pax_global_header00006660000000000000000000000064146474556650014540gustar00rootroot0000000000000052 comment=0bd66d901c7549051e21e8f648777f802eb20a73 colmap-3.10/000077500000000000000000000000001464745566500127365ustar00rootroot00000000000000colmap-3.10/.clang-format000077500000000000000000000005661464745566500153230ustar00rootroot00000000000000BasedOnStyle: Google BinPackArguments: false BinPackParameters: false DerivePointerAlignment: false IncludeBlocks: Regroup IncludeCategories: - Regex: '^"colmap' Priority: 1 - Regex: '^"pycolmap' Priority: 2 - Regex: '^"thirdparty' Priority: 3 - Regex: '^<[[:alnum:]_]+>' Priority: 4 - Regex: '.*' Priority: 5 SortIncludes: true colmap-3.10/.clang-tidy000066400000000000000000000010501464745566500147660ustar00rootroot00000000000000Checks: > performance-*, concurrency-*, bugprone-*, -bugprone-easily-swappable-parameters, -bugprone-exception-escape, -bugprone-implicit-widening-of-multiplication-result, -bugprone-narrowing-conversions, -bugprone-reserved-identifier, -bugprone-unchecked-optional-access, cppcoreguidelines-virtual-class-destructor, google-explicit-constructor, google-build-using-namespace, readability-avoid-const-params-in-decls, clang-analyzer-core*, clang-analyzer-cplusplus*, WarningsAsErrors: '*' FormatStyle: 'file' User: 'user' colmap-3.10/.github/000077500000000000000000000000001464745566500142765ustar00rootroot00000000000000colmap-3.10/.github/ISSUE_TEMPLATE/000077500000000000000000000000001464745566500164615ustar00rootroot00000000000000colmap-3.10/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000011531464745566500211530ustar00rootroot00000000000000--- name: Bug report about: Create a report to help us improve --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. Go to '...' 2. Click on '....' 3. Scroll down to '....' 4. See error **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. Input images and/or output reconstructions are usually helpful. **Environment:** - OS: [e.g. Windows 11] - COLMAP Version [e.g. 3.8 or git commit hash] - Capture Device [e.g. iPhone X] colmap-3.10/.github/ISSUE_TEMPLATE/feature_request.md000066400000000000000000000010601464745566500222030ustar00rootroot00000000000000--- name: Feature request about: Suggest an idea for this project --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. colmap-3.10/.github/workflows/000077500000000000000000000000001464745566500163335ustar00rootroot00000000000000colmap-3.10/.github/workflows/build-docker.yml000066400000000000000000000027221464745566500214250ustar00rootroot00000000000000name: COLMAP (Docker) on: push: branches: - main pull_request: types: [ assigned, opened, synchronize, reopened ] release: types: [ published, edited ] env: IS_RELEASE: ${{ github.event_name == 'release' || startsWith(github.ref, 'refs/tags') }} jobs: build: name: ubuntu-22.04 runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - name: Login to Docker Hub if: ${{ env.IS_RELEASE == 'true' }} uses: docker/login-action@v3 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} - name: Build and push run: | dockertag=$(date +%Y%m%d).${{ github.run_number }} if [[ "${{ env.IS_RELEASE }}" == "true" ]]; then cuda_archs="50;60;70;75;90" else cuda_archs="50" fi docker build . \ --file docker/Dockerfile \ --tag colmap/colmap:$dockertag \ --build-arg COLMAP_GIT_COMMIT="$GITHUB_SHA" \ --build-arg CUDA_ARCHITECTURES="$cuda_archs" if [[ "${{ env.IS_RELEASE }}" == "true" ]]; then docker tag colmap/colmap:$dockertag colmap/colmap:latest docker push colmap/colmap:$dockertag docker push colmap/colmap:latest fi colmap-3.10/.github/workflows/build-mac.yml000066400000000000000000000046771464745566500207310ustar00rootroot00000000000000name: COLMAP (Mac) on: push: branches: - main pull_request: types: [ assigned, opened, synchronize, reopened ] release: types: [ published, edited ] jobs: build: name: ${{ matrix.config.os }} ${{ matrix.config.arch }} ${{ matrix.config.cmakeBuildType }} runs-on: ${{ matrix.config.os }} strategy: matrix: config: [ { os: macos-14, arch: arm64, cmakeBuildType: Release, }, ] env: COMPILER_CACHE_VERSION: 1 COMPILER_CACHE_DIR: ${{ github.workspace }}/compiler-cache CCACHE_DIR: ${{ github.workspace }}/compiler-cache/ccache CCACHE_BASEDIR: ${{ github.workspace }} steps: - uses: actions/checkout@v4 - uses: actions/cache@v4 id: cache-builds with: key: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.arch }}-${{ matrix.config.cmakeBuildType }}-${{ github.run_id }}-${{ github.run_number }} restore-keys: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.arch }}-${{ matrix.config.cmakeBuildType }} path: ${{ env.COMPILER_CACHE_DIR }} - name: Setup Mac run: | # Fix `brew link` error. find /usr/local/bin -lname '*/Library/Frameworks/Python.framework/*' -delete brew install \ cmake \ ninja \ boost \ eigen \ flann \ freeimage \ metis \ glog \ googletest \ ceres-solver \ qt5 \ glew \ cgal \ sqlite3 \ ccache - name: Configure and build run: | export PATH="/usr/local/opt/qt@5/bin:$PATH" cmake --version mkdir build cd build cmake .. \ -GNinja \ -DCMAKE_BUILD_TYPE=${{ matrix.config.cmakeBuildType }} \ -DTESTS_ENABLED=ON \ -DCMAKE_PREFIX_PATH="/opt/homebrew/opt/flann;/opt/homebrew/opt/metis;/opt/homebrew/opt/suite-sparse;/opt/homebrew/opt/qt@5;/opt/homebrew/opt/freeimage" ninja - name: Run tests run: | cd build set +e ctest --output-on-failure - name: Cleanup compiler cache run: | set -x ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose colmap-3.10/.github/workflows/build-pycolmap.yml000066400000000000000000000175671464745566500220170ustar00rootroot00000000000000name: PyCOLMAP on: push: branches: - main pull_request: types: [ assigned, opened, synchronize, reopened ] release: types: [ published, edited ] jobs: build: name: ${{ matrix.config.os }} ${{ matrix.config.arch }} runs-on: ${{ matrix.config.os }} strategy: matrix: config: [ {os: ubuntu-latest}, {os: macos-14, arch: x86_64}, {os: macos-14, arch: arm64}, {os: windows-latest}, ] env: COMPILER_CACHE_VERSION: 1 COMPILER_CACHE_DIR: ${{ github.workspace }}/compiler-cache CCACHE_DIR: ${{ github.workspace }}/compiler-cache/ccache CCACHE_BASEDIR: ${{ github.workspace }} MACOSX_DEPLOYMENT_TARGET: 10.12 steps: - uses: actions/checkout@v4 - uses: actions/cache@v4 id: cache-builds with: key: pycolmap-v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.arch }}-${{ github.run_id }}-${{ github.run_number }} restore-keys: pycolmap-v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.arch }} path: ${{ env.COMPILER_CACHE_DIR }} - name: Set env (macOS) if: runner.os == 'macOS' run: | if [[ ${{ matrix.config.arch }} == "x86_64" ]]; then VCPKG_TARGET_TRIPLET="x64-osx-release" elif [[ ${{ matrix.config.arch }} == "arm64" ]]; then VCPKG_TARGET_TRIPLET="arm64-osx-release" else exit 1 fi echo "VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" >> "$GITHUB_ENV" VCPKG_INSTALLATION_ROOT="/Users/runner/work/vcpkg" CMAKE_TOOLCHAIN_FILE="${VCPKG_INSTALLATION_ROOT}/scripts/buildsystems/vcpkg.cmake" CMAKE_OSX_ARCHITECTURES=${{ matrix.config.arch }} echo "VCPKG_INSTALLATION_ROOT=${VCPKG_INSTALLATION_ROOT}" >> "$GITHUB_ENV" echo "CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" >> "$GITHUB_ENV" echo "CMAKE_OSX_ARCHITECTURES=${CMAKE_OSX_ARCHITECTURES}" >> "$GITHUB_ENV" # Fix: cibuildhweel cannot interpolate env variables. CONFIG_SETTINGS="cmake.define.CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" CONFIG_SETTINGS="${CONFIG_SETTINGS} cmake.define.VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" CONFIG_SETTINGS="${CONFIG_SETTINGS} cmake.define.VCPKG_INSTALLED_DIR=${{ github.workspace }}/build/vcpkg_installed" CONFIG_SETTINGS="${CONFIG_SETTINGS} cmake.define.CMAKE_OSX_ARCHITECTURES=${CMAKE_OSX_ARCHITECTURES}" echo "CIBW_CONFIG_SETTINGS_MACOS=${CONFIG_SETTINGS}" >> "$GITHUB_ENV" # vcpkg binary caching VCPKG_CACHE_DIR="${COMPILER_CACHE_DIR}/vcpkg" VCPKG_BINARY_SOURCES="clear;files,${VCPKG_CACHE_DIR},readwrite" echo "VCPKG_BINARY_SOURCES=${VCPKG_BINARY_SOURCES}" >> "$GITHUB_ENV" - name: Set env (Windows) if: runner.os == 'Windows' shell: pwsh run: | $VCPKG_INSTALLATION_ROOT = "${{ github.workspace }}/vcpkg" echo "VCPKG_INSTALLATION_ROOT=${VCPKG_INSTALLATION_ROOT}" >> "${env:GITHUB_ENV}" $CMAKE_TOOLCHAIN_FILE = "${VCPKG_INSTALLATION_ROOT}/scripts/buildsystems/vcpkg.cmake" echo "CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" >> "${env:GITHUB_ENV}" $VCPKG_TARGET_TRIPLET = "x64-windows-release" echo "VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" >> "${env:GITHUB_ENV}" # Fix: cibuildhweel cannot interpolate env variables. $CMAKE_TOOLCHAIN_FILE = $CMAKE_TOOLCHAIN_FILE.replace('\', '/') $VCPKG_INSTALLED_DIR = "${{ github.workspace }}/build/vcpkg_installed" $VCPKG_INSTALLED_DIR = $VCPKG_INSTALLED_DIR.replace('\', '/') $CONFIG_SETTINGS = "cmake.define.CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" $CONFIG_SETTINGS = "${CONFIG_SETTINGS} cmake.define.VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" $CONFIG_SETTINGS = "${CONFIG_SETTINGS} cmake.define.VCPKG_INSTALLED_DIR=${VCPKG_INSTALLED_DIR}" echo "CIBW_CONFIG_SETTINGS_WINDOWS=${CONFIG_SETTINGS}" >> "${env:GITHUB_ENV}" $CIBW_REPAIR_WHEEL_COMMAND = "delvewheel repair -v --add-path ${VCPKG_INSTALLED_DIR}/${VCPKG_TARGET_TRIPLET}/bin -w {dest_dir} {wheel}" echo "CIBW_REPAIR_WHEEL_COMMAND_WINDOWS=${CIBW_REPAIR_WHEEL_COMMAND}" >> "${env:GITHUB_ENV}" # vcpkg binary caching $VCPKG_CACHE_DIR = "${env:COMPILER_CACHE_DIR}/vcpkg" $VCPKG_BINARY_SOURCES = "clear;files,${VCPKG_CACHE_DIR},readwrite" echo "VCPKG_BINARY_SOURCES=${VCPKG_BINARY_SOURCES}" >> "${env:GITHUB_ENV}" - name: Set env (Linux) if: runner.os == 'Linux' run: | VCPKG_TARGET_TRIPLET="x64-linux-release" echo "VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" >> "$GITHUB_ENV" VCPKG_INSTALLATION_ROOT="${{ github.workspace }}/vcpkg" CMAKE_TOOLCHAIN_FILE="${VCPKG_INSTALLATION_ROOT}/scripts/buildsystems/vcpkg.cmake" echo "VCPKG_INSTALLATION_ROOT=${VCPKG_INSTALLATION_ROOT}" >> "$GITHUB_ENV" echo "CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" >> "$GITHUB_ENV" # Fix: cibuildhweel cannot interpolate env variables. CONFIG_SETTINGS="cmake.define.CMAKE_TOOLCHAIN_FILE=${CMAKE_TOOLCHAIN_FILE}" CONFIG_SETTINGS="${CONFIG_SETTINGS} cmake.define.VCPKG_TARGET_TRIPLET=${VCPKG_TARGET_TRIPLET}" CONFIG_SETTINGS="${CONFIG_SETTINGS} cmake.define.VCPKG_INSTALLED_DIR=/project/build/vcpkg_installed" echo "CIBW_CONFIG_SETTINGS_LINUX=${CONFIG_SETTINGS}" >> "$GITHUB_ENV" # Remap caching paths to the container CONTAINER_COMPILER_CACHE_DIR="/compiler-cache" CIBW_CONTAINER_ENGINE="docker; create_args: -v ${COMPILER_CACHE_DIR}:${CONTAINER_COMPILER_CACHE_DIR}" echo "CIBW_CONTAINER_ENGINE=${CIBW_CONTAINER_ENGINE}" >> "$GITHUB_ENV" echo "CONTAINER_COMPILER_CACHE_DIR=${CONTAINER_COMPILER_CACHE_DIR}" >> "$GITHUB_ENV" echo "CCACHE_DIR=${CONTAINER_COMPILER_CACHE_DIR}/ccache" >> "$GITHUB_ENV" echo "CCACHE_BASEDIR=/project" >> "$GITHUB_ENV" # vcpkg binary caching VCPKG_CACHE_DIR="${CONTAINER_COMPILER_CACHE_DIR}/vcpkg" VCPKG_BINARY_SOURCES="clear;files,${VCPKG_CACHE_DIR},readwrite" echo "VCPKG_BINARY_SOURCES=${VCPKG_BINARY_SOURCES}" >> "$GITHUB_ENV" CIBW_ENVIRONMENT_PASS_LINUX="VCPKG_TARGET_TRIPLET VCPKG_INSTALLATION_ROOT CMAKE_TOOLCHAIN_FILE VCPKG_BINARY_SOURCES CONTAINER_COMPILER_CACHE_DIR CCACHE_DIR CCACHE_BASEDIR" echo "CIBW_ENVIRONMENT_PASS_LINUX=${CIBW_ENVIRONMENT_PASS_LINUX}" >> "$GITHUB_ENV" CIBW_MANYLINUX_X86_64_IMAGE="quay.io/pypa/manylinux_2_28_x86_64" echo "CIBW_MANYLINUX_X86_64_IMAGE=${CIBW_MANYLINUX_X86_64_IMAGE}" >> "$GITHUB_ENV" - name: Build wheels uses: pypa/cibuildwheel@v2.19.2 with: package-dir: ./pycolmap/ env: CIBW_ARCHS_MACOS: ${{ matrix.config.arch }} - name: Archive wheels uses: actions/upload-artifact@v4 with: name: pycolmap-${{ matrix.config.os }}-${{ matrix.config.arch }} path: wheelhouse/pycolmap-*.whl pypi-publish: name: Publish wheels to PyPI needs: build runs-on: ubuntu-latest # We publish the wheel to pypi when a new tag is pushed, # either by creating a new GitHub release or explictly with `git tag` if: ${{ github.event_name == 'release' || startsWith(github.ref, 'refs/tags') }} steps: - name: Download wheels uses: actions/download-artifact@v4 with: path: ./artifacts/ - name: Move wheels run: mkdir ./wheelhouse && mv ./artifacts/**/*.whl ./wheelhouse/ - name: Publish package uses: pypa/gh-action-pypi-publish@release/v1 with: skip_existing: true user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} packages_dir: ./wheelhouse/ colmap-3.10/.github/workflows/build-ubuntu.yml000066400000000000000000000223671464745566500215070ustar00rootroot00000000000000name: COLMAP (Ubuntu) on: push: branches: - main pull_request: types: [ assigned, opened, synchronize, reopened ] release: types: [ published, edited ] jobs: build: name: ${{ matrix.config.os }} ${{ matrix.config.cmakeBuildType }} ${{ matrix.config.cudaEnabled && 'CUDA' || '' }} ${{ matrix.config.asanEnabled && 'ASan' || '' }} runs-on: ${{ matrix.config.os }} strategy: matrix: config: [ { os: ubuntu-22.04, cmakeBuildType: Release, asanEnabled: false, guiEnabled: true, cudaEnabled: false, e2eTests: true, checkCodeFormat: true, }, { os: ubuntu-22.04, cmakeBuildType: Release, asanEnabled: false, guiEnabled: false, cudaEnabled: true, e2eTests: false, checkCodeFormat: false, }, { os: ubuntu-22.04, cmakeBuildType: Release, asanEnabled: true, guiEnabled: false, cudaEnabled: false, e2eTests: false, checkCodeFormat: false, }, { os: ubuntu-22.04, cmakeBuildType: ClangTidy, asanEnabled: false, guiEnabled: false, cudaEnabled: false, e2eTests: false, checkCodeFormat: false, }, { os: ubuntu-20.04, cmakeBuildType: Release, asanEnabled: false, guiEnabled: true, cudaEnabled: false, e2eTests: false, checkCodeFormat: false, }, { os: ubuntu-20.04, cmakeBuildType: Release, asanEnabled: false, guiEnabled: false, cudaEnabled: true, e2eTests: false, checkCodeFormat: false, }, ] env: COMPILER_CACHE_VERSION: 1 COMPILER_CACHE_DIR: ${{ github.workspace }}/compiler-cache CCACHE_DIR: ${{ github.workspace }}/compiler-cache/ccache CCACHE_BASEDIR: ${{ github.workspace }} CTCACHE_DIR: ${{ github.workspace }}/compiler-cache/ctcache steps: - uses: actions/checkout@v4 - uses: actions/cache@v4 id: cache-builds with: key: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.cmakeBuildType }}-${{ matrix.config.asanEnabled }}--${{ matrix.config.cudaEnabled }}-${{ github.run_id }}-${{ github.run_number }} restore-keys: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.cmakeBuildType }}-${{ matrix.config.asanEnabled }}--${{ matrix.config.cudaEnabled }} path: ${{ env.COMPILER_CACHE_DIR }} - name: Install compiler cache run: | mkdir -p "$CCACHE_DIR" "$CTCACHE_DIR" echo "$COMPILER_CACHE_DIR/bin" >> $GITHUB_PATH if [ -f "$COMPILER_CACHE_DIR/bin/ccache" ]; then exit 0 fi set -x wget https://github.com/ccache/ccache/releases/download/v4.8.2/ccache-4.8.2-linux-x86_64.tar.xz echo "0b33f39766fe9db67f40418aed6a5b3d7b2f4f7fab025a8213264b77a2d0e1b1 ccache-4.8.2-linux-x86_64.tar.xz" | sha256sum --check tar xfv ccache-4.8.2-linux-x86_64.tar.xz mkdir -p "$COMPILER_CACHE_DIR/bin" mv ./ccache-4.8.2-linux-x86_64/ccache "$COMPILER_CACHE_DIR/bin" ctcache_commit_id="66c3614175fc650591488519333c411b2eac15a3" wget https://github.com/matus-chochlik/ctcache/archive/${ctcache_commit_id}.zip echo "108b087f156a9fe7da0c796de1ef73f5855d2a33a27983769ea39061359a40fc ${ctcache_commit_id}.zip" | sha256sum --check unzip "${ctcache_commit_id}.zip" mv ctcache-${ctcache_commit_id}/clang-tidy* "$COMPILER_CACHE_DIR/bin" - name: Check code format run: | if [ "${{ matrix.config.checkCodeFormat }}" != "true" ]; then exit 0 fi set +x -euo pipefail sudo apt-get update && sudo apt-get install -y clang-format-14 black ./scripts/format/clang_format.sh ./scripts/format/black.sh git diff --name-only git diff --exit-code || (echo "Code formatting failed" && exit 1) - name: Setup Ubuntu run: | sudo apt-get update && sudo apt-get install -y \ build-essential \ cmake \ ninja-build \ libboost-program-options-dev \ libboost-filesystem-dev \ libboost-graph-dev \ libboost-system-dev \ libeigen3-dev \ libceres-dev \ libflann-dev \ libfreeimage-dev \ libmetis-dev \ libgoogle-glog-dev \ libgtest-dev \ libsqlite3-dev \ libglew-dev \ qtbase5-dev \ libqt5opengl5-dev \ libcgal-dev \ libcgal-qt5-dev \ libgl1-mesa-dri \ libunwind-dev \ xvfb if [ "${{ matrix.config.cudaEnabled }}" == "true" ]; then if [ "${{ matrix.config.os }}" == "ubuntu-20.04" ]; then sudo apt-get install -y \ nvidia-cuda-toolkit \ nvidia-cuda-toolkit-gcc echo "CC=/usr/bin/cuda-gcc" >> $GITHUB_ENV echo "CXX=/usr/bin/cuda-g++" >> $GITHUB_ENV elif [ "${{ matrix.config.os }}" == "ubuntu-22.04" ]; then sudo apt-get install -y \ nvidia-cuda-toolkit \ nvidia-cuda-toolkit-gcc \ gcc-10 g++-10 echo "CC=/usr/bin/gcc-10" >> $GITHUB_ENV echo "CXX=/usr/bin/g++-10" >> $GITHUB_ENV echo "CUDAHOSTCXX=/usr/bin/g++-10" >> $GITHUB_ENV fi fi if [ "${{ matrix.config.asanEnabled }}" == "true" ]; then sudo apt-get install -y clang-15 libomp-15-dev echo "CC=/usr/bin/clang-15" >> $GITHUB_ENV echo "CXX=/usr/bin/clang++-15" >> $GITHUB_ENV fi if [ "${{ matrix.config.cmakeBuildType }}" == "ClangTidy" ]; then sudo apt-get install -y clang-15 clang-tidy-15 libomp-15-dev echo "CC=/usr/bin/clang-15" >> $GITHUB_ENV echo "CXX=/usr/bin/clang++-15" >> $GITHUB_ENV fi - name: Configure and build run: | set -x cmake --version mkdir build cd build cmake .. \ -GNinja \ -DCMAKE_BUILD_TYPE=${{ matrix.config.cmakeBuildType }} \ -DCMAKE_INSTALL_PREFIX=./install \ -DCMAKE_CUDA_ARCHITECTURES=50 \ -DTESTS_ENABLED=ON \ -DCUDA_ENABLED=${{ matrix.config.cudaEnabled }} \ -DGUI_ENABLED=${{ matrix.config.guiEnabled }} \ -DASAN_ENABLED=${{ matrix.config.asanEnabled }} ninja -k 10000 - name: Install and build sample run: | if [ "${{ matrix.config.asanEnabled }}" == "true" ] || [ "${{ matrix.config.cmakeBuildType }}" == "ClangTidy" ]; then exit 0 fi set -x cd build ninja install cd ../doc/sample-project mkdir build cd build export colmap_DIR=${{ github.workspace }}/build/install/share/colmap cmake .. \ -GNinja \ -DCMAKE_CUDA_ARCHITECTURES=50 ninja ./hello_world --message "world" - name: Run tests run: | if [ "${{ matrix.config.cmakeBuildType }}" == "ClangTidy" ]; then exit 0 fi if [ "${{ matrix.config.cudaEnabled }}" == "true" ]; then ctestExclusions="(feature/colmap_feature_sift_test)|(mvs/colmap_mvs_gpu_mat_test)" fi export DISPLAY=":99.0" export QT_QPA_PLATFORM="offscreen" Xvfb :99 & sleep 3 cd build ctest -E "$ctestExclusions" --output-on-failure - name: Run E2E tests run: | if [ "${{ matrix.config.e2eTests }}" != "true" ]; then exit 0 fi export DISPLAY=":99.0" export QT_QPA_PLATFORM="offscreen" Xvfb :99 & sleep 3 sudo apt install 7zip mkdir eth3d_benchmark # Error thresholds in degrees and meters, # as the ETH3D groundtruth has metric scale. GLOG_v=1 python ./scripts/python/benchmark_eth3d.py \ --workspace_path ./eth3d_benchmark \ --colmap_path ./build/src/colmap/exe/colmap \ --dataset_names boulders,door \ --max_rotation_error 1.0 \ --max_proj_center_error 0.05 - name: Cleanup compiler cache run: | set -x ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose echo "Size of ctcache before: $(du -sh $CTCACHE_DIR)" echo "Number of ctcache files before: $(find $CTCACHE_DIR | wc -l)" # Delete cache older than 10 days. find "$CTCACHE_DIR"/*/ -mtime +10 -print0 | xargs -0 rm -rf echo "Size of ctcache after: $(du -sh $CTCACHE_DIR)" echo "Number of ctcache files after: $(find $CTCACHE_DIR | wc -l)" colmap-3.10/.github/workflows/build-windows.yml000066400000000000000000000143531464745566500216530ustar00rootroot00000000000000name: COLMAP (Windows) on: push: branches: - main pull_request: types: [ assigned, opened, synchronize, reopened ] release: types: [ published, edited ] jobs: build: name: ${{ matrix.config.os }} ${{ matrix.config.cmakeBuildType }} ${{ matrix.config.cudaEnabled && 'CUDA' || '' }} runs-on: ${{ matrix.config.os }} strategy: matrix: config: [ { os: windows-2019, cmakeBuildType: Release, cudaEnabled: false, testsEnabled: true, exportPackage: false, }, { os: windows-2022, cmakeBuildType: Release, cudaEnabled: true, testsEnabled: true, exportPackage: true, }, { os: windows-2022, cmakeBuildType: Release, cudaEnabled: false, testsEnabled: true, exportPackage: true, }, ] env: COMPILER_CACHE_VERSION: 1 COMPILER_CACHE_DIR: ${{ github.workspace }}/compiler-cache CCACHE_DIR: ${{ github.workspace }}/compiler-cache/ccache CCACHE_BASEDIR: ${{ github.workspace }} VCPKG_COMMIT_ID: e01906b2ba7e645a76ee021a19de616edc98d29f VCPKG_BINARY_SOURCES: "clear;x-gha,readwrite" steps: - uses: actions/checkout@v4 - name: Export GitHub Actions cache env uses: actions/github-script@v7 with: script: | core.exportVariable('ACTIONS_CACHE_URL', process.env.ACTIONS_CACHE_URL || ''); core.exportVariable('ACTIONS_RUNTIME_TOKEN', process.env.ACTIONS_RUNTIME_TOKEN || ''); - name: Compiler cache uses: actions/cache@v4 id: cache-builds with: key: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.cmakeBuildType }}-${{ matrix.config.asanEnabled }}--${{ matrix.config.cudaEnabled }}-${{ github.run_id }}-${{ github.run_number }} restore-keys: v${{ env.COMPILER_CACHE_VERSION }}-${{ matrix.config.os }}-${{ matrix.config.cmakeBuildType }}-${{ matrix.config.asanEnabled }}--${{ matrix.config.cudaEnabled }} path: ${{ env.COMPILER_CACHE_DIR }} - name: Install ccache shell: pwsh run: | New-Item -ItemType Directory -Force -Path "${{ env.CCACHE_DIR }}" echo "${{ env.COMPILER_CACHE_DIR }}/bin" | Out-File -Encoding utf8 -Append -FilePath $env:GITHUB_PATH if (Test-Path -PathType Leaf "${{ env.COMPILER_CACHE_DIR }}/bin/ccache.exe") { exit } .github/workflows/install-ccache.ps1 -Destination "${{ env.COMPILER_CACHE_DIR }}/bin" - name: Install CUDA uses: Jimver/cuda-toolkit@v0.2.16 if: matrix.config.cudaEnabled id: cuda-toolkit with: cuda: '12.5.0' sub-packages: '["nvcc", "nvtx", "cudart", "curand", "curand_dev", "nvrtc_dev"]' method: 'network' - name: Install CMake and Ninja uses: lukka/get-cmake@latest - name: Setup vcpkg shell: pwsh run: | ./scripts/shell/enter_vs_dev_shell.ps1 cd ${{ github.workspace }} git clone https://github.com/microsoft/vcpkg cd vcpkg git reset --hard ${{ env.VCPKG_COMMIT_ID }} ./bootstrap-vcpkg.bat - name: Configure and build shell: pwsh run: | ./scripts/shell/enter_vs_dev_shell.ps1 cd ${{ github.workspace }} ./vcpkg/vcpkg.exe integrate install mkdir build cd build cmake .. ` -GNinja ` -DCMAKE_MAKE_PROGRAM=ninja ` -DCMAKE_BUILD_TYPE=Release ` -DTESTS_ENABLED=${{ matrix.config.testsEnabled }} ` -DGUI_ENABLED=ON ` -DCUDA_ENABLED=${{ matrix.config.cudaEnabled }} ` -DCMAKE_CUDA_ARCHITECTURES=all-major ` -DCUDAToolkit_ROOT="${{ steps.cuda-toolkit.outputs.CUDA_PATH }}" ` -DCMAKE_TOOLCHAIN_FILE="${{ github.workspace }}/vcpkg/scripts/buildsystems/vcpkg.cmake" ` -DVCPKG_TARGET_TRIPLET=x64-windows-release ` -DCMAKE_INSTALL_PREFIX=install ninja - name: Run tests shell: pwsh run: | ./vcpkg/vcpkg.exe integrate install cd build $EXCLUDED_TESTS = "(feature/colmap_feature_sift_test)|(util/colmap_util_opengl_utils_test)|(mvs/colmap_mvs_gpu_mat_test)" ctest -E ${EXCLUDED_TESTS} --output-on-failure - name: Export package if: matrix.config.exportPackage shell: pwsh run: | ./vcpkg/vcpkg.exe integrate install cd build ninja install ../vcpkg/vcpkg.exe install ` --triplet=x64-windows-release ` --x-feature=gui ` --x-feature=cgal ` $(if ($${{ matrix.config.testsEnabled }}) { echo "--x-feature=tests" }) ` $(if ($${{ matrix.config.cudaEnabled }}) { echo "--x-feature=cuda" }) ../vcpkg/vcpkg.exe export --raw --output-dir vcpkg_export --output colmap cp vcpkg_export/colmap/installed/x64-windows/bin/*.dll install/bin cp vcpkg_export/colmap/installed/x64-windows-release/bin/*.dll install/bin cp -r vcpkg_export/colmap/installed/x64-windows/plugins install if ($${{ matrix.config.cudaEnabled }}) { cp "${{ steps.cuda-toolkit.outputs.CUDA_PATH }}/bin/cudart64_*.dll" install/bin cp "${{ steps.cuda-toolkit.outputs.CUDA_PATH }}/bin/curand64_*.dll" install/bin } Remove-Item -Recurse -Force install/include,install/lib,install/share - name: Upload package uses: actions/upload-artifact@v4 if: ${{ matrix.config.exportPackage && matrix.config.cudaEnabled }} with: name: colmap-x64-windows-cuda path: build/install - name: Upload package uses: actions/upload-artifact@v4 if: ${{ matrix.config.exportPackage && !matrix.config.cudaEnabled }} with: name: colmap-x64-windows-nocuda path: build/install - name: Cleanup compiler cache shell: pwsh run: | ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose colmap-3.10/.github/workflows/install-ccache.ps1000066400000000000000000000022531464745566500216340ustar00rootroot00000000000000[CmdletBinding()] param ( [Parameter(Mandatory = $true)] [string] $Destination ) $version = "4.8" $folder = "ccache-$version-windows-x86_64" $url = "https://github.com/ccache/ccache/releases/download/v$version/$folder.zip" $expectedSha256 = "A2B3BAB4BB8318FFC5B3E4074DC25636258BC7E4B51261F7D9BEF8127FDA8309" $ErrorActionPreference = "Stop" try { New-Item -Path "$Destination" -ItemType Container -ErrorAction SilentlyContinue Write-Host "Download CCache" $zipFilePath = Join-Path "$env:TEMP" "$folder.zip" Invoke-WebRequest -Uri $url -UseBasicParsing -OutFile "$zipFilePath" -MaximumRetryCount 3 $hash = Get-FileHash $zipFilePath -Algorithm "sha256" if ($hash.Hash -ne $expectedSha256) { throw "File $Path hash $hash.Hash did not match expected hash $expectedHash" } Write-Host "Unzip CCache" Expand-Archive -Path "$zipFilePath" -DestinationPath "$env:TEMP" Write-Host "Move CCache" Move-Item -Force "$env:TEMP/$folder/ccache.exe" "$Destination" Remove-Item "$zipFilePath" Remove-Item -Recurse "$env:TEMP/$folder" } catch { Write-Host "Installation failed with an error" $_.Exception | Format-List exit -1 } colmap-3.10/.gitignore000077500000000000000000000006621464745566500147350ustar00rootroot00000000000000# Custom files LocalConfig.cmake src/colmap/util/version.cc # Custom directories .idea/ .vscode .vs .DS_Store build*/ install-debug/ install-release/ data/ doc/_build vcpkg_installed/ # Compiled Object files *.slo *.lo *.o *.obj *.pyc # Precompiled Headers *.gch *.pch # Compiled Dynamic libraries *.so *.dylib *.dll # Fortran module files *.mod # Compiled Static libraries *.lai *.la *.a *.lib # Executables *.exe *.out *.app colmap-3.10/.gitmodules000066400000000000000000000001721464745566500151130ustar00rootroot00000000000000[submodule "doc/_build/html"] path = doc/_build/html url = https://github.com/colmap/colmap.github.io.git ignore = all colmap-3.10/CHANGELOG.txt000066400000000000000000001252541464745566500147770ustar00rootroot00000000000000COLMAP 3.9.1 (01/08/2024) ------------------------- * Version 3.9 changelog by @ahojnnes in https://github.com/colmap/colmap/pull/2325 * Fully encapsulate freeimage in bitmap library (#2332) by @ahojnnes in https://github.com/colmap/colmap/pull/2334 COLMAP 3.9 (01/06/2024) ----------------------- * clang format all code and require clang-format-14 by @ahojnnes in https://github.com/colmap/colmap/pull/1785 * Fix compilation for vcpkg windows build by @ahojnnes in https://github.com/colmap/colmap/pull/1791 * Increment version number to 3.9 by @ahojnnes in https://github.com/colmap/colmap/pull/1794 * Remove unnecessary /arch:sse2 flag for MSVC by @ahojnnes in https://github.com/colmap/colmap/pull/1798 * Updated faq.rst by @CGCooke in https://github.com/colmap/colmap/pull/1801 * Fixed mistake in code comment for OpenCV Fisheye camera by @CGCooke in https://github.com/colmap/colmap/pull/1802 * Replace deprecated cudaThreadSynchronize with cudaDeviceSynchronize by @ahojnnes in https://github.com/colmap/colmap/pull/1806 * Replace deprecated Cuda texture references with texture objects by @ahojnnes in https://github.com/colmap/colmap/pull/1809 * Remove unused SIFT GPU cuda texture reference by @ahojnnes in https://github.com/colmap/colmap/pull/1823 * Upgrade SiftGPU to use CUDA texture objects by @ahojnnes in https://github.com/colmap/colmap/pull/1838 * Remove PBA as bundle adjustment backend to support CUDA 12+ by @ahojnnes in https://github.com/colmap/colmap/pull/1840 * Replace deprecated CUDA sature function call by @ahojnnes in https://github.com/colmap/colmap/pull/1841 * Avoid unnecessary mallocs during sampling by @ahojnnes in https://github.com/colmap/colmap/pull/1842 * Cleaned up docker readme and scripts by @ahojnnes in https://github.com/colmap/colmap/pull/1852 * add "Shared intrinsics per sub-folder" checkbox to automatic reconstruction window by @kenshi84 in https://github.com/colmap/colmap/pull/1853 * Update vcpkg by @ahojnnes in https://github.com/colmap/colmap/pull/1925 * Log the name of the file that causes Mat::Read() to checkfail by @SomeAlphabetGuy in https://github.com/colmap/colmap/pull/1923 * check Z_index correctly in ReadPly by @countywest in https://github.com/colmap/colmap/pull/1896 * Don't re-open files when reading and writing matrices by @SomeAlphabetGuy in https://github.com/colmap/colmap/pull/1926 * Update vcpkg to latest commit by @ahojnnes in https://github.com/colmap/colmap/pull/1948 * Remove unnecessary custom Eigen aligned allocator macros by @ahojnnes in https://github.com/colmap/colmap/pull/1947 * Prefix internal sources/includes with colmap by @ahojnnes in https://github.com/colmap/colmap/pull/1949 * Simplify clang-format config and sort includes by @ahojnnes in https://github.com/colmap/colmap/pull/1950 * Handle possible overflow in median function by @ahojnnes in https://github.com/colmap/colmap/pull/1951 * Run ASan pipeline under Ubuntu 22.04 by @ahojnnes in https://github.com/colmap/colmap/pull/1952 * Fix Ceres version test by @drkoller in https://github.com/colmap/colmap/pull/1954 * Fix deprecation warning for Qt font metrics width by @ahojnnes in https://github.com/colmap/colmap/pull/1958 * Setup clang-tidy and enable perf warnings by @ahojnnes in https://github.com/colmap/colmap/pull/1959 * VCPKG binary caching for windows CI by @ahojnnes in https://github.com/colmap/colmap/pull/1957 * Cosmetics for VS dev shell script by @ahojnnes in https://github.com/colmap/colmap/pull/1965 * Enable clang-tidy concurrency checks by @ahojnnes in https://github.com/colmap/colmap/pull/1967 * [Bug] fix finding shared points3D in FindLocalBundle by @wesleyliwei in https://github.com/colmap/colmap/pull/1963 * Enable compiler caching in CI by @ahojnnes in https://github.com/colmap/colmap/pull/1972 * Set number of features for different quality levels by @ahojnnes in https://github.com/colmap/colmap/pull/1975 * Specify parameter name using inline comment by @ahojnnes in https://github.com/colmap/colmap/pull/1976 * Fix Windows CCache by @ahojnnes in https://github.com/colmap/colmap/pull/1977 * Add e2e tests in CI pipeline using ETH3D datasets by @ahojnnes in https://github.com/colmap/colmap/pull/1397 * [feature] print verbose information for model analyzer by @wesleyliwei in https://github.com/colmap/colmap/pull/1978 * Add a missing include to compile with gcc13 by @EstebanDugueperoux2 in https://github.com/colmap/colmap/pull/1984 * Speed up snapshot construct in RigBundleAdjuster by @wesleyliwei in https://github.com/colmap/colmap/pull/1988 * Update outdated docker cuda image tag by @ahojnnes in https://github.com/colmap/colmap/pull/1992 * Add boulders ETH3D dataset to CI E2E tests by @ahojnnes in https://github.com/colmap/colmap/pull/1991 * Update executable paths in documentation by @ahojnnes in https://github.com/colmap/colmap/pull/1993 * Avoid unnecessary copy in ExtractTopScaleFeatures by @ahojnnes in https://github.com/colmap/colmap/pull/1994 * Move related code under new image library folder by @ahojnnes in https://github.com/colmap/colmap/pull/1995 * Move related code under new camera folder by @ahojnnes in https://github.com/colmap/colmap/pull/1996 * Added a virtual destructor to Sampler by @SomeAlphabetGuy in https://github.com/colmap/colmap/pull/2000 * Add a few more clang-tidy checks by @ahojnnes in https://github.com/colmap/colmap/pull/2001 * Move related code to new geometry module by @ahojnnes in https://github.com/colmap/colmap/pull/2006 * Use #pragma once as include guard by @ahojnnes in https://github.com/colmap/colmap/pull/2007 * Add bugprone-* clang-tidy checks by @ahojnnes in https://github.com/colmap/colmap/pull/2010 * Avoid const params in declarations by @ahojnnes in https://github.com/colmap/colmap/pull/2011 * Set and require C++14 by @ahojnnes in https://github.com/colmap/colmap/pull/2012 * Cleanup math functions that are now part of eigen/stdlib by @ahojnnes in https://github.com/colmap/colmap/pull/2013 * Add clang-analyzer checks by @ahojnnes in https://github.com/colmap/colmap/pull/2014 * Replace CMake provided find_package scripts and modern CMake targets by @ahojnnes in https://github.com/colmap/colmap/pull/2016 * Switch from Boost unit tests to Gtest by @ahojnnes in https://github.com/colmap/colmap/pull/2017 * Fix ccache restore keys in pipeline caching by @ahojnnes in https://github.com/colmap/colmap/pull/2018 * Add missing cacheHitVar to fix ccache by @ahojnnes in https://github.com/colmap/colmap/pull/2020 * Add missing Boost::graph import by @sarlinpe in https://github.com/colmap/colmap/pull/2021 * Compressed/flattened correspondence graph for faster triangulation / less memory by @ahojnnes in https://github.com/colmap/colmap/pull/2019 * Fix window ccache key by @ahojnnes in https://github.com/colmap/colmap/pull/2024 * Consistently use shared_ptr for shared pointers for SFM objects by @ahojnnes in https://github.com/colmap/colmap/pull/2023 * Remove check on Qt version by @sarlinpe in https://github.com/colmap/colmap/pull/2022 * Synthetics for E2E incremental mapper tests by @ahojnnes in https://github.com/colmap/colmap/pull/2025 * New math module by @ahojnnes in https://github.com/colmap/colmap/pull/2028 * Simplify similarity transform and more tests by @ahojnnes in https://github.com/colmap/colmap/pull/2030 * Extract reconstruction alignment functions into new file by @ahojnnes in https://github.com/colmap/colmap/pull/2032 * Add E2E hierarchical mapper tests by @ahojnnes in https://github.com/colmap/colmap/pull/2033 * Rename SimilarityTransform3 to Sim3d by @ahojnnes in https://github.com/colmap/colmap/pull/2034 * Add Rigid3d transform class by @ahojnnes in https://github.com/colmap/colmap/pull/2035 * Consolidate and simplify Rigid3d and Sim3d by @ahojnnes in https://github.com/colmap/colmap/pull/2037 * Some small improvements/cleanup for rigid3d/sim3d usage by @ahojnnes in https://github.com/colmap/colmap/pull/2041 * CamFromWorld replaces qvec/tvec by @ahojnnes in https://github.com/colmap/colmap/pull/2039 * Retry download of ETH3D datasets by @ahojnnes in https://github.com/colmap/colmap/pull/2043 * WorldToImage becomes CamToImg by @ahojnnes in https://github.com/colmap/colmap/pull/2044 * Camera models operate on camera rays by @ahojnnes in https://github.com/colmap/colmap/pull/2045 * Ignore directory .vs by @whuaegeanse in https://github.com/colmap/colmap/pull/2046 * Use the reference of Rigid3d to reduce memory consumption by @whuaegeanse in https://github.com/colmap/colmap/pull/2047 * Inline point to image projection by @ahojnnes in https://github.com/colmap/colmap/pull/2050 * Point2D becomes simpler pure data struct by @ahojnnes in https://github.com/colmap/colmap/pull/2051 * Use Eigen math for estimator utils by @ahojnnes in https://github.com/colmap/colmap/pull/2052 * Move cost functions under geometry module and rename by @ahojnnes in https://github.com/colmap/colmap/pull/2053 * Bundle adjuster is an estimator by @ahojnnes in https://github.com/colmap/colmap/pull/2054 * Remaining base targets move to new scene module by @ahojnnes in https://github.com/colmap/colmap/pull/2055 * Vote and verify improvements/speedup by @ahojnnes in https://github.com/colmap/colmap/pull/2056 * Generate version info in .cc file to reduce number of recompilations by @ahojnnes in https://github.com/colmap/colmap/pull/2057 * Option manager moves to controllers to disentangle circular deps by @ahojnnes in https://github.com/colmap/colmap/pull/2058 * Granular CMake modules and build targets by @ahojnnes in https://github.com/colmap/colmap/pull/2059 * Fix docker build by @ahojnnes in https://github.com/colmap/colmap/pull/2069 * Remove warnings about duplicated marco NOMINMAX by @whuaegeanse in https://github.com/colmap/colmap/pull/2067 * lib folder becomes thirdparty folder by @ahojnnes in https://github.com/colmap/colmap/pull/2068 * Remove unnecessary checks in image pair conversion by @ahojnnes in https://github.com/colmap/colmap/pull/2074 * Replace flaky ETH3D terrace with courtyard dataset by @ahojnnes in https://github.com/colmap/colmap/pull/2075 * Synthesize chained match graph for more mapper tests by @ahojnnes in https://github.com/colmap/colmap/pull/2076 * Introduce abstract feature extractor by @ahojnnes in https://github.com/colmap/colmap/pull/2077 * Avoid unnecessary data copies in feature conversion utils by @ahojnnes in https://github.com/colmap/colmap/pull/2078 * Abstract feature matcher by @ahojnnes in https://github.com/colmap/colmap/pull/2082 * Encapsulate feature matching controller/worker implementations by @ahojnnes in https://github.com/colmap/colmap/pull/2085 * Some cosmetics for util/feature types by @ahojnnes in https://github.com/colmap/colmap/pull/2084 * Use std:: when cmath included by @whuaegeanse in https://github.com/colmap/colmap/pull/2081 * Encapsulate feature extraction controller/worker implementations by @ahojnnes in https://github.com/colmap/colmap/pull/2086 * Reenable VS2022 CI pipeline by @ahojnnes in https://github.com/colmap/colmap/pull/1689 * Consistent transform convention for CenterAndNormalizeImagePoints by @ahojnnes in https://github.com/colmap/colmap/pull/2092 * Retire Mac 11 CI build by @ahojnnes in https://github.com/colmap/colmap/pull/2094 * Add ReprojErrorConstantPoint3DCostFunction to speed up the RefineAbsolutePose function by @whuaegeanse in https://github.com/colmap/colmap/pull/2089 * Numeric differentiation of camera model using partial piv LU by @ahojnnes in https://github.com/colmap/colmap/pull/2100 * cmake: add testing.cc to colmap_util only if TESTS_ENABLED=ON by @NeroBurner in https://github.com/colmap/colmap/pull/2102 * Set CUDA_STANDARD to 14 by @ahojnnes in https://github.com/colmap/colmap/pull/2108 * Transform back to existing images positions after mapper processing if set fixed by @ferreram in https://github.com/colmap/colmap/pull/2095 * Update documentation with new branch policy by @ahojnnes in https://github.com/colmap/colmap/pull/2110 * Update CMake find dependencies for vcpkg by @ahojnnes in https://github.com/colmap/colmap/pull/2116 * Decouple SIFT match from two view geometry options by @ahojnnes in https://github.com/colmap/colmap/pull/2118 * Fix docker build by @vnmsklnk in https://github.com/colmap/colmap/pull/2122 * Trigger build pipeline on main branch by @ahojnnes in https://github.com/colmap/colmap/pull/2123 * Update Linux install documentation with new branch policy by @joshuaoreilly in https://github.com/colmap/colmap/pull/2126 * Fix link in camera model documentation by @CFretter in https://github.com/colmap/colmap/pull/2152 * [Bugfix] Fix GUI_ENABLED=OFF and skip SiftGPU if no GUI and no CUDA by @sarlinpe in https://github.com/colmap/colmap/pull/2151 * [Bugfix] Properly handle CGAL_ENABLED by @sarlinpe in https://github.com/colmap/colmap/pull/2149 * Refinement of intrinsics in the point_triangulator by @tsattler in https://github.com/colmap/colmap/pull/2144 * Bugfix in handling COLMAP_GPU_ENABLED by @sarlinpe in https://github.com/colmap/colmap/pull/2163 * Expose exe as libs by @sarlinpe in https://github.com/colmap/colmap/pull/2165 * Add Sim3d::FromMatrix by @sarlinpe in https://github.com/colmap/colmap/pull/2147 * Check code format in CI by @ahojnnes in https://github.com/colmap/colmap/pull/2171 * Clean up dependencies by @sarlinpe in https://github.com/colmap/colmap/pull/2173 * Move tests into anonymous namespaces by @ahojnnes in https://github.com/colmap/colmap/pull/2175 * Fix glew/qopengl conflict warning by @ahojnnes in https://github.com/colmap/colmap/pull/2176 * Update documentation with new link to GitHub discussions by @ahojnnes in https://github.com/colmap/colmap/pull/2177 * Restore GLEW include by @sarlinpe in https://github.com/colmap/colmap/pull/2178 * Align reconstructions via shared 3D points by @sarlinpe in https://github.com/colmap/colmap/pull/2169 * Add clang-tidy-cachein CI by @ahojnnes in https://github.com/colmap/colmap/pull/2182 * Disable GUI build in one CI config by @ahojnnes in https://github.com/colmap/colmap/pull/2181 * Show verbose ccache stats by @ahojnnes in https://github.com/colmap/colmap/pull/2183 * Add EstimateGeneralizedAbsolutePose by @sarlinpe in https://github.com/colmap/colmap/pull/2174 * Fix bug in ReconstructionManagerWidget::Update by @whuaegeanse in https://github.com/colmap/colmap/pull/2186 * Fix missing retrieval dependency by @ahojnnes in https://github.com/colmap/colmap/pull/2189 * Removing clustering_options_ and mapper_options_ in Hierarchical Mapper Controller by @Serenitysmk in https://github.com/colmap/colmap/pull/2193 * Publish docker image to docker hub by @ahojnnes in https://github.com/colmap/colmap/pull/2195 * Fix Cuda architecture in docker build by @ahojnnes in https://github.com/colmap/colmap/pull/2196 * Fix all-major cuda arch missing in CMake < 3.23 by @ahojnnes in https://github.com/colmap/colmap/pull/2197 * Update triangulation.cc by @RayShark0605 in https://github.com/colmap/colmap/pull/2205 * Update author and acknowledgements by @ahojnnes in https://github.com/colmap/colmap/pull/2207 * Code formatting for Python by @ahojnnes in https://github.com/colmap/colmap/pull/2208 * Retire outdated build script by @ahojnnes in https://github.com/colmap/colmap/pull/2217 * Remove mention of deprecated build script by @sarlinpe in https://github.com/colmap/colmap/pull/2220 * Improve word spelling by @zchrissirhcz in https://github.com/colmap/colmap/pull/2235 * Stack allocate camera param idx arrays by @ahojnnes in https://github.com/colmap/colmap/pull/2234 * fix: typo in colmap/src/colmap/ui/project_widget.cc by @varundhand in https://github.com/colmap/colmap/pull/2241 * Update reconstruction.cc by @RayShark0605 in https://github.com/colmap/colmap/pull/2238 * Update to Docker CUDA 12.2.2 by @ahojnnes in https://github.com/colmap/colmap/pull/2244 * Stop setting C++ standard flags manually by @AdrianBunk in https://github.com/colmap/colmap/pull/2251 * Setting clear_points to true per default in point_triangulator by @tsattler in https://github.com/colmap/colmap/pull/2252 * Update cameras.rst to fix link to code by @tsattler in https://github.com/colmap/colmap/pull/2246 * Fix matching of imported features without descriptors by @ahojnnes in https://github.com/colmap/colmap/pull/2269 * Consistent versioning between documentation and code by @ahojnnes in https://github.com/colmap/colmap/pull/2275 * Reduce mallocs for RANSAC estimator models by @ahojnnes in https://github.com/colmap/colmap/pull/2283 * Migrate to glog logging by @ahojnnes in https://github.com/colmap/colmap/pull/2172 * Turn Point3D into simple data struct by @ahojnnes in https://github.com/colmap/colmap/pull/2285 * Camera becomes simple data struct by @ahojnnes in https://github.com/colmap/colmap/pull/2286 * Recover custom Eigen std::vector allocator for Eigen <3.4 support by @ahojnnes in https://github.com/colmap/colmap/pull/2293 * Replace result_of with invoke_result_t by @sarlinpe in https://github.com/colmap/colmap/pull/2300 * Allow getters FocalLength{X,Y} for isotropic models by @sarlinpe in https://github.com/colmap/colmap/pull/2301 * Add missing Boost targets and cleanup includes by @sarlinpe in https://github.com/colmap/colmap/pull/2304 * Expose IncrementalMapperOptions::{mapper,triangulation} by @sarlinpe in https://github.com/colmap/colmap/pull/2308 * Update install instructions for Mac by @Dawars in https://github.com/colmap/colmap/pull/2310 * Remove unused ceres reference in doc by @ahojnnes in https://github.com/colmap/colmap/pull/2315 * Fix typo by @whuaegeanse in https://github.com/colmap/colmap/pull/2317 * Stable version 3.9 release by @ahojnnes in https://github.com/colmap/colmap/pull/2319 COLMAP 3.8 (01/31/2023) ----------------------- * Updating geo-registration doc. by @ferreram in https://github.com/colmap/colmap/pull/1410 * Adding user-specified option for reconstructing purely planar scene. … by @ferreram in https://github.com/colmap/colmap/pull/1408 * Only apply sqlite vacuum command when elements are deleted from the database. by @ferreram in https://github.com/colmap/colmap/pull/1414 * Replace Graclus with Metis dependency by @ahojnnes in https://github.com/colmap/colmap/pull/1422 * Update ceres download URL in build script by @whuaegeanse in https://github.com/colmap/colmap/pull/1430 * Fix type errors when building colmap with build.py in windows by @whuaegeanse in https://github.com/colmap/colmap/pull/1440 * Fix bug in the computation of the statistics Global/Local BA by @whuaegeanse in https://github.com/colmap/colmap/pull/1449 * Add RefineGeneralizedAbsolutePose and covariance estimation by @Skydes in https://github.com/colmap/colmap/pull/1464 * Update docker image definition by @ahojnnes in https://github.com/colmap/colmap/pull/1478 * Upgrade deprecated ceres parameterizations to manifolds by @ahojnnes in https://github.com/colmap/colmap/pull/1477 * Use masks for stereo fusion on automatic reconstruction by @ibrarmalik in https://github.com/colmap/colmap/pull/1488 * fix random seed set failed from external interface by @WZG3661 in https://github.com/colmap/colmap/pull/1498 * Replace deprecated Eigen nonZeros() call for most recent Eigen versions. by @nackjaylor in https://github.com/colmap/colmap/pull/1494 * Fix ceres-solver folder name by @f-fl0 in https://github.com/colmap/colmap/pull/1501 * Improved convergence criterion for XYZ to ELL conversion by @ahojnnes in https://github.com/colmap/colmap/pull/1505 * Fix bug in the function SetPtr of Bitmap by @whuaegeanse in https://github.com/colmap/colmap/pull/1525 * Avoid the calling of copy constructor/assignment by @whuaegeanse in https://github.com/colmap/colmap/pull/1524 * Avoid calling copy constructors of FeatureKeypoints and FeatureDescriptors by @whuaegeanse in https://github.com/colmap/colmap/pull/1540 * Initialize freeimage if statically linked by @ahojnnes in https://github.com/colmap/colmap/pull/1549 * Avoid hard crash if Jacobian matrix is rank deficient by @mihaidusmanu in https://github.com/colmap/colmap/pull/1557 * visualize_model.py: added FULL_OPENCV model by @soeroesg in https://github.com/colmap/colmap/pull/1552 * Update vcpkg version to fix CI pipeline by @ahojnnes in https://github.com/colmap/colmap/pull/1568 * Replace deprecated Mac OS 10.15 with Mac OS 12 build in CI by @ahojnnes in https://github.com/colmap/colmap/pull/1569 * Fix inconsistent between the actual executed image reader option and the saved project.ini file by @XuChengHUST in https://github.com/colmap/colmap/pull/1564 * checkout the expected version of ceres solver by @scott-vsi in https://github.com/colmap/colmap/pull/1576 * use default qt5 brew install directory #1573 by @catapulta in https://github.com/colmap/colmap/pull/1574 * Fix image undistortion with nested image folders by @ahojnnes in https://github.com/colmap/colmap/pull/1606 * Fix source file permissions by @ahojnnes in https://github.com/colmap/colmap/pull/1607 * Fixed the collection of arguments in colmap.bat by @tdegraaff in https://github.com/colmap/colmap/pull/1121 * Add OpenMP to COLMAP_EXTERNAL_LIBRARIES if enabled by @logchan in https://github.com/colmap/colmap/pull/1632 * Fix output tile reconstructions are the same as the input reconstruction in `RunModelSplitter` (#1513) by @Serenitysmk in https://github.com/colmap/colmap/pull/1531 * add `libmetis-dev` to solve `METIS_INCLUDE_DIRS`. by @FavorMylikes in https://github.com/colmap/colmap/pull/1672 * Update install.rst by @tomer-grin in https://github.com/colmap/colmap/pull/1671 * Update freeimage links. by @Yulv-git in https://github.com/colmap/colmap/pull/1675 * fix small typo by @skal65535 in https://github.com/colmap/colmap/pull/1668 * Update build.py with new glew link by @aghand0ur in https://github.com/colmap/colmap/pull/1658 * Add use_cache in fusion options GUI by @hrflr in https://github.com/colmap/colmap/pull/1655 * Add CI pipeline for Ubuntu 22.04 by @ahojnnes in https://github.com/colmap/colmap/pull/1688 * Avoid unnecessary copies of data by @ahojnnes in https://github.com/colmap/colmap/pull/1691 * Reduce memory allocations in correspondence graph search by @ahojnnes in https://github.com/colmap/colmap/pull/1692 * Use FindCUDAToolkit when available. by @hanseuljun in https://github.com/colmap/colmap/pull/1693 * Fixed a crash due to inconsistent undistortion by @SomeAlphabetGuy in https://github.com/colmap/colmap/pull/1698 * Add CUDA Ubuntu 22.04 CI build by @ahojnnes in https://github.com/colmap/colmap/pull/1705 * Delete the redundancy install of libmetis-dev by @thomas-graphopti in https://github.com/colmap/colmap/pull/1721 * Fix broken loading of image masks on macOS by @buesma in https://github.com/colmap/colmap/pull/1639 * Update install instructions with latest hints and known issues by @ahojnnes in https://github.com/colmap/colmap/pull/1736 * Modernize smart pointer initialization, fix alloc/dealloc mismatch by @ahojnnes in https://github.com/colmap/colmap/pull/1737 * Fix typo in cli.rst by @ojhernandez in https://github.com/colmap/colmap/pull/1747 * Fix inconsistent image resizing between CPU/GPU implementations of SIFT by @Yzhbuaa in https://github.com/colmap/colmap/pull/1642 * Reduce number of SIFT test features to make tests run under WSL by @ahojnnes in https://github.com/colmap/colmap/pull/1748 * Tag documentation version with dev by @ahojnnes in https://github.com/colmap/colmap/pull/1749 * Update copyright to 2023 by @ahojnnes in https://github.com/colmap/colmap/pull/1750 * Fix max image dimension for positive first_octave by @ahojnnes in https://github.com/colmap/colmap/pull/1751 * Fix SIFT GPU match creation by @ahojnnes in https://github.com/colmap/colmap/pull/1757 * Fix SIFT tests for OpenGL by @ahojnnes in https://github.com/colmap/colmap/pull/1762 * Suppress CUDA stack size warning for ptxas by @ahojnnes in https://github.com/colmap/colmap/pull/1770 * Simplify CUDA CMake configuration by @ahojnnes in https://github.com/colmap/colmap/pull/1776 * Fixes for CUDA compilation by @ahojnnes in https://github.com/colmap/colmap/pull/1777 * Improvements to dockerfile and build pipeline by @ahojnnes in https://github.com/colmap/colmap/pull/1778 * Explicitly require CMAKE_CUDA_ARCHITECTURES to be defined by @ahojnnes in https://github.com/colmap/colmap/pull/1781 * Depend on system installed FLANN by @ahojnnes in https://github.com/colmap/colmap/pull/1782 * Option to store relative pose between two cameras in database by @yanxke in https://github.com/colmap/colmap/pull/1774 * Depend on system installed SQLite3 by @ahojnnes in https://github.com/colmap/colmap/pull/1783 COLMAP 3.7 (01/26/2022) ----------------------- * Allow to save fused point cloud in colmap format when using command line by @boitumeloruf in https://github.com/colmap/colmap/pull/799 * Fix typos in image.h by @Pascal-So in https://github.com/colmap/colmap/pull/936 * Fix for EPnP estimator by @vlarsson in https://github.com/colmap/colmap/pull/943 * Visualize models using Python in Open3D by @ahojnnes in https://github.com/colmap/colmap/pull/948 * Update tutorial.rst by @ignacio-rocco in https://github.com/colmap/colmap/pull/953 * 8 point algorithm internal contraint fix by @mihaidusmanu in https://github.com/colmap/colmap/pull/982 * Python script for writing depth/normal arrays by @SBCV in https://github.com/colmap/colmap/pull/957 * BuildImageModel: use std::vector instead of numbered arguments by @Pascal-So in https://github.com/colmap/colmap/pull/949 * Fix bugs of sift feature matching by @whuaegeanse in https://github.com/colmap/colmap/pull/985 * script for modifying fused results by @SBCV in https://github.com/colmap/colmap/pull/984 * fix camera model query by @Pascal-So in https://github.com/colmap/colmap/pull/997 * fixed small bug in visualize_model.py by @sniklaus in https://github.com/colmap/colmap/pull/1007 * Update .travis.yml by @srinivas32 in https://github.com/colmap/colmap/pull/989 * Ensure DecomposeHomographyMatrix() always returns rotations by @daithimaco in https://github.com/colmap/colmap/pull/1040 * Remove deprecated qt foreach by @UncleGene in https://github.com/colmap/colmap/pull/1039 * Fix AMD/Windows GUI visualization bug by @drkoller in https://github.com/colmap/colmap/pull/1079 * include colmap_cuda in COLMAP_LIBRARIES when compiled with cuda by @ClementPinard in https://github.com/colmap/colmap/pull/1084 * Fix runtime crash when sparsesuite is missing from ceres by @anmatako in https://github.com/colmap/colmap/pull/1115 * Store relative poses in two_view_geometry table by @Ahmed-Salama in https://github.com/colmap/colmap/pull/1103 * search src images for patch_match from all set, not only referenced subset by @DaniilSNikulin in https://github.com/colmap/colmap/pull/1038 * Replace Travis CI with Azure Pipelines for Linux/Mac builds by @ahojnnes in https://github.com/colmap/colmap/pull/1119 * Allow ReadPly to handle double precision files by @anmatako in https://github.com/colmap/colmap/pull/1131 * Update GPSTransform calculations to improve accuracy by @anmatako in https://github.com/colmap/colmap/pull/1132 * Add scale template flag in SimilarityTransform3::Estimate by @anmatako in https://github.com/colmap/colmap/pull/1133 * Add CopyFile utility that can copy or hard/soft-link files by @anmatako in https://github.com/colmap/colmap/pull/1134 * Expose BA options in IncrementalMapper by @anmatako in https://github.com/colmap/colmap/pull/1139 * Allow configurable paths for mvs::Model by @anmatako in https://github.com/colmap/colmap/pull/1141 * Change ReconstructionMaanger to write larger recons first by @anmatako in https://github.com/colmap/colmap/pull/1137 * Setup Azure pipelines for Windows build by @ahojnnes in https://github.com/colmap/colmap/pull/1150 * Add fixed extrinsics in rig config by @anmatako in https://github.com/colmap/colmap/pull/1144 * Allow custom config and missing dependencies for patch-match by @anmatako in https://github.com/colmap/colmap/pull/1142 * Update print statements for Python 3 compatibility by @UncleGene in https://github.com/colmap/colmap/pull/1126 * Allow cleanup of SQLite tables using new database_cleaner command by @anmatako in https://github.com/colmap/colmap/pull/1136 * Extend SceneClustering to support non-hierarchical (flat) clusters by @anmatako in https://github.com/colmap/colmap/pull/1140 * Support more formats in model_converter by @anmatako in https://github.com/colmap/colmap/pull/1147 * Fix Mac 10.15 build due to changed Qt5 path by @ahojnnes in https://github.com/colmap/colmap/pull/1157 * Fix bug in ReadCameraRigConfig when reading extrinsics by @anmatako in https://github.com/colmap/colmap/pull/1158 * Add utility to compare poses between two sparse models by @ahojnnes in https://github.com/colmap/colmap/pull/1159 * Modularize executable main functions into separate sources by @ahojnnes in https://github.com/colmap/colmap/pull/1160 * Fix unnecessary copies in for range loops by @ahojnnes in https://github.com/colmap/colmap/pull/1162 * Add script to clang-format all source code by @ahojnnes in https://github.com/colmap/colmap/pull/1163 * Add back new options and formats for model_converter by @anmatako in https://github.com/colmap/colmap/pull/1164 * ImageReder new option and bug fix in GPS priors by @anmatako in https://github.com/colmap/colmap/pull/1146 * Parallelize stereo fusion; needs pre-loading of entire workspace by @anmatako in https://github.com/colmap/colmap/pull/1148 * Refactoring and new functionality in Reconstruction class by @anmatako in https://github.com/colmap/colmap/pull/1169 * Add new functionality in image_undistorter by @anmatako in https://github.com/colmap/colmap/pull/1168 * Add new CMake option to disable GUI by @anmatako in https://github.com/colmap/colmap/pull/1165 * Fix the memory leak caused by not releasing the memory of the PRNG at the end of the thread by @whuaegeanse in https://github.com/colmap/colmap/pull/1170 * Fix fusion segfault bug by @anmatako in https://github.com/colmap/colmap/pull/1176 * Update SiftGPU to use floorf for floats by @anmatako in https://github.com/colmap/colmap/pull/1182 * fix typo in extraction.cc by @iuk in https://github.com/colmap/colmap/pull/1191 * Improvements to NVM, Cam, Recon3D, and Bundler exporters by @drkoller in https://github.com/colmap/colmap/pull/1187 * Update model_aligner functionality by @anmatako in https://github.com/colmap/colmap/pull/1177 * Add new model_cropper and model_splitter commands by @anmatako in https://github.com/colmap/colmap/pull/1179 * use type point2D_t instead of image_t by @iuk in https://github.com/colmap/colmap/pull/1199 * Fix radial distortion in Cam format exporter by @drkoller in https://github.com/colmap/colmap/pull/1196 * Add new model_transformer command by @anmatako in https://github.com/colmap/colmap/pull/1178 * Fix error of using urllib to download eigen from gitlab by @whuaegeanse in https://github.com/colmap/colmap/pull/1194 * Multi-line string fix in Python model script by @mihaidusmanu in https://github.com/colmap/colmap/pull/1217 * added visibility_sigma to CLI input options for delaunay_mesher. by @Matstah in https://github.com/colmap/colmap/pull/1236 * Backwards compatibility of model_aligner by @tsattler in https://github.com/colmap/colmap/pull/1240 * [update undistortion] update dumped commands by @hiakru in https://github.com/colmap/colmap/pull/1276 * Compute reprojection error in generalized absolute solver by @Skydes in https://github.com/colmap/colmap/pull/1257 * Modifying scripts/python/flickr_downloader.py to create files with correct extensions by @snavely in https://github.com/colmap/colmap/pull/1275 * revise Dockerfile and readme. by @MasahiroOgawa in https://github.com/colmap/colmap/pull/1281 * Update to latest vcpkg version by @ahojnnes in https://github.com/colmap/colmap/pull/1319 * Fix compiler warnings reported by GCC by @ahojnnes in https://github.com/colmap/colmap/pull/1317 * Auto-rotate JPEG images based on EXIF orientation by @ahojnnes in https://github.com/colmap/colmap/pull/1318 * Upgrade vcpkg to fix CI build issues by @ahojnnes in https://github.com/colmap/colmap/pull/1331 * Added descriptor normalization argument to feature_extractor. by @mihaidusmanu in https://github.com/colmap/colmap/pull/1332 * Fix memory leak in the function of StringAppendV by @whuaegeanse in https://github.com/colmap/colmap/pull/1337 * Add CUDA_SAFE_CALL to cudaGetDeviceCount. by @chpatrick in https://github.com/colmap/colmap/pull/1334 * Add missing include in case CUDA/GUI is not available by @ahojnnes in https://github.com/colmap/colmap/pull/1329 * Fix wrong WGS84 model and test cases in GPSTransform by @Freeverc in https://github.com/colmap/colmap/pull/1333 * Fixes bug in sprt.cc: num_inliers was not set. by @rmbrualla in https://github.com/colmap/colmap/pull/1360 * Prevent a divide by zero corner case. by @rmbrualla in https://github.com/colmap/colmap/pull/1361 * Adds missing header. by @rmbrualla in https://github.com/colmap/colmap/pull/1362 * Require Qt in COLMAPConfig only if GUI is enabled by @Skydes in https://github.com/colmap/colmap/pull/1365 * Keep precision in the process of storing in text. by @whuaegeanse in https://github.com/colmap/colmap/pull/1363 * Expose exe internals by @Skydes in https://github.com/colmap/colmap/pull/1366 * Fix inliers matches extraction in EstimateUncalibrated function. by @ferreram in https://github.com/colmap/colmap/pull/1369 * Expose exe internals - fix by @Skydes in https://github.com/colmap/colmap/pull/1368 * Remove deprecated Mac OSX 10.14 image in ADO pipeline by @ahojnnes in https://github.com/colmap/colmap/pull/1383 * Add Mac OSX 11 ADO pipeline job by @ahojnnes in https://github.com/colmap/colmap/pull/1384 * Fix warnings for latest compiler/libraries by @ahojnnes in https://github.com/colmap/colmap/pull/1382 * Fix clang compiler warnings by @ahojnnes in https://github.com/colmap/colmap/pull/1387 * Add Address Sanitizer options and fix reported issues by @ahojnnes in https://github.com/colmap/colmap/pull/1390 * User/joschonb/asan cleanup by @ahojnnes in https://github.com/colmap/colmap/pull/1391 * Add ADO pipeline for Visual Studio 2022 by @ahojnnes in https://github.com/colmap/colmap/pull/1392 * Add ccache option by @ahojnnes in https://github.com/colmap/colmap/pull/1395 * Update ModelAligner to handle GPS and custom coords. and more by @ferreram in https://github.com/colmap/colmap/pull/1371 COLMAP 3.6 (07/24/2020) ----------------------- - Improved robustness and faster incremental reconstruction process - Add ``image_deleter`` command to remove images from sparse model - Add ``image_filter`` command to filter bad registrations from sparse model - Add ``point_filtering`` command to filter sparse model point clouds - Add ``database_merger`` command to merge two databases, which is useful to parallelize matching across different machines - Add ``image_undistorter_standalone`` to enable undistorting images without a pre-existing full sparse model - Improved undistortion for fisheye cameras and FOV camera model - Support for masking input images in feature extraction stage - Improved HiDPI support in GUI for high-resolution monitors - Import sparse model when launching GUI from CLI - Faster CPU-based matching using approximate NN search - Support for bundle adjustment with fixed extrinsics - Support for fixing existing images when continuing reconstruction - Camera model colors in viewer can be customized - Support for latest GPU architectures in CUDA build - Support for writing sparse models in Python scripts - Scripts for building and running COLMAP in Docker - Many more bug fixes and improvements to code and documentation COLMAP 3.5 (08/22/2018) ----------------------- - COLMAP is now released under the BSD license instead of the GPL - COLMAP is now installed as a library, whose headers can be included and libraries linked against from other C/C++ code - Add hierarchical mapper for parallelized reconstruction or large scenes - Add sparse and dense Delaunay meshing algorithms, which reconstruct a watertight surface using a graph cut on the Delaunay triangulation of the reconstructed sparse or dense point cloud - Improved robustness when merging different models - Improved pre-trained vocabulary trees available for download - Add COLMAP as a software entry under Linux desktop systems - Add support to compile COLMAP on ARM platforms - Add example Python script to read/write COLMAP database - Add region of interest (ROI) cropping in image undistortion - Several import bug fixes for spatial verification in image retrieval - Add more extensive continuous integration across more compilation scenarios - Many more bug fixes and improvements to code and documentation COLMAP 3.4 (01/29/2018) ----------------------- - Unified command-line interface: The functionality of previous executables have been merged into the ``src/exe/colmap.cc`` executable. The GUI can now be started using the command ``colmap gui`` and other commands are available as ``colmap [command]``. For example, the feature extractor is now available as ``colmap feature_extractor [args]`` while all command-line arguments stay the same as before. This should result in much faster project compile times and smaller disk space usage of the program. More details about the new interface are documented at https://colmap.github.io/cli.html - More complete depth and normal maps with larger patch sizes - Faster dense stereo computation by skipping rows/columns in patch match, improved random sampling in patch match, and faster bilateral NCC - Better high DPI screen support for the graphical user interface - Improved model viewer under Windows, which now requires Qt 5.4 - Save computed two-view geometries in database - Images (keypoint/matches visualization, depth and normal maps) can now be saved from the graphical user interface - Support for PMVS format without sparse bundler file - Faster covariant feature detection - Many more bug fixes and improvements COLMAP 3.3 (11/21/2017) ----------------------- - Add DSP (Domain Size Pooling) SIFT implementation. DSP-SIFT outperforms standard SIFT in most cases, as shown in "Comparative Evaluation of Hand-Crafted and Learned Local Features", Schoenberger et al., CVPR 2017 - Improved parameters dense reconstruction of smaller models - Improved compile times due to various code optimizations - Add option to specify camera model in automatic reconstruction - Add new model orientation alignment based on upright image assumption - Improved numerical stability for generalized absolute pose solver - Support for image range specification in PMVS dense reconstruction format - Support for older Python versions in automatic build script - Fix OpenCV Fisheye camera model to exactly match OpenCV specifications COLMAP 3.2 (9/2/2017) ---------------------- - Fully automatic cross-platform build script (Windows, Mac, Linux) - Add multi-GPU feature extraction if multiple CUDA devices are available - Configurable dimension and data type for vocabulary tree implementation - Add new sequential matching mode for image sequences with high frame-rate - Add generalized relative pose solver for multi-camera systems - Add sparse least absolute deviation solver - Add CPU/GPU options to automatic reconstruction tool - Add continuous integration system under Windows, Mac, Linux through Github - Many more bug fixes and improvements COLMAP 3.1 (6/15/2017) ---------------------- - Add fast spatial verification to image retrieval module - Add binary file format for sparse models by default. Old text format still fully compatible and possible conversion in GUI and CLI - Add cross-platform little endian binary file reading and writing - Faster and less memory hungry stereo fusion by computing consistency on demand and possible limitation of image size in fusion - Simpler geometric stereo processing interface. Now geometric stereo output can be computed using a single pass - Faster and multi-architecture CUDA compilation - Add medium quality option in automatic reconstructor - Many more bug fixes and improvements COLMAP 3.0 (5/22/2017) ---------------------- - Add automatic end-to-end reconstruction tool that automatically performs sparse and dense reconstruction on a given set of images - Add multi-GPU dense stereo if multiple CUDA devices are available - Add multi-GPU feature matching if multiple CUDA devices are available - Add Manhattan-world / gravity alignment using line detection - Add CUDA-based feature extraction useful for usage on clusters - Add CPU-based feature matching for machines without GPU - Add new THIN_PRISM_FISHEYE camera model with tangential/radial correction - Add binary to triangulate existing/empty sparse reconstruction - Add binary to print summary statistics about sparse reconstruction - Add transitive feature matching to transitively complete match graph - Improved scalability of dense reconstruction by using caching - More stable GPU-based feature matching with informative warnings - Faster vocabulary tree matching using dynamic scheduling in FLANN - Faster spatial feature matching using linear index instead of kd-tree - More stable camera undistortion using numerical Newton iteration - Improved option parsing with some backwards incompatible option renaming - Faster compile times by optimizing includes and CUDA flags - More stable view selection for small baseline scenario in dense reconstruction - Many more bug fixes and improvements COLMAP 2.1 (12/7/2016) ---------------------- - Support to only index and match specific images in vocabulary tree matching - Support to perform image retrieval using vocabulary tree - Several bug fixes and improvements for multi-view stereo module - Improved Structure-from-Motion initialization strategy - Support to only reconstruct the scene using specific images in the database - Add support to merge two models using overlapping registered images - Add support to geo-register/align models using known camera locations - Support to only extract specific images in feature extraction module - Support for snapshot model export during reconstruction - Skip already undistorted images if they exist in output directory - Support to limit the number of features in image retrieval for improved speed - Miscellaneous bug fixes and improvements COLMAP 2.0 (9/8/2016) --------------------- - Implementation of dense reconstruction pipeline - Improved feature matching performance - New bundle adjuster for rigidly mounted multi-camera systems - New generalized absolute pose solver for multi-camera systems - New executable to extract colors from all images - Boost can now be linked in shared and static mode - Various bug fixes and performance improvements COLMAP 1.1 (5/19/2016) ---------------------- - Implementation of state-of-the-art image retrieval system using Hamming embedding for vocabulary tree matching. This should lead to much improved matching results as compared to the previous implementation. - Guided matching as an optional functionality. - New demo datasets for download. - Automatically switch to PBA if supported by the project. - Implementation of EPNP solver for local pose optimization in RANSAC. - Add option to extract upright SIFT features. - Saving JPEGs in superb quality by default in export. - Add option to clear matches and inlier matches in the project. - New fisheye camera models, including the FOV camera model used by Google Project Tango (Thomas Schoeps). - Extended documentation based on user feedback. - Fixed typo in documentation (Thomas Schoeps). COLMAP 1.0 (4/4/2016) --------------------- - Initial release of COLMAP. colmap-3.10/CMakeLists.txt000066400000000000000000000335171464745566500155070ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. cmake_minimum_required(VERSION 3.10) ################################################################################ # Options ################################################################################ option(SIMD_ENABLED "Whether to enable SIMD optimizations" ON) option(OPENMP_ENABLED "Whether to enable OpenMP parallelization" ON) option(IPO_ENABLED "Whether to enable interprocedural optimization" ON) option(CUDA_ENABLED "Whether to enable CUDA, if available" ON) option(GUI_ENABLED "Whether to enable the graphical UI" ON) option(OPENGL_ENABLED "Whether to enable OpenGL, if available" ON) option(TESTS_ENABLED "Whether to build test binaries" OFF) option(ASAN_ENABLED "Whether to enable AddressSanitizer flags" OFF) option(PROFILING_ENABLED "Whether to enable google-perftools linker flags" OFF) option(CCACHE_ENABLED "Whether to enable compiler caching, if available" ON) option(CGAL_ENABLED "Whether to enable the CGAL library" ON) option(LSD_ENABLED "Whether to enable the LSD library" ON) option(UNINSTALL_ENABLED "Whether to create a target to 'uninstall' colmap" ON) # Propagate options to vcpkg manifest. if(TESTS_ENABLED) enable_testing() list(APPEND VCPKG_MANIFEST_FEATURES "tests") endif() if(CUDA_ENABLED) list(APPEND VCPKG_MANIFEST_FEATURES "cuda") endif() if(GUI_ENABLED) list(APPEND VCPKG_MANIFEST_FEATURES "gui") endif() if(CGAL_ENABLED) list(APPEND VCPKG_MANIFEST_FEATURES "cgal") endif() if(LSD_ENABLED) message(STATUS "Enabling LSD support") add_definitions("-DCOLMAP_LSD_ENABLED") else() message(STATUS "Disabling LSD support") endif() project(COLMAP LANGUAGES C CXX) set(COLMAP_VERSION "3.10-dev") set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CUDA_STANDARD 14) set(CMAKE_CUDA_STANDARD_REQUIRED ON) set_property(GLOBAL PROPERTY GLOBAL_DEPENDS_NO_CYCLES ON) ################################################################################ # Include CMake dependencies ################################################################################ set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake) include(CheckCXXCompilerFlag) # Include helper macros and commands, and allow the included file to override # the CMake policies in this file include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/CMakeHelper.cmake NO_POLICY_SCOPE) # Build position-independent code, so that shared libraries can link against # COLMAP's static libraries. set(CMAKE_POSITION_INDEPENDENT_CODE ON) ################################################################################ # Dependency configuration ################################################################################ set(COLMAP_FIND_QUIETLY FALSE) include(cmake/FindDependencies.cmake) ################################################################################ # Compiler specific configuration ################################################################################ if(CMAKE_BUILD_TYPE) message(STATUS "Build type specified as ${CMAKE_BUILD_TYPE}") else() message(STATUS "Build type not specified, using Release") set(CMAKE_BUILD_TYPE Release) set(IS_DEBUG OFF) endif() if("${CMAKE_BUILD_TYPE}" STREQUAL "ClangTidy") find_program(CLANG_TIDY_EXE NAMES clang-tidy) if(NOT CLANG_TIDY_EXE) message(FATAL_ERROR "Could not find the clang-tidy executable, please set CLANG_TIDY_EXE") endif() else() unset(CLANG_TIDY_EXE) endif() if(IS_MSVC) # Some fixes for the Glog library. add_definitions("-DGLOG_USE_GLOG_EXPORT") add_definitions("-DGLOG_NO_ABBREVIATED_SEVERITIES") add_definitions("-DGL_GLEXT_PROTOTYPES") add_definitions("-DNOMINMAX") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc") # Disable warning: 'initializing': conversion from 'X' to 'Y', possible loss of data set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4244 /wd4267 /wd4305") # Enable object level parallel builds in Visual Studio. set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /MP") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP") if("${CMAKE_BUILD_TYPE}" STREQUAL "Debug" OR "${CMAKE_BUILD_TYPE}" STREQUAL "RelWithDebInfo") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /bigobj") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /bigobj") endif() endif() if(IS_GNU) if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9) message(FATAL_ERROR "GCC version 4.8 or older not supported") endif() # Hide incorrect warnings for uninitialized Eigen variables under GCC. set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-maybe-uninitialized") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-maybe-uninitialized") endif() if(IS_DEBUG) add_definitions("-DEIGEN_INITIALIZE_MATRICES_BY_NAN") endif() if(SIMD_ENABLED) message(STATUS "Enabling SIMD support") else() message(STATUS "Disabling SIMD support") endif() if(IPO_ENABLED AND NOT IS_DEBUG AND NOT IS_GNU) message(STATUS "Enabling interprocedural optimization") set_property(DIRECTORY PROPERTY INTERPROCEDURAL_OPTIMIZATION 1) else() message(STATUS "Disabling interprocedural optimization") endif() if(ASAN_ENABLED) message(STATUS "Enabling ASan support") if(IS_CLANG OR IS_GNU) add_compile_options(-fsanitize=address -fno-omit-frame-pointer -fsanitize-address-use-after-scope) add_link_options(-fsanitize=address) else() message(FATAL_ERROR "Unsupported compiler for ASan mode") endif() endif() if(CCACHE_ENABLED) find_program(CCACHE ccache) if(CCACHE) message(STATUS "Enabling ccache support") set(CMAKE_C_COMPILER_LAUNCHER ${CCACHE}) set(CMAKE_CXX_COMPILER_LAUNCHER ${CCACHE}) else() message(STATUS "Disabling ccache support") endif() else() message(STATUS "Disabling ccache support") endif() if(PROFILING_ENABLED) message(STATUS "Enabling profiling support") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -lprofiler -ltcmalloc") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lprofiler -ltcmalloc") else() message(STATUS "Disabling profiling support") endif() ################################################################################ # Add sources ################################################################################ # Generate source file with version definitions. include(GenerateVersionDefinitions) include_directories(src) link_directories(${COLMAP_LINK_DIRS}) add_subdirectory(src/colmap) add_subdirectory(src/thirdparty) ################################################################################ # Generate source groups for Visual Studio, XCode, etc. ################################################################################ COLMAP_ADD_SOURCE_DIR(src/colmap/controllers CONTROLLERS_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/estimators ESTIMATORS_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/exe EXE_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/feature FEATURE_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/geometry GEOMETRY_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/image IMAGE_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/math MATH_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/mvs MVS_SRCS *.h *.cc *.cu) COLMAP_ADD_SOURCE_DIR(src/colmap/optim OPTIM_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/retrieval RETRIEVAL_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/scene SCENE_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/sensor SENSOR_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/sfm SFM_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/tools TOOLS_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/ui UI_SRCS *.h *.cc) COLMAP_ADD_SOURCE_DIR(src/colmap/util UTIL_SRCS *.h *.cc) if(LSD_ENABLED) COLMAP_ADD_SOURCE_DIR(src/thirdparty/LSD THIRDPARTY_LSD_SRCS *.h *.c) endif() COLMAP_ADD_SOURCE_DIR(src/thirdparty/PoissonRecon THIRDPARTY_POISSON_RECON_SRCS *.h *.cpp *.inl) COLMAP_ADD_SOURCE_DIR(src/thirdparty/SiftGPU THIRDPARTY_SIFT_GPU_SRCS *.h *.cpp *.cu) COLMAP_ADD_SOURCE_DIR(src/thirdparty/VLFeat THIRDPARTY_VLFEAT_SRCS *.h *.c *.tc) # Add all of the source files to a regular library target, as using a custom # target does not allow us to set its C++ include directories (and thus # intellisense can't find any of the included files). set(ALL_SRCS ${CONTROLLERS_SRCS} ${ESTIMATORS_SRCS} ${EXE_SRCS} ${FEATURE_SRCS} ${GEOMETRY_SRCS} ${IMAGE_SRCS} ${MATH_SRCS} ${MVS_SRCS} ${OPTIM_SRCS} ${RETRIEVAL_SRCS} ${SCENE_SRCS} ${SENSOR_SRCS} ${SFM_SRCS} ${TOOLS_SRCS} ${UI_SRCS} ${UTIL_SRCS} ${THIRDPARTY_POISSON_RECON_SRCS} ${THIRDPARTY_SIFT_GPU_SRCS} ${THIRDPARTY_VLFEAT_SRCS} ) if(LSD_ENABLED) list(APPEND ALL_SRCS ${THIRDPARTY_LSD_SRCS} ) endif() add_library( ${COLMAP_SRC_ROOT_FOLDER} ${ALL_SRCS} ) # Prevent the library from being compiled automatically. set_target_properties( ${COLMAP_SRC_ROOT_FOLDER} PROPERTIES EXCLUDE_FROM_ALL 1 EXCLUDE_FROM_DEFAULT_BUILD 1) ################################################################################ # Install and uninstall scripts ################################################################################ # Install batch scripts under Windows. if(IS_MSVC) install(FILES "scripts/shell/COLMAP.bat" "scripts/shell/RUN_TESTS.bat" PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE DESTINATION "/") endif() # Install application meny entry under Linux/Unix. if(UNIX AND NOT APPLE) install(FILES "doc/COLMAP.desktop" DESTINATION "share/applications") endif() # Configure the uninstallation script. if(UNINSTALL_ENABLED) configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/CMakeUninstall.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/CMakeUninstall.cmake" IMMEDIATE @ONLY) add_custom_target(uninstall COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/CMakeUninstall.cmake) set_target_properties(uninstall PROPERTIES FOLDER ${CMAKE_TARGETS_ROOT_FOLDER}) endif() set(COLMAP_EXPORT_LIBS # Internal. colmap_controllers colmap_estimators colmap_exe colmap_feature_types colmap_feature colmap_geometry colmap_image colmap_math colmap_mvs colmap_optim colmap_retrieval colmap_scene colmap_sensor colmap_sfm colmap_util # Third-party. colmap_poisson_recon colmap_vlfeat ) if(LSD_ENABLED) list(APPEND COLMAP_EXPORT_LIBS # Third-party. colmap_lsd ) endif() if(GUI_ENABLED) list(APPEND COLMAP_EXPORT_LIBS colmap_ui ) endif() if(CUDA_ENABLED) list(APPEND COLMAP_EXPORT_LIBS colmap_util_cuda colmap_mvs_cuda ) endif() if(GPU_ENABLED) list(APPEND COLMAP_EXPORT_LIBS colmap_sift_gpu ) endif() # Add unified interface library target to export. add_library(colmap INTERFACE) target_link_libraries(colmap INTERFACE ${COLMAP_EXPORT_LIBS}) set(INSTALL_INCLUDE_DIR "${CMAKE_INSTALL_PREFIX}/include") target_include_directories( colmap INTERFACE $ $) install( TARGETS colmap ${COLMAP_EXPORT_LIBS} EXPORT colmap-targets LIBRARY DESTINATION thirdparty/) # Generate config and version. include(CMakePackageConfigHelpers) set(PACKAGE_CONFIG_FILE "${CMAKE_CURRENT_BINARY_DIR}/colmap-config.cmake") set(INSTALL_CONFIG_DIR "share/colmap") configure_package_config_file( ${CMAKE_CURRENT_SOURCE_DIR}/cmake/colmap-config.cmake.in ${PACKAGE_CONFIG_FILE} INSTALL_DESTINATION ${INSTALL_CONFIG_DIR}) install(FILES ${PACKAGE_CONFIG_FILE} DESTINATION ${INSTALL_CONFIG_DIR}) configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/colmap-config-version.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/colmap-config-version.cmake" @ONLY) install(FILES "${CMAKE_CURRENT_BINARY_DIR}/colmap-config-version.cmake" DESTINATION "share/colmap") # Install targets. install( EXPORT colmap-targets FILE colmap-targets.cmake NAMESPACE colmap:: DESTINATION ${INSTALL_CONFIG_DIR}) # Install header files. install( DIRECTORY src/colmap DESTINATION include FILES_MATCHING PATTERN "*.h") install( DIRECTORY src/thirdparty DESTINATION include/colmap FILES_MATCHING REGEX ".*[.]h|.*[.]hpp|.*[.]inl") # Install find_package scripts for dependencies. install( DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/cmake DESTINATION share/colmap FILES_MATCHING PATTERN "Find*.cmake") colmap-3.10/CONTRIBUTING.md000066400000000000000000000010721464745566500151670ustar00rootroot00000000000000Contributing ------------ Contributions (bug reports, bug fixes, improvements, etc.) are very welcome and should be submitted in the form of new issues and/or pull requests on GitHub. Please, adhere to the Google coding style guide: https://google.github.io/styleguide/cppguide.html by using the provided ".clang-format" file. Document code, functions, methods, classes, etc. Make sure to add unit tests for all newly added code and make sure that algorithmic "improvements" generalize and actually improve the results of the pipeline on a variety of datasets. colmap-3.10/COPYING.txt000066400000000000000000000035721464745566500146160ustar00rootroot00000000000000The COLMAP library is licensed under the new BSD license. Note that this text refers only to the license for COLMAP itself, independent of its dependencies, which are separately licensed. Building COLMAP with these dependencies may affect the resulting COLMAP license. Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. colmap-3.10/README.md000066400000000000000000000126541464745566500142250ustar00rootroot00000000000000COLMAP ====== About ----- COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. If you use this project for your research, please cite: @inproceedings{schoenberger2016sfm, author={Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael}, title={Structure-from-Motion Revisited}, booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2016}, } @inproceedings{schoenberger2016mvs, author={Sch\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael}, title={Pixelwise View Selection for Unstructured Multi-View Stereo}, booktitle={European Conference on Computer Vision (ECCV)}, year={2016}, } If you use the image retrieval / vocabulary tree engine, please also cite: @inproceedings{schoenberger2016vote, author={Sch\"{o}nberger, Johannes Lutz and Price, True and Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc}, title={A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval}, booktitle={Asian Conference on Computer Vision (ACCV)}, year={2016}, } The latest source code is available at https://github.com/colmap/colmap. COLMAP builds on top of existing works and when using specific algorithms within COLMAP, please also cite the original authors, as specified in the source code, and consider citing relevant third-party dependencies. Download -------- * Binaries for **Windows** and other resources can be downloaded from https://github.com/colmap/colmap/releases. * Binaries for **Linux/Unix/BSD** are available at https://repology.org/metapackage/colmap/versions. * Pre-built **Docker** images are available at https://hub.docker.com/r/colmap/colmap. * **Python bindings** are available at https://pypi.org/project/pycolmap. * To **build from source**, please see https://colmap.github.io/install.html. Getting Started --------------- 1. Download pre-built binaries or build from source. 