pax_global_header 0000666 0000000 0000000 00000000064 14007662526 0014522 g ustar 00root root 0000000 0000000 52 comment=7079e49418b1ef0a55764762699140451c5a13fa
BerryNet-3.10.2/ 0000775 0000000 0000000 00000000000 14007662526 0013337 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/.eslintrc.yaml 0000664 0000000 0000000 00000000135 14007662526 0016123 0 ustar 00root root 0000000 0000000 env:
node: true
es6: true
extends: google
rules:
comma-dangle: [2, only-multiline]
BerryNet-3.10.2/.github/ 0000775 0000000 0000000 00000000000 14007662526 0014677 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/.github/FUNDING.yml 0000664 0000000 0000000 00000001357 14007662526 0016522 0 ustar 00root root 0000000 0000000 # These are supported funding model platforms
github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
patreon: # Replace with a single Patreon username
open_collective: berrynet # Replace with a single Open Collective username
ko_fi: # Replace with a single Ko-fi username
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
liberapay: # Replace with a single Liberapay username
issuehunt: # Replace with a single IssueHunt username
otechie: # Replace with a single Otechie username
custom: ['https://paypal.me/berrynet'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
BerryNet-3.10.2/.github/ISSUE_TEMPLATE/ 0000775 0000000 0000000 00000000000 14007662526 0017062 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/.github/ISSUE_TEMPLATE/bug_report.md 0000664 0000000 0000000 00000001146 14007662526 0021556 0 ustar 00root root 0000000 0000000 ---
name: Bug report
about: Create a report to help us improve
title: "[Bug Report] "
labels: ''
assignees: ''
---
**Description**
A clear and concise description of what the bug is.
**Steps to Reproduce**
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Expected behavior**
* Expected: A clear and concise description of what you expected to happen.
* Actual: A clear and concise description of what actually happened.
**Logs**
If applicable, add logs or screenshots to help explain your problem.
**Environment**
* BerryNet: [e.g. v0.6.2, beta2, etc.]
* OS: [e.g. Ubuntu 20.04]
BerryNet-3.10.2/.github/ISSUE_TEMPLATE/feature_request.md 0000664 0000000 0000000 00000000767 14007662526 0022621 0 ustar 00root root 0000000 0000000 ---
name: Feature request
about: Create a new Feature request to help the project become better.
title: "[FR] "
labels: ''
assignees: ''
---
**Concept**
If we provide [Feature]
**Reason/Hypothesis**
We can help [who] to [Benefit] or solve [Problem] because we know [who] are [Fact about our target].
**More Description (if any)**
Put more description here to support the concept
**Suggested Implementation (if any)**
Put some suggested implementation here if you already have some ideas in mind
BerryNet-3.10.2/.github/workflows/ 0000775 0000000 0000000 00000000000 14007662526 0016734 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/.github/workflows/docker.yml 0000664 0000000 0000000 00000004330 14007662526 0020726 0 ustar 00root root 0000000 0000000 name: Docker Image CI
on:
push:
branches:
- "*"
pull_request:
branches:
- master
env:
RC_NAME: dt42/berrynet
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@master
# need to set dockerhub password in GitHub secrets
# - name: Login to docker hub
# uses: actions-hub/docker/login@master
# env:
# DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
# DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
# - name: Pull cache
# run: |
# docker login -u ${{ secrets.DOCKER_USERNAME }} -p ${{ secrets.DOCKER_PASSWORD }}
# docker pull ${RC_NAME}:cache
- name: Build image
if: always()
run: |
docker build --cache-from ${RC_NAME}:cache -t ${RC_NAME} -f docker/Dockerfile .
docker tag ${RC_NAME} ${RC_NAME}:${GITHUB_SHA}
docker tag ${RC_NAME} ${RC_NAME}:cache
# need to set dockerhub password in GitHub secrets
# - name: Push to docker registry
# uses: actions-hub/docker@master
# if: (github.ref == 'refs/heads/master' || github.ref == 'refs/heads/prod') && success()
# with:
# args: push ${RC_NAME}:${GITHUB_SHA}
# - name: Push Cache to docker registry
# uses: actions-hub/docker@master
# if: always()
# with:
# args: push ${RC_NAME}:cache
test:
env:
POETRY_VIRTUALENVS_CREATE: false
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.7
uses: actions/setup-python@v1
with:
python-version: 3.7
- name: Install dependencies
run: |
python3 -m pip install --upgrade pip
pip install poetry
poetry install
python3 setup.py install
- name: Download inception models
run: |
wget "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" -O berrynet/engine/inception_v3_2016_08_28_frozen.pb.tar.gz
tar -zxvf berrynet/engine/inception_v3_2016_08_28_frozen.pb.tar.gz -C berrynet/engine
- name: Test with pytest
run: |
python3 -m unittest
BerryNet-3.10.2/.github/workflows/main.yml 0000664 0000000 0000000 00000003627 14007662526 0020413 0 ustar 00root root 0000000 0000000 name: CI
on:
release:
types: [published]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout source
uses: actions/checkout@v2
- name: Build Debian Package
run: |
echo Add other actions to build,
echo test, and deploy your project.