2. Download one of the provided datasets at https://demuc.de/colmap/datasets/ or use your own images. 3. Use the **automatic reconstruction** to easily build models with a single click or command. Documentation ------------- The documentation is available at https://colmap.github.io/. Support ------- Please, use GitHub Discussions at https://github.com/colmap/colmap/discussions for questions and the GitHub issue tracker at https://github.com/colmap/colmap for bug reports, feature requests/additions, etc. Acknowledgments --------------- COLMAP was originally written by [Johannes Schönberger](https://demuc.de/) with funding provided by his PhD advisors Jan-Michael Frahm and Marc Pollefeys. The Python bindings in PyCOLMAP were originally added by [Mihai Dusmanu](https://github.com/mihaidusmanu), [Philipp Lindenberger](https://github.com/Phil26AT), and [Paul-Edouard Sarlin](https://github.com/Skydes). The project has also benefitted from countless community contributions, including bug fixes, improvements, new features, third-party tooling, and community support (special credits to [Torsten Sattler](https://tsattler.github.io)). Contribution ------------ Contributions (bug reports, bug fixes, improvements, etc.) are very welcome and should be submitted in the form of new issues and/or pull requests on GitHub. License ------- The COLMAP library is licensed under the new BSD license. Note that this text refers only to the license for COLMAP itself, independent of its thirdparty dependencies, which are separately licensed. Building COLMAP with these dependencies may affect the resulting COLMAP license. Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. colmap-3.10/benchmark/000077500000000000000000000000001464745566500146705ustar00rootroot00000000000000colmap-3.10/benchmark/CMakeLists.txt000066400000000000000000000004301464745566500174250ustar00rootroot00000000000000cmake_minimum_required(VERSION 3.10) project(colmap_benchmarks) find_package(colmap REQUIRED) find_package(benchmark REQUIRED) add_executable(benchmark_cost_functions cost_functions.cc) target_link_libraries(benchmark_cost_functions PRIVATE colmap::colmap benchmark::benchmark) colmap-3.10/benchmark/README.md000066400000000000000000000010211464745566500161410ustar00rootroot00000000000000# Benchmarking ## Installation 1. Install [google/benchmark](https://github.com/google/benchmark). 2. Build the benchmarking executables: ```bash mkdir build && cd build cmake .. -GNinja && ninja ``` To reduce the variance, consider setting up your system appropriately [following these instructions](https://github.com/google/benchmark/blob/main/docs/reducing_variance.md). ## Running the benchmarks Cost functions: ```bash ./benchmark_cost_functions --benchmark_display_aggregates_only=true --benchmark_repetitions=50 ``` colmap-3.10/benchmark/cost_functions.cc000066400000000000000000000070711464745566500202440ustar00rootroot00000000000000#include "colmap/estimators/cost_functions.h" #include "colmap/geometry/rigid3.h" #include "colmap/sensor/models.h" #include "colmap/util/eigen_alignment.h" #include "colmap/util/logging.h" #include #include using namespace colmap; using camera_model = SimpleRadialCameraModel; struct ReprojErrorData { Rigid3d cam_from_world; Eigen::Vector3d point3D; Eigen::Vector2d point2D; std::vector camera_params; }; static ReprojErrorData CreateReprojErrorData() { ReprojErrorData data{ Rigid3d(Eigen::Quaterniond(0.9, 0.1, 0.1, 0.1), Eigen::Vector3d::Zero()), Eigen::Vector3d(1, 2, 10), Eigen::Vector2d(0.1, 0.2), {1, 0, 0, 0.1}, }; CHECK_EQ(data.camera_params.size(), camera_model::num_params); return std::move(data); } class BM_ReprojErrorCostFunction : public benchmark::Fixture { public: void SetUp(::benchmark::State& state) { cost_function.reset( ReprojErrorCostFunction::Create(data.point2D)); } ReprojErrorData data = CreateReprojErrorData(); const double* parameters[4] = {data.cam_from_world.rotation.coeffs().data(), data.cam_from_world.translation.data(), data.point3D.data(), data.camera_params.data()}; double residuals[2]; double jacobian_q[2 * 4]; double jacobian_t[2 * 3]; double jacobian_p[2 * 3]; double jacobian_params[2 * camera_model::num_params]; double* jacobians[4] = {jacobian_q, jacobian_t, jacobian_p, jacobian_params}; std::unique_ptr cost_function; }; BENCHMARK_F(BM_ReprojErrorCostFunction, Run)(benchmark::State& state) { for (auto _ : state) { cost_function->Evaluate(parameters, residuals, jacobians); } } class BM_ReprojErrorConstantPoseCostFunction : public benchmark::Fixture { public: void SetUp(::benchmark::State& state) { cost_function.reset( ReprojErrorConstantPoseCostFunction::Create( data.cam_from_world, data.point2D)); } ReprojErrorData data = CreateReprojErrorData(); const double* parameters[2] = {data.point3D.data(), data.camera_params.data()}; double residuals[2]; double jacobian_p[2 * 3]; double jacobian_params[2 * camera_model::num_params]; double* jacobians[2] = {jacobian_p, jacobian_params}; std::unique_ptr cost_function; }; BENCHMARK_F(BM_ReprojErrorConstantPoseCostFunction, Run) (benchmark::State& state) { for (auto _ : state) { cost_function->Evaluate(parameters, residuals, jacobians); } } class BM_ReprojErrorConstantPoint3DCostFunction : public benchmark::Fixture { public: void SetUp(::benchmark::State& state) { cost_function.reset( ReprojErrorConstantPoint3DCostFunction::Create( data.point2D, data.point3D)); } ReprojErrorData data = CreateReprojErrorData(); const double* parameters[3] = {data.cam_from_world.rotation.coeffs().data(), data.cam_from_world.translation.data(), data.camera_params.data()}; double residuals[2]; double jacobian_q[2 * 4]; double jacobian_t[2 * 3]; double jacobian_params[2 * camera_model::num_params]; double* jacobians[3] = {jacobian_q, jacobian_t, jacobian_params}; std::unique_ptr cost_function; }; BENCHMARK_F(BM_ReprojErrorConstantPoint3DCostFunction, Run) (benchmark::State& state) { for (auto _ : state) { cost_function->Evaluate(parameters, residuals, jacobians); } } BENCHMARK_MAIN(); colmap-3.10/cmake/000077500000000000000000000000001464745566500140165ustar00rootroot00000000000000colmap-3.10/cmake/CMakeHelper.cmake000066400000000000000000000127751464745566500171540ustar00rootroot00000000000000if(POLICY CMP0043) cmake_policy(SET CMP0043 NEW) endif() if(POLICY CMP0054) cmake_policy(SET CMP0054 NEW) endif() # Determine project compiler. if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(IS_MSVC TRUE) endif() if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(IS_GNU TRUE) endif() if(CMAKE_CXX_COMPILER_ID MATCHES ".*Clang") set(IS_CLANG TRUE) endif() # Determine project architecture. if(CMAKE_SYSTEM_PROCESSOR MATCHES "[ix].?86|amd64|AMD64") set(IS_X86 TRUE) endif() # Determine project operating system. string(REGEX MATCH "Linux" IS_LINUX ${CMAKE_SYSTEM_NAME}) string(REGEX MATCH "DragonFly|BSD" IS_BSD ${CMAKE_SYSTEM_NAME}) string(REGEX MATCH "SunOS" IS_SOLARIS ${CMAKE_SYSTEM_NAME}) if(WIN32) set(IS_WINDOWS TRUE BOOL INTERNAL) endif() if(APPLE) set(IS_MACOS TRUE BOOL INTERNAL) endif() string(TOLOWER "${CMAKE_BUILD_TYPE}" CMAKE_BUILD_TYPE_LOWER) if(CMAKE_BUILD_TYPE_LOWER STREQUAL "debug" OR CMAKE_BUILD_TYPE_LOWER STREQUAL "relwithdebinfo") set(IS_DEBUG TRUE) endif() # Enable solution folders. set_property(GLOBAL PROPERTY USE_FOLDERS ON) set(CMAKE_TARGETS_ROOT_FOLDER "cmake") set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER ${CMAKE_TARGETS_ROOT_FOLDER}) set(COLMAP_TARGETS_ROOT_FOLDER "colmap_targets") set(COLMAP_SRC_ROOT_FOLDER "colmap_sources") # This macro will search for source files in a given directory, will add them # to a source group (folder within a project), and will then return paths to # each of the found files. The usage of the macro is as follows: # COLMAP_ADD_SOURCE_DIR( # # # ) macro(COLMAP_ADD_SOURCE_DIR SRC_DIR SRC_VAR) # Create the list of expressions to be used in the search. set(GLOB_EXPRESSIONS "") foreach(ARG ${ARGN}) list(APPEND GLOB_EXPRESSIONS ${SRC_DIR}/${ARG}) endforeach() # Perform the search for the source files. file(GLOB ${SRC_VAR} RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} ${GLOB_EXPRESSIONS}) # Create the source group. string(REPLACE "/" "\\" GROUP_NAME ${SRC_DIR}) source_group(${GROUP_NAME} FILES ${${SRC_VAR}}) # Clean-up. unset(GLOB_EXPRESSIONS) unset(ARG) unset(GROUP_NAME) endmacro(COLMAP_ADD_SOURCE_DIR) # Replacement for the normal add_library() command. The syntax remains the same # in that the first argument is the target name, and the following arguments # are the source files to use when building the target. macro(COLMAP_ADD_LIBRARY) set(options) set(oneValueArgs) set(multiValueArgs NAME SRCS PRIVATE_LINK_LIBS PUBLIC_LINK_LIBS) cmake_parse_arguments(COLMAP_ADD_LIBRARY "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) add_library(${COLMAP_ADD_LIBRARY_NAME} STATIC ${COLMAP_ADD_LIBRARY_SRCS}) set_target_properties(${COLMAP_ADD_LIBRARY_NAME} PROPERTIES FOLDER ${COLMAP_TARGETS_ROOT_FOLDER}/${FOLDER_NAME}) if(CLANG_TIDY_EXE) set_target_properties(${COLMAP_ADD_LIBRARY_NAME} PROPERTIES CXX_CLANG_TIDY "${CLANG_TIDY_EXE};-header-filter=.*") endif() target_link_libraries(${COLMAP_ADD_LIBRARY_NAME} PRIVATE ${COLMAP_ADD_LIBRARY_PRIVATE_LINK_LIBS} PUBLIC ${COLMAP_ADD_LIBRARY_PUBLIC_LINK_LIBS}) endmacro(COLMAP_ADD_LIBRARY) # Replacement for the normal add_executable() command. The syntax remains the # same in that the first argument is the target name, and the following # arguments are the source files to use when building the target. macro(COLMAP_ADD_EXECUTABLE) set(options) set(oneValueArgs) set(multiValueArgs NAME SRCS LINK_LIBS) cmake_parse_arguments(COLMAP_ADD_EXECUTABLE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) add_executable(${COLMAP_ADD_EXECUTABLE_NAME} ${COLMAP_ADD_EXECUTABLE_SRCS}) set_target_properties(${COLMAP_ADD_EXECUTABLE_NAME} PROPERTIES FOLDER ${COLMAP_TARGETS_ROOT_FOLDER}/${FOLDER_NAME}) target_link_libraries(${COLMAP_ADD_EXECUTABLE_NAME} ${COLMAP_ADD_EXECUTABLE_LINK_LIBS}) if(VCPKG_BUILD) install(TARGETS ${COLMAP_ADD_EXECUTABLE_NAME} DESTINATION tools/) else() install(TARGETS ${COLMAP_ADD_EXECUTABLE_NAME} DESTINATION bin/) endif() if(CLANG_TIDY_EXE) set_target_properties(${COLMAP_ADD_EXECUTABLE_NAME} PROPERTIES CXX_CLANG_TIDY "${CLANG_TIDY_EXE};-header-filter=.*") endif() endmacro(COLMAP_ADD_EXECUTABLE) # Wrapper for test executables. macro(COLMAP_ADD_TEST) set(options) set(oneValueArgs) set(multiValueArgs NAME SRCS LINK_LIBS) cmake_parse_arguments(COLMAP_ADD_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) if(TESTS_ENABLED) # ${ARGN} will store the list of link libraries. set(COLMAP_ADD_TEST_NAME "colmap_${FOLDER_NAME}_${COLMAP_ADD_TEST_NAME}") add_executable(${COLMAP_ADD_TEST_NAME} ${COLMAP_ADD_TEST_SRCS}) set_target_properties(${COLMAP_ADD_TEST_NAME} PROPERTIES FOLDER ${COLMAP_TARGETS_ROOT_FOLDER}/${FOLDER_NAME}) if(CLANG_TIDY_EXE) set_target_properties(${COLMAP_ADD_TEST_NAME} PROPERTIES CXX_CLANG_TIDY "${CLANG_TIDY_EXE};-header-filter=.*") endif() target_link_libraries(${COLMAP_ADD_TEST_NAME} ${COLMAP_ADD_TEST_LINK_LIBS} GTest::gtest GTest::gtest_main) add_test("${FOLDER_NAME}/${COLMAP_ADD_TEST_NAME}" ${COLMAP_ADD_TEST_NAME}) if(IS_MSVC) install(TARGETS ${COLMAP_ADD_TEST_NAME} DESTINATION bin/) endif() endif() endmacro(COLMAP_ADD_TEST) colmap-3.10/cmake/CMakeUninstall.cmake.in000066400000000000000000000015331464745566500203010ustar00rootroot00000000000000if(NOT EXISTS "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt") message(FATAL_ERROR "Cannot find install manifest: @CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt") endif() file(READ "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt" files) string(REGEX REPLACE "\n" ";" files "${files}") foreach(file ${files}) message(STATUS "Uninstalling $ENV{DESTDIR}${file}") if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}") exec_program("@CMAKE_COMMAND@" ARGS "-E remove \"$ENV{DESTDIR}${file}\"" OUTPUT_VARIABLE rm_out RETURN_VALUE rm_retval) if(NOT "${rm_retval}" STREQUAL 0) message(FATAL_ERROR "Problem when removing $ENV{DESTDIR}${file}") endif() else() message(STATUS "File $ENV{DESTDIR}${file} does not exist.") endif() endforeach() colmap-3.10/cmake/FindDependencies.cmake000066400000000000000000000154011464745566500202100ustar00rootroot00000000000000if(COLMAP_FIND_QUIETLY) set(COLMAP_FIND_TYPE QUIET) else() set(COLMAP_FIND_TYPE REQUIRED) endif() find_package(Boost ${COLMAP_FIND_TYPE} COMPONENTS filesystem graph program_options system) find_package(Eigen3 ${COLMAP_FIND_TYPE}) find_package(FreeImage ${COLMAP_FIND_TYPE}) find_package(FLANN ${COLMAP_FIND_TYPE}) find_package(LZ4 ${COLMAP_FIND_TYPE}) find_package(Metis ${COLMAP_FIND_TYPE}) find_package(Glog ${COLMAP_FIND_TYPE}) if(DEFINED glog_VERSION_MAJOR) # Older versions of glog don't export version variables. add_definitions("-DGLOG_VERSION_MAJOR=${glog_VERSION_MAJOR}") add_definitions("-DGLOG_VERSION_MINOR=${glog_VERSION_MINOR}") endif() find_package(SQLite3 ${COLMAP_FIND_TYPE}) set(OpenGL_GL_PREFERENCE GLVND) find_package(OpenGL ${COLMAP_FIND_TYPE}) find_package(Glew ${COLMAP_FIND_TYPE}) find_package(Git) find_package(Ceres ${COLMAP_FIND_TYPE}) if(NOT TARGET Ceres::ceres) # Older Ceres versions don't come with an imported interface target. add_library(Ceres::ceres INTERFACE IMPORTED) target_include_directories( Ceres::ceres INTERFACE ${CERES_INCLUDE_DIRS}) target_link_libraries( Ceres::ceres INTERFACE ${CERES_LIBRARIES}) endif() if(TESTS_ENABLED) find_package(GTest ${COLMAP_FIND_TYPE}) endif() if(OPENMP_ENABLED) find_package(OpenMP QUIET) endif() if(OPENMP_ENABLED AND OPENMP_FOUND) message(STATUS "Enabling OpenMP support") add_definitions("-DCOLMAP_OPENMP_ENABLED") else() message(STATUS "Disabling OpenMP support") endif() if(CGAL_ENABLED) set(CGAL_DO_NOT_WARN_ABOUT_CMAKE_BUILD_TYPE TRUE) # We do not use CGAL data. This prevents an unnecessary warning by CMake. set(CGAL_DATA_DIR "unused") find_package(CGAL ${COLMAP_FIND_TYPE}) endif() if(CGAL_FOUND) add_definitions("-DCOLMAP_CGAL_ENABLED") list(APPEND CGAL_LIBRARY ${CGAL_LIBRARIES}) message(STATUS "Found CGAL") message(STATUS " Includes : ${CGAL_INCLUDE_DIRS}") message(STATUS " Libraries : ${CGAL_LIBRARY}") if(NOT TARGET CGAL) # Older CGAL versions don't come with an imported interface target. add_library(CGAL INTERFACE IMPORTED) target_include_directories( CGAL INTERFACE ${CGAL_INCLUDE_DIRS} ${GMP_INCLUDE_DIR}) target_link_libraries( CGAL INTERFACE ${CGAL_LIBRARY} ${GMP_LIBRARIES}) endif() list(APPEND COLMAP_LINK_DIRS ${CGAL_LIBRARIES_DIR}) endif() set(COLMAP_LINK_DIRS ${Boost_LIBRARY_DIRS}) set(CUDA_MIN_VERSION "7.0") if(CUDA_ENABLED) if(CMAKE_VERSION VERSION_LESS 3.17) find_package(CUDA QUIET) if(CUDA_FOUND) message(STATUS "Found CUDA version ${CUDA_VERSION} installed in " "${CUDA_TOOLKIT_ROOT_DIR} via legacy CMake (<3.17) module. " "Using the legacy CMake module means that any installation of " "COLMAP will require that the CUDA libraries are " "available under LD_LIBRARY_PATH.") message(STATUS "Found CUDA ") message(STATUS " Includes : ${CUDA_INCLUDE_DIRS}") message(STATUS " Libraries : ${CUDA_LIBRARIES}") enable_language(CUDA) macro(declare_imported_cuda_target module) add_library(CUDA::${module} INTERFACE IMPORTED) target_include_directories( CUDA::${module} INTERFACE ${CUDA_INCLUDE_DIRS}) target_link_libraries( CUDA::${module} INTERFACE ${CUDA_${module}_LIBRARY} ${ARGN}) endmacro() declare_imported_cuda_target(cudart ${CUDA_LIBRARIES}) declare_imported_cuda_target(curand ${CUDA_LIBRARIES}) set(CUDAToolkit_VERSION "${CUDA_VERSION_STRING}") set(CUDAToolkit_BIN_DIR "${CUDA_TOOLKIT_ROOT_DIR}/bin") else() message(STATUS "CUDA not found") endif() else() find_package(CUDAToolkit QUIET) if(CUDAToolkit_FOUND) set(CUDA_FOUND ON) enable_language(CUDA) else() message(STATUS "CUDA not found") endif() endif() endif() if(CUDA_ENABLED AND CUDA_FOUND) if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) set(CMAKE_CUDA_ARCHITECTURES "native") endif() add_definitions("-DCOLMAP_CUDA_ENABLED") # Do not show warnings if the architectures are deprecated. set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") # Explicitly set PIC flags for CUDA targets. if(NOT IS_MSVC) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --compiler-options -fPIC") endif() message(STATUS "Enabling CUDA support (version: ${CUDAToolkit_VERSION}, " "archs: ${CMAKE_CUDA_ARCHITECTURES})") else() set(CUDA_ENABLED OFF) message(STATUS "Disabling CUDA support") endif() if(GUI_ENABLED) find_package(Qt5 5.4 ${COLMAP_FIND_TYPE} COMPONENTS Core OpenGL Widgets) message(STATUS "Found Qt") message(STATUS " Module : ${Qt5Core_DIR}") message(STATUS " Module : ${Qt5OpenGL_DIR}") message(STATUS " Module : ${Qt5Widgets_DIR}") if(Qt5_FOUND) # Qt5 was built with -reduce-relocations. if(Qt5_POSITION_INDEPENDENT_CODE) set(CMAKE_POSITION_INDEPENDENT_CODE ON) # Workaround for Qt5 CMake config bug under Ubuntu 20.04: https://gitlab.kitware.com/cmake/cmake/-/issues/16915 if(TARGET Qt5::Core) get_property(core_options TARGET Qt5::Core PROPERTY INTERFACE_COMPILE_OPTIONS) string(REPLACE "-fPIC" "" new_qt5_core_options "${core_options}") set_property(TARGET Qt5::Core PROPERTY INTERFACE_COMPILE_OPTIONS ${new_qt5_core_options}) set_property(TARGET Qt5::Core PROPERTY INTERFACE_POSITION_INDEPENDENT_CODE "ON") if(NOT IS_MSVC) set(CMAKE_CXX_COMPILE_OPTIONS_PIE "-fPIC") endif() endif() endif() # Enable automatic compilation of Qt resource files. set(CMAKE_AUTORCC ON) endif() endif() if(GUI_ENABLED AND Qt5_FOUND) add_definitions("-DCOLMAP_GUI_ENABLED") message(STATUS "Enabling GUI support") else() set(GUI_ENABLED OFF) message(STATUS "Disabling GUI support") endif() if(OPENGL_ENABLED) if(NOT GUI_ENABLED) message(STATUS "Disabling GUI also disables OpenGL") set(OPENGL_ENABLED OFF) else() add_definitions("-DCOLMAP_OPENGL_ENABLED") message(STATUS "Enabling OpenGL support") endif() else() message(STATUS "Disabling OpenGL support") endif() set(GPU_ENABLED OFF) if(OPENGL_ENABLED OR CUDA_ENABLED) add_definitions("-DCOLMAP_GPU_ENABLED") message(STATUS "Enabling GPU support (OpenGL: ${OPENGL_ENABLED}, CUDA: ${CUDA_ENABLED})") set(GPU_ENABLED ON) endif() colmap-3.10/cmake/FindFLANN.cmake000066400000000000000000000063671464745566500164730ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for FLANN library. # # The following variables are set by this module: # # FLANN_FOUND: TRUE if FLANN is found. # flann: Imported target to link against. # # The following variables control the behavior of this module: # # FLANN_INCLUDE_DIR_HINTS: List of additional directories in which to # search for FLANN includes. # FLANN_LIBRARY_DIR_HINTS: List of additional directories in which to # search for FLANN libraries. set(FLANN_INCLUDE_DIR_HINTS "" CACHE PATH "FLANN include directory") set(FLANN_LIBRARY_DIR_HINTS "" CACHE PATH "FLANN library directory") unset(FLANN_FOUND) unset(FLANN_INCLUDE_DIRS) unset(FLANN_LIBRARIES) list(APPEND FLANN_CHECK_INCLUDE_DIRS ${FLANN_INCLUDE_DIR_HINTS} /usr/include /usr/local/include /opt/include /opt/local/include ) list(APPEND FLANN_CHECK_LIBRARY_DIRS ${FLANN_LIBRARY_DIR_HINTS} /usr/lib /usr/local/lib /opt/lib /opt/local/lib ) find_path(FLANN_INCLUDE_DIRS NAMES flann/flann.hpp PATHS ${FLANN_CHECK_INCLUDE_DIRS}) find_library(FLANN_LIBRARIES NAMES flann PATHS ${FLANN_CHECK_LIBRARY_DIRS}) if(FLANN_INCLUDE_DIRS AND FLANN_LIBRARIES) set(FLANN_FOUND TRUE) endif() if(FLANN_FOUND) message(STATUS "Found FLANN") message(STATUS " Includes : ${FLANN_INCLUDE_DIRS}") message(STATUS " Libraries : ${FLANN_LIBRARIES}") else() if(FLANN_FIND_REQUIRED) message(FATAL_ERROR "Could not find FLANN") endif() endif() add_library(flann INTERFACE IMPORTED) target_include_directories( flann INTERFACE ${FLANN_INCLUDE_DIRS}) target_link_libraries( flann INTERFACE ${FLANN_LIBRARIES}) colmap-3.10/cmake/FindFreeImage.cmake000066400000000000000000000074261464745566500174560ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for FreeImage library. # # The following variables are set by this module: # # FREEIMAGE_FOUND: TRUE if FreeImage is found. # freeimage::FreeImage: Imported target to link against. # # The following variables control the behavior of this module: # # FREEIMAGE_INCLUDE_DIR_HINTS: List of additional directories in which to # search for FreeImage includes. # FREEIMAGE_LIBRARY_DIR_HINTS: List of additional directories in which to # search for FreeImage libraries. set(FREEIMAGE_INCLUDE_DIR_HINTS "" CACHE PATH "FreeImage include directory") set(FREEIMAGE_LIBRARY_DIR_HINTS "" CACHE PATH "FreeImage library directory") unset(FREEIMAGE_FOUND) find_package(FreeImage CONFIG QUIET) if(FreeImage_FOUND) if(TARGET freeimage::FreeImage) set(FREEIMAGE_FOUND TRUE) message(STATUS "Found FreeImage") message(STATUS " Target : freeimage::FreeImage") endif() else() list(APPEND FREEIMAGE_CHECK_INCLUDE_DIRS ${FREEIMAGE_INCLUDE_DIR_HINTS} /usr/include /usr/local/include /opt/include /opt/local/include ) list(APPEND FREEIMAGE_CHECK_LIBRARY_DIRS ${FREEIMAGE_LIBRARY_DIR_HINTS} /usr/lib /usr/local/lib /opt/lib /opt/local/lib ) find_path(FREEIMAGE_INCLUDE_DIRS NAMES FreeImage.h PATHS ${FREEIMAGE_CHECK_INCLUDE_DIRS}) find_library(FREEIMAGE_LIBRARIES NAMES freeimage PATHS ${FREEIMAGE_CHECK_LIBRARY_DIRS}) if(FREEIMAGE_INCLUDE_DIRS AND FREEIMAGE_LIBRARIES) set(FREEIMAGE_FOUND TRUE) endif() if(FREEIMAGE_FOUND) message(STATUS "Found FreeImage") message(STATUS " Includes : ${FREEIMAGE_INCLUDE_DIRS}") message(STATUS " Libraries : ${FREEIMAGE_LIBRARIES}") endif() add_library(freeimage::FreeImage INTERFACE IMPORTED) target_include_directories( freeimage::FreeImage INTERFACE ${FREEIMAGE_INCLUDE_DIRS}) target_link_libraries( freeimage::FreeImage INTERFACE ${FREEIMAGE_LIBRARIES}) endif() if(NOT FREEIMAGE_FOUND AND FREEIMAGE_FIND_REQUIRED) message(FATAL_ERROR "Could not find FreeImage") endif() colmap-3.10/cmake/FindGlew.cmake000066400000000000000000000067671464745566500165370ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for Glew library. # # The following variables are set by this module: # # GLEW_FOUND: TRUE if Glew is found. # GLEW::GLEW: Imported target to link against. # # The following variables control the behavior of this module: # # GLEW_INCLUDE_DIR_HINTS: List of additional directories in which to # search for Glew includes. # GLEW_LIBRARY_DIR_HINTS: List of additional directories in which to # search for Glew libraries. set(GLEW_INCLUDE_DIR_HINTS "" CACHE PATH "Glew include directory") set(GLEW_LIBRARY_DIR_HINTS "" CACHE PATH "Glew library directory") unset(GLEW_FOUND) unset(GLEW_INCLUDE_DIRS) unset(GLEW_LIBRARIES) find_package(Glew CONFIG QUIET) if(TARGET GLEW::GLEW) set(GLEW_FOUND TRUE) message(STATUS "Found Glew") message(STATUS " Target : GLEW::GLEW") else() find_path(GLEW_INCLUDE_DIRS NAMES GL/glew.h PATHS ${GLEW_INCLUDE_DIR_HINTS} /usr/include /usr/local/include /sw/include /opt/include /opt/local/include) find_library(GLEW_LIBRARIES NAMES GLEW Glew glew glew32 PATHS ${GLEW_LIBRARY_DIR_HINTS} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib /sw/lib /opt/lib /opt/local/lib) if(GLEW_INCLUDE_DIRS AND GLEW_LIBRARIES) set(GLEW_FOUND TRUE) message(STATUS "Found Glew") message(STATUS " Includes : ${GLEW_INCLUDE_DIRS}") message(STATUS " Libraries : ${GLEW_LIBRARIES}") else() set(GLEW_FOUND FALSE) endif() add_library(GLEW::GLEW INTERFACE IMPORTED) target_include_directories( GLEW::GLEW INTERFACE ${GLEW_INCLUDE_DIRS}) target_link_libraries( GLEW::GLEW INTERFACE ${GLEW_LIBRARIES}) endif() if(NOT GLEW_FOUND AND GLEW_FIND_REQUIRED) message(FATAL_ERROR "Could not find Glew") endif() colmap-3.10/cmake/FindGlog.cmake000066400000000000000000000101331464745566500165070ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for Glog library. # # The following variables are set by this module: # # GLOG_FOUND: TRUE if Glog is found. # glog::glog: Imported target to link against. # # The following variables control the behavior of this module: # # GLOG_INCLUDE_DIR_HINTS: List of additional directories in which to # search for Glog includes. # GLOG_LIBRARY_DIR_HINTS: List of additional directories in which to # search for Glog libraries. set(GLOG_INCLUDE_DIR_HINTS "" CACHE PATH "Glog include directory") set(GLOG_LIBRARY_DIR_HINTS "" CACHE PATH "Glog library directory") unset(GLOG_FOUND) find_package(glog CONFIG QUIET) if(TARGET glog::glog) set(GLOG_FOUND TRUE) message(STATUS "Found Glog") message(STATUS " Target : glog::glog") else() # Older versions of glog don't come with a find_package config. # Fall back to custom logic to find the library and remap to imported target. include(FindPackageHandleStandardArgs) list(APPEND GLOG_CHECK_INCLUDE_DIRS /usr/local/include /usr/local/homebrew/include /opt/local/var/macports/software /opt/local/include /usr/include) list(APPEND GLOG_CHECK_PATH_SUFFIXES glog/include glog/Include Glog/include Glog/Include src/windows) list(APPEND GLOG_CHECK_LIBRARY_DIRS /usr/local/lib /usr/local/homebrew/lib /opt/local/lib /usr/lib) list(APPEND GLOG_CHECK_LIBRARY_SUFFIXES glog/lib glog/Lib Glog/lib Glog/Lib x64/Release) find_path(GLOG_INCLUDE_DIRS NAMES glog/logging.h PATHS ${GLOG_INCLUDE_DIR_HINTS} ${GLOG_CHECK_INCLUDE_DIRS} PATH_SUFFIXES ${GLOG_CHECK_PATH_SUFFIXES}) find_library(GLOG_LIBRARIES NAMES glog libglog PATHS ${GLOG_LIBRARY_DIR_HINTS} ${GLOG_CHECK_LIBRARY_DIRS} PATH_SUFFIXES ${GLOG_CHECK_LIBRARY_SUFFIXES}) if(GLOG_INCLUDE_DIRS AND GLOG_LIBRARIES) set(GLOG_FOUND TRUE) message(STATUS "Found Glog") message(STATUS " Includes : ${GLOG_INCLUDE_DIRS}") message(STATUS " Libraries : ${GLOG_LIBRARIES}") endif() add_library(glog::glog INTERFACE IMPORTED) target_include_directories(glog::glog INTERFACE ${GLOG_INCLUDE_DIRS}) target_link_libraries(glog::glog INTERFACE ${GLOG_LIBRARIES}) endif() if(NOT GLOG_FOUND AND GLOG_FIND_REQUIRED) message(FATAL_ERROR "Could not find Glog") endif() colmap-3.10/cmake/FindLZ4.cmake000066400000000000000000000063731464745566500162430ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for LZ4 library. # # The following variables are set by this module: # # LZ4_FOUND: TRUE if LZ4 is found. # LZ4_INCLUDE_DIRS: Include directories for LZ4. # LZ4_LIBRARIES: Libraries required to link LZ4. # # The following variables control the behavior of this module: # # LZ4_INCLUDE_DIR_HINTS: List of additional directories in which to # search for LZ4 includes. # LZ4_LIBRARY_DIR_HINTS: List of additional directories in which to # search for LZ4 libraries. set(LZ4_INCLUDE_DIR_HINTS "" CACHE PATH "LZ4 include directory") set(LZ4_LIBRARY_DIR_HINTS "" CACHE PATH "LZ4 library directory") unset(LZ4_FOUND) unset(LZ4_INCLUDE_DIRS) unset(LZ4_LIBRARIES) list(APPEND LZ4_CHECK_INCLUDE_DIRS ${LZ4_INCLUDE_DIR_HINTS} /usr/include /usr/local/include /opt/include /opt/local/include ) list(APPEND LZ4_CHECK_LIBRARY_DIRS ${LZ4_LIBRARY_DIR_HINTS} /usr/lib /usr/local/lib /usr/lib/x86_64-linux-gnu /opt/lib /opt/local/lib ) find_path(LZ4_INCLUDE_DIRS NAMES lz4.h PATHS ${LZ4_CHECK_INCLUDE_DIRS}) find_library(LZ4_LIBRARIES NAMES lz4 PATHS ${LZ4_CHECK_LIBRARY_DIRS}) if(LZ4_INCLUDE_DIRS AND LZ4_LIBRARIES) set(LZ4_FOUND TRUE) endif() if(LZ4_FOUND) message(STATUS "Found LZ4") message(STATUS " Includes : ${LZ4_INCLUDE_DIRS}") message(STATUS " Libraries : ${LZ4_LIBRARIES}") else() if(LZ4_FIND_REQUIRED) message(FATAL_ERROR "Could not find LZ4") endif() endif() add_library(lz4 INTERFACE IMPORTED) target_include_directories( lz4 INTERFACE ${LZ4_INCLUDE_DIRS}) target_link_libraries( lz4 INTERFACE ${LZ4_LIBRARIES}) colmap-3.10/cmake/FindMetis.cmake000066400000000000000000000071041464745566500167040ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package module for Metis library. # # The following variables are set by this module: # # METIS_FOUND: TRUE if Metis is found. # metis: Imported target to link against. # # The following variables control the behavior of this module: # # METIS_INCLUDE_DIR_HINTS: List of additional directories in which to # search for Metis includes. # METIS_LIBRARY_DIR_HINTS: List of additional directories in which to # search for Metis libraries. set(METIS_INCLUDE_DIR_HINTS "" CACHE PATH "Metis include directory") set(METIS_LIBRARY_DIR_HINTS "" CACHE PATH "Metis library directory") unset(METIS_FOUND) find_package(metis CONFIG QUIET) if(TARGET metis) set(METIS_FOUND TRUE) message(STATUS "Found Metis") message(STATUS " Target : metis") else() list(APPEND METIS_CHECK_INCLUDE_DIRS ${METIS_INCLUDE_DIR_HINTS} /usr/include /usr/local/include /opt/include /opt/local/include ) list(APPEND METIS_CHECK_LIBRARY_DIRS ${METIS_LIBRARY_DIR_HINTS} /usr/lib /usr/local/lib /opt/lib /opt/local/lib ) find_path(METIS_INCLUDE_DIRS NAMES metis.h PATHS ${METIS_CHECK_INCLUDE_DIRS}) find_library(METIS_LIBRARIES NAMES metis PATHS ${METIS_CHECK_LIBRARY_DIRS}) find_library(GK_LIBRARIES NAMES GKlib PATHS ${METIS_CHECK_LIBRARY_DIRS}) if(METIS_INCLUDE_DIRS AND METIS_LIBRARIES) set(METIS_FOUND TRUE) message(STATUS "Found Metis") message(STATUS " Includes : ${METIS_INCLUDE_DIRS}") message(STATUS " Libraries : ${METIS_LIBRARIES}") endif() add_library(metis INTERFACE IMPORTED) target_include_directories( metis INTERFACE ${METIS_INCLUDE_DIRS}) target_link_libraries( metis INTERFACE ${METIS_LIBRARIES}) endif() if(NOT METIS_FOUND AND METIS_FIND_REQUIRED) message(FATAL_ERROR "Could not find Metis") endif() colmap-3.10/cmake/GenerateVersionDefinitions.cmake000066400000000000000000000052761464745566500223260ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. if (DEFINED GIT_COMMIT_ID OR DEFINED GIT_COMMIT_DATE) message(STATUS "Using custom-defined GIT_COMMIT_ID (${GIT_COMMIT_ID}) " "and GIT_COMMIT_DATE (${GIT_COMMIT_DATE})") elseif(Git_FOUND AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git") execute_process(COMMAND "${GIT_EXECUTABLE}" rev-parse --short HEAD WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" OUTPUT_VARIABLE GIT_COMMIT_ID ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) execute_process(COMMAND "${GIT_EXECUTABLE}" log -1 --format=%ad --date=short WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" OUTPUT_VARIABLE GIT_COMMIT_DATE ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) # Re-generate version.cc if the git index changes. set_property( DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/.git/index" ) else() set(GIT_COMMIT_ID "Unknown") set(GIT_COMMIT_DATE "Unknown") endif() configure_file("${CMAKE_CURRENT_SOURCE_DIR}/src/colmap/util/version.cc.in" "${CMAKE_CURRENT_SOURCE_DIR}/src/colmap/util/version.cc") colmap-3.10/cmake/colmap-config-version.cmake.in000066400000000000000000000034211464745566500216260ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. set(PACKAGE_VERSION "@COLMAP_VERSION@") if("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}") set(PACKAGE_VERSION_COMPATIBLE TRUE) else() set(PACKAGE_VERSION_COMPATIBLE FALSE) endif() colmap-3.10/cmake/colmap-config.cmake.in000066400000000000000000000060031464745566500201420ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Find package config for COLMAP library. # # The following variables are set by this config: # # COLMAP_FOUND: TRUE if COLMAP is found. # COLMAP_VERSION: COLMAP version. # # The colmap::colmap imported interface target is defined. @PACKAGE_INIT@ set(COLMAP_FOUND FALSE) # Set hints for finding dependency packages. set(FLANN_INCLUDE_DIR_HINTS @FLANN_INCLUDE_DIR_HINTS@) set(FLANN_LIBRARY_DIR_HINTS @FLANN_LIBRARY_DIR_HINTS@) set(LZ4_INCLUDE_DIR_HINTS @LZ4_INCLUDE_DIR_HINTS@) set(LZ4_LIBRARY_DIR_HINTS @LZ4_LIBRARY_DIR_HINTS@) set(FREEIMAGE_INCLUDE_DIR_HINTS @FREEIMAGE_INCLUDE_DIR_HINTS@) set(FREEIMAGE_LIBRARY_DIR_HINTS @FREEIMAGE_LIBRARY_DIR_HINTS@) set(METIS_INCLUDE_DIR_HINTS @METIS_INCLUDE_DIR_HINTS@) set(METIS_LIBRARY_DIR_HINTS @METIS_LIBRARY_DIR_HINTS@) set(GLEW_INCLUDE_DIR_HINTS @GLEW_INCLUDE_DIR_HINTS@) set(GLEW_LIBRARY_DIR_HINTS @GLEW_LIBRARY_DIR_HINTS@) # Find dependency packages. set(TEMP_CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH}) set(CMAKE_MODULE_PATH ${PACKAGE_PREFIX_DIR}/share/colmap/cmake) # Set the exported variables. set(COLMAP_FOUND TRUE) set(COLMAP_VERSION @COLMAP_VERSION@) set(OPENMP_ENABLED @OPENMP_ENABLED@) set(CUDA_ENABLED @CUDA_ENABLED@) set(CUDA_MIN_VERSION @CUDA_MIN_VERSION@) set(GUI_ENABLED @GUI_ENABLED@) set(CGAL_ENABLED @CGAL_ENABLED@) include(${PACKAGE_PREFIX_DIR}/share/colmap/colmap-targets.cmake) include(${PACKAGE_PREFIX_DIR}/share/colmap/cmake/FindDependencies.cmake) check_required_components(colmap) colmap-3.10/doc/000077500000000000000000000000001464745566500135035ustar00rootroot00000000000000colmap-3.10/doc/COLMAP.desktop000066400000000000000000000003601464745566500160500ustar00rootroot00000000000000[Desktop Entry] Name=COLMAP Comment=Structure-from-Motion and Multi-View Stereo Exec=colmap gui Icon=colmap Terminal=false Categories=Graphics;3DGraphics; Keywords=3d;reconstruction;structure-from-motion;multi-view-stereo; Type=Application colmap-3.10/doc/Makefile000077500000000000000000000151521464745566500151520ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) endif # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " xml to make Docutils-native XML files" @echo " pseudoxml to make pseudoxml-XML files for display purposes" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/COLMAP.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/COLMAP.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/COLMAP" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/COLMAP" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. 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The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." colmap-3.10/doc/_build/000077500000000000000000000000001464745566500147415ustar00rootroot00000000000000colmap-3.10/doc/_build/html/000077500000000000000000000000001464745566500157055ustar00rootroot00000000000000colmap-3.10/doc/bibliography.rst000077500000000000000000000034441464745566500167200ustar00rootroot00000000000000Bibliography ============ .. [schoenberger_thesis] Johannes L. Schönberger. "Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery." ETH Zürich, 2018. .. [furukawa10] Furukawa, Yasutaka, and Jean Ponce. "Accurate, dense, and robust multiview stereopsis." Transactions on Pattern Analysis and Machine Intelligence, 2010. .. [hofer16] Hofer, M., Maurer, M., and Bischof, H. Efficient 3D Scene Abstraction Using Line Segments, Computer Vision and Image Understanding, 2016. .. [jancosek11] Jancosek, Michal, and Tomás Pajdla. "Multi-view reconstruction preserving weakly-supported surfaces." Conference on Computer Vision and Pattern Recognition, 2011. .. [kazhdan2013] Kazhdan, Michael and Hoppe, Hugues "Screened poisson surface reconstruction." ACM Transactions on Graphics (TOG), 2013. .. [schoenberger16sfm] Schönberger, Johannes Lutz and Frahm, Jan-Michael. "Structure-from-Motion Revisited." Conference on Computer Vision and Pattern Recognition, 2016. .. [schoenberger16mvs] Schönberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael. "Pixelwise View Selection for Unstructured Multi-View Stereo." European Conference on Computer Vision, 2016. .. [schoenberger16vote] Schönberger, Johannes Lutz and Price, True and Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc "A Vote­-and­-Verify Strategy for Fast Spatial Verification in Image Retrieval." Asian Conference on Computer Vision, 2016. .. [lowe04] Lowe, David G. "Distinctive image features from scale-invariant keypoints". International journal of computer vision 60.2 (2004): 91-110. .. [wu13] Wu, Changchang. "Towards linear-time incremental structure from motion." International Conference 3D Vision, 2013. colmap-3.10/doc/cameras.rst000066400000000000000000000052251464745566500156540ustar00rootroot00000000000000Camera Models ============= COLMAP implements different camera models of varying complexity. If no intrinsic parameters are known a priori, it is generally best to use the simplest camera model that is complex enough to model the distortion effects: - ``SIMPLE_PINHOLE``, ``PINHOLE``: Use these camera models, if your images are undistorted a priori. These use one and two focal length parameters, respectively. Note that even in the case of undistorted images, COLMAP could try to improve the intrinsics with a more complex camera model. - ``SIMPLE_RADIAL``, ``RADIAL``: This should be the camera model of choice, if the intrinsics are unknown and every image has a different camera calibration, e.g., in the case of Internet photos. Both models are simplified versions of the ``OPENCV`` model only modeling radial distortion effects with one and two parameters, respectively. - ``OPENCV``, ``FULL_OPENCV``: Use these camera models, if you know the calibration parameters a priori. You can also try to let COLMAP estimate the parameters, if you share the intrinsics for multiple images. Note that the automatic estimation of parameters will most likely fail, if every image has a separate set of intrinsic parameters. - ``SIMPLE_RADIAL_FISHEYE``, ``RADIAL_FISHEYE``, ``OPENCV_FISHEYE``, ``FOV``, ``THIN_PRISM_FISHEYE``: Use these camera models for fisheye lenses and note that all other models are not really capable of modeling the distortion effects of fisheye lenses. The ``FOV`` model is used by Google Project Tango (make sure to not initialize `omega` to zero). You can inspect the estimated intrinsic parameters by double-clicking specific images in the model viewer or by exporting the model and opening the `cameras.txt` file. To achieve optimal reconstruction results, you might have to try different camera models for your problem. Generally, when the reconstruction fails and the estimated focal length values / distortion coefficients are grossly wrong, it is a sign of using a too complex camera model. Contrary, if COLMAP uses many iterative local and global bundle adjustments, it is a sign of using a too simple camera model that is not able to fully model the distortion effects. You can also share intrinsics between multiple images to obtain more reliable results (see :ref:`Share intrinsic camera parameters `) or you can fix the intrinsic parameters during the reconstruction (see :ref:`Fix intrinsic camera parameters `). Please, refer to the camera models header file for information on the parameters of the different camera models: https://github.com/colmap/colmap/blob/main/src/colmap/sensor/models.h colmap-3.10/doc/changelog.rst000066400000000000000000000001031464745566500161560ustar00rootroot00000000000000.. _changelog: Changelog ========= .. include:: ../CHANGELOG.txt colmap-3.10/doc/cli.rst000066400000000000000000000273441464745566500150160ustar00rootroot00000000000000.. _cli: Command-line Interface ====================== The command-line interface provides access to all of COLMAP's functionality for automated scripting. Each core functionality is implemented as a command to the ``colmap`` executable. Run ``colmap -h`` to list the available commands (or ``COLMAP.bat -h`` under Windows). Note that if you run COLMAP from the CMake build folder, the executable is located at ``./src/colmap/exe/colmap``. To start the graphical user interface, run ``colmap gui``. Example ------- Assuming you stored the images of your project in the following structure:: /path/to/project/... +── images │   +── image1.jpg │   +── image2.jpg │   +── ... │   +── imageN.jpg The command for the automatic reconstruction tool would be:: # The project folder must contain a folder "images" with all the images. $ DATASET_PATH=/path/to/project $ colmap automatic_reconstructor \ --workspace_path $DATASET_PATH \ --image_path $DATASET_PATH/images Note that any command lists all available options using the ``-h,--help`` command-line argument. In case you need more control over the individual parameters of the reconstruction process, you can execute the following sequence of commands as an alternative to the automatic reconstruction command:: # The project folder must contain a folder "images" with all the images. $ DATASET_PATH=/path/to/dataset $ colmap feature_extractor \ --database_path $DATASET_PATH/database.db \ --image_path $DATASET_PATH/images $ colmap exhaustive_matcher \ --database_path $DATASET_PATH/database.db $ mkdir $DATASET_PATH/sparse $ colmap mapper \ --database_path $DATASET_PATH/database.db \ --image_path $DATASET_PATH/images \ --output_path $DATASET_PATH/sparse $ mkdir $DATASET_PATH/dense $ colmap image_undistorter \ --image_path $DATASET_PATH/images \ --input_path $DATASET_PATH/sparse/0 \ --output_path $DATASET_PATH/dense \ --output_type COLMAP \ --max_image_size 2000 $ colmap patch_match_stereo \ --workspace_path $DATASET_PATH/dense \ --workspace_format COLMAP \ --PatchMatchStereo.geom_consistency true $ colmap stereo_fusion \ --workspace_path $DATASET_PATH/dense \ --workspace_format COLMAP \ --input_type geometric \ --output_path $DATASET_PATH/dense/fused.ply $ colmap poisson_mesher \ --input_path $DATASET_PATH/dense/fused.ply \ --output_path $DATASET_PATH/dense/meshed-poisson.ply $ colmap delaunay_mesher \ --input_path $DATASET_PATH/dense \ --output_path $DATASET_PATH/dense/meshed-delaunay.ply If you want to run COLMAP on a computer without an attached display (e.g., cluster or cloud service), COLMAP automatically switches to use CUDA if supported by your system. If no CUDA enabled device is available, you can manually select to use CPU-based feature extraction and matching by setting the ``--SiftExtraction.use_gpu 0`` and ``--SiftMatching.use_gpu 0`` options. Help ---- The available commands can be listed using the command:: $ colmap help Usage: colmap [command] [options] Documentation: https://colmap.github.io/ Example usage: colmap help [ -h, --help ] colmap gui colmap gui -h [ --help ] colmap automatic_reconstructor -h [ --help ] colmap automatic_reconstructor --image_path IMAGES --workspace_path WORKSPACE colmap feature_extractor --image_path IMAGES --database_path DATABASE colmap exhaustive_matcher --database_path DATABASE colmap mapper --image_path IMAGES --database_path DATABASE --output_path MODEL ... Available commands: help gui automatic_reconstructor bundle_adjuster color_extractor database_creator delaunay_mesher exhaustive_matcher feature_extractor feature_importer image_deleter image_rectifier image_registrator image_undistorter mapper matches_importer model_aligner model_analyzer model_converter model_merger model_orientation_aligner patch_match_stereo point_triangulator poisson_mesher rig_bundle_adjuster sequential_matcher spatial_matcher stereo_fusion transitive_matcher vocab_tree_builder vocab_tree_matcher vocab_tree_retriever And each command has a ``-h,--help`` command-line argument to show the usage and the available options, e.g.:: $ colmap feature_extractor -h Options can either be specified via command-line or by defining them in a .ini project file passed to `--project_path`. -h [ --help ] --project_path arg --database_path arg --image_path arg --image_list_path arg --ImageReader.camera_model arg (=SIMPLE_RADIAL) --ImageReader.single_camera arg (=0) --ImageReader.camera_params arg --ImageReader.default_focal_length_factor arg (=1.2) --SiftExtraction.num_threads arg (=-1) --SiftExtraction.use_gpu arg (=1) --SiftExtraction.gpu_index arg (=-1) --SiftExtraction.max_image_size arg (=3200) --SiftExtraction.max_num_features arg (=8192) --SiftExtraction.first_octave arg (=-1) --SiftExtraction.num_octaves arg (=4) --SiftExtraction.octave_resolution arg (=3) --SiftExtraction.peak_threshold arg (=0.0066666666666666671) --SiftExtraction.edge_threshold arg (=10) --SiftExtraction.estimate_affine_shape arg (=0) --SiftExtraction.max_num_orientations arg (=2) --SiftExtraction.upright arg (=0) --SiftExtraction.domain_size_pooling arg (=0) --SiftExtraction.dsp_min_scale arg (=0.16666666666666666) --SiftExtraction.dsp_max_scale arg (=3) --SiftExtraction.dsp_num_scales arg (=10) The available options can either be provided directly from the command-line or through a `.ini` file provided to ``--project_path``. Commands -------- The following list briefly documents the functionality of each command, that is available as ``colmap [command]``: - ``gui``: The graphical user interface, see :ref:`Graphical User Interface ` for more information. - ``automatic_reconstructor``: Automatically reconstruct sparse and dense model for a set of input images. - ``project_generator``: Generate project files at different quality settings. - ``feature_extractor``, ``feature_importer``: Perform feature extraction or import features for a set of images. - ``exhaustive_matcher``, ``vocab_tree_matcher``, ``sequential_matcher``, ``spatial_matcher``, ``transitive_matcher``, ``matches_importer``: Perform feature matching after performing feature extraction. - ``mapper``: Sparse 3D reconstruction / mapping of the dataset using SfM after performing feature extraction and matching. - ``hierarchical_mapper``: Sparse 3D reconstruction / mapping of the dataset using hierarchical SfM after performing feature extraction and matching. This parallelizes the reconstruction process by partitioning the scene into overlapping submodels and then reconstructing each submodel independently. Finally, the overlapping submodels are merged into a single reconstruction. It is recommended to run a few rounds of point triangulation and bundle adjustment after this step. - ``image_undistorter``: Undistort images and/or export them for MVS or to external dense reconstruction software, such as CMVS/PMVS. - ``image_rectifier``: Stereo rectify cameras and undistort images for stereo disparity estimation. - ``image_filterer``: Filter images from a sparse reconstruction. - ``image_deleter``: Delete specific images from a sparse reconstruction. - ``patch_match_stereo``: Dense 3D reconstruction / mapping using MVS after running the ``image_undistorter`` to initialize the workspace. - ``stereo_fusion``: Fusion of ``patch_match_stereo`` results into to a colored point cloud. - ``poisson_mesher``: Meshing of the fused point cloud using Poisson surface reconstruction. - ``delaunay_mesher``: Meshing of the reconstructed sparse or dense point cloud using a graph cut on the Delaunay triangulation and visibility voting. - ``image_registrator``: Register new images in the database against an existing model, e.g., when extracting features and matching newly added images in a database after running ``mapper``. Note that no bundle adjustment or triangulation is performed. - ``point_triangulator``: Triangulate all observations of registered images in an existing model using the feature matches in a database. - ``point_filtering``: Filter sparse points in model by enforcing criteria, such as minimum track length, maximum reprojection error, etc. - ``bundle_adjuster``: Run global bundle adjustment on a reconstructed scene, e.g., when a refinement of the intrinsics is needed or after running the ``image_registrator``. - ``database_creator``: Create an empty COLMAP SQLite database with the necessary database schema information. - ``database_merger``: Merge two databases into a new database. Note that the cameras will not be merged and that the unique camera and image identifiers might change during the merging process. - ``model_analyzer``: Print statistics about reconstructions. - ``model_aligner``: Align/geo-register model to coordinate system of given camera centers. - ``model_orientation_aligner``: Align the coordinate axis of a model using a Manhattan world assumption. - ``model_converter``: Convert the COLMAP export format to another format, such as PLY or NVM. - ``model_cropper``: Crop model to specific bounding box described in GPS or model coordinate system. - ``model_splitter``: Divide model in rectangular sub-models specified from file containing bounding box coordinates, or max extent of sub-model, or number of subdivisions in each dimension. - ``model_merger``: Attempt to merge two disconnected reconstructions, if they have common registered images. - ``color_extractor``: Extract mean colors for all 3D points of a model. - ``vocab_tree_builder``: Create a vocabulary tree from a database with extracted images. This is an offline procedure and can be run once, while the same vocabulary tree can be reused for other datasets. Note that, as a rule of thumb, you should use at least 10-100 times more features than visual words. Pre-trained trees can be downloaded from https://demuc.de/colmap/. This is useful if you want to build a custom tree with a different trade-off in terms of precision/recall vs. speed. - ``vocab_tree_retriever``: Perform vocabulary tree based image retrieval. Visualization ------------- If you want to quickly visualize the outputs of the sparse or dense reconstruction pipelines, COLMAP offers you the following possibilities: - The sparse point cloud obtained with the ``mapper`` can be visualized via the COLMAP GUI by importing the following files: choose ``File > Import Model`` and select the folder where the three files, ``cameras.txt``, ``images.txt``, and ``points3d.txt`` are located. - The dense point cloud obtained with the ``stereo_fusion`` can be visualized via the COLMAP GUI by importing ``fused.ply``: choose ``File > Import Model from...`` and then select the file ``fused.ply``. - The dense mesh model ``meshed-*.ply`` obtained with the ``poisson_mesher`` or the ``delaunay_mesher`` can currently not be visualized with COLMAP, instead you can use an external viewer, such as Meshlab. colmap-3.10/doc/colmap.1000066400000000000000000000021021464745566500150330ustar00rootroot00000000000000.TH colmap 1 "January 4 2018" .SH NAME colmap \- Structure-from-Motion and Multi-View Stereo .SH SYNOPSIS .B colmap .RI "[command] [options]" .SH DESCRIPTION This manual page documents briefly the .B colmap command. .PP COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. .SH OPTIONS This program offers a graphical and command-line interface. Each command offers summary of all available options. .TP .B help [ \-h, \-\-help ] Show summary of all options. .TP .B gui Start graphical interface. .TP .B gui \-h [ \-\-help ] Show summary of graphical interface options. .TP .B feature_extractor \-h [ \-\-help ] Show summary of feature extractor options. .TP .B feature_extractor \-\-image_path IMAGES \-\-database_path DATABASE Extract features for images in the given folder and store them in the database. .br .TP .B ... .br .SH ONLINE DOCUMENTATION The program is documented at https://colmap.github.io/ colmap-3.10/doc/conf.py000077500000000000000000000203771464745566500150160ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # COLMAP documentation build configuration file, created by # sphinx-quickstart on Wed Jan 28 09:31:25 2015. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.mathjax", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix of source filenames. source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = u"COLMAP" copyright = u"2023, Johannes L. Schoenberger" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = "3.10-dev" # The full version, including alpha/beta/rc tags. release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ["_build"] # The reST default role (used for this markup: `text`) to use for all # documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ["_themes"] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = "COLMAPdoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'a4paper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( "index", "COLMAP.tex", u"COLMAP Documentation", u"Johannes L. Schoenberger", "manual", ), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. latex_show_pagerefs = True # If true, show URL addresses after external links. latex_show_urls = "footnote" # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. latex_domain_indices = False # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ( "index", "colmap", u"COLMAP Documentation", [u"Johannes L. Schoenberger"], 1, ) ] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( "index", "COLMAP", u"COLMAP Documentation", u"Johannes L. Schoenberger", "COLMAP", "Structure-from-Motion and Multi-View Stereo.", "Miscellaneous", ), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False colmap-3.10/doc/contribution.rst000066400000000000000000000016731464745566500167630ustar00rootroot00000000000000Contribution ============ Contributions (bug reports, bug fixes, improvements, etc.) are very welcome and should be submitted in the form of new issues and/or pull requests on GitHub. Please, adhere to the Google coding style guide:: https://google.github.io/styleguide/cppguide.html by using the provided ".clang-format" file. Document functions, methods, classes, etc. with inline documentation strings describing the API, using the following format:: // Short description. // // Longer description with a few sentences and multiple lines. // // @param parameter1 Description for parameter 1. // @param parameter2 Description for parameter 2. // // @return Description of optional return value. Add unit tests for all newly added code and make sure that algorithmic "improvements" generalize and actually improve the results of the pipeline on a variety of datasets. colmap-3.10/doc/database.rst000077500000000000000000000113261464745566500160070ustar00rootroot00000000000000.. _database-format: Database Format =============== COLMAP stores all extracted information in a single SQLite database file. The database can be accessed with the database management toolkit in the COLMAP GUI, the provided C++ database API (see ``src/colmap/scene/database.h``), or with a scripting language of your choice (see ``scripts/python/database.py``). The database contains the following tables: - cameras - images - keypoints - descriptors - matches - two_view_geometries To initialize an empty SQLite database file with the required schema, you can either create a new project in the GUI or execute `src/colmap/exe/database_create.cc`. Cameras and Images ------------------ The relation between cameras and images is 1-to-N. This has important implications for Structure-from-Motion, since one camera shares the same intrinsic parameters (focal length, principal point, distortion, etc.), while every image has separate extrinsic parameters (orientation and location). The intrinsic parameters of cameras are stored as contiguous binary blobs in `float64`, ordered as specified in ``src/colmap/sensor/models.h``. COLMAP only uses cameras that are referenced by images, all other cameras are ignored. The ``name`` column in the images table is the unique relative path in the image folder. As such, the database file and image folder can be moved to different locations, as long as the relative folder structure is preserved. When manually inserting images and cameras into the database, make sure that all identifiers are positive and non-zero, i.e. ``image_id > 0`` and ``camera_id > 0``. Keypoints and Descriptors ------------------------- The detected keypoints are stored as row-major `float32` binary blobs, where the first two columns are the X and Y locations in the image, respectively. COLMAP uses the convention that the upper left image corner has coordinate `(0, 0)` and the center of the upper left most pixel has coordinate `(0.5, 0.5)`. If the keypoints have 4 columns, then the feature geometry is a similarity and the third column is the scale and the fourth column the orientation of the feature (according to SIFT conventions). If the keypoints have 6 columns, then the feature geometry is an affinity and the last 4 columns encode its affine shape (see ``src/feature/types.h`` for details). The extracted descriptors are stored as row-major `uint8` binary blobs, where each row describes the feature appearance of the corresponding entry in the keypoints table. Note that COLMAP only supports 128-D descriptors for now, i.e. the `cols` column must be 128. In both tables, the `rows` table specifies the number of detected features per image, while `rows=0` means that an image has no features. For feature matching and geometric verification, every image must have a corresponding keypoints and descriptors entry. Note that only vocabulary tree matching with fast spatial verification requires meaningful values for the local feature geometry, i.e., only X and Y must be provided and the other keypoint columns can be set to zero. The rest of the reconstruction pipeline only uses the keypoint locations. Matches ------- Feature matching stores its output in the `matches` table and geometric verification in the `two_view_geometries` table. COLMAP only uses the data in `two_view_geometries` for reconstruction. Every entry in the two tables stores the feature matches between two unique images, where the `pair_id` is the row-major, linear index in the upper-triangular match matrix, generated as follows:: def image_ids_to_pair_id(image_id1, image_id2): if image_id1 > image_id2: return 2147483647 * image_id2 + image_id1 else: return 2147483647 * image_id1 + image_id2 and image identifiers can be uniquely determined from the `pair_id` as:: def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2 The `pair_id` enables efficient database queries, as the matches tables may contain several hundred millions of entries. This scheme limits the maximum number of images in a database to 2147483647 (maximum value of signed 32-bit integers), i.e. `image_id` must be smaller than 2147483647. The binary blobs in the matches tables are row-major `uint32` matrices, where the left column are zero-based indices into the features of `image_id1` and the second column into the features of `image_id2`. The column `cols` must be 2 and the `rows` column specifies the number of feature matches. The F, E, H blobs in the `two_view_geometries` table are stored as 3x3 matrices in row-major `float64` format. The meaning of the `config` values are documented in the `src/estimators/two_view_geometry.h` source file. colmap-3.10/doc/datasets.rst000066400000000000000000000021461464745566500160500ustar00rootroot00000000000000.. _datasets: Datasets ======== A number of different datasets are available for download at: https://demuc.de/colmap/datasets/ - **Gerrard Hall**: 100 high-resolution images of the "Gerrard" hall at UNC Chapel Hill, which is the building right next to the "South" building. The images are taken with the same camera but different focus using a wide-angle lens. - **Graham Hall**: 1273 high-resolution images of the interior and exterior of "Graham" memorial hall at UNC Chapel Hill. The images are taken with the same camera but different focus using a wide-angle lens. - **Person Hall**: 330 high-resolution images of the "Person" hall at UNC Chapel Hill. The images are taken with the same camera using a wide-angle lens. - **South Building**: 128 images of the "South" building at UNC Chapel Hill. The images are taken with the same camera, kindly provided by Christopher Zach. A number of sample reconstructions produced by COLMAP can be viewed here: **Sparse reconstructions**: - https://youtu.be/PmXqdfBQxfQ - https://youtu.be/DIv1aGKqSIk **Dense reconstructions**: - https://youtu.be/11awtGWSqQU colmap-3.10/doc/faq.rst000066400000000000000000000663701464745566500150200ustar00rootroot00000000000000Frequently Asked Questions ========================== Adjusting the options for different reconstruction scenarios and output quality ------------------------------------------------------------------------------- COLMAP provides many options that can be tuned for different reconstruction scenarios and to trade off accuracy and completeness versus efficiency. The default options are set to for medium to high quality reconstruction of unstructured input data. There are several presets for different scenarios and quality levels, which can be set in the GUI as ``Extras > Set options for ...``. To use these presets from the command-line, you can save the current set of options as ``File > Save project`` after choosing the presets. The resulting project file can be opened with a text editor to view the different options. Alternatively, you can generate the project file also from the command-line by running ``colmap project_generator``. Extending COLMAP ---------------- If you need to simply analyze the produced sparse or dense reconstructions from COLMAP, you can load the sparse models in Python and Matlab using the provided scripts in ``scripts/python`` and ``scripts/matlab``. If you want to write a C/C++ executable that builds on top of COLMAP, there are two possible approaches. First, the COLMAP headers and library are installed to the ``CMAKE_INSTALL_PREFIX`` by default. Compiling against COLMAP as a library is described :ref:`here `. Alternatively, you can start from the ``src/tools/example.cc`` code template and implement the desired functionality directly as a new binary within COLMAP. .. _faq-share-intrinsics: Share intrinsics ---------------- COLMAP supports shared intrinsics for arbitrary groups of images and camera models. Images share the same intrinsics, if they refer to the same camera, as specified by the `camera_id` property in the database. You can add new cameras and set shared intrinsics in the database management tool. Please, refer to :ref:`Database Management ` for more information. .. _faq-fix-intrinsics: Fix intrinsics -------------- By default, COLMAP tries to refine the intrinsic camera parameters (except principal point) automatically during the reconstruction. Usually, if there are enough images in the dataset and you share the intrinsics between multiple images, the estimated intrinsic camera parameters in SfM should be better than parameters manually obtained with a calibration pattern. However, sometimes COLMAP's self-calibration routine might converge in degenerate parameters, especially in case of the more complex camera models with many distortion parameters. If you know the calibration parameters a priori, you can fix different parameter groups during the reconstruction. Choose ``Reconstruction > Reconstruction options > Bundle Adj. > refine_*`` and check which parameter group to refine or to keep constant. Even if you keep the parameters constant during the reconstruction, you can refine the parameters in a final global bundle adjustment by setting ``Reconstruction > Bundle adj. options > refine_*`` and then running ``Reconstruction > Bundle adjustment``. Principal point refinement -------------------------- By default, COLMAP keeps the principal point constant during the reconstruction, as principal point estimation is an ill-posed problem in general. Once all images are reconstructed, the problem is most often constrained enough that you can try to refine the principal point in global bundle adjustment, especially when sharing intrinsic parameters between multiple images. Please, refer to :ref:`Fix intrinsics ` for more information. Increase number of matches / sparse 3D points --------------------------------------------- To increase the number of matches, you should use the more discriminative DSP-SIFT features instead of plain SIFT and also estimate the affine feature shape using the options: ``--SiftExtraction.estimate_affine_shape=true`` and ``--SiftExtraction.domain_size_pooling=true``. In addition, you should enable guided feature matching using: ``--SiftMatching.guided_matching=true``. By default, COLMAP ignores two-view feature tracks in triangulation, resulting in fewer 3D points than possible. Triangulation of two-view tracks can in rare cases improve the stability of sparse image collections by providing additional constraints in bundle adjustment. To also triangulate two-view tracks, unselect the option ``Reconstruction > Reconstruction options > Triangulation > ignore_two_view_tracks``. If your images are taken from far distance with respect to the scene, you can try to reduce the minimum triangulation angle. Reconstruct sparse/dense model from known camera poses ------------------------------------------------------ If the camera poses are known and you want to reconstruct a sparse or dense model of the scene, you must first manually construct a sparse model by creating a ``cameras.txt``, ``points3D.txt``, and ``images.txt`` under a new folder:: +── path/to/manually/created/sparse/model │   +── cameras.txt │   +── images.txt │   +── points3D.txt The ``points3D.txt`` file should be empty while every other line in the ``images.txt`` should also be empty, since the sparse features are computed, as described below. You can refer to :ref:`this article ` for more information about the structure of a sparse model. Example of images.txt:: 1 0.695104 0.718385 -0.024566 0.012285 -0.046895 0.005253 -0.199664 1 image0001.png # Make sure every other line is left empty 2 0.696445 0.717090 -0.023185 0.014441 -0.041213 0.001928 -0.134851 2 image0002.png 3 0.697457 0.715925 -0.025383 0.018967 -0.054056 0.008579 -0.378221 1 image0003.png 4 0.698777 0.714625 -0.023996 0.021129 -0.048184 0.004529 -0.313427 2 image0004.png Each image above must have the same ``image_id`` (first column) as in the database (next step). This database can be inspected either in the GUI (under ``Database management > Processing``), or, one can create a reconstruction with colmap and later export it as text in order to see the images.txt file it creates. To reconstruct a sparse map, you first have to recompute features from the images of the known camera poses as follows:: colmap feature_extractor \ --database_path $PROJECT_PATH/database.db \ --image_path $PROJECT_PATH/images If your known camera intrinsics have large distortion coefficients, you should now manually copy the parameters from your ``cameras.txt`` to the database, such that the matcher can leverage the intrinsics. Modifying the database is possible in many ways, but an easy option is to use the provided ``scripts/python/database.py`` script. Otherwise, you can skip this step and simply continue as follows:: colmap exhaustive_matcher \ # or alternatively any other matcher --database_path $PROJECT_PATH/database.db colmap point_triangulator \ --database_path $PROJECT_PATH/database.db \ --image_path $PROJECT_PATH/images --input_path path/to/manually/created/sparse/model \ --output_path path/to/triangulated/sparse/model Note that the sparse reconstruction step is not necessary in order to compute a dense model from known camera poses. Assuming you computed a sparse model from the known camera poses, you can compute a dense model as follows:: colmap image_undistorter \ --image_path $PROJECT_PATH/images \ --input_path path/to/triangulated/sparse/model \ --output_path path/to/dense/workspace colmap patch_match_stereo \ --workspace_path path/to/dense/workspace colmap stereo_fusion \ --workspace_path path/to/dense/workspace \ --output_path path/to/dense/workspace/fused.ply Alternatively, you can also produce a dense model without a sparse model as:: colmap image_undistorter \ --image_path $PROJECT_PATH/images \ --input_path path/to/manually/created/sparse/model \ --output_path path/to/dense/workspace Since the sparse point cloud is used to automatically select neighboring images during the dense stereo stage, you have to manually specify the source images, as described :ref:`here `. The dense stereo stage now also requires a manual specification of the depth range:: colmap patch_match_stereo \ --workspace_path path/to/dense/workspace \ --PatchMatchStereo.depth_min $MIN_DEPTH \ --PatchMatchStereo.depth_max $MAX_DEPTH colmap stereo_fusion \ --workspace_path path/to/dense/workspace \ --output_path path/to/dense/workspace/fused.ply .. _faq-merge-models: Merge disconnected models ------------------------- Sometimes COLMAP fails to reconstruct all images into the same model and hence produces multiple sub-models. If those sub-models have common registered images, they can be merged into a single model as post-processing step:: colmap model_merger \ --input_path1 /path/to/sub-model1 \ --input_path2 /path/to/sub-model2 \ --output_path /path/to/merged-model To improve the quality of the alignment between the two sub-models, it is recommended to run another global bundle adjustment after the merge:: colmap bundle_adjuster \ --input_path /path/to/merged-model \ --output_path /path/to/refined-merged-model Geo-registration ---------------- Geo-registration of models is possible by providing the 3D locations for the camera centers of a subset or all registered images. The 3D similarity transformation between the reconstructed model and the target coordinate frame of the geo-registration is determined from these correspondences. The geo-registered 3D coordinates can either be extracted from the database (tvec_prior field) or from a user specified text file. For text-files, the geo-registered 3D coordinates of the camera centers for images must be specified with the following format:: image_name1.jpg X1 Y1 Z1 image_name2.jpg X2 Y2 Z2 image_name3.jpg X3 Y3 Z3 ... The coordinates can be either GPS-based (lat/lon/alt) or cartesian-based (x/y/z). In case of GPS coordinates, a conversion will be performed to turn those into cartesian coordinates. The conversion can be done from GPS to ECEF (Earth-Centered-Earth-Fixed) or to ENU (East-North-Up) coordinates. If ENU coordinates are used, the first image GPS coordinates will define the origin of the ENU frame. It is also possible to use ECEF coordinates for alignment and then rotate the aligned reconstruction into the ENU plane. Note that at least 3 images must be specified to estimate a 3D similarity transformation. Then, the model can be geo-registered using:: colmap model_aligner \ --input_path /path/to/model \ --output_path /path/to/geo-registered-model \ --ref_images_path /path/to/text-file (or --database_path /path/to/database.db) \ --ref_is_gps 1 \ --alignment_type ecef \ --robust_alignment 1 \ --robust_alignment_max_error 3.0 (where 3.0 is the error threshold to be used in RANSAC) By default, the robust_alignment flag is set to 1. If this flag is set, a 3D similarity transformation will be estimated with a RANSAC estimator to be robust to potential outliers in the data. In such case, it is required to provide the error threshold to be used in the RANSAC estimator. Manhattan world alignment ------------------------- COLMAP has functionality to align the coordinate axes of a reconstruction using a Manhattan world assumption, i.e. COLMAP can automatically determine the gravity axis and the major horizontal axis of the Manhattan world through vanishing point detection in the images. Please, refer to the ``model_orientation_aligner`` for more details. Mask image regions ------------------ COLMAP supports masking of keypoints during feature extraction two different ways: 1. Passing ``mask_path`` to a folder with image masks. For a given image, the corresponding mask must have the same sub-path below this root as the image has below ``image_path``. The filename must be equal, aside from the added extension ``.png``. For example, for an image ``image_path/abc/012.jpg``, the mask would be ``mask_path/abc/012.jpg.png``. 2. Passing ``camera_mask_path`` to a single mask image. This single mask is applied to all images. In both cases no features will be extracted in regions, where the mask image is black (pixel intensity value 0 in grayscale). Register/localize new images into an existing reconstruction ------------------------------------------------------------ If you have an existing reconstruction of images and want to register/localize new images within this reconstruction, you can follow these steps:: colmap feature_extractor \ --database_path $PROJECT_PATH/database.db \ --image_path $PROJECT_PATH/images \ --image_list_path /path/to/image-list.txt colmap vocab_tree_matcher \ --database_path $PROJECT_PATH/database.db \ --VocabTreeMatching.vocab_tree_path /path/to/vocab-tree.bin \ --VocabTreeMatching.match_list_path /path/to/image-list.txt colmap image_registrator \ --database_path $PROJECT_PATH/database.db \ --input_path /path/to/existing-model \ --output_path /path/to/model-with-new-images colmap bundle_adjuster \ --input_path /path/to/model-with-new-images \ --output_path /path/to/model-with-new-images Note that this first extracts features for the new images, then matches them to the existing images in the database, and finally registers them into the model. The image list text file contains a list of images to extract and match, specified as one image file name per line. The bundle adjustment is optional. If you need a more accurate image registration with triangulation, then you should restart or continue the reconstruction process rather than just registering the images to the model. Instead of running the ``image_registrator``, you should run the ``mapper`` to continue the reconstruction process from the existing model:: colmap mapper \ --database_path $PROJECT_PATH/database.db \ --image_path $PROJECT_PATH/images \ --input_path /path/to/existing-model \ --output_path /path/to/model-with-new-images Or, alternatively, you can start the reconstruction from scratch:: colmap mapper \ --database_path $PROJECT_PATH/database.db \ --image_path $PROJECT_PATH/images \ --output_path /path/to/model-with-new-images Note that dense reconstruction must be re-run from scratch after running the ``mapper`` or the ``bundle_adjuster``, as the coordinate frame of the model can change during these steps. Available functionality without GPU/CUDA ---------------------------------------- If you do not have a CUDA-enabled GPU but some other GPU, you can use all COLMAP functionality except the dense reconstruction part. However, you can use external dense reconstruction software as an alternative, as described in the :ref:`Tutorial `. If you have a GPU with low compute power or you want to execute COLMAP on a machine without an attached display and without CUDA support, you can run all steps on the CPU by specifying the appropriate options (e.g., ``--SiftExtraction.use_gpu=false`` for the feature extraction step). But note that this might result in a significant slow-down of the reconstruction pipeline. Please, also note that feature extraction on the CPU can consume excessive RAM for large images in the default settings, which might require manually reducing the maximum image size using ``--SiftExtraction.max_image_size`` and/or setting ``--SiftExtraction.first_octave 0`` or by manually limiting the number of threads using ``--SiftExtraction.num_threads``. Multi-GPU support in feature extraction/matching ------------------------------------------------ You can run feature extraction/matching on multiple GPUs by specifying multiple indices for CUDA-enabled GPUs, e.g., ``--SiftExtraction.gpu_index=0,1,2,3`` and ``--SiftMatching.gpu_index=0,1,2,3`` runs the feature extraction/matching on 4 GPUs in parallel. Note that you can only run one thread per GPU and this typically also gives the best performance. By default, COLMAP runs one feature extraction/matching thread per CUDA-enabled GPU and this usually gives the best performance as compared to running multiple threads on the same GPU. Feature matching fails due to illegal memory access --------------------------------------------------- If you encounter the following error message:: MultiplyDescriptor: an illegal memory access was encountered or the following: ERROR: Feature matching failed. This probably caused by insufficient GPU memory. Consider reducing the maximum number of features. during feature matching, your GPU runs out of memory. Try decreasing the option ``--SiftMatching.max_num_matches`` until the error disappears. Note that this might lead to inferior feature matching results, since the lower-scale input features will be clamped in order to fit them into GPU memory. Alternatively, you could change to CPU-based feature matching, but this can become very slow, or better you buy a GPU with more memory. The maximum required GPU memory can be approximately estimated using the following formula: ``4 * num_matches * num_matches + 4 * num_matches * 256``. For example, if you set ``--SiftMatching.max_num_matches 10000``, the maximum required GPU memory will be around 400MB, which are only allocated if one of your images actually has that many features. Trading off completeness and accuracy in dense reconstruction ------------------------------------------------------------- If the dense point cloud contains too many outliers and too much noise, try to increase the value of option ``--StereoFusion.min_num_pixels``. If the reconstructed dense surface mesh model using Poisson reconstruction contains no surface or there are too many outlier surfaces, you should reduce the value of option ``--PoissonMeshing.trim`` to decrease the surface are and vice versa to increase it. Also consider to try the reduce the outliers or increase the completeness in the fusion stage, as described above. If the reconstructed dense surface mesh model using Delaunay reconstruction contains too noisy or incomplete surfaces, you should increase the ``--DenaunayMeshing.quality_regularization`` parameter to obtain a smoother surface. If the resolution of the mesh is too coarse, you should reduce the ``--DelaunayMeshing.max_proj_dist`` option to a lower value. Improving dense reconstruction results for weakly textured surfaces ------------------------------------------------------------------- For scenes with weakly textured surfaces it can help to have a high resolution of the input images (``--PatchMatchStereo.max_image_size``) and a large patch window radius (``--PatchMatchStereo.window_radius``). You may also want to reduce the filtering threshold for the photometric consistency cost (``--PatchMatchStereo.filter_min_ncc``). Surface mesh reconstruction --------------------------- COLMAP supports two types of surface reconstruction algorithms. Poisson surface reconstruction [kazhdan2013]_ and graph-cut based surface extraction from a Delaunay triangulation. Poisson surface reconstruction typically requires an almost outlier-free input point cloud and it often produces bad surfaces in the presence of outliers or large holes in the input data. The Delaunay triangulation based meshing algorithm is more robust to outliers and in general more scalable to large datasets than the Poisson algorithm, but it usually produces less smooth surfaces. Furthermore, the Delaunay based meshing can be applied to sparse and dense reconstruction results. To increase the smoothness of the surface as a post-processing step, you could use Laplacian smoothing, as e.g. implemented in Meshlab. Note that the two algorithms can also be combined by first running the Delaunay meshing to robustly filter outliers from the sparse or dense point cloud and then, in the second step, performing Poisson surface reconstruction to obtain a smooth surface. Speedup dense reconstruction ---------------------------- The dense reconstruction can be speeded up in multiple ways: - Put more GPUs in your system as the dense reconstruction can make use of multiple GPUs during the stereo reconstruction step. Put more RAM into your system and increase the ``--PatchMatchStereo.cache_size``, ``--StereoFusion.cache_size`` to the largest possible value in order to speed up the dense fusion step. - Do not perform geometric dense stereo reconstruction ``--PatchMatchStereo.geom_consistency false``. Make sure to also enable ``--PatchMatchStereo.filter true`` in this case. - Reduce the ``--PatchMatchStereo.max_image_size``, ``--StereoFusion.max_image_size`` values to perform dense reconstruction on a maximum image resolution. - Reduce the number of source images per reference image to be considered, as described :ref:`here `. - Increase the patch windows step ``--PatchMatchStereo.window_step`` to 2. - Reduce the patch window radius ``--PatchMatchStereo.window_radius``. - Reduce the number of patch match iterations ``--PatchMatchStereo.num_iterations``. - Reduce the number of sampled views ``--PatchMatchStereo.num_samples``. - To speedup the dense stereo and fusion step for very large reconstructions, you can use CMVS to partition your scene into multiple clusters and to prune redundant images, as described :ref:`here `. Note that apart from upgrading your hardware, the proposed changes might degrade the quality of the dense reconstruction results. When canceling the stereo reconstruction process and restarting it later, the previous progress is not lost and any already processed views will be skipped. .. _faq-dense-memory: Reduce memory usage during dense reconstruction ----------------------------------------------- If you run out of GPU memory during patch match stereo, you can either reduce the maximum image size by setting the option ``--PatchMatchStereo.max_image_size`` or reduce the number of source images in the ``stereo/patch-match.cfg`` file from e.g. ``__auto__, 30`` to ``__auto__, 10``. Note that enabling the ``geom_consistency`` option increases the required GPU memory. If you run out of CPU memory during stereo or fusion, you can reduce the ``--PatchMatchStereo.cache_size`` or ``--StereoFusion.cache_size`` specified in gigabytes or you can reduce ``--PatchMatchStereo.max_image_size`` or ``--StereoFusion.max_image_size``. Note that a too low value might lead to very slow processing and heavy load on the hard disk. For large-scale reconstructions of several thousands of images, you should consider splitting your sparse reconstruction into more manageable clusters of images using e.g. CMVS [furukawa10]_. In addition, CMVS allows to prune redundant images observing the same scene elements. Note that, for this use case, COLMAP's dense reconstruction pipeline also supports the PMVS/CMVS folder structure when executed from the command-line. Please, refer to the workspace folder for example shell scripts. Note that the example shell scripts for PMVS/CMVS are only generated, if the output type is set to PMVS. Since CMVS produces highly overlapping clusters, it is recommended to increase the default value of 100 images per cluster to as high as possible according to your available system resources and speed requirements. To change the number of images using CMVS, you must modify the shell scripts accordingly. For example, ``cmvs pmvs/ 500`` to limit each cluster to 500 images. If you want to use CMVS to prune redundant images but not to cluster the scene, you can simply set this number to a very large value. .. _faq-dense-manual-source: Manual specification of source images during dense reconstruction ----------------------------------------------------------------- You can change the number of source images in the ``stereo/patch-match.cfg`` file from e.g. ``__auto__, 30`` to ``__auto__, 10``. This selects the images with the most visual overlap automatically as source images. You can also use all other images as source images, by specifying ``__all__``. Alternatively, you can manually specify images with their name, for example:: image1.jpg image2.jpg, image3.jpg image2.jpg image1.jpg, image3.jpg image3.jpg image1.jpg, image2.jpg Here, ``image2.jpg`` and ``image3.jpg`` are used as source images for ``image1.jpg``, etc. Multi-GPU support in dense reconstruction ----------------------------------------- You can run dense reconstruction on multiple GPUs by specifying multiple indices for CUDA-enabled GPUs, e.g., ``--PatchMatchStereo.gpu_index=0,1,2,3`` runs the dense reconstruction on 4 GPUs in parallel. You can also run multiple dense reconstruction threads on the same GPU by specifying the same GPU index twice, e.g., ``--PatchMatchStereo.gpu_index=0,0,1,1,2,3``. By default, COLMAP runs one dense reconstruction thread per CUDA-enabled GPU. .. _faq-dense-timeout: Fix GPU freezes and timeouts during dense reconstruction -------------------------------------------------------- The stereo reconstruction pipeline runs on the GPU using CUDA and puts the GPU under heavy load. You might experience a display freeze or even a program crash during the reconstruction. As a solution to this problem, you could use a secondary GPU in your system, that is not connected to your display by setting the GPU indices explicitly (usually index 0 corresponds to the card that the display is attached to). Alternatively, you can increase the GPU timeouts of your system, as detailed in the following. By default, the Windows operating system detects response problems from the GPU, and recovers to a functional desktop by resetting the card and aborting the stereo reconstruction process. The solution is to increase the so-called "Timeout Detection & Recovery" (TDR) delay to a larger value. Please, refer to the `NVIDIA Nsight documentation `_ or to the `Microsoft documentation `_ on how to increase the delay time under Windows. You can increase the delay using the following Windows Registry entries:: [HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers] "TdrLevel"=dword:00000001 "TdrDelay"=dword:00000120 To set the registry entries, execute the following commands using administrator privileges (e.g., in ``cmd.exe`` or ``powershell.exe``):: reg add HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers /v TdrLevel /t REG_DWORD /d 00000001 reg add HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers /v TdrDelay /t REG_DWORD /d 00000120 and restart your machine afterwards to make the changes effective. The X window system under Linux/Unix has a similar feature and detects response problems of the GPU. The easiest solution to avoid timeout problems under the X window system is to shut it down and run the stereo reconstruction from the command-line. Under Ubuntu, you could first stop X using:: sudo service lightdm stop And then run the dense reconstruction code from the command-line:: colmap patch_match_stereo ... Finally, you can restart your desktop environment with the following command:: sudo service lightdm start If the dense reconstruction still crashes after these changes, the reason is probably insufficient GPU memory, as discussed in a separate item in this list. colmap-3.10/doc/format.rst000066400000000000000000000225051464745566500155310ustar00rootroot00000000000000.. _output-format: Output Format ============= ================== Binary File Format ================== Note that all binary data is stored using little endian byte ordering. All x86 processors are little endian and thus no special care has to be taken when reading COLMAP binary data on most platforms. ======================= Indices and Identifiers ======================= Any variable name ending with ``*_idx`` should be considered as an ordered, contiguous zero-based index. In general, any variable name ending with ``*_id`` should be considered as an unordered, non-contiguous identifier. For example, the unique identifiers of cameras (`CAMERA_ID`), images (`IMAGE_ID`), and 3D points (`POINT3D_ID`) are unordered and are most likely not contiguous. This also means that the maximum `POINT3D_ID` does not necessarily correspond to the number 3D points, since some `POINT3D_ID`'s are missing due to filtering during the reconstruction, etc. ===================== Sparse Reconstruction ===================== By default, COLMAP uses a binary file format (machine-readable, fast) for storing sparse models. In addition, COLMAP provides the option to store the sparse models as text files (human-readable, slow). In both cases, the information is split into three files for the information about `cameras`, `images`, and `points`. Any directory containing those three files constitutes a sparse model. The binary files have the file extension `.bin` and the text files the file extension `.txt`. Note that when loading a model from a directory which contains both binary and text files, COLMAP prefers the binary format. To export the currently selected model in the GUI, choose ``File > Export model``. To export all reconstructed models in the current dataset, choose ``File > Export all``. The selected folder then contains the three files, and for convenience, the current project configuration for importing the model to COLMAP. To import the exported models, e.g., for visualization or to resume the reconstruction, choose ``File > Import model`` and select the folder containing the `cameras`, `images`, and `points3D` files. To convert between the binary and text format in the GUI, you can load the model using ``File > Import model`` and then export the model in the desired output format using ``File > Export model`` (binary) or ``File > Export model as text`` (text). In addition, you can export sparse models to other formats, such as VisualSfM's NVM, Bundler files, PLY, VRML, etc., using ``File > Export as...