echo GITHUB_SHA is $GITHUB_SHA
echo GITHUB_REF is $GITHUB_REF
sudo apt-get -y update
sudo apt-get -y install git-buildpackage
sudo apt-get -y install build-essential devscripts
sudo apt-get -y install lsb-release
sudo apt-get -y install debhelper dh-apache2 dh-python git python3 python3-setuptools
sudo apt-get -y install pristine-tar
sudo apt-get -y install p7zip-full
lsb_release -a
curl -sL https://raw.githubusercontent.com/DT42/BerryNet-repo/master/setup.sh | sudo -E bash
sudo apt-get -y install freeboard
git remote add github1 https://github.com/grandpaul/BerryNet.git
git remote update
git branch -a
git checkout github1/upstream -b upstream
git checkout github1/pristine-tar -b pristine-tar
git checkout github1/debian/sid -b debian/sid
echo gbp buildpackage --no-sign
OLDPWD=`pwd`
echo OLDPWD is $OLDPWD
sleep 30; uscan --verbose
VER1=`ls ../berrynet_*.orig.tar.gz | sed 's/.*berrynet_//' | sed 's/.orig.tar.gz//'`
echo VER1 is $VER1
cd ../berrynet-$VER1; debuild --no-sign; cd $OLDPWD
ls ..
7z a -snl debpackage.7z ../*.dsc ../*.debian.tar.xz ../*.orig.tar.gz ../*.deb ../*.changes ../*.build ../*.buildinfo ../berrynet-*.tar.gz
- name: Upload to release
uses: JasonEtco/upload-to-release@master
with:
args: debpackage.7z application/x-7z-compressed
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
BerryNet-3.10.2/.gitignore 0000664 0000000 0000000 00000000235 14007662526 0015327 0 ustar 00root root 0000000 0000000 # Byte-compiled / optimized / DLL files
__pycache__
*.pyc
# Distribution / packaging
build
dist
*.egg-info
# Environments
env
venv
.vscode
# Others
*.swp
BerryNet-3.10.2/.gitlab-ci.yml 0000664 0000000 0000000 00000003331 14007662526 0015773 0 ustar 00root root 0000000 0000000 # Official language image. Look for the different tagged releases at:
# https://hub.docker.com/r/library/python/tags/
image: gitlab-ci:base
# Change pip's cache directory to be inside the project directory since we can
# only cache local items.
variables:
PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache"
# Pip's cache doesn't store the python packages
# https://pip.pypa.io/en/stable/reference/pip_install/#caching
#
# If you want to also cache the installed packages, you have to install
# them in a virtualenv and cache it as well.
cache:
paths:
- .cache/pip
stages:
- build
- test
- deploy
before_script:
- python3 -V # Print out python version for debugging
- eval $(ssh-agent -s)
- echo "$SSH_PRIVATE_KEY" | tr -d '\r' | ssh-add - > /dev/null
- mkdir -p ~/.ssh && chmod 700 ~/.ssh
- echo "$SSH_KNOWN_HOSTS" > ~/.ssh/known_hosts && chmod 644 ~/.ssh/known_hosts
build-wheel:
stage: build
script:
- pip3 wheel --wheel-dir=./dist .
artifacts:
paths:
- dist/
unit-test:
stage: test
dependencies:
- build-wheel
script:
- pip3 install dist/*.whl tensorflow
- apt update && apt install -y libsm6 libxrender-dev
- python3 setup.py test
allow_failure: true
pep8-codestyle:
stage: test
script:
- pycodestyle .
allow_failure: true
pylint-codestyle:
stage: test
script:
- pylint berrynet
allow_failure: true
deploy-pypi:
stage: deploy
dependencies:
- build-wheel
script:
- git clone git@gitlab.com:DT42/infrastructure42/dt42pypi.git
- cp dist/berrynet*.whl dt42pypi/ && cd dt42pypi
- dir2pi -n .
- git add . && git commit -m "Add $CI_PROJECT_NAME $CI_COMMIT_TAG wheel. This is an auto-commit by GitLab-CI Runner."
- git pull && git push
only:
- tags
BerryNet-3.10.2/.gitmodules 0000664 0000000 0000000 00000000153 14007662526 0015513 0 ustar 00root root 0000000 0000000 [submodule "inference/darkflow"]
path = inference/darkflow
url = https://github.com/thtrieu/darkflow.git
BerryNet-3.10.2/AUTHORS 0000664 0000000 0000000 00000000554 14007662526 0014413 0 ustar 00root root 0000000 0000000 # Authors and contributors ordered by first contribution.
Bofu Chen - bofu AT dt42 dot io
Joseph Liu - joseph AT dt42 dot io
Kai-Heng Feng - khfeng AT dt42 dot io
Tammy Yang - tammy AT dt42 dot io
Paul Liu - paulliu AT debian dot org
Katsuya Hyodo (PINTO0309) - rmsdh122 AT yahoo dot co dot jp
Sherry Chung - sherry AT dt42 dot io
Mei Mei - meimei AT dt42 dot io
BerryNet-3.10.2/BACKERS.md 0000664 0000000 0000000 00000001116 14007662526 0014732 0 ustar 00root root 0000000 0000000
Sponsors & Backers
BerryNet is a GPL-licensed FLOSS project. It's an independent project with its ongoing development made possible entirely thanks to the support by these awesome [backers](https://github.com/DT42/BerryNet/blob/master/BACKERS.md). If you'd like to join them, please consider:
* [Become a backer or sponsor on Open Collective](https://opencollective.com/berrynet).
* [One-time donation via PayPal or crypto-currencies.](https://github.com/DT42/BerryNet/wiki/Donation#one-time-donations)
One-Time Donations
* Penk Chen
BerryNet-3.10.2/CONTRIBUTING.md 0000664 0000000 0000000 00000000304 14007662526 0015565 0 ustar 00root root 0000000 0000000 We use [Developer Certificate of Origin](https://developercertificate.org/).
To use DCO, you only need to add your signature into a Git commit:
```
$ git commit -s -m "your commit message."
```
BerryNet-3.10.2/LICENSE.txt 0000664 0000000 0000000 00000104513 14007662526 0015166 0 ustar 00root root 0000000 0000000 GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc.
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
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To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
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For example, if you distribute copies of such a program, whether
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Developers that use the GNU GPL protect your rights with two steps:
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For the developers' and authors' protection, the GPL clearly explains
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The precise terms and conditions for copying, distribution and
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.
BerryNet-3.10.2/README.md 0000664 0000000 0000000 00000021062 14007662526 0014617 0 ustar 00root root 0000000 0000000
Deep Learning Gateway on Raspberry Pi And Other Edge Devices
[Supporting BerryNet](community/donation)
* [Become a backer or sponsor on Open Collective](https://opencollective.com/berrynet).
* [One-time donation via PayPal or crypto-currencies](community/donation#one-time-donations).
## Introduction
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network.
At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud.
Figure 1: BerryNet architecture
Figure 1 shows the software architecture of the project, we use Node.js/Python, MQTT and an AI engine to analyze images or video frames with deep learning. So far, there are two default types of AI engines, the classification engine (with Inception v3 [[1]](https://arxiv.org/pdf/1512.00567.pdf) model) and the object detection engine (with TinyYOLO [[2]](https://pjreddie.com/media/files/papers/YOLO9000.pdf) model or MobileNet SSD [[3]](https://arxiv.org/pdf/1704.04861.pdf) model). Figure 2 shows the differences between classification and object detection.
Figure 2: Classification vs detection
One of the application of this intelligent gateway is to use the camera to monitor the place you care about. For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. The frames were captured by the IP camera and they were submitted into the AI engine. The output from the AI engine will be shown in the dashboard. We are working on the Email and IM notification so you can get a notification when there is a dog coming into the meeting area with the next release.
Figure 3: Object detection result example
To bring easy and flexible edge AI experience to user, we keep expending support of the AI engines and the reference HWs.
Figure 4: Reference hardwares
## Installation
You can install BerryNet by using pre-built image or from source. Please refer to the [installation guide](https://dt42.io/berrynet-doc/tutorials/installation/) for the details.
We are pushing BerryNet into Debian repository, so you will be able to install by only typing one command in the future.
Here is the quick steps to install from source:
```
$ git clone https://github.com/DT42/BerryNet.git
$ cd BerryNet
$ ./configure
```
## Start and Stop BerryNet
BerryNet performs an AIoT application by connecting independent components together. Component types include but not limited to AI engine, I/O processor, data processor (algorithm), or data collector.
We recommend to manage BerryNet componetns by [supervisor](http://supervisord.org/), but you can also run BerryNet components manually. You can manage BerryNet via `supervisorctl`:
```
# Check status of BerryNet components
$ sudo supervisorctl status all
# Stop Camera client
$ sudo supervisorctl stop camera
# Restart all components
$ sudo supervisorctl restart all
# Show last stderr logs of camera client
$ sudo supervisorctl tail camera stderr
```
For more possibilities of supervisorctl, please refer to the [official tutorial](http://supervisord.org/running.html#running-supervisorctl).
The default application has three components:
* Camera client to provide input images
* Object detection engine to find type and position of the detected objects in an image
* Dashboard to display the detection results
You will learn how to configure or change the components in the [Configuration](#configuration) section.