``. To convert between various formats from the CLI, use the ``model_converter`` executable. There are two source files to conveniently read the sparse reconstructions using Python (``scripts/python/read_write_model.py`` supporting binary and text) and Matlab (``scripts/matlab/read_model.m`` supporting text). ----------- Text Format ----------- COLMAP exports the following three text files for every reconstructed model: `cameras.txt`, `images.txt`, and `points3D.txt`. Comments start with a leading "#" character and are ignored. The first comment lines briefly describe the format of the text files, as described in more detailed on this page. cameras.txt ----------- This file contains the intrinsic parameters of all reconstructed cameras in the dataset using one line per camera, e.g.:: # Camera list with one line of data per camera: # CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[] # Number of cameras: 3 1 SIMPLE_PINHOLE 3072 2304 2559.81 1536 1152 2 PINHOLE 3072 2304 2560.56 2560.56 1536 1152 3 SIMPLE_RADIAL 3072 2304 2559.69 1536 1152 -0.0218531 Here, the dataset contains 3 cameras based using different distortion models with the same sensor dimensions (width: 3072, height: 2304). The length of parameters is variable and depends on the camera model. For the first camera, there are 3 parameters with a single focal length of 2559.81 pixels and a principal point at pixel location `(1536, 1152)`. The intrinsic parameters of a camera can be shared by multiple images, which refer to cameras using the unique identifier `CAMERA_ID`. images.txt ---------- This file contains the pose and keypoints of all reconstructed images in the dataset using two lines per image, e.g.:: # Image list with two lines of data per image: # IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME # POINTS2D[] as (X, Y, POINT3D_ID) # Number of images: 2, mean observations per image: 2 1 0.851773 0.0165051 0.503764 -0.142941 -0.737434 1.02973 3.74354 1 P1180141.JPG 2362.39 248.498 58396 1784.7 268.254 59027 1784.7 268.254 -1 2 0.851773 0.0165051 0.503764 -0.142941 -0.737434 1.02973 3.74354 1 P1180142.JPG 1190.83 663.957 23056 1258.77 640.354 59070 Here, the first two lines define the information of the first image, and so on. The reconstructed pose of an image is specified as the projection from world to the camera coordinate system of an image using a quaternion `(QW, QX, QY, QZ)` and a translation vector `(TX, TY, TZ)`. The quaternion is defined using the Hamilton convention, which is, for example, also used by the Eigen library. The coordinates of the projection/camera center are given by ``-R^t * T``, where ``R^t`` is the inverse/transpose of the 3x3 rotation matrix composed from the quaternion and ``T`` is the translation vector. The local camera coordinate system of an image is defined in a way that the X axis points to the right, the Y axis to the bottom, and the Z axis to the front as seen from the image. Both images in the example above use the same camera model and share intrinsics (`CAMERA_ID = 1`). The image name is relative to the selected base image folder of the project. The first image has 3 keypoints and the second image has 2 keypoints, while the location of the keypoints is specified in pixel coordinates. Both images observe 2 3D points and note that the last keypoint of the first image does not observe a 3D point in the reconstruction as the 3D point identifier is -1. points3D.txt ------------ This file contains the information of all reconstructed 3D points in the dataset using one line per point, e.g.:: # 3D point list with one line of data per point: # POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX) # Number of points: 3, mean track length: 3.3334 63390 1.67241 0.292931 0.609726 115 121 122 1.33927 16 6542 15 7345 6 6714 14 7227 63376 2.01848 0.108877 -0.0260841 102 209 250 1.73449 16 6519 15 7322 14 7212 8 3991 63371 1.71102 0.28566 0.53475 245 251 249 0.612829 118 4140 117 4473 Here, there are three reconstructed 3D points, where `POINT2D_IDX` defines the zero-based index of the keypoint in the `images.txt` file. The error is given in pixels of reprojection error and is only updated after global bundle adjustment. ==================== Dense Reconstruction ==================== COLMAP uses the following workspace folder structure:: +── images │   +── image1.jpg │   +── image2.jpg │   +── ... +── sparse │   +── cameras.txt │   +── images.txt │   +── points3D.txt +── stereo │   +── consistency_graphs │   │   +── image1.jpg.photometric.bin │   │   +── image2.jpg.photometric.bin │   │   +── ... │   +── depth_maps │   │   +── image1.jpg.photometric.bin │   │   +── image2.jpg.photometric.bin │   │   +── ... │   +── normal_maps │   │   +── image1.jpg.photometric.bin │   │   +── image2.jpg.photometric.bin │   │   +── ... │   +── patch-match.cfg │   +── fusion.cfg +── fused.ply +── meshed-poisson.ply +── meshed-delaunay.ply +── run-colmap-geometric.sh +── run-colmap-photometric.sh Here, the `images` folder contains the undistorted images, the `sparse` folder contains the sparse reconstruction with undistorted cameras, the `stereo` folder contains the stereo reconstruction results, `point-cloud.ply` and `mesh.ply` are the results of the fusion and meshing procedure, and `run-colmap-geometric.sh` and `run-colmap-photometric.sh` contain example command-line usage to perform the dense reconstruction. --------------------- Depth and Normal Maps --------------------- The depth maps are stored as mixed text and binary files. The text header defines the dimensions of the image in the format ``with&height&channels&`` followed by row-major `float32` binary data. For depth maps ``channels=1`` and for normal maps ``channels=3``. The depth and normal maps can be conveniently read with Python using the functions in ``scripts/python/read_dense.py`` and with Matlab using the functions in ``scripts/matlab/read_depth_map.m`` and ``scripts/matlab/read_normal_map.m``. ------------------ Consistency Graphs ------------------ The consistency graph defines, for all pixels in an image, the source images a pixel is consistent with. The graph is stored as a mixed text and binary file, while the text part is equivalent to the depth and normal maps and the binary part is a continuous list of `int32` values in the format ``...``. Here, ``(row, col)`` defines the location of the pixel in the image followed by a list of ``N`` image indices. The indices are specified w.r.t. the ordering in the ``images.txt`` file. colmap-3.10/doc/gui.rst000066400000000000000000000060071464745566500150240ustar00rootroot00000000000000.. _gui: Graphical User Interface ======================== The graphical user interface of COLMAP provides access to most of the available functionality and visualizes the reconstruction process in "real-time". To start the GUI, you can run the pre-built packages (Windows: `COLMAP.bat`, Mac: `COLMAP.app`), execute ``colmap gui`` if you installed COLMAP or execute ``./src/colmap/exe/colmap gui`` from the CMake build folder. The GUI application requires an attached display with at least OpenGL 3.2 support. Registered images are visualized in red and reconstructed points in their average point color extracted from the images. The viewer can also visualize dense point clouds produced from Multi-View Stereo. Model Viewer Controls --------------------- - **Rotate model**: Left-click and drag. - **Shift model**: Right-click or -click (-click) and drag. - **Zoom model**: Scroll. - **Change point size**: -scroll (-scroll). - **Change camera size**: -scroll. - **Adjust clipping plane**: -scroll. - **Select point**: Double-left-click point (change point size if too small). The green lines visualize the projections into the images that see the point. The opening window shows the projected locations of the point in all images. - **Select camera**: Double-left-click camera (change camera size if too small). The purple lines visualize images that see at least one common point with the selected image. The opening window shows a few statistics of the image. - **Reset view**: To reset all viewing settings, choose ``Render > Reset view``. Render Options -------------- The model viewer allows you to render the model with different settings, projections, colormaps, etc. Please, choose ``Render > Render options``. Create Screenshots ------------------ To create screenshots of the current viewpoint (without coordinate axes), choose ``Extras > Grab image`` and save the image in the format of your choice. Create Screencast ----------------- To create a video screen capture of the reconstructed model, choose ``Extras > Grab movie``. This dialog allows you to set individual control viewpoints by choosing ``Add``. COLMAP generates a fixed number of frames per second between each control viewpoint by smoothly interpolating the linear trajectory, and to interpolate the configured point and the camera sizes at the time of clicking ``Add``. To change the number of frames between two viewpoints or to reorder individual viewpoints, modify the time of the viewpoint by double-clicking the respective cell in the table. 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About ----- COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. If you use this project for your research, please cite:: @inproceedings{schoenberger2016sfm, author={Sch\"{o}nberger, Johannes Lutz and Frahm, Jan-Michael}, title={Structure-from-Motion Revisited}, booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2016}, } @inproceedings{schoenberger2016mvs, author={Sch\"{o}nberger, Johannes Lutz and Zheng, Enliang and Pollefeys, Marc and Frahm, Jan-Michael}, title={Pixelwise View Selection for Unstructured Multi-View Stereo}, booktitle={European Conference on Computer Vision (ECCV)}, year={2016}, } If you use the image retrieval / vocabulary tree engine, please also cite:: @inproceedings{schoenberger2016vote, author={Sch\"{o}nberger, Johannes Lutz and Price, True and Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc}, title={A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval}, booktitle={Asian Conference on Computer Vision (ACCV)}, year={2016}, } The latest source code is available at `GitHub `_. COLMAP builds on top of existing works and when using specific algorithms within COLMAP, please also cite the original authors, as specified in the source code. Download -------- Executables and other resources can be downloaded from https://demuc.de/colmap/. Getting Started --------------- 1. Download the `pre-built binaries `_ or build the library manually from `source `_ (see :ref:`Installation `). 2. Download one of the provided datasets (see :ref:`Datasets `) or use your own images. 3. Use the **automatic reconstruction** to easily build models with a single click (see :ref:`Quickstart `). Support ------- Please, use `GitHub Discussions `_ for questions and the `GitHub issue tracker `_ for bug reports, feature requests/additions, etc. Acknowledgments --------------- The library was originally written by `Johannes L. Schönberger `_ with funding provided by his PhD advisors Jan-Michael Frahm and Marc Pollefeys. Since then the project has benefited from countless community contributions, including bug fixes, improvements, new features, third-party tooling, and community support. .. toctree:: :hidden: :maxdepth: 2 install tutorial database cameras format datasets gui cli faq changelog contribution license bibliography colmap-3.10/doc/install.rst000077500000000000000000000233571464745566500157200ustar00rootroot00000000000000.. _installation: Installation ============ You can either download one of the pre-built binaries or build the source code manually. Executables for Windows and Mac and other resources can be downloaded from https://demuc.de/colmap/. Executables for Linux/Unix/BSD are available at https://repology.org/metapackage/colmap/versions. Note that the COLMAP packages in the default repositories for Linux/Unix/BSD do not come with CUDA support, which requires manual compilation but is relatively easy on these platforms. COLMAP can be used as an independent application through the command-line or graphical user interface. Alternatively, COLMAP is also built as a reusable library, i.e., you can include and link COLMAP against your own source code, as described further below. ------------------ Pre-built Binaries ------------------ Windows ------- For convenience, the pre-built binaries for Windows contain both the graphical and command-line interface executables. To start the COLMAP GUI, you can simply double-click the ``COLMAP.bat`` batch script or alternatively run it from the Windows command shell or Powershell. The command-line interface is also accessible through this batch script, which automatically sets the necessary library paths. To list the available COLMAP commands, run ``COLMAP.bat -h`` in the command shell ``cmd.exe`` or in Powershell. Mac --- The pre-built application package for Mac contains both the GUI and command-line version of COLMAP. To open the GUI, simply open the application and note that COLMAP is shipped as an unsigned application, i.e., when your first open the application, you have to right-click the application and select *Open* and then accept to trust the application. In the future, you can then simply double-click the application to open COLMAP. The command-line interface is accessible by running the packaged binary ``COLMAP.app/Contents/MacOS/colmap``. To list the available COLMAP commands, run ``COLMAP.app/Contents/MacOS/colmap -h``. ----------------- Build from Source ----------------- COLMAP builds on all major platforms (Linux, Mac, Windows) with little effort. First, checkout the latest source code:: git clone https://github.com/colmap/colmap On Linux and Mac it is generally recommended to follow the installation instructions below, which use the system package managers to install the required dependencies. Alternatively, there is a Python build script that builds COLMAP and its dependencies locally. This script is useful under Windows and on a (cluster) system if you do not have root access under Linux or Mac. Linux ----- *Recommended dependencies:* CUDA (at least version 7.X) Dependencies from the default Ubuntu repositories:: sudo apt-get install \ git \ cmake \ ninja-build \ build-essential \ libboost-program-options-dev \ libboost-filesystem-dev \ libboost-graph-dev \ libboost-system-dev \ libeigen3-dev \ libflann-dev \ libfreeimage-dev \ libmetis-dev \ libgoogle-glog-dev \ libgtest-dev \ libsqlite3-dev \ libglew-dev \ qtbase5-dev \ libqt5opengl5-dev \ libcgal-dev \ libceres-dev Configure and compile COLMAP:: git clone https://github.com/colmap/colmap.git cd colmap mkdir build cd build cmake .. -GNinja ninja sudo ninja install Run COLMAP:: colmap -h colmap gui To compile with **CUDA support**, also install Ubuntu's default CUDA package:: sudo apt-get install -y \ nvidia-cuda-toolkit \ nvidia-cuda-toolkit-gcc Or, manually install the latest CUDA from NVIDIA's homepage. During CMake configuration specify `CMAKE_CUDA_ARCHITECTURES` as "native", if you want to run COLMAP on your current machine only (default), "all"/"all-major" to be able to distribute to other machines, or a specific CUDA architecture like "75", etc. Under **Ubuntu 18.04**, the CMake configuration scripts of CGAL are broken and you must also install the CGAL Qt5 package:: sudo apt-get install libcgal-qt5-dev Under **Ubuntu 22.04**, there is a problem when compiling with Ubuntu's default CUDA package and GCC, and you must compile against GCC 10:: sudo apt-get install gcc-10 g++-10 export CC=/usr/bin/gcc-10 export CXX=/usr/bin/g++-10 export CUDAHOSTCXX=/usr/bin/g++-10 # ... and then run CMake against COLMAP's sources. Mac --- Dependencies from `Homebrew `_:: brew install \ cmake \ ninja \ boost \ eigen \ flann \ freeimage \ metis \ glog \ googletest \ ceres-solver \ qt5 \ glew \ cgal \ sqlite3 Configure and compile COLMAP:: git clone https://github.com/colmap/colmap.git cd colmap export PATH="/usr/local/opt/qt@5/bin:$PATH" mkdir build cd build cmake .. -GNinja -DQt5_DIR=/usr/local/opt/qt/lib/cmake/Qt5 ninja sudo ninja install On Macs with ARM the brew paths are different so you need this cmake .. -GNinja -DCMAKE_PREFIX_PATH="/opt/homebrew/opt/flann;/opt/homebrew/opt/metis;/opt/homebrew/opt/suite-sparse;/opt/homebrew/opt/qt@5;/opt/homebrew/opt/freeimage" If you have Qt 6 installed on your system as well, you might have to temporarily link your Qt 5 installation while configuring CMake:: brew link qt5 cmake configuration (from previous code block) brew unlink qt5 Run COLMAP:: colmap -h colmap gui Windows ------- *Recommended dependencies:* CUDA (at least version 7.X), Visual Studio 2019 On Windows, the recommended way is to build COLMAP using vcpkg:: git clone https://github.com/microsoft/vcpkg cd vcpkg .\bootstrap-vcpkg.bat .\vcpkg install colmap[cuda,tests]:x64-windows To compile CUDA for multiple compute architectures, please use:: .\vcpkg install colmap[cuda-redist]:x64-windows Please refer to the next section for more details. VCPKG ----- COLMAP ships as part of the vcpkg distribution. This enables to conveniently build COLMAP and all of its dependencies from scratch under different platforms. Note that VCPKG requires you to install CUDA manually in the standard way on your platform. To compile COLMAP using VCPKG, you run:: git clone https://github.com/microsoft/vcpkg cd vcpkg ./bootstrap-vcpkg.sh ./vcpkg install colmap:x64-linux VCPKG ships with support for various other platforms (e.g., x64-osx, x64-windows, etc.). To compile with CUDA support and to build all tests:: ./vcpkg install colmap[cuda,tests]:x64-linux The above commands will build the latest release version of COLMAP. To compile the latest commit in the dev branch, you can use the following options:: ./vcpkg install colmap:x64-linux --head To modify the source code, you can further add ``--editable --no-downloads``. Or, if you want to build from another folder and use the dependencies from vcpkg, first run `./vcpkg integrate install` (under Windows use pwsh and `./scripts/shell/enter_vs_dev_shell.ps1`) and then configure COLMAP as:: cd path/to/colmap mkdir build cd build cmake .. -DCMAKE_TOOLCHAIN_FILE=path/to/vcpkg/scripts/buildsystems/vcpkg.cmake -DCMAKE_BUILD_TYPE=Release cmake --build . --config release --target colmap --parallel 24 .. _installation-library: ------- Library ------- If you want to include and link COLMAP against your own library, the easiest way is to use CMake as a build configuration tool. After configuring the COLMAP build and running `ninja/make install`, COLMAP automatically installs all headers to ``${CMAKE_INSTALL_PREFIX}/include/colmap``, all libraries to ``${CMAKE_INSTALL_PREFIX}/lib/colmap``, and the CMake configuration to ``${CMAKE_INSTALL_PREFIX}/share/colmap``. For example, compiling your own source code against COLMAP is as simple as using the following ``CMakeLists.txt``:: cmake_minimum_required(VERSION 3.10) project(SampleProject) find_package(colmap REQUIRED) # or to require a specific version: find_package(colmap 3.4 REQUIRED) add_executable(hello_world hello_world.cc) target_link_libraries(hello_world colmap::colmap) with the source code ``hello_world.cc``:: #include #include #include #include int main(int argc, char** argv) { colmap::InitializeGlog(argv); std::string message; colmap::OptionManager options; options.AddRequiredOption("message", &message); options.Parse(argc, argv); std::cout << colmap::StringPrintf("Hello %s!", message.c_str()) << std::endl; return EXIT_SUCCESS; } Then compile and run your code as:: mkdir build cd build export colmap_DIR=${CMAKE_INSTALL_PREFIX}/share/colmap cmake .. -GNinja ninja ./hello_world --message "world" The sources of this example are stored under ``doc/sample-project``. ---------------- AddressSanitizer ---------------- If you want to build COLMAP with address sanitizer flags enabled, you need to use a recent compiler with ASan support. For example, you can manually install a recent clang version on your Ubuntu machine and invoke CMake as follows:: CC=/usr/bin/clang CXX=/usr/bin/clang++ cmake .. \ -DASAN_ENABLED=ON \ -DTESTS_ENABLED=ON \ -DCMAKE_BUILD_TYPE=RelWithDebInfo Note that it is generally useful to combine ASan with debug symbols to get meaningful traces for reported issues. ------------- Documentation ------------- You need Python and Sphinx to build the HTML documentation:: cd path/to/colmap/doc sudo apt-get install python pip install sphinx make html open _build/html/index.html Alternatively, you can build the documentation as PDF, EPUB, etc.:: make latexpdf open _build/pdf/COLMAP.pdf colmap-3.10/doc/license.rst000066400000000000000000000036541464745566500156670ustar00rootroot00000000000000License ======= The COLMAP library is licensed under the new BSD license. Note that this text refers only to the license for COLMAP itself, independent of its thirdparty dependencies, which are separately licensed. Building COLMAP with these dependencies may affect the resulting COLMAP license. .. code-block:: text Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 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The XML files are in %BUILDDIR%/xml. goto end ) if "%1" == "pseudoxml" ( %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml if errorlevel 1 exit /b 1 echo. echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. goto end ) :end colmap-3.10/doc/sample-project/000077500000000000000000000000001464745566500164305ustar00rootroot00000000000000colmap-3.10/doc/sample-project/CMakeLists.txt000066400000000000000000000004001464745566500211620ustar00rootroot00000000000000cmake_minimum_required(VERSION 3.10) project(SampleProject) find_package(colmap REQUIRED) # or to require a specific version: find_package(colmap 3.4 REQUIRED) add_executable(hello_world hello_world.cc) target_link_libraries(hello_world colmap::colmap) colmap-3.10/doc/sample-project/hello_world.cc000066400000000000000000000006551464745566500212570ustar00rootroot00000000000000#include #include #include #include int main(int argc, char** argv) { colmap::InitializeGlog(argv); std::string message; colmap::OptionManager options; options.AddRequiredOption("message", &message); options.Parse(argc, argv); std::cout << colmap::StringPrintf("Hello %s!", message.c_str()) << std::endl; return EXIT_SUCCESS; } colmap-3.10/doc/tutorial.rst000077500000000000000000000637471464745566500161240ustar00rootroot00000000000000.. _tutorial: Tutorial ======== This tutorial covers the topic of image-based 3D reconstruction by demonstrating the individual processing steps in COLMAP. If you are interested in a more general and mathematical introduction to the topic of image-based 3D reconstruction, please also refer to the `CVPR 2017 Tutorial on Large-scale 3D Modeling from Crowdsourced Data `_ and [schoenberger_thesis]_. Image-based 3D reconstruction from images traditionally first recovers a sparse representation of the scene and the camera poses of the input images using Structure-from-Motion. This output then serves as the input to Multi-View Stereo to recover a dense representation of the scene. .. _quick-start: Quickstart ---------- First, start the graphical user interface of COLMAP, as described :ref:`here `. COLMAP provides an automatic reconstruction tool that simply takes a folder of input images and produces a sparse and dense reconstruction in a workspace folder. Click ``Reconstruction > Automatic Reconstruction`` in the GUI and specify the relevant options. The output is written to the workspace folder. For example, if your images are located in ``path/to/project/images``, you could select ``path/to/project`` as a workspace folder and after running the automatic reconstruction tool, the folder would look similar to this:: +── images │   +── image1.jpg │   +── image2.jpg │   +── ... +── sparse │   +── 0 │   │ +── cameras.bin │   │ +── images.bin │   │ +── points3D.bin │ +── ... +── dense │   +── 0 │   │ +── images │   │ +── sparse │   │ +── stereo │   │ +── fused.ply │   │ +── meshed-poisson.ply │   │ +── meshed-delaunay.ply │ +── ... +── database.db Here, the ``path/to/project/sparse`` contains the sparse models for all reconstructed components, while ``path/to/project/dense`` contains their corresponding dense models. The dense point cloud ``fused.ply`` can be imported in COLMAP using ``File > Import model from ...``, while the dense mesh must be visualized with an external viewer such as Meshlab. The following sections give general recommendations and describe the reconstruction process in more detail, if you need more control over the reconstruction process/parameters or if you are interested in the underlying technology in COLMAP. Structure-from-Motion --------------------- .. figure:: images/incremental-sfm.png :alt: Incremental Structure-from-Motion pipeline :figclass: align-center COLMAP's incremental Structure-from-Motion pipeline. Structure-from-Motion (SfM) is the process of reconstructing 3D structure from its projections into a series of images. The input is a set of overlapping images of the same object, taken from different viewpoints. The output is a 3-D reconstruction of the object, and the reconstructed intrinsic and extrinsic camera parameters of all images. Typically, Structure-from-Motion systems divide this process into three stages: 1) Feature detection and extraction 2) Feature matching and geometric verification 3) Structure and motion reconstruction COLMAP reflects these stages in different modules, that can be combined depending on the application. More information on Structure-from-Motion in general and the algorithms in COLMAP can be found in [schoenberger16sfm]_ and [schoenberger16mvs]_. If you have control over the picture capture process, please follow these guidelines for optimal reconstruction results: - Capture images with **good texture**. Avoid completely texture-less images (e.g., a white wall or empty desk). If the scene does not contain enough texture itself, you could place additional background objects, such as posters, etc. - Capture images at **similar illumination** conditions. Avoid high dynamic range scenes (e.g., pictures against the sun with shadows or pictures through doors/windows). Avoid specularities on shiny surfaces. - Capture images with **high visual overlap**. Make sure that each object is seen in at least 3 images -- the more images the better. - Capture images from **different viewpoints**. Do not take images from the same location by only rotating the camera, e.g., make a few steps after each shot. At the same time, try to have enough images from a relatively similar viewpoint. Note that more images is not necessarily better and might lead to a slow reconstruction process. If you use a video as input, consider down-sampling the frame rate. Multi-View Stereo ----------------- Multi-View Stereo (MVS) takes the output of SfM to compute depth and/or normal information for every pixel in an image. Fusion of the depth and normal maps of multiple images in 3D then produces a dense point cloud of the scene. Using the depth and normal information of the fused point cloud, algorithms such as the (screened) Poisson surface reconstruction [kazhdan2013]_ can then recover the 3D surface geometry of the scene. More information on Multi-View Stereo in general and the algorithms in COLMAP can be found in [schoenberger16mvs]_. Preface ------- COLMAP requires only few steps to do a standard reconstruction for a general user. For more experienced users, the program exposes many different parameters, only some of which are intuitive to a beginner. The program should usually work without the need to modify any parameters. The defaults are chosen as a trade- off between reconstruction robustness/quality and speed. You can set "optimal" options for different reconstruction scenarios by choosing ``Extras > Set options for ... data``. If in doubt what settings to choose, stick to the defaults. The source code contains more documentation about all parameters. COLMAP is research software and in rare cases it may exit ungracefully if some constraints are not fulfilled. In this case, the program prints a traceback to stdout. To see this traceback or more debug information, it is recommended to run the executables (including the GUI) from the command-line, where you can define various levels of logging verbosity. Terminology ----------- The term **camera** is associated with the physical object of a camera using the same zoom-factor and lens. A camera defines the intrinsic projection model in COLMAP. A single camera can take multiple images with the same resolution, intrinsic parameters, and distortion characteristics. The term **image** is associated with a bitmap file, e.g., a JPEG or PNG file on disk. COLMAP detects **keypoints** in each image whose appearance is described by numerical **descriptors**. Pure appearance-based correspondences between keypoints/descriptors are defined by **matches**, while **inlier matches** are geometrically verified and used for the reconstruction procedure. Data Structure -------------- COLMAP assumes that all input images are in one input directory with potentially nested sub-directories. It recursively considers all images stored in this directory, and it supports various different image formats (see `FreeImage `_). Other files are automatically ignored. If high performance is a requirement, then you should separate any files that are not images. Images are identified uniquely by their relative file path. For later processing, such as image undistortion or dense reconstruction, the relative folder structure should be preserved. COLMAP does not modify the input images or directory and all extracted data is stored in a single, self-contained SQLite database file (see :doc:`database`). The first step is to start the graphical user interface of COLMAP by running the pre-built binaries (Windows: `COLMAP.bat`, Mac: `COLMAP.app`) or by executing ``./src/colmap/exe/colmap gui`` from the CMake build folder. Next, create a new project by choosing ``File > New project``. In this dialog, you must select where to store the database and the folder that contains the input images. For convenience, you can save the entire project settings to a configuration file by choosing ``File > Save project``. The project configuration stores the absolute path information of the database and image folder in addition to any other parameter settings. If you decide to move the database or image folder, you must change the paths accordingly by creating a new project. Alternatively, the resulting `.ini` configuration file can be directly modified in a text editor of your choice. To reopen an existing project, you can simply open the configuration file by choosing ``File > Open project`` and all parameter settings should be recovered. Note that all COLMAP executables can be started from the command-line by either specifying individual settings as command-line arguments or by providing the path to the project configuration file (see :ref:`Interface `). An example folder structure could look like this:: /path/to/project/... +── images │   +── image1.jpg │   +── image2.jpg │   +── ... │   +── imageN.jpg +── database.db +── project.ini In this example, you would select `/path/to/project/images` as the image folder path, `/path/to/project/database.db` as the database file path, and save the project configuration to `/path/to/project/project.ini`. Feature Detection and Extraction -------------------------------- In the first step, feature detection/extraction finds sparse feature points in the image and describes their appearance using a numerical descriptor. COLMAP imports images and performs feature detection/extraction in one step in order to only load images from disk once. Next, choose ``Processing > Extract features``. In this dialog, you must first decide on the employed intrinsic camera model. You can either automatically extract focal length information from the embedded EXIF information or manually specify intrinsic parameters, e.g., as obtained in a lab calibration. If an image has partial EXIF information, COLMAP tries to find the missing camera specifications in a large database of camera models automatically. If all your images were captured by the same physical camera with identical zoom factor, it is recommended to share intrinsics between all images. Note that the program will exit ungracefully if the same camera model is shared among all images but not all images have the same size or EXIF focal length. If you have several groups of images that share the same intrinsic camera parameters, you can easily modify the camera models at a later point as well (see :ref:`Database Management `). If in doubt what to choose in this step, simply stick to the default parameters. You can either detect and extract new features from the images or import existing features from text files. COLMAP extracts SIFT [lowe04]_ features either on the GPU or the CPU. The GPU version requires an attached display, while the CPU version is recommended for use on a server. In general, the GPU version is favorable as it has a customized feature detection mode that often produces higher quality features in the case of high contrast images. If you import existing features, every image must have a text file next to it (e.g., `/path/to/image1.jpg` and `/path/to/image1.jpg.txt`) in the following format:: NUM_FEATURES 128 X Y SCALE ORIENTATION D_1 D_2 D_3 ... D_128 ... X Y SCALE ORIENTATION D_1 D_2 D_3 ... D_128 where `X, Y, SCALE, ORIENTATION` are floating point numbers and `D_1...D_128` values in the range `0...255`. The file should have `NUM_FEATURES` lines with one line per feature. For example, if an image has 4 features, then the text file should look something like this:: 4 128 1.2 2.3 0.1 0.3 1 2 3 4 ... 21 2.2 3.3 1.1 0.3 3 2 3 2 ... 32 0.2 1.3 1.1 0.3 3 2 3 2 ... 2 1.2 2.3 1.1 0.3 3 2 3 2 ... 3 Note that by convention the upper left corner of an image has coordinate `(0, 0)` and the center of the upper left most pixel has coordinate `(0.5, 0.5)`. If you must import features for large image collections, it is much more efficient to directly access the database with your favorite scripting language (see :ref:`Database Format `). If you are done setting all options, choose ``Extract`` and wait for the extraction to finish or cancel. If you cancel during the extraction process, the next time you start extracting images for the same project, COLMAP automatically continues where it left off. This also allows you to add images to an existing project/reconstruction. In this case, be sure to verify the camera parameters when using shared intrinsics. All extracted data will be stored in the database file and can be reviewed/managed in the database management tool (see :ref:`Database Management `) or, for experts, directly modified using SQLite (see :ref:`Database Format `). Feature Matching and Geometric Verification ------------------------------------------- In the second step, feature matching and geometric verification finds correspondences between the feature points in different images. Please, choose ``Processing > Match features`` and select one of the provided matching modes, that are intended for different input scenarios: - **Exhaustive Matching**: If the number of images in your dataset is relatively low (up to several hundreds), this matching mode should be fast enough and leads to the best reconstruction results. Here, every image is matched against every other image, while the block size determines how many images are loaded from disk into memory at the same time. - **Sequential Matching**: This mode is useful if the images are acquired in sequential order, e.g., by a video camera. In this case, consecutive frames have visual overlap and there is no need to match all image pairs exhaustively. Instead, consecutively captured images are matched against each other. This matching mode has built-in loop detection based on a vocabulary tree, where every N-th image (`loop_detection_period`) is matched against its visually most similar images (`loop_detection_num_images`). Note that image file names must be ordered sequentially (e.g., `image0001.jpg`, `image0002.jpg`, etc.). The order in the database is not relevant, since the images are explicitly ordered according to their file names. Note that loop detection requires a pre-trained vocabulary tree, that can be downloaded from https://demuc.de/colmap/. - **Vocabulary Tree Matching**: In this matching mode [schoenberger16vote]_, every image is matched against its visual nearest neighbors using a vocabulary tree with spatial re-ranking. This is the recommended matching mode for large image collections (several thousands). This requires a pre-trained vocabulary tree, that can be downloaded from https://demuc.de/colmap/. - **Spatial Matching**: This matching mode matches every image against its spatial nearest neighbors. Spatial locations can be manually set in the database management. By default, COLMAP also extracts GPS information from EXIF and uses it for spatial nearest neighbor search. If accurate prior location information is available, this is the recommended matching mode. - **Transitive Matching**: This matching mode uses the transitive relations of already existing feature matches to produce a more complete matching graph. If an image A matches to an image B and B matches to C, then this matcher attempts to match A to C directly. - **Custom Matching**: This mode allows to specify individual image pairs for matching or to import individual feature matches. To specify image pairs, you have to provide a text file with one image pair per line:: image1.jpg image2.jpg image1.jpg image3.jpg ... where `image1.jpg` is the relative path in the image folder. You have two options to import individual feature matches. Either raw feature matches, which are not geometrically verified or already geometrically verified feature matches. In both cases, the expected format is:: image1.jpg image2.jpg 0 1 1 2 3 4 image1.jpg image3.jpg 0 1 1 2 3 4 4 5 ... where `image1.jpg` is the relative path in the image folder and the pairs of numbers are zero-based feature indices in the respective images. If you must import many matches for large image collections, it is more efficient to directly access the database with a scripting language of your choice. If you are done setting all options, choose ``Match`` and wait for the matching to finish or cancel in between. Note that this step can take a significant amount of time depending on the number of images, the number of features per image, and the chosen matching mode. Expected times for exhaustive matching are from a few minutes for tens of images to a few hours for hundreds of images to days or weeks for thousands of images. If you cancel the matching process or import new images after matching, COLMAP only matches image pairs that have not been matched previously. The overhead of skipping already matched image pairs is low. This also enables to match additional images imported after an initial matching and it enables to combine different matching modes for the same dataset. All extracted data will be stored in the database file and can be reviewed/managed in the database management tool (see :ref:`Database Management `) or, for experts, directly modified using SQLite (see :ref:`Database Format `). Note that feature matching requires a GPU and that the display performance of your computer might degrade significantly during the matching process. If your system has multiple CUDA-enabled GPUs, you can select specific GPUs with the `gpu_index` option. Sparse Reconstruction --------------------- After producing the scene graph in the previous two steps, you can start the incremental reconstruction process by choosing ``Reconstruction > Start``. COLMAP first loads all extracted data from the database into memory and seeds the reconstruction from an initial image pair. Then, the scene is incrementally extended by registering new images and triangulating new points. The results are visualized in "real-time" during this reconstruction process. Refer to the :ref:`Graphical User Interface ` section for more details about the available controls. COLMAP attempts to reconstruct multiple models if not all images are registered into the same model. The different models can be selected from the drop-down menu in the toolbar. If the different models have common registered images, you can use the ``model_converter`` executable to merge them into a single reconstruction (see :ref:`FAQ ` for details). Ideally, the reconstruction works fine and all images are registered. If this is not the case, it is recommended to: - Perform additional matching. For best results, use exhaustive matching, enable guided matching, increase the number of nearest neighbors in vocabulary tree matching, or increase the overlap in sequential matching, etc. - Manually choose an initial image pair, if COLMAP fails to initialize. Choose ``Reconstruction > Reconstruction options > Init`` and set images from the database management tool that have enough matches from different viewpoints. Importing and Exporting ----------------------- COLMAP provides several export options for further processing. For full flexibility, it is recommended to export the reconstruction in COLMAP's data format by choosing ``File > Export`` to export the currently viewed model or ``File > Export all`` to export all reconstructed models. The model is exported in the selected folder using separate text files for the reconstructed cameras, images, and points. When exporting in COLMAP's data format, you can re- import the reconstruction for later visualization, image undistortion, or to continue an existing reconstruction from where it left off (e.g., after importing and matching new images). To import a model, choose ``File > Import`` and select the export folder path. Alternatively, you can also export the model in various other formats, such as Bundler, VisualSfM [#f1]_, PLY, or VRML by choosing ``File > Export as...``. COLMAP can visualize plain PLY point cloud files with RGB information by choosing ``File > Import From...``. Further information about the format of the exported models can be found :ref:`here `. .. _dense-reconstruction: Dense Reconstruction -------------------- After reconstructing a sparse representation of the scene and the camera poses of the input images, MVS can now recover denser scene geometry. COLMAP has an integrated dense reconstruction pipeline to produce depth and normal maps for all registered images, to fuse the depth and normal maps into a dense point cloud with normal information, and to finally estimate a dense surface from the fused point cloud using Poisson [kazhdan2013]_ or Delaunay reconstruction. To get started, import your sparse 3D model into COLMAP (or select the reconstructed model after finishing the previous sparse reconstruction steps). Then, choose ``Reconstruction > Multi-view stereo`` and select an empty or existing workspace folder, which is used for the output and of all dense reconstruction results. The first step is to ``undistort`` the images, second to compute the depth and normal maps using ``stereo``, third to ``fuse`` the depth and normals maps to a point cloud, followed by a final, optional point cloud ``meshing`` step. During the stereo reconstruction process, the display might freeze due to heavy compute load and, if your GPU does not have enough memory, the reconstruction process might ungracefully crash. Please, refer to the FAQ (:ref:`freeze ` and :ref:`memory `) for information on how to avoid these problems. Note that the reconstructed normals of the point cloud cannot be directly visualized in COLMAP, but e.g. in Meshlab by enabling ``Render > Show Normal/Curvature``. Similarly, the reconstructed dense surface mesh model must be visualized with external software. In addition to the internal dense reconstruction functionality, COLMAP exports to several other dense reconstruction libraries, such as CMVS/PMVS [furukawa10]_ or CMP-MVS [jancosek11]_. Please choose ``Extras > Undistort images`` and select the appropriate format. The output folders contain the reconstruction and the undistorted images. In addition, the folders contain sample shell scripts to perform the dense reconstruction. To run PMVS2, execute the following commands: ./path/to/pmvs2 /path/to/undistortion/folder/pmvs/ option-all where `/path/to/undistortion/folder` is the folder selected in the undistortion dialog. Make sure not to forget the trailing slash in `/path/to/undistortion/folder/pmvs/` in the above command-line arguments. For large datasets, you probably want to first run CMVS to cluster the scene into more manageable parts and then run COLMAP or PMVS2. Please, refer to the sample shell scripts in the undistortion output folder on how to run CMVS in combination with COLMAP or PMVS2. Moreover, there are a number of external libraries that support COLMAP's output: - `CMVS/PMVS `_ [furukawa10]_ - `CMP-MVS `_ [jancosek11]_ - `Line3D++ `_ [hofer16]_. .. _database-management: Database Management ------------------- You can review and manage the imported cameras, images, and feature matches in the database management tool. Choose ``Processing > Manage database``. In the opening dialog, you can see the list of imported images and cameras. You can view the features and matches for each image by clicking ``Show image`` and ``Overlapping images``. Individual entries in the database tables can be modified by double clicking specific cells. Note that any changes to the database are only effective after clicking ``Save``. To share intrinsic camera parameters between arbitrary groups of images, select a single or multiple images, choose ``Set camera`` and set the `camera_id`, which corresponds to the unique `camera_id` column in the cameras table. You can also add new cameras with specific parameters. By setting the `prior_focal_length` flag to 0 or 1, you can give a hint whether the reconstruction algorithm should trust the focal length value. In case of a prior lab calibration, you want to set this value to 1. Without prior knowledge about the focal length, it is recommended to set this value to `1.25 * max(width_in_px, height_in_px)`. The database management tool has only limited functionality and, for full control over the data, you must directly modify the SQLite database (see :ref:`Database Format `). By accessing the database directly, you can use COLMAP only for feature extraction and matching or you can import your own features and matches to only use COLMAP's incremental reconstruction algorithm. .. _interface: Graphical and Command-line Interface ------------------------------------ Most of COLMAP's features are accessible from both the graphical and the command-line interface, which are both embedded in the same executable. You can provide the options directly as command-line arguments or you can provide a `.ini` project configuration file containing the options using the ``--project_path path/to/project.ini`` argument. To start the GUI application, please execute ``colmap gui`` or directly specify a project configuration as ``colmap gui --project_path path/to/project.ini`` to avoid tedious selection in the GUI. To list the different commands available from the command-line, execute ``colmap help``. For example, to run feature extraction from the command-line, you must execute ``colmap feature_extractor``. The :ref:`graphical user interface ` and :ref:`command-line Interface ` sections provide more details about the available commands. .. rubric:: Footnotes .. [#f1] VisualSfM's [wu13]_ projection model applies the distortion to the measurements and COLMAP to the projection, hence the exported NVM file is not fully compatible with VisualSfM. colmap-3.10/docker/000077500000000000000000000000001464745566500142055ustar00rootroot00000000000000colmap-3.10/docker/Dockerfile000066400000000000000000000047741464745566500162130ustar00rootroot00000000000000ARG UBUNTU_VERSION=22.04 ARG NVIDIA_CUDA_VERSION=12.3.1 # # Docker builder stage. # FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} as builder ARG COLMAP_GIT_COMMIT=main ARG CUDA_ARCHITECTURES=native ENV QT_XCB_GL_INTEGRATION=xcb_egl # Prevent stop building ubuntu at time zone selection. ENV DEBIAN_FRONTEND=noninteractive # Prepare and empty machine for building. RUN apt-get update && \ apt-get install -y --no-install-recommends --no-install-suggests \ git \ cmake \ ninja-build \ build-essential \ libboost-program-options-dev \ libboost-filesystem-dev \ libboost-graph-dev \ libboost-system-dev \ libeigen3-dev \ libflann-dev \ libfreeimage-dev \ libmetis-dev \ libgoogle-glog-dev \ libgtest-dev \ libsqlite3-dev \ libglew-dev \ qtbase5-dev \ libqt5opengl5-dev \ libcgal-dev \ libceres-dev # Build and install COLMAP. RUN git clone https://github.com/colmap/colmap.git RUN cd colmap && \ git fetch https://github.com/colmap/colmap.git ${COLMAP_GIT_COMMIT} && \ git checkout FETCH_HEAD && \ mkdir build && \ cd build && \ cmake .. -GNinja -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES} \ -DCMAKE_INSTALL_PREFIX=/colmap_installed && \ ninja install # # Docker runtime stage. # FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} as runtime # Minimal dependencies to run COLMAP binary compiled in the builder stage. # Note: this reduces the size of the final image considerably, since all the # build dependencies are not needed. RUN apt-get update && \ apt-get install -y --no-install-recommends --no-install-suggests \ libboost-filesystem1.74.0 \ libboost-program-options1.74.0 \ libc6 \ libceres2 \ libfreeimage3 \ libgcc-s1 \ libgl1 \ libglew2.2 \ libgoogle-glog0v5 \ libqt5core5a \ libqt5gui5 \ libqt5widgets5 # Copy all files from /colmap_installed/ in the builder stage to /usr/local/ in # the runtime stage. This simulates installing COLMAP in the default location # (/usr/local/), which simplifies environment variables. It also allows the user # of this Docker image to use it as a base image for compiling against COLMAP as # a library. For instance, CMake will be able to find COLMAP easily with the # command: find_package(COLMAP REQUIRED). COPY --from=builder /colmap_installed/ /usr/local/ colmap-3.10/docker/README.md000066400000000000000000000031431464745566500154650ustar00rootroot00000000000000# How to build COLMAP using Docker ## Requirements - Host machine with at least one NVIDIA GPU/CUDA support and installed drivers (to support dense reconstruction). - Docker (for CUDA support 19.03+). ## Quick Start 1. Check that Docker >=19.03 installed on your host machine: ``` docker --version ``` 2. Check that you have an NVIDIA driver installed on your host machine: ``` nvidia-smi ``` 3. Setup the nvidia-toolkit on your host machine: For Ubuntu host machines: `./setup-ubuntu.sh` For CentOS host machines: `./setup-centos.sh` 4. Run the *run* script, using the *full local path* to your preferred local working directory (a folder with your input files/images, etc.): ``` ./run.sh /path/where/your/working/folder/is ``` This will put you in a directory (inside the Docker container) mounted to the local path you specified. Now you can run COLMAP binaries on your own inputs like this: ``` colmap automatic_reconstructor --image_path ./images --workspace_path . ``` ## Build from Scratch After completing steps 1-3, you can alternatively build the docker image from scratch based on the **Dockerfile** (e.g., with your own modifications) using: ``` ./build.sh ./run.sh /path/where/your/working/folder/is ``` ## Troubleshooting Install an NVIDIA driver and NVIDIA container runtime: ``` sudo apt install ubuntu-drivers-common sudo ubuntu-drivers autoinstall ``` If you failed to install the above, check the appropriate NVIDIA driver by yourself and install it: ``` ubuntu-drivers devices e.g. sudo apt install nvidia-driver-455 ``` colmap-3.10/docker/build.sh000077500000000000000000000002701464745566500156420ustar00rootroot00000000000000docker build -t="colmap:latest" . # In some cases, you may have to explicitly specify the compute architecture: # docker build -t="colmap:latest" --build-arg CUDA_ARCHITECTURES=75 . colmap-3.10/docker/run-gui.sh000077500000000000000000000003421464745566500161310ustar00rootroot00000000000000docker pull colmap/colmap:latest docker run \ -e QT_XCB_GL_INTEGRATION=xcb_egl \ -e DISPLAY=:0 \ -v /tmp/.X11-unix:/tmp/.X11-unix \ --gpus all \ --privileged \ -it colmap/colmap:latest \ colmap gui colmap-3.10/docker/run.sh000077500000000000000000000001531464745566500153470ustar00rootroot00000000000000docker pull colmap/colmap:latest docker run --gpus all -w /working -v $1:/working -it colmap/colmap:latest colmap-3.10/docker/setup-centos.sh000077500000000000000000000006261464745566500172010ustar00rootroot00000000000000# Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo # Install nvidia-container-toolkit sudo yum install -y nvidia-container-toolkit sudo systemctl restart docker # Check that it worked! docker run --gpus all nvidia/cuda:10.2-base nvidia-smi colmap-3.10/docker/setup-ubuntu.sh000077500000000000000000000010061464745566500172210ustar00rootroot00000000000000# Add the package repositories distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list # Install nvidia-container-toolkit sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker # Check that it worked! docker run --gpus all nvidia/cuda:10.2-base nvidia-smi colmap-3.10/pycolmap/000077500000000000000000000000001464745566500145625ustar00rootroot00000000000000colmap-3.10/pycolmap/.gitignore000066400000000000000000000000341464745566500165470ustar00rootroot00000000000000*.egg-info/ build/ example/ colmap-3.10/pycolmap/CMakeLists.txt000066400000000000000000000023471464745566500173300ustar00rootroot00000000000000cmake_minimum_required(VERSION 3.10) project(${SKBUILD_PROJECT_NAME} VERSION ${SKBUILD_PROJECT_VERSION}) set(CMAKE_POSITION_INDEPENDENT_CODE ON) set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CUDA_STANDARD 14) set(CMAKE_CUDA_STANDARD_REQUIRED ON) if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") # Some fixes for the Glog library. add_definitions("-DGLOG_NO_ABBREVIATED_SEVERITIES") add_definitions("-DGL_GLEXT_PROTOTYPES") add_definitions("-DNOMINMAX") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc") # Enable object level parallel builds in Visual Studio. set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /MP") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP") endif() find_package(colmap REQUIRED) find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) find_package(pybind11 2.12.0 REQUIRED) file(GLOB_RECURSE SOURCE_FILES "${PROJECT_SOURCE_DIR}/../src/pycolmap/*.cc") pybind11_add_module(pycolmap ${SOURCE_FILES}) target_include_directories(pycolmap PRIVATE ${PROJECT_SOURCE_DIR}/../src/) target_link_libraries(pycolmap PRIVATE colmap::colmap glog::glog Ceres::ceres) target_compile_definitions(pycolmap PRIVATE VERSION_INFO="${PROJECT_VERSION}") install(TARGETS pycolmap LIBRARY DESTINATION .) colmap-3.10/pycolmap/README.md000066400000000000000000000240171464745566500160450ustar00rootroot00000000000000# Python bindings for COLMAP PyCOLMAP exposes to Python most capabilities of [COLMAP](https://colmap.github.io/) for Structure-from-Motion and Multiview-stereo, such as reconstruction pipelines & objects and geometric estimators. ## Installation Pre-built wheels for Python 3.8/3.9/3.10 on Linux, macOS 10/11/12 (both Intel and Apple Silicon), and Windows can be installed using pip: ```bash pip install pycolmap ``` The wheels are automatically built and pushed to [PyPI](https://pypi.org/project/pycolmap/) at each release. They are currently not built with CUDA support, which requires building from source.

[Building PyCOLMAP from source - click to expand] 1. Install COLMAP from source following [the official guide](https://colmap.github.io/install.html). 3. Build PyCOLMAP: - On Linux and macOS: ```bash python -m pip install . ``` - On Windows, after installing COLMAP [via VCPKG](https://colmap.github.io/install.html#id3), run in powershell: ```powershell python -m pip install . ` --cmake.define.CMAKE_TOOLCHAIN_FILE="$VCPKG_INSTALLATION_ROOT/scripts/buildsystems/vcpkg.cmake" ` --cmake.define.VCPKG_TARGET_TRIPLET="x64-windows" ```
## Reconstruction pipeline PyCOLMAP provides bindings for multiple steps of the standard reconstruction pipeline: - extracting and matching SIFT features - importing an image folder into a COLMAP database - inferring the camera parameters from the EXIF metadata of an image file - running two-view geometric verification of matches on a COLMAP database - triangulating points into an existing COLMAP model - running incremental reconstruction from a COLMAP database - dense reconstruction with multi-view stereo Sparse & Dense reconstruction from a folder of images can be performed with: ```python output_path: pathlib.Path image_dir: pathlib.Path output_path.mkdir() mvs_path = output_path / "mvs" database_path = output_path / "database.db" pycolmap.extract_features(database_path, image_dir) pycolmap.match_exhaustive(database_path) maps = pycolmap.incremental_mapping(database_path, image_dir, output_path) maps[0].write(output_path) # dense reconstruction pycolmap.undistort_images(mvs_path, output_path, image_dir) pycolmap.patch_match_stereo(mvs_path) # requires compilation with CUDA pycolmap.stereo_fusion(mvs_path / "dense.ply", mvs_path) ``` PyCOLMAP can leverage the GPU for feature extraction, matching, and multi-view stereo if COLMAP was compiled with CUDA support. Similarly, PyCOLMAP can run Delaunay Triangulation if COLMAP was compiled with CGAL support. This requires to build the package from source and is not available with the PyPI wheels. All of the above steps are easily configurable with python dicts which are recursively merged into their respective defaults, for example: ```python pycolmap.extract_features(database_path, image_dir, sift_options={"max_num_features": 512}) # equivalent to ops = pycolmap.SiftExtractionOptions() ops.max_num_features = 512 pycolmap.extract_features(database_path, image_dir, sift_options=ops) ``` To list available options and their default parameters: ```python help(pycolmap.SiftExtractionOptions) ``` For another example of usage, see [`example.py`](./example.py) or [`hloc/reconstruction.py`](https://github.com/cvg/Hierarchical-Localization/blob/master/hloc/reconstruction.py). ## Reconstruction object We can load and manipulate an existing COLMAP 3D reconstruction: ```python import pycolmap reconstruction = pycolmap.Reconstruction("path/to/reconstruction/dir") print(reconstruction.summary()) for image_id, image in reconstruction.images.items(): print(image_id, image) for point3D_id, point3D in reconstruction.points3D.items(): print(point3D_id, point3D) for camera_id, camera in reconstruction.cameras.items(): print(camera_id, camera) reconstruction.write("path/to/reconstruction/dir/") ``` The object API mirrors the COLMAP C++ library. The bindings support many other operations, for example: - projecting a 3D point into an image with arbitrary camera model: ```python uv = camera.img_from_cam(image.cam_from_world * point3D.xyz) ``` - aligning two 3D reconstructions by their camera poses: ```python rec2_from_rec1 = pycolmap.align_reconstructions_via_reprojections(reconstruction1, reconstrution2) reconstruction1.transform(rec2_from_rec1) print(rec2_from_rec1.scale, rec2_from_rec1.rotation, rec2_from_rec1.translation) ``` - exporting reconstructions to text, PLY, or other formats: ```python reconstruction.write_text("path/to/new/reconstruction/dir/") # text format reconstruction.export_PLY("rec.ply") # PLY format ``` ## Estimators We provide robust RANSAC-based estimators for absolute camera pose (single-camera and multi-camera-rig), essential matrix, fundamental matrix, homography, and two-view relative pose for calibrated cameras. All RANSAC and estimation parameters are exposed as objects that behave similarly as Python dataclasses. The RANSAC options are described in [`colmap/optim/ransac.h`](https://github.com/colmap/colmap/blob/main/src/colmap/optim/ransac.h#L43-L72) and their default values are: ```python ransac_options = pycolmap.RANSACOptions( max_error=4.0, # for example the reprojection error in pixels min_inlier_ratio=0.01, confidence=0.9999, min_num_trials=1000, max_num_trials=100000, ) ``` ### Absolute pose estimation For instance, to estimate the absolute pose of a query camera given 2D-3D correspondences: ```python # Parameters: # - points2D: Nx2 array; pixel coordinates # - points3D: Nx3 array; world coordinates # - camera: pycolmap.Camera # Optional parameters: # - estimation_options: dict or pycolmap.AbsolutePoseEstimationOptions # - refinement_options: dict or pycolmap.AbsolutePoseRefinementOptions answer = pycolmap.absolute_pose_estimation(points2D, points3D, camera) # Returns: dictionary of estimation outputs or None if failure ``` 2D and 3D points are passed as Numpy arrays or lists. The options are defined in [`estimators/absolute_pose.cc`](./pycolmap/estimators/absolute_pose.h#L100-L122) and can be passed as regular (nested) Python dictionaries: ```python pycolmap.absolute_pose_estimation( points2D, points3D, camera, estimation_options=dict(ransac=dict(max_error=12.0)), refinement_options=dict(refine_focal_length=True), ) ``` ### Absolute Pose Refinement ```python # Parameters: # - cam_from_world: pycolmap.Rigid3d, initial pose # - points2D: Nx2 array; pixel coordinates # - points3D: Nx3 array; world coordinates # - inlier_mask: array of N bool; inlier_mask[i] is true if correpondence i is an inlier # - camera: pycolmap.Camera # Optional parameters: # - refinement_options: dict or pycolmap.AbsolutePoseRefinementOptions answer = pycolmap.pose_refinement(cam_from_world, points2D, points3D, inlier_mask, camera) # Returns: dictionary of refinement outputs or None if failure ``` ### Essential matrix estimation ```python # Parameters: # - points1: Nx2 array; 2D pixel coordinates in image 1 # - points2: Nx2 array; 2D pixel coordinates in image 2 # - camera1: pycolmap.Camera of image 1 # - camera2: pycolmap.Camera of image 2 # Optional parameters: # - options: dict or pycolmap.RANSACOptions (default inlier threshold is 4px) answer = pycolmap.essential_matrix_estimation(points1, points2, camera1, camera2) # Returns: dictionary of estimation outputs or None if failure ``` ### Fundamental matrix estimation ```python answer = pycolmap.fundamental_matrix_estimation( points1, points2, [options], # optional dict or pycolmap.RANSACOptions ) ``` ### Homography estimation ```python answer = pycolmap.homography_matrix_estimation( points1, points2, [options], # optional dict or pycolmap.RANSACOptions ) ``` ### Two-view geometry estimation COLMAP can also estimate a relative pose between two calibrated cameras by estimating both E and H and accounting for the degeneracies of each model. ```python # Parameters: # - camera1: pycolmap.Camera of image 1 # - points1: Nx2 array; 2D pixel coordinates in image 1 # - camera2: pycolmap.Camera of image 2 # - points2: Nx2 array; 2D pixel coordinates in image 2 # Optional parameters: # - matches: Nx2 integer array; correspondences across images # - options: dict or pycolmap.TwoViewGeometryOptions answer = pycolmap.estimate_calibrated_two_view_geometry(camera1, points1, camera2, points2) # Returns: pycolmap.TwoViewGeometry ``` The `TwoViewGeometryOptions` control how each model is selected. The output structure contains the geometric model, inlier matches, the relative pose (if `options.compute_relative_pose=True`), and the type of camera configuration, which is an instance of the enum `pycolmap.TwoViewGeometryConfiguration`. ### Camera argument Some estimators expect a COLMAP camera object, which can be created as follows: ```python camera = pycolmap.Camera( model=camera_model_name_or_id, width=width, height=height, params=params, ) ``` The different camera models and their extra parameters are defined in [`colmap/src/colmap/sensor/models.h`](https://github.com/colmap/colmap/blob/main/src/colmap/sensor/models.h). For example for a pinhole camera: ```python camera = pycolmap.Camera( model='SIMPLE_PINHOLE', width=width, height=height, params=[focal_length, cx, cy], ) ``` Alternatively, we can also pass a camera dictionary: ```python camera_dict = { 'model': COLMAP_CAMERA_MODEL_NAME_OR_ID, 'width': IMAGE_WIDTH, 'height': IMAGE_HEIGHT, 'params': EXTRA_CAMERA_PARAMETERS_LIST } ``` ## SIFT feature extraction ```python import numpy as np import pycolmap from PIL import Image, ImageOps # Input should be grayscale image with range [0, 1]. img = Image.open('image.jpg').convert('RGB') img = ImageOps.grayscale(img) img = np.array(img).astype(np.float) / 255. # Optional parameters: # - options: dict or pycolmap.SiftExtractionOptions # - device: default pycolmap.Device.auto uses the GPU if available sift = pycolmap.Sift() # Parameters: # - image: HxW float array keypoints, descriptors = sift.extract(img) # Returns: # - keypoints: Nx4 array; format: x (j), y (i), scale, orientation # - descriptors: Nx128 array; L2-normalized descriptors ``` colmap-3.10/pycolmap/ci/000077500000000000000000000000001464745566500151555ustar00rootroot00000000000000colmap-3.10/pycolmap/ci/install-colmap-centos.sh000077500000000000000000000027211464745566500217260ustar00rootroot00000000000000#!/bin/bash set -e -x uname -a CURRDIR=$(pwd) export PATH="/usr/bin" # Install toolchain under AlmaLinux 8, # see https://almalinux.pkgs.org/8/almalinux-appstream-x86_64/ yum install -y \ gcc \ gcc-c++ \ gcc-gfortran \ git \ cmake3 \ ninja-build \ curl \ zip \ unzip \ tar # ccache shipped by CentOS is too old so we download and cache it. COMPILER_TOOLS_DIR="${CONTAINER_COMPILER_CACHE_DIR}/bin" mkdir -p ${COMPILER_TOOLS_DIR} if [ ! -f "${COMPILER_TOOLS_DIR}/ccache" ]; then FILE="ccache-4.10.1-linux-x86_64" curl -sSLO https://github.com/ccache/ccache/releases/download/v4.10.1/${FILE}.tar.xz tar -xf ${FILE}.tar.xz cp ${FILE}/ccache ${COMPILER_TOOLS_DIR} fi export PATH="${COMPILER_TOOLS_DIR}:${PATH}" # Setup vcpkg git clone https://github.com/microsoft/vcpkg ${VCPKG_INSTALLATION_ROOT} cd ${VCPKG_INSTALLATION_ROOT} git checkout ${VCPKG_COMMIT_ID} ./bootstrap-vcpkg.sh ./vcpkg integrate install # Build COLMAP cd ${CURRDIR} mkdir build && cd build cmake3 .. -GNinja \ -DCUDA_ENABLED=OFF \ -DGUI_ENABLED=OFF \ -DCGAL_ENABLED=OFF \ -DLSD_ENABLED=OFF \ -DCCACHE_ENABLED=ON \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_MAKE_PROGRAM=/usr/bin/ninja \ -DCMAKE_TOOLCHAIN_FILE="${CMAKE_TOOLCHAIN_FILE}" \ -DVCPKG_TARGET_TRIPLET="${VCPKG_TARGET_TRIPLET}" \ -DCMAKE_EXE_LINKER_FLAGS_INIT="-ldl" ninja install ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose colmap-3.10/pycolmap/ci/install-colmap-macos.sh000077500000000000000000000024251464745566500215360ustar00rootroot00000000000000#!/bin/bash set -x -e CURRDIR=$(pwd) # Fix `brew link` error. find /usr/local/bin -lname '*/Library/Frameworks/Python.framework/*' -delete brew update brew install git cmake ninja gfortran ccache sudo xcode-select --reset export ARCHFLAGS="-arch ${CMAKE_OSX_ARCHITECTURES}" # When building lapack-reference, vcpkg/cmake looks for gfortran. ln -sf $(which gfortran-14) "$(dirname $(which gfortran-14))/gfortran" # Setup vcpkg git clone https://github.com/microsoft/vcpkg ${VCPKG_INSTALLATION_ROOT} cd ${VCPKG_INSTALLATION_ROOT} git checkout ${VCPKG_COMMIT_ID} ./bootstrap-vcpkg.sh ./vcpkg integrate install # Build COLMAP cd ${CURRDIR} mkdir build && cd build "$(brew --prefix cmake)/bin/cmake" .. -GNinja \ -DCUDA_ENABLED=OFF \ -DGUI_ENABLED=OFF \ -DCGAL_ENABLED=OFF \ -DLSD_ENABLED=OFF \ -DCCACHE_ENABLED=ON \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_MAKE_PROGRAM="$(brew --prefix ninja)/bin/ninja" \ -DCMAKE_TOOLCHAIN_FILE="${CMAKE_TOOLCHAIN_FILE}" \ -DVCPKG_TARGET_TRIPLET="${VCPKG_TARGET_TRIPLET}" \ -DCMAKE_OSX_ARCHITECTURES="${CMAKE_OSX_ARCHITECTURES}" \ `if [[ ${CIBW_ARCHS_MACOS} == "arm64" ]]; then echo "-DSIMD_ENABLED=OFF"; fi` ninja sudo ninja install ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose colmap-3.10/pycolmap/ci/install-colmap-windows.ps1000077500000000000000000000021351464745566500222150ustar00rootroot00000000000000$CURRDIR = $PWD $COMPILER_TOOLS_DIR = "${env:COMPILER_CACHE_DIR}/bin" New-Item -ItemType Directory -Force -Path ${COMPILER_TOOLS_DIR} $env:Path = "${COMPILER_TOOLS_DIR};" + $env:Path If (!(Test-Path -path "${COMPILER_TOOLS_DIR}/ccache.exe" -PathType Leaf)) { .github/workflows/install-ccache.ps1 -Destination "${COMPILER_TOOLS_DIR}" } # Setup vcpkg cd ${CURRDIR} git clone https://github.com/microsoft/vcpkg ${env:VCPKG_INSTALLATION_ROOT} cd ${env:VCPKG_INSTALLATION_ROOT} git checkout "${env:VCPKG_COMMIT_ID}" ./bootstrap-vcpkg.bat cd ${CURRDIR} & "./scripts/shell/enter_vs_dev_shell.ps1" & "${env:VCPKG_INSTALLATION_ROOT}/vcpkg.exe" integrate install # Build COLMAP mkdir build cd build cmake .. ` -GNinja ` -DCMAKE_MAKE_PROGRAM=ninja ` -DCUDA_ENABLED="OFF" ` -DGUI_ENABLED="OFF" ` -DCGAL_ENABLED="OFF" ` -DLSD_ENABLED="OFF" ` -DCMAKE_BUILD_TYPE="Release" ` -DCMAKE_TOOLCHAIN_FILE="${env:CMAKE_TOOLCHAIN_FILE}" ` -DVCPKG_TARGET_TRIPLET="${env:VCPKG_TARGET_TRIPLET}" ninja install ccache --show-stats --verbose ccache --evict-older-than 1d ccache --show-stats --verbose colmap-3.10/pycolmap/ci/test-colmap-windows.ps1000066400000000000000000000002461464745566500215240ustar00rootroot00000000000000& "./scripts/shell/enter_vs_dev_shell.ps1" & "${env:VCPKG_INSTALLATION_ROOT}/vcpkg.exe" integrate install & python -c "import pycolmap; print(pycolmap.