## Dashboard: Freeboard
### Open Freeboard on RPi (with touch screen)
Freeboard is a web-based dashboard. Here are the steps to show the detection result iamge and text on Freeboard:
* 1: Enter `http://127.0.0.1:8080` in browser's URL bar, and press enter
* 2: [Download](https://raw.githubusercontent.com/DT42/BerryNet/master/config/dashboard-tflitedetector.json) the Freeboard configuration for default application, `dashboard-tflitedetector.json`
* 2: Click `LOAD FREEBOARD`, and select the newly downloaded `dashboard-tflitedetector.json`
* 3: Wait for seconds, you should see the inference result image and text on Freeboard
### Open Freeboard on another computer
Assuming that you have two devices:
* Device A with IP `192.168.1.42`, BerryNet default application runs on it
* Device B with IP `192.168.1.43`, you want to open Freeboard and see the detection result on it
Here are the steps:
* 1: Enter `http://192.168.1.42:8080` in browser's URL bar, and press enter
* 2: [Download](https://raw.githubusercontent.com/DT42/BerryNet/master/config/dashboard-tflitedetector.json) the Freeboard configuration for default application, `dashboard-tflitedetector.json`
* 3: Replace all the `localhost` to `192.168.1.42` in `dashboard-tflitedetector.json`
* 2: Click `LOAD FREEBOARD`, and select the newly downloaded `dashboard-tflitedetector.json`
* 3: Wait for seconds, you should see the inference result image and text on Freeboard
For more details about dashboard configuration (e.g. how to add widgets), please refer to [Freeboard project](https://github.com/Freeboard/freeboard).
## Enable Data Collector
You might want to store the snapshot and inference results for data analysis.
To run BerryNet data collector manually, you can run the command below:
```
$ bn_data_collector --topic-config --data-dirpath
```
The topic config indicates what MQTT topic the data collector will listen, and what handler will be triggered. Here is a topic config exmaple:
```
{
"berrynet/engine/tflitedetector/result": "self.update"
}
```
The inference result image and text will be saved into the indicated result directory.
## Configuration
The default supervisor config is at `/etc/supervisor/conf.d/berrynet-tflite.conf`. To write your own supervisor config, you can refer to [here](https://github.com/DT42/BerryNet/tree/master/config/supervisor/conf.d) for more example supervisor configs of BerryNet
### Camera Client
BerryNet camera client can run in two modes: stream or file. In stream mode, local camera (e.g. USB camera and RPi camera) and IP camera can be supported, and input frame rate (FPS) can be changed on demand (default is 1). In file mode, user can indicate filepath as input source.
To run camera client in stream mode:
```
$ bn_camera --fps 5
```
To run camera client in file mode:
```
$ bn_camera --mode file --filepath
```
## Use Your Data To Train
The original instruction of retraining YOLOv2 model see [github repository of darknet](https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects)
In the current of BerryNet, TinyYolo is used instead of YOLOv2.
The major differences are:
1. Create file yolo-obj.cfg with the same content as in `tiny-yolo.cfg`
2. Download pre-trained weights of darknet reference model, `darknet.weights.12`, for the convolutional layers (6.1MB)
https://drive.google.com/drive/folders/0B-oZJEwmkAObMzAtc2QzZDhyVGM?usp=sharing
The rest parts are the same as retraining YOLO.
If you use [LabelMe](http://labelme.csail.mit.edu/Release3.0/) to annotate data, `utils/xmlTotxt.py` can help convert the xml format to the text format that darknet uses.
## Discussion
Please refer to the [Slack](https://join.slack.com/t/berrynet/shared_invite/enQtODg5MjA0ODExMjUzLWIwMDNkZWExZGE2Njc1ZDljMmFiOWJlZDdmZmEwYmQ4YTJiNzg2NDc1NTJhMDVkMzhmNzA3YTU0ZTc4M2JiNTE) or [Telegram Group](https://t.me/berrynetdev) for questions, suggestions, or any idea discussion.
BerryNet-3.10.2/berrynet-manager 0000775 0000000 0000000 00000003412 14007662526 0016527 0 ustar 00root root 0000000 0000000 #! /bin/sh
#
# Copyright 2017 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
help() {
echo "Usage: $0 "
echo " start : launch dlsystem"
echo " stop : terminate dlsystem"
echo " status : check the state of all services"
echo " log : dump logfiles of all services"
exit 1
}
if [ $# -lt 1 ]; then
help
fi
case $1 in
start | stop | status)
sudo systemctl $1 \
detection_fast_server.service \
agent.service \
broker.service \
dashboard.service \
localimg.service \
camera.service \
journal.service \
data_collector.service
;;
log)
sudo journalctl -x --no-pager -u detection_fast_server.service
sudo journalctl -x --no-pager -u agent.service
sudo journalctl -x --no-pager -u broker.service
sudo journalctl -x --no-pager -u dashboard.service
sudo journalctl -x --no-pager -u localimg.service
sudo journalctl -x --no-pager -u camera.service
sudo journalctl -x --no-pager -u journal.service
sudo journalctl -x --no-pager -u data_collector.service
;;
*)
help
esac
BerryNet-3.10.2/berrynet/ 0000775 0000000 0000000 00000000000 14007662526 0015171 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/__init__.py 0000664 0000000 0000000 00000000514 14007662526 0017302 0 ustar 00root root 0000000 0000000 import os
from logzero import setup_logger
# Save log file at different place to prevent permission error.
if os.geteuid() == 0: # root
LOGGING_FLLEPATH='/tmp/berrynet.log'
else:
LOGGING_FLLEPATH='{}/.cache/berrynet.log'.format(os.getenv('HOME'))
logger = setup_logger(name='berrynet-logger', logfile=LOGGING_FLLEPATH)
BerryNet-3.10.2/berrynet/bndyda/ 0000775 0000000 0000000 00000000000 14007662526 0016432 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/bndyda/__init__.py 0000664 0000000 0000000 00000000000 14007662526 0020531 0 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/bndyda/bnpipeline.py 0000664 0000000 0000000 00000041414 14007662526 0021135 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Pipeline service with relay engine (default engine).
"""
import cv2
import argparse
import json
import logging
import os
import sys
import time
from datetime import datetime
import numpy as np
from berrynet import logger
from berrynet.bndyda.launcher_berrynet import BerryNetPipelineLauncher
from berrynet.comm import payload
from berrynet.engine import DLEngine
from berrynet.service import EngineService
from dyda_utils import tools
class PipelineEngine(DLEngine):
def __init__(self, config,
dyda_config_path='',
warmup_size=(480, 640, 3),
disable_warmup=False,
benchmark=False,
verbosity=0):
self.launcher = BerryNetPipelineLauncher(
config,
dyda_config_path=dyda_config_path,
verbosity=verbosity, benchmark=benchmark)
self.pipeline_config = tools.parse_json(config)
if not disable_warmup:
self.warmup(shape=warmup_size)
# def process_input(self, tensor):
# return tensor
def inference(self, tensor, meta={}, base_name=None):
"""
Args:
tensor: Image data in BGR format (numpy array)
Returns:
Dictionary following generic inference spec and
pipeline spec (by project).
"""
return self.launcher.run(tensor, meta=meta, base_name=base_name)
# return {
# 'annotations': {
# 'label': 'dt42',
# 'confidence': 0.99
# }
# }
def process_output(self, output):
return output
def get_dl_component_config(self, pipeline_config):
"""Get pipeline def list containing only DL components
Args:
pipeline_config: pipeline config JSON object
Returns:
List of DL components definitions
"""
dl_comp_config = []
try:
pipeline_def = pipeline_config['pipeline_def']
except KeyError:
logger.warning('Invalid pipeline config')
pipeline_def = []
for comp_config in pipeline_def:
if ('classifier' in comp_config['name'] or
'detector' in comp_config['name']):
dl_comp_config.append(comp_config)
return dl_comp_config
def warmup(self, shape=(480, 640, 3), iteration=5):
"""Warmup pipeline engine
Use all-zero numpy array as input to warmup pipeline engine.
Args:
meta: Metadata of image data
shape: Warmup image shape in (w, h, c) format
iteration: How many times to feed in warmup image
Returns:
N/A
"""
logger.debug('Warmup shape: {}'.format(shape))
input_data = [np.zeros(shape=shape, dtype=np.uint8)] * iteration
# FIXME: get engines programatically
dl_comp_config = self.get_dl_component_config(self.pipeline_config)
for comp_config in dl_comp_config:
t_start = time.time()
comp_name = comp_config['name']
inst = self.launcher.pipeline.pipeline[comp_name]['instance']
inst.input_data = input_data
inst.main_process()
t_duration = time.time() - t_start
logger.debug('Warmup {0} costs {1} sec'.format(comp_name,
t_duration))
def duration(t):
return (datetime.now() - t).microseconds / 1000
class PipelineDummyEngine(DLEngine):
def inference(self, tensor, meta={}):
output = None
return output
class PipelineService(EngineService):
def __init__(self, service_name, engine, comm_config,
pid=None,
pipeline_config_path=None,
disable_engine=False,
disable_warmup=False,
warmup_size=(480, 640, 3)):
super().__init__(service_name,
engine,
comm_config)
self.pipeline_config_path = pipeline_config_path
self.dyda_config_path = ''
self.warmup_size = warmup_size
self.disable_engine = disable_engine
if not os.path.exists('/tmp/dlbox-pipeline'):
os.mkdir('/tmp/dlbox-pipeline')
self.counter = 0
self.pid = pid
logger.debug('Pipeline result topic: berrynet/engine/pipeline/result')
def inference(self, pl):
logger.debug('Disable engine: {}'.format(self.disable_engine))
if self.disable_engine:
self.dummy_inference(pl)
else:
self.dl_inference(pl)
def dl_inference(self, pl):
def empty_inference_result(count):
return [
{
'channel': i,
'annotations': []
}
for i in range(count)]
t = datetime.now()
base_name = None
logger.debug('counter #{}'.format(self.counter))