__version__)" colmap-3.10/pycolmap/custom_bundle_adjustment.py000066400000000000000000000425571464745566500222520ustar00rootroot00000000000000""" Python reimplementation of the bundle adjustment for the incremental mapper of C++ with equivalent logic. As a result, one can add customized residuals on top of the exposed ceres problem from conventional bundle adjustment. pyceres is needed as a dependency for this file. """ import pyceres import pycolmap from pycolmap import logging import copy class PyBundleAdjuster(object): # Python implementation of COLMAP bundle adjuster with pyceres def __init__( self, options: pycolmap.BundleAdjustmentOptions, config: pycolmap.BundleAdjustmentConfig, ): self.options = options self.config = config self.problem = pyceres.Problem() self.summary = pyceres.SolverSummary() self.camera_ids = set() self.point3D_num_observations = dict() def solve(self, reconstruction: pycolmap.Reconstruction): loss = self.options.create_loss_function() self.set_up_problem(reconstruction, loss) if self.problem.num_residuals() == 0: return False solver_options = self.set_up_solver_options( self.problem, self.options.solver_options ) pyceres.solve(solver_options, self.problem, self.summary) return True def set_up_problem( self, reconstruction: pycolmap.Reconstruction, loss: pyceres.LossFunction, ): assert reconstruction is not None self.problem = pyceres.Problem() for image_id in self.config.image_ids: self.add_image_to_problem(image_id, reconstruction, loss) for point3D_id in self.config.variable_point3D_ids: self.add_point_to_problem(point3D_id, reconstruction, loss) for point3D_id in self.config.constant_point3D_ids: self.add_point_to_problem(point3D_id, reconstruction, loss) self.parameterize_cameras(reconstruction) self.parameterize_points(reconstruction) return self.problem def set_up_solver_options( self, problem: pyceres.Problem, solver_options: pyceres.SolverOptions ): bundle_adjuster = pycolmap.BundleAdjuster(self.options, self.config) return bundle_adjuster.set_up_solver_options(problem, solver_options) def add_image_to_problem( self, image_id: int, reconstruction: pycolmap.Reconstruction, loss: pyceres.LossFunction, ): image = reconstruction.images[image_id] pose = image.cam_from_world camera = reconstruction.cameras[image.camera_id] constant_cam_pose = ( not self.options.refine_extrinsics ) or self.config.has_constant_cam_pose(image.image_id) num_observations = 0 for point2D in image.points2D: if not point2D.has_point3D(): continue num_observations += 1 if point2D.point3D_id not in self.point3D_num_observations: self.point3D_num_observations[point2D.point3D_id] = 0 self.point3D_num_observations[point2D.point3D_id] += 1 point3D = reconstruction.points3D[point2D.point3D_id] assert point3D.track.length() > 1 if constant_cam_pose: cost = pycolmap.cost_functions.ReprojErrorCost( camera.model, pose, point2D.xy ) self.problem.add_residual_block( cost, loss, [point3D.xyz, camera.params] ) else: cost = pycolmap.cost_functions.ReprojErrorCost( camera.model, point2D.xy ) self.problem.add_residual_block( cost, loss, [ pose.rotation.quat, pose.translation, point3D.xyz, camera.params, ], ) if num_observations > 0: self.camera_ids.add(image.camera_id) # Set pose parameterization if not constant_cam_pose: self.problem.set_manifold( pose.rotation.quat, pyceres.QuaternionManifold() ) if self.config.has_constant_cam_positions(image_id): constant_position_idxs = self.config.constant_cam_positions( image_id ) self.problem.set_manifold( pose.translation, pyceres.SubsetManifold(3, constant_position_idxs), ) def add_point_to_problem( self, point3D_id: int, reconstruction: pycolmap.Reconstruction, loss: pyceres.LossFunction, ): point3D = reconstruction.points3D[point3D_id] if point3D_id in self.point3D_num_observations: if ( self.point3D_num_observations[point3D_id] == point3D.track.length() ): return else: self.point3D_num_observations[point3D_id] = 0 for track_el in point3D.track.elements: if self.config.has_image(track_el.image_id): continue self.point3D_num_observations[point3D_id] += 1 image = reconstruction.images[track_el.image_id] camera = reconstruction.cameras[image.camera_id] point2D = image.point2D(track_el.point2D_idx) if image.camera_id not in self.camera_ids: self.camera_ids.add(image.camera_id) self.config.set_constant_cam_intrinsics(image.camera_id) cost = pycolmap.cost_functions.ReprojErrorCost( camera.model, image.cam_from_world, point2D.xy ) self.problem.add_residual_block( cost, loss, [point3D.xyz, camera.params] ) def parameterize_cameras(self, reconstruction: pycolmap.Reconstruction): constant_camera = ( (not self.options.refine_focal_length) and (not self.options.refine_principal_point) and (not options.refine_extra_params) ) for camera_id in self.camera_ids: camera = reconstruction.cameras[camera_id] if constant_camera or self.config.has_constant_cam_intrinsics( camera_id ): self.problem.set_parameter_block_constant(camera.params) continue const_camera_params = [] if not self.options.refine_focal_length: const_camera_params.extend(camera.focal_length_idxs()) if not self.options.refine_principal_point: const_camera_params.extend(camera.principal_point_idxs()) if not self.options.refine_extra_params: const_camera_params.extend(camera.extra_point_idxs()) if len(const_camera_params) > 0: self.problem.set_manifold( camera.params, pyceres.SubsetManifold( len(camera.params), const_camera_params ), ) def parameterize_points(self, reconstruction: pycolmap.Reconstruction): for ( point3D_id, num_observations, ) in self.point3D_num_observations.items(): point3D = reconstruction.points3D[point3D_id] if point3D.track.length() > num_observations: self.problem.set_parameter_block_constant(point3D.xyz) for point3D_id in self.config.constant_point3D_ids: point3D = reconstruction.points3D[point3D_id] self.problem.set_parameter_block_constant(point3D.xyz) def solve_bundle_adjustment(reconstruction, ba_options, ba_config): bundle_adjuster = pycolmap.BundleAdjuster(ba_options, ba_config) # alternative equivalent python-based bundle adjustment (slower): # bundle_adjuster = PyBundleAdjuster(ba_options, ba_config) bundle_adjuster.set_up_problem( reconstruction, ba_options.create_loss_function() ) solver_options = bundle_adjuster.set_up_solver_options( bundle_adjuster.problem, ba_options.solver_options ) summary = pyceres.SolverSummary() pyceres.solve(solver_options, bundle_adjuster.problem, summary) return summary def adjust_global_bundle(mapper, mapper_options, ba_options): """Equivalent to mapper.adjust_global_bundle(...)""" reconstruction = mapper.reconstruction assert reconstruction is not None reg_image_ids = reconstruction.reg_image_ids() if len(reg_image_ids) < 2: logging.fatal( "At least two images must be registered for global bundle-adjustment" ) ba_options_tmp = copy.deepcopy(ba_options) # Use stricter convergence criteria for first registered images if len(reg_image_ids) < 10: # kMinNumRegImagesForFastBA = 10 ba_options_tmp.solver_options.function_tolerance /= 10 ba_options_tmp.solver_options.gradient_tolerance /= 10 ba_options_tmp.solver_options.parameter_tolerance /= 10 ba_options_tmp.solver_options.max_num_iterations *= 2 ba_options_tmp.solver_options.max_linear_solver_iterations = 200 # Avoid degeneracies in bundle adjustment mapper.observation_manager.filter_observations_with_negative_depth() # Configure bundle adjustment ba_config = pycolmap.BundleAdjustmentConfig() for image_id in reg_image_ids: ba_config.add_image(image_id) # Fix the existing images, if option specified if mapper_options.fix_existing_images: for image_id in reg_image_ids: if image_id in mapper.existing_image_ids: ba_config.set_constant_cam_pose(image_id) # Fix 7-DOFs of the bundle adjustment problem ba_config.set_constant_cam_pose(reg_image_ids[0]) if (not mapper_options.fix_existing_images) or ( reg_image_ids[1] not in mapper.existing_image_ids ): ba_config.set_constant_cam_positions(reg_image_ids[1], [0]) # Run bundle adjustment summary = solve_bundle_adjustment(reconstruction, ba_options_tmp, ba_config) logging.info("Global Bundle Adjustment") logging.info(summary.BriefReport()) def iterative_global_refinement( mapper, max_num_refinements, max_refinement_change, mapper_options, ba_options, tri_options, normalize_reconstruction=True, ): """Equivalent to mapper.iterative_global_refinement(...)""" reconstruction = mapper.reconstruction mapper.complete_and_merge_tracks(tri_options) num_retriangulated_observations = mapper.retriangulate(tri_options) logging.verbose( 1, f"=> Retriangulated observations: {num_retriangulated_observations}" ) for i in range(max_num_refinements): num_observations = reconstruction.compute_num_observations() # mapper.adjust_global_bundle(mapper_options, ba_options) adjust_global_bundle(mapper, mapper_options, ba_options) if normalize_reconstruction: reconstruction.normalize() num_changed_observations = mapper.complete_and_merge_tracks(tri_options) num_changed_observations += mapper.filter_points(mapper_options) changed = ( num_changed_observations / num_observations if num_observations > 0 else 0 ) logging.verbose(1, f"=> Changed observations: {changed:.6f}") if changed < max_refinement_change: break def adjust_local_bundle( mapper, mapper_options, ba_options, tri_options, image_id, point3D_ids ): """Equivalent to mapper.adjust_local_bundle(...)""" reconstruction = mapper.reconstruction assert reconstruction is not None report = pycolmap.LocalBundleAdjustmentReport() # Find images that have most 3D points with given image in common local_bundle = mapper.find_local_bundle(mapper_options, image_id) # Do the bundle adjustment only if there is any connected images if local_bundle: ba_config = pycolmap.BundleAdjustmentConfig() ba_config.add_image(image_id) for local_image_id in local_bundle: ba_config.add_image(local_image_id) # Fix the existing images, if options specified if mapper_options.fix_existing_images: for local_image_id in local_bundle: if local_image_id in mapper.existing_image_ids: ba_config.set_constant_cam_pose(local_image_id) # Determine which cameras to fix, when not all the registered images are within the current local bundle. num_images_per_camera = {} for image_id in ba_config.image_ids: image = reconstruction.images[image_id] if image.camera_id not in num_images_per_camera: num_images_per_camera[image.camera_id] = 0 num_images_per_camera[image.camera_id] += 1 for camera_id, num_images_local in num_images_per_camera.items(): if num_images_local < mapper.num_reg_images_per_camera[camera_id]: ba_config.set_constant_cam_intrinsics(camera_id) # Fix 7 DOF to avoid scale/rotation/translation drift in bundle adjustment if len(local_bundle) == 1: ba_config.set_constant_cam_pose(local_bundle[0]) ba_config.set_constant_cam_positions(image_id, [0]) elif len(local_bundle) > 1: image_id1, image_id2 = local_bundle[-1], local_bundle[-2] ba_config.set_constant_cam_pose(image_id1) if (not mapper_options.fix_existing_images) or ( image_id2 not in mapper.existing_image_ids ): ba_config.set_constant_cam_positions(image_id2, [0]) # Make sure, we refine all new and short-track 3D points, no matter if # they are fully contained in the local image set or not. Do not include # long track 3D points as they are usually already very stable and adding # to them to bundle adjustment and track merging/completion would slow # down the local bundle adjustment significantly. variable_point3D_ids = set() for point3D_id in list(point3D_ids): point3D = reconstruction.point3D(point3D_id) kMaxTrackLength = 15 if ( point3D.error == -1.0 ) or point3D.track.length() <= kMaxTrackLength: ba_config.add_variable_point(point3D_id) variable_point3D_ids.add(point3D_id) # Adjust the local bundle summary = solve_bundle_adjustment( mapper.reconstruction, ba_options, ba_config ) logging.info("Local Bundle Adjustment") logging.info(summary.BriefReport()) report.num_adjusted_observations = int(summary.num_residuals / 2) # Merge refined tracks with other existing points report.num_merged_observations = mapper.triangulator.merge_tracks( tri_options, variable_point3D_ids ) # Complete tracks that may have failed to triangulate before refinement # of camera pose and calibration in bundle adjustment. This may avoid that # some points are filtered and it helps for subsequent image registrations report.num_completed_observations = mapper.triangulator.complete_tracks( tri_options, variable_point3D_ids ) report.num_completed_observations += mapper.triangulator.complete_image( tri_options, image_id ) filter_image_ids = {image_id, *local_bundle} report.num_filtered_observations = ( mapper.observation_manager.filter_points3D_in_images( mapper_options.filter_max_reproj_error, mapper_options.filter_min_tri_angle, filter_image_ids, ) ) report.num_filtered_observations += ( mapper.observation_manager.filter_points3D( mapper_options.filter_max_reproj_error, mapper_options.filter_min_tri_angle, point3D_ids, ) ) return report def iterative_local_refinement( mapper, max_num_refinements, max_refinement_change, mapper_options, ba_options, tri_options, image_id, ): """Equivalent to mapper.iterative_local_refinement(...)""" ba_options_tmp = copy.deepcopy(ba_options) for i in range(max_num_refinements): # report = mapper.adjust_local_bundle(mapper_options, ba_options_tmp, tri_options, image_id, mapper.get_modified_points3D()) report = adjust_local_bundle( mapper, mapper_options, ba_options_tmp, tri_options, image_id, mapper.get_modified_points3D(), ) logging.verbose( 1, f"=> Merged observations: {report.num_merged_observations}" ) logging.verbose( 1, f"=> Completed observations: {report.num_completed_observations}" ) logging.verbose( 1, f"=> Filtered observations: {report.num_filtered_observations}" ) changed = 0 if report.num_adjusted_observations > 0: changed = ( report.num_merged_observations + report.num_completed_observations + report.num_filtered_observations ) / report.num_adjusted_observations logging.verbose(1, f"=> Changed observations: {changed:.6f}") if changed < max_refinement_change: break # Only use robust cost function for first iteration ba_options_tmp.loss_function_type = pycolmap.LossFunctionType.TRIVIAL mapper.clear_modified_points3D() colmap-3.10/pycolmap/custom_incremental_mapping.py000066400000000000000000000313511464745566500225450ustar00rootroot00000000000000""" Python reimplementation of the C++ incremental mapper with equivalent logic. """ import shutil import time import urllib.request import zipfile from pathlib import Path import enlighten import pycolmap from pycolmap import logging import custom_bundle_adjustment def extract_colors(image_path, image_id, reconstruction): if not reconstruction.extract_colors_for_image(image_id, image_path): logging.warning(f"Could not read image {image_id} at path {image_path}") def write_snapshot(reconstruction, snapshot_path): logging.info("Creating snapshot") timestamp = time.time() * 1000 path = snapshot_path / f"{timestamp:010d}" path.mkdir(exist_ok=True, parents=True) logging.verbose(1, f"=> Writing to {path}") reconstruction.write(path) def iterative_global_refinement(options, mapper_options, mapper): logging.info("Retriangulation and Global bundle adjustment") # The following is equivalent to mapper.iterative_global_refinement(...) custom_bundle_adjustment.iterative_global_refinement( mapper, options.ba_global_max_refinements, options.ba_global_max_refinement_change, mapper_options, options.get_global_bundle_adjustment(), options.get_triangulation(), ) mapper.filter_images(mapper_options) def initialize_reconstruction( controller, mapper, mapper_options, reconstruction ): """Equivalent to IncrementalMapperController.initialize_reconstruction(...)""" options = controller.options init_pair = (options.init_image_id1, options.init_image_id2) # Try to find good initial pair if not options.is_initial_pair_provided(): logging.info("Finding good initial image pair") ret = mapper.find_initial_image_pair(mapper_options, *init_pair) if ret is None: logging.info("No good initial image pair found.") return pycolmap.IncrementalMapperStatus.NO_INITIAL_PAIR init_pair, two_view_geometry = ret else: if not all(reconstruction.exists_image(i) for i in init_pair): logging.info(f"=> Initial image pair {init_pair} does not exist.") return pycolmap.IncrementalMapperStatus.BAD_INITIAL_PAIR two_view_geometry = mapper.estimate_initial_two_view_geometry( mapper_options, *init_pair ) if two_view_geometry is None: logging.info("Provided pair is insuitable for initialization") return pycolmap.IncrementalMapperStatus.BAD_INITIAL_PAIR logging.info(f"Initializing with image pair {init_pair}") mapper.register_initial_image_pair( mapper_options, two_view_geometry, *init_pair ) logging.info("Global bundle adjustment") # The following is equivalent to: mapper.adjust_global_bundle(...) custom_bundle_adjustment.adjust_global_bundle( mapper, mapper_options, options.get_global_bundle_adjustment() ) reconstruction.normalize() mapper.filter_points(mapper_options) mapper.filter_images(mapper_options) # Initial image pair failed to register if ( reconstruction.num_reg_images() == 0 or reconstruction.num_points3D() == 0 ): return pycolmap.IncrementalMapperStatus.BAD_INITIAL_PAIR if options.extract_colors: extract_colors(controller.image_path, init_pair[0], reconstruction) return pycolmap.IncrementalMapperStatus.SUCCESS def reconstruct_sub_model(controller, mapper, mapper_options, reconstruction): """Equivalent to IncrementalMapperController.reconstruct_sub_model(...)""" # register initial pair mapper.begin_reconstruction(reconstruction) if reconstruction.num_reg_images() == 0: init_status = initialize_reconstruction( controller, mapper, mapper_options, reconstruction ) if init_status != pycolmap.IncrementalMapperStatus.SUCCESS: return init_status controller.callback( pycolmap.IncrementalMapperCallback.INITIAL_IMAGE_PAIR_REG_CALLBACK ) # incremental mapping options = controller.options snapshot_prev_num_reg_images = reconstruction.num_reg_images() ba_prev_num_reg_images = reconstruction.num_reg_images() ba_prev_num_points = reconstruction.num_points3D() reg_next_success, prev_reg_next_success = True, True while True: if not (reg_next_success or prev_reg_next_success): break prev_reg_next_success = reg_next_success reg_next_success = False next_images = mapper.find_next_images(mapper_options) if len(next_images) == 0: break for reg_trial in range(len(next_images)): next_image_id = next_images[reg_trial] logging.info( f"Registering image #{next_image_id} " f"({reconstruction.num_reg_images() + 1})" ) num_vis = mapper.observation_manager.num_visible_points3D( next_image_id ) num_obs = mapper.observation_manager.num_observations(next_image_id) logging.info(f"=> Image sees {num_vis} / {num_obs} points") reg_next_success = mapper.register_next_image( mapper_options, next_image_id ) if reg_next_success: break else: logging.info("=> Could not register, trying another image.") # If initial pair fails to continue for some time, # abort and try different initial pair. kMinNumInitialRegTrials = 30 if ( reg_trial >= kMinNumInitialRegTrials and reconstruction.num_reg_images() < options.min_model_size ): break if reg_next_success: mapper.triangulate_image(options.get_triangulation(), next_image_id) # The following is equivalent to mapper.iterative_local_refinement(...) custom_bundle_adjustment.iterative_local_refinement( mapper, options.ba_local_max_refinements, options.ba_local_max_refinement_change, mapper_options, options.get_local_bundle_adjustment(), options.get_triangulation(), next_image_id, ) if controller.check_run_global_refinement( reconstruction, ba_prev_num_reg_images, ba_prev_num_points ): iterative_global_refinement(options, mapper_options, mapper) ba_prev_num_points = reconstruction.num_points3D() ba_prev_num_reg_images = reconstruction.num_reg_images() if options.extract_colors: extract_colors( controller.image_path, next_image_id, reconstruction ) if ( options.snapshot_images_freq > 0 and reconstruction.num_reg_images() >= options.snapshot_images_freq + snapshot_prev_num_reg_images ): snapshot_prev_num_reg_images = reconstruction.num_reg_images() write_snapshot(reconstruction, Path(options.snapshot_path)) controller.callback( pycolmap.IncrementalMapperCallback.NEXT_IMAGE_REG_CALLBACK ) if mapper.num_shared_reg_images() >= int(options.max_model_overlap): break if (not reg_next_success) and prev_reg_next_success: iterative_global_refinement(options, mapper_options, mapper) if ( reconstruction.num_reg_images() >= 2 and reconstruction.num_reg_images() != ba_prev_num_reg_images and reconstruction.num_points3D != ba_prev_num_points ): iterative_global_refinement(options, mapper_options, mapper) return pycolmap.IncrementalMapperStatus.SUCCESS def reconstruct(controller, mapper_options): """Equivalent to IncrementalMapperController.reconstruct(...)""" options = controller.options reconstruction_manager = controller.reconstruction_manager database_cache = controller.database_cache mapper = pycolmap.IncrementalMapper(database_cache) initial_reconstruction_given = reconstruction_manager.size() > 0 if reconstruction_manager.size() > 1: logging.fatal( "Can only resume from a single reconstruction, but multiple are given" ) for num_trials in range(options.init_num_trials): if (not initial_reconstruction_given) or num_trials > 0: reconstruction_idx = reconstruction_manager.add() else: reconstruction_idx = 0 reconstruction = reconstruction_manager.get(reconstruction_idx) status = reconstruct_sub_model( controller, mapper, mapper_options, reconstruction ) if status == pycolmap.IncrementalMapperStatus.INTERRUPTED: mapper.end_reconstruction(False) elif status in ( pycolmap.IncrementalMapperStatus.NO_INITIAL_PAIR, pycolmap.IncrementalMapperStatus.BAD_INITIAL_PAIR, ): mapper.end_reconstruction(True) reconstruction_manager.delete(reconstruction_idx) if options.is_initial_pair_provided(): return elif status == pycolmap.IncrementalMapperStatus.SUCCESS: total_num_reg_images = mapper.num_total_reg_images() min_model_size = min( 0.8 * database_cache.num_images(), options.min_model_size ) if ( options.multiple_models and reconstruction_manager.size() > 1 and ( reconstruction.num_reg_images() < min_model_size or reconstruction.num_reg_images() == 0 ) ): mapper.end_reconstruction(True) reconstruction_manager.delete(reconstruction_idx) else: mapper.end_reconstruction(False) controller.callback( pycolmap.IncrementalMapperCallback.LAST_IMAGE_REG_CALLBACK ) if ( initial_reconstruction_given or (not options.multiple_models) or reconstruction_manager.size() >= options.max_num_models or total_num_reg_images >= database_cache.num_images() - 1 ): return else: logging.fatal(f"Unknown reconstruction status: {status}") def main_incremental_mapper(controller): """Equivalent to IncrementalMapperController.run()""" timer = pycolmap.Timer() timer.start() if not controller.load_database(): return init_mapper_options = controller.options.get_mapper() reconstruct(controller, init_mapper_options) for i in range(2): # number of relaxations if controller.reconstruction_manager.size() > 0: break logging.info("=> Relaxing the initialization constraints") init_mapper_options.init_min_num_inliers = int( init_mapper_options.init_min_num_inliers / 2 ) reconstruct(controller, init_mapper_options) if controller.reconstruction_manager.size() > 0: break logging.info("=> Relaxing the initialization constraints") init_mapper_options.init_min_tri_angle /= 2 reconstruct(controller, init_mapper_options) timer.print_minutes() def main( database_path, image_path, output_path, options=pycolmap.IncrementalPipelineOptions(), input_path=None, ): if not database_path.exists(): logging.fatal(f"Database path does not exist: {database_path}") if not image_path.exists(): logging.fatal(f"Image path does not exist: {image_path}") output_path.mkdir(exist_ok=True, parents=True) reconstruction_manager = pycolmap.ReconstructionManager() if input_path is not None and input_path != "": reconstruction_manager.read(input_path) mapper = pycolmap.IncrementalMapperController( options, image_path, database_path, reconstruction_manager ) # main runner num_images = pycolmap.Database(database_path).num_images with enlighten.Manager() as manager: with manager.counter( total=num_images, desc="Images registered:" ) as pbar: pbar.update(0, force=True) mapper.add_callback( pycolmap.IncrementalMapperCallback.INITIAL_IMAGE_PAIR_REG_CALLBACK, lambda: pbar.update(2), ) mapper.add_callback( pycolmap.IncrementalMapperCallback.NEXT_IMAGE_REG_CALLBACK, lambda: pbar.update(1), ) main_incremental_mapper(mapper) # write and output reconstruction_manager.write(output_path) reconstructions = {} for i in range(reconstruction_manager.size()): reconstructions[i] = reconstruction_manager.get(i) return reconstructions colmap-3.10/pycolmap/example.py000066400000000000000000000043731464745566500165760ustar00rootroot00000000000000""" An example for running incremental SfM on a set of images with pycolmap interfaces. """ import shutil import urllib.request import zipfile from pathlib import Path import enlighten import pycolmap from pycolmap import logging def incremental_mapping_with_pbar(database_path, image_path, sfm_path): num_images = pycolmap.Database(database_path).num_images with enlighten.Manager() as manager: with manager.counter( total=num_images, desc="Images registered:" ) as pbar: pbar.update(0, force=True) reconstructions = pycolmap.incremental_mapping( database_path, image_path, sfm_path, initial_image_pair_callback=lambda: pbar.update(2), next_image_callback=lambda: pbar.update(1), ) return reconstructions def run(): output_path = Path("example/") image_path = output_path / "Fountain/images" database_path = output_path / "database.db" sfm_path = output_path / "sfm" output_path.mkdir(exist_ok=True) # The log filename is postfixed with the execution timestamp. logging.set_log_destination(logging.INFO, output_path / "INFO.log.") data_url = "https://cvg-data.inf.ethz.ch/local-feature-evaluation-schoenberger2017/Strecha-Fountain.zip" if not image_path.exists(): logging.info("Downloading the data.") zip_path = output_path / "data.zip" urllib.request.urlretrieve(data_url, zip_path) with zipfile.ZipFile(zip_path, "r") as fid: fid.extractall(output_path) logging.info(f"Data extracted to {output_path}.") if database_path.exists(): database_path.unlink() pycolmap.set_random_seed(0) pycolmap.extract_features(database_path, image_path) pycolmap.match_exhaustive(database_path) if sfm_path.exists(): shutil.rmtree(sfm_path) sfm_path.mkdir(exist_ok=True) recs = incremental_mapping_with_pbar(database_path, image_path, sfm_path) # alternatively, use: # import custom_incremental_mapping # recs = custom_incremental_mapping.main(database_path, image_path, sfm_path) for idx, rec in recs.items(): logging.info(f"#{idx} {rec.summary()}") if __name__ == "__main__": run() colmap-3.10/pycolmap/pyproject.toml000066400000000000000000000026571464745566500175100ustar00rootroot00000000000000[build-system] requires = ["scikit-build-core>=0.3.3", "pybind11==2.12.0"] build-backend = "scikit_build_core.build" [project] name = "pycolmap" version = "3.10-dev" description="COLMAP bindings" readme = "README.md" authors = [ { name = "Mihai Dusmanu", email = "mihai.dusmanu@gmail.com" }, { name = "Paul-Edouard Sarlin", email = "psarlin@ethz.ch" }, { name = "Philipp Lindenberger", email = "plindenbe@ethz.ch" }, ] license = {text = "BSD-3-Clause"} urls = {Repository = "https://github.com/colmap/pycolmap"} requires-python = ">=3.7" dependencies = ["numpy"] classifiers = [ "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3 :: Only", ] [tool.scikit-build] wheel.expand-macos-universal-tags = true [tool.cibuildwheel] build = "cp3{8,9,10,11,12}-{macosx,manylinux,win}*" archs = ["auto64"] [tool.cibuildwheel.environment] VCPKG_COMMIT_ID = "e01906b2ba7e645a76ee021a19de616edc98d29f" [tool.cibuildwheel.linux] before-all = "{package}/ci/install-colmap-centos.sh" test-command = "python -c \"import pycolmap; print(pycolmap.__version__)\"" [tool.cibuildwheel.macos] before-all = "{package}/ci/install-colmap-macos.sh" test-command = "python -c \"import pycolmap; print(pycolmap.__version__)\"" [tool.cibuildwheel.windows] before-all = "powershell -File {package}/ci/install-colmap-windows.ps1" before-build = "pip install delvewheel" test-command = "powershell -File {package}/ci/test-colmap-windows.ps1" colmap-3.10/scripts/000077500000000000000000000000001464745566500144255ustar00rootroot00000000000000colmap-3.10/scripts/format/000077500000000000000000000000001464745566500157155ustar00rootroot00000000000000colmap-3.10/scripts/format/black.sh000077500000000000000000000015001464745566500173240ustar00rootroot00000000000000#!/usr/bin/env bash # This script runs the black Python formatter on the whole repository. # Check version version_string=$(black --version | sed -E 's/^.*(\d+\.\d+-.*).*$/\1/') expected_version_string='21.12' if [[ "$version_string" =~ "$expected_version_string" ]]; then echo "black version '$version_string' matches '$expected_version_string'" else echo "black version '$version_string' doesn't match '$expected_version_string'" exit 1 fi # Get all C++ files checked into the repo, excluding submodules root_folder=$(git rev-parse --show-toplevel) all_files=$( \ git ls-tree --full-tree -r --name-only HEAD . \ | grep "^.*\(\.py\)$" \ | sed "s~^~$root_folder/~") num_files=$(echo $all_files | wc -w) echo "Formatting ${num_files} files" # shellcheck disable=SC2086 black --line-length 80 ${all_files} colmap-3.10/scripts/format/clang_format.sh000077500000000000000000000023111464745566500207050ustar00rootroot00000000000000#!/usr/bin/env bash # This script applies clang-format to the whole repository. # Find clang-format tools=' clang-format-8 clang-format ' clang_format='' for tool in ${tools}; do if type -p "${tool}" > /dev/null; then clang_format=$tool break fi done if [ -z "$clang_format" ]; then echo "Could not locate clang-format" exit 1 fi echo "Found clang-format: $(which ${clang_format})" # Check version version_string=$($clang_format --version | sed -E 's/^.*(\d+\.\d+\.\d+-.*).*$/\1/') expected_version_string='14.0.0' if [[ "$version_string" =~ "$expected_version_string" ]]; then echo "clang-format version '$version_string' matches '$expected_version_string'" else echo "clang-format version '$version_string' doesn't match '$expected_version_string'" exit 1 fi # Get all C++ files checked into the repo, excluding submodules root_folder=$(git rev-parse --show-toplevel) all_files=$( \ git ls-tree --full-tree -r --name-only HEAD . \ | grep "^src/\(colmap\|pycolmap\).*\(\.cc\|\.h\|\.hpp\|\.cpp\|\.cu\)$" \ | sed "s~^~$root_folder/~") num_files=$(echo $all_files | wc -w) echo "Formatting ${num_files} files" # Run clang-format ${clang_format} -i $all_files colmap-3.10/scripts/matlab/000077500000000000000000000000001464745566500156655ustar00rootroot00000000000000colmap-3.10/scripts/matlab/cmap2rgb.m000066400000000000000000000036371464745566500175510ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function rgb = cmap2rgb(image, cmap, varargin) if length(varargin) == 1 clim = varargin{1}; image(image <= clim(1)) = clim(1); image(image > clim(2)) = clim(2); end image_min = min(image(:)); image_max = max(image(:)); image = (image - image_min) / (image_max - image_min) * size(cmap, 1); rgb = ind2rgb(uint32(image), cmap); end colmap-3.10/scripts/matlab/plot_model.m000077500000000000000000000045761464745566500202200ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function plot_model(cameras, images, points) % Visualize COLMAP model. keys = images.keys; camera_centers = zeros(images.length, 3); view_dirs = zeros(3 * images.length, 3); for i = 1:images.length image_id = keys{i}; image = images(image_id); camera_centers(i,:) = -image.R' * image.t; view_dirs(3 * i - 2,:) = camera_centers(i,:); view_dirs(3 * i - 1,:) = camera_centers(i,:)' + image.R' * [0; 0; 0.3]; view_dirs(3 * i,:) = nan; end keys = points.keys; xyz = zeros(points.length, 3); for i = 1:points.length point_id = keys{i}; point = points(point_id); xyz(i,:) = point.xyz; end hold on; plot3(camera_centers(:,1), camera_centers(:,2), camera_centers(:,3), 'xr'); plot3(view_dirs(:,1), view_dirs(:,2), view_dirs(:,3), '-b'); plot3(xyz(:,1), xyz(:,2), xyz(:,3), '.k'); hold off; end colmap-3.10/scripts/matlab/quat2rotmat.m000077500000000000000000000041651464745566500203370ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function rotmat = quat2rotmat(qvec) rotmat = [1 - 2 * qvec(3).^2 - 2 * qvec(4).^2, ... 2 * qvec(2) * qvec(3) - 2 * qvec(1) * qvec(4), ... 2 * qvec(4) * qvec(2) + 2 * qvec(1) * qvec(3); ... 2 * qvec(2) * qvec(3) + 2 * qvec(1) * qvec(4), ... 1 - 2 * qvec(2).^2 - 2 * qvec(4).^2, ... 2 * qvec(3) * qvec(4) - 2 * qvec(1) * qvec(2); ... 2 * qvec(4) * qvec(2) - 2 * qvec(1) * qvec(3), ... 2 * qvec(3) * qvec(4) + 2 * qvec(1) * qvec(2), ... 1 - 2 * qvec(2).^2 - 2 * qvec(3).^2]; end colmap-3.10/scripts/matlab/read_array.m000066400000000000000000000037761464745566500201710ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function array = read_array(path, varargin) if length(varargin) == 1 dtype = varargin{1}; else dtype = 'single'; end fid = fopen(path); line = fscanf(fid, '%d&%d&%d&', [1, 3]); width = line(1); height = line(2); channels = line(3); num = width * height * channels; array = fread(fid, num, dtype); array = reshape(array, [width, height, channels]); array = permute(array, [2 1 3]); array = cast(array, dtype); fclose(fid); end colmap-3.10/scripts/matlab/read_depth_map.m000066400000000000000000000035101464745566500207760ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function [depth_map, depth_map_rgb] = read_depth_map(path) depth_map = read_array(path); depth_range = prctile(depth_map(depth_map(:) > 0), [2, 98]); depth_map(depth_map<=0) = nan; depth_map_rgb = cmap2rgb(depth_map, [0 0 0; jet(2^15)], depth_range); end colmap-3.10/scripts/matlab/read_model.m000077500000000000000000000111611464745566500201410ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function [cameras, images, points3D] = read_model(path) % Read COLMAP model from folder, which contains a % cameras.txt, images.txt, and points3D.txt. if numel(path) > 0 && path(end) ~= '/' path = [path '/']; end cameras = read_cameras([path 'cameras.txt']); images = read_images([path 'images.txt']); points3D = read_points3D([path 'points3D.txt']); end function cameras = read_cameras(path) cameras = containers.Map('KeyType', 'int64', 'ValueType', 'any'); fid = fopen(path); tline = fgets(fid); while ischar(tline) elems = strsplit(tline); if numel(elems) < 4 || strcmp(elems(1), '#') tline = fgets(fid); continue end if mod(cameras.Count, 10) == 0 fprintf('Reading camera %d\n', cameras.length); end camera = struct; camera.camera_id = str2num(elems{1}); camera.model = elems{2}; camera.width = str2num(elems{3}); camera.height = str2num(elems{4}); camera.params = zeros(numel(elems) - 5, 1); for i = 5:numel(elems) - 1 camera.params(i - 4) = str2double(elems{i}); end cameras(camera.camera_id) = camera; tline = fgets(fid); end fclose(fid); end function images = read_images(path) images = containers.Map('KeyType', 'int64', 'ValueType', 'any'); fid = fopen(path); tline = fgets(fid); while ischar(tline) elems = strsplit(tline); if numel(elems) < 4 || strcmp(elems(1), '#') tline = fgets(fid); continue end if mod(images.Count, 10) == 0 fprintf('Reading image %d\n', images.length); end image = struct; image.image_id = str2num(elems{1}); qw = str2double(elems{2}); qx = str2double(elems{3}); qy = str2double(elems{4}); qz = str2double(elems{5}); image.R = quat2rotmat([qw, qx, qy, qz]); tx = str2double(elems{6}); ty = str2double(elems{7}); tz = str2double(elems{8}); image.t = [tx; ty; tz]; image.camera_id = str2num(elems{9}); image.name = elems{10}; tline = fgets(fid); elems = sscanf(tline, '%f'); elems = reshape(elems, [3, numel(elems) / 3]); image.xys = elems(1:2,:)'; image.point3D_ids = elems(3,:)'; images(image.image_id) = image; tline = fgets(fid); end fclose(fid); end function points3D = read_points3D(path) points3D = containers.Map('KeyType', 'int64', 'ValueType', 'any'); fid = fopen(path); tline = fgets(fid); while ischar(tline) if numel(tline) == 0 || strcmp(tline(1), '#') tline = fgets(fid); continue; end elems = sscanf(tline, '%f'); if numel(elems) == 0 tline = fgets(fid); continue; end if mod(points3D.Count, 1000) == 0 fprintf('Reading point %d\n', points3D.length); end point = struct; point.point3D_id = int64(elems(1)); point.xyz = elems(2:4); point.rgb = uint8(elems(5:7)); point.error = elems(8); point.track = int64(elems(9:end)); point.track = reshape(point.track, [2, numel(point.track) / 2])'; point.track(:,2) = point.track(:,2) + 1; points3D(point.point3D_id) = point; tline = fgets(fid); end fclose(fid); end colmap-3.10/scripts/matlab/read_normal_map.m000066400000000000000000000041031464745566500211610ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function [normal_map, normal_map_rgb] = read_normal_map(path, varargin) normal_map = read_array(path); normal_map_rgb = -[normal_map(:,:,1) normal_map(:,:,2) ... 2 * normal_map(:,:,3) + 1]; normal_map_rgb = reshape(normal_map_rgb, ... size(normal_map,1), size(normal_map,2), 3); normal_map_rgb = uint8(((normal_map_rgb + 1) ./ 2) .* 255); if length(varargin) == 1 depth_map = varargin{1}; normal_map_rgb(repmat(isnan(depth_map), [1, 1, 3])) = 0; end end colmap-3.10/scripts/matlab/read_ply.m000077500000000000000000000044231464745566500176500ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function [xyz, normals, rgb] = read_ply(path) % Read point cloud from PLY text file. file = fopen(path, 'r'); type = fscanf(file, '%s', 1); format = fscanf(file, '%s', 3); data = fscanf(file, '%s', 2); num_points = fscanf(file, '%d', 1); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 3); fscanf(file, '%s', 1); points_data = textscan(file, '%f %f %f %f %f %f %f %f %f', num_points); xyz = [points_data{1}, points_data{2}, points_data{3}]; rgb = [points_data{7}, points_data{8}, points_data{9}]; normals = [points_data{4}, points_data{5}, points_data{6}]; end colmap-3.10/scripts/matlab/write_array.m000066400000000000000000000034401464745566500203740ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function write_array(path, array) fid = fopen(path, 'w'); fprintf(fid, '%d&%d&%d&', size(array, 2), size(array, 1), size(array, 3)); array = permute(array, [2 1 3]); fwrite(fid, array, class(array)); fclose(fid); end colmap-3.10/scripts/matlab/write_ply.m000077500000000000000000000046461464745566500200760ustar00rootroot00000000000000% Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % % * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of % its contributors may be used to endorse or promote products derived % from this software without specific prior written permission. % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. function write_ply(path, xyz, normals, rgb) % Write point cloud to PLY text file. file = fopen(path, 'W'); fprintf(file,'ply\n'); fprintf(file,'format ascii 1.0\n'); fprintf(file,'element vertex %d\n',size(xyz,1)); fprintf(file,'property float x\n'); fprintf(file,'property float y\n'); fprintf(file,'property float z\n'); fprintf(file,'property float nx\n'); fprintf(file,'property float ny\n'); fprintf(file,'property float nz\n'); fprintf(file,'property uchar diffuse_red\n'); fprintf(file,'property uchar diffuse_green\n'); fprintf(file,'property uchar diffuse_blue\n'); fprintf(file,'end_header\n'); for i = 1:size(xyz, 1) fprintf(file, '%f %f %f %f %f %f %d %d %d\n', ... xyz(i,1), xyz(i,2), xyz(i,3), ... normals(i,1), normals(i,2), normals(i,3), ... uint8(rgb(i,1)), uint8(rgb(i,2)), uint8(rgb(i,3))); end fclose(file); end colmap-3.10/scripts/python/000077500000000000000000000000001464745566500157465ustar00rootroot00000000000000colmap-3.10/scripts/python/benchmark_eth3d.py000066400000000000000000000126561464745566500213530ustar00rootroot00000000000000import os import sys import argparse import urllib.request import subprocess def download_file(url, file_path, max_retries=3): if os.path.exists(file_path): return print(f"Downloading {url} to {file_path}") for retry in range(max_retries): try: urllib.request.urlretrieve(url, file_path) return except Exception as exc: print( f"Failed to download {url} (trial={retry+1}) to {file_path} due to {exc}" ) def check_small_errors_or_exit( dataset_name, max_rotation_error, max_proj_center_error, expected_num_images, errors_csv_path, ): print(f"Evaluating errors for {dataset_name}") error = False with open(errors_csv_path, "r") as fid: num_images = 0 for line in fid: line = line.strip() if len(line) == 0 or line.startswith("#"): continue rotation_error, proj_center_error = map(float, line.split(",")) num_images += 1 if rotation_error > max_rotation_error: print("Exceeded rotation error threshold:", rotation_error) error = True if proj_center_error > max_proj_center_error: print( "Exceeded projection center error threshold:", proj_center_error, ) error = True if num_images != expected_num_images: print("Unexpected number of images:", num_images) error = True if error: sys.exit(1) def process_dataset(args, dataset_name): print("Processing dataset:", dataset_name) workspace_path = os.path.join( os.path.realpath(args.workspace_path), dataset_name ) os.makedirs(workspace_path, exist_ok=True) dataset_archive_path = os.path.join(workspace_path, f"{dataset_name}.7z") download_file( f"https://www.eth3d.net/data/{dataset_name}_dslr_undistorted.7z", dataset_archive_path, ) subprocess.check_call( ["7zz", "x", "-y", f"{dataset_name}.7z"], cwd=workspace_path ) # Find undistorted parameters of first camera and initialize all images with it. with open( os.path.join( workspace_path, f"{dataset_name}/dslr_calibration_undistorted/cameras.txt", ), "r", ) as fid: for line in fid: if not line.startswith("#"): first_camera_data = line.split() camera_model = first_camera_data[1] assert camera_model == "PINHOLE" camera_params = first_camera_data[4:] assert len(camera_params) == 4 break # Count the number of expected images in the GT. expected_num_images = 0 with open( os.path.join( workspace_path, f"{dataset_name}/dslr_calibration_undistorted/images.txt", ), "r", ) as fid: for line in fid: if not line.startswith("#") and line.strip(): expected_num_images += 1 # Each image uses two consecutive lines. assert expected_num_images % 2 == 0 expected_num_images /= 2 # Run automatic reconstruction pipeline. subprocess.check_call( [ os.path.realpath(args.colmap_path), "automatic_reconstructor", "--image_path", f"{dataset_name}/images/", "--workspace_path", workspace_path, "--use_gpu", "1" if args.use_gpu else "0", "--num_threads", str(args.num_threads), "--quality", "low", "--camera_model", "PINHOLE", "--camera_params", ",".join(camera_params), ], cwd=workspace_path, ) # Compare reconstructed model to GT model. subprocess.check_call( [ os.path.realpath(args.colmap_path), "model_comparer", "--input_path1", "sparse/0", "--input_path2", f"{dataset_name}/dslr_calibration_undistorted/", "--output_path", ".", "--alignment_error", "proj_center", "--max_proj_center_error", str(args.max_proj_center_error), ], cwd=workspace_path, ) # Ensure discrepancy between reconstructed model and GT is small. check_small_errors_or_exit( dataset_name, args.max_rotation_error, args.max_proj_center_error, expected_num_images, os.path.join(workspace_path, "errors.csv"), ) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--dataset_names", required=True) parser.add_argument("--workspace_path", required=True) parser.add_argument("--colmap_path", required=True) parser.add_argument("--use_gpu", default=True, action="store_true") parser.add_argument("--use_cpu", dest="use_gpu", action="store_false") parser.add_argument("--num_threads", type=int, default=-1) parser.add_argument("--quality", default="medium") parser.add_argument("--max_rotation_error", type=float, default=1.0) parser.add_argument("--max_proj_center_error", type=float, default=0.1) return parser.parse_args() def main(): args = parse_args() for dataset_name in args.dataset_names.split(","): process_dataset(args, dataset_name.strip()) if __name__ == "__main__": main() colmap-3.10/scripts/python/build_windows_app.py000077500000000000000000000071341464745566500220410ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import os import glob import shutil import argparse def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--install_path", required=True, help="The installation prefix, e.g., build/__install__", ) parser.add_argument( "--app_path", required=True, help="The application path, e.g., " "build/COLMAP-dev-windows", ) args = parser.parse_args() return args def mkdir_if_not_exists(path): assert os.path.exists(os.path.dirname(os.path.abspath(path))) if not os.path.exists(path): os.makedirs(path) def main(): args = parse_args() mkdir_if_not_exists(args.app_path) mkdir_if_not_exists(os.path.join(args.app_path, "bin")) mkdir_if_not_exists(os.path.join(args.app_path, "lib")) mkdir_if_not_exists(os.path.join(args.app_path, "lib/platforms")) # Copy batch scripts to app directory. shutil.copyfile( os.path.join(args.install_path, "COLMAP.bat"), os.path.join(args.app_path, "COLMAP.bat"), ) shutil.copyfile( os.path.join(args.install_path, "RUN_TESTS.bat"), os.path.join(args.app_path, "RUN_TESTS.bat"), ) # Copy executables to app directory. exe_files = glob.glob(os.path.join(args.install_path, "bin/*.exe")) for exe_file in exe_files: shutil.copyfile( exe_file, os.path.join(args.app_path, "bin", os.path.basename(exe_file)), ) # Copy shared libraries to app directory. dll_files = glob.glob(os.path.join(args.install_path, "lib/*.dll")) for dll_file in dll_files: shutil.copyfile( dll_file, os.path.join(args.app_path, "lib", os.path.basename(dll_file)), ) shutil.copyfile( os.path.join(args.install_path, "lib/platforms/qwindows.dll"), os.path.join(args.app_path, "lib/platforms/qwindows.dll"), ) # Create zip archive for deployment. shutil.make_archive(args.app_path, "zip", root_dir=args.app_path) if __name__ == "__main__": main() colmap-3.10/scripts/python/bundler_to_ply.py000077500000000000000000000113131464745566500213430ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script converts a Bundler reconstruction file to a PLY point cloud. import argparse import numpy as np def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--bundler_path", required=True) parser.add_argument("--ply_path", required=True) parser.add_argument("--normalize", type=bool, default=True) parser.add_argument("--normalize_p0", type=float, default=0.2) parser.add_argument("--normalize_p1", type=float, default=0.8) parser.add_argument("--min_track_length", type=int, default=3) args = parser.parse_args() return args def main(): args = parse_args() with open(args.bundler_path, "r") as fid: line = fid.readline() line = fid.readline() num_images, num_points = map(int, line.split()) for i in range(5 * num_images): fid.readline() xyz = np.zeros((num_points, 3), dtype=np.float64) rgb = np.zeros((num_points, 3), dtype=np.uint16) track_lengths = np.zeros((num_points,), dtype=np.uint32) for i in range(num_points): if i % 1000 == 0: print("Reading point", i, "/", num_points) xyz[i] = map(float, fid.readline().split()) rgb[i] = map(int, fid.readline().split()) track_lengths[i] = int(fid.readline().split()[0]) mask = track_lengths >= args.min_track_length xyz = xyz[mask] rgb = rgb[mask] if args.normalize: sorted_x = np.sort(xyz[:, 0]) sorted_y = np.sort(xyz[:, 1]) sorted_z = np.sort(xyz[:, 2]) num_coords = sorted_x.size min_coord = int(args.normalize_p0 * num_coords) max_coord = int(args.normalize_p1 * num_coords) mean_coords = xyz.mean(0) bbox_min = np.array( [sorted_x[min_coord], sorted_y[min_coord], sorted_z[min_coord]] ) bbox_max = np.array( [sorted_x[max_coord], sorted_y[max_coord], sorted_z[max_coord]] ) extent = np.linalg.norm(bbox_max - bbox_min) scale = 10.0 / extent xyz -= mean_coords xyz *= scale xyz[:, 2] *= -1 with open(args.ply_path, "w") as fid: fid.write("ply\n") fid.write("format ascii 1.0\n") fid.write("element vertex %d\n" % xyz.shape[0]) fid.write("property float x\n") fid.write("property float y\n") fid.write("property float z\n") fid.write("property float nx\n") fid.write("property float ny\n") fid.write("property float nz\n") fid.write("property uchar diffuse_red\n") fid.write("property uchar diffuse_green\n") fid.write("property uchar diffuse_blue\n") fid.write("end_header\n") for i in range(xyz.shape[0]): if i % 1000 == 0: print("Writing point", i, "/", xyz.shape[0]) fid.write( "%f %f %f 0 0 0 %d %d %d\n" % ( xyz[i, 0], xyz[i, 1], xyz[i, 2], rgb[i, 0], rgb[i, 1], rgb[i, 2], ) ) if __name__ == "__main__": main() colmap-3.10/scripts/python/clang_format_code.py000077500000000000000000000050031464745566500217470ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import os import string import argparse import subprocess def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--path", required=True) parser.add_argument("--exts", default=".h,.cc") parser.add_argument("--style", default="File") args = parser.parse_args() return args def main(): args = parse_args() exts = map(string.lower, args.exts.split(",")) for root, subdirs, files in os.walk(args.path): for f in files: name, ext = os.path.splitext(f) if ext.lower() in exts: file_path = os.path.join(root, f) proc = subprocess.Popen( ["clang-format", "--style", args.style, file_path], stdout=subprocess.PIPE, ) text = "".join(proc.stdout) with open(file_path, "w") as fd: fd.write(text) if __name__ == "__main__": main() colmap-3.10/scripts/python/crawl_camera_specs.py000077500000000000000000000130041464745566500221360ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import re import argparse import requests from lxml.html import soupparser MAX_REQUEST_TRIALS = 10 def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--lib_path", required=True) args = parser.parse_args() return args def request_trial(func, *args, **kwargs): for i in range(MAX_REQUEST_TRIALS): try: response = func(*args, **kwargs) except: continue else: return response raise SystemError def main(): args = parse_args() ########################################################################## # Header file ########################################################################## with open(args.lib_path + ".h", "w") as f: f.write("#include \n") f.write("#include \n") f.write("#include \n\n") f.write("// { make1 : ({ model1 : sensor-width in mm }, ...), ... }\n") f.write( "typedef std::vector> make_specs_t;\n" ) f.write( "typedef std::unordered_map camera_specs_t;;\n\n" ) f.write("camera_specs_t InitializeCameraSpecs();\n\n") ########################################################################## # Source file ########################################################################## makes_response = requests.get("http://www.digicamdb.com") makes_tree = soupparser.fromstring(makes_response.text) makes_node = makes_tree.find('.//select[@id="select_brand"]') makes = [b.attrib["value"] for b in makes_node.iter("option")] with open(args.lib_path + ".cc", "w") as f: f.write("camera_specs_t InitializeCameraSpecs() {\n") f.write(" camera_specs_t specs;\n\n") for make in makes: f.write(" {\n") f.write( ' auto& make_specs = specs["%s"];\n' % make.lower().replace(" ", "") ) models_response = request_trial( requests.post, "http://www.digicamdb.com/inc/ajax.php", data={"b": make, "role": "header_search"}, ) models_tree = soupparser.fromstring(models_response.text) models_code = "" num_models = 0 for model_node in models_tree.iter("option"): model = model_node.attrib.get("value") model_name = model_node.text if model is None: continue url = "http://www.digicamdb.com/specs/{0}_{1}".format( make, model ) specs_response = request_trial(requests.get, url) specs_tree = soupparser.fromstring(specs_response.text) for spec in specs_tree.findall('.//td[@class="info_key"]'): if spec.text.strip() == "Sensor:": sensor_text = spec.find("..").find( './td[@class="bold"]' ) sensor_text = sensor_text.text.strip() m = re.match(".*?([\d.]+) x ([\d.]+).*?", sensor_text) sensor_width = m.group(1) data = ( model_name.lower().replace(" ", ""), float(sensor_width.replace(" ", "")), ) models_code += ( ' make_specs.emplace_back("%s", %.4ff);\n' % data ) print(make, model_name) print(" ", sensor_text) num_models += 1 f.write(" make_specs.reserve(%d);\n" % num_models) f.write(models_code) f.write(" }\n\n") f.write(" return specs;\n") f.write("}\n") if __name__ == "__main__": main() colmap-3.10/scripts/python/database.py000077500000000000000000000326771464745566500201060ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script is based on an original implementation by True Price. import sys import sqlite3 import numpy as np IS_PYTHON3 = sys.version_info[0] >= 3 MAX_IMAGE_ID = 2 ** 31 - 1 CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras ( camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, model INTEGER NOT NULL, width INTEGER NOT NULL, height INTEGER NOT NULL, params BLOB, prior_focal_length INTEGER NOT NULL)""" CREATE_DESCRIPTORS_TABLE = """CREATE TABLE IF NOT EXISTS descriptors ( image_id INTEGER PRIMARY KEY NOT NULL, rows INTEGER NOT NULL, cols INTEGER NOT NULL, data BLOB, FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)""" CREATE_IMAGES_TABLE = """CREATE TABLE IF NOT EXISTS images ( image_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, name TEXT NOT NULL UNIQUE, camera_id INTEGER NOT NULL, CONSTRAINT image_id_check CHECK(image_id >= 0 and image_id < {}), FOREIGN KEY(camera_id) REFERENCES cameras(camera_id)) """.format( MAX_IMAGE_ID ) CREATE_POSE_PRIORS_TABLE = """CREATE TABLE IF NOT EXISTS pose_priors ( image_id INTEGER PRIMARY KEY NOT NULL, position BLOB, coordinate_system INTEGER NOT NULL, FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)""" CREATE_TWO_VIEW_GEOMETRIES_TABLE = """ CREATE TABLE IF NOT EXISTS two_view_geometries ( pair_id INTEGER PRIMARY KEY NOT NULL, rows INTEGER NOT NULL, cols INTEGER NOT NULL, data BLOB, config INTEGER NOT NULL, F BLOB, E BLOB, H BLOB, qvec BLOB, tvec BLOB) """ CREATE_KEYPOINTS_TABLE = """CREATE TABLE IF NOT EXISTS keypoints ( image_id INTEGER PRIMARY KEY NOT NULL, rows INTEGER NOT NULL, cols INTEGER NOT NULL, data BLOB, FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE) """ CREATE_MATCHES_TABLE = """CREATE TABLE IF NOT EXISTS matches ( pair_id INTEGER PRIMARY KEY NOT NULL, rows INTEGER NOT NULL, cols INTEGER NOT NULL, data BLOB)""" CREATE_NAME_INDEX = ( "CREATE UNIQUE INDEX IF NOT EXISTS index_name ON images(name)" ) CREATE_ALL = "; ".join( [ CREATE_CAMERAS_TABLE, CREATE_IMAGES_TABLE, CREATE_POSE_PRIORS_TABLE, CREATE_KEYPOINTS_TABLE, CREATE_DESCRIPTORS_TABLE, CREATE_MATCHES_TABLE, CREATE_TWO_VIEW_GEOMETRIES_TABLE, CREATE_NAME_INDEX, ] ) def image_ids_to_pair_id(image_id1, image_id2): if image_id1 > image_id2: image_id1, image_id2 = image_id2, image_id1 return image_id1 * MAX_IMAGE_ID + image_id2 def pair_id_to_image_ids(pair_id): image_id2 = pair_id % MAX_IMAGE_ID image_id1 = (pair_id - image_id2) / MAX_IMAGE_ID return image_id1, image_id2 def array_to_blob(array): if IS_PYTHON3: return array.tostring() else: return np.getbuffer(array) def blob_to_array(blob, dtype, shape=(-1,)): if IS_PYTHON3: return np.fromstring(blob, dtype=dtype).reshape(*shape) else: return np.frombuffer(blob, dtype=dtype).reshape(*shape) class COLMAPDatabase(sqlite3.Connection): @staticmethod def connect(database_path): return sqlite3.connect(database_path, factory=COLMAPDatabase) def __init__(self, *args, **kwargs): super(COLMAPDatabase, self).__init__(*args, **kwargs) self.create_tables = lambda: self.executescript(CREATE_ALL) self.create_cameras_table = lambda: self.executescript( CREATE_CAMERAS_TABLE ) self.create_descriptors_table = lambda: self.executescript( CREATE_DESCRIPTORS_TABLE ) self.create_images_table = lambda: self.executescript( CREATE_IMAGES_TABLE ) self.create_pose_priors_table = lambda: self.executescript( CREATE_POSE_PRIORS_TABLE ) self.create_two_view_geometries_table = lambda: self.executescript( CREATE_TWO_VIEW_GEOMETRIES_TABLE ) self.create_keypoints_table = lambda: self.executescript( CREATE_KEYPOINTS_TABLE ) self.create_matches_table = lambda: self.executescript( CREATE_MATCHES_TABLE ) self.create_name_index = lambda: self.executescript(CREATE_NAME_INDEX) def add_camera( self, model, width, height, params, prior_focal_length=False, camera_id=None, ): params = np.asarray(params, np.float64) cursor = self.execute( "INSERT INTO cameras VALUES (?, ?, ?, ?, ?, ?)", ( camera_id, model, width, height, array_to_blob(params), prior_focal_length, ), ) return cursor.lastrowid def add_image( self, name, camera_id, prior_q=np.full(4, np.NaN), prior_t=np.full(3, np.NaN), image_id=None, ): cursor = self.execute( "INSERT INTO images VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", ( image_id, name, camera_id, prior_q[0], prior_q[1], prior_q[2], prior_q[3], prior_t[0], prior_t[1], prior_t[2], ), ) return cursor.lastrowid def add_pose_prior(self, image_id, position, coordinate_system=-1): position = np.asarray(position, dtype=np.float64) self.execute( "INSERT INTO pose_priors VALUES (?, ?, ?)", (image_id, array_to_blob(position), coordinate_system), ) def add_keypoints(self, image_id, keypoints): assert len(keypoints.shape) == 2 assert keypoints.shape[1] in [2, 4, 6] keypoints = np.asarray(keypoints, np.float32) self.execute( "INSERT INTO keypoints VALUES (?, ?, ?, ?)", (image_id,) + keypoints.shape + (array_to_blob(keypoints),), ) def add_descriptors(self, image_id, descriptors): descriptors = np.ascontiguousarray(descriptors, np.uint8) self.execute( "INSERT INTO descriptors VALUES (?, ?, ?, ?)", (image_id,) + descriptors.shape + (array_to_blob(descriptors),), ) def add_matches(self, image_id1, image_id2, matches): assert len(matches.shape) == 2 assert matches.shape[1] == 2 if image_id1 > image_id2: matches = matches[:, ::-1] pair_id = image_ids_to_pair_id(image_id1, image_id2) matches = np.asarray(matches, np.uint32) self.execute( "INSERT INTO matches VALUES (?, ?, ?, ?)", (pair_id,) + matches.shape + (array_to_blob(matches),), ) def add_two_view_geometry( self, image_id1, image_id2, matches, F=np.eye(3), E=np.eye(3), H=np.eye(3), qvec=np.array([1.0, 0.0, 0.0, 0.0]), tvec=np.zeros(3), config=2, ): assert len(matches.shape) == 2 assert matches.shape[1] == 2 if image_id1 > image_id2: matches = matches[:, ::-1] pair_id = image_ids_to_pair_id(image_id1, image_id2) matches = np.asarray(matches, np.uint32) F = np.asarray(F, dtype=np.float64) E = np.asarray(E, dtype=np.float64) H = np.asarray(H, dtype=np.float64) qvec = np.asarray(qvec, dtype=np.float64) tvec = np.asarray(tvec, dtype=np.float64) self.execute( "INSERT INTO two_view_geometries VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", (pair_id,) + matches.shape + ( array_to_blob(matches), config, array_to_blob(F), array_to_blob(E), array_to_blob(H), array_to_blob(qvec), array_to_blob(tvec), ), ) def example_usage(): import os import argparse parser = argparse.ArgumentParser() parser.add_argument("--database_path", default="database.db") args = parser.parse_args() if os.path.exists(args.database_path): print("ERROR: database path already exists -- will not modify it.") return # Open the database. db = COLMAPDatabase.connect(args.database_path) # For convenience, try creating all the tables upfront. db.create_tables() # Create dummy cameras. model1, width1, height1, params1 = ( 0, 1024, 768, np.array((1024.0, 512.0, 384.0)), ) model2, width2, height2, params2 = ( 2, 1024, 768, np.array((1024.0, 512.0, 384.0, 0.1)), ) camera_id1 = db.add_camera(model1, width1, height1, params1) camera_id2 = db.add_camera(model2, width2, height2, params2) # Create dummy images. image_id1 = db.add_image("image1.png", camera_id1) image_id2 = db.add_image("image2.png", camera_id1) image_id3 = db.add_image("image3.png", camera_id2) image_id4 = db.add_image("image4.png", camera_id2) # Create dummy keypoints. # # Note that COLMAP supports: # - 2D keypoints: (x, y) # - 4D keypoints: (x, y, theta, scale) # - 6D affine keypoints: (x, y, a_11, a_12, a_21, a_22) num_keypoints = 1000 keypoints1 = np.random.rand(num_keypoints, 2) * (width1, height1) keypoints2 = np.random.rand(num_keypoints, 2) * (width1, height1) keypoints3 = np.random.rand(num_keypoints, 2) * (width2, height2) keypoints4 = np.random.rand(num_keypoints, 2) * (width2, height2) db.add_keypoints(image_id1, keypoints1) db.add_keypoints(image_id2, keypoints2) db.add_keypoints(image_id3, keypoints3) db.add_keypoints(image_id4, keypoints4) # Create dummy matches. M = 50 matches12 = np.random.randint(num_keypoints, size=(M, 2)) matches23 = np.random.randint(num_keypoints, size=(M, 2)) matches34 = np.random.randint(num_keypoints, size=(M, 2)) db.add_matches(image_id1, image_id2, matches12) db.add_matches(image_id2, image_id3, matches23) db.add_matches(image_id3, image_id4, matches34) # Commit the data to the file. db.commit() # Read and check cameras. rows = db.execute("SELECT * FROM cameras") camera_id, model, width, height, params, prior = next(rows) params = blob_to_array(params, np.float64) assert camera_id == camera_id1 assert model == model1 and width == width1 and height == height1 assert np.allclose(params, params1) camera_id, model, width, height, params, prior = next(rows) params = blob_to_array(params, np.float64) assert camera_id == camera_id2 assert model == model2 and width == width2 and height == height2 assert np.allclose(params, params2) # Read and check keypoints. keypoints = dict( (image_id, blob_to_array(data, np.float32, (-1, 2))) for image_id, data in db.execute("SELECT image_id, data FROM keypoints") ) assert np.allclose(keypoints[image_id1], keypoints1) assert np.allclose(keypoints[image_id2], keypoints2) assert np.allclose(keypoints[image_id3], keypoints3) assert np.allclose(keypoints[image_id4], keypoints4) # Read and check matches. pair_ids = [ image_ids_to_pair_id(*pair) for pair in ( (image_id1, image_id2), (image_id2, image_id3), (image_id3, image_id4), ) ] matches = dict( (pair_id_to_image_ids(pair_id), blob_to_array(data, np.uint32, (-1, 2))) for pair_id, data in db.execute("SELECT pair_id, data FROM matches") ) assert np.all(matches[(image_id1, image_id2)] == matches12) assert np.all(matches[(image_id2, image_id3)] == matches23) assert np.all(matches[(image_id3, image_id4)] == matches34) # Clean up. db.close() if os.path.exists(args.database_path): os.remove(args.database_path) if __name__ == "__main__": example_usage() colmap-3.10/scripts/python/export_inlier_matches.py000077500000000000000000000064601464745566500227200ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script exports inlier matches from a COLMAP database to a text file. import os import argparse import sqlite3 import numpy as np def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--database_path", required=True) parser.add_argument("--output_path", required=True) parser.add_argument("--min_num_matches", type=int, default=15) args = parser.parse_args() return args def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2 def main(): args = parse_args() connection = sqlite3.connect(args.database_path) cursor = connection.cursor() images = {} cursor.execute("SELECT image_id, camera_id, name FROM images;") for row in cursor: image_id = row[0] image_name = row[2] images[image_id] = image_name with open(os.path.join(args.output_path), "w") as fid: cursor.execute( "SELECT pair_id, data FROM two_view_geometries WHERE rows>=?;", (args.min_num_matches,), ) for row in cursor: pair_id = row[0] inlier_matches = np.fromstring(row[1], dtype=np.uint32).reshape( -1, 2 ) image_id1, image_id2 = pair_id_to_image_ids(pair_id) image_name1 = images[image_id1] image_name2 = images[image_id2] fid.write( "%s %s %d\n" % (image_name1, image_name2, inlier_matches.shape[0]) ) for i in range(inlier_matches.shape[0]): fid.write("%d %d\n" % tuple(inlier_matches[i])) cursor.close() connection.close() if __name__ == "__main__": main() colmap-3.10/scripts/python/export_inlier_pairs.py000077500000000000000000000061521464745566500224100ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script exports inlier image pairs from a COLMAP database to a text file. import sqlite3 import argparse def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--database_path", required=True) parser.add_argument("--match_list_path", required=True) parser.add_argument("--min_num_matches", type=int, default=15) args = parser.parse_args() return args def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2 def main(): args = parse_args() connection = sqlite3.connect(args.database_path) cursor = connection.cursor() # Get a mapping between image ids and image names image_id_to_name = dict() cursor.execute("SELECT image_id, name FROM images;") for row in cursor: image_id = row[0] name = row[1] image_id_to_name[image_id] = name # Iterate over entries in the two_view_geometries table output = open(args.match_list_path, "w") cursor.execute("SELECT pair_id, rows FROM two_view_geometries;") for row in cursor: pair_id = row[0] rows = row[1] if rows < args.min_num_matches: continue image_id1, image_id2 = pair_id_to_image_ids(pair_id) image_name1 = image_id_to_name[image_id1] image_name2 = image_id_to_name[image_id2] output.write("%s %s\n" % (image_name1, image_name2)) output.close() cursor.close() connection.close() if __name__ == "__main__": main() colmap-3.10/scripts/python/export_to_bundler.py000077500000000000000000000152271464745566500220700ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script exports a COLMAP database to the file structure to run Bundler. import os import argparse import sqlite3 import shutil import gzip import numpy as np def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--database_path", required=True) parser.add_argument("--image_path", required=True) parser.add_argument("--output_path", required=True) parser.add_argument("--min_num_matches", type=int, default=15) args = parser.parse_args() return args def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2 def main(): args = parse_args() connection = sqlite3.connect(args.database_path) cursor = connection.cursor() try: os.makedirs(args.output_path) except: pass cameras = {} cursor.execute("SELECT camera_id, params FROM cameras;") for row in cursor: camera_id = row[0] params = np.fromstring(row[1], dtype=np.double) cameras[camera_id] = params images = {} with open(os.path.join(args.output_path, "list.txt"), "w") as fid: cursor.execute("SELECT image_id, camera_id, name FROM images;") for row in cursor: image_id = row[0] camera_id = row[1] image_name = row[2] print("Copying image", image_name) images[image_id] = (len(images), image_name) fid.write("./%s 0 %f\n" % (image_name, cameras[camera_id][0])) if not os.path.exists(os.path.join(args.output_path, image_name)): shutil.copyfile( os.path.join(args.image_path, image_name), os.path.join(args.output_path, image_name), ) for image_id, (image_idx, image_name) in images.iteritems(): print("Exporting key file for", image_name) base_name, ext = os.path.splitext(image_name) key_file_name = os.path.join(args.output_path, base_name + ".key") key_file_name_gz = key_file_name + ".gz" if os.path.exists(key_file_name_gz): continue cursor.execute( "SELECT data FROM keypoints WHERE image_id=?;", (image_id,) ) row = next(cursor) if row[0] is None: keypoints = np.zeros((0, 6), dtype=np.float32) descriptors = np.zeros((0, 128), dtype=np.uint8) else: keypoints = np.fromstring(row[0], dtype=np.float32).reshape(-1, 6) cursor.execute( "SELECT data FROM descriptors WHERE image_id=?;", (image_id,) ) row = next(cursor) descriptors = np.fromstring(row[0], dtype=np.uint8).reshape(-1, 128) with open(key_file_name, "w") as fid: fid.write("%d %d\n" % (keypoints.shape[0], descriptors.shape[1])) for r in range(keypoints.shape[0]): fid.write( "%f %f %f %f\n" % ( keypoints[r, 1], keypoints[r, 0], keypoints[r, 2], keypoints[r, 3], ) ) for i in range(0, 128, 20): desc_block = descriptors[r, i : i + 20] fid.write(" ".join(map(str, desc_block.ravel().tolist()))) fid.write("\n") with open(key_file_name, "rb") as fid_in: with gzip.open(key_file_name + ".gz", "wb") as fid_out: fid_out.writelines(fid_in) os.remove(key_file_name) with open(os.path.join(args.output_path, "matches.init.txt"), "w") as fid: cursor.execute( "SELECT pair_id, data FROM two_view_geometries " "WHERE rows>=?;", (args.min_num_matches,), ) for row in cursor: pair_id = row[0] inlier_matches = np.fromstring(row[1], dtype=np.uint32).reshape( -1, 2 ) image_id1, image_id2 = pair_id_to_image_ids(pair_id) image_idx1 = images[image_id1][0] image_idx2 = images[image_id2][0] fid.write( "%d %d\n%d\n" % (image_idx1, image_idx2, inlier_matches.shape[0]) ) for i in range(inlier_matches.shape[0]): fid.write( "%d %d\n" % (inlier_matches[i, 0], inlier_matches[i, 1]) ) with open(os.path.join(args.output_path, "run_bundler.sh"), "w") as fid: fid.write("bin/Bundler list.txt \\\n") fid.write("--run_bundle \\\n") fid.write("--use_focal_estimate \\\n") fid.write("--output_all bundle_ \\\n") fid.write("--constrain_focal \\\n") fid.write("--estimate_distortion \\\n") fid.write("--match_table matches.init.txt \\\n") fid.write("--variable_focal_length \\\n") fid.write("--output_dir bundle \\\n") fid.write("--output bundle.out \\\n") fid.write("--constrain_focal_weight 0.0001 \\\n") cursor.close() connection.close() if __name__ == "__main__": main() colmap-3.10/scripts/python/export_to_visualsfm.py000077500000000000000000000145371464745566500224510ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script exports a COLMAP database to the file structure to run VisualSfM. import os import sys import argparse import sqlite3 import shutil import gzip import numpy as np def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--database_path", required=True) parser.add_argument("--image_path", required=True) parser.add_argument("--output_path", required=True) parser.add_argument("--min_num_matches", type=int, default=15) parser.add_argument("--binary_feature_files", type=bool, default=True) args = parser.parse_args() return args def pair_id_to_image_ids(pair_id): image_id2 = pair_id % 2147483647 image_id1 = (pair_id - image_id2) / 2147483647 return image_id1, image_id2 def main(): args = parse_args() connection = sqlite3.connect(args.database_path) cursor = connection.cursor() try: os.makedirs(args.output_path) except: pass cameras = {} cursor.execute("SELECT camera_id, params FROM cameras;") for row in cursor: camera_id = row[0] params = np.fromstring(row[1], dtype=np.double) cameras[camera_id] = params images = {} cursor.execute("SELECT image_id, camera_id, name FROM images;") for row in cursor: image_id = row[0] camera_id = row[1] image_name = row[2] print("Copying image", image_name) images[image_id] = (len(images), image_name) if not os.path.exists(os.path.join(args.output_path, image_name)): shutil.copyfile( os.path.join(args.image_path, image_name), os.path.join(args.output_path, image_name), ) # The magic numbers used in VisualSfM's binary file format for storing the # feature descriptors. sift_name = 1413892435 sift_version_v4 = 808334422 sift_eof_marker = 1179600383 for image_id, (image_idx, image_name) in images.iteritems(): print("Exporting key file for", image_name) base_name, ext = os.path.splitext(image_name) key_file_name = os.path.join(args.output_path, base_name + ".sift") if os.path.exists(key_file_name): continue cursor.execute( "SELECT data FROM keypoints WHERE image_id=?;", (image_id,) ) row = next(cursor) if row[0] is None: keypoints = np.zeros((0, 6), dtype=np.float32) descriptors = np.zeros((0, 128), dtype=np.uint8) else: keypoints = np.fromstring(row[0], dtype=np.float32).reshape(-1, 6) cursor.execute( "SELECT data FROM descriptors WHERE image_id=?;", (image_id,) ) row = next(cursor) descriptors = np.fromstring(row[0], dtype=np.uint8).reshape(-1, 128) if args.binary_feature_files: with open(key_file_name, "wb") as fid: fid.write(struct.pack("i", sift_name)) fid.write(struct.pack("i", sift_version_v4)) fid.write(struct.pack("i", keypoints.shape[0])) fid.write(struct.pack("i", 4)) fid.write(struct.pack("i", 128)) keypoints[:, :4].astype(np.float32).tofile(fid) descriptors.astype(np.uint8).tofile(fid) fid.write(struct.pack("i", sift_eof_marker)) else: with open(key_file_name, "w") as fid: fid.write( "%d %d\n" % (keypoints.shape[0], descriptors.shape[1]) ) for r in range(keypoints.shape[0]): fid.write("%f %f 0 0 " % (keypoints[r, 0], keypoints[r, 1])) fid.write( " ".join(map(str, descriptors[r].ravel().tolist())) ) fid.write("\n") with open(os.path.join(args.output_path, "matches.txt"), "w") as fid: cursor.execute( "SELECT pair_id, data FROM two_view_geometries " "WHERE rows>=?;", (args.min_num_matches,), ) for row in cursor: pair_id = row[0] inlier_matches = np.fromstring(row[1], dtype=np.uint32).reshape( -1, 2 ) image_id1, image_id2 = pair_id_to_image_ids(pair_id) image_name1 = images[image_id1][1] image_name2 = images[image_id2][1] fid.write( "%s %s %d\n" % (image_name1, image_name2, inlier_matches.shape[0]) ) line1 = "" line2 = "" for i in range(inlier_matches.shape[0]): line1 += "%d " % inlier_matches[i, 0] line2 += "%d " % inlier_matches[i, 1] fid.write(line1 + "\n") fid.write(line2 + "\n") cursor.close() connection.close() if __name__ == "__main__": main() colmap-3.10/scripts/python/flickr_downloader.py000077500000000000000000000143301464745566500220140ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import os import time import datetime import urllib import urllib2 import urlparse import socket import argparse import multiprocessing import xml.etree.ElementTree as ElementTree PER_PAGE = 500 SORT = "date-posted-desc" URL = ( "https://api.flickr.com/services/rest/?method=flickr.photos.search&" "api_key=%s&text=%s&sort=%s&per_page=%d&page=%d&min_upload_date=%s&" "max_upload_date=%s&format=rest&extras=url_o,url_l,url_c,url_z,url_n" ) MAX_PAGE_REQUESTS = 5 MAX_PAGE_TIMEOUT = 20 MAX_IMAGE_REQUESTS = 3 TIME_SKIP = 24 * 60 * 60 MAX_DATE = time.time() MIN_DATE = MAX_DATE - TIME_SKIP def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--search_text", required=True) parser.add_argument("--api_key", required=True) parser.add_argument("--image_path", required=True) parser.add_argument("--num_procs", type=int, default=10) parser.add_argument("--max_days_without_image", type=int, default=365) args = parser.parse_args() return args def compose_url(page, api_key, text, min_date, max_date): return URL % ( api_key, text, SORT, PER_PAGE, page, str(min_date), str(max_date), ) def parse_page(page, api_key, text, min_date, max_date): f = None for _ in range(MAX_PAGE_REQUESTS): try: f = urllib2.urlopen( compose_url(page, api_key, text, min_date, max_date), timeout=MAX_PAGE_TIMEOUT, ) except socket.timeout: continue else: break if f is None: return { "pages": "0", "total": "0", "page": "0", "perpage": "0", }, tuple() response = f.read() root = ElementTree.fromstring(response) if root.attrib["stat"] != "ok": raise IOError photos = [] for photo in root.iter("photo"): photos.append(photo.attrib) return root.find("photos").attrib, photos class PhotoDownloader(object): def __init__(self, image_path): self.image_path = image_path def __call__(self, photo): # Find the URL corresponding to the highest image resolution. We will # need this URL here to determine the image extension (typically .jpg, # but could be .png, .gif, etc). url = None for url_suffix in ("o", "l", "k", "h", "b", "c", "z"): url_attr = "url_%s" % url_suffix if photo.get(url_attr) is not None: url = photo.get(url_attr) break if url is not None: # Note that the following statement may fail in Python 3. urlparse # may need to be replaced with urllib.parse. url_filename = urlparse.urlparse(url).path image_ext = os.path.splitext(url_filename)[1] image_name = "%s_%s%s" % (photo["id"], photo["secret"], image_ext) path = os.path.join(self.image_path, image_name) if not os.path.exists(path): print(url) for _ in range(MAX_IMAGE_REQUESTS): try: urllib.urlretrieve(url, path) except urllib.ContentTooShortError: continue else: break def main(): args = parse_args() downloader = PhotoDownloader(args.image_path) pool = multiprocessing.Pool(processes=args.num_procs) num_pages = float("inf") page = 0 min_date = MIN_DATE max_date = MAX_DATE days_in_row = 0 search_text = args.search_text.replace(" ", "-") while num_pages > page: page += 1 metadata, photos = parse_page( page, args.api_key, search_text, min_date, max_date ) num_pages = int(metadata["pages"]) print(78 * "=") print("Page:\t\t", page, "of", num_pages) print("Min-Date:\t", datetime.datetime.fromtimestamp(min_date)) print("Max-Date:\t", datetime.datetime.fromtimestamp(max_date)) print("Num-Photos:\t", len(photos)) print(78 * "=") try: pool.map_async(downloader, photos).get(1e10) except KeyboardInterrupt: pool.wait() break if page >= num_pages: max_date -= TIME_SKIP min_date -= TIME_SKIP page = 0 if num_pages == 0: days_in_row = days_in_row + 1 num_pages = float("inf") print(" No images in", days_in_row, "days in a row") if days_in_row == args.max_days_without_image: break else: days_in_row = 0 if __name__ == "__main__": main() colmap-3.10/scripts/python/merge_ply_files.py000077500000000000000000000051111464745566500214660ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script merges multiple homogeneous PLY files into a single PLY file. import os import glob import argparse import numpy as np import plyfile def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--folder_path", required=True) parser.add_argument("--merged_path", required=True) args = parser.parse_args() return args def main(): args = parse_args() files = [] for file_name in os.listdir(args.folder_path): if len(file_name) < 4 or file_name[-4:].lower() != ".ply": continue print("Reading file", file_name) file = plyfile.PlyData.read(os.path.join(args.folder_path, file_name)) for element in file.elements: files.append(element.data) print("Merging files") merged_file = np.concatenate(files, -1) merged_el = plyfile.PlyElement.describe(merged_file, "vertex") print("Writing merged file") plyfile.PlyData([merged_el]).write(args.merged_path) if __name__ == "__main__": main() colmap-3.10/scripts/python/migrate_database_pose_prior.py000077500000000000000000000065531464745566500240510ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import numpy as np import argparse from database import COLMAPDatabase if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--database_path", type=str, required=True) parser.add_argument("--is_cartesian", action="store_true") parser.add_argument("--cleanup", action="store_true") args = parser.parse_args() db = COLMAPDatabase.connect(args.database_path) pose_priors = {} rows = db.execute("SELECT * FROM images") for image_id, _, _, *cam_from_world_prior in rows: if not cam_from_world_prior: # newer format database continue qvec = np.array(cam_from_world_prior[:4], dtype=float) tvec = np.array(cam_from_world_prior[4:], dtype=float) if np.isfinite(qvec).any(): print( f"Warning: rotation prior for image {image_id} " "will be lost during migration." ) if np.isfinite(tvec).any(): pose_priors[image_id] = tvec print(f"Found location priors for {len(pose_priors)} images.") coordinate_systems = {"UNKNOWN": -1, "WGS84": 0, "CARTESIAN": 1} coordinate_system = coordinate_systems[ "CARTESIAN" if args.is_cartesian else "WGS84" ] db.create_pose_priors_table() for image_id, position in pose_priors.items(): (exists,) = db.execute( "SELECT COUNT(*) FROM pose_priors WHERE image_id = ?", (image_id,), ).fetchone() if exists: print(f"Location prior for {image_id} already exists, skipping.") continue db.add_pose_prior(image_id, position, coordinate_system) if args.cleanup: for col in ["qw", "qx", "qy", "qz", "tx", "ty", "tz"]: db.execute(f"ALTER TABLE images DROP COLUMN prior_{col}") db.commit() colmap-3.10/scripts/python/nvm_to_ply.py000077500000000000000000000113271464745566500205150ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script converts a VisualSfM reconstruction file to a PLY point cloud. import os import argparse import numpy as np def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--nvm_path", required=True) parser.add_argument("--ply_path", required=True) parser.add_argument("--normalize", type=bool, default=True) parser.add_argument("--normalize_p0", type=float, default=0.2) parser.add_argument("--normalize_p1", type=float, default=0.8) parser.add_argument("--min_track_length", type=int, default=3) args = parser.parse_args() return args def main(): args = parse_args() with open(args.nvm_path, "r") as fid: line = fid.readline() line = fid.readline() num_images = int(fid.readline()) for i in range(num_images + 1): fid.readline() num_points = int(fid.readline()) xyz = np.zeros((num_points, 3), dtype=np.float64) rgb = np.zeros((num_points, 3), dtype=np.uint16) track_lengths = np.zeros((num_points,), dtype=np.uint32) for i in range(num_points): if i % 1000 == 0: print("Reading point", i, "/", num_points) elems = fid.readline().split() xyz[i] = map(float, elems[0:3]) rgb[i] = map(int, elems[3:6]) track_lengths[i] = int(elems[6]) mask = track_lengths >= args.min_track_length xyz = xyz[mask] rgb = rgb[mask] if args.normalize: sorted_x = np.sort(xyz[:, 0]) sorted_y = np.sort(xyz[:, 1]) sorted_z = np.sort(xyz[:, 2]) num_coords = sorted_x.size min_coord = int(args.normalize_p0 * num_coords) max_coord = int(args.normalize_p1 * num_coords) mean_coords = xyz.mean(0) bbox_min = np.array( [sorted_x[min_coord], sorted_y[min_coord], sorted_z[min_coord]] ) bbox_max = np.array( [sorted_x[max_coord], sorted_y[max_coord], sorted_z[max_coord]] ) extent = np.linalg.norm(bbox_max - bbox_min) scale = 10.0 / extent xyz -= mean_coords xyz *= scale with open(args.ply_path, "w") as fid: fid.write("ply\n") fid.write("format ascii 1.0\n") fid.write("element vertex %d\n" % xyz.shape[0]) fid.write("property float x\n") fid.write("property float y\n") fid.write("property float z\n") fid.write("property float nx\n") fid.write("property float ny\n") fid.write("property float nz\n") fid.write("property uchar diffuse_red\n") fid.write("property uchar diffuse_green\n") fid.write("property uchar diffuse_blue\n") fid.write("end_header\n") for i in range(xyz.shape[0]): if i % 1000 == 0: print("Writing point", i, "/", xyz.shape[0]) fid.write( "%f %f %f 0 0 0 %d %d %d\n" % ( xyz[i, 0], xyz[i, 1], xyz[i, 2], rgb[i, 0], rgb[i, 1], rgb[i, 2], ) ) if __name__ == "__main__": main() colmap-3.10/scripts/python/plyfile.py000077500000000000000000000624471464745566500200040ustar00rootroot00000000000000# Copyright 2014 Darsh Ranjan # # This file is part of python-plyfile. # # python-plyfile is free software: you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # python-plyfile is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with python-plyfile. If not, see # . from itertools import islice as _islice import numpy as _np from sys import byteorder as _byteorder try: _range = xrange except NameError: _range = range # Many-many relation _data_type_relation = [ ("int8", "i1"), ("char", "i1"), ("uint8", "u1"), ("uchar", "b1"), ("uchar", "u1"), ("int16", "i2"), ("short", "i2"), ("uint16", "u2"), ("ushort", "u2"), ("int32", "i4"), ("int", "i4"), ("uint32", "u4"), ("uint", "u4"), ("float32", "f4"), ("float", "f4"), ("float64", "f8"), ("double", "f8"), ] _data_types = dict(_data_type_relation) _data_type_reverse = dict((b, a) for (a, b) in _data_type_relation) _types_list = [] _types_set = set() for (_a, _b) in _data_type_relation: if _a not in _types_set: _types_list.append(_a) _types_set.add(_a) if _b not in _types_set: _types_list.append(_b) _types_set.add(_b) _byte_order_map = { "ascii": "=", "binary_little_endian": "<", "binary_big_endian": ">", } _byte_order_reverse = {"<": "binary_little_endian", ">": "binary_big_endian"} _native_byte_order = {"little": "<", "big": ">"}[_byteorder] def _lookup_type(type_str): if type_str not in _data_type_reverse: try: type_str = _data_types[type_str] except KeyError: raise ValueError( "field type %r not in %r" % (type_str, _types_list) ) return _data_type_reverse[type_str] def _split_line(line, n): fields = line.split(None, n) if len(fields) == n: fields.append("") assert len(fields) == n + 1 return fields def make2d(array, cols=None, dtype=None): """ Make a 2D array from an array of arrays. The `cols' and `dtype' arguments can be omitted if the array is not empty. """ if (cols is None or dtype is None) and not len(array): raise RuntimeError( "cols and dtype must be specified for empty " "array" ) if cols is None: cols = len(array[0]) if dtype is None: dtype = array[0].dtype return _np.fromiter(array, [("_", dtype, (cols,))], count=len(array))["_"] class PlyParseError(Exception): """ Raised when a PLY file cannot be parsed. The attributes `element', `row', `property', and `message' give additional information. """ def __init__(self, message, element=None, row=None, prop=None): self.message = message self.element = element self.row = row self.prop = prop s = "" if self.element: s += "element %r: " % self.element.name if self.row is not None: s += "row %d: " % self.row if self.prop: s += "property %r: " % self.prop.name s += self.message Exception.__init__(self, s) def __repr__(self): return ( "PlyParseError(%r, element=%r, row=%r, prop=%r)" % self.message, self.element, self.row, self.prop, ) class PlyData(object): """ PLY file header and data. A PlyData instance is created in one of two ways: by the static method PlyData.read (to read a PLY file), or directly from __init__ given a sequence of elements (which can then be written to a PLY file). """ def __init__( self, elements=[], text=False, byte_order="=", comments=[], obj_info=[] ): """ elements: sequence of PlyElement instances. text: whether the resulting PLY file will be text (True) or binary (False). byte_order: '<' for little-endian, '>' for big-endian, or '=' for native. This is only relevant if `text' is False. comments: sequence of strings that will be placed in the header between the 'ply' and 'format ...' lines. obj_info: like comments, but will be placed in the header with "obj_info ..." instead of "comment ...". """ if byte_order == "=" and not text: byte_order = _native_byte_order self.byte_order = byte_order self.text = text self.comments = list(comments) self.obj_info = list(obj_info) self.elements = elements def _get_elements(self): return self._elements def _set_elements(self, elements): self._elements = tuple(elements) self._index() elements = property(_get_elements, _set_elements) def _get_byte_order(self): return self._byte_order def _set_byte_order(self, byte_order): if byte_order not in ["<", ">", "="]: raise ValueError("byte order must be '<', '>', or '='") self._byte_order = byte_order byte_order = property(_get_byte_order, _set_byte_order) def _index(self): self._element_lookup = dict((elt.name, elt) for elt in self._elements) if len(self._element_lookup) != len(self._elements): raise ValueError("two elements with same name") @staticmethod def _parse_header(stream): """ Parse a PLY header from a readable file-like stream. """ lines = [] comments = {"comment": [], "obj_info": []} while True: line = stream.readline().decode("ascii").strip() fields = _split_line(line, 1) if fields[0] == "end_header": break elif fields[0] in comments.keys(): lines.append(fields) else: lines.append(line.split()) a = 0 if lines[a] != ["ply"]: raise PlyParseError("expected 'ply'") a += 1 while lines[a][0] in comments.keys(): comments[lines[a][0]].append(lines[a][1]) a += 1 if lines[a][0] != "format": raise PlyParseError("expected 'format'") if lines[a][2] != "1.0": raise PlyParseError("expected version '1.0'") if len(lines[a]) != 3: raise PlyParseError("too many fields after 'format'") fmt = lines[a][1] if fmt not in _byte_order_map: raise PlyParseError("don't understand format %r" % fmt) byte_order = _byte_order_map[fmt] text = fmt == "ascii" a += 1 while a < len(lines) and lines[a][0] in comments.keys(): comments[lines[a][0]].append(lines[a][1]) a += 1 return PlyData( PlyElement._parse_multi(lines[a:]), text, byte_order, comments["comment"], comments["obj_info"], ) @staticmethod def read(stream): """ Read PLY data from a readable file-like object or filename. """ must_close = False try: if isinstance(stream, str): stream = open(stream, "rb") must_close = True data = PlyData._parse_header(stream) for elt in data: elt._read(stream, data.text, data.byte_order) finally: if must_close: stream.close() return data def write(self, stream): """ Write PLY data to a writeable file-like object or filename. """ must_close = False try: if isinstance(stream, str): stream = open(stream, "wb") must_close = True stream.write(self.header.encode("ascii")) stream.write(b"\r\n") for elt in self: elt._write(stream, self.text, self.byte_order) finally: if must_close: stream.close() @property def header(self): """ Provide PLY-formatted metadata for the instance. """ lines = ["ply"] if self.text: lines.append("format ascii 1.0") else: lines.append( "format " + _byte_order_reverse[self.byte_order] + " 1.0" ) # Some information is lost here, since all comments are placed # between the 'format' line and the first element. for c in self.comments: lines.append("comment " + c) for c in self.obj_info: lines.append("obj_info " + c) lines.extend(elt.header for elt in self.elements) lines.append("end_header") return "\r\n".join(lines) def __iter__(self): return iter(self.elements) def __len__(self): return len(self.elements) def __contains__(self, name): return name in self._element_lookup def __getitem__(self, name): return self._element_lookup[name] def __str__(self): return self.header def __repr__(self): return ( "PlyData(%r, text=%r, byte_order=%r, " "comments=%r, obj_info=%r)" % ( self.elements, self.text, self.byte_order, self.comments, self.obj_info, ) ) class PlyElement(object): """ PLY file element. A client of this library doesn't normally need to instantiate this directly, so the following is only for the sake of documenting the internals. Creating a PlyElement instance is generally done in one of two ways: as a byproduct of PlyData.read (when reading a PLY file) and by PlyElement.describe (before writing a PLY file). """ def __init__(self, name, properties, count, comments=[]): """ This is not part of the public interface. The preferred methods of obtaining PlyElement instances are PlyData.read (to read from a file) and PlyElement.describe (to construct from a numpy array). """ self._name = str(name) self._check_name() self._count = count self._properties = tuple(properties) self._index() self.comments = list(comments) self._have_list = any( isinstance(p, PlyListProperty) for p in self.properties ) @property def count(self): return self._count def _get_data(self): return self._data def _set_data(self, data): self._data = data self._count = len(data) self._check_sanity() data = property(_get_data, _set_data) def _check_sanity(self): for prop in self.properties: if prop.name not in self._data.dtype.fields: raise ValueError("dangling property %r" % prop.name) def _get_properties(self): return self._properties def _set_properties(self, properties): self._properties = tuple(properties) self._check_sanity() self._index() properties = property(_get_properties, _set_properties) def _index(self): self._property_lookup = dict( (prop.name, prop) for prop in self._properties ) if len(self._property_lookup) != len(self._properties): raise ValueError("two properties with same name") def ply_property(self, name): return self._property_lookup[name] @property def name(self): return self._name def _check_name(self): if any(c.isspace() for c in self._name): msg = "element name %r contains spaces" % self._name raise ValueError(msg) def dtype(self, byte_order="="): """ Return the numpy dtype of the in-memory representation of the data. (If there are no list properties, and the PLY format is binary, then this also accurately describes the on-disk representation of the element.) """ return [(prop.name, prop.dtype(byte_order)) for prop in self.properties] @staticmethod def _parse_multi(header_lines): """ Parse a list of PLY element definitions. """ elements = [] while header_lines: (elt, header_lines) = PlyElement._parse_one(header_lines) elements.append(elt) return elements @staticmethod def _parse_one(lines): """ Consume one element definition. The unconsumed input is returned along with a PlyElement instance. """ a = 0 line = lines[a] if line[0] != "element": raise PlyParseError("expected 'element'") if len(line) > 3: raise PlyParseError("too many fields after 'element'") if len(line) < 3: raise PlyParseError("too few fields after 'element'") (name, count) = (line[1], int(line[2])) comments = [] properties = [] while True: a += 1 if a >= len(lines): break if lines[a][0] == "comment": comments.append(lines[a][1]) elif lines[a][0] == "property": properties.append(PlyProperty._parse_one(lines[a])) else: break return (PlyElement(name, properties, count, comments), lines[a:]) @staticmethod def describe(data, name, len_types={}, val_types={}, comments=[]): """ Construct a PlyElement from an array's metadata. len_types and val_types can be given as mappings from list property names to type strings (like 'u1', 'f4', etc., or 'int8', 'float32', etc.). These can be used to define the length and value types of list properties. List property lengths always default to type 'u1' (8-bit unsigned integer), and value types default to 'i4' (32-bit integer). """ if not isinstance(data, _np.ndarray): raise TypeError("only numpy arrays are supported") if len(data.shape) != 1: raise ValueError("only one-dimensional arrays are " "supported") count = len(data) properties = [] descr = data.dtype.descr for t in descr: if not isinstance(t[1], str): raise ValueError("nested records not supported") if not t[0]: raise ValueError("field with empty name") if len(t) != 2 or t[1][1] == "O": # non-scalar field, which corresponds to a list # property in PLY. if t[1][1] == "O": if len(t) != 2: raise ValueError( "non-scalar object fields not " "supported" ) len_str = _data_type_reverse[len_types.get(t[0], "u1")] if t[1][1] == "O": val_type = val_types.get(t[0], "i4") val_str = _lookup_type(val_type) else: val_str = _lookup_type(t[1][1:]) prop = PlyListProperty(t[0], len_str, val_str) else: val_str = _lookup_type(t[1][1:]) prop = PlyProperty(t[0], val_str) properties.append(prop) elt = PlyElement(name, properties, count, comments) elt.data = data return elt def _read(self, stream, text, byte_order): """ Read the actual data from a PLY file. """ if text: self._read_txt(stream) else: if self._have_list: # There are list properties, so a simple load is # impossible. self._read_bin(stream, byte_order) else: # There are no list properties, so loading the data is # much more straightforward. self._data = _np.fromfile( stream, self.dtype(byte_order), self.count ) if len(self._data) < self.count: k = len(self._data) del self._data raise PlyParseError("early end-of-file", self, k) self._check_sanity() def _write(self, stream, text, byte_order): """ Write the data to a PLY file. """ if text: self._write_txt(stream) else: if self._have_list: # There are list properties, so serialization is # slightly complicated. self._write_bin(stream, byte_order) else: # no list properties, so serialization is # straightforward. self.data.astype(self.dtype(byte_order), copy=False).tofile( stream ) def _read_txt(self, stream): """ Load a PLY element from an ASCII-format PLY file. The element may contain list properties. """ self._data = _np.empty(self.count, dtype=self.dtype()) k = 0 for line in _islice(iter(stream.readline, b""), self.count): fields = iter(line.strip().split()) for prop in self.properties: try: self._data[prop.name][k] = prop._from_fields(fields) except StopIteration: raise PlyParseError("early end-of-line", self, k, prop) except ValueError: raise PlyParseError("malformed input", self, k, prop) try: next(fields) except StopIteration: pass else: raise PlyParseError("expected end-of-line", self, k) k += 1 if k < self.count: del self._data raise PlyParseError("early end-of-file", self, k) def _write_txt(self, stream): """ Save a PLY element to an ASCII-format PLY file. The element may contain list properties. """ for rec in self.data: fields = [] for prop in self.properties: fields.extend(prop._to_fields(rec[prop.name])) _np.savetxt(stream, [fields], "%.18g", newline="\r\n") def _read_bin(self, stream, byte_order): """ Load a PLY element from a binary PLY file. The element may contain list properties. """ self._data = _np.empty(self.count, dtype=self.dtype(byte_order)) for k in _range(self.count): for prop in self.properties: try: self._data[prop.name][k] = prop._read_bin( stream, byte_order ) except StopIteration: raise PlyParseError("early end-of-file", self, k, prop) def _write_bin(self, stream, byte_order): """ Save a PLY element to a binary PLY file. The element may contain list properties. """ for rec in self.data: for prop in self.properties: prop._write_bin(rec[prop.name], stream, byte_order) @property def header(self): """ Format this element's metadata as it would appear in a PLY header. """ lines = ["element %s %d" % (self.name, self.count)] # Some information is lost here, since all comments are placed # between the 'element' line and the first property definition. for c in self.comments: lines.append("comment " + c) lines.extend(list(map(str, self.properties))) return "\r\n".join(lines) def __getitem__(self, key): return self.data[key] def __setitem__(self, key, value): self.data[key] = value def __str__(self): return self.header def __repr__(self): return "PlyElement(%r, %r, count=%d, comments=%r)" % ( self.name, self.properties, self.count, self.comments, ) class PlyProperty(object): """ PLY property description. This class is pure metadata; the data itself is contained in PlyElement instances. """ def __init__(self, name, val_dtype): self._name = str(name) self._check_name() self.val_dtype = val_dtype def _get_val_dtype(self): return self._val_dtype def _set_val_dtype(self, val_dtype): self._val_dtype = _data_types[_lookup_type(val_dtype)] val_dtype = property(_get_val_dtype, _set_val_dtype) @property def name(self): return self._name def _check_name(self): if any(c.isspace() for c in self._name): msg = "Error: property name %r contains spaces" % self._name raise RuntimeError(msg) @staticmethod def _parse_one(line): assert line[0] == "property" if line[1] == "list": if len(line) > 5: raise PlyParseError("too many fields after " "'property list'") if len(line) < 5: raise PlyParseError("too few fields after " "'property list'") return PlyListProperty(line[4], line[2], line[3]) else: if len(line) > 3: raise PlyParseError("too many fields after " "'property'") if len(line) < 3: raise PlyParseError("too few fields after " "'property'") return PlyProperty(line[2], line[1]) def dtype(self, byte_order="="): """ Return the numpy dtype description for this property (as a tuple of strings). """ return byte_order + self.val_dtype def _from_fields(self, fields): """ Parse from generator. Raise StopIteration if the property could not be read. """ return _np.dtype(self.dtype()).type(next(fields)) def _to_fields(self, data): """ Return generator over one item. """ yield _np.dtype(self.dtype()).type(data) def _read_bin(self, stream, byte_order): """ Read data from a binary stream. Raise StopIteration if the property could not be read. """ try: return _np.fromfile(stream, self.dtype(byte_order), 1)[0] except IndexError: raise StopIteration def _write_bin(self, data, stream, byte_order): """ Write data to a binary stream. """ _np.dtype(self.dtype(byte_order)).type(data).tofile(stream) def __str__(self): val_str = _data_type_reverse[self.val_dtype] return "property %s %s" % (val_str, self.name) def __repr__(self): return "PlyProperty(%r, %r)" % (self.name, _lookup_type(self.val_dtype)) class PlyListProperty(PlyProperty): """ PLY list property description. """ def __init__(self, name, len_dtype, val_dtype): PlyProperty.