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
# Unify the type of input payload to a list, so that
# bnpipeline can process the input in the same way.
#
# If the payload is
# - a list of items: keep the list
# - a single item: convert to a list with an item
mqtt_payload = payload.deserialize_payload(pl.decode('utf-8'))
if isinstance(mqtt_payload, list):
jpg_json = mqtt_payload
else:
jpg_json = [mqtt_payload]
logger.info('Convert input type from {0} to {1}'.format(
type(mqtt_payload),
type(jpg_json)))
jpg_bytes_list = [
payload.destringify_jpg(img['bytes']) for img in jpg_json]
metas = [img.get('meta', {}) for img in jpg_json]
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_arrays = [
payload.jpg2bgr(jpg_bytes) for jpg_bytes in jpg_bytes_list]
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
# FIXME: Galaxy pipeline may or may not use a list as input, so we
# check the length here and then choose whether to send a list or not.
# We may drop it when Galaxy Pipline unite their input.
if len(bgr_arrays) > 1:
image_data = self.engine.process_input(bgr_arrays)
else:
image_data = self.engine.process_input(bgr_arrays[0])
# FIXME: Galaxy pipeline doesn't support multiple metadata for multiple
# images at the moment (which will be needed), so we provide the first
# metadata here. This commit should be revert when Galaxy pipeline
# support it: https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/120
meta_data = metas[0]
try:
logger.debug(meta_data)
output = self.engine.inference(image_data,
meta=meta_data,
base_name=base_name)
model_outputs = self.engine.process_output(output)
except IndexError as e:
# FIXME: workaround for pipeline
# Pipeline throw IndexError when there's no results, see:
# https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/86
# So we catch the exeception, and produce a dummy result
# to hook. This workaround should be removed after the issue
# has been fixed.
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'pipeline raised IndexError'))
if model_outputs is None:
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'severe error happened in pipeline'))
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} ms'.format(duration(t)))
# self.engine.cache_data('model_output', model_outputs)
# self.engine.cache_data('model_output_filepath', output_name)
# self.engine.save_cache()
self.send_result(self.generalize_result(jpg_json, model_outputs))
self.counter += 1
def dummy_inference(self, pl):
logger.debug('dummy_inference is called')
def switch_mode(self, pl):
"""Switch pipeline service between inference and non-inference modes
If Pipeline service receives berrynet/data/mode topic with
"inference" in payload, service will switch to inference mode;
If "idle" or "learning" in payload, service will switch to
non-inference mode.
Pipeline service will create pipeline engine and
listen to specified topics only in inference mode.
Args:
pl: MQTT message payload
valid value: {'inference', 'idle', 'learning'}
Returns:
N/A
"""
mode = pl.decode('utf-8')
if mode == 'inference':
self.disable_engine = False
self.engine = PipelineEngine(
self.pipeline_config_path,
dyda_config_path=self.dyda_config_path,
disable_warmup=self.disable_warmup,
warmup_size=self.warmup_size)
else:
self.disable_engine = True
self.engine = PipelineDummyEngine()
def deploy(self, pl):
"""Deploy newly retrained model for pipeline engine
New dyda config filepath is in the payload.
Args:
pl: MQTT message payload w/ new dyda config filepath.
Returns:
N/A
"""
dyda_config_path = pl.decode('utf-8')
self.dyda_config_path = dyda_config_path
self.comm.send('berrynet/data/deployed', '')
logger.info(('New model has been deployed, '
'dyda config: {}'.format(self.dyda_config_path)))
def generalize_result(self, eng_input, eng_output):
# Pipeline returns None if any error happened
if eng_output is None:
eng_output = {}
# If pipeline generate multiple outputs simultaneously
#
# In this case, the format of engine output is
#
# {
# 'annotations': {...},
# 'bytes': '...'
# }
if all(key in eng_output.keys() for key in ['annotations', 'bytes']):
logger.debug('Pipeline output type: multiple')
logger.debug('eng_input type = {0}, len = {1}'.format(type(eng_input), len(eng_input)))
# FIXME: Re-cap why eng_input is a list and only contains 1 item.
eng_input = eng_input[0]
try:
eng_input['annotations'] = eng_output['annotations']
logger.debug('output image type: {0}, len: {1}'.format(type(eng_output['bytes']),
len(eng_output['bytes'])))
pipeline_img = eng_output['bytes'][0]
retval, jpg_bytes = cv2.imencode('.jpg', pipeline_img)
eng_input['bytes'] = payload.stringify_jpg(jpg_bytes)
except Exception as e:
logger.critical(e)
else:
logger.debug('Pipeline output type: simple')
# FIXME: Workaround for spec incompatibility
# DLBox spec use 'image_blob', but BerryNet use 'bytes', so we have to
# do a convert here
if isinstance(eng_output, list):
inf_output = eng_output[0]
else:
inf_output = eng_output
if len(eng_input) > 1:
for i in range(len(eng_input)):
try:
retval, jpg_bytes = cv2.imencode('.jpg', inf_output)
eng_input[i]['bytes'] = payload.stringify_jpg(jpg_bytes)
#eng_input[i].pop('bytes')
except Exception as e:
print(e)
else:
try:
eng_input, = eng_input
retval, jpg_bytes = cv2.imencode('.jpg', inf_output)
eng_input['bytes'] = payload.stringify_jpg(jpg_bytes)
#eng_input.pop('bytes')
except Exception as e:
print(e)
return eng_input
def send_result(self, generalized_result):
# NOTE: There are numpy float in pipeline output, so we use
# tools.dump_json instead of payload.serialize_payload
if self.pid is None:
self.comm.send(
'berrynet/engine/pipeline/result',
tools.dump_json(generalized_result))
else:
self.comm.send(
'berrynet/engine/pipeline/result/{}'.format(self.pid),
tools.dump_json(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--pipeline-id',
help=('Indicate pipeline ID which will be attached '
'to result topic (optional)'))
ap.add_argument('--pipeline-config',
help='File contains the definition '
'of pipeline application')
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle')
ap.add_argument('--benchmark',
action='store_true',
help='Benchmark mode toggle')
ap.add_argument('--broker-ip',
default='localhost',
help='MQTT broker IP')
ap.add_argument('--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.')