__init__(self, name, val_dtype) self.len_dtype = len_dtype def _get_len_dtype(self): return self._len_dtype def _set_len_dtype(self, len_dtype): self._len_dtype = _data_types[_lookup_type(len_dtype)] len_dtype = property(_get_len_dtype, _set_len_dtype) def dtype(self, byte_order="="): """ List properties always have a numpy dtype of "object". """ return "|O" def list_dtype(self, byte_order="="): """ Return the pair (len_dtype, val_dtype) (both numpy-friendly strings). """ return (byte_order + self.len_dtype, byte_order + self.val_dtype) def _from_fields(self, fields): (len_t, val_t) = self.list_dtype() n = int(_np.dtype(len_t).type(next(fields))) data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1) if len(data) < n: raise StopIteration return data def _to_fields(self, data): """ Return generator over the (numerical) PLY representation of the list data (length followed by actual data). """ (len_t, val_t) = self.list_dtype() data = _np.asarray(data, dtype=val_t).ravel() yield _np.dtype(len_t).type(data.size) for x in data: yield x def _read_bin(self, stream, byte_order): (len_t, val_t) = self.list_dtype(byte_order) try: n = _np.fromfile(stream, len_t, 1)[0] except IndexError: raise StopIteration data = _np.fromfile(stream, val_t, n) if len(data) < n: raise StopIteration return data def _write_bin(self, data, stream, byte_order): """ Write data to a binary stream. """ (len_t, val_t) = self.list_dtype(byte_order) data = _np.asarray(data, dtype=val_t).ravel() _np.array(data.size, dtype=len_t).tofile(stream) data.tofile(stream) def __str__(self): len_str = _data_type_reverse[self.len_dtype] val_str = _data_type_reverse[self.val_dtype] return "property list %s %s %s" % (len_str, val_str, self.name) def __repr__(self): return "PlyListProperty(%r, %r, %r)" % ( self.name, _lookup_type(self.len_dtype), _lookup_type(self.val_dtype), ) colmap-3.10/scripts/python/read_write_dense.py000077500000000000000000000120161464745566500216260ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import argparse import numpy as np import os import struct def read_array(path): with open(path, "rb") as fid: width, height, channels = np.genfromtxt( fid, delimiter="&", max_rows=1, usecols=(0, 1, 2), dtype=int ) fid.seek(0) num_delimiter = 0 byte = fid.read(1) while True: if byte == b"&": num_delimiter += 1 if num_delimiter >= 3: break byte = fid.read(1) array = np.fromfile(fid, np.float32) array = array.reshape((width, height, channels), order="F") return np.transpose(array, (1, 0, 2)).squeeze() def write_array(array, path): """ see: src/mvs/mat.h void Mat::Write(const std::string& path) """ assert array.dtype == np.float32 if len(array.shape) == 2: height, width = array.shape channels = 1 elif len(array.shape) == 3: height, width, channels = array.shape else: assert False with open(path, "w") as fid: fid.write(str(width) + "&" + str(height) + "&" + str(channels) + "&") with open(path, "ab") as fid: if len(array.shape) == 2: array_trans = np.transpose(array, (1, 0)) elif len(array.shape) == 3: array_trans = np.transpose(array, (1, 0, 2)) else: assert False data_1d = array_trans.reshape(-1, order="F") data_list = data_1d.tolist() endian_character = "<" format_char_sequence = "".join(["f"] * len(data_list)) byte_data = struct.pack( endian_character + format_char_sequence, *data_list ) fid.write(byte_data) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "-d", "--depth_map", help="path to depth map", type=str, required=True ) parser.add_argument( "-n", "--normal_map", help="path to normal map", type=str, required=True ) parser.add_argument( "--min_depth_percentile", help="minimum visualization depth percentile", type=float, default=5, ) parser.add_argument( "--max_depth_percentile", help="maximum visualization depth percentile", type=float, default=95, ) args = parser.parse_args() return args def main(): args = parse_args() if args.min_depth_percentile > args.max_depth_percentile: raise ValueError( "min_depth_percentile should be less than or equal " "to the max_depth_percentile." ) # Read depth and normal maps corresponding to the same image. if not os.path.exists(args.depth_map): raise FileNotFoundError("File not found: {}".format(args.depth_map)) if not os.path.exists(args.normal_map): raise FileNotFoundError("File not found: {}".format(args.normal_map)) depth_map = read_array(args.depth_map) normal_map = read_array(args.normal_map) min_depth, max_depth = np.percentile( depth_map, [args.min_depth_percentile, args.max_depth_percentile] ) depth_map[depth_map < min_depth] = min_depth depth_map[depth_map > max_depth] = max_depth import pylab as plt # Visualize the depth map. plt.figure() plt.imshow(depth_map) plt.title("depth map") # Visualize the normal map. plt.figure() plt.imshow(normal_map) plt.title("normal map") plt.show() if __name__ == "__main__": main() colmap-3.10/scripts/python/read_write_fused_vis.py000077500000000000000000000121211464745566500225140ustar00rootroot00000000000000#!/usr/bin/env python # Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import os import collections import numpy as np import pandas as pd from pyntcloud import PyntCloud from read_write_model import read_next_bytes, write_next_bytes MeshPoint = collections.namedtuple( "MeshingPoint", ["position", "color", "normal", "num_visible_images", "visible_image_idxs"], ) def read_fused(path_to_fused_ply, path_to_fused_ply_vis): """ see: src/mvs/meshing.cc void ReadDenseReconstruction(const std::string& path """ assert os.path.isfile(path_to_fused_ply) assert os.path.isfile(path_to_fused_ply_vis) point_cloud = PyntCloud.from_file(path_to_fused_ply) xyz_arr = point_cloud.points.loc[:, ["x", "y", "z"]].to_numpy() normal_arr = point_cloud.points.loc[:, ["nx", "ny", "nz"]].to_numpy() color_arr = point_cloud.points.loc[:, ["red", "green", "blue"]].to_numpy() with open(path_to_fused_ply_vis, "rb") as fid: num_points = read_next_bytes(fid, 8, "Q")[0] mesh_points = [0] * num_points for i in range(num_points): num_visible_images = read_next_bytes(fid, 4, "I")[0] visible_image_idxs = read_next_bytes( fid, num_bytes=4 * num_visible_images, format_char_sequence="I" * num_visible_images, ) visible_image_idxs = np.array(tuple(map(int, visible_image_idxs))) mesh_point = MeshPoint( position=xyz_arr[i], color=color_arr[i], normal=normal_arr[i], num_visible_images=num_visible_images, visible_image_idxs=visible_image_idxs, ) mesh_points[i] = mesh_point return mesh_points def write_fused_ply(mesh_points, path_to_fused_ply): columns = ["x", "y", "z", "nx", "ny", "nz", "red", "green", "blue"] points_data_frame = pd.DataFrame( np.zeros((len(mesh_points), len(columns))), columns=columns ) positions = np.asarray([point.position for point in mesh_points]) normals = np.asarray([point.normal for point in mesh_points]) colors = np.asarray([point.color for point in mesh_points]) points_data_frame.loc[:, ["x", "y", "z"]] = positions points_data_frame.loc[:, ["nx", "ny", "nz"]] = normals points_data_frame.loc[:, ["red", "green", "blue"]] = colors points_data_frame = points_data_frame.astype( { "x": positions.dtype, "y": positions.dtype, "z": positions.dtype, "red": colors.dtype, "green": colors.dtype, "blue": colors.dtype, "nx": normals.dtype, "ny": normals.dtype, "nz": normals.dtype, } ) point_cloud = PyntCloud(points_data_frame) point_cloud.to_file(path_to_fused_ply) def write_fused_ply_vis(mesh_points, path_to_fused_ply_vis): """ see: src/mvs/fusion.cc void WritePointsVisibility(const std::string& path, const std::vector>& points_visibility) """ with open(path_to_fused_ply_vis, "wb") as fid: write_next_bytes(fid, len(mesh_points), "Q") for point in mesh_points: write_next_bytes(fid, point.num_visible_images, "I") format_char_sequence = "I" * point.num_visible_images write_next_bytes( fid, [*point.visible_image_idxs], format_char_sequence ) def write_fused(points, path_to_fused_ply, path_to_fused_ply_vis): write_fused_ply(points, path_to_fused_ply) write_fused_ply_vis(points, path_to_fused_ply_vis) colmap-3.10/scripts/python/read_write_model.py000077500000000000000000000540131464745566500216330ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import os import collections import numpy as np import struct import argparse CameraModel = collections.namedtuple( "CameraModel", ["model_id", "model_name", "num_params"] ) Camera = collections.namedtuple( "Camera", ["id", "model", "width", "height", "params"] ) BaseImage = collections.namedtuple( "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"] ) Point3D = collections.namedtuple( "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"] ) class Image(BaseImage): def qvec2rotmat(self): return qvec2rotmat(self.qvec) CAMERA_MODELS = { CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), CameraModel(model_id=1, model_name="PINHOLE", num_params=4), CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), CameraModel(model_id=3, model_name="RADIAL", num_params=5), CameraModel(model_id=4, model_name="OPENCV", num_params=8), CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), CameraModel(model_id=7, model_name="FOV", num_params=5), CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), } CAMERA_MODEL_IDS = dict( [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] ) CAMERA_MODEL_NAMES = dict( [(camera_model.model_name, camera_model) for camera_model in CAMERA_MODELS] ) def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): """Read and unpack the next bytes from a binary file. :param fid: :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. :param endian_character: Any of {@, =, <, >, !} :return: Tuple of read and unpacked values. """ data = fid.read(num_bytes) return struct.unpack(endian_character + format_char_sequence, data) def write_next_bytes(fid, data, format_char_sequence, endian_character="<"): """pack and write to a binary file. :param fid: :param data: data to send, if multiple elements are sent at the same time, they should be encapsuled either in a list or a tuple :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. should be the same length as the data list or tuple :param endian_character: Any of {@, =, <, >, !} """ if isinstance(data, (list, tuple)): bytes = struct.pack(endian_character + format_char_sequence, *data) else: bytes = struct.pack(endian_character + format_char_sequence, data) fid.write(bytes) def read_cameras_text(path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::WriteCamerasText(const std::string& path) void Reconstruction::ReadCamerasText(const std::string& path) """ cameras = {} with open(path, "r") as fid: while True: line = fid.readline() if not line: break line = line.strip() if len(line) > 0 and line[0] != "#": elems = line.split() camera_id = int(elems[0]) model = elems[1] width = int(elems[2]) height = int(elems[3]) params = np.array(tuple(map(float, elems[4:]))) cameras[camera_id] = Camera( id=camera_id, model=model, width=width, height=height, params=params, ) return cameras def read_cameras_binary(path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::WriteCamerasBinary(const std::string& path) void Reconstruction::ReadCamerasBinary(const std::string& path) """ cameras = {} with open(path_to_model_file, "rb") as fid: num_cameras = read_next_bytes(fid, 8, "Q")[0] for _ in range(num_cameras): camera_properties = read_next_bytes( fid, num_bytes=24, format_char_sequence="iiQQ" ) camera_id = camera_properties[0] model_id = camera_properties[1] model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name width = camera_properties[2] height = camera_properties[3] num_params = CAMERA_MODEL_IDS[model_id].num_params params = read_next_bytes( fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params, ) cameras[camera_id] = Camera( id=camera_id, model=model_name, width=width, height=height, params=np.array(params), ) assert len(cameras) == num_cameras return cameras def write_cameras_text(cameras, path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::WriteCamerasText(const std::string& path) void Reconstruction::ReadCamerasText(const std::string& path) """ HEADER = ( "# Camera list with one line of data per camera:\n" + "# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n" + "# Number of cameras: {}\n".format(len(cameras)) ) with open(path, "w") as fid: fid.write(HEADER) for _, cam in cameras.items(): to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params] line = " ".join([str(elem) for elem in to_write]) fid.write(line + "\n") def write_cameras_binary(cameras, path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::WriteCamerasBinary(const std::string& path) void Reconstruction::ReadCamerasBinary(const std::string& path) """ with open(path_to_model_file, "wb") as fid: write_next_bytes(fid, len(cameras), "Q") for _, cam in cameras.items(): model_id = CAMERA_MODEL_NAMES[cam.model].model_id camera_properties = [cam.id, model_id, cam.width, cam.height] write_next_bytes(fid, camera_properties, "iiQQ") for p in cam.params: write_next_bytes(fid, float(p), "d") return cameras def read_images_text(path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadImagesText(const std::string& path) void Reconstruction::WriteImagesText(const std::string& path) """ images = {} with open(path, "r") as fid: while True: line = fid.readline() if not line: break line = line.strip() if len(line) > 0 and line[0] != "#": elems = line.split() image_id = int(elems[0]) qvec = np.array(tuple(map(float, elems[1:5]))) tvec = np.array(tuple(map(float, elems[5:8]))) camera_id = int(elems[8]) image_name = elems[9] elems = fid.readline().split() xys = np.column_stack( [ tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3])), ] ) point3D_ids = np.array(tuple(map(int, elems[2::3]))) images[image_id] = Image( id=image_id, qvec=qvec, tvec=tvec, camera_id=camera_id, name=image_name, xys=xys, point3D_ids=point3D_ids, ) return images def read_images_binary(path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadImagesBinary(const std::string& path) void Reconstruction::WriteImagesBinary(const std::string& path) """ images = {} with open(path_to_model_file, "rb") as fid: num_reg_images = read_next_bytes(fid, 8, "Q")[0] for _ in range(num_reg_images): binary_image_properties = read_next_bytes( fid, num_bytes=64, format_char_sequence="idddddddi" ) image_id = binary_image_properties[0] qvec = np.array(binary_image_properties[1:5]) tvec = np.array(binary_image_properties[5:8]) camera_id = binary_image_properties[8] binary_image_name = b"" current_char = read_next_bytes(fid, 1, "c")[0] while current_char != b"\x00": # look for the ASCII 0 entry binary_image_name += current_char current_char = read_next_bytes(fid, 1, "c")[0] image_name = binary_image_name.decode("utf-8") num_points2D = read_next_bytes( fid, num_bytes=8, format_char_sequence="Q" )[0] x_y_id_s = read_next_bytes( fid, num_bytes=24 * num_points2D, format_char_sequence="ddq" * num_points2D, ) xys = np.column_stack( [ tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3])), ] ) point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) images[image_id] = Image( id=image_id, qvec=qvec, tvec=tvec, camera_id=camera_id, name=image_name, xys=xys, point3D_ids=point3D_ids, ) return images def write_images_text(images, path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadImagesText(const std::string& path) void Reconstruction::WriteImagesText(const std::string& path) """ if len(images) == 0: mean_observations = 0 else: mean_observations = sum( (len(img.point3D_ids) for _, img in images.items()) ) / len(images) HEADER = ( "# Image list with two lines of data per image:\n" + "# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n" + "# POINTS2D[] as (X, Y, POINT3D_ID)\n" + "# Number of images: {}, mean observations per image: {}\n".format( len(images), mean_observations ) ) with open(path, "w") as fid: fid.write(HEADER) for _, img in images.items(): image_header = [ img.id, *img.qvec, *img.tvec, img.camera_id, img.name, ] first_line = " ".join(map(str, image_header)) fid.write(first_line + "\n") points_strings = [] for xy, point3D_id in zip(img.xys, img.point3D_ids): points_strings.append(" ".join(map(str, [*xy, point3D_id]))) fid.write(" ".join(points_strings) + "\n") def write_images_binary(images, path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadImagesBinary(const std::string& path) void Reconstruction::WriteImagesBinary(const std::string& path) """ with open(path_to_model_file, "wb") as fid: write_next_bytes(fid, len(images), "Q") for _, img in images.items(): write_next_bytes(fid, img.id, "i") write_next_bytes(fid, img.qvec.tolist(), "dddd") write_next_bytes(fid, img.tvec.tolist(), "ddd") write_next_bytes(fid, img.camera_id, "i") for char in img.name: write_next_bytes(fid, char.encode("utf-8"), "c") write_next_bytes(fid, b"\x00", "c") write_next_bytes(fid, len(img.point3D_ids), "Q") for xy, p3d_id in zip(img.xys, img.point3D_ids): write_next_bytes(fid, [*xy, p3d_id], "ddq") def read_points3D_text(path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadPoints3DText(const std::string& path) void Reconstruction::WritePoints3DText(const std::string& path) """ points3D = {} with open(path, "r") as fid: while True: line = fid.readline() if not line: break line = line.strip() if len(line) > 0 and line[0] != "#": elems = line.split() point3D_id = int(elems[0]) xyz = np.array(tuple(map(float, elems[1:4]))) rgb = np.array(tuple(map(int, elems[4:7]))) error = float(elems[7]) image_ids = np.array(tuple(map(int, elems[8::2]))) point2D_idxs = np.array(tuple(map(int, elems[9::2]))) points3D[point3D_id] = Point3D( id=point3D_id, xyz=xyz, rgb=rgb, error=error, image_ids=image_ids, point2D_idxs=point2D_idxs, ) return points3D def read_points3D_binary(path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadPoints3DBinary(const std::string& path) void Reconstruction::WritePoints3DBinary(const std::string& path) """ points3D = {} with open(path_to_model_file, "rb") as fid: num_points = read_next_bytes(fid, 8, "Q")[0] for _ in range(num_points): binary_point_line_properties = read_next_bytes( fid, num_bytes=43, format_char_sequence="QdddBBBd" ) point3D_id = binary_point_line_properties[0] xyz = np.array(binary_point_line_properties[1:4]) rgb = np.array(binary_point_line_properties[4:7]) error = np.array(binary_point_line_properties[7]) track_length = read_next_bytes( fid, num_bytes=8, format_char_sequence="Q" )[0] track_elems = read_next_bytes( fid, num_bytes=8 * track_length, format_char_sequence="ii" * track_length, ) image_ids = np.array(tuple(map(int, track_elems[0::2]))) point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) points3D[point3D_id] = Point3D( id=point3D_id, xyz=xyz, rgb=rgb, error=error, image_ids=image_ids, point2D_idxs=point2D_idxs, ) return points3D def write_points3D_text(points3D, path): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadPoints3DText(const std::string& path) void Reconstruction::WritePoints3DText(const std::string& path) """ if len(points3D) == 0: mean_track_length = 0 else: mean_track_length = sum( (len(pt.image_ids) for _, pt in points3D.items()) ) / len(points3D) HEADER = ( "# 3D point list with one line of data per point:\n" + "# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n" + "# Number of points: {}, mean track length: {}\n".format( len(points3D), mean_track_length ) ) with open(path, "w") as fid: fid.write(HEADER) for _, pt in points3D.items(): point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error] fid.write(" ".join(map(str, point_header)) + " ") track_strings = [] for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs): track_strings.append(" ".join(map(str, [image_id, point2D]))) fid.write(" ".join(track_strings) + "\n") def write_points3D_binary(points3D, path_to_model_file): """ see: src/colmap/scene/reconstruction.cc void Reconstruction::ReadPoints3DBinary(const std::string& path) void Reconstruction::WritePoints3DBinary(const std::string& path) """ with open(path_to_model_file, "wb") as fid: write_next_bytes(fid, len(points3D), "Q") for _, pt in points3D.items(): write_next_bytes(fid, pt.id, "Q") write_next_bytes(fid, pt.xyz.tolist(), "ddd") write_next_bytes(fid, pt.rgb.tolist(), "BBB") write_next_bytes(fid, pt.error, "d") track_length = pt.image_ids.shape[0] write_next_bytes(fid, track_length, "Q") for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs): write_next_bytes(fid, [image_id, point2D_id], "ii") def detect_model_format(path, ext): if ( os.path.isfile(os.path.join(path, "cameras" + ext)) and os.path.isfile(os.path.join(path, "images" + ext)) and os.path.isfile(os.path.join(path, "points3D" + ext)) ): print("Detected model format: '" + ext + "'") return True return False def read_model(path, ext=""): # try to detect the extension automatically if ext == "": if detect_model_format(path, ".bin"): ext = ".bin" elif detect_model_format(path, ".txt"): ext = ".txt" else: print("Provide model format: '.bin' or '.txt'") return if ext == ".txt": cameras = read_cameras_text(os.path.join(path, "cameras" + ext)) images = read_images_text(os.path.join(path, "images" + ext)) points3D = read_points3D_text(os.path.join(path, "points3D") + ext) else: cameras = read_cameras_binary(os.path.join(path, "cameras" + ext)) images = read_images_binary(os.path.join(path, "images" + ext)) points3D = read_points3D_binary(os.path.join(path, "points3D") + ext) return cameras, images, points3D def write_model(cameras, images, points3D, path, ext=".bin"): if ext == ".txt": write_cameras_text(cameras, os.path.join(path, "cameras" + ext)) write_images_text(images, os.path.join(path, "images" + ext)) write_points3D_text(points3D, os.path.join(path, "points3D") + ext) else: write_cameras_binary(cameras, os.path.join(path, "cameras" + ext)) write_images_binary(images, os.path.join(path, "images" + ext)) write_points3D_binary(points3D, os.path.join(path, "points3D") + ext) return cameras, images, points3D def qvec2rotmat(qvec): return np.array( [ [ 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], ], [ 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], ], [ 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, ], ] ) def rotmat2qvec(R): Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat K = ( np.array( [ [Rxx - Ryy - Rzz, 0, 0, 0], [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0], [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0], [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz], ] ) / 3.0 ) eigvals, eigvecs = np.linalg.eigh(K) qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)] if qvec[0] < 0: qvec *= -1 return qvec def main(): parser = argparse.ArgumentParser( description="Read and write COLMAP binary and text models" ) parser.add_argument("--input_model", help="path to input model folder") parser.add_argument( "--input_format", choices=[".bin", ".txt"], help="input model format", default="", ) parser.add_argument("--output_model", help="path to output model folder") parser.add_argument( "--output_format", choices=[".bin", ".txt"], help="output model format", default=".txt", ) args = parser.parse_args() cameras, images, points3D = read_model( path=args.input_model, ext=args.input_format ) print("num_cameras:", len(cameras)) print("num_images:", len(images)) print("num_points3D:", len(points3D)) if args.output_model is not None: write_model( cameras, images, points3D, path=args.output_model, ext=args.output_format, ) if __name__ == "__main__": main() colmap-3.10/scripts/python/test_read_write_dense.py000077500000000000000000000046211464745566500226700ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import numpy as np from read_write_dense import read_array, write_array def main(): import sys if len(sys.argv) != 3: print( "Usage: python test_read_write_dense.py " "path/to/dense/input.bin path/to/dense/output.bin" ) return print( "Checking consistency of reading and writing dense arrays " + "(depth maps / normal maps) ..." ) path_to_dense_input = sys.argv[1] path_to_dense_output = sys.argv[2] dense_input = read_array(path_to_dense_input) print("Input shape: " + str(dense_input.shape)) write_array(dense_input, path_to_dense_output) dense_output = read_array(path_to_dense_output) np.testing.assert_array_equal(dense_input, dense_output) print("... dense arrays are equal.") if __name__ == "__main__": main() colmap-3.10/scripts/python/test_read_write_fused_vis.py000077500000000000000000000052151464745566500235610ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import filecmp from read_write_fused_vis import read_fused, write_fused def main(): import sys if len(sys.argv) != 5: print( "Usage: python test_read_write_fused_vis.py " "path/to/input_fused.ply path/to/input_fused.ply.vis " "path/to/output_fused.ply path/to/output_fused.ply.vis" ) return print( "Checking consistency of reading and writing fused.ply and fused.ply.vis files ..." ) path_to_fused_ply_input = sys.argv[1] path_to_fused_ply_vis_input = sys.argv[2] path_to_fused_ply_output = sys.argv[3] path_to_fused_ply_vis_output = sys.argv[4] mesh_points = read_fused( path_to_fused_ply_input, path_to_fused_ply_vis_input ) write_fused( mesh_points, path_to_fused_ply_output, path_to_fused_ply_vis_output ) assert filecmp.cmp(path_to_fused_ply_input, path_to_fused_ply_output) assert filecmp.cmp( path_to_fused_ply_vis_input, path_to_fused_ply_vis_output ) print("... Results are equal.") if __name__ == "__main__": main() colmap-3.10/scripts/python/test_read_write_model.py000077500000000000000000000112321464745566500226660ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import numpy as np from read_write_model import read_model, write_model from tempfile import mkdtemp def compare_cameras(cameras1, cameras2): assert len(cameras1) == len(cameras2) for camera_id1 in cameras1: camera1 = cameras1[camera_id1] camera2 = cameras2[camera_id1] assert camera1.id == camera2.id assert camera1.width == camera2.width assert camera1.height == camera2.height assert np.allclose(camera1.params, camera2.params) def compare_images(images1, images2): assert len(images1) == len(images2) for image_id1 in images1: image1 = images1[image_id1] image2 = images2[image_id1] assert image1.id == image2.id assert np.allclose(image1.qvec, image2.qvec) assert np.allclose(image1.tvec, image2.tvec) assert image1.camera_id == image2.camera_id assert image1.name == image2.name assert np.allclose(image1.xys, image2.xys) assert np.array_equal(image1.point3D_ids, image2.point3D_ids) def compare_points(points3D1, points3D2): for point3D_id1 in points3D1: point3D1 = points3D1[point3D_id1] point3D2 = points3D2[point3D_id1] assert point3D1.id == point3D2.id assert np.allclose(point3D1.xyz, point3D2.xyz) assert np.array_equal(point3D1.rgb, point3D2.rgb) assert np.allclose(point3D1.error, point3D2.error) assert np.array_equal(point3D1.image_ids, point3D2.image_ids) assert np.array_equal(point3D1.point2D_idxs, point3D2.point2D_idxs) def main(): import sys if len(sys.argv) != 3: print( "Usage: python read_model.py " "path/to/model/folder/txt path/to/model/folder/bin" ) return print("Comparing text and binary models ...") path_to_model_txt_folder = sys.argv[1] path_to_model_bin_folder = sys.argv[2] cameras_txt, images_txt, points3D_txt = read_model( path_to_model_txt_folder, ext=".txt" ) cameras_bin, images_bin, points3D_bin = read_model( path_to_model_bin_folder, ext=".bin" ) compare_cameras(cameras_txt, cameras_bin) compare_images(images_txt, images_bin) compare_points(points3D_txt, points3D_bin) print("... text and binary models are equal.") print("Saving text model and reloading it ...") tmpdir = mkdtemp() write_model(cameras_bin, images_bin, points3D_bin, tmpdir, ext=".txt") cameras_txt, images_txt, points3D_txt = read_model(tmpdir, ext=".txt") compare_cameras(cameras_txt, cameras_bin) compare_images(images_txt, images_bin) compare_points(points3D_txt, points3D_bin) print("... saved text and loaded models are equal.") print("Saving binary model and reloading it ...") write_model(cameras_bin, images_bin, points3D_bin, tmpdir, ext=".bin") cameras_bin, images_bin, points3D_bin = read_model(tmpdir, ext=".bin") compare_cameras(cameras_txt, cameras_bin) compare_images(images_txt, images_bin) compare_points(points3D_txt, points3D_bin) print("... saved binary and loaded models are equal.") if __name__ == "__main__": main() colmap-3.10/scripts/python/visualize_model.py000077500000000000000000000160301464745566500215160ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import argparse import numpy as np import open3d from read_write_model import read_model, write_model, qvec2rotmat, rotmat2qvec class Model: def __init__(self): self.cameras = [] self.images = [] self.points3D = [] self.__vis = None def read_model(self, path, ext=""): self.cameras, self.images, self.points3D = read_model(path, ext) def add_points(self, min_track_len=3, remove_statistical_outlier=True): pcd = open3d.geometry.PointCloud() xyz = [] rgb = [] for point3D in self.points3D.values(): track_len = len(point3D.point2D_idxs) if track_len < min_track_len: continue xyz.append(point3D.xyz) rgb.append(point3D.rgb / 255) pcd.points = open3d.utility.Vector3dVector(xyz) pcd.colors = open3d.utility.Vector3dVector(rgb) # remove obvious outliers if remove_statistical_outlier: [pcd, _] = pcd.remove_statistical_outlier( nb_neighbors=20, std_ratio=2.0 ) # open3d.visualization.draw_geometries([pcd]) self.__vis.add_geometry(pcd) self.__vis.poll_events() self.__vis.update_renderer() def add_cameras(self, scale=1): frames = [] for img in self.images.values(): # rotation R = qvec2rotmat(img.qvec) # translation t = img.tvec # invert t = -R.T @ t R = R.T # intrinsics cam = self.cameras[img.camera_id] if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): fx = fy = cam.params[0] cx = cam.params[1] cy = cam.params[2] elif cam.model in ( "PINHOLE", "OPENCV", "OPENCV_FISHEYE", "FULL_OPENCV", ): fx = cam.params[0] fy = cam.params[1] cx = cam.params[2] cy = cam.params[3] else: raise Exception("Camera model not supported") # intrinsics K = np.identity(3) K[0, 0] = fx K[1, 1] = fy K[0, 2] = cx K[1, 2] = cy # create axis, plane and pyramed geometries that will be drawn cam_model = draw_camera(K, R, t, cam.width, cam.height, scale) frames.extend(cam_model) # add geometries to visualizer for i in frames: self.__vis.add_geometry(i) def create_window(self): self.__vis = open3d.visualization.Visualizer() self.__vis.create_window() def show(self): self.__vis.poll_events() self.__vis.update_renderer() self.__vis.run() self.__vis.destroy_window() def draw_camera(K, R, t, w, h, scale=1, color=[0.8, 0.2, 0.8]): """Create axis, plane and pyramed geometries in Open3D format. :param K: calibration matrix (camera intrinsics) :param R: rotation matrix :param t: translation :param w: image width :param h: image height :param scale: camera model scale :param color: color of the image plane and pyramid lines :return: camera model geometries (axis, plane and pyramid) """ # intrinsics K = K.copy() / scale Kinv = np.linalg.inv(K) # 4x4 transformation T = np.column_stack((R, t)) T = np.vstack((T, (0, 0, 0, 1))) # axis axis = open3d.geometry.TriangleMesh.create_coordinate_frame( size=0.5 * scale ) axis.transform(T) # points in pixel points_pixel = [ [0, 0, 0], [0, 0, 1], [w, 0, 1], [0, h, 1], [w, h, 1], ] # pixel to camera coordinate system points = [Kinv @ p for p in points_pixel] # image plane width = abs(points[1][0]) + abs(points[3][0]) height = abs(points[1][1]) + abs(points[3][1]) plane = open3d.geometry.TriangleMesh.create_box(width, height, depth=1e-6) plane.paint_uniform_color(color) plane.translate([points[1][0], points[1][1], scale]) plane.transform(T) # pyramid points_in_world = [(R @ p + t) for p in points] lines = [ [0, 1], [0, 2], [0, 3], [0, 4], ] colors = [color for i in range(len(lines))] line_set = open3d.geometry.LineSet( points=open3d.utility.Vector3dVector(points_in_world), lines=open3d.utility.Vector2iVector(lines), ) line_set.colors = open3d.utility.Vector3dVector(colors) # return as list in Open3D format return [axis, plane, line_set] def parse_args(): parser = argparse.ArgumentParser( description="Visualize COLMAP binary and text models" ) parser.add_argument( "--input_model", required=True, help="path to input model folder" ) parser.add_argument( "--input_format", choices=[".bin", ".txt"], help="input model format", default="", ) args = parser.parse_args() return args def main(): args = parse_args() # read COLMAP model model = Model() model.read_model(args.input_model, ext=args.input_format) print("num_cameras:", len(model.cameras)) print("num_images:", len(model.images)) print("num_points3D:", len(model.points3D)) # display using Open3D visualization tools model.create_window() model.add_points() model.add_cameras(scale=0.25) model.show() if __name__ == "__main__": main() colmap-3.10/scripts/shell/000077500000000000000000000000001464745566500155345ustar00rootroot00000000000000colmap-3.10/scripts/shell/build_mac_app.sh000077500000000000000000000064231464745566500206570ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # This script creates a deployable package of COLMAP for Mac OS X. BASE_PATH=$(dirname $1) echo "Creating bundle directory" mkdir -p "$BASE_PATH/COLMAP.app/Contents/MacOS" echo "Copying binary" cp "$BASE_PATH/colmap" "$BASE_PATH/COLMAP.app/Contents/MacOS/colmap" echo "Writing Info.plist" cat <"$BASE_PATH/COLMAP.app/Contents/Info.plist" CFBundlePackageType APPL CFBundleExecutable colmap CFBundleIdentifier COLMAP CFBundleName COLMAP CFBundleDisplayName COLMAP NSHighResolutionCapable NSAppSleepDisabled EOM install_name_tool -change @rpath/libtbb.dylib /usr/local/lib/libtbb.dylib $BASE_PATH/COLMAP.app/Contents/MacOS/COLMAP install_name_tool -change @rpath/libtbbmalloc.dylib /usr/local/lib/libtbbmalloc.dylib $BASE_PATH/COLMAP.app/Contents/MacOS/COLMAP echo "Linking dynamic libraries" /usr/local/opt/qt5/bin/macdeployqt "$BASE_PATH/COLMAP.app" echo "Wrapping binary" cat <"$BASE_PATH/COLMAP.app/Contents/MacOS/colmap_gui.sh" #!/bin/bash script_path="\$(cd "\$(dirname "\${BASH_SOURCE[0]}")" && pwd)" \$script_path/colmap gui EOM chmod +x $BASE_PATH/COLMAP.app/Contents/MacOS/colmap_gui.sh sed -i '' 's#colmap#colmap_gui.sh#g' $BASE_PATH/COLMAP.app/Contents/Info.plist echo "Compressing application" cd "$BASE_PATH" zip -r "COLMAP-mac.zip" "COLMAP.app" colmap-3.10/scripts/shell/colmap.bat000077500000000000000000000035331464745566500175060ustar00rootroot00000000000000@echo off rem Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. rem All rights reserved. rem rem Redistribution and use in source and binary forms, with or without rem modification, are permitted provided that the following conditions are met: rem rem * Redistributions of source code must retain the above copyright rem notice, this list of conditions and the following disclaimer. rem rem * Redistributions in binary form must reproduce the above copyright rem notice, this list of conditions and the following disclaimer in the rem documentation and/or other materials provided with the distribution. rem rem * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of rem its contributors may be used to endorse or promote products derived rem from this software without specific prior written permission. rem rem THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" rem AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE rem IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE rem ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE rem LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR rem CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF rem SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS rem INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN rem CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) rem ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE rem POSSIBILITY OF SUCH DAMAGE. set SCRIPT_PATH=%~dp0 set PATH=%SCRIPT_PATH%\bin;%PATH% set QT_PLUGIN_PATH=%SCRIPT_PATH%\plugins;%QT_PLUGIN_PATH% set ARGUMENTS=%* if "%ARGUMENTS%"=="" set ARGUMENTS=gui "%SCRIPT_PATH%\bin\colmap" %ARGUMENTS% colmap-3.10/scripts/shell/enter_vs_dev_shell.ps1000066400000000000000000000015031464745566500220320ustar00rootroot00000000000000if (!$env:VisualStudioDevShell) { $vswhere = "${Env:ProgramFiles(x86)}/Microsoft Visual Studio/Installer/vswhere.exe" if (!(Test-Path $vswhere)) { throw "Failed to find vswhere.exe" } & $vswhere -latest -format json $vsInstance = & $vswhere -latest -format json | ConvertFrom-Json if ($LASTEXITCODE) { throw "vswhere.exe returned exit code $LASTEXITCODE" } Import-Module "$($vsInstance.installationPath)/Common7/Tools/Microsoft.VisualStudio.DevShell.dll" $prevCwd = Get-Location try { Enter-VsDevShell $vsInstance.instanceId -DevCmdArguments "-no_logo -host_arch=amd64 -arch=amd64" } catch { Write-Host $_ Write-Error "Failed to enter Visual Studio Dev Shell" exit 1 } Set-Location $prevCwd $env:VisualStudioDevShell = $true } colmap-3.10/scripts/shell/images_to_video.sh000077500000000000000000000033161464745566500212330ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Command to produce video from images produced by COLMAP movie grabber tool. ffmpeg -i frame%06d.png -r 30 -vf scale=1680:1050 out.mp4 colmap-3.10/scripts/shell/profile_binary.sh000077500000000000000000000050021464745566500210740ustar00rootroot00000000000000#!/bin/bash # Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # Script to perform profiling on a command. For example: # ./profile_binary.sh ./src/colmap/exe/colmap automatic_reconstructor ... perf_bin=$(find /usr/lib/linux-tools -name perf | head -1) if [[ -z $perf_bin ]]; then echo "Error: Perf tool not found. Under ubuntu, install as:" echo " sudo apt-get install linux-tools-generic" exit 1 fi "$perf_bin" record -e cycles:u -g "$@" binary_filename=$(basename -- "${binary_path}") profile_path="$binary_filename.perf.data" mv perf.data "$profile_path" echo "#####################################################################" echo "###### Profiling finished. Inspect results using the commands: ######" echo "#####################################################################" echo "If the perf output contains unknown list items, recompile with RelWithDebInfo" echo "$perf_bin report -i $profile_path" echo "$perf_bin report --stdio -g graph,0.5,caller -i $profile_path" colmap-3.10/scripts/shell/restore_git_submodules.sh000077500000000000000000000035261464745566500226710ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. set -e git config -f .gitmodules --get-regexp '^submodule\..*\.path$' | while read path_key path do url_key=$(echo $path_key | sed 's/\.path/.url/') url=$(git config -f .gitmodules --get "$url_key") git submodule add -f $url $path done colmap-3.10/scripts/shell/run_tests.bat000077500000000000000000000035561464745566500202660ustar00rootroot00000000000000@echo off rem Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. rem All rights reserved. rem rem Redistribution and use in source and binary forms, with or without rem modification, are permitted provided that the following conditions are met: rem rem * Redistributions of source code must retain the above copyright rem notice, this list of conditions and the following disclaimer. rem rem * Redistributions in binary form must reproduce the above copyright rem notice, this list of conditions and the following disclaimer in the rem documentation and/or other materials provided with the distribution. rem rem * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of rem its contributors may be used to endorse or promote products derived rem from this software without specific prior written permission. rem rem THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" rem AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE rem IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE rem ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE rem LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR rem CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF rem SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS rem INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN rem CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) rem ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE rem POSSIBILITY OF SUCH DAMAGE. set SCRIPT_PATH=%~dp0 set PATH=%SCRIPT_PATH%\bin;%PATH% set QT_PLUGIN_PATH=%SCRIPT_PATH%\plugins;%QT_PLUGIN_PATH% @echo on for %%i in (%SCRIPT_PATH%\bin\*_test.exe) do ( %%i if %errorlevel% neq 0 goto end ) :end pause colmap-3.10/src/000077500000000000000000000000001464745566500135255ustar00rootroot00000000000000colmap-3.10/src/colmap/000077500000000000000000000000001464745566500150005ustar00rootroot00000000000000colmap-3.10/src/colmap/CMakeLists.txt000066400000000000000000000054761464745566500175540ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. if(IS_MSVC) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /W3") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W3") # Avoid pulling in too many header files through add_definitions("-DWIN32_LEAN_AND_MEAN") elseif(IS_GNU OR IS_CLANG) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall") endif() if(CUDA_ENABLED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --use_fast_math") # Use a separate stream per thread to allow for concurrent kernel execution # between multiple threads on the same device. set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --default-stream per-thread") # Suppress warnings: # ptxas warning : Stack size for entry function X cannot be statically determined. set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xptxas=-suppress-stack-size-warning") endif() add_subdirectory(controllers) add_subdirectory(estimators) add_subdirectory(exe) add_subdirectory(feature) add_subdirectory(geometry) add_subdirectory(image) add_subdirectory(math) add_subdirectory(mvs) add_subdirectory(optim) add_subdirectory(retrieval) add_subdirectory(scene) add_subdirectory(sensor) add_subdirectory(sfm) add_subdirectory(tools) add_subdirectory(util) if (GUI_ENABLED) add_subdirectory(ui) endif() colmap-3.10/src/colmap/controllers/000077500000000000000000000000001464745566500173465ustar00rootroot00000000000000colmap-3.10/src/colmap/controllers/CMakeLists.txt000066400000000000000000000053011464745566500221050ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. set(FOLDER_NAME "controllers") COLMAP_ADD_LIBRARY( NAME colmap_controllers SRCS automatic_reconstruction.h automatic_reconstruction.cc bundle_adjustment.h bundle_adjustment.cc hierarchical_mapper.h hierarchical_mapper.cc feature_extraction.h feature_extraction.cc feature_matching.h feature_matching.cc feature_matching_utils.h feature_matching_utils.cc image_reader.h image_reader.cc incremental_mapper.h incremental_mapper.cc option_manager.h option_manager.cc PUBLIC_LINK_LIBS colmap_estimators colmap_feature colmap_geometry colmap_scene colmap_util Eigen3::Eigen Boost::program_options PRIVATE_LINK_LIBS colmap_image colmap_math colmap_mvs colmap_sfm Ceres::ceres Boost::filesystem Boost::boost ) COLMAP_ADD_TEST( NAME hierarchical_mapper_test SRCS hierarchical_mapper_test.cc LINK_LIBS colmap_controllers ) COLMAP_ADD_TEST( NAME incremental_mapper_test SRCS incremental_mapper_test.cc LINK_LIBS colmap_controllers ) colmap-3.10/src/colmap/controllers/automatic_reconstruction.cc000066400000000000000000000277241464745566500250200ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/automatic_reconstruction.h" #include "colmap/controllers/feature_extraction.h" #include "colmap/controllers/feature_matching.h" #include "colmap/controllers/incremental_mapper.h" #include "colmap/controllers/option_manager.h" #include "colmap/image/undistortion.h" #include "colmap/mvs/fusion.h" #include "colmap/mvs/meshing.h" #include "colmap/mvs/patch_match.h" #include "colmap/util/logging.h" #include "colmap/util/misc.h" namespace colmap { AutomaticReconstructionController::AutomaticReconstructionController( const Options& options, std::shared_ptr reconstruction_manager) : options_(options), reconstruction_manager_(std::move(reconstruction_manager)), active_thread_(nullptr) { THROW_CHECK_DIR_EXISTS(options_.workspace_path); THROW_CHECK_DIR_EXISTS(options_.image_path); THROW_CHECK_NOTNULL(reconstruction_manager_); option_manager_.AddAllOptions(); *option_manager_.image_path = options_.image_path; *option_manager_.database_path = JoinPaths(options_.workspace_path, "database.db"); if (options_.data_type == DataType::VIDEO) { option_manager_.ModifyForVideoData(); } else if (options_.data_type == DataType::INDIVIDUAL) { option_manager_.ModifyForIndividualData(); } else if (options_.data_type == DataType::INTERNET) { option_manager_.ModifyForInternetData(); } else { LOG(FATAL_THROW) << "Data type not supported"; } THROW_CHECK(ExistsCameraModelWithName(options_.camera_model)); if (options_.quality == Quality::LOW) { option_manager_.ModifyForLowQuality(); } else if (options_.quality == Quality::MEDIUM) { option_manager_.ModifyForMediumQuality(); } else if (options_.quality == Quality::HIGH) { option_manager_.ModifyForHighQuality(); } else if (options_.quality == Quality::EXTREME) { option_manager_.ModifyForExtremeQuality(); } option_manager_.sift_extraction->num_threads = options_.num_threads; option_manager_.sift_matching->num_threads = options_.num_threads; option_manager_.mapper->num_threads = options_.num_threads; option_manager_.poisson_meshing->num_threads = options_.num_threads; ImageReaderOptions& reader_options = *option_manager_.image_reader; reader_options.database_path = *option_manager_.database_path; reader_options.image_path = *option_manager_.image_path; if (!options_.mask_path.empty()) { reader_options.mask_path = options_.mask_path; option_manager_.image_reader->mask_path = options_.mask_path; option_manager_.stereo_fusion->mask_path = options_.mask_path; } reader_options.single_camera = options_.single_camera; reader_options.single_camera_per_folder = options_.single_camera_per_folder; reader_options.camera_model = options_.camera_model; reader_options.camera_params = options_.camera_params; option_manager_.sift_extraction->use_gpu = options_.use_gpu; option_manager_.sift_matching->use_gpu = options_.use_gpu; option_manager_.sift_extraction->gpu_index = options_.gpu_index; option_manager_.sift_matching->gpu_index = options_.gpu_index; option_manager_.patch_match_stereo->gpu_index = options_.gpu_index; feature_extractor_ = CreateFeatureExtractorController( reader_options, *option_manager_.sift_extraction); exhaustive_matcher_ = CreateExhaustiveFeatureMatcher(*option_manager_.exhaustive_matching, *option_manager_.sift_matching, *option_manager_.two_view_geometry, *option_manager_.database_path); if (!options_.vocab_tree_path.empty()) { option_manager_.sequential_matching->loop_detection = true; option_manager_.sequential_matching->vocab_tree_path = options_.vocab_tree_path; } sequential_matcher_ = CreateSequentialFeatureMatcher(*option_manager_.sequential_matching, *option_manager_.sift_matching, *option_manager_.two_view_geometry, *option_manager_.database_path); if (!options_.vocab_tree_path.empty()) { option_manager_.vocab_tree_matching->vocab_tree_path = options_.vocab_tree_path; vocab_tree_matcher_ = CreateVocabTreeFeatureMatcher(*option_manager_.vocab_tree_matching, *option_manager_.sift_matching, *option_manager_.two_view_geometry, *option_manager_.database_path); } } void AutomaticReconstructionController::Stop() { if (active_thread_ != nullptr) { active_thread_->Stop(); } Thread::Stop(); } void AutomaticReconstructionController::Run() { if (IsStopped()) { return; } RunFeatureExtraction(); if (IsStopped()) { return; } RunFeatureMatching(); if (IsStopped()) { return; } if (options_.sparse) { RunSparseMapper(); } if (IsStopped()) { return; } if (options_.dense) { RunDenseMapper(); } } void AutomaticReconstructionController::RunFeatureExtraction() { THROW_CHECK_NOTNULL(feature_extractor_); active_thread_ = feature_extractor_.get(); feature_extractor_->Start(); feature_extractor_->Wait(); feature_extractor_.reset(); active_thread_ = nullptr; } void AutomaticReconstructionController::RunFeatureMatching() { Thread* matcher = nullptr; if (options_.data_type == DataType::VIDEO) { matcher = sequential_matcher_.get(); } else if (options_.data_type == DataType::INDIVIDUAL || options_.data_type == DataType::INTERNET) { Database database(*option_manager_.database_path); const size_t num_images = database.NumImages(); if (options_.vocab_tree_path.empty() || num_images < 200) { matcher = exhaustive_matcher_.get(); } else { matcher = vocab_tree_matcher_.get(); } } THROW_CHECK_NOTNULL(matcher); active_thread_ = matcher; matcher->Start(); matcher->Wait(); exhaustive_matcher_.reset(); sequential_matcher_.reset(); vocab_tree_matcher_.reset(); active_thread_ = nullptr; } void AutomaticReconstructionController::RunSparseMapper() { const auto sparse_path = JoinPaths(options_.workspace_path, "sparse"); if (ExistsDir(sparse_path)) { auto dir_list = GetDirList(sparse_path); std::sort(dir_list.begin(), dir_list.end()); if (dir_list.size() > 0) { LOG(WARNING) << "Skipping sparse reconstruction because it is already computed"; for (const auto& dir : dir_list) { reconstruction_manager_->Read(dir); } return; } } IncrementalMapperController mapper(option_manager_.mapper, *option_manager_.image_path, *option_manager_.database_path, reconstruction_manager_); mapper.SetCheckIfStoppedFunc([&]() { return IsStopped(); }); mapper.Run(); CreateDirIfNotExists(sparse_path); reconstruction_manager_->Write(sparse_path); option_manager_.Write(JoinPaths(sparse_path, "project.ini")); } void AutomaticReconstructionController::RunDenseMapper() { CreateDirIfNotExists(JoinPaths(options_.workspace_path, "dense")); for (size_t i = 0; i < reconstruction_manager_->Size(); ++i) { if (IsStopped()) { return; } const std::string dense_path = JoinPaths(options_.workspace_path, "dense", std::to_string(i)); const std::string fused_path = JoinPaths(dense_path, "fused.ply"); std::string meshing_path; if (options_.mesher == Mesher::POISSON) { meshing_path = JoinPaths(dense_path, "meshed-poisson.ply"); } else if (options_.mesher == Mesher::DELAUNAY) { meshing_path = JoinPaths(dense_path, "meshed-delaunay.ply"); } if (ExistsFile(fused_path) && ExistsFile(meshing_path)) { continue; } // Image undistortion. if (!ExistsDir(dense_path)) { CreateDirIfNotExists(dense_path); UndistortCameraOptions undistortion_options; undistortion_options.max_image_size = option_manager_.patch_match_stereo->max_image_size; COLMAPUndistorter undistorter(undistortion_options, *reconstruction_manager_->Get(i), *option_manager_.image_path, dense_path); undistorter.SetCheckIfStoppedFunc([&]() { return IsStopped(); }); undistorter.Run(); } if (IsStopped()) { return; } // Patch match stereo. #if defined(COLMAP_CUDA_ENABLED) { mvs::PatchMatchController patch_match_controller( *option_manager_.patch_match_stereo, dense_path, "COLMAP", ""); patch_match_controller.SetCheckIfStoppedFunc( [&]() { return IsStopped(); }); patch_match_controller.Run(); } #else // COLMAP_CUDA_ENABLED LOG(WARNING) << "Skipping patch match stereo because CUDA is not available"; return; #endif // COLMAP_CUDA_ENABLED if (IsStopped()) { return; } // Stereo fusion. if (!ExistsFile(fused_path)) { auto fusion_options = *option_manager_.stereo_fusion; const int num_reg_images = reconstruction_manager_->Get(i)->NumRegImages(); fusion_options.min_num_pixels = std::min(num_reg_images + 1, fusion_options.min_num_pixels); mvs::StereoFusion fuser( fusion_options, dense_path, "COLMAP", "", options_.quality == Quality::HIGH ? "geometric" : "photometric"); fuser.SetCheckIfStoppedFunc([&]() { return IsStopped(); }); fuser.Run(); LOG(INFO) << "Writing output: " << fused_path; WriteBinaryPlyPoints(fused_path, fuser.GetFusedPoints()); mvs::WritePointsVisibility(fused_path + ".vis", fuser.GetFusedPointsVisibility()); } if (IsStopped()) { return; } // Surface meshing. if (!ExistsFile(meshing_path)) { if (options_.mesher == Mesher::POISSON) { mvs::PoissonMeshing( *option_manager_.poisson_meshing, fused_path, meshing_path); } else if (options_.mesher == Mesher::DELAUNAY) { #if defined(COLMAP_CGAL_ENABLED) mvs::DenseDelaunayMeshing( *option_manager_.delaunay_meshing, dense_path, meshing_path); #else // COLMAP_CGAL_ENABLED LOG(WARNING) << "Skipping Delaunay meshing because CGAL is not available"; return; #endif // COLMAP_CGAL_ENABLED } } } } } // namespace colmap colmap-3.10/src/colmap/controllers/automatic_reconstruction.h000066400000000000000000000105071464745566500246510ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/controllers/option_manager.h" #include "colmap/scene/reconstruction_manager.h" #include "colmap/util/threading.h" #include #include namespace colmap { class AutomaticReconstructionController : public Thread { public: enum class DataType { INDIVIDUAL, VIDEO, INTERNET }; enum class Quality { LOW, MEDIUM, HIGH, EXTREME }; enum class Mesher { POISSON, DELAUNAY }; struct Options { // The path to the workspace folder in which all results are stored. std::string workspace_path; // The path to the image folder which are used as input. std::string image_path; // The path to the mask folder which are used as input. std::string mask_path; // The path to the vocabulary tree for feature matching. std::string vocab_tree_path; // The type of input data used to choose optimal mapper settings. DataType data_type = DataType::INDIVIDUAL; // Whether to perform low- or high-quality reconstruction. Quality quality = Quality::HIGH; // Whether to use shared intrinsics or not. bool single_camera = false; // Whether to use shared intrinsics or not for all images in the same // sub-folder. bool single_camera_per_folder = false; // Which camera model to use for images. std::string camera_model = "SIMPLE_RADIAL"; // Initial camera params for all images. std::string camera_params; // Whether to perform sparse mapping. bool sparse = true; // Whether to perform dense mapping. #if defined(COLMAP_CUDA_ENABLED) bool dense = true; #else bool dense = false; #endif // The meshing algorithm to be used. Mesher mesher = Mesher::POISSON; // The number of threads to use in all stages. int num_threads = -1; // Whether to use the GPU in feature extraction and matching. bool use_gpu = true; // Index of the GPU used for GPU stages. For multi-GPU computation, // you should separate multiple GPU indices by comma, e.g., "0,1,2,3". // By default, all GPUs will be used in all stages. std::string gpu_index = "-1"; }; AutomaticReconstructionController( const Options& options, std::shared_ptr reconstruction_manager); void Stop() override; private: void Run() override; void RunFeatureExtraction(); void RunFeatureMatching(); void RunSparseMapper(); void RunDenseMapper(); const Options options_; OptionManager option_manager_; std::shared_ptr reconstruction_manager_; Thread* active_thread_; std::unique_ptr feature_extractor_; std::unique_ptr exhaustive_matcher_; std::unique_ptr sequential_matcher_; std::unique_ptr vocab_tree_matcher_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/bundle_adjustment.cc000066400000000000000000000074071464745566500233740ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/bundle_adjustment.h" #include "colmap/estimators/bundle_adjustment.h" #include "colmap/sfm/observation_manager.h" #include "colmap/util/misc.h" #include "colmap/util/timer.h" #include namespace colmap { namespace { // Callback functor called after each bundle adjustment iteration. class BundleAdjustmentIterationCallback : public ceres::IterationCallback { public: explicit BundleAdjustmentIterationCallback(BaseController* controller) : controller_(controller) {} virtual ceres::CallbackReturnType operator()( const ceres::IterationSummary& summary) { THROW_CHECK_NOTNULL(controller_); if (controller_->CheckIfStopped()) { return ceres::SOLVER_TERMINATE_SUCCESSFULLY; } else { return ceres::SOLVER_CONTINUE; } } private: BaseController* controller_; }; } // namespace BundleAdjustmentController::BundleAdjustmentController( const OptionManager& options, std::shared_ptr reconstruction) : options_(options), reconstruction_(std::move(reconstruction)) {} void BundleAdjustmentController::Run() { THROW_CHECK_NOTNULL(reconstruction_); PrintHeading1("Global bundle adjustment"); Timer run_timer; run_timer.Start(); const std::vector& reg_image_ids = reconstruction_->RegImageIds(); if (reg_image_ids.size() < 2) { LOG(ERROR) << "Need at least two views."; return; } // Avoid degeneracies in bundle adjustment. ObservationManager(*reconstruction_).FilterObservationsWithNegativeDepth(); BundleAdjustmentOptions ba_options = *options_.bundle_adjustment; BundleAdjustmentIterationCallback iteration_callback(this); ba_options.solver_options.callbacks.push_back(&iteration_callback); // Configure bundle adjustment. BundleAdjustmentConfig ba_config; for (const image_t image_id : reg_image_ids) { ba_config.AddImage(image_id); } ba_config.SetConstantCamPose(reg_image_ids[0]); ba_config.SetConstantCamPositions(reg_image_ids[1], {0}); // Run bundle adjustment. BundleAdjuster bundle_adjuster(ba_options, ba_config); bundle_adjuster.Solve(reconstruction_.get()); run_timer.PrintMinutes(); } } // namespace colmap colmap-3.10/src/colmap/controllers/bundle_adjustment.h000066400000000000000000000042301464745566500232250ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/controllers/option_manager.h" #include "colmap/scene/reconstruction.h" #include "colmap/util/base_controller.h" namespace colmap { // Class that controls the global bundle adjustment procedure. class BundleAdjustmentController : public BaseController { public: BundleAdjustmentController(const OptionManager& options, std::shared_ptr reconstruction); void Run(); private: const OptionManager options_; std::shared_ptr reconstruction_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/feature_extraction.cc000066400000000000000000000513501464745566500235540ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/feature_extraction.h" #include "colmap/feature/sift.h" #include "colmap/geometry/gps.h" #include "colmap/scene/database.h" #include "colmap/util/cuda.h" #include "colmap/util/misc.h" #include "colmap/util/opengl_utils.h" #include "colmap/util/timer.h" #include namespace colmap { namespace { void ScaleKeypoints(const Bitmap& bitmap, const Camera& camera, FeatureKeypoints* keypoints) { if (static_cast(bitmap.Width()) != camera.width || static_cast(bitmap.Height()) != camera.height) { const float scale_x = static_cast(camera.width) / bitmap.Width(); const float scale_y = static_cast(camera.height) / bitmap.Height(); for (auto& keypoint : *keypoints) { keypoint.Rescale(scale_x, scale_y); } } } void MaskKeypoints(const Bitmap& mask, FeatureKeypoints* keypoints, FeatureDescriptors* descriptors) { size_t out_index = 0; BitmapColor color; for (size_t i = 0; i < keypoints->size(); ++i) { if (!mask.GetPixel(static_cast(keypoints->at(i).x), static_cast(keypoints->at(i).y), &color) || color.r == 0) { // Delete this keypoint by not copying it to the output. } else { // Retain this keypoint by copying it to the output index (in case this // index differs from its current position). if (out_index != i) { keypoints->at(out_index) = keypoints->at(i); for (int col = 0; col < descriptors->cols(); ++col) { (*descriptors)(out_index, col) = (*descriptors)(i, col); } } out_index += 1; } } keypoints->resize(out_index); descriptors->conservativeResize(out_index, descriptors->cols()); } struct ImageData { ImageReader::Status status = ImageReader::Status::FAILURE; Camera camera; Image image; PosePrior pose_prior; Bitmap bitmap; Bitmap mask; FeatureKeypoints keypoints; FeatureDescriptors descriptors; }; class ImageResizerThread : public Thread { public: ImageResizerThread(int max_image_size, JobQueue* input_queue, JobQueue* output_queue) : max_image_size_(max_image_size), input_queue_(input_queue), output_queue_(output_queue) {} private: void Run() override { while (true) { if (IsStopped()) { break; } auto input_job = input_queue_->Pop(); if (input_job.IsValid()) { auto& image_data = input_job.Data(); if (image_data.status == ImageReader::Status::SUCCESS) { if (static_cast(image_data.bitmap.Width()) > max_image_size_ || static_cast(image_data.bitmap.Height()) > max_image_size_) { // Fit the down-sampled version exactly into the max dimensions. const double scale = static_cast(max_image_size_) / std::max(image_data.bitmap.Width(), image_data.bitmap.Height()); const int new_width = static_cast(image_data.bitmap.Width() * scale); const int new_height = static_cast(image_data.bitmap.Height() * scale); image_data.bitmap.Rescale(new_width, new_height); } } output_queue_->Push(std::move(image_data)); } else { break; } } } const int max_image_size_; JobQueue* input_queue_; JobQueue* output_queue_; }; class SiftFeatureExtractorThread : public Thread { public: SiftFeatureExtractorThread(const SiftExtractionOptions& sift_options, const std::shared_ptr& camera_mask, JobQueue* input_queue, JobQueue* output_queue) : sift_options_(sift_options), camera_mask_(camera_mask), input_queue_(input_queue), output_queue_(output_queue) { THROW_CHECK(sift_options_.Check()); #if !defined(COLMAP_CUDA_ENABLED) if (sift_options_.use_gpu) { opengl_context_ = std::make_unique(); } #endif } private: void Run() override { if (sift_options_.use_gpu) { #if !defined(COLMAP_CUDA_ENABLED) THROW_CHECK_NOTNULL(opengl_context_); THROW_CHECK(opengl_context_->MakeCurrent()); #endif } std::unique_ptr extractor = CreateSiftFeatureExtractor(sift_options_); if (extractor == nullptr) { LOG(ERROR) << "Failed to create feature extractor."; SignalInvalidSetup(); return; } SignalValidSetup(); while (true) { if (IsStopped()) { break; } auto input_job = input_queue_->Pop(); if (input_job.IsValid()) { auto& image_data = input_job.Data(); if (image_data.status == ImageReader::Status::SUCCESS) { if (extractor->Extract(image_data.bitmap, &image_data.keypoints, &image_data.descriptors)) { ScaleKeypoints( image_data.bitmap, image_data.camera, &image_data.keypoints); if (camera_mask_) { MaskKeypoints(*camera_mask_, &image_data.keypoints, &image_data.descriptors); } if (image_data.mask.Data()) { MaskKeypoints(image_data.mask, &image_data.keypoints, &image_data.descriptors); } } else { image_data.status = ImageReader::Status::FAILURE; } } image_data.bitmap.Deallocate(); output_queue_->Push(std::move(image_data)); } else { break; } } } const SiftExtractionOptions sift_options_; std::shared_ptr camera_mask_; std::unique_ptr opengl_context_; JobQueue* input_queue_; JobQueue* output_queue_; }; class FeatureWriterThread : public Thread { public: FeatureWriterThread(size_t num_images, Database* database, JobQueue* input_queue) : num_images_(num_images), database_(database), input_queue_(input_queue) {} private: void Run() override { size_t image_index = 0; while (true) { if (IsStopped()) { break; } auto input_job = input_queue_->Pop(); if (input_job.IsValid()) { auto& image_data = input_job.Data(); image_index += 1; LOG(INFO) << StringPrintf( "Processed file [%d/%d]", image_index, num_images_); LOG(INFO) << StringPrintf(" Name: %s", image_data.image.Name().c_str()); if (image_data.status == ImageReader::Status::IMAGE_EXISTS) { LOG(INFO) << " SKIP: Features for image already extracted."; } else if (image_data.status == ImageReader::Status::BITMAP_ERROR) { LOG(ERROR) << "Failed to read image file format."; } else if (image_data.status == ImageReader::Status::CAMERA_SINGLE_DIM_ERROR) { LOG(ERROR) << "Single camera specified, " "but images have different dimensions."; } else if (image_data.status == ImageReader::Status::CAMERA_EXIST_DIM_ERROR) { LOG(ERROR) << "Image previously processed, but current image " "has different dimensions."; } else if (image_data.status == ImageReader::Status::CAMERA_PARAM_ERROR) { LOG(ERROR) << "Camera has invalid parameters."; } else if (image_data.status == ImageReader::Status::FAILURE) { LOG(ERROR) << "Failed to extract features."; } if (image_data.status != ImageReader::Status::SUCCESS) { continue; } LOG(INFO) << StringPrintf(" Dimensions: %d x %d", image_data.camera.width, image_data.camera.height); LOG(INFO) << StringPrintf(" Camera: #%d - %s", image_data.camera.camera_id, image_data.camera.ModelName().c_str()); LOG(INFO) << StringPrintf( " Focal Length: %.2fpx%s", image_data.camera.MeanFocalLength(), image_data.camera.has_prior_focal_length ? " (Prior)" : ""); LOG(INFO) << StringPrintf(" Features: %d", image_data.keypoints.size()); DatabaseTransaction database_transaction(database_); if (image_data.image.ImageId() == kInvalidImageId) { image_data.image.SetImageId(database_->WriteImage(image_data.image)); if (image_data.pose_prior.IsValid()) { LOG(INFO) << StringPrintf( " GPS: LAT=%.3f, LON=%.3f, ALT=%.3f", image_data.pose_prior.position.x(), image_data.pose_prior.position.y(), image_data.pose_prior.position.z()); database_->WritePosePrior(image_data.image.ImageId(), image_data.pose_prior); } } if (!database_->ExistsKeypoints(image_data.image.ImageId())) { database_->WriteKeypoints(image_data.image.ImageId(), image_data.keypoints); } if (!database_->ExistsDescriptors(image_data.image.ImageId())) { database_->WriteDescriptors(image_data.image.ImageId(), image_data.descriptors); } } else { break; } } } const size_t num_images_; Database* database_; JobQueue* input_queue_; }; // Feature extraction class to extract features for all images in a directory. class FeatureExtractorController : public Thread { public: FeatureExtractorController(const ImageReaderOptions& reader_options, const SiftExtractionOptions& sift_options) : reader_options_(reader_options), sift_options_(sift_options), database_(reader_options_.database_path), image_reader_(reader_options_, &database_) { THROW_CHECK(reader_options_.Check()); THROW_CHECK(sift_options_.Check()); std::shared_ptr camera_mask; if (!reader_options_.camera_mask_path.empty()) { camera_mask = std::make_shared(); if (!camera_mask->Read(reader_options_.camera_mask_path, /*as_rgb*/ false)) { LOG(ERROR) << "Cannot read camera mask file: " << reader_options_.camera_mask_path << ". No mask is going to be used."; camera_mask.reset(); } } const int num_threads = GetEffectiveNumThreads(sift_options_.num_threads); THROW_CHECK_GT(num_threads, 0); // Make sure that we only have limited number of objects in the queue to // avoid excess in memory usage since images and features take lots of // memory. const int kQueueSize = 1; resizer_queue_ = std::make_unique>(kQueueSize); extractor_queue_ = std::make_unique>(kQueueSize); writer_queue_ = std::make_unique>(kQueueSize); if (sift_options_.max_image_size > 0) { for (int i = 0; i < num_threads; ++i) { resizers_.emplace_back( std::make_unique(sift_options_.max_image_size, resizer_queue_.get(), extractor_queue_.get())); } } if (!sift_options_.domain_size_pooling && !sift_options_.estimate_affine_shape && sift_options_.use_gpu) { std::vector gpu_indices = CSVToVector(sift_options_.gpu_index); THROW_CHECK_GT(gpu_indices.size(), 0); #if defined(COLMAP_CUDA_ENABLED) if (gpu_indices.size() == 1 && gpu_indices[0] == -1) { const int num_cuda_devices = GetNumCudaDevices(); THROW_CHECK_GT(num_cuda_devices, 0); gpu_indices.resize(num_cuda_devices); std::iota(gpu_indices.begin(), gpu_indices.end(), 0); } #endif // COLMAP_CUDA_ENABLED auto sift_gpu_options = sift_options_; for (const auto& gpu_index : gpu_indices) { sift_gpu_options.gpu_index = std::to_string(gpu_index); extractors_.emplace_back( std::make_unique(sift_gpu_options, camera_mask, extractor_queue_.get(), writer_queue_.get())); } } else { if (sift_options_.num_threads == -1 && sift_options_.max_image_size == SiftExtractionOptions().max_image_size && sift_options_.first_octave == SiftExtractionOptions().first_octave) { LOG(WARNING) << "Your current options use the maximum number of " "threads on the machine to extract features. Extracting SIFT " "features on the CPU can consume a lot of RAM per thread for " "large images. Consider reducing the maximum image size and/or " "the first octave or manually limit the number of extraction " "threads. Ignore this warning, if your machine has sufficient " "memory for the current settings."; } auto custom_sift_options = sift_options_; custom_sift_options.use_gpu = false; for (int i = 0; i < num_threads; ++i) { extractors_.emplace_back( std::make_unique(custom_sift_options, camera_mask, extractor_queue_.get(), writer_queue_.get())); } } writer_ = std::make_unique( image_reader_.NumImages(), &database_, writer_queue_.get()); } private: void Run() override { PrintHeading1("Feature extraction"); Timer run_timer; run_timer.Start(); for (auto& resizer : resizers_) { resizer->Start(); } for (auto& extractor : extractors_) { extractor->Start(); } writer_->Start(); for (auto& extractor : extractors_) { if (!extractor->CheckValidSetup()) { return; } } while (image_reader_.NextIndex() < image_reader_.NumImages()) { if (IsStopped()) { resizer_queue_->Stop(); extractor_queue_->Stop(); resizer_queue_->Clear(); extractor_queue_->Clear(); break; } ImageData image_data; image_data.status = image_reader_.Next(&image_data.camera, &image_data.image, &image_data.pose_prior, &image_data.bitmap, &image_data.mask); if (image_data.status != ImageReader::Status::SUCCESS) { image_data.bitmap.Deallocate(); } if (sift_options_.max_image_size > 0) { THROW_CHECK(resizer_queue_->Push(std::move(image_data))); } else { THROW_CHECK(extractor_queue_->Push(std::move(image_data))); } } resizer_queue_->Wait(); resizer_queue_->Stop(); for (auto& resizer : resizers_) { resizer->Wait(); } extractor_queue_->Wait(); extractor_queue_->Stop(); for (auto& extractor : extractors_) { extractor->Wait(); } writer_queue_->Wait(); writer_queue_->Stop(); writer_->Wait(); run_timer.PrintMinutes(); } const ImageReaderOptions reader_options_; const SiftExtractionOptions sift_options_; Database database_; ImageReader image_reader_; std::vector> resizers_; std::vector> extractors_; std::unique_ptr writer_; std::unique_ptr> resizer_queue_; std::unique_ptr> extractor_queue_; std::unique_ptr> writer_queue_; }; // Import features from text files. Each image must have a corresponding text // file with the same name and an additional ".txt" suffix. class FeatureImporterController : public Thread { public: FeatureImporterController(const ImageReaderOptions& reader_options, const std::string& import_path) : reader_options_(reader_options), import_path_(import_path) {} private: void Run() override { PrintHeading1("Feature import"); Timer run_timer; run_timer.Start(); if (!ExistsDir(import_path_)) { LOG(ERROR) << "Import directory does not exist."; return; } Database database(reader_options_.database_path); ImageReader image_reader(reader_options_, &database); while (image_reader.NextIndex() < image_reader.NumImages()) { if (IsStopped()) { break; } LOG(INFO) << StringPrintf("Processing file [%d/%d]", image_reader.NextIndex() + 1, image_reader.NumImages()); // Load image data and possibly save camera to database. Camera camera; Image image; PosePrior pose_prior; Bitmap bitmap; if (image_reader.Next(&camera, &image, &pose_prior, &bitmap, nullptr) != ImageReader::Status::SUCCESS) { continue; } const std::string path = JoinPaths(import_path_, image.Name() + ".txt"); if (ExistsFile(path)) { FeatureKeypoints keypoints; FeatureDescriptors descriptors; LoadSiftFeaturesFromTextFile(path, &keypoints, &descriptors); LOG(INFO) << "Features: " << keypoints.size(); DatabaseTransaction database_transaction(&database); if (image.ImageId() == kInvalidImageId) { image.SetImageId(database.WriteImage(image)); if (pose_prior.IsValid()) { database.WritePosePrior(image.ImageId(), pose_prior); } } if (!database.ExistsKeypoints(image.ImageId())) { database.WriteKeypoints(image.ImageId(), keypoints); } if (!database.ExistsDescriptors(image.ImageId())) { database.WriteDescriptors(image.ImageId(), descriptors); } } else { LOG(INFO) << "SKIP: No features found at " << path; } } run_timer.PrintMinutes(); } const ImageReaderOptions reader_options_; const std::string import_path_; }; } // namespace std::unique_ptr CreateFeatureExtractorController( const ImageReaderOptions& reader_options, const SiftExtractionOptions& sift_options) { return std::make_unique(reader_options, sift_options); } std::unique_ptr CreateFeatureImporterController( const ImageReaderOptions& reader_options, const std::string& import_path) { return std::make_unique(reader_options, import_path); } } // namespace colmap colmap-3.10/src/colmap/controllers/feature_extraction.h000066400000000000000000000044061464745566500234160ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/controllers/image_reader.h" #include "colmap/feature/sift.h" #include "colmap/util/threading.h" namespace colmap { // Reads images from a folder, extracts features, and writes them to database. std::unique_ptr CreateFeatureExtractorController( const ImageReaderOptions& reader_options, const SiftExtractionOptions& sift_options); // Import features from text files. Each image must have a corresponding text // file with the same name and an additional ".txt" suffix. std::unique_ptr CreateFeatureImporterController( const ImageReaderOptions& reader_options, const std::string& import_path); } // namespace colmap colmap-3.10/src/colmap/controllers/feature_matching.cc000066400000000000000000000357401464745566500231730ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/feature_matching.h" #include "colmap/controllers/feature_matching_utils.h" #include "colmap/estimators/two_view_geometry.h" #include "colmap/feature/matcher.h" #include "colmap/feature/utils.h" #include "colmap/util/misc.h" #include "colmap/util/timer.h" #include #include namespace colmap { namespace { void PrintElapsedTime(const Timer& timer) { LOG(INFO) << StringPrintf(" in %.3fs", timer.ElapsedSeconds()); } template class GenericFeatureMatcher : public Thread { public: GenericFeatureMatcher( const typename DerivedPairGenerator::PairOptions& pair_options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) : pair_options_(pair_options), database_(std::make_shared(database_path)), cache_(std::make_shared( DerivedPairGenerator::CacheSize(pair_options_), database_)), matcher_( matching_options, geometry_options, database_.get(), cache_.get()) { THROW_CHECK(matching_options.Check()); THROW_CHECK(geometry_options.Check()); } private: void Run() override { PrintHeading1("Feature matching"); Timer run_timer; run_timer.Start(); if (!matcher_.Setup()) { return; } cache_->Setup(); DerivedPairGenerator pair_generator(pair_options_, cache_); while (!pair_generator.HasFinished()) { if (IsStopped()) { run_timer.PrintMinutes(); return; } Timer timer; timer.Start(); const std::vector> image_pairs = pair_generator.Next(); DatabaseTransaction database_transaction(database_.get()); matcher_.Match(image_pairs); PrintElapsedTime(timer); } run_timer.PrintMinutes(); } const typename DerivedPairGenerator::PairOptions pair_options_; const std::shared_ptr database_; const std::shared_ptr cache_; FeatureMatcherController matcher_; }; } // namespace std::unique_ptr CreateExhaustiveFeatureMatcher( const ExhaustiveMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique>( options, matching_options, geometry_options, database_path); } std::unique_ptr CreateVocabTreeFeatureMatcher( const VocabTreeMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique>( options, matching_options, geometry_options, database_path); } std::unique_ptr CreateSequentialFeatureMatcher( const SequentialMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique>( options, matching_options, geometry_options, database_path); } std::unique_ptr CreateSpatialFeatureMatcher( const SpatialMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique>( options, matching_options, geometry_options, database_path); } namespace { class TransitiveFeatureMatcher : public Thread { public: TransitiveFeatureMatcher(const TransitiveMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) : options_(options), matching_options_(matching_options), database_(std::make_shared(database_path)), cache_(std::make_shared(options_.batch_size, database_)), matcher_( matching_options, geometry_options, database_.get(), cache_.get()) { THROW_CHECK(options.Check()); THROW_CHECK(matching_options.Check()); THROW_CHECK(geometry_options.Check()); } private: void Run() override { PrintHeading1("Transitive feature matching"); Timer run_timer; run_timer.Start(); if (!matcher_.Setup()) { return; } cache_->Setup(); const std::vector image_ids = cache_->GetImageIds(); std::vector> image_pairs; std::unordered_set image_pair_ids; for (int iteration = 0; iteration < options_.num_iterations; ++iteration) { if (IsStopped()) { run_timer.PrintMinutes(); return; } Timer timer; timer.Start(); LOG(INFO) << StringPrintf( "Iteration [%d/%d]", iteration + 1, options_.num_iterations); std::vector> existing_image_pairs; std::vector existing_num_inliers; database_->ReadTwoViewGeometryNumInliers(&existing_image_pairs, &existing_num_inliers); THROW_CHECK_EQ(existing_image_pairs.size(), existing_num_inliers.size()); std::unordered_map> adjacency; for (const auto& image_pair : existing_image_pairs) { adjacency[image_pair.first].push_back(image_pair.second); adjacency[image_pair.second].push_back(image_pair.first); } const size_t batch_size = static_cast(options_.batch_size); size_t num_batches = 0; image_pairs.clear(); image_pair_ids.clear(); for (const auto& image : adjacency) { const auto image_id1 = image.first; for (const auto& image_id2 : image.second) { if (adjacency.count(image_id2) > 0) { for (const auto& image_id3 : adjacency.at(image_id2)) { const auto image_pair_id = Database::ImagePairToPairId(image_id1, image_id3); if (image_pair_ids.count(image_pair_id) == 0) { image_pairs.emplace_back(image_id1, image_id3); image_pair_ids.insert(image_pair_id); if (image_pairs.size() >= batch_size) { num_batches += 1; LOG(INFO) << StringPrintf(" Batch %d", num_batches); DatabaseTransaction database_transaction(database_.get()); matcher_.Match(image_pairs); image_pairs.clear(); PrintElapsedTime(timer); timer.Restart(); if (IsStopped()) { run_timer.PrintMinutes(); return; } } } } } } } num_batches += 1; LOG(INFO) << StringPrintf(" Batch %d", num_batches); DatabaseTransaction database_transaction(database_.get()); matcher_.Match(image_pairs); PrintElapsedTime(timer); } run_timer.PrintMinutes(); } const TransitiveMatchingOptions options_; const SiftMatchingOptions matching_options_; const std::shared_ptr database_; const std::shared_ptr cache_; FeatureMatcherController matcher_; }; } // namespace std::unique_ptr CreateTransitiveFeatureMatcher( const TransitiveMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique( options, matching_options, geometry_options, database_path); } std::unique_ptr CreateImagePairsFeatureMatcher( const ImagePairsMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique>( options, matching_options, geometry_options, database_path); } namespace { class FeaturePairsFeatureMatcher : public Thread { public: FeaturePairsFeatureMatcher(const FeaturePairsMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) : options_(options), matching_options_(matching_options), geometry_options_(geometry_options), database_(std::make_shared(database_path)), cache_(std::make_shared(/*cache_size=*/100, database_)) { THROW_CHECK(options.Check()); THROW_CHECK(matching_options.Check()); THROW_CHECK(geometry_options.Check()); } private: void Run() override { PrintHeading1("Importing matches"); Timer run_timer; run_timer.Start(); cache_->Setup(); std::unordered_map image_name_to_image; image_name_to_image.reserve(cache_->GetImageIds().size()); for (const auto image_id : cache_->GetImageIds()) { const auto& image = cache_->GetImage(image_id); image_name_to_image.emplace(image.Name(), &image); } std::ifstream file(options_.match_list_path); THROW_CHECK_FILE_OPEN(file, options_.match_list_path); std::string line; while (std::getline(file, line)) { if (IsStopped()) { run_timer.PrintMinutes(); return; } StringTrim(&line); if (line.empty()) { continue; } std::istringstream line_stream(line); std::string image_name1, image_name2; try { line_stream >> image_name1 >> image_name2; } catch (...) { LOG(ERROR) << "Could not read image pair."; break; } LOG(INFO) << StringPrintf( "%s - %s", image_name1.c_str(), image_name2.c_str()); if (image_name_to_image.count(image_name1) == 0) { LOG(INFO) << StringPrintf("SKIP: Image %s not found in database.", image_name1.c_str()); break; } if (image_name_to_image.count(image_name2) == 0) { LOG(INFO) << StringPrintf("SKIP: Image %s not found in database.", image_name2.c_str()); break; } const Image& image1 = *image_name_to_image[image_name1]; const Image& image2 = *image_name_to_image[image_name2]; bool skip_pair = false; if (database_->ExistsInlierMatches(image1.ImageId(), image2.ImageId())) { LOG(INFO) << "SKIP: Matches for image pair already exist in database."; skip_pair = true; } FeatureMatches matches; while (std::getline(file, line)) { StringTrim(&line); if (line.empty()) { break; } std::istringstream line_stream(line); FeatureMatch match; try { line_stream >> match.point2D_idx1 >> match.point2D_idx2; } catch (...) { LOG(ERROR) << "Cannot read feature matches."; break; } matches.push_back(match); } if (skip_pair) { continue; } const Camera& camera1 = cache_->GetCamera(image1.CameraId()); const Camera& camera2 = cache_->GetCamera(image2.CameraId()); if (options_.verify_matches) { database_->WriteMatches(image1.ImageId(), image2.ImageId(), matches); const auto keypoints1 = cache_->GetKeypoints(image1.ImageId()); const auto keypoints2 = cache_->GetKeypoints(image2.ImageId()); TwoViewGeometry two_view_geometry = EstimateTwoViewGeometry(camera1, FeatureKeypointsToPointsVector(*keypoints1), camera2, FeatureKeypointsToPointsVector(*keypoints2), matches, geometry_options_); database_->WriteTwoViewGeometry( image1.ImageId(), image2.ImageId(), two_view_geometry); } else { TwoViewGeometry two_view_geometry; if (camera1.has_prior_focal_length && camera2.has_prior_focal_length) { two_view_geometry.config = TwoViewGeometry::CALIBRATED; } else { two_view_geometry.config = TwoViewGeometry::UNCALIBRATED; } two_view_geometry.inlier_matches = matches; database_->WriteTwoViewGeometry( image1.ImageId(), image2.ImageId(), two_view_geometry); } } run_timer.PrintMinutes(); } const FeaturePairsMatchingOptions options_; const SiftMatchingOptions matching_options_; const TwoViewGeometryOptions geometry_options_; const std::shared_ptr database_; const std::shared_ptr cache_; }; } // namespace std::unique_ptr CreateFeaturePairsFeatureMatcher( const FeaturePairsMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path) { return std::make_unique( options, matching_options, geometry_options, database_path); } } // namespace colmap colmap-3.10/src/colmap/controllers/feature_matching.h000066400000000000000000000147751464745566500230420ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/estimators/two_view_geometry.h" #include "colmap/feature/pairing.h" #include "colmap/feature/sift.h" #include "colmap/util/threading.h" #include #include namespace colmap { // Exhaustively match images by processing each block in the exhaustive match // matrix in one batch: // // +----+----+-----------------> images[i] // |#000|0000| // |1#00|1000| <- Above the main diagonal, the block diagonal is not matched // |11#0|1100| ^ // |111#|1110| | // +----+----+ | // |1000|#000|\ | // |1100|1#00| \ One block | // |1110|11#0| / of image pairs | // |1111|111#|/ | // +----+----+ | // | ^ | // | | | // | Below the main diagonal, the block diagonal is matched <--------------+ // | // v // images[i] // // Pairs will only be matched if 1, to avoid duplicate pairs. Pairs with # // are on the main diagonal and denote pairs of the same image. std::unique_ptr CreateExhaustiveFeatureMatcher( const ExhaustiveMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Match each image against its nearest neighbors using a vocabulary tree. std::unique_ptr CreateVocabTreeFeatureMatcher( const VocabTreeMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Sequentially match images within neighborhood: // // +-------------------------------+-----------------------> images[i] // ^ | ^ // | Current image[i] | // | | | // +----------+-----------+ // | // Match image_i against // // image_[i - o, i + o] with o = [1 .. overlap] // image_[i - 2^o, i + 2^o] (for quadratic overlap) // // Sequential order is determined based on the image names in ascending order. // // Invoke loop detection if `(i mod loop_detection_period) == 0`, retrieve // most similar `loop_detection_num_images` images from vocabulary tree, // and perform matching and verification. std::unique_ptr CreateSequentialFeatureMatcher( const SequentialMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Match images against spatial nearest neighbors using prior location // information, e.g. provided manually or extracted from EXIF. std::unique_ptr CreateSpatialFeatureMatcher( const SpatialMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Match transitive image pairs in a database with existing feature matches. // This matcher transitively closes loops. For example, if image pairs A-B and // B-C match but A-C has not been matched, then this matcher attempts to match // A-C. This procedure is performed for multiple iterations. std::unique_ptr CreateTransitiveFeatureMatcher( const TransitiveMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Match images manually specified in a list of image pairs. // // Read matches file with the following format: // // image_name1 image_name2 // image_name1 image_name3 // image_name2 image_name3 // ... // std::unique_ptr CreateImagePairsFeatureMatcher( const ImagePairsMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); // Import feature matches from a text file. // // Read matches file with the following format: // // image_name1 image_name2 // 0 1 // 1 2 // 2 3 // // image_name1 image_name3 // 0 1 // 1 2 // 2 3 // ... // std::unique_ptr CreateFeaturePairsFeatureMatcher( const FeaturePairsMatchingOptions& options, const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, const std::string& database_path); } // namespace colmap colmap-3.10/src/colmap/controllers/feature_matching_utils.cc000066400000000000000000000370161464745566500244110ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/feature_matching_utils.h" #include "colmap/estimators/two_view_geometry.h" #include "colmap/feature/utils.h" #include "colmap/util/cuda.h" #include "colmap/util/misc.h" #include #include #include namespace colmap { FeatureMatcherWorker::FeatureMatcherWorker( const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, FeatureMatcherCache* cache, JobQueue* input_queue, JobQueue* output_queue) : matching_options_(matching_options), geometry_options_(geometry_options), cache_(cache), input_queue_(input_queue), output_queue_(output_queue) { THROW_CHECK(matching_options_.Check()); prev_keypoints_image_ids_[0] = kInvalidImageId; prev_keypoints_image_ids_[1] = kInvalidImageId; prev_descriptors_image_ids_[0] = kInvalidImageId; prev_descriptors_image_ids_[1] = kInvalidImageId; if (matching_options_.use_gpu) { #if !defined(COLMAP_CUDA_ENABLED) opengl_context_ = std::make_unique(); #endif } } void FeatureMatcherWorker::SetMaxNumMatches(int max_num_matches) { matching_options_.max_num_matches = max_num_matches; } void FeatureMatcherWorker::Run() { if (matching_options_.use_gpu) { #if !defined(COLMAP_CUDA_ENABLED) THROW_CHECK_NOTNULL(opengl_context_); THROW_CHECK(opengl_context_->MakeCurrent()); #endif } std::unique_ptr matcher = CreateSiftFeatureMatcher(matching_options_); if (matcher == nullptr) { LOG(ERROR) << "Failed to create feature matcher."; SignalInvalidSetup(); return; } SignalValidSetup(); while (true) { if (IsStopped()) { break; } auto input_job = input_queue_->Pop(); if (input_job.IsValid()) { auto& data = input_job.Data(); if (!cache_->ExistsDescriptors(data.image_id1) || !cache_->ExistsDescriptors(data.image_id2)) { THROW_CHECK(output_queue_->Push(std::move(data))); continue; } if (matching_options_.guided_matching) { matcher->MatchGuided(geometry_options_.ransac_options.max_error, GetKeypointsPtr(0, data.image_id1), GetKeypointsPtr(1, data.image_id2), GetDescriptorsPtr(0, data.image_id1), GetDescriptorsPtr(1, data.image_id2), &data.two_view_geometry); } else { matcher->Match(GetDescriptorsPtr(0, data.image_id1), GetDescriptorsPtr(1, data.image_id2), &data.matches); } THROW_CHECK(output_queue_->Push(std::move(data))); } } } std::shared_ptr FeatureMatcherWorker::GetKeypointsPtr( const int index, const image_t image_id) { THROW_CHECK_GE(index, 0); THROW_CHECK_LE(index, 1); if (prev_keypoints_image_ids_[index] == image_id) { return nullptr; } else { prev_keypoints_image_ids_[index] = image_id; prev_keypoints_[index] = cache_->GetKeypoints(image_id); return prev_keypoints_[index]; } } std::shared_ptr FeatureMatcherWorker::GetDescriptorsPtr( const int index, const image_t image_id) { THROW_CHECK_GE(index, 0); THROW_CHECK_LE(index, 1); if (prev_descriptors_image_ids_[index] == image_id) { return nullptr; } else { prev_descriptors_image_ids_[index] = image_id; prev_descriptors_[index] = cache_->GetDescriptors(image_id); return prev_descriptors_[index]; } } namespace { class VerifierWorker : public Thread { public: typedef FeatureMatcherData Input; typedef FeatureMatcherData Output; VerifierWorker(const TwoViewGeometryOptions& options, FeatureMatcherCache* cache, JobQueue* input_queue, JobQueue* output_queue) : options_(options), cache_(cache), input_queue_(input_queue), output_queue_(output_queue) { THROW_CHECK(options_.Check()); } protected: void Run() override { while (true) { if (IsStopped()) { break; } auto input_job = input_queue_->Pop(); if (input_job.IsValid()) { auto& data = input_job.Data(); if (data.matches.size() < static_cast(options_.min_num_inliers)) { THROW_CHECK(output_queue_->Push(std::move(data))); continue; } const auto& camera1 = cache_->GetCamera(cache_->GetImage(data.image_id1).CameraId()); const auto& camera2 = cache_->GetCamera(cache_->GetImage(data.image_id2).CameraId()); const auto keypoints1 = cache_->GetKeypoints(data.image_id1); const auto keypoints2 = cache_->GetKeypoints(data.image_id2); const std::vector points1 = FeatureKeypointsToPointsVector(*keypoints1); const std::vector points2 = FeatureKeypointsToPointsVector(*keypoints2); data.two_view_geometry = EstimateTwoViewGeometry( camera1, points1, camera2, points2, data.matches, options_); THROW_CHECK(output_queue_->Push(std::move(data))); } } } private: const TwoViewGeometryOptions options_; FeatureMatcherCache* cache_; JobQueue* input_queue_; JobQueue* output_queue_; }; } // namespace FeatureMatcherController::FeatureMatcherController( const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, Database* database, FeatureMatcherCache* cache) : matching_options_(matching_options), geometry_options_(geometry_options), database_(database), cache_(cache), is_setup_(false) { THROW_CHECK(matching_options_.Check()); THROW_CHECK(geometry_options_.Check()); const int num_threads = GetEffectiveNumThreads(matching_options_.num_threads); THROW_CHECK_GT(num_threads, 0); std::vector gpu_indices = CSVToVector(matching_options_.gpu_index); THROW_CHECK_GT(gpu_indices.size(), 0); #if defined(COLMAP_CUDA_ENABLED) if (matching_options_.use_gpu && gpu_indices.size() == 1 && gpu_indices[0] == -1) { const int num_cuda_devices = GetNumCudaDevices(); THROW_CHECK_GT(num_cuda_devices, 0); gpu_indices.resize(num_cuda_devices); std::iota(gpu_indices.begin(), gpu_indices.end(), 0); } #endif // COLMAP_CUDA_ENABLED if (matching_options_.use_gpu) { auto matching_options_copy = matching_options_; // The first matching is always without guided matching. matching_options_copy.guided_matching = false; matchers_.reserve(gpu_indices.size()); for (const auto& gpu_index : gpu_indices) { matching_options_copy.gpu_index = std::to_string(gpu_index); matchers_.emplace_back( std::make_unique(matching_options_copy, geometry_options_, cache, &matcher_queue_, &verifier_queue_)); } } else { auto matching_options_copy = matching_options_; // The first matching is always without guided matching. matching_options_copy.guided_matching = false; matchers_.reserve(num_threads); for (int i = 0; i < num_threads; ++i) { matchers_.emplace_back( std::make_unique(matching_options_copy, geometry_options_, cache, &matcher_queue_, &verifier_queue_)); } } verifiers_.reserve(num_threads); if (matching_options_.guided_matching) { // Redirect the verification output to final round of guided matching. for (int i = 0; i < num_threads; ++i) { verifiers_.emplace_back(std::make_unique( geometry_options_, cache, &verifier_queue_, &guided_matcher_queue_)); } if (matching_options_.use_gpu) { auto matching_options_copy = matching_options_; guided_matchers_.reserve(gpu_indices.size()); for (const auto& gpu_index : gpu_indices) { matching_options_copy.gpu_index = std::to_string(gpu_index); guided_matchers_.emplace_back( std::make_unique(matching_options_copy, geometry_options_, cache, &guided_matcher_queue_, &output_queue_)); } } else { guided_matchers_.reserve(num_threads); for (int i = 0; i < num_threads; ++i) { guided_matchers_.emplace_back( std::make_unique(matching_options_, geometry_options_, cache, &guided_matcher_queue_, &output_queue_)); } } } else { for (int i = 0; i < num_threads; ++i) { verifiers_.emplace_back(std::make_unique( geometry_options_, cache, &verifier_queue_, &output_queue_)); } } } FeatureMatcherController::~FeatureMatcherController() { matcher_queue_.Wait(); verifier_queue_.Wait(); guided_matcher_queue_.Wait(); output_queue_.Wait(); for (auto& matcher : matchers_) { matcher->Stop(); } for (auto& verifier : verifiers_) { verifier->Stop(); } for (auto& guided_matcher : guided_matchers_) { guided_matcher->Stop(); } matcher_queue_.Stop(); verifier_queue_.Stop(); guided_matcher_queue_.Stop(); output_queue_.Stop(); for (auto& matcher : matchers_) { matcher->Wait(); } for (auto& verifier : verifiers_) { verifier->Wait(); } for (auto& guided_matcher : guided_matchers_) { guided_matcher->Wait(); } } bool FeatureMatcherController::Setup() { // Minimize the amount of allocated GPU memory by computing the maximum number // of descriptors for any image over the whole database. const int max_num_features = THROW_CHECK_NOTNULL(database_)->MaxNumKeypoints(); matching_options_.max_num_matches = std::min(matching_options_.max_num_matches, max_num_features); for (auto& matcher : matchers_) { matcher->SetMaxNumMatches(matching_options_.max_num_matches); matcher->Start(); } for (auto& verifier : verifiers_) { verifier->Start(); } for (auto& guided_matcher : guided_matchers_) { guided_matcher->SetMaxNumMatches(matching_options_.max_num_matches); guided_matcher->Start(); } for (auto& matcher : matchers_) { if (!matcher->CheckValidSetup()) { return false; } } for (auto& guided_matcher : guided_matchers_) { if (!guided_matcher->CheckValidSetup()) { return false; } } is_setup_ = true; return true; } void FeatureMatcherController::Match( const std::vector>& image_pairs) { THROW_CHECK_NOTNULL(database_); THROW_CHECK_NOTNULL(cache_); THROW_CHECK(is_setup_); if (image_pairs.empty()) { return; } ////////////////////////////////////////////////////////////////////////////// // Match the image pairs ////////////////////////////////////////////////////////////////////////////// std::unordered_set image_pair_ids; image_pair_ids.reserve(image_pairs.size()); size_t num_outputs = 0; for (const auto& image_pair : image_pairs) { // Avoid self-matches. if (image_pair.first == image_pair.second) { continue; } // Avoid duplicate image pairs. const image_pair_t pair_id = Database::ImagePairToPairId(image_pair.first, image_pair.second); if (image_pair_ids.count(pair_id) > 0) { continue; } image_pair_ids.insert(pair_id); const bool exists_matches = cache_->ExistsMatches(image_pair.first, image_pair.second); const bool exists_inlier_matches = cache_->ExistsInlierMatches(image_pair.first, image_pair.second); if (exists_matches && exists_inlier_matches) { continue; } num_outputs += 1; // If only one of the matches or inlier matches exist, we recompute them // from scratch and delete the existing results. This must be done before // pushing the jobs to the queue, otherwise database constraints might fail // when writing an existing result into the database. if (exists_inlier_matches) { cache_->DeleteInlierMatches(image_pair.first, image_pair.second); } FeatureMatcherData data; data.image_id1 = image_pair.first; data.image_id2 = image_pair.second; if (exists_matches) { data.matches = cache_->GetMatches(image_pair.first, image_pair.second); cache_->DeleteMatches(image_pair.first, image_pair.second); THROW_CHECK(verifier_queue_.Push(std::move(data))); } else { THROW_CHECK(matcher_queue_.Push(std::move(data))); } } ////////////////////////////////////////////////////////////////////////////// // Write results to database ////////////////////////////////////////////////////////////////////////////// for (size_t i = 0; i < num_outputs; ++i) { auto output_job = output_queue_.Pop(); THROW_CHECK(output_job.IsValid()); auto& output = output_job.Data(); if (output.matches.size() < static_cast(geometry_options_.min_num_inliers)) { output.matches = {}; } if (output.two_view_geometry.inlier_matches.size() < static_cast(geometry_options_.min_num_inliers)) { output.two_view_geometry = TwoViewGeometry(); } cache_->WriteMatches(output.image_id1, output.image_id2, output.matches); cache_->WriteTwoViewGeometry( output.image_id1, output.image_id2, output.two_view_geometry); } THROW_CHECK_EQ(output_queue_.Size(), 0); } } // namespace colmap colmap-3.10/src/colmap/controllers/feature_matching_utils.h000066400000000000000000000114251464745566500242470ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/estimators/two_view_geometry.h" #include "colmap/feature/matcher.h" #include "colmap/feature/sift.h" #include "colmap/scene/database.h" #include "colmap/util/opengl_utils.h" #include "colmap/util/threading.h" #include #include #include #include namespace colmap { struct FeatureMatcherData { image_t image_id1 = kInvalidImageId; image_t image_id2 = kInvalidImageId; FeatureMatches matches; TwoViewGeometry two_view_geometry; }; class FeatureMatcherWorker : public Thread { public: typedef FeatureMatcherData Input; typedef FeatureMatcherData Output; FeatureMatcherWorker(const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& geometry_options, FeatureMatcherCache* cache, JobQueue* input_queue, JobQueue* output_queue); void SetMaxNumMatches(int max_num_matches); private: void Run() override; std::shared_ptr GetKeypointsPtr(int index, image_t image_id); std::shared_ptr GetDescriptorsPtr(int index, image_t image_id); SiftMatchingOptions matching_options_; TwoViewGeometryOptions geometry_options_; FeatureMatcherCache* cache_; JobQueue* input_queue_; JobQueue* output_queue_; std::unique_ptr opengl_context_; std::array prev_keypoints_image_ids_; std::array, 2> prev_keypoints_; std::array prev_descriptors_image_ids_; std::array, 2> prev_descriptors_; }; // Multi-threaded and multi-GPU SIFT feature matcher, which writes the computed // results to the database and skips already matched image pairs. To improve // performance of the matching by taking advantage of caching and database // transactions, pass multiple images to the `Match` function. Note that the // database should be in an active transaction while calling `Match`. class FeatureMatcherController { public: FeatureMatcherController( const SiftMatchingOptions& matching_options, const TwoViewGeometryOptions& two_view_geometry_options, Database* database, FeatureMatcherCache* cache); ~FeatureMatcherController(); // Setup the matchers and return if successful. bool Setup(); // Match one batch of multiple image pairs. void Match(const std::vector>& image_pairs); private: SiftMatchingOptions matching_options_; TwoViewGeometryOptions geometry_options_; Database* database_; FeatureMatcherCache* cache_; bool is_setup_; std::vector> matchers_; std::vector> guided_matchers_; std::vector> verifiers_; std::unique_ptr thread_pool_; JobQueue matcher_queue_; JobQueue verifier_queue_; JobQueue guided_matcher_queue_; JobQueue output_queue_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/hierarchical_mapper.cc000066400000000000000000000224351464745566500236450ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/hierarchical_mapper.h" #include "colmap/estimators/alignment.h" #include "colmap/scene/scene_clustering.h" #include "colmap/sfm/observation_manager.h" #include "colmap/util/misc.h" #include "colmap/util/threading.h" namespace colmap { namespace { void MergeClusters(const SceneClustering::Cluster& cluster, std::unordered_map>* reconstruction_managers) { // Extract all reconstructions from all child clusters. std::vector> reconstructions; for (const auto& child_cluster : cluster.child_clusters) { if (!child_cluster.child_clusters.empty()) { MergeClusters(child_cluster, reconstruction_managers); } auto& reconstruction_manager = reconstruction_managers->at(&child_cluster); for (size_t i = 0; i < reconstruction_manager->Size(); ++i) { reconstructions.push_back(reconstruction_manager->Get(i)); } } // Try to merge all child cluster reconstruction. while (reconstructions.size() > 1) { bool merge_success = false; for (size_t i = 0; i < reconstructions.size(); ++i) { const int num_reg_images_i = reconstructions[i]->NumRegImages(); for (size_t j = 0; j < i; ++j) { const double kMaxReprojError = 8.0; const int num_reg_images_j = reconstructions[j]->NumRegImages(); if (MergeAndFilterReconstructions( kMaxReprojError, *reconstructions[j], *reconstructions[i])) { LOG(INFO) << StringPrintf( "=> Merged clusters with %d and %d images into %d images", num_reg_images_i, num_reg_images_j, reconstructions[i]->NumRegImages()); reconstructions.erase(reconstructions.begin() + j); merge_success = true; break; } } if (merge_success) { break; } } if (!merge_success) { break; } } // Insert a new reconstruction manager for merged cluster. auto& reconstruction_manager = (*reconstruction_managers)[&cluster]; reconstruction_manager = std::make_shared(); for (const auto& reconstruction : reconstructions) { reconstruction_manager->Get(reconstruction_manager->Add()) = reconstruction; } // Delete all merged child cluster reconstruction managers. for (const auto& child_cluster : cluster.child_clusters) { reconstruction_managers->erase(&child_cluster); } } } // namespace bool HierarchicalMapperController::Options::Check() const { CHECK_OPTION_GT(init_num_trials, -1); CHECK_OPTION_GE(num_workers, -1); clustering_options.Check(); THROW_CHECK_EQ(clustering_options.branching, 2); incremental_options.Check(); return true; } HierarchicalMapperController::HierarchicalMapperController( const Options& options, std::shared_ptr reconstruction_manager) : options_(options), reconstruction_manager_(std::move(reconstruction_manager)) { THROW_CHECK(options_.Check()); } void HierarchicalMapperController::Run() { PrintHeading1("Partitioning scene"); Timer run_timer; run_timer.Start(); ////////////////////////////////////////////////////////////////////////////// // Cluster scene graph ////////////////////////////////////////////////////////////////////////////// const Database database(options_.database_path); LOG(INFO) << "Reading images..."; const auto images = database.ReadAllImages(); std::unordered_map image_id_to_name; image_id_to_name.reserve(images.size()); for (const auto& image : images) { image_id_to_name.emplace(image.ImageId(), image.Name()); } SceneClustering scene_clustering = SceneClustering::Create(options_.clustering_options, database); auto leaf_clusters = scene_clustering.GetLeafClusters(); size_t total_num_images = 0; for (size_t i = 0; i < leaf_clusters.size(); ++i) { total_num_images += leaf_clusters[i]->image_ids.size(); LOG(INFO) << StringPrintf(" Cluster %d with %d images", i + 1, leaf_clusters[i]->image_ids.size()); } LOG(INFO) << StringPrintf("Clusters have %d images", total_num_images); ////////////////////////////////////////////////////////////////////////////// // Reconstruct clusters ////////////////////////////////////////////////////////////////////////////// PrintHeading1("Reconstructing clusters"); // Determine the number of workers and threads per worker. const int kMaxNumThreads = -1; const int num_eff_threads = GetEffectiveNumThreads(kMaxNumThreads); const int kDefaultNumWorkers = 8; const int num_eff_workers = options_.num_workers < 1 ? std::min(static_cast(leaf_clusters.size()), std::min(kDefaultNumWorkers, num_eff_threads)) : options_.num_workers; const int num_threads_per_worker = std::max(1, num_eff_threads / num_eff_workers); // Function to reconstruct one cluster using incremental mapping. auto ReconstructCluster = [&, this](const SceneClustering::Cluster& cluster, std::shared_ptr reconstruction_manager) { if (cluster.image_ids.empty()) { return; } auto incremental_options = std::make_shared( options_.incremental_options); incremental_options->max_model_overlap = 3; incremental_options->init_num_trials = options_.init_num_trials; if (incremental_options->num_threads < 0) { incremental_options->num_threads = num_threads_per_worker; } for (const auto image_id : cluster.image_ids) { incremental_options->image_names.insert( image_id_to_name.at(image_id)); } IncrementalMapperController mapper(std::move(incremental_options), options_.image_path, options_.database_path, std::move(reconstruction_manager)); mapper.Run(); }; // Start reconstructing the bigger clusters first for better resource usage. std::sort(leaf_clusters.begin(), leaf_clusters.end(), [](const SceneClustering::Cluster* cluster1, const SceneClustering::Cluster* cluster2) { return cluster1->image_ids.size() > cluster2->image_ids.size(); }); // Start the reconstruction workers. Use a separate reconstruction manager per // thread to avoid race conditions. std::unordered_map> reconstruction_managers; reconstruction_managers.reserve(leaf_clusters.size()); ThreadPool thread_pool(num_eff_workers); for (const auto& cluster : leaf_clusters) { reconstruction_managers[cluster] = std::make_shared(); thread_pool.AddTask( ReconstructCluster, *cluster, reconstruction_managers[cluster]); } thread_pool.Wait(); ////////////////////////////////////////////////////////////////////////////// // Merge clusters ////////////////////////////////////////////////////////////////////////////// if (leaf_clusters.size() > 1) { PrintHeading1("Merging clusters"); MergeClusters(*scene_clustering.GetRootCluster(), &reconstruction_managers); } THROW_CHECK_EQ(reconstruction_managers.size(), 1); THROW_CHECK_GT( reconstruction_managers.begin()->second->Get(0)->NumRegImages(), 0); *reconstruction_manager_ = *reconstruction_managers.begin()->second; run_timer.PrintMinutes(); } } // namespace colmap colmap-3.10/src/colmap/controllers/hierarchical_mapper.h000066400000000000000000000062421464745566500235050ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/controllers/incremental_mapper.h" #include "colmap/scene/reconstruction_manager.h" #include "colmap/scene/scene_clustering.h" #include "colmap/util/base_controller.h" #include namespace colmap { // Hierarchical mapping first hierarchically partitions the scene into multiple // overlapping clusters, then reconstructs them separately using incremental // mapping, and finally merges them all into a globally consistent // reconstruction. This is especially useful for larger-scale scenes, since // incremental mapping becomes slow with an increasing number of images. class HierarchicalMapperController : public BaseController { public: struct Options { // The path to the image folder which are used as input. std::string image_path; // The path to the database file which is used as input. std::string database_path; // The maximum number of trials to initialize a cluster. int init_num_trials = 10; // The number of workers used to reconstruct clusters in parallel. int num_workers = -1; // Options for clustering the scene graph. SceneClustering::Options clustering_options; // Options used to reconstruction each cluster individually. IncrementalMapperOptions incremental_options; bool Check() const; }; HierarchicalMapperController( const Options& options, std::shared_ptr reconstruction_manager); void Run() override; private: const Options options_; std::shared_ptr reconstruction_manager_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/hierarchical_mapper_test.cc000066400000000000000000000144641464745566500247070ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/hierarchical_mapper.h" #include "colmap/estimators/alignment.h" #include "colmap/scene/synthetic.h" #include "colmap/util/testing.h" #include namespace colmap { namespace { void ExpectEqualReconstructions(const Reconstruction& gt, const Reconstruction& computed, const double max_rotation_error_deg, const double max_proj_center_error, const double num_obs_tolerance) { EXPECT_EQ(computed.NumCameras(), gt.NumCameras()); EXPECT_EQ(computed.NumImages(), gt.NumImages()); EXPECT_EQ(computed.NumRegImages(), gt.NumRegImages()); EXPECT_GE(computed.ComputeNumObservations(), (1 - num_obs_tolerance) * gt.ComputeNumObservations()); Sim3d gtFromComputed; AlignReconstructionsViaProjCenters(computed, gt, /*max_proj_center_error=*/0.1, >FromComputed); const std::vector errors = ComputeImageAlignmentError(computed, gt, gtFromComputed); EXPECT_EQ(errors.size(), gt.NumImages()); for (const auto& error : errors) { EXPECT_LT(error.rotation_error_deg, max_rotation_error_deg); EXPECT_LT(error.proj_center_error, max_proj_center_error); } } TEST(HierarchicalMapperController, WithoutNoise) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 2; synthetic_dataset_options.num_images = 20; synthetic_dataset_options.num_points3D = 50; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, >_reconstruction, &database); auto reconstruction_manager = std::make_shared(); HierarchicalMapperController::Options mapper_options; mapper_options.database_path = database_path; mapper_options.clustering_options.leaf_max_num_images = 5; mapper_options.clustering_options.image_overlap = 3; HierarchicalMapperController mapper(mapper_options, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 1); ExpectEqualReconstructions(gt_reconstruction, *reconstruction_manager->Get(0), /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); } TEST(HierarchicalMapperController, MultiReconstruction) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction1; Reconstruction gt_reconstruction2; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 1; synthetic_dataset_options.num_images = 5; synthetic_dataset_options.num_points3D = 50; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, >_reconstruction1, &database); synthetic_dataset_options.num_images = 4; SynthesizeDataset(synthetic_dataset_options, >_reconstruction2, &database); auto reconstruction_manager = std::make_shared(); HierarchicalMapperController::Options mapper_options; mapper_options.database_path = database_path; mapper_options.clustering_options.leaf_max_num_images = 5; mapper_options.clustering_options.image_overlap = 3; HierarchicalMapperController mapper(mapper_options, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 2); Reconstruction* computed_reconstruction1 = nullptr; Reconstruction* computed_reconstruction2 = nullptr; if (reconstruction_manager->Get(0)->NumRegImages() == 5) { computed_reconstruction1 = reconstruction_manager->Get(0).get(); computed_reconstruction2 = reconstruction_manager->Get(1).get(); } else { computed_reconstruction1 = reconstruction_manager->Get(1).get(); computed_reconstruction2 = reconstruction_manager->Get(0).get(); } ExpectEqualReconstructions(gt_reconstruction1, *computed_reconstruction1, /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); ExpectEqualReconstructions(gt_reconstruction2, *computed_reconstruction2, /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); } } // namespace } // namespace colmap colmap-3.10/src/colmap/controllers/image_reader.cc000066400000000000000000000257041464745566500222710ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/image_reader.h" #include "colmap/sensor/models.h" #include "colmap/util/misc.h" namespace colmap { bool ImageReaderOptions::Check() const { CHECK_OPTION_GT(default_focal_length_factor, 0.0); CHECK_OPTION(ExistsCameraModelWithName(camera_model)); const CameraModelId model_id = CameraModelNameToId(camera_model); if (!camera_params.empty()) { CHECK_OPTION( CameraModelVerifyParams(model_id, CSVToVector(camera_params))); } return true; } ImageReader::ImageReader(const ImageReaderOptions& options, Database* database) : options_(options), database_(database), image_index_(0) { THROW_CHECK(options_.Check()); // Ensure trailing slash, so that we can build the correct image name. options_.image_path = EnsureTrailingSlash(StringReplace(options_.image_path, "\\", "/")); options_.mask_path = EnsureTrailingSlash(StringReplace(options_.mask_path, "\\", "/")); // Get a list of all files in the image path, sorted by image name. if (options_.image_list.empty()) { options_.image_list = GetRecursiveFileList(options_.image_path); std::sort(options_.image_list.begin(), options_.image_list.end()); } else { if (!std::is_sorted(options_.image_list.begin(), options_.image_list.end())) { std::sort(options_.image_list.begin(), options_.image_list.end()); } for (auto& image_name : options_.image_list) { image_name = JoinPaths(options_.image_path, image_name); } } if (static_cast(options_.existing_camera_id) != kInvalidCameraId) { THROW_CHECK(database->ExistsCamera(options_.existing_camera_id)); prev_camera_ = database->ReadCamera(options_.existing_camera_id); } else { // Set the manually specified camera parameters. prev_camera_.camera_id = kInvalidCameraId; THROW_CHECK(ExistsCameraModelWithName(options_.camera_model)); prev_camera_.model_id = CameraModelNameToId(options_.camera_model); prev_camera_.params.resize(CameraModelNumParams(prev_camera_.model_id), 0.); if (!options_.camera_params.empty()) { THROW_CHECK(prev_camera_.SetParamsFromString(options_.camera_params)); prev_camera_.has_prior_focal_length = true; } } } ImageReader::Status ImageReader::Next(Camera* camera, Image* image, PosePrior* pose_prior, Bitmap* bitmap, Bitmap* mask) { THROW_CHECK_NOTNULL(camera); THROW_CHECK_NOTNULL(image); THROW_CHECK_NOTNULL(bitmap); image_index_ += 1; THROW_CHECK_LE(image_index_, options_.image_list.size()); const std::string image_path = options_.image_list.at(image_index_ - 1); DatabaseTransaction database_transaction(database_); ////////////////////////////////////////////////////////////////////////////// // Set the image name. ////////////////////////////////////////////////////////////////////////////// image->SetName(image_path); image->SetName(StringReplace(image->Name(), "\\", "/")); image->SetName( image->Name().substr(options_.image_path.size(), image->Name().size() - options_.image_path.size())); const std::string image_folder = GetParentDir(image->Name()); ////////////////////////////////////////////////////////////////////////////// // Check if image already read. ////////////////////////////////////////////////////////////////////////////// const bool exists_image = database_->ExistsImageWithName(image->Name()); if (exists_image) { *image = database_->ReadImageWithName(image->Name()); const bool exists_keypoints = database_->ExistsKeypoints(image->ImageId()); const bool exists_descriptors = database_->ExistsDescriptors(image->ImageId()); if (exists_keypoints && exists_descriptors) { return Status::IMAGE_EXISTS; } } ////////////////////////////////////////////////////////////////////////////// // Read image. ////////////////////////////////////////////////////////////////////////////// if (!bitmap->Read(image_path, false)) { return Status::BITMAP_ERROR; } ////////////////////////////////////////////////////////////////////////////// // Read mask. ////////////////////////////////////////////////////////////////////////////// if (mask && !options_.mask_path.empty()) { const std::string mask_path = JoinPaths(options_.mask_path, image->Name() + ".png"); if (ExistsFile(mask_path) && !mask->Read(mask_path, false)) { // NOTE: Maybe introduce a separate error type MASK_ERROR? return Status::BITMAP_ERROR; } } ////////////////////////////////////////////////////////////////////////////// // Check for well-formed data. ////////////////////////////////////////////////////////////////////////////// if (exists_image) { Camera current_camera = database_->ReadCamera(image->CameraId()); if (options_.single_camera && prev_camera_.camera_id != kInvalidCameraId && (current_camera.width != prev_camera_.width || current_camera.height != prev_camera_.height)) { return Status::CAMERA_SINGLE_DIM_ERROR; } if (static_cast(bitmap->Width()) != current_camera.width || static_cast(bitmap->Height()) != current_camera.height) { return Status::CAMERA_EXIST_DIM_ERROR; } prev_camera_ = std::move(current_camera); } else { ////////////////////////////////////////////////////////////////////////////// // Check image dimensions. ////////////////////////////////////////////////////////////////////////////// if (prev_camera_.camera_id != kInvalidCameraId && ((options_.single_camera && !options_.single_camera_per_folder) || (options_.single_camera_per_folder && image_folder == prev_image_folder_)) && (prev_camera_.width != static_cast(bitmap->Width()) || prev_camera_.height != static_cast(bitmap->Height()))) { return Status::CAMERA_SINGLE_DIM_ERROR; } ////////////////////////////////////////////////////////////////////////////// // Read camera model and check for consistency if it exists ////////////////////////////////////////////////////////////////////////////// std::string camera_model; const bool valid_camera_model = bitmap->ExifCameraModel(&camera_model); if (camera_model_to_id_.count(camera_model) > 0) { Camera camera = database_->ReadCamera(camera_model_to_id_.at(camera_model)); if (camera.width != static_cast(bitmap->Width()) || camera.height != static_cast(bitmap->Height())) { return Status::CAMERA_EXIST_DIM_ERROR; } prev_camera_ = std::move(camera); } ////////////////////////////////////////////////////////////////////////////// // Extract camera model and focal length ////////////////////////////////////////////////////////////////////////////// if (prev_camera_.camera_id == kInvalidCameraId || options_.single_camera_per_image || (!options_.single_camera && !options_.single_camera_per_folder && static_cast(options_.existing_camera_id) == kInvalidCameraId && camera_model_to_id_.count(camera_model) == 0) || (options_.single_camera_per_folder && image_folders_.count(image_folder) == 0)) { if (options_.camera_params.empty()) { // Extract focal length. double focal_length = 0.0; bool has_focal_length = false; if (bitmap->ExifFocalLength(&focal_length)) { has_focal_length = true; } else { focal_length = options_.default_focal_length_factor * std::max(bitmap->Width(), bitmap->Height()); } prev_camera_ = Camera::CreateFromModelId(prev_camera_.camera_id, prev_camera_.model_id, focal_length, bitmap->Width(), bitmap->Height()); prev_camera_.has_prior_focal_length = has_focal_length; } prev_camera_.width = static_cast(bitmap->Width()); prev_camera_.height = static_cast(bitmap->Height()); if (!prev_camera_.VerifyParams()) { return Status::CAMERA_PARAM_ERROR; } prev_camera_.camera_id = database_->WriteCamera(prev_camera_); if (valid_camera_model) { camera_model_to_id_[camera_model] = prev_camera_.camera_id; } } image->SetCameraId(prev_camera_.camera_id); ////////////////////////////////////////////////////////////////////////////// // Extract GPS data. ////////////////////////////////////////////////////////////////////////////// Eigen::Vector3d position_prior; if (bitmap->ExifLatitude(&position_prior.x()) && bitmap->ExifLongitude(&position_prior.y()) && bitmap->ExifAltitude(&position_prior.z())) { pose_prior->position = position_prior; pose_prior->coordinate_system = PosePrior::CoordinateSystem::WGS84; } } *camera = prev_camera_; image_folders_.insert(image_folder); prev_image_folder_ = image_folder; return Status::SUCCESS; } size_t ImageReader::NextIndex() const { return image_index_; } size_t ImageReader::NumImages() const { return options_.image_list.size(); } } // namespace colmap colmap-3.10/src/colmap/controllers/image_reader.h000066400000000000000000000117401464745566500221260ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/geometry/gps.h" #include "colmap/scene/database.h" #include "colmap/sensor/bitmap.h" #include "colmap/util/threading.h" #include namespace colmap { struct ImageReaderOptions { // Path to database in which to store the extracted data. std::string database_path = ""; // Root path to folder which contains the images. std::string image_path = ""; // Optional root path to folder which contains image masks. For a given image, // the corresponding mask must have the same sub-path below this root as the // image has below image_path. The filename must be equal, aside from the // added extension .png. For example, for an image image_path/abc/012.jpg, the // mask would be mask_path/abc/012.jpg.png. No features will be extracted in // regions where the mask image is black (pixel intensity value 0 in // grayscale). std::string mask_path = ""; // Optional list of images to read. The list must contain the relative path // of the images with respect to the image_path. std::vector image_list; // Name of the camera model. std::string camera_model = "SIMPLE_RADIAL"; // Whether to use the same camera for all images. bool single_camera = false; // Whether to use the same camera for all images in the same sub-folder. bool single_camera_per_folder = false; // Whether to use a different camera for each image. bool single_camera_per_image = false; // Whether to explicitly use an existing camera for all images. Note that in // this case the specified camera model and parameters are ignored. int existing_camera_id = kInvalidCameraId; // Manual specification of camera parameters. If empty, camera parameters // will be extracted from EXIF, i.e. principal point and focal length. std::string camera_params = ""; // If camera parameters are not specified manually and the image does not // have focal length EXIF information, the focal length is set to the // value `default_focal_length_factor * max(width, height)`. double default_focal_length_factor = 1.2; // Optional path to an image file specifying a mask for all images. No // features will be extracted in regions where the mask is black (pixel // intensity value 0 in grayscale). std::string camera_mask_path = ""; bool Check() const; }; // Recursively iterate over the images in a directory. Skips an image if it // already exists in the database. Extracts the camera intrinsics from EXIF and // writes the camera information to the database. class ImageReader { public: enum class Status { FAILURE, SUCCESS, IMAGE_EXISTS, BITMAP_ERROR, CAMERA_SINGLE_DIM_ERROR, CAMERA_EXIST_DIM_ERROR, CAMERA_PARAM_ERROR }; ImageReader(const ImageReaderOptions& options, Database* database); Status Next(Camera* camera, Image* image, PosePrior* pose_prior, Bitmap* bitmap, Bitmap* mask); size_t NextIndex() const; size_t NumImages() const; private: // Image reader options. ImageReaderOptions options_; Database* database_; // Index of previously processed image. size_t image_index_; // Previously processed camera. Camera prev_camera_; std::unordered_map camera_model_to_id_; // Names of image sub-folders. std::string prev_image_folder_; std::unordered_set image_folders_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/incremental_mapper.cc000066400000000000000000000542271464745566500235340ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/incremental_mapper.h" #include "colmap/util/misc.h" #include "colmap/util/timer.h" namespace colmap { namespace { void IterativeGlobalRefinement(const IncrementalMapperOptions& options, const IncrementalMapper::Options& mapper_options, IncrementalMapper& mapper) { LOG(INFO) << "Retriangulation and Global bundle adjustment"; mapper.IterativeGlobalRefinement(options.ba_global_max_refinements, options.ba_global_max_refinement_change, mapper_options, options.GlobalBundleAdjustment(), options.Triangulation()); mapper.FilterImages(mapper_options); } void ExtractColors(const std::string& image_path, const image_t image_id, Reconstruction& reconstruction) { if (!reconstruction.ExtractColorsForImage(image_id, image_path)) { LOG(WARNING) << StringPrintf("Could not read image %s at path %s.", reconstruction.Image(image_id).Name().c_str(), image_path.c_str()); } } void WriteSnapshot(const Reconstruction& reconstruction, const std::string& snapshot_path) { LOG(INFO) << "Creating snapshot"; // Get the current timestamp in milliseconds. const size_t timestamp = std::chrono::duration_cast( std::chrono::high_resolution_clock::now().time_since_epoch()) .count(); // Write reconstruction to unique path with current timestamp. const std::string path = JoinPaths(snapshot_path, StringPrintf("%010d", timestamp)); CreateDirIfNotExists(path); VLOG(1) << "=> Writing to " << path; reconstruction.Write(path); } } // namespace IncrementalMapper::Options IncrementalMapperOptions::Mapper() const { IncrementalMapper::Options options = mapper; options.abs_pose_refine_focal_length = ba_refine_focal_length; options.abs_pose_refine_extra_params = ba_refine_extra_params; options.min_focal_length_ratio = min_focal_length_ratio; options.max_focal_length_ratio = max_focal_length_ratio; options.max_extra_param = max_extra_param; options.num_threads = num_threads; options.local_ba_num_images = ba_local_num_images; options.fix_existing_images = fix_existing_images; return options; } IncrementalTriangulator::Options IncrementalMapperOptions::Triangulation() const { IncrementalTriangulator::Options options = triangulation; options.min_focal_length_ratio = min_focal_length_ratio; options.max_focal_length_ratio = max_focal_length_ratio; options.max_extra_param = max_extra_param; return options; } BundleAdjustmentOptions IncrementalMapperOptions::LocalBundleAdjustment() const { BundleAdjustmentOptions options; options.solver_options.function_tolerance = ba_local_function_tolerance; options.solver_options.gradient_tolerance = 10.0; options.solver_options.parameter_tolerance = 0.0; options.solver_options.max_num_iterations = ba_local_max_num_iterations; options.solver_options.max_linear_solver_iterations = 100; options.solver_options.logging_type = ceres::LoggingType::SILENT; options.solver_options.num_threads = num_threads; #if CERES_VERSION_MAJOR < 2 options.solver_options.num_linear_solver_threads = num_threads; #endif // CERES_VERSION_MAJOR options.print_summary = false; options.refine_focal_length = ba_refine_focal_length; options.refine_principal_point = ba_refine_principal_point; options.refine_extra_params = ba_refine_extra_params; options.min_num_residuals_for_multi_threading = ba_min_num_residuals_for_multi_threading; options.loss_function_scale = 1.0; options.loss_function_type = BundleAdjustmentOptions::LossFunctionType::SOFT_L1; return options; } BundleAdjustmentOptions IncrementalMapperOptions::GlobalBundleAdjustment() const { BundleAdjustmentOptions options; options.solver_options.function_tolerance = ba_global_function_tolerance; options.solver_options.gradient_tolerance = 1.0; options.solver_options.parameter_tolerance = 0.0; options.solver_options.max_num_iterations = ba_global_max_num_iterations; options.solver_options.max_linear_solver_iterations = 100; options.solver_options.logging_type = ceres::LoggingType::PER_MINIMIZER_ITERATION; options.solver_options.minimizer_progress_to_stdout = false; options.solver_options.num_threads = num_threads; #if CERES_VERSION_MAJOR < 2 options.solver_options.num_linear_solver_threads = num_threads; #endif // CERES_VERSION_MAJOR options.print_summary = false; options.refine_focal_length = ba_refine_focal_length; options.refine_principal_point = ba_refine_principal_point; options.refine_extra_params = ba_refine_extra_params; options.min_num_residuals_for_multi_threading = ba_min_num_residuals_for_multi_threading; options.loss_function_type = BundleAdjustmentOptions::LossFunctionType::TRIVIAL; return options; } bool IncrementalMapperOptions::Check() const { CHECK_OPTION_GT(min_num_matches, 0); CHECK_OPTION_GT(max_num_models, 0); CHECK_OPTION_GT(max_model_overlap, 0); CHECK_OPTION_GE(min_model_size, 0); CHECK_OPTION_GT(init_num_trials, 0); CHECK_OPTION_GT(min_focal_length_ratio, 0); CHECK_OPTION_GT(max_focal_length_ratio, 0); CHECK_OPTION_GE(max_extra_param, 0); CHECK_OPTION_GE(ba_local_num_images, 2); CHECK_OPTION_GE(ba_local_max_num_iterations, 0); CHECK_OPTION_GT(ba_global_images_ratio, 1.0); CHECK_OPTION_GT(ba_global_points_ratio, 1.0); CHECK_OPTION_GT(ba_global_images_freq, 0); CHECK_OPTION_GT(ba_global_points_freq, 0); CHECK_OPTION_GT(ba_global_max_num_iterations, 0); CHECK_OPTION_GT(ba_local_max_refinements, 0); CHECK_OPTION_GE(ba_local_max_refinement_change, 0); CHECK_OPTION_GT(ba_global_max_refinements, 0); CHECK_OPTION_GE(ba_global_max_refinement_change, 0); CHECK_OPTION_GE(snapshot_images_freq, 0); CHECK_OPTION(Mapper().Check()); CHECK_OPTION(Triangulation().Check()); return true; } IncrementalMapperController::IncrementalMapperController( std::shared_ptr options, const std::string& image_path, const std::string& database_path, std::shared_ptr reconstruction_manager) : options_(std::move(options)), image_path_(image_path), database_path_(database_path), reconstruction_manager_(std::move(reconstruction_manager)) { THROW_CHECK(options_->Check()); RegisterCallback(INITIAL_IMAGE_PAIR_REG_CALLBACK); RegisterCallback(NEXT_IMAGE_REG_CALLBACK); RegisterCallback(LAST_IMAGE_REG_CALLBACK); } void IncrementalMapperController::Run() { Timer run_timer; run_timer.Start(); if (!LoadDatabase()) { return; } IncrementalMapper::Options init_mapper_options = options_->Mapper(); Reconstruct(init_mapper_options); const size_t kNumInitRelaxations = 2; for (size_t i = 0; i < kNumInitRelaxations; ++i) { if (reconstruction_manager_->Size() > 0 || CheckIfStopped()) { break; } LOG(INFO) << "=> Relaxing the initialization constraints."; init_mapper_options.init_min_num_inliers /= 2; Reconstruct(init_mapper_options); if (reconstruction_manager_->Size() > 0 || CheckIfStopped()) { break; } LOG(INFO) << "=> Relaxing the initialization constraints."; init_mapper_options.init_min_tri_angle /= 2; Reconstruct(init_mapper_options); } run_timer.PrintMinutes(); } bool IncrementalMapperController::LoadDatabase() { LOG(INFO) << "Loading database"; // Make sure images of the given reconstruction are also included when // manually specifying images for the reconstruction procedure. std::unordered_set image_names = options_->image_names; if (reconstruction_manager_->Size() == 1 && !options_->image_names.empty()) { const auto& reconstruction = reconstruction_manager_->Get(0); for (const image_t image_id : reconstruction->RegImageIds()) { const auto& image = reconstruction->Image(image_id); image_names.insert(image.Name()); } } Database database(database_path_); Timer timer; timer.Start(); const size_t min_num_matches = static_cast(options_->min_num_matches); database_cache_ = DatabaseCache::Create( database, min_num_matches, options_->ignore_watermarks, image_names); timer.PrintMinutes(); if (database_cache_->NumImages() == 0) { LOG(WARNING) << "No images with matches found in the database"; return false; } return true; } IncrementalMapperController::Status IncrementalMapperController::InitializeReconstruction( IncrementalMapper& mapper, const IncrementalMapper::Options& mapper_options, Reconstruction& reconstruction) { image_t image_id1 = static_cast(options_->init_image_id1); image_t image_id2 = static_cast(options_->init_image_id2); // Try to find good initial pair. TwoViewGeometry two_view_geometry; if (!options_->IsInitialPairProvided()) { LOG(INFO) << "Finding good initial image pair"; const bool find_init_success = mapper.FindInitialImagePair( mapper_options, two_view_geometry, image_id1, image_id2); if (!find_init_success) { LOG(INFO) << "=> No good initial image pair found."; return Status::NO_INITIAL_PAIR; } } else { if (!reconstruction.ExistsImage(image_id1) || !reconstruction.ExistsImage(image_id2)) { LOG(INFO) << StringPrintf( "=> Initial image pair #%d and #%d do not exist.", image_id1, image_id2); return Status::BAD_INITIAL_PAIR; } const bool provided_init_success = mapper.EstimateInitialTwoViewGeometry( mapper_options, two_view_geometry, image_id1, image_id2); if (!provided_init_success) { LOG(INFO) << "Provided pair is insuitable for intialization."; return Status::BAD_INITIAL_PAIR; } } LOG(INFO) << StringPrintf( "Initializing with image pair #%d and #%d", image_id1, image_id2); mapper.RegisterInitialImagePair( mapper_options, two_view_geometry, image_id1, image_id2); LOG(INFO) << "Global bundle adjustment"; mapper.AdjustGlobalBundle(mapper_options, options_->GlobalBundleAdjustment()); reconstruction.Normalize(); mapper.FilterPoints(mapper_options); mapper.FilterImages(mapper_options); // Initial image pair failed to register. if (reconstruction.NumRegImages() == 0 || reconstruction.NumPoints3D() == 0) { return Status::BAD_INITIAL_PAIR; } if (options_->extract_colors) { ExtractColors(image_path_, image_id1, reconstruction); } return Status::SUCCESS; } bool IncrementalMapperController::CheckRunGlobalRefinement( const Reconstruction& reconstruction, const size_t ba_prev_num_reg_images, const size_t ba_prev_num_points) { return reconstruction.NumRegImages() >= options_->ba_global_images_ratio * ba_prev_num_reg_images || reconstruction.NumRegImages() >= options_->ba_global_images_freq + ba_prev_num_reg_images || reconstruction.NumPoints3D() >= options_->ba_global_points_ratio * ba_prev_num_points || reconstruction.NumPoints3D() >= options_->ba_global_points_freq + ba_prev_num_points; } IncrementalMapperController::Status IncrementalMapperController::ReconstructSubModel( IncrementalMapper& mapper, const IncrementalMapper::Options& mapper_options, const std::shared_ptr& reconstruction) { mapper.BeginReconstruction(reconstruction); //////////////////////////////////////////////////////////////////////////// // Register initial pair //////////////////////////////////////////////////////////////////////////// if (reconstruction->NumRegImages() == 0) { const Status init_status = IncrementalMapperController::InitializeReconstruction( mapper, mapper_options, *reconstruction); if (init_status != Status::SUCCESS) { return init_status; } } Callback(INITIAL_IMAGE_PAIR_REG_CALLBACK); //////////////////////////////////////////////////////////////////////////// // Incremental mapping //////////////////////////////////////////////////////////////////////////// size_t snapshot_prev_num_reg_images = reconstruction->NumRegImages(); size_t ba_prev_num_reg_images = reconstruction->NumRegImages(); size_t ba_prev_num_points = reconstruction->NumPoints3D(); bool reg_next_success = true; bool prev_reg_next_success = true; do { if (CheckIfStopped()) { break; } prev_reg_next_success = reg_next_success; reg_next_success = false; const std::vector next_images = mapper.FindNextImages(mapper_options); if (next_images.empty()) { break; } image_t next_image_id; for (size_t reg_trial = 0; reg_trial < next_images.size(); ++reg_trial) { next_image_id = next_images[reg_trial]; LOG(INFO) << StringPrintf("Registering image #%d (%d)", next_image_id, reconstruction->NumRegImages() + 1); LOG(INFO) << StringPrintf( "=> Image sees %d / %d points", mapper.ObservationManager().NumVisiblePoints3D(next_image_id), mapper.ObservationManager().NumObservations(next_image_id)); reg_next_success = mapper.RegisterNextImage(mapper_options, next_image_id); if (reg_next_success) { break; } else { LOG(INFO) << "=> Could not register, trying another image."; // If initial pair fails to continue for some time, // abort and try different initial pair. const size_t kMinNumInitialRegTrials = 30; if (reg_trial >= kMinNumInitialRegTrials && reconstruction->NumRegImages() < static_cast(options_->min_model_size)) { break; } } } if (reg_next_success) { mapper.TriangulateImage(options_->Triangulation(), next_image_id); mapper.IterativeLocalRefinement(options_->ba_local_max_refinements, options_->ba_local_max_refinement_change, mapper_options, options_->LocalBundleAdjustment(), options_->Triangulation(), next_image_id); if (CheckRunGlobalRefinement( *reconstruction, ba_prev_num_reg_images, ba_prev_num_points)) { IterativeGlobalRefinement(*options_, mapper_options, mapper); ba_prev_num_points = reconstruction->NumPoints3D(); ba_prev_num_reg_images = reconstruction->NumRegImages(); } if (options_->extract_colors) { ExtractColors(image_path_, next_image_id, *reconstruction); } if (options_->snapshot_images_freq > 0 && reconstruction->NumRegImages() >= options_->snapshot_images_freq + snapshot_prev_num_reg_images) { snapshot_prev_num_reg_images = reconstruction->NumRegImages(); WriteSnapshot(*reconstruction, options_->snapshot_path); } Callback(NEXT_IMAGE_REG_CALLBACK); } const size_t max_model_overlap = static_cast(options_->max_model_overlap); if (mapper.NumSharedRegImages() >= max_model_overlap) { break; } // If no image could be registered, try a single final global iterative // bundle adjustment and try again to register one image. If this fails // once, then exit the incremental mapping. if (!reg_next_success && prev_reg_next_success) { IterativeGlobalRefinement(*options_, mapper_options, mapper); } } while (reg_next_success || prev_reg_next_success); if (CheckIfStopped()) { return Status::INTERRUPTED; } // Only run final global BA, if last incremental BA was not global. if (reconstruction->NumRegImages() >= 2 && reconstruction->NumRegImages() != ba_prev_num_reg_images && reconstruction->NumPoints3D() != ba_prev_num_points) { IterativeGlobalRefinement(*options_, mapper_options, mapper); } return Status::SUCCESS; } void IncrementalMapperController::Reconstruct( const IncrementalMapper::Options& mapper_options) { IncrementalMapper mapper(database_cache_); // Is there a sub-model before we start the reconstruction? I.e. the user // has imported an existing reconstruction. const bool initial_reconstruction_given = reconstruction_manager_->Size() > 0; THROW_CHECK_LE(reconstruction_manager_->Size(), 1) << "Can only resume from a " "single reconstruction, but " "multiple are given."; for (int num_trials = 0; num_trials < options_->init_num_trials; ++num_trials) { if (CheckIfStopped()) { break; } size_t reconstruction_idx; if (!initial_reconstruction_given || num_trials > 0) { reconstruction_idx = reconstruction_manager_->Add(); } else { reconstruction_idx = 0; } std::shared_ptr reconstruction = reconstruction_manager_->Get(reconstruction_idx); const Status status = ReconstructSubModel(mapper, mapper_options, reconstruction); switch (status) { case Status::INTERRUPTED: mapper.EndReconstruction(/*discard=*/false); return; case Status::NO_INITIAL_PAIR: case Status::BAD_INITIAL_PAIR: mapper.EndReconstruction(/*discard=*/true); reconstruction_manager_->Delete(reconstruction_idx); // If both initial images are manually specified, there is no need for // further initialization trials. if (options_->IsInitialPairProvided()) { return; } break; case Status::SUCCESS: { // Remember the total number of registered images before potentially // discarding it below due to small size, so we can out of the main // loop, if all images were registered. const size_t total_num_reg_images = mapper.NumTotalRegImages(); // If the total number of images is small then do not enforce the // minimum model size so that we can reconstruct small image // collections. Always keep the first reconstruction, independent of // size. const size_t min_model_size = std::min( 0.8 * database_cache_->NumImages(), options_->min_model_size); if ((options_->multiple_models && reconstruction_manager_->Size() > 1 && reconstruction->NumRegImages() < min_model_size) || reconstruction->NumRegImages() == 0) { mapper.EndReconstruction(/*discard=*/true); reconstruction_manager_->Delete(reconstruction_idx); } else { mapper.EndReconstruction(/*discard=*/false); } Callback(LAST_IMAGE_REG_CALLBACK); if (initial_reconstruction_given || !options_->multiple_models || reconstruction_manager_->Size() >= static_cast(options_->max_num_models) || total_num_reg_images >= database_cache_->NumImages() - 1) { return; } } break; default: LOG(FATAL_THROW) << "Unknown reconstruction status."; } } } void IncrementalMapperController::TriangulateReconstruction( const std::shared_ptr& reconstruction) { THROW_CHECK(LoadDatabase()); IncrementalMapper mapper(database_cache_); mapper.BeginReconstruction(reconstruction); LOG(INFO) << "Iterative triangulation"; const std::vector& reg_image_ids = reconstruction->RegImageIds(); for (size_t i = 0; i < reg_image_ids.size(); ++i) { const image_t image_id = reg_image_ids[i]; const auto& image = reconstruction->Image(image_id); LOG(INFO) << StringPrintf("Triangulating image #%d (%d)", image_id, i); const size_t num_existing_points3D = image.NumPoints3D(); LOG(INFO) << "=> Image sees " << num_existing_points3D << " / " << mapper.ObservationManager().NumObservations(image_id) << " points"; mapper.TriangulateImage(options_->Triangulation(), image_id); VLOG(1) << "=> Triangulated " << (image.NumPoints3D() - num_existing_points3D) << " points"; } LOG(INFO) << "Retriangulation and Global bundle adjustment"; mapper.IterativeGlobalRefinement(options_->ba_global_max_refinements, options_->ba_global_max_refinement_change, options_->Mapper(), options_->GlobalBundleAdjustment(), options_->Triangulation(), /*normalize_reconstruction=*/false); mapper.EndReconstruction(/*discard=*/false); LOG(INFO) << "Extracting colors"; reconstruction->ExtractColorsForAllImages(image_path_); } } // namespace colmap colmap-3.10/src/colmap/controllers/incremental_mapper.h000066400000000000000000000172621464745566500233740ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/scene/reconstruction_manager.h" #include "colmap/sfm/incremental_mapper.h" #include "colmap/util/base_controller.h" namespace colmap { struct IncrementalMapperOptions { // The minimum number of matches for inlier matches to be considered. int min_num_matches = 15; // Whether to ignore the inlier matches of watermark image pairs. bool ignore_watermarks = false; // Whether to reconstruct multiple sub-models. bool multiple_models = true; // The number of sub-models to reconstruct. int max_num_models = 50; // The maximum number of overlapping images between sub-models. If the // current sub-models shares more than this number of images with another // model, then the reconstruction is stopped. int max_model_overlap = 20; // The minimum number of registered images of a sub-model, otherwise the // sub-model is discarded. Note that the first sub-model is always kept // independent of size. int min_model_size = 10; // The image identifiers used to initialize the reconstruction. Note that // only one or both image identifiers can be specified. In the former case, // the second image is automatically determined. int init_image_id1 = -1; int init_image_id2 = -1; // The number of trials to initialize the reconstruction. int init_num_trials = 200; // Whether to extract colors for reconstructed points. bool extract_colors = true; // The number of threads to use during reconstruction. int num_threads = -1; // Thresholds for filtering images with degenerate intrinsics. double min_focal_length_ratio = 0.1; double max_focal_length_ratio = 10.0; double max_extra_param = 1.0; // Which intrinsic parameters to optimize during the reconstruction. bool ba_refine_focal_length = true; bool ba_refine_principal_point = false; bool ba_refine_extra_params = true; // The minimum number of residuals per bundle adjustment problem to // enable multi-threading solving of the problems. int ba_min_num_residuals_for_multi_threading = 50000; // The number of images to optimize in local bundle adjustment. int ba_local_num_images = 6; // Ceres solver function tolerance for local bundle adjustment double ba_local_function_tolerance = 0.0; // The maximum number of local bundle adjustment iterations. int ba_local_max_num_iterations = 25; // The growth rates after which to perform global bundle adjustment. double ba_global_images_ratio = 1.1; double ba_global_points_ratio = 1.1; int ba_global_images_freq = 500; int ba_global_points_freq = 250000; // Ceres solver function tolerance for global bundle adjustment double ba_global_function_tolerance = 0.0; // The maximum number of global bundle adjustment iterations. int ba_global_max_num_iterations = 50; // The thresholds for iterative bundle adjustment refinements. int ba_local_max_refinements = 2; double ba_local_max_refinement_change = 0.001; int ba_global_max_refinements = 5; double ba_global_max_refinement_change = 0.0005; // Path to a folder with reconstruction snapshots during incremental // reconstruction. Snapshots will be saved according to the specified // frequency of registered images. std::string snapshot_path = ""; int snapshot_images_freq = 0; // Which images to reconstruct. If no images are specified, all images will // be reconstructed by default. std::unordered_set image_names; // If reconstruction is provided as input, fix the existing image poses. bool fix_existing_images = false; IncrementalMapper::Options mapper; IncrementalTriangulator::Options triangulation; IncrementalMapper::Options Mapper() const; IncrementalTriangulator::Options Triangulation() const; BundleAdjustmentOptions LocalBundleAdjustment() const; BundleAdjustmentOptions GlobalBundleAdjustment() const; inline bool IsInitialPairProvided() const { return init_image_id1 != -1 && init_image_id2 != -1; } bool Check() const; }; // Class that controls the incremental mapping procedure by iteratively // initializing reconstructions from the same scene graph. class IncrementalMapperController : public BaseController { public: enum CallbackType { INITIAL_IMAGE_PAIR_REG_CALLBACK, NEXT_IMAGE_REG_CALLBACK, LAST_IMAGE_REG_CALLBACK, }; enum class Status { NO_INITIAL_PAIR, BAD_INITIAL_PAIR, SUCCESS, INTERRUPTED }; IncrementalMapperController( std::shared_ptr options, const std::string& image_path, const std::string& database_path, std::shared_ptr reconstruction_manager); void Run(); void TriangulateReconstruction( const std::shared_ptr& reconstruction); bool LoadDatabase(); // getter functions for python pipelines const std::string& ImagePath() const { return image_path_; } const std::string& DatabasePath() const { return database_path_; } const std::shared_ptr& Options() const { return options_; } const std::shared_ptr& ReconstructionManager() const { return reconstruction_manager_; } const std::shared_ptr& DatabaseCache() const { return database_cache_; } void Reconstruct(const IncrementalMapper::Options& init_mapper_options); Status ReconstructSubModel( IncrementalMapper& mapper, const IncrementalMapper::Options& mapper_options, const std::shared_ptr& reconstruction); Status InitializeReconstruction( IncrementalMapper& mapper, const IncrementalMapper::Options& mapper_options, Reconstruction& reconstruction); bool CheckRunGlobalRefinement(const Reconstruction& reconstruction, size_t ba_prev_num_reg_images, size_t ba_prev_num_points); private: const std::shared_ptr options_; const std::string image_path_; const std::string database_path_; std::shared_ptr reconstruction_manager_; std::shared_ptr database_cache_; }; } // namespace colmap colmap-3.10/src/colmap/controllers/incremental_mapper_test.cc000066400000000000000000000207321464745566500245650ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/incremental_mapper.h" #include "colmap/estimators/alignment.h" #include "colmap/scene/synthetic.h" #include "colmap/util/testing.h" #include namespace colmap { namespace { void ExpectEqualReconstructions(const Reconstruction& gt, const Reconstruction& computed, const double max_rotation_error_deg, const double max_proj_center_error, const double num_obs_tolerance) { EXPECT_EQ(computed.NumCameras(), gt.NumCameras()); EXPECT_EQ(computed.NumImages(), gt.NumImages()); EXPECT_EQ(computed.NumRegImages(), gt.NumRegImages()); EXPECT_GE(computed.ComputeNumObservations(), (1 - num_obs_tolerance) * gt.ComputeNumObservations()); Sim3d gtFromComputed; AlignReconstructionsViaProjCenters(computed, gt, /*max_proj_center_error=*/0.1, >FromComputed); const std::vector errors = ComputeImageAlignmentError(computed, gt, gtFromComputed); EXPECT_EQ(errors.size(), gt.NumImages()); for (const auto& error : errors) { EXPECT_LT(error.rotation_error_deg, max_rotation_error_deg); EXPECT_LT(error.proj_center_error, max_proj_center_error); } } TEST(IncrementalMapperController, WithoutNoise) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 2; synthetic_dataset_options.num_images = 7; synthetic_dataset_options.num_points3D = 50; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, >_reconstruction, &database); auto reconstruction_manager = std::make_shared(); IncrementalMapperController mapper( std::make_shared(), /*image_path=*/"", database_path, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 1); ExpectEqualReconstructions(gt_reconstruction, *reconstruction_manager->Get(0), /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); } TEST(IncrementalMapperController, WithNoise) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 2; synthetic_dataset_options.num_images = 7; synthetic_dataset_options.num_points3D = 100; synthetic_dataset_options.point2D_stddev = 0.5; SynthesizeDataset(synthetic_dataset_options, >_reconstruction, &database); auto reconstruction_manager = std::make_shared(); IncrementalMapperController mapper( std::make_shared(), /*image_path=*/"", database_path, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 1); ExpectEqualReconstructions(gt_reconstruction, *reconstruction_manager->Get(0), /*max_rotation_error_deg=*/1e-1, /*max_proj_center_error=*/1e-1, /*num_obs_tolerance=*/0.02); } TEST(IncrementalMapperController, MultiReconstruction) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction1; Reconstruction gt_reconstruction2; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 1; synthetic_dataset_options.num_images = 5; synthetic_dataset_options.num_points3D = 50; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, >_reconstruction1, &database); synthetic_dataset_options.num_images = 4; SynthesizeDataset(synthetic_dataset_options, >_reconstruction2, &database); auto reconstruction_manager = std::make_shared(); auto mapper_options = std::make_shared(); mapper_options->min_model_size = 4; IncrementalMapperController mapper(mapper_options, /*image_path=*/"", database_path, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 2); Reconstruction* computed_reconstruction1 = nullptr; Reconstruction* computed_reconstruction2 = nullptr; if (reconstruction_manager->Get(0)->NumRegImages() == 5) { computed_reconstruction1 = reconstruction_manager->Get(0).get(); computed_reconstruction2 = reconstruction_manager->Get(1).get(); } else { computed_reconstruction1 = reconstruction_manager->Get(1).get(); computed_reconstruction2 = reconstruction_manager->Get(0).get(); } ExpectEqualReconstructions(gt_reconstruction1, *computed_reconstruction1, /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); ExpectEqualReconstructions(gt_reconstruction2, *computed_reconstruction2, /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); } TEST(IncrementalMapperController, ChainedMatches) { const std::string database_path = CreateTestDir() + "/database.db"; Database database(database_path); Reconstruction gt_reconstruction; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.match_config = SyntheticDatasetOptions::MatchConfig::CHAINED; synthetic_dataset_options.num_cameras = 1; synthetic_dataset_options.num_images = 4; synthetic_dataset_options.num_points3D = 100; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, >_reconstruction, &database); auto reconstruction_manager = std::make_shared(); IncrementalMapperController mapper( std::make_shared(), /*image_path=*/"", database_path, reconstruction_manager); mapper.Run(); ASSERT_EQ(reconstruction_manager->Size(), 1); ExpectEqualReconstructions(gt_reconstruction, *reconstruction_manager->Get(0), /*max_rotation_error_deg=*/1e-2, /*max_proj_center_error=*/1e-4, /*num_obs_tolerance=*/0); } } // namespace } // namespace colmap colmap-3.10/src/colmap/controllers/option_manager.cc000066400000000000000000001216001464745566500226570ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/controllers/option_manager.h" #include "colmap/controllers/feature_extraction.h" #include "colmap/controllers/image_reader.h" #include "colmap/controllers/incremental_mapper.h" #include "colmap/estimators/bundle_adjustment.h" #include "colmap/estimators/two_view_geometry.h" #include "colmap/feature/pairing.h" #include "colmap/feature/sift.h" #include "colmap/math/random.h" #include "colmap/mvs/fusion.h" #include "colmap/mvs/meshing.h" #include "colmap/mvs/patch_match.h" #include "colmap/ui/render_options.h" #include "colmap/util/misc.h" #include "colmap/util/version.h" #include #include namespace config = boost::program_options; namespace colmap { OptionManager::OptionManager(bool add_project_options) { project_path = std::make_shared(); database_path = std::make_shared(); image_path = std::make_shared(); image_reader = std::make_shared(); sift_extraction = std::make_shared(); sift_matching = std::make_shared(); two_view_geometry = std::make_shared(); exhaustive_matching = std::make_shared(); sequential_matching = std::make_shared(); vocab_tree_matching = std::make_shared(); spatial_matching = std::make_shared(); transitive_matching = std::make_shared(); image_pairs_matching = std::make_shared(); bundle_adjustment = std::make_shared(); mapper = std::make_shared(); patch_match_stereo = std::make_shared(); stereo_fusion = std::make_shared(); poisson_meshing = std::make_shared(); delaunay_meshing = std::make_shared(); render = std::make_shared(); Reset(); desc_->add_options()("help,h", ""); AddRandomOptions(); AddLogOptions(); if (add_project_options) { desc_->add_options()("project_path", config::value()); } } void OptionManager::ModifyForIndividualData() { mapper->min_focal_length_ratio = 0.1; mapper->max_focal_length_ratio = 10; mapper->max_extra_param = std::numeric_limits::max(); } void OptionManager::ModifyForVideoData() { const bool kResetPaths = false; ResetOptions(kResetPaths); mapper->mapper.init_min_tri_angle /= 2; mapper->ba_global_images_ratio = 1.4; mapper->ba_global_points_ratio = 1.4; mapper->min_focal_length_ratio = 0.1; mapper->max_focal_length_ratio = 10; mapper->max_extra_param = std::numeric_limits::max(); stereo_fusion->min_num_pixels = 15; } void OptionManager::ModifyForInternetData() { stereo_fusion->min_num_pixels = 10; } void OptionManager::ModifyForLowQuality() { sift_extraction->max_image_size = 1000; sift_extraction->max_num_features = 2048; sequential_matching->loop_detection_num_images /= 2; vocab_tree_matching->num_images /= 2; mapper->ba_local_max_num_iterations /= 2; mapper->ba_global_max_num_iterations /= 2; mapper->ba_global_images_ratio *= 1.2; mapper->ba_global_points_ratio *= 1.2; mapper->ba_global_max_refinements = 2; patch_match_stereo->max_image_size = 1000; patch_match_stereo->window_radius = 4; patch_match_stereo->window_step = 2; patch_match_stereo->num_samples /= 2; patch_match_stereo->num_iterations = 3; patch_match_stereo->geom_consistency = false; stereo_fusion->check_num_images /= 2; stereo_fusion->max_image_size = 1000; } void OptionManager::ModifyForMediumQuality() { sift_extraction->max_image_size = 1600; sift_extraction->max_num_features = 4096; sequential_matching->loop_detection_num_images /= 1.5; vocab_tree_matching->num_images /= 1.5; mapper->ba_local_max_num_iterations /= 1.5; mapper->ba_global_max_num_iterations /= 1.5; mapper->ba_global_images_ratio *= 1.1; mapper->ba_global_points_ratio *= 1.1; mapper->ba_global_max_refinements = 2; patch_match_stereo->max_image_size = 1600; patch_match_stereo->window_radius = 4; patch_match_stereo->window_step = 2; patch_match_stereo->num_samples /= 1.5; patch_match_stereo->num_iterations = 5; patch_match_stereo->geom_consistency = false; stereo_fusion->check_num_images /= 1.5; stereo_fusion->max_image_size = 1600; } void OptionManager::ModifyForHighQuality() { sift_extraction->estimate_affine_shape = true; sift_extraction->max_image_size = 2400; sift_extraction->max_num_features = 8192; sift_matching->guided_matching = true; mapper->ba_local_max_num_iterations = 30; mapper->ba_local_max_refinements = 3; mapper->ba_global_max_num_iterations = 75; patch_match_stereo->max_image_size = 2400; stereo_fusion->max_image_size = 2400; } void OptionManager::ModifyForExtremeQuality() { // Most of the options are set to extreme quality by default. sift_extraction->estimate_affine_shape = true; sift_extraction->domain_size_pooling = true; sift_matching->guided_matching = true; mapper->ba_local_max_num_iterations = 40; mapper->ba_local_max_refinements = 3; mapper->ba_global_max_num_iterations = 100; } void OptionManager::AddAllOptions() { AddLogOptions(); AddRandomOptions(); AddDatabaseOptions(); AddImageOptions(); AddExtractionOptions(); AddMatchingOptions(); AddExhaustiveMatchingOptions(); AddSequentialMatchingOptions(); AddVocabTreeMatchingOptions(); AddSpatialMatchingOptions(); AddTransitiveMatchingOptions(); AddImagePairsMatchingOptions(); AddBundleAdjustmentOptions(); AddMapperOptions(); AddPatchMatchStereoOptions(); AddStereoFusionOptions(); AddPoissonMeshingOptions(); AddDelaunayMeshingOptions(); AddRenderOptions(); } void OptionManager::AddLogOptions() { if (added_log_options_) { return; } added_log_options_ = true; AddAndRegisterDefaultOption("log_to_stderr", &FLAGS_logtostderr); AddAndRegisterDefaultOption("log_level", &FLAGS_v); } void OptionManager::AddRandomOptions() { if (added_random_options_) { return; } added_random_options_ = true; AddAndRegisterDefaultOption("random_seed", &kDefaultPRNGSeed); } void OptionManager::AddDatabaseOptions() { if (added_database_options_) { return; } added_database_options_ = true; AddAndRegisterRequiredOption("database_path", database_path.get()); } void OptionManager::AddImageOptions() { if (added_image_options_) { return; } added_image_options_ = true; AddAndRegisterRequiredOption("image_path", image_path.get()); } void OptionManager::AddExtractionOptions() { if (added_extraction_options_) { return; } added_extraction_options_ = true; AddAndRegisterDefaultOption("ImageReader.mask_path", &image_reader->mask_path); AddAndRegisterDefaultOption("ImageReader.camera_model", &image_reader->camera_model); AddAndRegisterDefaultOption("ImageReader.single_camera", &image_reader->single_camera); AddAndRegisterDefaultOption("ImageReader.single_camera_per_folder", &image_reader->single_camera_per_folder); AddAndRegisterDefaultOption("ImageReader.single_camera_per_image", &image_reader->single_camera_per_image); AddAndRegisterDefaultOption("ImageReader.existing_camera_id", &image_reader->existing_camera_id); AddAndRegisterDefaultOption("ImageReader.camera_params", &image_reader->camera_params); AddAndRegisterDefaultOption("ImageReader.default_focal_length_factor", &image_reader->default_focal_length_factor); AddAndRegisterDefaultOption("ImageReader.camera_mask_path", &image_reader->camera_mask_path); AddAndRegisterDefaultOption("SiftExtraction.num_threads", &sift_extraction->num_threads); AddAndRegisterDefaultOption("SiftExtraction.use_gpu", &sift_extraction->use_gpu); AddAndRegisterDefaultOption("SiftExtraction.gpu_index", &sift_extraction->gpu_index); AddAndRegisterDefaultOption("SiftExtraction.max_image_size", &sift_extraction->max_image_size); AddAndRegisterDefaultOption("SiftExtraction.max_num_features", &sift_extraction->max_num_features); AddAndRegisterDefaultOption("SiftExtraction.first_octave", &sift_extraction->first_octave); AddAndRegisterDefaultOption("SiftExtraction.num_octaves", &sift_extraction->num_octaves); AddAndRegisterDefaultOption("SiftExtraction.octave_resolution", &sift_extraction->octave_resolution); AddAndRegisterDefaultOption("SiftExtraction.peak_threshold", &sift_extraction->peak_threshold); AddAndRegisterDefaultOption("SiftExtraction.edge_threshold", &sift_extraction->edge_threshold); AddAndRegisterDefaultOption("SiftExtraction.estimate_affine_shape", &sift_extraction->estimate_affine_shape); AddAndRegisterDefaultOption("SiftExtraction.max_num_orientations", &sift_extraction->max_num_orientations); AddAndRegisterDefaultOption("SiftExtraction.upright", &sift_extraction->upright); AddAndRegisterDefaultOption("SiftExtraction.domain_size_pooling", &sift_extraction->domain_size_pooling); AddAndRegisterDefaultOption("SiftExtraction.dsp_min_scale", &sift_extraction->dsp_min_scale); AddAndRegisterDefaultOption("SiftExtraction.dsp_max_scale", &sift_extraction->dsp_max_scale); AddAndRegisterDefaultOption("SiftExtraction.dsp_num_scales", &sift_extraction->dsp_num_scales); } void OptionManager::AddMatchingOptions() { if (added_match_options_) { return; } added_match_options_ = true; AddAndRegisterDefaultOption("SiftMatching.num_threads", &sift_matching->num_threads); AddAndRegisterDefaultOption("SiftMatching.use_gpu", &sift_matching->use_gpu); AddAndRegisterDefaultOption("SiftMatching.gpu_index", &sift_matching->gpu_index); AddAndRegisterDefaultOption("SiftMatching.max_ratio", &sift_matching->max_ratio); AddAndRegisterDefaultOption("SiftMatching.max_distance", &sift_matching->max_distance); AddAndRegisterDefaultOption("SiftMatching.cross_check", &sift_matching->cross_check); AddAndRegisterDefaultOption("SiftMatching.guided_matching", &sift_matching->guided_matching); AddAndRegisterDefaultOption("SiftMatching.max_num_matches", &sift_matching->max_num_matches); AddAndRegisterDefaultOption("TwoViewGeometry.min_num_inliers", &two_view_geometry->min_num_inliers); AddAndRegisterDefaultOption("TwoViewGeometry.multiple_models", &two_view_geometry->multiple_models); AddAndRegisterDefaultOption("TwoViewGeometry.compute_relative_pose", &two_view_geometry->compute_relative_pose); AddAndRegisterDefaultOption("TwoViewGeometry.max_error", &two_view_geometry->ransac_options.max_error); AddAndRegisterDefaultOption("TwoViewGeometry.confidence", &two_view_geometry->ransac_options.confidence); AddAndRegisterDefaultOption( "TwoViewGeometry.max_num_trials", &two_view_geometry->ransac_options.max_num_trials); AddAndRegisterDefaultOption( "TwoViewGeometry.min_inlier_ratio", &two_view_geometry->ransac_options.min_inlier_ratio); } void