ap.add_argument('--topic',
nargs=2,
action='append',
default=None,
help=('Two params in "" format. '
'It can be declared multiple times.'))
ap.add_argument('--disable-engine',
action='store_true',
help='Service disable engine initially')
ap.add_argument('--disable-warmup',
action='store_true',
help='Skip warming up pipeline by black image')
ap.add_argument('-v', '--verbosity',
action='count', default=0,
help='Output verbosity')
ap.add_argument('-w', '--warmup-size',
nargs=2,
type=int,
default=(640, 480),
help='Warmup image\'s size, in format "w h", '
'e.g., "640 480"')
return vars(ap.parse_args())
def main():
# Process CLI arguments
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
topic_config['berrynet/data/mode'] = 'self.switch_mode'
topic_config['berrynet/data/deploy'] = 'self.deploy'
if args['topic'] is not None:
for t, h in args['topic']:
topic_config[t] = h
w, h = args['warmup_size']
# Setup pipeline service
if args['disable_engine']:
eng = PipelineDummyEngine()
else:
eng = PipelineEngine(args['pipeline_config'],
disable_warmup=args['disable_warmup'],
verbosity=args['verbosity'],
benchmark=args['benchmark'],
warmup_size=(h, w, 3))
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': 1883
}
}
engine_service = PipelineService(
'pipeline service',
eng,
comm_config,
pid=args['pipeline_id'],
pipeline_config_path=args['pipeline_config'],
disable_engine=args['disable_engine'],
disable_warmup=args['disable_warmup'],
warmup_size=(h, w, 3))
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/bndyda/launcher_berrynet.py 0000664 0000000 0000000 00000011671 14007662526 0022525 0 ustar 00root root 0000000 0000000 import argparse
import json
import logging
from os.path import join as pjoin
import cv2
from dyda.pipelines import pipeline as dydapl
logger = logging.getLogger('launcher')
class BerryNetPipelineLauncher(object):
def __init__(self, config, dyda_config_path='',
output_dirpath='', verbosity=0, benchmark=False,
lab_flag=False):
self.pipeline = dydapl.Pipeline(
config,
dyda_config_path=dyda_config_path,
parent_result_folder=output_dirpath,
verbosity=verbosity,
lab_flag=lab_flag)
self.benchmark = benchmark
def run(self, bitmap, meta={}, base_name=None):
"""Run pipeline.
Args:
bitmap: Image data in BGR format (numpy array)
Returns:
Dictionary with contents or empty list.
"""
self.pipeline.run(bitmap,
external_meta=meta,
benchmark=self.benchmark,
base_name=base_name)
try:
# You can get pipeline output(s) in two ways:
#
# 1. Simple (and single) output from pipeline.output.
# - 100% available
# - Data type is dynamic (it might be a JSON object, an image, etc.)
#
# 2. Multiple outputs from components defined in pipeline config.
# - Currently it is used in the scenario that
# you want to have JSON result and image simultaneously.
# - final_json_output and final_img_output are component name,
# not component types. We are considering to define them as standard.
if all(key in self.pipeline.pipeline.keys()
for key in ['final_json_output', 'final_img_output']):
logger.debug('Pipeline output type: multiple (launcher_berrynet)')
output = {
'annotations': self.pipeline.pipeline['final_json_output']['output']['annotations'],
'bytes': self.pipeline.pipeline['final_img_output']['output']
}
else:
logger.debug('Pipeline output type: simple (launcher_berrynet)')
output = self.pipeline.output
except Exception as e:
logger.critical(e)
return output
def get_args(argv=None):
""" Prepare auguments for running the script. """
parser = argparse.ArgumentParser(
description='Pipeline.'
)
parser.add_argument(
'-i', '--input',
type=str,
default=(
'/home/shared/customer_data/acti/201711-ACTi-A/'
'20171207_recording/acti_2017-12-07-1701/frame'),
help='Input folder for ')
parser.add_argument(
'-o', '--output',
type=str,
default='/home/shared/DT42/test_data/'
'test_auto_labeler_with_tracker/results/',
help='Output folder for output_metadata')
parser.add_argument(
'--lab_flag',
dest='lab_flag',
action='store_true',
default=False,
help='True to enable related lab process.'