OptionManager::AddExhaustiveMatchingOptions() { if (added_exhaustive_match_options_) { return; } added_exhaustive_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("ExhaustiveMatching.block_size", &exhaustive_matching->block_size); } void OptionManager::AddSequentialMatchingOptions() { if (added_sequential_match_options_) { return; } added_sequential_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("SequentialMatching.overlap", &sequential_matching->overlap); AddAndRegisterDefaultOption("SequentialMatching.quadratic_overlap", &sequential_matching->quadratic_overlap); AddAndRegisterDefaultOption("SequentialMatching.loop_detection", &sequential_matching->loop_detection); AddAndRegisterDefaultOption("SequentialMatching.loop_detection_period", &sequential_matching->loop_detection_period); AddAndRegisterDefaultOption("SequentialMatching.loop_detection_num_images", &sequential_matching->loop_detection_num_images); AddAndRegisterDefaultOption( "SequentialMatching.loop_detection_num_nearest_neighbors", &sequential_matching->loop_detection_num_nearest_neighbors); AddAndRegisterDefaultOption("SequentialMatching.loop_detection_num_checks", &sequential_matching->loop_detection_num_checks); AddAndRegisterDefaultOption( "SequentialMatching.loop_detection_num_images_after_verification", &sequential_matching->loop_detection_num_images_after_verification); AddAndRegisterDefaultOption( "SequentialMatching.loop_detection_max_num_features", &sequential_matching->loop_detection_max_num_features); AddAndRegisterDefaultOption("SequentialMatching.vocab_tree_path", &sequential_matching->vocab_tree_path); } void OptionManager::AddVocabTreeMatchingOptions() { if (added_vocab_tree_match_options_) { return; } added_vocab_tree_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("VocabTreeMatching.num_images", &vocab_tree_matching->num_images); AddAndRegisterDefaultOption("VocabTreeMatching.num_nearest_neighbors", &vocab_tree_matching->num_nearest_neighbors); AddAndRegisterDefaultOption("VocabTreeMatching.num_checks", &vocab_tree_matching->num_checks); AddAndRegisterDefaultOption( "VocabTreeMatching.num_images_after_verification", &vocab_tree_matching->num_images_after_verification); AddAndRegisterDefaultOption("VocabTreeMatching.max_num_features", &vocab_tree_matching->max_num_features); AddAndRegisterDefaultOption("VocabTreeMatching.vocab_tree_path", &vocab_tree_matching->vocab_tree_path); AddAndRegisterDefaultOption("VocabTreeMatching.match_list_path", &vocab_tree_matching->match_list_path); } void OptionManager::AddSpatialMatchingOptions() { if (added_spatial_match_options_) { return; } added_spatial_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("SpatialMatching.ignore_z", &spatial_matching->ignore_z); AddAndRegisterDefaultOption("SpatialMatching.max_num_neighbors", &spatial_matching->max_num_neighbors); AddAndRegisterDefaultOption("SpatialMatching.max_distance", &spatial_matching->max_distance); } void OptionManager::AddTransitiveMatchingOptions() { if (added_transitive_match_options_) { return; } added_transitive_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("TransitiveMatching.batch_size", &transitive_matching->batch_size); AddAndRegisterDefaultOption("TransitiveMatching.num_iterations", &transitive_matching->num_iterations); } void OptionManager::AddImagePairsMatchingOptions() { if (added_image_pairs_match_options_) { return; } added_image_pairs_match_options_ = true; AddMatchingOptions(); AddAndRegisterDefaultOption("ImagePairsMatching.block_size", &image_pairs_matching->block_size); } void OptionManager::AddBundleAdjustmentOptions() { if (added_ba_options_) { return; } added_ba_options_ = true; AddAndRegisterDefaultOption( "BundleAdjustment.max_num_iterations", &bundle_adjustment->solver_options.max_num_iterations); AddAndRegisterDefaultOption( "BundleAdjustment.max_linear_solver_iterations", &bundle_adjustment->solver_options.max_linear_solver_iterations); AddAndRegisterDefaultOption( "BundleAdjustment.function_tolerance", &bundle_adjustment->solver_options.function_tolerance); AddAndRegisterDefaultOption( "BundleAdjustment.gradient_tolerance", &bundle_adjustment->solver_options.gradient_tolerance); AddAndRegisterDefaultOption( "BundleAdjustment.parameter_tolerance", &bundle_adjustment->solver_options.parameter_tolerance); AddAndRegisterDefaultOption("BundleAdjustment.refine_focal_length", &bundle_adjustment->refine_focal_length); AddAndRegisterDefaultOption("BundleAdjustment.refine_principal_point", &bundle_adjustment->refine_principal_point); AddAndRegisterDefaultOption("BundleAdjustment.refine_extra_params", &bundle_adjustment->refine_extra_params); AddAndRegisterDefaultOption("BundleAdjustment.refine_extrinsics", &bundle_adjustment->refine_extrinsics); } void OptionManager::AddMapperOptions() { if (added_mapper_options_) { return; } added_mapper_options_ = true; AddAndRegisterDefaultOption("Mapper.min_num_matches", &mapper->min_num_matches); AddAndRegisterDefaultOption("Mapper.ignore_watermarks", &mapper->ignore_watermarks); AddAndRegisterDefaultOption("Mapper.multiple_models", &mapper->multiple_models); AddAndRegisterDefaultOption("Mapper.max_num_models", &mapper->max_num_models); AddAndRegisterDefaultOption("Mapper.max_model_overlap", &mapper->max_model_overlap); AddAndRegisterDefaultOption("Mapper.min_model_size", &mapper->min_model_size); AddAndRegisterDefaultOption("Mapper.init_image_id1", &mapper->init_image_id1); AddAndRegisterDefaultOption("Mapper.init_image_id2", &mapper->init_image_id2); AddAndRegisterDefaultOption("Mapper.init_num_trials", &mapper->init_num_trials); AddAndRegisterDefaultOption("Mapper.extract_colors", &mapper->extract_colors); AddAndRegisterDefaultOption("Mapper.num_threads", &mapper->num_threads); AddAndRegisterDefaultOption("Mapper.min_focal_length_ratio", &mapper->min_focal_length_ratio); AddAndRegisterDefaultOption("Mapper.max_focal_length_ratio", &mapper->max_focal_length_ratio); AddAndRegisterDefaultOption("Mapper.max_extra_param", &mapper->max_extra_param); AddAndRegisterDefaultOption("Mapper.ba_refine_focal_length", &mapper->ba_refine_focal_length); AddAndRegisterDefaultOption("Mapper.ba_refine_principal_point", &mapper->ba_refine_principal_point); AddAndRegisterDefaultOption("Mapper.ba_refine_extra_params", &mapper->ba_refine_extra_params); AddAndRegisterDefaultOption( "Mapper.ba_min_num_residuals_for_multi_threading", &mapper->ba_min_num_residuals_for_multi_threading); AddAndRegisterDefaultOption("Mapper.ba_local_num_images", &mapper->ba_local_num_images); AddAndRegisterDefaultOption("Mapper.ba_local_function_tolerance", &mapper->ba_local_function_tolerance); AddAndRegisterDefaultOption("Mapper.ba_local_max_num_iterations", &mapper->ba_local_max_num_iterations); AddAndRegisterDefaultOption("Mapper.ba_global_images_ratio", &mapper->ba_global_images_ratio); AddAndRegisterDefaultOption("Mapper.ba_global_points_ratio", &mapper->ba_global_points_ratio); AddAndRegisterDefaultOption("Mapper.ba_global_images_freq", &mapper->ba_global_images_freq); AddAndRegisterDefaultOption("Mapper.ba_global_points_freq", &mapper->ba_global_points_freq); AddAndRegisterDefaultOption("Mapper.ba_global_function_tolerance", &mapper->ba_global_function_tolerance); AddAndRegisterDefaultOption("Mapper.ba_global_max_num_iterations", &mapper->ba_global_max_num_iterations); AddAndRegisterDefaultOption("Mapper.ba_global_max_refinements", &mapper->ba_global_max_refinements); AddAndRegisterDefaultOption("Mapper.ba_global_max_refinement_change", &mapper->ba_global_max_refinement_change); AddAndRegisterDefaultOption("Mapper.ba_local_max_refinements", &mapper->ba_local_max_refinements); AddAndRegisterDefaultOption("Mapper.ba_local_max_refinement_change", &mapper->ba_local_max_refinement_change); AddAndRegisterDefaultOption("Mapper.snapshot_path", &mapper->snapshot_path); AddAndRegisterDefaultOption("Mapper.snapshot_images_freq", &mapper->snapshot_images_freq); AddAndRegisterDefaultOption("Mapper.fix_existing_images", &mapper->fix_existing_images); // IncrementalMapper. AddAndRegisterDefaultOption("Mapper.init_min_num_inliers", &mapper->mapper.init_min_num_inliers); AddAndRegisterDefaultOption("Mapper.init_max_error", &mapper->mapper.init_max_error); AddAndRegisterDefaultOption("Mapper.init_max_forward_motion", &mapper->mapper.init_max_forward_motion); AddAndRegisterDefaultOption("Mapper.init_min_tri_angle", &mapper->mapper.init_min_tri_angle); AddAndRegisterDefaultOption("Mapper.init_max_reg_trials", &mapper->mapper.init_max_reg_trials); AddAndRegisterDefaultOption("Mapper.abs_pose_max_error", &mapper->mapper.abs_pose_max_error); AddAndRegisterDefaultOption("Mapper.abs_pose_min_num_inliers", &mapper->mapper.abs_pose_min_num_inliers); AddAndRegisterDefaultOption("Mapper.abs_pose_min_inlier_ratio", &mapper->mapper.abs_pose_min_inlier_ratio); AddAndRegisterDefaultOption("Mapper.filter_max_reproj_error", &mapper->mapper.filter_max_reproj_error); AddAndRegisterDefaultOption("Mapper.filter_min_tri_angle", &mapper->mapper.filter_min_tri_angle); AddAndRegisterDefaultOption("Mapper.max_reg_trials", &mapper->mapper.max_reg_trials); AddAndRegisterDefaultOption("Mapper.local_ba_min_tri_angle", &mapper->mapper.local_ba_min_tri_angle); // IncrementalTriangulator. AddAndRegisterDefaultOption("Mapper.tri_max_transitivity", &mapper->triangulation.max_transitivity); AddAndRegisterDefaultOption("Mapper.tri_create_max_angle_error", &mapper->triangulation.create_max_angle_error); AddAndRegisterDefaultOption("Mapper.tri_continue_max_angle_error", &mapper->triangulation.continue_max_angle_error); AddAndRegisterDefaultOption("Mapper.tri_merge_max_reproj_error", &mapper->triangulation.merge_max_reproj_error); AddAndRegisterDefaultOption("Mapper.tri_complete_max_reproj_error", &mapper->triangulation.complete_max_reproj_error); AddAndRegisterDefaultOption("Mapper.tri_complete_max_transitivity", &mapper->triangulation.complete_max_transitivity); AddAndRegisterDefaultOption("Mapper.tri_re_max_angle_error", &mapper->triangulation.re_max_angle_error); AddAndRegisterDefaultOption("Mapper.tri_re_min_ratio", &mapper->triangulation.re_min_ratio); AddAndRegisterDefaultOption("Mapper.tri_re_max_trials", &mapper->triangulation.re_max_trials); AddAndRegisterDefaultOption("Mapper.tri_min_angle", &mapper->triangulation.min_angle); AddAndRegisterDefaultOption("Mapper.tri_ignore_two_view_tracks", &mapper->triangulation.ignore_two_view_tracks); } void OptionManager::AddPatchMatchStereoOptions() { if (added_patch_match_stereo_options_) { return; } added_patch_match_stereo_options_ = true; AddAndRegisterDefaultOption("PatchMatchStereo.max_image_size", &patch_match_stereo->max_image_size); AddAndRegisterDefaultOption("PatchMatchStereo.gpu_index", &patch_match_stereo->gpu_index); AddAndRegisterDefaultOption("PatchMatchStereo.depth_min", &patch_match_stereo->depth_min); AddAndRegisterDefaultOption("PatchMatchStereo.depth_max", &patch_match_stereo->depth_max); AddAndRegisterDefaultOption("PatchMatchStereo.window_radius", &patch_match_stereo->window_radius); AddAndRegisterDefaultOption("PatchMatchStereo.window_step", &patch_match_stereo->window_step); AddAndRegisterDefaultOption("PatchMatchStereo.sigma_spatial", &patch_match_stereo->sigma_spatial); AddAndRegisterDefaultOption("PatchMatchStereo.sigma_color", &patch_match_stereo->sigma_color); AddAndRegisterDefaultOption("PatchMatchStereo.num_samples", &patch_match_stereo->num_samples); AddAndRegisterDefaultOption("PatchMatchStereo.ncc_sigma", &patch_match_stereo->ncc_sigma); AddAndRegisterDefaultOption("PatchMatchStereo.min_triangulation_angle", &patch_match_stereo->min_triangulation_angle); AddAndRegisterDefaultOption("PatchMatchStereo.incident_angle_sigma", &patch_match_stereo->incident_angle_sigma); AddAndRegisterDefaultOption("PatchMatchStereo.num_iterations", &patch_match_stereo->num_iterations); AddAndRegisterDefaultOption("PatchMatchStereo.geom_consistency", &patch_match_stereo->geom_consistency); AddAndRegisterDefaultOption( "PatchMatchStereo.geom_consistency_regularizer", &patch_match_stereo->geom_consistency_regularizer); AddAndRegisterDefaultOption("PatchMatchStereo.geom_consistency_max_cost", &patch_match_stereo->geom_consistency_max_cost); AddAndRegisterDefaultOption("PatchMatchStereo.filter", &patch_match_stereo->filter); AddAndRegisterDefaultOption("PatchMatchStereo.filter_min_ncc", &patch_match_stereo->filter_min_ncc); AddAndRegisterDefaultOption( "PatchMatchStereo.filter_min_triangulation_angle", &patch_match_stereo->filter_min_triangulation_angle); AddAndRegisterDefaultOption("PatchMatchStereo.filter_min_num_consistent", &patch_match_stereo->filter_min_num_consistent); AddAndRegisterDefaultOption( "PatchMatchStereo.filter_geom_consistency_max_cost", &patch_match_stereo->filter_geom_consistency_max_cost); AddAndRegisterDefaultOption("PatchMatchStereo.cache_size", &patch_match_stereo->cache_size); AddAndRegisterDefaultOption("PatchMatchStereo.allow_missing_files", &patch_match_stereo->allow_missing_files); AddAndRegisterDefaultOption("PatchMatchStereo.write_consistency_graph", &patch_match_stereo->write_consistency_graph); } void OptionManager::AddStereoFusionOptions() { if (added_stereo_fusion_options_) { return; } added_stereo_fusion_options_ = true; AddAndRegisterDefaultOption("StereoFusion.mask_path", &stereo_fusion->mask_path); AddAndRegisterDefaultOption("StereoFusion.num_threads", &stereo_fusion->num_threads); AddAndRegisterDefaultOption("StereoFusion.max_image_size", &stereo_fusion->max_image_size); AddAndRegisterDefaultOption("StereoFusion.min_num_pixels", &stereo_fusion->min_num_pixels); AddAndRegisterDefaultOption("StereoFusion.max_num_pixels", &stereo_fusion->max_num_pixels); AddAndRegisterDefaultOption("StereoFusion.max_traversal_depth", &stereo_fusion->max_traversal_depth); AddAndRegisterDefaultOption("StereoFusion.max_reproj_error", &stereo_fusion->max_reproj_error); AddAndRegisterDefaultOption("StereoFusion.max_depth_error", &stereo_fusion->max_depth_error); AddAndRegisterDefaultOption("StereoFusion.max_normal_error", &stereo_fusion->max_normal_error); AddAndRegisterDefaultOption("StereoFusion.check_num_images", &stereo_fusion->check_num_images); AddAndRegisterDefaultOption("StereoFusion.cache_size", &stereo_fusion->cache_size); AddAndRegisterDefaultOption("StereoFusion.use_cache", &stereo_fusion->use_cache); } void OptionManager::AddPoissonMeshingOptions() { if (added_poisson_meshing_options_) { return; } added_poisson_meshing_options_ = true; AddAndRegisterDefaultOption("PoissonMeshing.point_weight", &poisson_meshing->point_weight); AddAndRegisterDefaultOption("PoissonMeshing.depth", &poisson_meshing->depth); AddAndRegisterDefaultOption("PoissonMeshing.color", &poisson_meshing->color); AddAndRegisterDefaultOption("PoissonMeshing.trim", &poisson_meshing->trim); AddAndRegisterDefaultOption("PoissonMeshing.num_threads", &poisson_meshing->num_threads); } void OptionManager::AddDelaunayMeshingOptions() { if (added_delaunay_meshing_options_) { return; } added_delaunay_meshing_options_ = true; AddAndRegisterDefaultOption("DelaunayMeshing.max_proj_dist", &delaunay_meshing->max_proj_dist); AddAndRegisterDefaultOption("DelaunayMeshing.max_depth_dist", &delaunay_meshing->max_depth_dist); AddAndRegisterDefaultOption("DelaunayMeshing.visibility_sigma", &delaunay_meshing->visibility_sigma); AddAndRegisterDefaultOption("DelaunayMeshing.distance_sigma_factor", &delaunay_meshing->distance_sigma_factor); AddAndRegisterDefaultOption("DelaunayMeshing.quality_regularization", &delaunay_meshing->quality_regularization); AddAndRegisterDefaultOption("DelaunayMeshing.max_side_length_factor", &delaunay_meshing->max_side_length_factor); AddAndRegisterDefaultOption("DelaunayMeshing.max_side_length_percentile", &delaunay_meshing->max_side_length_percentile); AddAndRegisterDefaultOption("DelaunayMeshing.num_threads", &delaunay_meshing->num_threads); } void OptionManager::AddRenderOptions() { if (added_render_options_) { return; } added_render_options_ = true; AddAndRegisterDefaultOption("Render.min_track_len", &render->min_track_len); AddAndRegisterDefaultOption("Render.max_error", &render->max_error); AddAndRegisterDefaultOption("Render.refresh_rate", &render->refresh_rate); AddAndRegisterDefaultOption("Render.adapt_refresh_rate", &render->adapt_refresh_rate); AddAndRegisterDefaultOption("Render.image_connections", &render->image_connections); AddAndRegisterDefaultOption("Render.projection_type", &render->projection_type); } void OptionManager::Reset() { FLAGS_logtostderr = true; const bool kResetPaths = true; ResetOptions(kResetPaths); desc_ = std::make_shared(); options_bool_.clear(); options_int_.clear(); options_double_.clear(); options_string_.clear(); added_log_options_ = false; added_random_options_ = false; added_database_options_ = false; added_image_options_ = false; added_extraction_options_ = false; added_match_options_ = false; added_exhaustive_match_options_ = false; added_sequential_match_options_ = false; added_vocab_tree_match_options_ = false; added_spatial_match_options_ = false; added_transitive_match_options_ = false; added_image_pairs_match_options_ = false; added_ba_options_ = false; added_mapper_options_ = false; added_patch_match_stereo_options_ = false; added_stereo_fusion_options_ = false; added_poisson_meshing_options_ = false; added_delaunay_meshing_options_ = false; added_render_options_ = false; } void OptionManager::ResetOptions(const bool reset_paths) { if (reset_paths) { *project_path = ""; *database_path = ""; *image_path = ""; } *image_reader = ImageReaderOptions(); *sift_extraction = SiftExtractionOptions(); *sift_matching = SiftMatchingOptions(); *exhaustive_matching = ExhaustiveMatchingOptions(); *sequential_matching = SequentialMatchingOptions(); *vocab_tree_matching = VocabTreeMatchingOptions(); *spatial_matching = SpatialMatchingOptions(); *transitive_matching = TransitiveMatchingOptions(); *image_pairs_matching = ImagePairsMatchingOptions(); *bundle_adjustment = BundleAdjustmentOptions(); *mapper = IncrementalMapperOptions(); *patch_match_stereo = mvs::PatchMatchOptions(); *stereo_fusion = mvs::StereoFusionOptions(); *poisson_meshing = mvs::PoissonMeshingOptions(); *delaunay_meshing = mvs::DelaunayMeshingOptions(); *render = RenderOptions(); } bool OptionManager::Check() { bool success = true; if (added_database_options_) { const auto database_parent_path = GetParentDir(*database_path); success = success && CHECK_OPTION_IMPL(!ExistsDir(*database_path)) && CHECK_OPTION_IMPL(database_parent_path == "" || ExistsDir(database_parent_path)); } if (added_image_options_) success = success && CHECK_OPTION_IMPL(ExistsDir(*image_path)); if (image_reader) success = success && image_reader->Check(); if (sift_extraction) success = success && sift_extraction->Check(); if (sift_matching) success = success && sift_matching->Check(); if (two_view_geometry) success = success && two_view_geometry->Check(); if (exhaustive_matching) success = success && exhaustive_matching->Check(); if (sequential_matching) success = success && sequential_matching->Check(); if (vocab_tree_matching) success = success && vocab_tree_matching->Check(); if (spatial_matching) success = success && spatial_matching->Check(); if (transitive_matching) success = success && transitive_matching->Check(); if (image_pairs_matching) success = success && image_pairs_matching->Check(); if (bundle_adjustment) success = success && bundle_adjustment->Check(); if (mapper) success = success && mapper->Check(); if (patch_match_stereo) success = success && patch_match_stereo->Check(); if (stereo_fusion) success = success && stereo_fusion->Check(); if (poisson_meshing) success = success && poisson_meshing->Check(); if (delaunay_meshing) success = success && delaunay_meshing->Check(); #if defined(COLMAP_GUI_ENABLED) if (render) success = success && render->Check(); #endif return success; } void OptionManager::Parse(const int argc, char** argv) { config::variables_map vmap; try { config::store(config::parse_command_line(argc, argv, *desc_), vmap); if (vmap.count("help")) { LOG(INFO) << StringPrintf( "%s (%s)", GetVersionInfo().c_str(), GetBuildInfo().c_str()); LOG(INFO) << "Options can either be specified via command-line or by defining " "them in a .ini project file passed to `--project_path`.\n" << *desc_; // NOLINTNEXTLINE(concurrency-mt-unsafe) exit(EXIT_SUCCESS); } if (vmap.count("project_path")) { *project_path = vmap["project_path"].as(); if (!Read(*project_path)) { // NOLINTNEXTLINE(concurrency-mt-unsafe) exit(EXIT_FAILURE); } } else { vmap.notify(); } } catch (std::exception& exc) { LOG(ERROR) << "Failed to parse options - " << exc.what() << "."; // NOLINTNEXTLINE(concurrency-mt-unsafe) exit(EXIT_FAILURE); } catch (...) { LOG(ERROR) << "Failed to parse options for unknown reason."; // NOLINTNEXTLINE(concurrency-mt-unsafe) exit(EXIT_FAILURE); } if (!Check()) { LOG(ERROR) << "Invalid options provided."; // NOLINTNEXTLINE(concurrency-mt-unsafe) exit(EXIT_FAILURE); } } bool OptionManager::Read(const std::string& path) { config::variables_map vmap; if (!ExistsFile(path)) { LOG(ERROR) << "Configuration file does not exist."; return false; } try { std::ifstream file(path); THROW_CHECK_FILE_OPEN(file, path); config::store(config::parse_config_file(file, *desc_), vmap); vmap.notify(); } catch (std::exception& e) { LOG(ERROR) << "Failed to parse options " << e.what() << "."; return false; } catch (...) { LOG(ERROR) << "Failed to parse options for unknown reason."; return false; } return Check(); } bool OptionManager::ReRead(const std::string& path) { Reset(); AddAllOptions(); return Read(path); } void OptionManager::Write(const std::string& path) const { boost::property_tree::ptree pt; // First, put all options without a section and then those with a section. // This is necessary as otherwise older Boost versions will write the // options without a section in between other sections and therefore // the errors will be assigned to the wrong section if read later. for (const auto& option : options_bool_) { if (!StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_int_) { if (!StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_double_) { if (!StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_string_) { if (!StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_bool_) { if (StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_int_) { if (StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_double_) { if (StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } for (const auto& option : options_string_) { if (StringContains(option.first, ".")) { pt.put(option.first, *option.second); } } boost::property_tree::write_ini(path, pt); } } // namespace colmap colmap-3.10/src/colmap/controllers/option_manager.h000066400000000000000000000222741464745566500225300ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/util/logging.h" #include #include namespace colmap { struct ImageReaderOptions; struct SiftExtractionOptions; struct SiftMatchingOptions; struct TwoViewGeometryOptions; struct ExhaustiveMatchingOptions; struct SequentialMatchingOptions; struct VocabTreeMatchingOptions; struct SpatialMatchingOptions; struct TransitiveMatchingOptions; struct ImagePairsMatchingOptions; struct BundleAdjustmentOptions; struct IncrementalMapperOptions; struct RenderOptions; namespace mvs { struct PatchMatchOptions; struct StereoFusionOptions; struct PoissonMeshingOptions; struct DelaunayMeshingOptions; } // namespace mvs class OptionManager { public: explicit OptionManager(bool add_project_options = true); // Create "optimal" set of options for different reconstruction scenarios. // Note that the existing options are modified, so if your parameters are // already low quality, they will be further modified. void ModifyForIndividualData(); void ModifyForVideoData(); void ModifyForInternetData(); // Create "optimal" set of options for different quality settings. // Note that the existing options are modified, so if your parameters are // already low quality, they will be further degraded. void ModifyForLowQuality(); void ModifyForMediumQuality(); void ModifyForHighQuality(); void ModifyForExtremeQuality(); void AddAllOptions(); void AddLogOptions(); void AddRandomOptions(); void AddDatabaseOptions(); void AddImageOptions(); void AddExtractionOptions(); void AddMatchingOptions(); void AddExhaustiveMatchingOptions(); void AddSequentialMatchingOptions(); void AddVocabTreeMatchingOptions(); void AddSpatialMatchingOptions(); void AddTransitiveMatchingOptions(); void AddImagePairsMatchingOptions(); void AddBundleAdjustmentOptions(); void AddMapperOptions(); void AddPatchMatchStereoOptions(); void AddStereoFusionOptions(); void AddPoissonMeshingOptions(); void AddDelaunayMeshingOptions(); void AddRenderOptions(); template void AddRequiredOption(const std::string& name, T* option, const std::string& help_text = ""); template void AddDefaultOption(const std::string& name, T* option, const std::string& help_text = ""); void Reset(); void ResetOptions(bool reset_paths); bool Check(); void Parse(int argc, char** argv); bool Read(const std::string& path); bool ReRead(const std::string& path); void Write(const std::string& path) const; std::shared_ptr project_path; std::shared_ptr database_path; std::shared_ptr image_path; std::shared_ptr image_reader; std::shared_ptr sift_extraction; std::shared_ptr sift_matching; std::shared_ptr two_view_geometry; std::shared_ptr exhaustive_matching; std::shared_ptr sequential_matching; std::shared_ptr vocab_tree_matching; std::shared_ptr spatial_matching; std::shared_ptr transitive_matching; std::shared_ptr image_pairs_matching; std::shared_ptr bundle_adjustment; std::shared_ptr mapper; std::shared_ptr patch_match_stereo; std::shared_ptr stereo_fusion; std::shared_ptr poisson_meshing; std::shared_ptr delaunay_meshing; std::shared_ptr render; private: template void AddAndRegisterRequiredOption(const std::string& name, T* option, const std::string& help_text = ""); template void AddAndRegisterDefaultOption(const std::string& name, T* option, const std::string& help_text = ""); template void RegisterOption(const std::string& name, const T* option); std::shared_ptr desc_; std::vector> options_bool_; std::vector> options_int_; std::vector> options_double_; std::vector> options_string_; bool added_log_options_; bool added_random_options_; bool added_database_options_; bool added_image_options_; bool added_extraction_options_; bool added_match_options_; bool added_exhaustive_match_options_; bool added_sequential_match_options_; bool added_vocab_tree_match_options_; bool added_spatial_match_options_; bool added_transitive_match_options_; bool added_image_pairs_match_options_; bool added_ba_options_; bool added_mapper_options_; bool added_patch_match_stereo_options_; bool added_stereo_fusion_options_; bool added_poisson_meshing_options_; bool added_delaunay_meshing_options_; bool added_render_options_; }; //////////////////////////////////////////////////////////////////////////////// // Implementation //////////////////////////////////////////////////////////////////////////////// template void OptionManager::AddRequiredOption(const std::string& name, T* option, const std::string& help_text) { desc_->add_options()(name.c_str(), boost::program_options::value(option)->required(), help_text.c_str()); } template void OptionManager::AddDefaultOption(const std::string& name, T* option, const std::string& help_text) { desc_->add_options()( name.c_str(), boost::program_options::value(option)->default_value(*option), help_text.c_str()); } template void OptionManager::AddAndRegisterRequiredOption(const std::string& name, T* option, const std::string& help_text) { desc_->add_options()(name.c_str(), boost::program_options::value(option)->required(), help_text.c_str()); RegisterOption(name, option); } template void OptionManager::AddAndRegisterDefaultOption(const std::string& name, T* option, const std::string& help_text) { desc_->add_options()( name.c_str(), boost::program_options::value(option)->default_value(*option), help_text.c_str()); RegisterOption(name, option); } template void OptionManager::RegisterOption(const std::string& name, const T* option) { if (std::is_same::value) { options_bool_.emplace_back(name, reinterpret_cast(option)); } else if (std::is_same::value) { options_int_.emplace_back(name, reinterpret_cast(option)); } else if (std::is_same::value) { options_double_.emplace_back(name, reinterpret_cast(option)); } else if (std::is_same::value) { options_string_.emplace_back(name, reinterpret_cast(option)); } else { LOG(FATAL_THROW) << "Unsupported option type"; } } } // namespace colmap colmap-3.10/src/colmap/estimators/000077500000000000000000000000001464745566500171725ustar00rootroot00000000000000colmap-3.10/src/colmap/estimators/CMakeLists.txt000066400000000000000000000113131464745566500217310ustar00rootroot00000000000000# Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of # its contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. set(FOLDER_NAME "estimators") COLMAP_ADD_LIBRARY( NAME colmap_estimators SRCS absolute_pose.h absolute_pose.cc affine_transform.h affine_transform.cc alignment.h alignment.cc bundle_adjustment.h bundle_adjustment.cc coordinate_frame.h coordinate_frame.cc cost_functions.h covariance.h covariance.cc essential_matrix.h essential_matrix.cc euclidean_transform.h fundamental_matrix.h fundamental_matrix.cc generalized_absolute_pose.h generalized_absolute_pose.cc generalized_absolute_pose_coeffs.h generalized_absolute_pose_coeffs.cc generalized_relative_pose.h generalized_relative_pose.cc homography_matrix.h homography_matrix.cc pose.h pose.cc generalized_pose.h generalized_pose.cc similarity_transform.h translation_transform.h triangulation.h triangulation.cc two_view_geometry.h two_view_geometry.cc utils.h utils.cc PUBLIC_LINK_LIBS colmap_util colmap_math colmap_feature_types colmap_geometry colmap_sensor colmap_image colmap_scene colmap_optim Eigen3::Eigen Ceres::ceres ) COLMAP_ADD_TEST( NAME absolute_pose_test SRCS absolute_pose_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME affine_transform_test SRCS affine_transform_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME alignment_test SRCS alignment_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME bundle_adjustment_test SRCS bundle_adjustment_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME coordinate_frame_test SRCS coordinate_frame_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME covariance_test SRCS covariance_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME cost_functions_test SRCS cost_functions_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME essential_matrix_test SRCS essential_matrix_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME estimators_utils_test SRCS utils_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME fundamental_matrix_test SRCS fundamental_matrix_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME generalized_absolute_pose_test SRCS generalized_absolute_pose_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME generalized_pose_test SRCS generalized_pose_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME generalized_relative_pose_test SRCS generalized_relative_pose_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME homography_matrix_test SRCS homography_matrix_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME pose_test SRCS pose_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME similarity_transform_test SRCS similarity_transform_test.cc LINK_LIBS colmap_estimators ) COLMAP_ADD_TEST( NAME translation_transform_test SRCS translation_transform_test.cc LINK_LIBS colmap_estimators ) colmap-3.10/src/colmap/estimators/absolute_pose.cc000066400000000000000000000510241464745566500223470ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/absolute_pose.h" #include "colmap/estimators/utils.h" #include "colmap/math/polynomial.h" #include "colmap/util/eigen_alignment.h" #include "colmap/util/logging.h" #include namespace colmap { void P3PEstimator::Estimate(const std::vector& points2D, const std::vector& points3D, std::vector* models) { THROW_CHECK_EQ(points2D.size(), 3); THROW_CHECK_EQ(points3D.size(), 3); THROW_CHECK(models != nullptr); models->clear(); Eigen::Matrix3d points3D_world; points3D_world.col(0) = points3D[0]; points3D_world.col(1) = points3D[1]; points3D_world.col(2) = points3D[2]; const Eigen::Vector3d u = points2D[0].homogeneous().normalized(); const Eigen::Vector3d v = points2D[1].homogeneous().normalized(); const Eigen::Vector3d w = points2D[2].homogeneous().normalized(); // Angles between 2D points. const double cos_uv = u.transpose() * v; const double cos_uw = u.transpose() * w; const double cos_vw = v.transpose() * w; // Distances between 2D points. const double dist_AB_2 = (points3D[0] - points3D[1]).squaredNorm(); const double dist_AC_2 = (points3D[0] - points3D[2]).squaredNorm(); const double dist_BC_2 = (points3D[1] - points3D[2]).squaredNorm(); const double dist_AB = std::sqrt(dist_AB_2); const double a = dist_BC_2 / dist_AB_2; const double b = dist_AC_2 / dist_AB_2; // Helper variables for calculation of coefficients. const double a2 = a * a; const double b2 = b * b; const double p = 2 * cos_vw; const double q = 2 * cos_uw; const double r = 2 * cos_uv; const double p2 = p * p; const double p3 = p2 * p; const double q2 = q * q; const double r2 = r * r; const double r3 = r2 * r; const double r4 = r3 * r; const double r5 = r4 * r; // Build polynomial coefficients: a4*x^4 + a3*x^3 + a2*x^2 + a1*x + a0 = 0. Eigen::Matrix coeffs; coeffs(0) = -2 * b + b2 + a2 + 1 + a * b * (2 - r2) - 2 * a; coeffs(1) = -2 * q * a2 - r * p * b2 + 4 * q * a + (2 * q + p * r) * b + (r2 * q - 2 * q + r * p) * a * b - 2 * q; coeffs(2) = (2 + q2) * a2 + (p2 + r2 - 2) * b2 - (4 + 2 * q2) * a - (p * q * r + p2) * b - (p * q * r + r2) * a * b + q2 + 2; coeffs(3) = -2 * q * a2 - r * p * b2 + 4 * q * a + (p * r + q * p2 - 2 * q) * b + (r * p + 2 * q) * a * b - 2 * q; coeffs(4) = a2 + b2 - 2 * a + (2 - p2) * b - 2 * a * b + 1; Eigen::VectorXd roots_real; Eigen::VectorXd roots_imag; if (!FindPolynomialRootsCompanionMatrix(coeffs, &roots_real, &roots_imag)) { return; } models->reserve(roots_real.size()); for (Eigen::VectorXd::Index i = 0; i < roots_real.size(); ++i) { const double kMaxRootImag = 1e-10; if (std::abs(roots_imag(i)) > kMaxRootImag) { continue; } const double x = roots_real(i); if (x < 0) { continue; } const double x2 = x * x; const double x3 = x2 * x; // Build polynomial coefficients: b1*y + b0 = 0. const double bb1 = (p2 - p * q * r + r2) * a + (p2 - r2) * b - p2 + p * q * r - r2; const double b1 = b * bb1 * bb1; const double b0 = ((1 - a - b) * x2 + (a - 1) * q * x - a + b + 1) * (r3 * (a2 + b2 - 2 * a - 2 * b + (2 - r2) * a * b + 1) * x3 + r2 * (p + p * a2 - 2 * r * q * a * b + 2 * r * q * b - 2 * r * q - 2 * p * a - 2 * p * b + p * r2 * b + 4 * r * q * a + q * r3 * a * b - 2 * r * q * a2 + 2 * p * a * b + p * b2 - r2 * p * b2) * x2 + (r5 * (b2 - a * b) - r4 * p * q * b + r3 * (q2 - 4 * a - 2 * q2 * a + q2 * a2 + 2 * a2 - 2 * b2 + 2) + r2 * (4 * p * q * a - 2 * p * q * a * b + 2 * p * q * b - 2 * p * q - 2 * p * q * a2) + r * (p2 * b2 - 2 * p2 * b + 2 * p2 * a * b - 2 * p2 * a + p2 + p2 * a2)) * x + (2 * p * r2 - 2 * r3 * q + p3 - 2 * p2 * q * r + p * q2 * r2) * a2 + (p3 - 2 * p * r2) * b2 + (4 * q * r3 - 4 * p * r2 - 2 * p3 + 4 * p2 * q * r - 2 * p * q2 * r2) * a + (-2 * q * r3 + p * r4 + 2 * p2 * q * r - 2 * p3) * b + (2 * p3 + 2 * q * r3 - 2 * p2 * q * r) * a * b + p * q2 * r2 - 2 * p2 * q * r + 2 * p * r2 + p3 - 2 * r3 * q); // Solve for y. const double y = b0 / b1; const double y2 = y * y; const double nu = x2 + y2 - 2 * x * y * cos_uv; const double dist_PC = dist_AB / std::sqrt(nu); const double dist_PB = y * dist_PC; const double dist_PA = x * dist_PC; Eigen::Matrix3d points3D_camera; points3D_camera.col(0) = u * dist_PA; // A' points3D_camera.col(1) = v * dist_PB; // B' points3D_camera.col(2) = w * dist_PC; // C' // Find transformation from the world to the camera system. const Eigen::Matrix4d transform = Eigen::umeyama(points3D_world, points3D_camera, false); models->push_back(transform.topLeftCorner<3, 4>()); } } void P3PEstimator::Residuals(const std::vector& points2D, const std::vector& points3D, const M_t& proj_matrix, std::vector* residuals) { ComputeSquaredReprojectionError(points2D, points3D, proj_matrix, residuals); } void EPNPEstimator::Estimate(const std::vector& points2D, const std::vector& points3D, std::vector* models) { THROW_CHECK_GE(points2D.size(), 4); THROW_CHECK_EQ(points2D.size(), points3D.size()); THROW_CHECK(models != nullptr); models->clear(); EPNPEstimator epnp; M_t proj_matrix; if (!epnp.ComputePose(points2D, points3D, &proj_matrix)) { return; } models->resize(1); (*models)[0] = proj_matrix; } void EPNPEstimator::Residuals(const std::vector& points2D, const std::vector& points3D, const M_t& proj_matrix, std::vector* residuals) { ComputeSquaredReprojectionError(points2D, points3D, proj_matrix, residuals); } bool EPNPEstimator::ComputePose(const std::vector& points2D, const std::vector& points3D, Eigen::Matrix3x4d* proj_matrix) { points2D_ = &points2D; points3D_ = &points3D; ChooseControlPoints(); if (!ComputeBarycentricCoordinates()) { return false; } const Eigen::Matrix M = ComputeM(); const Eigen::Matrix MtM = M.transpose() * M; Eigen::JacobiSVD> svd( MtM, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix Ut = svd.matrixU().transpose(); const Eigen::Matrix L6x10 = ComputeL6x10(Ut); const Eigen::Matrix rho = ComputeRho(); Eigen::Vector4d betas[4]; std::array reproj_errors; std::array Rs; std::array ts; FindBetasApprox1(L6x10, rho, &betas[1]); RunGaussNewton(L6x10, rho, &betas[1]); reproj_errors[1] = ComputeRT(Ut, betas[1], &Rs[1], &ts[1]); FindBetasApprox2(L6x10, rho, &betas[2]); RunGaussNewton(L6x10, rho, &betas[2]); reproj_errors[2] = ComputeRT(Ut, betas[2], &Rs[2], &ts[2]); FindBetasApprox3(L6x10, rho, &betas[3]); RunGaussNewton(L6x10, rho, &betas[3]); reproj_errors[3] = ComputeRT(Ut, betas[3], &Rs[3], &ts[3]); int best_idx = 1; if (reproj_errors[2] < reproj_errors[1]) { best_idx = 2; } if (reproj_errors[3] < reproj_errors[best_idx]) { best_idx = 3; } proj_matrix->leftCols<3>() = Rs[best_idx]; proj_matrix->rightCols<1>() = ts[best_idx]; return true; } void EPNPEstimator::ChooseControlPoints() { // Take C0 as the reference points centroid: cws_[0].setZero(); for (size_t i = 0; i < points3D_->size(); ++i) { cws_[0] += (*points3D_)[i]; } cws_[0] /= points3D_->size(); Eigen::Matrix PW0(points3D_->size(), 3); for (size_t i = 0; i < points3D_->size(); ++i) { PW0.row(i) = (*points3D_)[i] - cws_[0]; } const Eigen::Matrix3d PW0tPW0 = PW0.transpose() * PW0; Eigen::JacobiSVD svd( PW0tPW0, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Vector3d& D = svd.singularValues(); const Eigen::Matrix3d Ut = svd.matrixU().transpose(); for (int i = 1; i < 4; ++i) { const double k = std::sqrt(D(i - 1) / points3D_->size()); cws_[i] = cws_[0] + k * Ut.row(i - 1).transpose(); } } bool EPNPEstimator::ComputeBarycentricCoordinates() { Eigen::Matrix3d CC; for (int i = 0; i < 3; ++i) { for (int j = 1; j < 4; ++j) { CC(i, j - 1) = cws_[j][i] - cws_[0][i]; } } if (CC.colPivHouseholderQr().rank() < 3) { return false; } const Eigen::Matrix3d CC_inv = CC.inverse(); alphas_.resize(points2D_->size()); for (size_t i = 0; i < points3D_->size(); ++i) { for (int j = 0; j < 3; ++j) { alphas_[i][1 + j] = CC_inv(j, 0) * ((*points3D_)[i][0] - cws_[0][0]) + CC_inv(j, 1) * ((*points3D_)[i][1] - cws_[0][1]) + CC_inv(j, 2) * ((*points3D_)[i][2] - cws_[0][2]); } alphas_[i][0] = 1.0 - alphas_[i][1] - alphas_[i][2] - alphas_[i][3]; } return true; } Eigen::Matrix EPNPEstimator::ComputeM() { Eigen::Matrix M(2 * points2D_->size(), 12); for (size_t i = 0; i < points3D_->size(); ++i) { for (size_t j = 0; j < 4; ++j) { M(2 * i, 3 * j) = alphas_[i][j]; M(2 * i, 3 * j + 1) = 0.0; M(2 * i, 3 * j + 2) = -alphas_[i][j] * (*points2D_)[i].x(); M(2 * i + 1, 3 * j) = 0.0; M(2 * i + 1, 3 * j + 1) = alphas_[i][j]; M(2 * i + 1, 3 * j + 2) = -alphas_[i][j] * (*points2D_)[i].y(); } } return M; } Eigen::Matrix EPNPEstimator::ComputeL6x10( const Eigen::Matrix& Ut) { Eigen::Matrix L6x10; std::array, 4> dv; for (int i = 0; i < 4; ++i) { int a = 0, b = 1; for (int j = 0; j < 6; ++j) { dv[i][j][0] = Ut(11 - i, 3 * a) - Ut(11 - i, 3 * b); dv[i][j][1] = Ut(11 - i, 3 * a + 1) - Ut(11 - i, 3 * b + 1); dv[i][j][2] = Ut(11 - i, 3 * a + 2) - Ut(11 - i, 3 * b + 2); b += 1; if (b > 3) { a += 1; b = a + 1; } } } for (int i = 0; i < 6; ++i) { L6x10(i, 0) = dv[0][i].transpose() * dv[0][i]; L6x10(i, 1) = 2.0 * dv[0][i].transpose() * dv[1][i]; L6x10(i, 2) = dv[1][i].transpose() * dv[1][i]; L6x10(i, 3) = 2.0 * dv[0][i].transpose() * dv[2][i]; L6x10(i, 4) = 2.0 * dv[1][i].transpose() * dv[2][i]; L6x10(i, 5) = dv[2][i].transpose() * dv[2][i]; L6x10(i, 6) = 2.0 * dv[0][i].transpose() * dv[3][i]; L6x10(i, 7) = 2.0 * dv[1][i].transpose() * dv[3][i]; L6x10(i, 8) = 2.0 * dv[2][i].transpose() * dv[3][i]; L6x10(i, 9) = dv[3][i].transpose() * dv[3][i]; } return L6x10; } Eigen::Matrix EPNPEstimator::ComputeRho() { Eigen::Matrix rho; rho[0] = (cws_[0] - cws_[1]).squaredNorm(); rho[1] = (cws_[0] - cws_[2]).squaredNorm(); rho[2] = (cws_[0] - cws_[3]).squaredNorm(); rho[3] = (cws_[1] - cws_[2]).squaredNorm(); rho[4] = (cws_[1] - cws_[3]).squaredNorm(); rho[5] = (cws_[2] - cws_[3]).squaredNorm(); return rho; } // betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] // betas_approx_1 = [B11 B12 B13 B14] void EPNPEstimator::FindBetasApprox1(const Eigen::Matrix& L6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas) { Eigen::Matrix L_6x4; for (int i = 0; i < 6; ++i) { L_6x4(i, 0) = L6x10(i, 0); L_6x4(i, 1) = L6x10(i, 1); L_6x4(i, 2) = L6x10(i, 3); L_6x4(i, 3) = L6x10(i, 6); } Eigen::JacobiSVD> svd( L_6x4, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix b4 = svd.solve(rho); if (b4[0] < 0) { (*betas)[0] = std::sqrt(-b4[0]); (*betas)[1] = -b4[1] / (*betas)[0]; (*betas)[2] = -b4[2] / (*betas)[0]; (*betas)[3] = -b4[3] / (*betas)[0]; } else { (*betas)[0] = std::sqrt(b4[0]); (*betas)[1] = b4[1] / (*betas)[0]; (*betas)[2] = b4[2] / (*betas)[0]; (*betas)[3] = b4[3] / (*betas)[0]; } } // betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] // betas_approx_2 = [B11 B12 B22 ] void EPNPEstimator::FindBetasApprox2(const Eigen::Matrix& L6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas) { Eigen::Matrix L_6x3(6, 3); for (int i = 0; i < 6; ++i) { L_6x3(i, 0) = L6x10(i, 0); L_6x3(i, 1) = L6x10(i, 1); L_6x3(i, 2) = L6x10(i, 2); } Eigen::JacobiSVD> svd( L_6x3, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix b3 = svd.solve(rho); if (b3[0] < 0) { (*betas)[0] = std::sqrt(-b3[0]); (*betas)[1] = (b3[2] < 0) ? std::sqrt(-b3[2]) : 0.0; } else { (*betas)[0] = std::sqrt(b3[0]); (*betas)[1] = (b3[2] > 0) ? std::sqrt(b3[2]) : 0.0; } if (b3[1] < 0) { (*betas)[0] = -(*betas)[0]; } (*betas)[2] = 0.0; (*betas)[3] = 0.0; } // betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] // betas_approx_3 = [B11 B12 B22 B13 B23 ] void EPNPEstimator::FindBetasApprox3(const Eigen::Matrix& L6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas) { Eigen::JacobiSVD> svd( L6x10.leftCols<5>(), Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix b5 = svd.solve(rho); if (b5[0] < 0) { (*betas)[0] = std::sqrt(-b5[0]); (*betas)[1] = (b5[2] < 0) ? std::sqrt(-b5[2]) : 0.0; } else { (*betas)[0] = std::sqrt(b5[0]); (*betas)[1] = (b5[2] > 0) ? std::sqrt(b5[2]) : 0.0; } if (b5[1] < 0) { (*betas)[0] = -(*betas)[0]; } (*betas)[2] = b5[3] / (*betas)[0]; (*betas)[3] = 0.0; } void EPNPEstimator::RunGaussNewton(const Eigen::Matrix& L6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas) { Eigen::Matrix A; Eigen::Matrix b; const int kNumIterations = 5; for (int k = 0; k < kNumIterations; ++k) { for (int i = 0; i < 6; ++i) { A(i, 0) = 2 * L6x10(i, 0) * (*betas)[0] + L6x10(i, 1) * (*betas)[1] + L6x10(i, 3) * (*betas)[2] + L6x10(i, 6) * (*betas)[3]; A(i, 1) = L6x10(i, 1) * (*betas)[0] + 2 * L6x10(i, 2) * (*betas)[1] + L6x10(i, 4) * (*betas)[2] + L6x10(i, 7) * (*betas)[3]; A(i, 2) = L6x10(i, 3) * (*betas)[0] + L6x10(i, 4) * (*betas)[1] + 2 * L6x10(i, 5) * (*betas)[2] + L6x10(i, 8) * (*betas)[3]; A(i, 3) = L6x10(i, 6) * (*betas)[0] + L6x10(i, 7) * (*betas)[1] + L6x10(i, 8) * (*betas)[2] + 2 * L6x10(i, 9) * (*betas)[3]; b(i) = rho[i] - (L6x10(i, 0) * (*betas)[0] * (*betas)[0] + L6x10(i, 1) * (*betas)[0] * (*betas)[1] + L6x10(i, 2) * (*betas)[1] * (*betas)[1] + L6x10(i, 3) * (*betas)[0] * (*betas)[2] + L6x10(i, 4) * (*betas)[1] * (*betas)[2] + L6x10(i, 5) * (*betas)[2] * (*betas)[2] + L6x10(i, 6) * (*betas)[0] * (*betas)[3] + L6x10(i, 7) * (*betas)[1] * (*betas)[3] + L6x10(i, 8) * (*betas)[2] * (*betas)[3] + L6x10(i, 9) * (*betas)[3] * (*betas)[3]); } const Eigen::Vector4d x = A.colPivHouseholderQr().solve(b); (*betas) += x; } } double EPNPEstimator::ComputeRT(const Eigen::Matrix& Ut, const Eigen::Vector4d& betas, Eigen::Matrix3d* R, Eigen::Vector3d* t) { ComputeCcs(betas, Ut); ComputePcs(); SolveForSign(); EstimateRT(R, t); return ComputeTotalReprojectionError(*R, *t); } void EPNPEstimator::ComputeCcs(const Eigen::Vector4d& betas, const Eigen::Matrix& Ut) { for (int i = 0; i < 4; ++i) { ccs_[i][0] = ccs_[i][1] = ccs_[i][2] = 0.0; } for (int i = 0; i < 4; ++i) { for (int j = 0; j < 4; ++j) { for (int k = 0; k < 3; ++k) { ccs_[j][k] += betas[i] * Ut(11 - i, 3 * j + k); } } } } void EPNPEstimator::ComputePcs() { pcs_.resize(points2D_->size()); for (size_t i = 0; i < points3D_->size(); ++i) { for (int j = 0; j < 3; ++j) { pcs_[i][j] = alphas_[i][0] * ccs_[0][j] + alphas_[i][1] * ccs_[1][j] + alphas_[i][2] * ccs_[2][j] + alphas_[i][3] * ccs_[3][j]; } } } void EPNPEstimator::SolveForSign() { if (pcs_[0][2] < 0.0) { for (int i = 0; i < 4; ++i) { ccs_[i] = -ccs_[i]; } for (size_t i = 0; i < points3D_->size(); ++i) { pcs_[i] = -pcs_[i]; } } } void EPNPEstimator::EstimateRT(Eigen::Matrix3d* R, Eigen::Vector3d* t) { Eigen::Vector3d pc0 = Eigen::Vector3d::Zero(); Eigen::Vector3d pw0 = Eigen::Vector3d::Zero(); for (size_t i = 0; i < points3D_->size(); ++i) { pc0 += pcs_[i]; pw0 += (*points3D_)[i]; } pc0 /= points3D_->size(); pw0 /= points3D_->size(); Eigen::Matrix3d abt = Eigen::Matrix3d::Zero(); for (size_t i = 0; i < points3D_->size(); ++i) { for (int j = 0; j < 3; ++j) { abt(j, 0) += (pcs_[i][j] - pc0[j]) * ((*points3D_)[i][0] - pw0[0]); abt(j, 1) += (pcs_[i][j] - pc0[j]) * ((*points3D_)[i][1] - pw0[1]); abt(j, 2) += (pcs_[i][j] - pc0[j]) * ((*points3D_)[i][2] - pw0[2]); } } Eigen::JacobiSVD svd( abt, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix3d& abt_U = svd.matrixU(); const Eigen::Matrix3d& abt_V = svd.matrixV(); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { (*R)(i, j) = abt_U.row(i) * abt_V.row(j).transpose(); } } if (R->determinant() < 0) { Eigen::Matrix3d Abt_v_prime = abt_V; Abt_v_prime.col(2) = -abt_V.col(2); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { (*R)(i, j) = abt_U.row(i) * Abt_v_prime.row(j).transpose(); } } } *t = pc0 - *R * pw0; } double EPNPEstimator::ComputeTotalReprojectionError(const Eigen::Matrix3d& R, const Eigen::Vector3d& t) { Eigen::Matrix3x4d proj_matrix; proj_matrix.leftCols<3>() = R; proj_matrix.rightCols<1>() = t; std::vector residuals; ComputeSquaredReprojectionError( *points2D_, *points3D_, proj_matrix, &residuals); double reproj_error = 0.0; for (const double residual : residuals) { reproj_error += std::sqrt(residual); } return reproj_error; } } // namespace colmap colmap-3.10/src/colmap/estimators/absolute_pose.h000066400000000000000000000165301464745566500222140ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/util/eigen_alignment.h" #include "colmap/util/types.h" #include #include #include namespace colmap { // Analytic solver for the P3P (Perspective-Three-Point) problem. // // The algorithm is based on the following paper: // // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang. Complete Solution // Classification for the Perspective-Three-Point Problem. // http://www.mmrc.iss.ac.cn/~xgao/paper/ieee.pdf class P3PEstimator { public: // The 2D image feature observations. typedef Eigen::Vector2d X_t; // The observed 3D features in the world frame. typedef Eigen::Vector3d Y_t; // The transformation from the world to the camera frame. typedef Eigen::Matrix3x4d M_t; // The minimum number of samples needed to estimate a model. static const int kMinNumSamples = 3; // Estimate the most probable solution of the P3P problem from a set of // three 2D-3D point correspondences. // // @param points2D Normalized 2D image points as 3x2 matrix. // @param points3D 3D world points as 3x3 matrix. // // @return Most probable pose as length-1 vector of a 3x4 matrix. static void Estimate(const std::vector& points2D, const std::vector& points3D, std::vector* models); // Calculate the squared reprojection error given a set of 2D-3D point // correspondences and a projection matrix. // // @param points2D Normalized 2D image points as Nx2 matrix. // @param points3D 3D world points as Nx3 matrix. // @param proj_matrix 3x4 projection matrix. // @param residuals Output vector of residuals. static void Residuals(const std::vector& points2D, const std::vector& points3D, const M_t& proj_matrix, std::vector* residuals); }; // EPNP solver for the PNP (Perspective-N-Point) problem. The solver needs a // minimum of 4 2D-3D correspondences. // // The algorithm is based on the following paper: // // Lepetit, Vincent, Francesc Moreno-Noguer, and Pascal Fua. // "Epnp: An accurate o (n) solution to the pnp problem." // International journal of computer vision 81.2 (2009): 155-166. // // The implementation is based on their original open-source release, but is // ported to Eigen and contains several improvements over the original code. class EPNPEstimator { public: // The 2D image feature observations. typedef Eigen::Vector2d X_t; // The observed 3D features in the world frame. typedef Eigen::Vector3d Y_t; // The transformation from the world to the camera frame. typedef Eigen::Matrix3x4d M_t; // The minimum number of samples needed to estimate a model. static const int kMinNumSamples = 4; // Estimate the most probable solution of the P3P problem from a set of // three 2D-3D point correspondences. // // @param points2D Normalized 2D image points as 3x2 matrix. // @param points3D 3D world points as 3x3 matrix. // // @return Most probable pose as length-1 vector of a 3x4 matrix. static void Estimate(const std::vector& points2D, const std::vector& points3D, std::vector* models); // Calculate the squared reprojection error given a set of 2D-3D point // correspondences and a projection matrix. // // @param points2D Normalized 2D image points as Nx2 matrix. // @param points3D 3D world points as Nx3 matrix. // @param proj_matrix 3x4 projection matrix. // @param residuals Output vector of residuals. static void Residuals(const std::vector& points2D, const std::vector& points3D, const M_t& proj_matrix, std::vector* residuals); private: bool ComputePose(const std::vector& points2D, const std::vector& points3D, Eigen::Matrix3x4d* proj_matrix); void ChooseControlPoints(); bool ComputeBarycentricCoordinates(); Eigen::Matrix ComputeM(); Eigen::Matrix ComputeL6x10( const Eigen::Matrix& Ut); Eigen::Matrix ComputeRho(); void FindBetasApprox1(const Eigen::Matrix& L_6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas); void FindBetasApprox2(const Eigen::Matrix& L_6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas); void FindBetasApprox3(const Eigen::Matrix& L_6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas); void RunGaussNewton(const Eigen::Matrix& L_6x10, const Eigen::Matrix& rho, Eigen::Vector4d* betas); double ComputeRT(const Eigen::Matrix& Ut, const Eigen::Vector4d& betas, Eigen::Matrix3d* R, Eigen::Vector3d* t); void ComputeCcs(const Eigen::Vector4d& betas, const Eigen::Matrix& Ut); void ComputePcs(); void SolveForSign(); void EstimateRT(Eigen::Matrix3d* R, Eigen::Vector3d* t); double ComputeTotalReprojectionError(const Eigen::Matrix3d& R, const Eigen::Vector3d& t); const std::vector* points2D_ = nullptr; const std::vector* points3D_ = nullptr; std::vector pcs_; std::vector alphas_; std::array cws_; std::array ccs_; }; } // namespace colmap colmap-3.10/src/colmap/estimators/absolute_pose_test.cc000066400000000000000000000204161464745566500234070ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/absolute_pose.h" #include "colmap/estimators/essential_matrix.h" #include "colmap/geometry/pose.h" #include "colmap/geometry/rigid3.h" #include "colmap/math/random.h" #include "colmap/optim/ransac.h" #include "colmap/util/eigen_alignment.h" #include #include namespace colmap { namespace { TEST(AbsolutePose, P3P) { const std::vector points3D = { Eigen::Vector3d(1, 1, 1), Eigen::Vector3d(0, 1, 1), Eigen::Vector3d(3, 1.0, 4), Eigen::Vector3d(3, 1.1, 4), Eigen::Vector3d(3, 1.2, 4), Eigen::Vector3d(3, 1.3, 4), Eigen::Vector3d(3, 1.4, 4), Eigen::Vector3d(2, 1, 7), }; auto points3D_faulty = points3D; for (size_t i = 0; i < points3D.size(); ++i) { points3D_faulty[i](0) = 20; } // NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter) for (double qx = 0; qx < 1; qx += 0.2) { // NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter) for (double tx = 0; tx < 1; tx += 0.1) { const Rigid3d expected_cam_from_world( Eigen::Quaterniond(1, qx, 0, 0).normalized(), Eigen::Vector3d(tx, 0, 0)); // Project points to camera coordinate system. std::vector points2D; for (size_t i = 0; i < points3D.size(); ++i) { points2D.push_back( (expected_cam_from_world * points3D[i]).hnormalized()); } RANSACOptions options; options.max_error = 1e-5; RANSAC ransac(options); const auto report = ransac.Estimate(points2D, points3D); EXPECT_TRUE(report.success); EXPECT_LT((expected_cam_from_world.ToMatrix() - report.model).norm(), 1e-2); // Test residuals of exact points. std::vector residuals; P3PEstimator::Residuals(points2D, points3D, report.model, &residuals); for (size_t i = 0; i < residuals.size(); ++i) { EXPECT_TRUE(residuals[i] < 1e-3); } // Test residuals of faulty points. P3PEstimator::Residuals( points2D, points3D_faulty, report.model, &residuals); for (size_t i = 0; i < residuals.size(); ++i) { EXPECT_TRUE(residuals[i] > 0.1); } } } } TEST(AbsolutePose, EPNP) { const std::vector points3D = { Eigen::Vector3d(1, 1, 1), Eigen::Vector3d(0, 1, 1), Eigen::Vector3d(3, 1.0, 4), Eigen::Vector3d(3, 1.1, 4), Eigen::Vector3d(3, 1.2, 4), Eigen::Vector3d(3, 1.3, 4), Eigen::Vector3d(3, 1.4, 4), Eigen::Vector3d(2, 1, 7), }; auto points3D_faulty = points3D; for (size_t i = 0; i < points3D.size(); ++i) { points3D_faulty[i](0) = 20; } // NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter) for (double qx = 0; qx < 1; qx += 0.2) { // NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter) for (double tx = 0; tx < 1; tx += 0.1) { const Rigid3d expected_cam_from_world( Eigen::Quaterniond(1, qx, 0, 0).normalized(), Eigen::Vector3d(tx, 0, 0)); // Project points to camera coordinate system. std::vector points2D; for (size_t i = 0; i < points3D.size(); ++i) { points2D.push_back( (expected_cam_from_world * points3D[i]).hnormalized()); } RANSACOptions options; options.max_error = 1e-5; RANSAC ransac(options); const auto report = ransac.Estimate(points2D, points3D); EXPECT_TRUE(report.success); EXPECT_LT((expected_cam_from_world.ToMatrix() - report.model).norm(), 1e-4); // Test residuals of exact points. std::vector residuals; EPNPEstimator::Residuals(points2D, points3D, report.model, &residuals); for (size_t i = 0; i < residuals.size(); ++i) { EXPECT_TRUE(residuals[i] < 1e-3); } // Test residuals of faulty points. EPNPEstimator::Residuals( points2D, points3D_faulty, report.model, &residuals); for (size_t i = 0; i < residuals.size(); ++i) { EXPECT_TRUE(residuals[i] > 0.1); } } } } TEST(AbsolutePose, EPNP_BrokenSolveSignCase) { std::vector points2D; points2D.emplace_back(-2.6783007931074532e-01, 5.3457197430746251e-01); points2D.emplace_back(-4.2629907287470264e-01, 7.5623350319519789e-01); points2D.emplace_back(-1.6767413005963930e-01, -1.3387172544910089e-01); points2D.emplace_back(-5.6616329720373559e-02, 2.3621156497739373e-01); points2D.emplace_back(-1.7721225948969935e-01, 2.3395366792735982e-02); points2D.emplace_back(-5.1836259886632222e-02, -4.4380694271927049e-02); points2D.emplace_back(-3.5897765845560037e-01, 1.6252721078589397e-01); points2D.emplace_back(2.7057324473684058e-01, -1.4067450104631887e-01); points2D.emplace_back(-2.5811166424334520e-01, 8.0167171300227366e-02); points2D.emplace_back(2.0239567448222310e-02, -3.2845953375344145e-01); points2D.emplace_back(4.2571014715170657e-01, -2.8321173570154773e-01); points2D.emplace_back(-5.4597596412987237e-01, 9.1431935871671977e-02); std::vector points3D; points3D.emplace_back( 4.4276865308679305e+00, -1.3384364366019632e+00, -3.5997423085253892e+00); points3D.emplace_back( 2.7278555252512309e+00, -3.8152996187231392e-01, -2.6558518399902824e+00); points3D.emplace_back( 4.8548566083054894e+00, -1.4756197433631739e+00, -6.8274946022490501e-01); points3D.emplace_back( 3.1523013527998449e+00, -1.3377020437938025e+00, -1.6443269301929087e+00); points3D.emplace_back( 3.8551679771512073e+00, -1.0557700545885551e+00, -1.1695994508851486e+00); points3D.emplace_back( 5.9571373150353812e+00, -2.6120646101684555e+00, -1.0841441206050342e+00); points3D.emplace_back( 6.3287088499358894e+00, -1.1761274755817175e+00, -2.5951879774151583e+00); points3D.emplace_back( 2.3005305990121250e+00, -1.4019796626800123e+00, -4.4485464455072321e-01); points3D.emplace_back( 5.9816859934587354e+00, -1.4211814511691452e+00, -2.0285923889293449e+00); points3D.emplace_back( 5.2543344690665457e+00, -2.3389255564264144e+00, 4.3708173185524052e-01); points3D.emplace_back( 3.2181599245991688e+00, -2.8906671988445098e+00, 2.6825718150064348e-01); points3D.emplace_back( 4.4592895306946758e+00, -9.1235241641579902e-03, -1.6555237117970871e+00); std::vector models; EPNPEstimator::Estimate(points2D, points3D, &models); ASSERT_EQ(models.size(), 1); double reproj = 0.0; for (size_t i = 0; i < points3D.size(); ++i) { reproj += ((models[0] * points3D[i].homogeneous()).hnormalized() - points2D[i]) .norm(); } EXPECT_TRUE(reproj < 0.2); } } // namespace } // namespace colmap colmap-3.10/src/colmap/estimators/affine_transform.cc000066400000000000000000000100121464745566500230160ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/affine_transform.h" #include "colmap/util/eigen_alignment.h" #include "colmap/util/logging.h" #include namespace colmap { void AffineTransformEstimator::Estimate(const std::vector& points1, const std::vector& points2, std::vector* models) { THROW_CHECK_EQ(points1.size(), points2.size()); THROW_CHECK_GE(points1.size(), 3); THROW_CHECK(models != nullptr); models->clear(); // Sets up the linear system that we solve to obtain a least squared solution // for the affine transformation. Eigen::MatrixXd C(2 * points1.size(), 6); C.setZero(); Eigen::VectorXd b(2 * points1.size(), 1); for (size_t i = 0; i < points1.size(); ++i) { const Eigen::Vector2d& x1 = points1[i]; const Eigen::Vector2d& x2 = points2[i]; C(2 * i, 0) = x1(0); C(2 * i, 1) = x1(1); C(2 * i, 2) = 1.0f; b(2 * i) = x2(0); C(2 * i + 1, 3) = x1(0); C(2 * i + 1, 4) = x1(1); C(2 * i + 1, 5) = 1.0f; b(2 * i + 1) = x2(1); } const Eigen::VectorXd nullspace = C.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(b); Eigen::Map> A_t(nullspace.data()); models->resize(1); (*models)[0] = A_t.transpose(); } void AffineTransformEstimator::Residuals(const std::vector& points1, const std::vector& points2, const M_t& A, std::vector* residuals) { THROW_CHECK_EQ(points1.size(), points2.size()); residuals->resize(points1.size()); // Note that this code might not be as nice as Eigen expressions, // but it is significantly faster in various tests. const double A_00 = A(0, 0); const double A_01 = A(0, 1); const double A_02 = A(0, 2); const double A_10 = A(1, 0); const double A_11 = A(1, 1); const double A_12 = A(1, 2); for (size_t i = 0; i < points1.size(); ++i) { const double s_0 = points1[i](0); const double s_1 = points1[i](1); const double d_0 = points2[i](0); const double d_1 = points2[i](1); const double pd_0 = A_00 * s_0 + A_01 * s_1 + A_02; const double pd_1 = A_10 * s_0 + A_11 * s_1 + A_12; const double dd_0 = d_0 - pd_0; const double dd_1 = d_1 - pd_1; (*residuals)[i] = dd_0 * dd_0 + dd_1 * dd_1; } } } // namespace colmap colmap-3.10/src/colmap/estimators/affine_transform.h000066400000000000000000000047671464745566500227040ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/util/eigen_alignment.h" #include "colmap/util/types.h" #include #include namespace colmap { class AffineTransformEstimator { public: typedef Eigen::Vector2d X_t; typedef Eigen::Vector2d Y_t; typedef Eigen::Matrix M_t; // The minimum number of samples needed to estimate a model. static const int kMinNumSamples = 3; // Estimate the affine transformation from at least 3 correspondences. static void Estimate(const std::vector& points1, const std::vector& points2, std::vector* models); // Compute the squared transformation error. static void Residuals(const std::vector& points1, const std::vector& points2, const M_t& E, std::vector* residuals); }; } // namespace colmap colmap-3.10/src/colmap/estimators/affine_transform_test.cc000066400000000000000000000051101464745566500240600ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/affine_transform.h" #include "colmap/util/eigen_alignment.h" #include #include namespace colmap { namespace { TEST(AffineTransform, Nominal) { for (int x = 0; x < 10; ++x) { Eigen::Matrix A; A << x / 10.0, 0.2, 0.3, 30, 0.2, 0.1; std::vector src; src.emplace_back(x, 0); src.emplace_back(1, 0); src.emplace_back(2, 1); std::vector dst; for (size_t i = 0; i < 3; ++i) { dst.push_back(A * src[i].homogeneous()); } AffineTransformEstimator estimator; std::vector> models; estimator.Estimate(src, dst, &models); ASSERT_EQ(models.size(), 1); std::vector residuals; estimator.Residuals(src, dst, models[0], &residuals); EXPECT_EQ(residuals.size(), 3); for (size_t i = 0; i < 3; ++i) { EXPECT_LT(residuals[i], 1e-6); } } } } // namespace } // namespace colmap colmap-3.10/src/colmap/estimators/alignment.cc000066400000000000000000000450411464745566500214630ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/alignment.h" #include "colmap/estimators/similarity_transform.h" #include "colmap/geometry/pose.h" #include "colmap/optim/loransac.h" #include "colmap/scene/projection.h" #include "colmap/util/logging.h" #include namespace colmap { namespace { struct ReconstructionAlignmentEstimator { static const int kMinNumSamples = 3; typedef const Image* X_t; typedef const Image* Y_t; typedef Sim3d M_t; void SetMaxReprojError(const double max_reproj_error) { max_squared_reproj_error_ = max_reproj_error * max_reproj_error; } void SetReconstructions(const Reconstruction* src_reconstruction, const Reconstruction* tgt_reconstruction) { THROW_CHECK_NOTNULL(src_reconstruction); THROW_CHECK_NOTNULL(tgt_reconstruction); src_reconstruction_ = src_reconstruction; tgt_reconstruction_ = tgt_reconstruction; } // Estimate 3D similarity transform from corresponding projection centers. void Estimate(const std::vector& src_images, const std::vector& tgt_images, std::vector* models) const { THROW_CHECK_GE(src_images.size(), 3); THROW_CHECK_GE(tgt_images.size(), 3); THROW_CHECK_EQ(src_images.size(), tgt_images.size()); THROW_CHECK(models != nullptr); models->clear(); std::vector proj_centers1(src_images.size()); std::vector proj_centers2(tgt_images.size()); for (size_t i = 0; i < src_images.size(); ++i) { THROW_CHECK_EQ(src_images[i]->ImageId(), tgt_images[i]->ImageId()); proj_centers1[i] = src_images[i]->ProjectionCenter(); proj_centers2[i] = tgt_images[i]->ProjectionCenter(); } Sim3d tgt_from_src; if (!EstimateSim3d(proj_centers1, proj_centers2, tgt_from_src)) { return; } models->resize(1); (*models)[0] = tgt_from_src; } // For each image, determine the ratio of 3D points that correctly project // from one image to the other image and vice versa for the given // tgt_from_src. The residual is then defined as 1 minus this ratio, i.e., an // error threshold of 0.3 means that 70% of the points for that image must // reproject within the given maximum reprojection error threshold. void Residuals(const std::vector& src_images, const std::vector& tgt_images, const M_t& tgt_from_src, std::vector* residuals) const { THROW_CHECK_EQ(src_images.size(), tgt_images.size()); THROW_CHECK_NOTNULL(src_reconstruction_); THROW_CHECK_NOTNULL(tgt_reconstruction_); const Sim3d srcFromTgt = Inverse(tgt_from_src); residuals->resize(src_images.size()); for (size_t i = 0; i < src_images.size(); ++i) { const auto& src_image = *src_images[i]; const auto& tgt_image = *tgt_images[i]; THROW_CHECK_EQ(src_image.ImageId(), tgt_image.ImageId()); const auto& src_camera = src_reconstruction_->Camera(src_image.CameraId()); const auto& tgt_camera = tgt_reconstruction_->Camera(tgt_image.CameraId()); const Eigen::Matrix3x4d src_cam_from_world = src_image.CamFromWorld().ToMatrix(); const Eigen::Matrix3x4d tgt_cam_from_world = tgt_image.CamFromWorld().ToMatrix(); THROW_CHECK_EQ(src_image.NumPoints2D(), tgt_image.NumPoints2D()); size_t num_inliers = 0; size_t num_common_points = 0; for (point2D_t point2D_idx = 0; point2D_idx < src_image.NumPoints2D(); ++point2D_idx) { // Check if both images have a 3D point. const auto& src_point2D = src_image.Point2D(point2D_idx); if (!src_point2D.HasPoint3D()) { continue; } const auto& tgt_point2D = tgt_image.Point2D(point2D_idx); if (!tgt_point2D.HasPoint3D()) { continue; } num_common_points += 1; const Eigen::Vector3d src_point_in_tgt = tgt_from_src * src_reconstruction_->Point3D(src_point2D.point3D_id).xyz; if (CalculateSquaredReprojectionError(tgt_point2D.xy, src_point_in_tgt, tgt_cam_from_world, tgt_camera) > max_squared_reproj_error_) { continue; } const Eigen::Vector3d tgt_point_in_src = srcFromTgt * tgt_reconstruction_->Point3D(tgt_point2D.point3D_id).xyz; if (CalculateSquaredReprojectionError(src_point2D.xy, tgt_point_in_src, src_cam_from_world, src_camera) > max_squared_reproj_error_) { continue; } num_inliers += 1; } if (num_common_points == 0) { (*residuals)[i] = 1.0; } else { const double negative_inlier_ratio = 1.0 - static_cast(num_inliers) / static_cast(num_common_points); (*residuals)[i] = negative_inlier_ratio * negative_inlier_ratio; } } } private: double max_squared_reproj_error_ = 0.0; const Reconstruction* src_reconstruction_ = nullptr; const Reconstruction* tgt_reconstruction_ = nullptr; }; } // namespace bool AlignReconstructionToLocations( const Reconstruction& src_reconstruction, const std::vector& tgt_image_names, const std::vector& tgt_image_locations, const int min_common_images, const RANSACOptions& ransac_options, Sim3d* tgt_from_src) { THROW_CHECK_GE(min_common_images, 3); THROW_CHECK_EQ(tgt_image_names.size(), tgt_image_locations.size()); // Find out which images are contained in the reconstruction and get the // positions of their camera centers. std::unordered_set common_image_ids; std::vector src; std::vector dst; for (size_t i = 0; i < tgt_image_names.size(); ++i) { const class Image* src_image = src_reconstruction.FindImageWithName(tgt_image_names[i]); if (src_image == nullptr) { continue; } if (!src_reconstruction.IsImageRegistered(src_image->ImageId())) { continue; } // Ignore duplicate images. if (common_image_ids.count(src_image->ImageId()) > 0) { continue; } common_image_ids.insert(src_image->ImageId()); src.push_back(src_image->ProjectionCenter()); dst.push_back(tgt_image_locations[i]); } // Only compute the alignment if there are enough correspondences. if (common_image_ids.size() < static_cast(min_common_images)) { return false; } LORANSAC, SimilarityTransformEstimator<3, true>> ransac(ransac_options); const auto report = ransac.Estimate(src, dst); if (report.support.num_inliers < static_cast(min_common_images)) { return false; } if (tgt_from_src != nullptr) { *tgt_from_src = Sim3d::FromMatrix(report.model); } return true; } bool AlignReconstructionsViaReprojections( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, const double min_inlier_observations, const double max_reproj_error, Sim3d* tgt_from_src) { THROW_CHECK_GE(min_inlier_observations, 0.0); THROW_CHECK_LE(min_inlier_observations, 1.0); RANSACOptions ransac_options; ransac_options.max_error = 1.0 - min_inlier_observations; ransac_options.min_inlier_ratio = 0.2; LORANSAC ransac(ransac_options); ransac.estimator.SetMaxReprojError(max_reproj_error); ransac.estimator.SetReconstructions(&src_reconstruction, &tgt_reconstruction); ransac.local_estimator.SetMaxReprojError(max_reproj_error); ransac.local_estimator.SetReconstructions(&src_reconstruction, &tgt_reconstruction); const std::vector> common_image_ids = src_reconstruction.FindCommonRegImageIds(tgt_reconstruction); if (common_image_ids.size() < 3) { return false; } std::vector src_images(common_image_ids.size()); std::vector tgt_images(common_image_ids.size()); for (size_t i = 0; i < common_image_ids.size(); ++i) { src_images[i] = &src_reconstruction.Image(common_image_ids[i].first); tgt_images[i] = &tgt_reconstruction.Image(common_image_ids[i].second); } const auto report = ransac.Estimate(src_images, tgt_images); if (report.success) { *tgt_from_src = report.model; } return report.success; } bool AlignReconstructionsViaProjCenters( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, const double max_proj_center_error, Sim3d* tgt_from_src) { THROW_CHECK_GT(max_proj_center_error, 0); std::vector ref_image_names; std::vector ref_proj_centers; for (const auto& image : tgt_reconstruction.Images()) { if (image.second.IsRegistered()) { ref_image_names.push_back(image.second.Name()); ref_proj_centers.push_back(image.second.ProjectionCenter()); } } Sim3d tform; RANSACOptions ransac_options; ransac_options.max_error = max_proj_center_error; return AlignReconstructionToLocations(src_reconstruction, ref_image_names, ref_proj_centers, /*min_common_images=*/3, ransac_options, tgt_from_src); } std::vector ComputeImageAlignmentError( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, const Sim3d& tgt_from_src) { const std::vector> common_image_ids = src_reconstruction.FindCommonRegImageIds(tgt_reconstruction); const int num_common_images = common_image_ids.size(); std::vector errors; errors.reserve(num_common_images); for (const auto& image_ids : common_image_ids) { const auto& src_image = src_reconstruction.Image(image_ids.first); const Rigid3d tgt_world_from_src_cam = Inverse(TransformCameraWorld(tgt_from_src, src_image.CamFromWorld())); const Rigid3d tgt_world_from_tgt_cam = Inverse(tgt_reconstruction.Image(image_ids.second).CamFromWorld()); ImageAlignmentError error; error.image_name = src_image.Name(); error.rotation_error_deg = RadToDeg(tgt_world_from_src_cam.rotation.angularDistance( tgt_world_from_tgt_cam.rotation)); error.proj_center_error = (tgt_world_from_src_cam.translation - tgt_world_from_tgt_cam.translation) .norm(); errors.push_back(error); } return errors; } bool AlignReconstructionsViaPoints(const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, const size_t min_common_observations, const double max_error, const double min_inlier_ratio, Sim3d* tgt_from_src) { THROW_CHECK_GT(min_common_observations, 0); THROW_CHECK_GT(max_error, 0.0); THROW_CHECK_GE(min_inlier_ratio, 0.0); THROW_CHECK_LE(min_inlier_ratio, 1.0); std::vector src_xyz; std::vector tgt_xyz; std::unordered_map counts; // Associate 3D points using point2D_idx for (const auto& src_point3D : src_reconstruction.Points3D()) { counts.clear(); // Count how often a 3D point in tgt is associated to this 3D point. for (const auto& track_el : src_point3D.second.track.Elements()) { if (!tgt_reconstruction.IsImageRegistered(track_el.image_id)) { continue; } const Point2D& tgt_point2D = tgt_reconstruction.Image(track_el.image_id) .Point2D(track_el.point2D_idx); if (tgt_point2D.HasPoint3D()) { if (counts.find(tgt_point2D.point3D_id) != counts.end()) { counts[tgt_point2D.point3D_id]++; } else { counts[tgt_point2D.point3D_id] = 0; } } } if (counts.empty()) { continue; } // The 3D point in tgt who is associated the most is selected auto best_p3D = std::max_element(counts.begin(), counts.end(), [](const std::pair& p1, const std::pair& p2) { return p1.second < p2.second; }); if (best_p3D->second >= min_common_observations) { src_xyz.push_back(src_point3D.second.xyz); tgt_xyz.push_back(tgt_reconstruction.Point3D(best_p3D->first).xyz); } } THROW_CHECK_EQ(src_xyz.size(), tgt_xyz.size()); LOG(INFO) << "Found " << src_xyz.size() << " / " << src_reconstruction.NumPoints3D() << " valid correspondences."; RANSACOptions ransac_options; ransac_options.max_error = max_error; ransac_options.min_inlier_ratio = min_inlier_ratio; LORANSAC, SimilarityTransformEstimator<3, true>> ransac(ransac_options); const auto report = ransac.Estimate(src_xyz, tgt_xyz); if (report.success) { *tgt_from_src = Sim3d::FromMatrix(report.model); } return report.success; } bool MergeReconstructions(const double max_reproj_error, const Reconstruction& src_reconstruction, Reconstruction& tgt_reconstruction) { Sim3d tgt_from_src; if (!AlignReconstructionsViaReprojections(src_reconstruction, tgt_reconstruction, /*min_inlier_observations=*/0.3, max_reproj_error, &tgt_from_src)) { return false; } // Find common and missing images in the two reconstructions. std::unordered_set common_image_ids; common_image_ids.reserve(src_reconstruction.NumRegImages()); std::unordered_set missing_image_ids; missing_image_ids.reserve(src_reconstruction.NumRegImages()); for (const auto& image_id : src_reconstruction.RegImageIds()) { if (tgt_reconstruction.ExistsImage(image_id)) { common_image_ids.insert(image_id); } else { missing_image_ids.insert(image_id); } } // Register the missing images in this src_reconstruction. for (const auto image_id : missing_image_ids) { auto src_image = src_reconstruction.Image(image_id); src_image.SetRegistered(false); src_image.CamFromWorld() = TransformCameraWorld(tgt_from_src, src_image.CamFromWorld()); tgt_reconstruction.AddImage(src_image); tgt_reconstruction.RegisterImage(image_id); if (!tgt_reconstruction.ExistsCamera(src_image.CameraId())) { tgt_reconstruction.AddCamera( src_reconstruction.Camera(src_image.CameraId())); } } // Merge the two point clouds using the following two rules: // - copy points to this src_reconstruction with non-conflicting tracks, // i.e. points that do not have an already triangulated observation // in this src_reconstruction. // - merge tracks that are unambiguous, i.e. only merge points in the two // reconstructions if they have a one-to-one mapping. // Note that in both cases no cheirality or reprojection test is performed. for (const auto& point3D : src_reconstruction.Points3D()) { Track new_track; Track old_track; std::unordered_set old_point3D_ids; for (const auto& track_el : point3D.second.track.Elements()) { if (common_image_ids.count(track_el.image_id) > 0) { const auto& point2D = tgt_reconstruction.Image(track_el.image_id) .Point2D(track_el.point2D_idx); if (point2D.HasPoint3D()) { old_track.AddElement(track_el); old_point3D_ids.insert(point2D.point3D_id); } else { new_track.AddElement(track_el); } } else if (missing_image_ids.count(track_el.image_id) > 0) { tgt_reconstruction.Image(track_el.image_id) .ResetPoint3DForPoint2D(track_el.point2D_idx); new_track.AddElement(track_el); } } const bool create_new_point = new_track.Length() >= 2; const bool merge_new_and_old_point = (new_track.Length() + old_track.Length()) >= 2 && old_point3D_ids.size() == 1; if (create_new_point || merge_new_and_old_point) { const Eigen::Vector3d xyz = tgt_from_src * point3D.second.xyz; const auto point3D_id = tgt_reconstruction.AddPoint3D(xyz, new_track, point3D.second.color); if (old_point3D_ids.size() == 1) { tgt_reconstruction.MergePoints3D(point3D_id, *old_point3D_ids.begin()); } } } return true; } } // namespace colmap colmap-3.10/src/colmap/estimators/alignment.h000066400000000000000000000106031464745566500213210ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/geometry/sim3.h" #include "colmap/optim/ransac.h" #include "colmap/scene/reconstruction.h" namespace colmap { bool AlignReconstructionToLocations( const Reconstruction& reconstruction, const std::vector& image_names, const std::vector& locations, int min_common_images, const RANSACOptions& ransac_options, Sim3d* tform); // Robustly compute alignment between reconstructions by finding images that // are registered in both reconstructions. The alignment is then estimated // robustly inside RANSAC from corresponding projection centers. An alignment // is verified by reprojecting common 3D point observations. // The min_inlier_observations threshold determines how many observations // in a common image must reproject within the given threshold. bool AlignReconstructionsViaReprojections( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, double min_inlier_observations, double max_reproj_error, Sim3d* tgt_from_src); // Robustly compute alignment between reconstructions by finding images that // are registered in both reconstructions. The alignment is then estimated // robustly inside RANSAC from corresponding projection centers and by // minimizing the Euclidean distance between them in world space. bool AlignReconstructionsViaProjCenters( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, double max_proj_center_error, Sim3d* tgt_from_src); // Robustly compute the alignment between reconstructions that share the // same 2D points. It is estimated by minimizing the 3D distance between // corresponding 3D points. bool AlignReconstructionsViaPoints(const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, size_t min_common_observations, double max_error, double min_inlier_ratio, Sim3d* tgt_from_src); // Compute image alignment errors in the target coordinate frame. struct ImageAlignmentError { std::string image_name; double rotation_error_deg = -1; double proj_center_error = -1; }; std::vector ComputeImageAlignmentError( const Reconstruction& src_reconstruction, const Reconstruction& tgt_reconstruction, const Sim3d& tgt_from_src); // Aligns the source to the target reconstruction and merges cameras, images, // points3D into the target using the alignment. Returns false on failure. bool MergeReconstructions(double max_reproj_error, const Reconstruction& src_reconstruction, Reconstruction& tgt_reconstruction); } // namespace colmap colmap-3.10/src/colmap/estimators/alignment_test.cc000066400000000000000000000117231464745566500225220ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/alignment.h" #include "colmap/geometry/sim3.h" #include "colmap/math/random.h" #include "colmap/scene/database.h" #include "colmap/scene/reconstruction.h" #include "colmap/scene/synthetic.h" #include "colmap/util/testing.h" #include namespace colmap { Sim3d TestSim3d() { return Sim3d(RandomUniformReal(0.5, 2), Eigen::Quaterniond::UnitRandom(), Eigen::Vector3d::Random()); } void ExpectEqualSim3d(const Sim3d& gt_tgt_from_src, const Sim3d& tgt_from_src) { EXPECT_NEAR(gt_tgt_from_src.scale, tgt_from_src.scale, 1e-6); EXPECT_LT(gt_tgt_from_src.rotation.angularDistance(tgt_from_src.rotation), 1e-6); EXPECT_LT((gt_tgt_from_src.translation - tgt_from_src.translation).norm(), 1e-6); } Reconstruction GenerateReconstructionForAlignment() { // const std::string database_path = CreateTestDir() + "/database.db"; // Database database(database_path); Reconstruction reconstruction; SyntheticDatasetOptions synthetic_dataset_options; synthetic_dataset_options.num_cameras = 2; synthetic_dataset_options.num_images = 20; synthetic_dataset_options.num_points3D = 50; synthetic_dataset_options.point2D_stddev = 0; SynthesizeDataset(synthetic_dataset_options, &reconstruction); return reconstruction; } TEST(Alignment, AlignReconstructionsViaReprojections) { Reconstruction src_reconstruction = GenerateReconstructionForAlignment(); Reconstruction tgt_reconstruction = src_reconstruction; Sim3d gt_tgt_from_src = TestSim3d(); tgt_reconstruction.Transform(gt_tgt_from_src); Sim3d tgt_from_src; THROW_CHECK( AlignReconstructionsViaReprojections(src_reconstruction, tgt_reconstruction, /*min_inlier_observations=*/0.9, /*max_reproj_error=*/2, &tgt_from_src)); ExpectEqualSim3d(gt_tgt_from_src, tgt_from_src); } TEST(Alignment, AlignReconstructionsViaProjCenters) { Reconstruction src_reconstruction = GenerateReconstructionForAlignment(); Reconstruction tgt_reconstruction = src_reconstruction; Sim3d gt_tgt_from_src = TestSim3d(); tgt_reconstruction.Transform(gt_tgt_from_src); Sim3d tgt_from_src; THROW_CHECK(AlignReconstructionsViaProjCenters(src_reconstruction, tgt_reconstruction, /*max_proj_center_error=*/0.1, &tgt_from_src)); ExpectEqualSim3d(gt_tgt_from_src, tgt_from_src); } TEST(Alignment, AlignReconstructionsViaPoints) { Reconstruction src_reconstruction = GenerateReconstructionForAlignment(); Reconstruction tgt_reconstruction = src_reconstruction; Sim3d gt_tgt_from_src = TestSim3d(); tgt_reconstruction.Transform(gt_tgt_from_src); Sim3d tgt_from_src; THROW_CHECK(AlignReconstructionsViaPoints(src_reconstruction, tgt_reconstruction, /*min_common_observations=*/3, /*max_error=*/0.01, /*min_inlier_ratio=*/0.9, &tgt_from_src)); ExpectEqualSim3d(gt_tgt_from_src, tgt_from_src); } } // namespace colmap colmap-3.10/src/colmap/estimators/bundle_adjustment.cc000066400000000000000000000761571464745566500232300ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/bundle_adjustment.h" #include "colmap/estimators/cost_functions.h" #include "colmap/scene/projection.h" #include "colmap/sensor/models.h" #include "colmap/util/misc.h" #include "colmap/util/threading.h" #include "colmap/util/timer.h" #include namespace colmap { //////////////////////////////////////////////////////////////////////////////// // BundleAdjustmentOptions //////////////////////////////////////////////////////////////////////////////// ceres::LossFunction* BundleAdjustmentOptions::CreateLossFunction() const { ceres::LossFunction* loss_function = nullptr; switch (loss_function_type) { case LossFunctionType::TRIVIAL: loss_function = new ceres::TrivialLoss(); break; case LossFunctionType::SOFT_L1: loss_function = new ceres::SoftLOneLoss(loss_function_scale); break; case LossFunctionType::CAUCHY: loss_function = new ceres::CauchyLoss(loss_function_scale); break; } THROW_CHECK_NOTNULL(loss_function); return loss_function; } bool BundleAdjustmentOptions::Check() const { CHECK_OPTION_GE(loss_function_scale, 0); return true; } //////////////////////////////////////////////////////////////////////////////// // BundleAdjustmentConfig //////////////////////////////////////////////////////////////////////////////// BundleAdjustmentConfig::BundleAdjustmentConfig() {} size_t BundleAdjustmentConfig::NumImages() const { return image_ids_.size(); } size_t BundleAdjustmentConfig::NumPoints() const { return variable_point3D_ids_.size() + constant_point3D_ids_.size(); } size_t BundleAdjustmentConfig::NumConstantCamIntrinsics() const { return constant_intrinsics_.size(); } size_t BundleAdjustmentConfig::NumConstantCamPoses() const { return constant_cam_poses_.size(); } size_t BundleAdjustmentConfig::NumConstantCamPositions() const { return constant_cam_positions_.size(); } size_t BundleAdjustmentConfig::NumVariablePoints() const { return variable_point3D_ids_.size(); } size_t BundleAdjustmentConfig::NumConstantPoints() const { return constant_point3D_ids_.size(); } size_t BundleAdjustmentConfig::NumResiduals( const Reconstruction& reconstruction) const { // Count the number of observations for all added images. size_t num_observations = 0; for (const image_t image_id : image_ids_) { num_observations += reconstruction.Image(image_id).NumPoints3D(); } // Count the number of observations for all added 3D points that are not // already added as part of the images above. auto NumObservationsForPoint = [this, &reconstruction](const point3D_t point3D_id) { size_t num_observations_for_point = 0; const auto& point3D = reconstruction.Point3D(point3D_id); for (const auto& track_el : point3D.track.Elements()) { if (image_ids_.count(track_el.image_id) == 0) { num_observations_for_point += 1; } } return num_observations_for_point; }; for (const auto point3D_id : variable_point3D_ids_) { num_observations += NumObservationsForPoint(point3D_id); } for (const auto point3D_id : constant_point3D_ids_) { num_observations += NumObservationsForPoint(point3D_id); } return 2 * num_observations; } void BundleAdjustmentConfig::AddImage(const image_t image_id) { image_ids_.insert(image_id); } bool BundleAdjustmentConfig::HasImage(const image_t image_id) const { return image_ids_.find(image_id) != image_ids_.end(); } void BundleAdjustmentConfig::RemoveImage(const image_t image_id) { image_ids_.erase(image_id); } void BundleAdjustmentConfig::SetConstantCamIntrinsics( const camera_t camera_id) { constant_intrinsics_.insert(camera_id); } void BundleAdjustmentConfig::SetVariableCamIntrinsics( const camera_t camera_id) { constant_intrinsics_.erase(camera_id); } bool BundleAdjustmentConfig::HasConstantCamIntrinsics( const camera_t camera_id) const { return constant_intrinsics_.find(camera_id) != constant_intrinsics_.end(); } void BundleAdjustmentConfig::SetConstantCamPose(const image_t image_id) { THROW_CHECK(HasImage(image_id)); THROW_CHECK(!HasConstantCamPositions(image_id)); constant_cam_poses_.insert(image_id); } void BundleAdjustmentConfig::SetVariableCamPose(const image_t image_id) { constant_cam_poses_.erase(image_id); } bool BundleAdjustmentConfig::HasConstantCamPose(const image_t image_id) const { return constant_cam_poses_.find(image_id) != constant_cam_poses_.end(); } void BundleAdjustmentConfig::SetConstantCamPositions( const image_t image_id, const std::vector& idxs) { THROW_CHECK_GT(idxs.size(), 0); THROW_CHECK_LE(idxs.size(), 3); THROW_CHECK(HasImage(image_id)); THROW_CHECK(!HasConstantCamPose(image_id)); THROW_CHECK(!VectorContainsDuplicateValues(idxs)) << "Tvec indices must not contain duplicates"; constant_cam_positions_.emplace(image_id, idxs); } void BundleAdjustmentConfig::RemoveConstantCamPositions( const image_t image_id) { constant_cam_positions_.erase(image_id); } bool BundleAdjustmentConfig::HasConstantCamPositions( const image_t image_id) const { return constant_cam_positions_.find(image_id) != constant_cam_positions_.end(); } const std::unordered_set BundleAdjustmentConfig::ConstantIntrinsics() const { return constant_intrinsics_; } const std::unordered_set& BundleAdjustmentConfig::Images() const { return image_ids_; } const std::unordered_set& BundleAdjustmentConfig::VariablePoints() const { return variable_point3D_ids_; } const std::unordered_set& BundleAdjustmentConfig::ConstantPoints() const { return constant_point3D_ids_; } const std::unordered_set& BundleAdjustmentConfig::ConstantCamPoses() const { return constant_cam_poses_; } const std::vector& BundleAdjustmentConfig::ConstantCamPositions( const image_t image_id) const { return constant_cam_positions_.at(image_id); } void BundleAdjustmentConfig::AddVariablePoint(const point3D_t point3D_id) { THROW_CHECK(!HasConstantPoint(point3D_id)); variable_point3D_ids_.insert(point3D_id); } void BundleAdjustmentConfig::AddConstantPoint(const point3D_t point3D_id) { THROW_CHECK(!HasVariablePoint(point3D_id)); constant_point3D_ids_.insert(point3D_id); } bool BundleAdjustmentConfig::HasPoint(const point3D_t point3D_id) const { return HasVariablePoint(point3D_id) || HasConstantPoint(point3D_id); } bool BundleAdjustmentConfig::HasVariablePoint( const point3D_t point3D_id) const { return variable_point3D_ids_.find(point3D_id) != variable_point3D_ids_.end(); } bool BundleAdjustmentConfig::HasConstantPoint( const point3D_t point3D_id) const { return constant_point3D_ids_.find(point3D_id) != constant_point3D_ids_.end(); } void BundleAdjustmentConfig::RemoveVariablePoint(const point3D_t point3D_id) { variable_point3D_ids_.erase(point3D_id); } void BundleAdjustmentConfig::RemoveConstantPoint(const point3D_t point3D_id) { constant_point3D_ids_.erase(point3D_id); } //////////////////////////////////////////////////////////////////////////////// // BundleAdjuster //////////////////////////////////////////////////////////////////////////////// BundleAdjuster::BundleAdjuster(const BundleAdjustmentOptions& options, const BundleAdjustmentConfig& config) : options_(options), config_(config) { THROW_CHECK(options_.Check()); } bool BundleAdjuster::Solve(Reconstruction* reconstruction) { loss_function_ = std::unique_ptr(options_.CreateLossFunction()); SetUpProblem(reconstruction, loss_function_.get()); if (problem_->NumResiduals() == 0) { return false; } ceres::Solver::Options solver_options = SetUpSolverOptions(*problem_, options_.solver_options); ceres::Solve(solver_options, problem_.get(), &summary_); if (options_.print_summary || VLOG_IS_ON(1)) { PrintSolverSummary(summary_, "Bundle adjustment report"); } return true; } const BundleAdjustmentOptions& BundleAdjuster::Options() const { return options_; } const BundleAdjustmentConfig& BundleAdjuster::Config() const { return config_; } std::shared_ptr BundleAdjuster::Problem() { return problem_; } const ceres::Solver::Summary& BundleAdjuster::Summary() const { return summary_; } void BundleAdjuster::SetUpProblem(Reconstruction* reconstruction, ceres::LossFunction* loss_function) { THROW_CHECK_NOTNULL(reconstruction); // Initialize an empty problem ceres::Problem::Options problem_options; problem_options.loss_function_ownership = ceres::DO_NOT_TAKE_OWNERSHIP; problem_ = std::make_shared(problem_options); // Set up problem // Warning: AddPointsToProblem assumes that AddImageToProblem is called first. // Do not change order of instructions! for (const image_t image_id : config_.Images()) { AddImageToProblem(image_id, reconstruction, loss_function); } for (const auto point3D_id : config_.VariablePoints()) { AddPointToProblem(point3D_id, reconstruction, loss_function); } for (const auto point3D_id : config_.ConstantPoints()) { AddPointToProblem(point3D_id, reconstruction, loss_function); } ParameterizeCameras(reconstruction); ParameterizePoints(reconstruction); } ceres::Solver::Options BundleAdjuster::SetUpSolverOptions( const ceres::Problem& problem, const ceres::Solver::Options& input_solver_options) const { ceres::Solver::Options solver_options = input_solver_options; const bool has_sparse = solver_options.sparse_linear_algebra_library_type != ceres::NO_SPARSE; // Empirical choice. const size_t kMaxNumImagesDirectDenseSolver = 50; const size_t kMaxNumImagesDirectSparseSolver = 1000; const size_t num_images = config_.NumImages(); if (num_images <= kMaxNumImagesDirectDenseSolver) { solver_options.linear_solver_type = ceres::DENSE_SCHUR; } else if (num_images <= kMaxNumImagesDirectSparseSolver && has_sparse) { solver_options.linear_solver_type = ceres::SPARSE_SCHUR; } else { // Indirect sparse (preconditioned CG) solver. solver_options.linear_solver_type = ceres::ITERATIVE_SCHUR; solver_options.preconditioner_type = ceres::SCHUR_JACOBI; } if (problem.NumResiduals() < options_.min_num_residuals_for_multi_threading) { solver_options.num_threads = 1; #if CERES_VERSION_MAJOR < 2 solver_options.num_linear_solver_threads = 1; #endif // CERES_VERSION_MAJOR } else { solver_options.num_threads = GetEffectiveNumThreads(solver_options.num_threads); #if CERES_VERSION_MAJOR < 2 solver_options.num_linear_solver_threads = GetEffectiveNumThreads(solver_options.num_linear_solver_threads); #endif // CERES_VERSION_MAJOR } std::string solver_error; THROW_CHECK(solver_options.IsValid(&solver_error)) << solver_error; return solver_options; } void BundleAdjuster::AddImageToProblem(const image_t image_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function) { Image& image = reconstruction->Image(image_id); Camera& camera = reconstruction->Camera(image.CameraId()); // CostFunction assumes unit quaternions. image.CamFromWorld().rotation.normalize(); double* cam_from_world_rotation = image.CamFromWorld().rotation.coeffs().data(); double* cam_from_world_translation = image.CamFromWorld().translation.data(); double* camera_params = camera.params.data(); const bool constant_cam_pose = !options_.refine_extrinsics || config_.HasConstantCamPose(image_id); // Add residuals to bundle adjustment problem. size_t num_observations = 0; for (const Point2D& point2D : image.Points2D()) { if (!point2D.HasPoint3D()) { continue; } num_observations += 1; point3D_num_observations_[point2D.point3D_id] += 1; Point3D& point3D = reconstruction->Point3D(point2D.point3D_id); assert(point3D.track.Length() > 1); if (constant_cam_pose) { problem_->AddResidualBlock( CameraCostFunction( camera.model_id, image.CamFromWorld(), point2D.xy), loss_function, point3D.xyz.data(), camera_params); } else { problem_->AddResidualBlock(CameraCostFunction( camera.model_id, point2D.xy), loss_function, cam_from_world_rotation, cam_from_world_translation, point3D.xyz.data(), camera_params); } } if (num_observations > 0) { camera_ids_.insert(image.CameraId()); // Set pose parameterization. if (!constant_cam_pose) { SetQuaternionManifold(problem_.get(), cam_from_world_rotation); if (config_.HasConstantCamPositions(image_id)) { const std::vector& constant_position_idxs = config_.ConstantCamPositions(image_id); SetSubsetManifold(3, constant_position_idxs, problem_.get(), cam_from_world_translation); } } } } void BundleAdjuster::AddPointToProblem(const point3D_t point3D_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function) { Point3D& point3D = reconstruction->Point3D(point3D_id); // Is 3D point already fully contained in the problem? I.e. its entire track // is contained in `variable_image_ids`, `constant_image_ids`, // `constant_x_image_ids`. if (point3D_num_observations_[point3D_id] == point3D.track.Length()) { return; } for (const auto& track_el : point3D.track.Elements()) { // Skip observations that were already added in `FillImages`. if (config_.HasImage(track_el.image_id)) { continue; } point3D_num_observations_[point3D_id] += 1; Image& image = reconstruction->Image(track_el.image_id); Camera& camera = reconstruction->Camera(image.CameraId()); const Point2D& point2D = image.Point2D(track_el.point2D_idx); // CostFunction assumes unit quaternions. image.CamFromWorld().rotation.normalize(); // We do not want to refine the camera of images that are not // part of `constant_image_ids_`, `constant_image_ids_`, // `constant_x_image_ids_`. if (camera_ids_.count(image.CameraId()) == 0) { camera_ids_.insert(image.CameraId()); config_.SetConstantCamIntrinsics(image.CameraId()); } problem_->AddResidualBlock( CameraCostFunction( camera.model_id, image.CamFromWorld(), point2D.xy), loss_function, point3D.xyz.data(), camera.params.data()); } } void BundleAdjuster::ParameterizeCameras(Reconstruction* reconstruction) { const bool constant_camera = !options_.refine_focal_length && !options_.refine_principal_point && !options_.refine_extra_params; for (const camera_t camera_id : camera_ids_) { Camera& camera = reconstruction->Camera(camera_id); if (constant_camera || config_.HasConstantCamIntrinsics(camera_id)) { problem_->SetParameterBlockConstant(camera.params.data()); continue; } else { std::vector const_camera_params; if (!options_.refine_focal_length) { const span params_idxs = camera.FocalLengthIdxs(); const_camera_params.insert( const_camera_params.end(), params_idxs.begin(), params_idxs.end()); } if (!options_.refine_principal_point) { const span params_idxs = camera.PrincipalPointIdxs(); const_camera_params.insert( const_camera_params.end(), params_idxs.begin(), params_idxs.end()); } if (!options_.refine_extra_params) { const span params_idxs = camera.ExtraParamsIdxs(); const_camera_params.insert( const_camera_params.end(), params_idxs.begin(), params_idxs.end()); } if (const_camera_params.size() > 0) { SetSubsetManifold(static_cast(camera.params.size()), const_camera_params, problem_.get(), camera.params.data()); } } } } void BundleAdjuster::ParameterizePoints(Reconstruction* reconstruction) { for (const auto elem : point3D_num_observations_) { Point3D& point3D = reconstruction->Point3D(elem.first); if (point3D.track.Length() > elem.second) { problem_->SetParameterBlockConstant(point3D.xyz.data()); } } for (const point3D_t point3D_id : config_.ConstantPoints()) { Point3D& point3D = reconstruction->Point3D(point3D_id); problem_->SetParameterBlockConstant(point3D.xyz.data()); } } //////////////////////////////////////////////////////////////////////////////// // RigBundleAdjuster //////////////////////////////////////////////////////////////////////////////// RigBundleAdjuster::RigBundleAdjuster(const BundleAdjustmentOptions& options, const Options& rig_options, const BundleAdjustmentConfig& config) : BundleAdjuster(options, config), rig_options_(rig_options) {} bool RigBundleAdjuster::Solve(Reconstruction* reconstruction, std::vector* camera_rigs) { loss_function_ = std::unique_ptr(options_.CreateLossFunction()); SetUpProblem(reconstruction, camera_rigs, loss_function_.get()); if (problem_->NumResiduals() == 0) { return false; } ceres::Solver::Options solver_options = SetUpSolverOptions(*problem_, options_.solver_options); ceres::Solve(solver_options, problem_.get(), &summary_); if (options_.print_summary || VLOG_IS_ON(1)) { PrintSolverSummary(summary_, "Rig Bundle adjustment report"); } TearDown(reconstruction, *camera_rigs); return true; } void RigBundleAdjuster::SetUpProblem(Reconstruction* reconstruction, std::vector* camera_rigs, ceres::LossFunction* loss_function) { THROW_CHECK_NOTNULL(reconstruction); THROW_CHECK_NOTNULL(camera_rigs); THROW_CHECK(!problem_) << "Cannot set up problem from the same BundleAdjuster multiple times"; // Check the validity of the provided camera rigs. std::unordered_set rig_camera_ids; for (auto& camera_rig : *camera_rigs) { camera_rig.Check(*reconstruction); for (const auto& camera_id : camera_rig.GetCameraIds()) { THROW_CHECK_EQ(rig_camera_ids.count(camera_id), 0) << "Camera must not be part of multiple camera rigs"; rig_camera_ids.insert(camera_id); } for (const auto& snapshot : camera_rig.Snapshots()) { for (const auto& image_id : snapshot) { THROW_CHECK_EQ(image_id_to_camera_rig_.count(image_id), 0) << "Image must not be part of multiple camera rigs"; image_id_to_camera_rig_.emplace(image_id, &camera_rig); } } } // Initialize an empty problem ceres::Problem::Options problem_options; problem_options.loss_function_ownership = ceres::DO_NOT_TAKE_OWNERSHIP; problem_ = std::make_shared(problem_options); // Set up problem ComputeCameraRigPoses(*reconstruction, *camera_rigs); for (const image_t image_id : config_.Images()) { AddImageToProblem(image_id, reconstruction, camera_rigs, loss_function); } for (const auto point3D_id : config_.VariablePoints()) { AddPointToProblem(point3D_id, reconstruction, loss_function); } for (const auto point3D_id : config_.ConstantPoints()) { AddPointToProblem(point3D_id, reconstruction, loss_function); } ParameterizeCameras(reconstruction); ParameterizePoints(reconstruction); ParameterizeCameraRigs(reconstruction); } void RigBundleAdjuster::TearDown(Reconstruction* reconstruction, const std::vector& camera_rigs) { for (const auto& elem : image_id_to_camera_rig_) { const auto image_id = elem.first; const auto& camera_rig = *elem.second; auto& image = reconstruction->Image(image_id); image.CamFromWorld() = camera_rig.CamFromRig(image.CameraId()) * (*image_id_to_rig_from_world_.at(image_id)); } } void RigBundleAdjuster::AddImageToProblem(const image_t image_id, Reconstruction* reconstruction, std::vector* camera_rigs, ceres::LossFunction* loss_function) { const double max_squared_reproj_error = rig_options_.max_reproj_error * rig_options_.max_reproj_error; Image& image = reconstruction->Image(image_id); Camera& camera = reconstruction->Camera(image.CameraId()); const bool constant_cam_pose = config_.HasConstantCamPose(image_id); const bool constant_cam_position = config_.HasConstantCamPositions(image_id); double* camera_params = camera.params.data(); double* cam_from_rig_rotation = nullptr; double* cam_from_rig_translation = nullptr; double* rig_from_world_rotation = nullptr; double* rig_from_world_translation = nullptr; CameraRig* camera_rig = nullptr; Eigen::Matrix3x4d cam_from_world_mat = Eigen::Matrix3x4d::Zero(); if (image_id_to_camera_rig_.count(image_id) > 0) { THROW_CHECK(!constant_cam_pose) << "Images contained in a camera rig must not have constant pose"; THROW_CHECK(!constant_cam_position) << "Images contained in a camera rig must not have constant tvec"; camera_rig = image_id_to_camera_rig_.at(image_id); Rigid3d& rig_from_world = *image_id_to_rig_from_world_.at(image_id); rig_from_world_rotation = rig_from_world.rotation.coeffs().data(); rig_from_world_translation = rig_from_world.translation.data(); Rigid3d& cam_from_rig = camera_rig->CamFromRig(image.CameraId()); cam_from_rig_rotation = cam_from_rig.rotation.coeffs().data(); cam_from_rig_translation = cam_from_rig.translation.data(); cam_from_world_mat = (cam_from_rig * rig_from_world).ToMatrix(); } else { // CostFunction assumes unit quaternions. image.CamFromWorld().rotation.normalize(); cam_from_rig_rotation = image.CamFromWorld().rotation.coeffs().data(); cam_from_rig_translation = image.CamFromWorld().translation.data(); } // Collect cameras for final parameterization. THROW_CHECK(image.HasCamera()); camera_ids_.insert(image.CameraId()); // The number of added observations for the current image. size_t num_observations = 0; // Add residuals to bundle adjustment problem. for (const Point2D& point2D : image.Points2D()) { if (!point2D.HasPoint3D()) { continue; } Point3D& point3D = reconstruction->Point3D(point2D.point3D_id); assert(point3D.track.Length() > 1); if (camera_rig != nullptr && CalculateSquaredReprojectionError( point2D.xy, point3D.xyz, cam_from_world_mat, camera) > max_squared_reproj_error) { continue; } num_observations += 1; point3D_num_observations_[point2D.point3D_id] += 1; if (camera_rig == nullptr) { if (constant_cam_pose) { problem_->AddResidualBlock( CameraCostFunction( camera.model_id, image.CamFromWorld(), point2D.xy), loss_function, point3D.xyz.data(), camera_params); } else { problem_->AddResidualBlock(CameraCostFunction( camera.model_id, point2D.xy), loss_function, cam_from_rig_rotation, // rig == world cam_from_rig_translation, // rig == world point3D.xyz.data(), camera_params); } } else { problem_->AddResidualBlock(CameraCostFunction( camera.model_id, point2D.xy), loss_function, cam_from_rig_rotation, cam_from_rig_translation, rig_from_world_rotation, rig_from_world_translation, point3D.xyz.data(), camera_params); } } if (num_observations > 0) { parameterized_quats_.insert(cam_from_rig_rotation); if (camera_rig != nullptr) { parameterized_quats_.insert(rig_from_world_rotation); // Set the relative pose of the camera constant if relative pose // refinement is disabled or if it is the reference camera to avoid over- // parameterization of the camera pose. if (!rig_options_.refine_relative_poses || image.CameraId() == camera_rig->RefCameraId()) { problem_->SetParameterBlockConstant(cam_from_rig_rotation); problem_->SetParameterBlockConstant(cam_from_rig_translation); } } // Set pose parameterization. if (!constant_cam_pose && constant_cam_position) { const std::vector& constant_position_idxs = config_.ConstantCamPositions(image_id); SetSubsetManifold( 3, constant_position_idxs, problem_.get(), cam_from_rig_translation); } } } void RigBundleAdjuster::AddPointToProblem(const point3D_t point3D_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function) { Point3D& point3D = reconstruction->Point3D(point3D_id); // Is 3D point already fully contained in the problem? I.e. its entire track // is contained in `variable_image_ids`, `constant_image_ids`, // `constant_x_image_ids`. if (point3D_num_observations_[point3D_id] == point3D.track.Length()) { return; } for (const auto& track_el : point3D.track.Elements()) { // Skip observations that were already added in `AddImageToProblem`. if (config_.HasImage(track_el.image_id)) { continue; } point3D_num_observations_[point3D_id] += 1; Image& image = reconstruction->Image(track_el.image_id); Camera& camera = reconstruction->Camera(image.CameraId()); const Point2D& point2D = image.Point2D(track_el.point2D_idx); // We do not want to refine the camera of images that are not // part of `constant_image_ids_`, `constant_image_ids_`, // `constant_x_image_ids_`. if (camera_ids_.count(image.CameraId()) == 0) { camera_ids_.insert(image.CameraId()); config_.SetConstantCamIntrinsics(image.CameraId()); } problem_->AddResidualBlock( CameraCostFunction( camera.model_id, image.CamFromWorld(), point2D.xy), loss_function, point3D.xyz.data(), camera.params.data()); } } void RigBundleAdjuster::ComputeCameraRigPoses( const Reconstruction& reconstruction, const std::vector& camera_rigs) { rigs_from_world_.reserve(camera_rigs.size()); for (const auto& camera_rig : camera_rigs) { rigs_from_world_.emplace_back(); auto& rig_from_world = rigs_from_world_.back(); const size_t num_snapshots = camera_rig.NumSnapshots(); rig_from_world.resize(num_snapshots); for (size_t snapshot_idx = 0; snapshot_idx < num_snapshots; ++snapshot_idx) { rig_from_world[snapshot_idx] = camera_rig.ComputeRigFromWorld(snapshot_idx, reconstruction); for (const auto image_id : camera_rig.Snapshots()[snapshot_idx]) { image_id_to_rig_from_world_.emplace(image_id, &rig_from_world[snapshot_idx]); } } } } void RigBundleAdjuster::ParameterizeCameraRigs(Reconstruction* reconstruction) { for (double* cam_from_rig_rotation : parameterized_quats_) { SetQuaternionManifold(problem_.get(), cam_from_rig_rotation); } } void PrintSolverSummary(const ceres::Solver::Summary& summary, const std::string& header) { std::ostringstream log; log << "\n" << header << ":\n"; log << std::right << std::setw(16) << "Residuals : "; log << std::left << summary.num_residuals_reduced << "\n"; log << std::right << std::setw(16) << "Parameters : "; log << std::left << summary.num_effective_parameters_reduced << "\n"; log << std::right << std::setw(16) << "Iterations : "; log << std::left << summary.num_successful_steps + summary.num_unsuccessful_steps << "\n"; log << std::right << std::setw(16) << "Time : "; log << std::left << summary.total_time_in_seconds << " [s]\n"; log << std::right << std::setw(16) << "Initial cost : "; log << std::right << std::setprecision(6) << std::sqrt(summary.initial_cost / summary.num_residuals_reduced) << " [px]\n"; log << std::right << std::setw(16) << "Final cost : "; log << std::right << std::setprecision(6) << std::sqrt(summary.final_cost / summary.num_residuals_reduced) << " [px]\n"; log << std::right << std::setw(16) << "Termination : "; std::string termination = ""; switch (summary.termination_type) { case ceres::CONVERGENCE: termination = "Convergence"; break; case ceres::NO_CONVERGENCE: termination = "No convergence"; break; case ceres::FAILURE: termination = "Failure"; break; case ceres::USER_SUCCESS: termination = "User success"; break; case ceres::USER_FAILURE: termination = "User failure"; break; default: termination = "Unknown"; break; } log << std::right << termination << "\n\n"; LOG(INFO) << log.str(); } } // namespace colmap colmap-3.10/src/colmap/estimators/bundle_adjustment.h000066400000000000000000000254351464745566500230630ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/scene/camera_rig.h" #include "colmap/scene/reconstruction.h" #include "colmap/util/eigen_alignment.h" #include #include #include #include namespace colmap { struct BundleAdjustmentOptions { // Loss function types: Trivial (non-robust) and Cauchy (robust) loss. enum class LossFunctionType { TRIVIAL, SOFT_L1, CAUCHY }; LossFunctionType loss_function_type = LossFunctionType::TRIVIAL; // Scaling factor determines residual at which robustification takes place. double loss_function_scale = 1.0; // Whether to refine the focal length parameter group. bool refine_focal_length = true; // Whether to refine the principal point parameter group. bool refine_principal_point = false; // Whether to refine the extra parameter group. bool refine_extra_params = true; // Whether to refine the extrinsic parameter group. bool refine_extrinsics = true; // Whether to print a final summary. bool print_summary = true; // Minimum number of residuals to enable multi-threading. Note that // single-threaded is typically better for small bundle adjustment problems // due to the overhead of threading. int min_num_residuals_for_multi_threading = 50000; // Ceres-Solver options. ceres::Solver::Options solver_options; BundleAdjustmentOptions() { solver_options.function_tolerance = 0.0; solver_options.gradient_tolerance = 0.0; solver_options.parameter_tolerance = 0.0; solver_options.logging_type = ceres::LoggingType::SILENT; solver_options.max_num_iterations = 100; solver_options.max_linear_solver_iterations = 200; solver_options.max_num_consecutive_invalid_steps = 10; solver_options.max_consecutive_nonmonotonic_steps = 10; solver_options.num_threads = -1; #if CERES_VERSION_MAJOR < 2 solver_options.num_linear_solver_threads = -1; #endif // CERES_VERSION_MAJOR } // Create a new loss function based on the specified options. The caller // takes ownership of the loss function. ceres::LossFunction* CreateLossFunction() const; bool Check() const; }; // Configuration container to setup bundle adjustment problems. class BundleAdjustmentConfig { public: BundleAdjustmentConfig(); size_t NumImages() const; size_t NumPoints() const; size_t NumConstantCamIntrinsics() const; size_t NumConstantCamPoses() const; size_t NumConstantCamPositions() const; size_t NumVariablePoints() const; size_t NumConstantPoints() const; // Determine the number of residuals for the given reconstruction. The number // of residuals equals the number of observations times two. size_t NumResiduals(const Reconstruction& reconstruction) const; // Add / remove images from the configuration. void AddImage(image_t image_id); bool HasImage(image_t image_id) const; void RemoveImage(image_t image_id); // Set cameras of added images as constant or variable. By default all // cameras of added images are variable. Note that the corresponding images // have to be added prior to calling these methods. void SetConstantCamIntrinsics(camera_t camera_id); void SetVariableCamIntrinsics(camera_t camera_id); bool HasConstantCamIntrinsics(camera_t camera_id) const; // Set the pose of added images as constant. The pose is defined as the // rotational and translational part of the projection matrix. void SetConstantCamPose(image_t image_id); void SetVariableCamPose(image_t image_id); bool HasConstantCamPose(image_t image_id) const; // Set the translational part of the pose, hence the constant pose // indices may be in [0, 1, 2] and must be unique. Note that the // corresponding images have to be added prior to calling these methods. void SetConstantCamPositions(image_t image_id, const std::vector& idxs); void RemoveConstantCamPositions(image_t image_id); bool HasConstantCamPositions(image_t image_id) const; // Add / remove points from the configuration. Note that points can either // be variable or constant but not both at the same time. void AddVariablePoint(point3D_t point3D_id); void AddConstantPoint(point3D_t point3D_id); bool HasPoint(point3D_t point3D_id) const; bool HasVariablePoint(point3D_t point3D_id) const; bool HasConstantPoint(point3D_t point3D_id) const; void RemoveVariablePoint(point3D_t point3D_id); void RemoveConstantPoint(point3D_t point3D_id); // Access configuration data. const std::unordered_set ConstantIntrinsics() const; const std::unordered_set& Images() const; const std::unordered_set& VariablePoints() const; const std::unordered_set& ConstantPoints() const; const std::unordered_set& ConstantCamPoses() const; const std::vector& ConstantCamPositions(image_t image_id) const; private: std::unordered_set constant_intrinsics_; std::unordered_set image_ids_; std::unordered_set variable_point3D_ids_; std::unordered_set constant_point3D_ids_; std::unordered_set constant_cam_poses_; std::unordered_map> constant_cam_positions_; }; // Bundle adjustment based on Ceres-Solver. Enables most flexible configurations // and provides best solution quality. class BundleAdjuster { public: BundleAdjuster(const BundleAdjustmentOptions& options, const BundleAdjustmentConfig& config); bool Solve(Reconstruction* reconstruction); // Set up the problem void SetUpProblem(Reconstruction* reconstruction, ceres::LossFunction* loss_function); ceres::Solver::Options SetUpSolverOptions( const ceres::Problem& problem, const ceres::Solver::Options& input_solver_options) const; // Getter functions below const BundleAdjustmentOptions& Options() const; const BundleAdjustmentConfig& Config() const; // Get the Ceres problem after the last call to "set_up" std::shared_ptr Problem(); // Get the Ceres solver summary after the last call to `Solve`. const ceres::Solver::Summary& Summary() const; private: void AddImageToProblem(image_t image_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function); void AddPointToProblem(point3D_t point3D_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function); protected: void ParameterizeCameras(Reconstruction* reconstruction); void ParameterizePoints(Reconstruction* reconstruction); const BundleAdjustmentOptions options_; BundleAdjustmentConfig config_; std::shared_ptr problem_; ceres::Solver::Summary summary_; std::unordered_set camera_ids_; std::unordered_map point3D_num_observations_; // Hold the life of loss function for Solve() std::unique_ptr loss_function_; }; class RigBundleAdjuster : public BundleAdjuster { public: struct Options { // Whether to optimize the relative poses of the camera rigs. bool refine_relative_poses = true; // The maximum allowed reprojection error for an observation to be // considered in the bundle adjustment. Some observations might have large // reprojection errors due to the concatenation of the absolute and relative // rig poses, which might be different from the absolute pose of the image // in the reconstruction. double max_reproj_error = 1000.0; }; RigBundleAdjuster(const BundleAdjustmentOptions& options, const Options& rig_options, const BundleAdjustmentConfig& config); bool Solve(Reconstruction* reconstruction, std::vector* camera_rigs); void SetUpProblem(Reconstruction* reconstruction, std::vector* camera_rigs, ceres::LossFunction* loss_function); void TearDown(Reconstruction* reconstruction, const std::vector& camera_rigs); private: void AddImageToProblem(image_t image_id, Reconstruction* reconstruction, std::vector* camera_rigs, ceres::LossFunction* loss_function); void AddPointToProblem(point3D_t point3D_id, Reconstruction* reconstruction, ceres::LossFunction* loss_function); void ComputeCameraRigPoses(const Reconstruction& reconstruction, const std::vector& camera_rigs); void ParameterizeCameraRigs(Reconstruction* reconstruction); const Options rig_options_; // Mapping from images to camera rigs. std::unordered_map image_id_to_camera_rig_; // Mapping from images to the absolute camera rig poses. std::unordered_map image_id_to_rig_from_world_; // For each camera rig, the absolute camera rig poses for all snapshots. std::vector> rigs_from_world_; // The Quaternions added to the problem, used to set the local // parameterization once after setting up the problem. std::unordered_set parameterized_quats_; }; void PrintSolverSummary(const ceres::Solver::Summary& summary, const std::string& header); } // namespace colmap colmap-3.10/src/colmap/estimators/bundle_adjustment_test.cc000066400000000000000000001000121464745566500242410ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/bundle_adjustment.h" #include "colmap/math/random.h" #include "colmap/scene/correspondence_graph.h" #include "colmap/scene/projection.h" #include "colmap/sensor/models.h" #include #define CheckVariableCamera(camera, orig_camera) \ { \ const size_t focal_length_idx = \ SimpleRadialCameraModel::focal_length_idxs[0]; \ const size_t extra_param_idx = \ SimpleRadialCameraModel::extra_params_idxs[0]; \ EXPECT_NE((camera).params[focal_length_idx], \ (orig_camera).params[focal_length_idx]); \ EXPECT_NE((camera).params[extra_param_idx], \ (orig_camera).params[extra_param_idx]); \ } #define CheckConstantCamera(camera, orig_camera) \ { \ const size_t focal_length_idx = \ SimpleRadialCameraModel::focal_length_idxs[0]; \ const size_t extra_param_idx = \ SimpleRadialCameraModel::extra_params_idxs[0]; \ EXPECT_EQ((camera).params[focal_length_idx], \ (orig_camera).params[focal_length_idx]); \ EXPECT_EQ((camera).params[extra_param_idx], \ (orig_camera).params[extra_param_idx]); \ } #define CheckVariableImage(image, orig_image) \ { \ EXPECT_NE((image).CamFromWorld().rotation.coeffs(), \ (orig_image).CamFromWorld().rotation.coeffs()); \ EXPECT_NE((image).CamFromWorld().translation, \ (orig_image).CamFromWorld().translation); \ } #define CheckConstantImage(image, orig_image) \ { \ EXPECT_EQ((image).CamFromWorld().rotation.coeffs(), \ (orig_image).CamFromWorld().rotation.coeffs()); \ EXPECT_EQ((image).CamFromWorld().translation, \ (orig_image).CamFromWorld().translation); \ } #define CheckConstantXImage(image, orig_image) \ { \ CheckVariableImage(image, orig_image); \ EXPECT_EQ((image).CamFromWorld().translation.x(), \ (orig_image).CamFromWorld().translation.x()); \ } #define CheckConstantCameraRig(camera_rig, orig_camera_rig, camera_id) \ { \ EXPECT_EQ((camera_rig).CamFromRig(camera_id).rotation.coeffs(), \ (orig_camera_rig).CamFromRig(camera_id).rotation.coeffs()); \ EXPECT_EQ((camera_rig).CamFromRig(camera_id).translation, \ (orig_camera_rig).CamFromRig(camera_id).translation); \ } #define CheckVariableCameraRig(camera_rig, orig_camera_rig, camera_id) \ { \ if ((camera_rig).RefCameraId() == (camera_id)) { \ CheckConstantCameraRig(camera_rig, orig_camera_rig, camera_id); \ } else { \ EXPECT_NE((camera_rig).CamFromRig(camera_id).rotation.coeffs(), \ (orig_camera_rig).CamFromRig(camera_id).rotation.coeffs()); \ EXPECT_NE((camera_rig).CamFromRig(camera_id).translation, \ (orig_camera_rig).CamFromRig(camera_id).translation); \ } \ } #define CheckVariablePoint(point, orig_point) \ { EXPECT_NE((point).xyz, (orig_point).xyz); } #define CheckConstantPoint(point, orig_point) \ { EXPECT_EQ((point).xyz, (orig_point).xyz); } namespace colmap { namespace { void GeneratePointCloud(const size_t num_points, const Eigen::Vector3d& min, const Eigen::Vector3d& max, Reconstruction* reconstruction) { for (size_t i = 0; i < num_points; ++i) { Eigen::Vector3d xyz; xyz.x() = RandomUniformReal(min.x(), max.x()); xyz.y() = RandomUniformReal(min.y(), max.y()); xyz.z() = RandomUniformReal(min.z(), max.z()); reconstruction->AddPoint3D(xyz, Track()); } } void GenerateReconstruction(const size_t num_images, const size_t num_points, Reconstruction* reconstruction) { SetPRNGSeed(0); GeneratePointCloud(num_points, Eigen::Vector3d(-1, -1, -1), Eigen::Vector3d(1, 1, 1), reconstruction); const double kFocalLengthFactor = 1.2; const size_t kImageSize = 1000; for (size_t i = 0; i < num_images; ++i) { const camera_t camera_id = static_cast(i); const image_t image_id = static_cast(i); const Camera camera = Camera::CreateFromModelId(camera_id, SimpleRadialCameraModel::model_id, kFocalLengthFactor * kImageSize, kImageSize, kImageSize); reconstruction->AddCamera(camera); Image image; image.SetImageId(image_id); image.SetCameraId(camera_id); image.SetName(std::to_string(i)); image.CamFromWorld() = Rigid3d( Eigen::Quaterniond::Identity(), Eigen::Vector3d( RandomUniformReal(-1.0, 1.0), RandomUniformReal(-1.0, 1.0), 10)); image.SetRegistered(true); reconstruction->AddImage(image); const Eigen::Matrix3x4d cam_from_world_matrix = image.CamFromWorld().ToMatrix(); std::vector points2D; for (const auto& point3D : reconstruction->Points3D()) { EXPECT_TRUE( HasPointPositiveDepth(cam_from_world_matrix, point3D.second.xyz)); // Get exact projection of 3D point. Eigen::Vector2d point2D = camera.ImgFromCam( (image.CamFromWorld() * point3D.second.xyz).hnormalized()); // Add some uniform noise. point2D += Eigen::Vector2d(RandomUniformReal(-2.0, 2.0), RandomUniformReal(-2.0, 2.0)); points2D.push_back(point2D); } reconstruction->Image(image_id).SetPoints2D(points2D); } for (size_t i = 0; i < num_images; ++i) { const image_t image_id = static_cast(i); TrackElement track_el; track_el.image_id = image_id; track_el.point2D_idx = 0; for (const auto& point3D : reconstruction->Points3D()) { reconstruction->AddObservation(point3D.first, track_el); track_el.point2D_idx += 1; } } } TEST(BundleAdjustment, ConfigNumObservations) { Reconstruction reconstruction; GenerateReconstruction(4, 100, &reconstruction); BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); EXPECT_EQ(config.NumResiduals(reconstruction), 400); config.AddVariablePoint(1); EXPECT_EQ(config.NumResiduals(reconstruction), 404); config.AddConstantPoint(2); EXPECT_EQ(config.NumResiduals(reconstruction), 408); config.AddImage(2); EXPECT_EQ(config.NumResiduals(reconstruction), 604); config.AddImage(3); EXPECT_EQ(config.NumResiduals(reconstruction), 800); } TEST(BundleAdjustment, TwoView) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPositions(1, {0}); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 5 image parameters (pose of second image) // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 309); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantXImage(reconstruction.Image(1), orig_reconstruction.Image(1)); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, TwoViewConstantCamera) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPose(1); config.SetConstantCamIntrinsics(0); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 302); CheckConstantCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantImage(reconstruction.Image(1), orig_reconstruction.Image(1)); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, PartiallyContainedTracks) { Reconstruction reconstruction; GenerateReconstruction(3, 100, &reconstruction); const auto variable_point3D_id = reconstruction.Image(2).Point2D(0).point3D_id; reconstruction.DeleteObservation(2, 0); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPose(1); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 1 x 3 point parameters // 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 7); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckConstantCamera(reconstruction.Camera(2), orig_reconstruction.Camera(2)); CheckConstantImage(reconstruction.Image(2), orig_reconstruction.Image(2)); for (const auto& point3D : reconstruction.Points3D()) { if (point3D.first == variable_point3D_id) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } else { CheckConstantPoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } } TEST(BundleAdjustment, PartiallyContainedTracksForceToOptimizePoint) { Reconstruction reconstruction; GenerateReconstruction(3, 100, &reconstruction); const point3D_t variable_point3D_id = reconstruction.Image(2).Point2D(0).point3D_id; const point3D_t add_variable_point3D_id = reconstruction.Image(2).Point2D(1).point3D_id; const point3D_t add_constant_point3D_id = reconstruction.Image(2).Point2D(2).point3D_id; reconstruction.DeleteObservation(2, 0); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPose(1); config.AddVariablePoint(add_variable_point3D_id); config.AddConstantPoint(add_constant_point3D_id); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image // + 2 residuals in 3rd image for added variable 3D point // (added constant point does not add residuals since the image/camera // is also constant). EXPECT_EQ(summary.num_residuals_reduced, 402); // 2 x 3 point parameters // 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 10); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckConstantCamera(reconstruction.Camera(2), orig_reconstruction.Camera(2)); CheckConstantImage(reconstruction.Image(2), orig_reconstruction.Image(2)); for (const auto& point3D : reconstruction.Points3D()) { if (point3D.first == variable_point3D_id || point3D.first == add_variable_point3D_id) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } else { CheckConstantPoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } } TEST(BundleAdjustment, ConstantPoints) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; const point3D_t constant_point3D_id1 = 1; const point3D_t constant_point3D_id2 = 2; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPose(1); config.AddConstantPoint(constant_point3D_id1); config.AddConstantPoint(constant_point3D_id2); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 98 x 3 point parameters // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 298); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantImage(reconstruction.Image(1), orig_reconstruction.Image(1)); for (const auto& point3D : reconstruction.Points3D()) { if (point3D.first == constant_point3D_id1 || point3D.first == constant_point3D_id2) { CheckConstantPoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } else { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } } TEST(BundleAdjustment, VariableImage) { Reconstruction reconstruction; GenerateReconstruction(3, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.AddImage(2); config.SetConstantCamPose(0); config.SetConstantCamPositions(1, {0}); BundleAdjustmentOptions options; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 3 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 600); // 100 x 3 point parameters // + 5 image parameters (pose of second image) // + 6 image parameters (pose of third image) // + 3 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 317); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckConstantXImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckVariableCamera(reconstruction.Camera(2), orig_reconstruction.Camera(2)); CheckVariableImage(reconstruction.Image(2), orig_reconstruction.Image(2)); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, ConstantFocalLength) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPositions(1, {0}); BundleAdjustmentOptions options; options.refine_focal_length = false; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 3 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 5 image parameters (pose of second image) // + 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 307); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckConstantXImage(reconstruction.Image(1), orig_reconstruction.Image(1)); const size_t focal_length_idx = SimpleRadialCameraModel::focal_length_idxs[0]; const size_t extra_param_idx = SimpleRadialCameraModel::extra_params_idxs[0]; const auto& camera0 = reconstruction.Camera(0); const auto& orig_camera0 = orig_reconstruction.Camera(0); EXPECT_TRUE(camera0.params[focal_length_idx] == orig_camera0.params[focal_length_idx]); EXPECT_TRUE(camera0.params[extra_param_idx] != orig_camera0.params[extra_param_idx]); const auto& camera1 = reconstruction.Camera(1); const auto& orig_camera1 = orig_reconstruction.Camera(1); EXPECT_TRUE(camera1.params[focal_length_idx] == orig_camera1.params[focal_length_idx]); EXPECT_TRUE(camera1.params[extra_param_idx] != orig_camera1.params[extra_param_idx]); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, VariablePrincipalPoint) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPositions(1, {0}); BundleAdjustmentOptions options; options.refine_principal_point = true; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 3 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 5 image parameters (pose of second image) // + 8 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 313); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckConstantXImage(reconstruction.Image(1), orig_reconstruction.Image(1)); const size_t focal_length_idx = SimpleRadialCameraModel::focal_length_idxs[0]; const size_t principal_point_idx_x = SimpleRadialCameraModel::principal_point_idxs[0]; const size_t principal_point_idx_y = SimpleRadialCameraModel::principal_point_idxs[0]; const size_t extra_param_idx = SimpleRadialCameraModel::extra_params_idxs[0]; const auto& camera0 = reconstruction.Camera(0); const auto& orig_camera0 = orig_reconstruction.Camera(0); EXPECT_TRUE(camera0.params[focal_length_idx] != orig_camera0.params[focal_length_idx]); EXPECT_TRUE(camera0.params[principal_point_idx_x] != orig_camera0.params[principal_point_idx_x]); EXPECT_TRUE(camera0.params[principal_point_idx_y] != orig_camera0.params[principal_point_idx_y]); EXPECT_TRUE(camera0.params[extra_param_idx] != orig_camera0.params[extra_param_idx]); const auto& camera1 = reconstruction.Camera(1); const auto& orig_camera1 = orig_reconstruction.Camera(1); EXPECT_TRUE(camera1.params[focal_length_idx] != orig_camera1.params[focal_length_idx]); EXPECT_TRUE(camera1.params[principal_point_idx_x] != orig_camera1.params[principal_point_idx_x]); EXPECT_TRUE(camera1.params[principal_point_idx_y] != orig_camera1.params[principal_point_idx_y]); EXPECT_TRUE(camera1.params[extra_param_idx] != orig_camera1.params[extra_param_idx]); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, ConstantExtraParam) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.SetConstantCamPose(0); config.SetConstantCamPositions(1, {0}); BundleAdjustmentOptions options; options.refine_extra_params = false; BundleAdjuster bundle_adjuster(options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction)); const auto summary = bundle_adjuster.Summary(); // 100 points, 3 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 5 image parameters (pose of second image) // + 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 307); CheckConstantImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckConstantXImage(reconstruction.Image(1), orig_reconstruction.Image(1)); const size_t focal_length_idx = SimpleRadialCameraModel::focal_length_idxs[0]; const size_t extra_param_idx = SimpleRadialCameraModel::extra_params_idxs[0]; const auto& camera0 = reconstruction.Camera(0); const auto& orig_camera0 = orig_reconstruction.Camera(0); EXPECT_TRUE(camera0.params[focal_length_idx] != orig_camera0.params[focal_length_idx]); EXPECT_TRUE(camera0.params[extra_param_idx] == orig_camera0.params[extra_param_idx]); const auto& camera1 = reconstruction.Camera(1); const auto& orig_camera1 = orig_reconstruction.Camera(1); EXPECT_TRUE(camera1.params[focal_length_idx] != orig_camera1.params[focal_length_idx]); EXPECT_TRUE(camera1.params[extra_param_idx] == orig_camera1.params[extra_param_idx]); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, RigTwoView) { Reconstruction reconstruction; GenerateReconstruction(2, 100, &reconstruction); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); std::vector camera_rigs; camera_rigs.emplace_back(); camera_rigs[0].AddCamera(0, Rigid3d()); camera_rigs[0].AddCamera(1, Rigid3d()); camera_rigs[0].AddSnapshot({0, 1}); camera_rigs[0].SetRefCameraId(0); const auto orig_camera_rigs = camera_rigs; BundleAdjustmentOptions options; RigBundleAdjuster::Options rig_options; RigBundleAdjuster bundle_adjuster(options, rig_options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction, &camera_rigs)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 400); // 100 x 3 point parameters // + 6 pose parameters for camera rig // + 1 x 6 relative pose parameters for camera rig // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 316); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckVariableImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckVariableImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 0); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 1); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, RigFourView) { Reconstruction reconstruction; GenerateReconstruction(4, 100, &reconstruction); reconstruction.Image(2).SetCameraId(0); reconstruction.Image(3).SetCameraId(1); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.AddImage(2); config.AddImage(3); std::vector camera_rigs; camera_rigs.emplace_back(); camera_rigs[0].AddCamera(0, Rigid3d()); camera_rigs[0].AddCamera(1, Rigid3d()); camera_rigs[0].AddSnapshot({0, 1}); camera_rigs[0].AddSnapshot({2, 3}); camera_rigs[0].SetRefCameraId(0); const auto orig_camera_rigs = camera_rigs; BundleAdjustmentOptions options; RigBundleAdjuster::Options rig_options; RigBundleAdjuster bundle_adjuster(options, rig_options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction, &camera_rigs)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 800); // 100 x 3 point parameters // + 2 x 6 pose parameters for camera rig // + 1 x 6 relative pose parameters for camera rig // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 322); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckVariableImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckVariableImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 0); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 1); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, ConstantRigFourView) { Reconstruction reconstruction; GenerateReconstruction(4, 100, &reconstruction); reconstruction.Image(2).SetCameraId(0); reconstruction.Image(3).SetCameraId(1); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.AddImage(2); config.AddImage(3); std::vector camera_rigs; camera_rigs.emplace_back(); camera_rigs[0].AddCamera(0, Rigid3d()); camera_rigs[0].AddCamera(1, Rigid3d()); camera_rigs[0].AddSnapshot({0, 1}); camera_rigs[0].AddSnapshot({2, 3}); camera_rigs[0].SetRefCameraId(0); const auto orig_camera_rigs = camera_rigs; BundleAdjustmentOptions options; RigBundleAdjuster::Options rig_options; rig_options.refine_relative_poses = false; RigBundleAdjuster bundle_adjuster(options, rig_options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction, &camera_rigs)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 800); // 100 x 3 point parameters // + 2 x 6 pose parameters for camera rig // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 316); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckVariableImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckVariableImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckConstantCameraRig(camera_rigs[0], orig_camera_rigs[0], 0); CheckConstantCameraRig(camera_rigs[0], orig_camera_rigs[0], 1); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } TEST(BundleAdjustment, RigFourViewPartial) { Reconstruction reconstruction; GenerateReconstruction(4, 100, &reconstruction); reconstruction.Image(2).SetCameraId(0); reconstruction.Image(3).SetCameraId(1); const auto orig_reconstruction = reconstruction; BundleAdjustmentConfig config; config.AddImage(0); config.AddImage(1); config.AddImage(2); config.AddImage(3); std::vector camera_rigs; camera_rigs.emplace_back(); camera_rigs[0].AddCamera(0, Rigid3d()); camera_rigs[0].AddCamera(1, Rigid3d()); camera_rigs[0].AddSnapshot({0, 1}); camera_rigs[0].AddSnapshot({2}); camera_rigs[0].SetRefCameraId(0); const auto orig_camera_rigs = camera_rigs; BundleAdjustmentOptions options; RigBundleAdjuster::Options rig_options; RigBundleAdjuster bundle_adjuster(options, rig_options, config); ASSERT_TRUE(bundle_adjuster.Solve(&reconstruction, &camera_rigs)); const auto summary = bundle_adjuster.Summary(); // 100 points, 2 images, 2 residuals per point per image EXPECT_EQ(summary.num_residuals_reduced, 800); // 100 x 3 point parameters // + 2 x 6 pose parameters for camera rig // + 1 x 6 relative pose parameters for camera rig // + 1 x 6 pose parameters for individual image // + 2 x 2 camera parameters EXPECT_EQ(summary.num_effective_parameters_reduced, 328); CheckVariableCamera(reconstruction.Camera(0), orig_reconstruction.Camera(0)); CheckVariableImage(reconstruction.Image(0), orig_reconstruction.Image(0)); CheckVariableCamera(reconstruction.Camera(1), orig_reconstruction.Camera(1)); CheckVariableImage(reconstruction.Image(1), orig_reconstruction.Image(1)); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 0); CheckVariableCameraRig(camera_rigs[0], orig_camera_rigs[0], 1); for (const auto& point3D : reconstruction.Points3D()) { CheckVariablePoint(point3D.second, orig_reconstruction.Point3D(point3D.first)); } } } // namespace } // namespace colmap colmap-3.10/src/colmap/estimators/coordinate_frame.cc000066400000000000000000000333631464745566500230120ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/coordinate_frame.h" #include "colmap/estimators/utils.h" #include "colmap/geometry/gps.h" #include "colmap/geometry/pose.h" #include "colmap/image/line.h" #include "colmap/image/undistortion.h" #include "colmap/optim/ransac.h" #include "colmap/util/logging.h" #include "colmap/util/misc.h" namespace colmap { namespace { Eigen::Vector3d FindBestConsensusAxis(const std::vector& axes, const double max_distance) { if (axes.empty()) { return Eigen::Vector3d::Zero(); } std::vector inlier_idxs; inlier_idxs.reserve(axes.size()); std::vector best_inlier_idxs; best_inlier_idxs.reserve(axes.size()); double best_inlier_distance_sum = std::numeric_limits::max(); for (size_t i = 0; i < axes.size(); ++i) { const Eigen::Vector3d& ref_axis = axes[i]; double inlier_distance_sum = 0; inlier_idxs.clear(); for (size_t j = 0; j < axes.size(); ++j) { if (i == j) { inlier_idxs.push_back(j); } else { const double distance = 1 - ref_axis.dot(axes[j]); if (distance <= max_distance) { inlier_distance_sum += distance; inlier_idxs.push_back(j); } } } if (inlier_idxs.size() > best_inlier_idxs.size() || (inlier_idxs.size() == best_inlier_idxs.size() && inlier_distance_sum < best_inlier_distance_sum)) { best_inlier_distance_sum = inlier_distance_sum; best_inlier_idxs = inlier_idxs; } } if (best_inlier_idxs.empty()) { return Eigen::Vector3d::Zero(); } Eigen::Vector3d best_axis(0, 0, 0); for (const auto idx : best_inlier_idxs) { best_axis += axes[idx]; } best_axis /= best_inlier_idxs.size(); return best_axis; } } // namespace Eigen::Vector3d EstimateGravityVectorFromImageOrientation( const Reconstruction& reconstruction, const double max_axis_distance) { std::vector downward_axes; downward_axes.reserve(reconstruction.NumRegImages()); for (const auto image_id : reconstruction.RegImageIds()) { const auto& image = reconstruction.Image(image_id); downward_axes.push_back( image.CamFromWorld().rotation.toRotationMatrix().row(1)); } return FindBestConsensusAxis(downward_axes, max_axis_distance); } #ifdef COLMAP_LSD_ENABLED struct VanishingPointEstimator { // The line segments. typedef LineSegment X_t; // The line representation of the segments. typedef Eigen::Vector3d Y_t; // The vanishing point. typedef Eigen::Vector3d M_t; // The minimum number of samples needed to estimate a model. static const int kMinNumSamples = 2; // Estimate the vanishing point from at least two line segments. static void Estimate(const std::vector& line_segments, const std::vector& lines, std::vector* models) { THROW_CHECK_EQ(line_segments.size(), 2); THROW_CHECK_EQ(lines.size(), 2); THROW_CHECK(models != nullptr); models->resize(1); (*models)[0] = lines[0].cross(lines[1]); } // Calculate the squared distance of each line segment's end point to the line // connecting the vanishing point and the midpoint of the line segment. static void Residuals(const std::vector& line_segments, const std::vector& lines, const M_t& vanishing_point, std::vector* residuals) { residuals->resize(line_segments.size()); // Check if vanishing point is at infinity. if (vanishing_point[2] == 0) { std::fill(residuals->begin(), residuals->end(), std::numeric_limits::max()); return; } for (size_t i = 0; i < lines.size(); ++i) { const Eigen::Vector3d midpoint = (0.5 * (line_segments[i].start + line_segments[i].end)).homogeneous(); const Eigen::Vector3d connecting_line = midpoint.cross(vanishing_point); const double signed_distance = connecting_line.dot(line_segments[i].end.homogeneous()) / connecting_line.head<2>().norm(); (*residuals)[i] = signed_distance * signed_distance; } } }; Eigen::Matrix3d EstimateManhattanWorldFrame( const ManhattanWorldFrameEstimationOptions& options, const Reconstruction& reconstruction, const std::string& image_path) { std::vector rightward_axes; std::vector downward_axes; for (size_t i = 0; i < reconstruction.NumRegImages(); ++i) { const auto image_id = reconstruction.RegImageIds()[i]; const auto& image = reconstruction.Image(image_id); const auto& camera = reconstruction.Camera(image.CameraId()); PrintHeading1(StringPrintf("Processing image %s (%d / %d)", image.Name().c_str(), i + 1, reconstruction.NumRegImages())); LOG(INFO) << "Reading image..."; colmap::Bitmap bitmap; THROW_CHECK(bitmap.Read(colmap::JoinPaths(image_path, image.Name()))); LOG(INFO) << "Undistorting image..."; UndistortCameraOptions undistortion_options; undistortion_options.max_image_size = options.max_image_size; Bitmap undistorted_bitmap; Camera undistorted_camera; UndistortImage(undistortion_options, bitmap, camera, &undistorted_bitmap, &undistorted_camera); LOG(INFO) << "Detecting lines..."; const std::vector line_segments = DetectLineSegments(undistorted_bitmap, options.min_line_length); const std::vector line_orientations = ClassifyLineSegmentOrientations(line_segments, options.line_orientation_tolerance); LOG(INFO) << StringPrintf(" %d", line_segments.size()); std::vector horizontal_line_segments; std::vector vertical_line_segments; std::vector horizontal_lines; std::vector vertical_lines; for (size_t i = 0; i < line_segments.size(); ++i) { const auto& line_segment = line_segments[i]; const Eigen::Vector3d line_segment_start = line_segment.start.homogeneous(); const Eigen::Vector3d line_segment_end = line_segment.end.homogeneous(); const Eigen::Vector3d line = line_segment_start.cross(line_segment_end); if (line_orientations[i] == LineSegmentOrientation::HORIZONTAL) { horizontal_line_segments.push_back(line_segment); horizontal_lines.push_back(line); } else if (line_orientations[i] == LineSegmentOrientation::VERTICAL) { vertical_line_segments.push_back(line_segment); vertical_lines.push_back(line); } } LOG(INFO) << StringPrintf(" (%d horizontal, %d vertical)", horizontal_lines.size(), vertical_lines.size()); LOG(INFO) << "Estimating vanishing points..."; RANSACOptions ransac_options; ransac_options.max_error = options.max_line_vp_distance; RANSAC ransac(ransac_options); const auto horizontal_report = ransac.Estimate(horizontal_line_segments, horizontal_lines); const auto vertical_report = ransac.Estimate(vertical_line_segments, vertical_lines); LOG(INFO) << StringPrintf(" (%d horizontal inliers, %d vertical inliers)", horizontal_report.support.num_inliers, vertical_report.support.num_inliers); LOG(INFO) << "Composing coordinate axes..."; const Eigen::Matrix3d inv_calib_matrix = undistorted_camera.CalibrationMatrix().inverse(); const Eigen::Quaterniond world_from_cam_rotation = image.CamFromWorld().rotation.inverse(); if (horizontal_report.success) { Eigen::Vector3d horizontal_axis_in_world = world_from_cam_rotation * (inv_calib_matrix * horizontal_report.model).normalized(); // Make sure all axes point into the same direction. if (rightward_axes.size() > 0 && rightward_axes[0].dot(horizontal_axis_in_world) < 0) { horizontal_axis_in_world = -horizontal_axis_in_world; } rightward_axes.push_back(horizontal_axis_in_world); LOG(INFO) << "Horizontal: " << horizontal_axis_in_world.transpose(); } if (vertical_report.success) { const Eigen::Vector3d vertical_axis_in_cam = (inv_calib_matrix * vertical_report.model).normalized(); Eigen::Vector3d vertical_axis_in_world = (world_from_cam_rotation * vertical_axis_in_cam).normalized(); // Make sure axis points downwards in the image, assuming that the image // was taken in upright orientation. if (vertical_axis_in_world.dot(Eigen::Vector3d(0, 1, 0)) < 0) { vertical_axis_in_world = -vertical_axis_in_world; } downward_axes.push_back(vertical_axis_in_world); LOG(INFO) << "Vertical: " << vertical_axis_in_world.transpose(); } } PrintHeading1("Computing coordinate frame"); Eigen::Matrix3d frame = Eigen::Matrix3d::Zero(); if (rightward_axes.size() > 0) { frame.col(0) = FindBestConsensusAxis(rightward_axes, options.max_axis_distance); } LOG(INFO) << "Found rightward axis: " << frame.col(0).transpose(); if (downward_axes.size() > 0) { frame.col(1) = FindBestConsensusAxis(downward_axes, options.max_axis_distance); } LOG(INFO) << "Found downward axis: " << frame.col(1).transpose(); if (rightward_axes.size() > 0 && downward_axes.size() > 0) { frame.col(2) = frame.col(0).cross(frame.col(1)); Eigen::JacobiSVD svd( frame, Eigen::ComputeFullV | Eigen::ComputeFullU); const Eigen::Matrix3d orthonormal_frame = svd.matrixU() * Eigen::Matrix3d::Identity() * svd.matrixV().transpose(); frame = orthonormal_frame; } LOG(INFO) << "Found orthonormal frame:\n" << frame; return frame; } #endif void AlignToPrincipalPlane(Reconstruction* reconstruction, Sim3d* tform) { // Perform SVD on the 3D points to estimate the ground plane basis const Eigen::Vector3d centroid = reconstruction->ComputeCentroid(0.0, 1.0); Eigen::MatrixXd normalized_points3D(3, reconstruction->NumPoints3D()); int pidx = 0; for (const auto& point : reconstruction->Points3D()) { normalized_points3D.col(pidx++) = point.second.xyz - centroid; } const Eigen::Matrix3d basis = normalized_points3D.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV) .matrixU(); Eigen::Matrix3d rot_mat; rot_mat << basis.col(0), basis.col(1), basis.col(0).cross(basis.col(1)); rot_mat.transposeInPlace(); *tform = Sim3d(1.0, Eigen::Quaterniond(rot_mat), -rot_mat * centroid); // If camera plane ends up below ground then flip basis vectors. const Rigid3d cam0_from_aligned_world = TransformCameraWorld( *tform, reconstruction->Image(reconstruction->RegImageIds()[0]).CamFromWorld()); if (Inverse(cam0_from_aligned_world).translation.z() < 0.0) { rot_mat << basis.col(0), -basis.col(1), basis.col(0).cross(-basis.col(1)); rot_mat.transposeInPlace(); *tform = Sim3d(1.0, Eigen::Quaterniond(rot_mat), -rot_mat * centroid); } reconstruction->Transform(*tform); } void AlignToENUPlane(Reconstruction* reconstruction, Sim3d* tform, bool unscaled) { const Eigen::Vector3d centroid = reconstruction->ComputeCentroid(0.0, 1.0); GPSTransform gps_tform; const Eigen::Vector3d ell_centroid = gps_tform.XYZToEll({centroid}).at(0); // Create rotation matrix from ECEF to ENU coordinates const double sin_lat = std::sin(DegToRad(ell_centroid(0))); const double sin_lon = std::sin(DegToRad(ell_centroid(1))); const double cos_lat = std::cos(DegToRad(ell_centroid(0))); const double cos_lon = std::cos(DegToRad(ell_centroid(1))); // Create ECEF to ENU rotation matrix Eigen::Matrix3d rot_mat; rot_mat << -sin_lon, cos_lon, 0, -cos_lon * sin_lat, -sin_lon * sin_lat, cos_lat, cos_lon * cos_lat, sin_lon * cos_lat, sin_lat; const double scale = unscaled ? 1.0 / tform->scale : 1.0; *tform = Sim3d(scale, Eigen::Quaterniond(rot_mat), -scale * rot_mat * centroid); reconstruction->Transform(*tform); } } // namespace colmap colmap-3.10/src/colmap/estimators/coordinate_frame.h000066400000000000000000000100461464745566500226450ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/scene/reconstruction.h" #include "colmap/util/eigen_alignment.h" #include namespace colmap { struct ManhattanWorldFrameEstimationOptions { // The maximum image size for line detection. int max_image_size = 1024; // The minimum length of line segments in pixels. double min_line_length = 3; // The tolerance for classifying lines into horizontal/vertical. double line_orientation_tolerance = 0.2; // The maximum distance in pixels between lines and the vanishing points. double max_line_vp_distance = 0.5; // The maximum cosine distance between estimated axes to be inliers. double max_axis_distance = 0.05; }; // Estimate gravity vector by assuming gravity-aligned image orientation, i.e. // the majority of images is assumed to have the gravity vector aligned with an // upright image plane. Eigen::Vector3d EstimateGravityVectorFromImageOrientation( const Reconstruction& reconstruction, double max_axis_distance = 0.05); // Estimate the coordinate frame of the reconstruction assuming a Manhattan // world by finding the major vanishing points in each image. This function // assumes that the majority of images is taken in upright direction, i.e. // people are standing upright in the image. The orthonormal axes of the // estimated coordinate frame will be given in the columns of the returned // matrix. If one axis could not be determined, the respective column will be // zero. The axes are specified in the world coordinate system in the order // rightward, downward, forward. #ifdef COLMAP_LSD_ENABLED Eigen::Matrix3d EstimateManhattanWorldFrame( const ManhattanWorldFrameEstimationOptions& options, const Reconstruction& reconstruction, const std::string& image_path); #endif // Aligns the reconstruction to the plane defined by running PCA on the 3D // points. The model centroid is at the origin of the new coordinate system // and the X axis is the first principal component with the Y axis being the // second principal component void AlignToPrincipalPlane(Reconstruction* recon, Sim3d* tform); // Aligns the reconstruction to the local ENU plane orientation. Rotates the // reconstruction such that the x-y plane aligns with the ENU tangent plane at // the point cloud centroid and translates the origin to the centroid. // If unscaled == true, then the original scale of the model remains unchanged. void AlignToENUPlane(Reconstruction* recon, Sim3d* tform, bool unscaled); } // namespace colmap colmap-3.10/src/colmap/estimators/coordinate_frame_test.cc000066400000000000000000000146471464745566500240550ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #include "colmap/estimators/coordinate_frame.h" #include "colmap/geometry/gps.h" #include namespace colmap { namespace { TEST(CoordinateFrame, EstimateGravityVectorFromImageOrientation) { Reconstruction reconstruction; EXPECT_EQ(EstimateGravityVectorFromImageOrientation(reconstruction), Eigen::Vector3d::Zero()); } TEST(CoordinateFrame, EstimateManhattanWorldFrame) { Reconstruction reconstruction; std::string image_path; EXPECT_EQ( EstimateManhattanWorldFrame( ManhattanWorldFrameEstimationOptions(), reconstruction, image_path), Eigen::Matrix3d::Zero()); } TEST(CoordinateFrame, AlignToPrincipalPlane) { // Start with reconstruction containing points on the Y-Z plane and cameras // "above" the plane on the positive X axis. After alignment the points should // be on the X-Y plane and the cameras "above" the plane on the positive Z // axis. Sim3d tform; Reconstruction reconstruction; // Setup image with projection center at (1, 0, 0) Image image; image.SetImageId(1); image.SetRegistered(true); image.CamFromWorld() = Rigid3d(Eigen::Quaterniond::Identity(), Eigen::Vector3d(-1, 0, 0)); reconstruction.AddImage(image); // Setup 4 points on the Y-Z plane const point3D_t p1 = reconstruction.AddPoint3D(Eigen::Vector3d(0, -1, 0), Track()); const point3D_t p2 = reconstruction.AddPoint3D(Eigen::Vector3d(0, 1, 0), Track()); const point3D_t p3 = reconstruction.AddPoint3D(Eigen::Vector3d(0, 0, -1), Track()); const point3D_t p4 = reconstruction.AddPoint3D(Eigen::Vector3d(0, 0, 1), Track()); AlignToPrincipalPlane(&reconstruction, &tform); // Note that the final X and Y axes may be inverted after alignment, so we // need to account for both cases when checking for correctness const bool inverted = tform.rotation.y() < 0; // Verify that points lie on the correct locations of the X-Y plane EXPECT_LE((reconstruction.Point3D(p1).xyz - Eigen::Vector3d(inverted ? 1 : -1, 0, 0)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(p2).xyz - Eigen::Vector3d(inverted ? -1 : 1, 0, 0)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(p3).xyz - Eigen::Vector3d(0, inverted ? 1 : -1, 0)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(p4).xyz - Eigen::Vector3d(0, inverted ? -1 : 1, 0)) .norm(), 1e-6); // Verify that projection center is at (0, 0, 1) EXPECT_LE( (reconstruction.Image(1).ProjectionCenter() - Eigen::Vector3d(0, 0, 1)) .norm(), 1e-6); // Verify that transform matrix does shuffling of axes Eigen::Matrix3x4d expected; if (inverted) { expected << 0, -1, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0; } else { expected << 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0; } EXPECT_LT((tform.ToMatrix() - expected).norm(), 1e-6); } TEST(CoordinateFrame, AlignToENUPlane) { // Create reconstruction with 4 points with known LLA coordinates. After the // ENU transform all 4 points should land approximately on the X-Y plane. GPSTransform gps; auto points = gps.EllToXYZ({Eigen::Vector3d(50, 10.1, 100), Eigen::Vector3d(50.1, 10, 100), Eigen::Vector3d(50.1, 10.1, 100), Eigen::Vector3d(50, 10, 100)}); Sim3d tform; Reconstruction reconstruction; std::vector point_ids; for (size_t i = 0; i < points.size(); ++i) { point_ids.push_back(reconstruction.AddPoint3D(points[i], Track())); LOG(INFO) << points[i].transpose(); } AlignToENUPlane(&reconstruction, &tform, false); // Verify final locations of points EXPECT_LE((reconstruction.Point3D(point_ids[0]).xyz - Eigen::Vector3d(3584.8565215, -5561.5336506, 0.0742643)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(point_ids[1]).xyz - Eigen::Vector3d(-3577.3888622, 5561.6397107, 0.0783761)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(point_ids[2]).xyz - Eigen::Vector3d(3577.4152111, 5561.6397283, 0.0783613)) .norm(), 1e-6); EXPECT_LE((reconstruction.Point3D(point_ids[3]).xyz - Eigen::Vector3d(-3584.8301178, -5561.5336683, 0.0742791)) .norm(), 1e-6); // Verify that straight line distance between points is preserved for (size_t i = 1; i < points.size(); ++i) { const double dist_orig = (points[i] - points[i - 1]).norm(); const double dist_tform = (reconstruction.Point3D(point_ids[i]).xyz - reconstruction.Point3D(point_ids[i - 1]).xyz) .norm(); EXPECT_LE(std::abs(dist_orig - dist_tform), 1e-6); } } } // namespace } // namespace colmap colmap-3.10/src/colmap/estimators/cost_functions.h000066400000000000000000000533711464745566500224140ustar00rootroot00000000000000// Copyright (c) 2023, ETH Zurich and UNC Chapel Hill. // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of // its contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. #pragma once #include "colmap/geometry/rigid3.h" #include "colmap/sensor/models.h" #include "colmap/util/eigen_alignment.h" #include "colmap/util/logging.h" #include #include #include #include namespace colmap { template using EigenVector3Map = Eigen::Map>; template using EigenQuaternionMap = Eigen::Map>; using EigenMatrix6d = Eigen::Matrix; inline Eigen::MatrixXd SqrtInformation(const Eigen::MatrixXd& covariance) { return covariance.inverse().llt().matrixL().transpose(); } // Standard bundle adjustment cost function for variable // camera pose, calibration, and point parameters. template class ReprojErrorCostFunction { public: explicit ReprojErrorCostFunction(const Eigen::Vector2d& point2D) : observed_x_(point2D(0)), observed_y_(point2D(1)) {} static ceres::CostFunction* Create(const Eigen::Vector2d& point2D) { return ( new ceres::AutoDiffCostFunction, 2, 4, 3, 3, CameraModel::num_params>( new ReprojErrorCostFunction(point2D))); } template bool operator()(const T* const cam_from_world_rotation, const T* const cam_from_world_translation, const T* const point3D, const T* const camera_params, T* residuals) const { const Eigen::Matrix point3D_in_cam = EigenQuaternionMap(cam_from_world_rotation) * EigenVector3Map(point3D) + EigenVector3Map(cam_from_world_translation); CameraModel::ImgFromCam(camera_params, point3D_in_cam[0], point3D_in_cam[1], point3D_in_cam[2], &residuals[0], &residuals[1]); residuals[0] -= T(observed_x_); residuals[1] -= T(observed_y_); return true; } private: const double observed_x_; const double observed_y_; }; // Bundle adjustment cost function for variable // camera calibration and point parameters, and fixed camera pose. template class ReprojErrorConstantPoseCostFunction : public ReprojErrorCostFunction { using Parent = ReprojErrorCostFunction; public: ReprojErrorConstantPoseCostFunction(const Rigid3d& cam_from_world, const Eigen::Vector2d& point2D) : Parent(point2D), cam_from_world_(cam_from_world) {} static ceres::CostFunction* Create(const Rigid3d& cam_from_world, const Eigen::Vector2d& point2D) { return (new ceres::AutoDiffCostFunction< ReprojErrorConstantPoseCostFunction, 2, 3, CameraModel::num_params>( new ReprojErrorConstantPoseCostFunction(cam_from_world, point2D))); } template bool operator()(const T* const point3D, const T* const camera_params, T* residuals) const { const Eigen::Quaternion cam_from_world_rotation = cam_from_world_.rotation.cast(); const Eigen::Matrix cam_from_world_translation = cam_from_world_.translation.cast(); return Parent::operator()(cam_from_world_rotation.coeffs().data(), cam_from_world_translation.data(), point3D, camera_params, residuals); } private: const Rigid3d& cam_from_world_; }; // Bundle adjustment cost function for variable // camera pose and calibration parameters, and fixed point. template class ReprojErrorConstantPoint3DCostFunction : public ReprojErrorCostFunction { using Parent = ReprojErrorCostFunction; public: ReprojErrorConstantPoint3DCostFunction(const Eigen::Vector2d& point2D, const Eigen::Vector3d& point3D) : Parent(point2D), point3D_x_(point3D(0)), point3D_y_(point3D(1)), point3D_z_(point3D(2)) {} static ceres::CostFunction* Create(const Eigen::Vector2d& point2D, const Eigen::Vector3d& point3D) { return (new ceres::AutoDiffCostFunction< ReprojErrorConstantPoint3DCostFunction, 2, 4, 3, CameraModel::num_params>( new ReprojErrorConstantPoint3DCostFunction(point2D, point3D))); } template bool operator()(const T* const cam_from_world_rotation, const T* const cam_from_world_translation, const T* const camera_params, T* residuals) const { const T point3D[3] = {T(point3D_x_), T(point3D_y_), T(point3D_z_)}; return Parent::operator()(cam_from_world_rotation, cam_from_world_translation, point3D, camera_params, residuals); } private: const double point3D_x_; const double point3D_y_; const double point3D_z_; }; // Rig bundle adjustment cost function for variable camera pose and calibration // and point parameters. Different from the standard bundle adjustment function, // this cost function is suitable for camera rigs with consistent relative poses // of the cameras within the rig. The cost function first projects points into // the local system of the camera rig and then into the local system of the // camera within the rig. template class RigReprojErrorCostFunction { public: explicit RigReprojErrorCostFunction(const Eigen::Vector2d& point2D) : observed_x_(point2D(0)), observed_y_(point2D(1)) {} static ceres::CostFunction* Create(const Eigen::Vector2d& point2D) { return ( new ceres::AutoDiffCostFunction, 2, 4, 3, 4, 3, 3, CameraModel::num_params>( new RigReprojErrorCostFunction(point2D))); } template bool operator()(const T* const cam_from_rig_rotation, const T* const cam_from_rig_translation, const T* const rig_from_world_rotation, const T* const rig_from_world_translation, const T* const point3D, const T* const camera_params, T* residuals) const { const Eigen::Matrix point3D_in_cam = EigenQuaternionMap(cam_from_rig_rotation) * (EigenQuaternionMap(rig_from_world_rotation) * EigenVector3Map(point3D) + EigenVector3Map(rig_from_world_translation)) + EigenVector3Map(cam_from_rig_translation); CameraModel::ImgFromCam(camera_params, point3D_in_cam[0], point3D_in_cam[1], point3D_in_cam[2], &residuals[0], &residuals[1]); residuals[0] -= T(observed_x_); residuals[1] -= T(observed_y_); return true; } private: const double observed_x_; const double observed_y_; }; // Rig bundle adjustment cost function for variable camera pose and camera // calibration and point parameters but fixed rig extrinsic poses. template class RigReprojErrorConstantRigCostFunction : public RigReprojErrorCostFunction { using Parent = RigReprojErrorCostFunction; public: explicit RigReprojErrorConstantRigCostFunction(const Rigid3d& cam_from_rig, const Eigen::Vector2d& point2D) : Parent(point2D), cam_from_rig_(cam_from_rig) {} static ceres::CostFunction* Create(const Rigid3d& cam_from_rig, const Eigen::Vector2d& point2D) { return (new ceres::AutoDiffCostFunction< RigReprojErrorConstantRigCostFunction, 2, 4, 3, 3, CameraModel::num_params>( new RigReprojErrorConstantRigCostFunction(cam_from_rig, point2D))); } template bool operator()(const T* const rig_from_world_rotation, const T* const rig_from_world_translation, const T* const point3D, const T* const camera_params, T* residuals) const { const Eigen::Quaternion cam_from_rig_rotation = cam_from_rig_.rotation.cast(); const Eigen::Matrix cam_from_rig_translation = cam_from_rig_.translation.cast(); return Parent::operator()(cam_from_rig_rotation.coeffs().data(), cam_from_rig_translation.data(), rig_from_world_rotation, rig_from_world_translation, point3D, camera_params, residuals); } private: const Rigid3d& cam_from_rig_; }; // Cost function for refining two-view geometry based on the Sampson-Error. // // First pose is assumed to be located at the origin with 0 rotation. Second // pose is assumed to be on the unit sphere around the first pose, i.e. the // pose of the second camera is parameterized by a 3D rotation and a // 3D translation with unit norm. `tvec` is therefore over-parameterized as is // and should be down-projected using `SphereManifold`. class SampsonErrorCostFunction { public: SampsonErrorCostFunction(const Eigen::Vector2d& x1, const Eigen::Vector2d& x2) : x1_(x1(0)), y1_(x1(1)), x2_(x2(0)), y2_(x2(1)) {} static ceres::CostFunction* Create(const Eigen::Vector2d& x1, const Eigen::Vector2d& x2) { return (new ceres::AutoDiffCostFunction( new SampsonErrorCostFunction(x1, x2))); } template bool operator()(const T* const cam2_from_cam1_rotation, const T* const cam2_from_cam1_translation, T* residuals) const { const Eigen::Matrix R = EigenQuaternionMap(cam2_from_cam1_rotation).toRotationMatrix(); // Matrix representation of the cross product t x R. Eigen::Matrix t_x; t_x << T(0), -cam2_from_cam1_translation[2], cam2_from_cam1_translation[1], cam2_from_cam1_translation[2], T(0), -cam2_from_cam1_translation[0], -cam2_from_cam1_translation[1], cam2_from_cam1_translation[0], T(0); // Essential matrix. const Eigen::Matrix E = t_x * R; // Homogeneous image coordinates. const Eigen::Matrix x1_h(T(x1_), T(y1_), T(1)); const Eigen::Matrix x2_h(T(x2_), T(y2_), T(1)); // Squared sampson error. const Eigen::Matrix Ex1 = E * x1_h; const Eigen::Matrix Etx2 = E.transpose() * x2_h; const T x2tEx1 = x2_h.transpose() * Ex1; residuals[0] = x2tEx1 * x2tEx1 / (Ex1(0) * Ex1(0) + Ex1(1) * Ex1(1) + Etx2(0) * Etx2(0) + Etx2(1) * Etx2(1)); return true; } private: const double x1_; const double y1_; const double x2_; const double y2_; }; template inline void EigenQuaternionToAngleAxis(const T* eigen_quaternion, T* angle_axis) { const T quaternion[4] = {eigen_quaternion[3], eigen_quaternion[0], eigen_quaternion[1], eigen_quaternion[2]}; ceres::QuaternionToAngleAxis(quaternion, angle_axis); } // 6-DoF error on the absolute pose. The residual is the log of the error pose, // splitting SE(3) into SO(3) x R^3. The 6x6 covariance matrix is defined in the // reference frame of the camera. Its first and last three components correspond // to the rotation and translation errors, respectively. struct AbsolutePoseErrorCostFunction { public: AbsolutePoseErrorCostFunction(const Rigid3d& cam_from_world, const EigenMatrix6d& covariance_cam) : world_from_cam_(Inverse(cam_from_world)), sqrt_information_cam_(SqrtInformation(covariance_cam)) {} static ceres::CostFunction* Create(const Rigid3d& cam_from_world, const EigenMatrix6d& covariance_cam) { return ( new ceres::AutoDiffCostFunction( new AbsolutePoseErrorCostFunction(cam_from_world, covariance_cam))); } template bool operator()(const T* const cam_from_world_q, const T* const cam_from_world_t, T* residuals_ptr) const { const Eigen::Quaternion param_from_measured_q = EigenQuaternionMap(cam_from_world_q) * world_from_cam_.rotation.cast(); EigenQuaternionToAngleAxis(param_from_measured_q.coeffs().data(), residuals_ptr); Eigen::Map> param_from_measured_t(residuals_ptr + 3); param_from_measured_t = EigenVector3Map(cam_from_world_t) + EigenQuaternionMap(cam_from_world_q) * world_from_cam_.translation.cast(); Eigen::Map> residuals(residuals_ptr); residuals.applyOnTheLeft(sqrt_information_cam_.template cast()); return true; } private: const Rigid3d world_from_cam_; const EigenMatrix6d sqrt_information_cam_; }; // 6-DoF error between two absolute poses based on a measurement that is their // relative pose, with identical scale for the translation. The covariance is // defined in the reference frame of the camera j. // Its first and last three components correspond to the rotation and // translation errors, respectively. // // Derivation: // j_T_w = ΔT_j·j_T_i·i_T_w // where ΔT_j = exp(η_j) is the residual in SE(3) and η_j in tangent space. // Thus η_j = log(j_T_w·i_T_w⁻¹·i_T_j) // Rotation term: ΔR = log(j_R_w·i_R_w⁻¹·i_R_j) // Translation term: Δt = j_t_w + j_R_w·i_R_w⁻¹·(i_t_j -i_t_w) struct MetricRelativePoseErrorCostFunction { public: MetricRelativePoseErrorCostFunction(const Rigid3d& i_from_j, const EigenMatrix6d& covariance_j) : i_from_j_(i_from_j), sqrt_information_j_(SqrtInformation(covariance_j)) {} static ceres::CostFunction* Create(const Rigid3d& i_from_j, const EigenMatrix6d& covariance_j) { return (new ceres::AutoDiffCostFunction( new MetricRelativePoseErrorCostFunction(i_from_j, covariance_j))); } template bool operator()(const T* const i_from_world_q, const T* const i_from_world_t, const T* const j_from_world_q, const T* const j_from_world_t, T* residuals_ptr) const { const Eigen::Quaternion j_from_i_q = EigenQuaternionMap(j_from_world_q) * EigenQuaternionMap(i_from_world_q).inverse(); const Eigen::Quaternion param_from_measured_q = j_from_i_q * i_from_j_.rotation.cast(); EigenQuaternionToAngleAxis(param_from_measured_q.coeffs().data(), residuals_ptr); Eigen::Matrix i_from_jw_t = i_from_j_.translation.cast() - EigenVector3Map(i_from_world_t); Eigen::Map> param_from_measured_t(residuals_ptr + 3); param_from_measured_t = EigenVector3Map(j_from_world_t) + j_from_i_q * i_from_jw_t; Eigen::Map> residuals(residuals_ptr); residuals.applyOnTheLeft(sqrt_information_j_.template cast()); return true; } private: const Rigid3d& i_from_j_; const EigenMatrix6d sqrt_information_j_; }; // A cost function that wraps another one and whiten its residuals with an // isotropic covariance, i.e. assuming that the variance is identical in and // independent between each dimension of the residual. template class IsotropicNoiseCostFunctionWrapper { class LinearCostFunction : public ceres::CostFunction { public: explicit LinearCostFunction(const double s) : s_(s) { set_num_residuals(1); mutable_parameter_block_sizes()->push_back(1); } bool Evaluate(double const* const* parameters, double* residuals, double** jacobians) const final { *residuals = **parameters * s_; if (jacobians && *jacobians) { **jacobians = s_; } return true; } private: const double s_; }; public: template static ceres::CostFunction* Create(const double stddev, Args&&... args) { THROW_CHECK_GT(stddev, 0.0); ceres::CostFunction* cost_function = CostFunction::Create(std::forward(args)...); const double scale = 1.0 / stddev; std::vector conditioners( #if CERES_VERSION_MAJOR < 2 cost_function->num_residuals()); // Ceres <2.0 does not allow reusing the same conditioner multiple times. for (size_t i = 0; i < conditioners.size(); ++i) { conditioners[i] = new LinearCostFunction(scale); } #else cost_function->num_residuals(), new LinearCostFunction(scale)); #endif return new ceres::ConditionedCostFunction( cost_function, conditioners, ceres::TAKE_OWNERSHIP); } }; template

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