)
parser.add_argument(
'-p', '--pipeline_config',
type=str,
default='/home/lab/dyda/pipeline.config',
help='File contains the definition of pipeline application.'
)
parser.add_argument(
'-t', '--dyda_config',
type=str,
default='',
help='File contains the component definitions.'
)
parser.add_argument(
"-v", "--verbosity",
action="count",
default=0,
help="increase output verbosity"
)
return parser.parse_args(argv)
def main():
""" Example for testing pipeline. """
args = get_args()
log_level = logging.WARNING
if args.verbosity == 1:
log_level = logging.INFO
elif args.verbosity >= 2:
log_level = logging.DEBUG
formatter = logging.Formatter('[launcher] %(levelname)s %(message)s')
console = logging.StreamHandler()
console.setFormatter(formatter)
logger.setLevel(log_level)
logger.addHandler(console)
logger.debug('lab_flag is %r' % args.lab_flag)
pipeline = BerryNetPipelineLauncher(
config=args.pipeline_config,
dyda_config_path=args.dyda_config,
output_dirpath=args.output,
verbosity=args.verbosity,
lab_flag=args.lab_flag
)
logger.debug('Running Reader and Selector for frames')
source_dirpath = args.input
input_number = 100
for i in range(input_number):
input_data = pjoin(source_dirpath, '00000{}.png'.format(570 + i))
ext_data = cv2.imread(input_data)
output_data = pipeline.run(ext_data)
logger.debug('===== frame #{} ====='.format(i))
logger.debug('input: {}'.format(input_data))
if (len(output_data) > 0):
with open('output_{}.json'.format(i), 'w') as f:
json.dump(output_data, f, indent=4)
if __name__ == "__main__":
main()
BerryNet-3.10.2/berrynet/bndyda/output_processor.py 0000664 0000000 0000000 00000005277 14007662526 0022456 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Data collector service.
"""
import argparse
import json
import os
from datetime import datetime
from os.path import join as pjoin
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class DataCollectorService(object):
def __init__(self, comm_config, data_dirpath):
self.comm_config = comm_config
self.comm_config['subscribe']['berrynet/engine/mockup/result'] = \
self.update
self.comm = Communicator(self.comm_config, debug=True)
self.data_dirpath = data_dirpath
def update(self, pl):
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(payload_json['bytes'])
payload_json.pop('bytes')
logger.debug('inference text result: {}'.format(payload_json))
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--data_dirpath',
default='/tmp/berrynet-data',
help='Dirpath where to store collected data.'
)
return vars(ap.parse_args())
def main():
args = parse_args()
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
dc_service = DataCollectorService(comm_config,
args['data_dirpath'])
dc_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/bndyda/pipeline.py 0000664 0000000 0000000 00000036534 14007662526 0020624 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Pipeline service with relay engine (default engine).
"""
import argparse
import json
import logging
import os
import sys
import time
from datetime import datetime
import paho.mqtt.publish as publish
import activation.verify
import numpy as np
import cv2
from berrynet import logger
from berrynet.comm import payload
from berrynet.engine import DLEngine
from berrynet.service import EngineService
from dt42lab.core import tools
from bntrainer.launcher_berrynet import BerryNetPipelineLauncher
class PipelineEngine(DLEngine):
def __init__(self, config,
trainer_config_path='',
warmup_size=(480, 640, 3),
disable_warmup=False,
benchmark=False,
verbosity=0):
self.launcher = BerryNetPipelineLauncher(
config,
trainer_config_path=trainer_config_path,
verbosity=verbosity, benchmark=benchmark)
self.pipeline_config = tools.parse_json(config)
if not disable_warmup:
self.warmup(shape=warmup_size)
# def process_input(self, tensor):
# return tensor
def inference(self, tensor, meta={}, base_name=None):
"""
Args:
tensor: Image data in BGR format (numpy array)
Returns:
Dictionary following generic inference spec and
pipeline spec (by project).
"""
return self.launcher.run(tensor, meta=meta, base_name=base_name)
# return {
# 'annotations': {
# 'label': 'dt42',
# 'confidence': 0.99
# }
# }
def process_output(self, output):
return output
def get_dl_component_config(self, pipeline_config):
"""Get pipeline def list containing only DL components
Args:
pipeline_config: pipeline config JSON object
Returns:
List of DL components definitions
"""
dl_comp_config = []
try:
pipeline_def = pipeline_config['pipeline_def']
except KeyError:
logger.warning('Invalid pipeline config')
pipeline_def = []
for comp_config in pipeline_def:
if ('classifier' in comp_config['name'] or
'detector' in comp_config['name']):
dl_comp_config.append(comp_config)
return dl_comp_config
def warmup(self, shape=(480, 640, 3), iteration=5):
"""Warmup pipeline engine
Use all-zero numpy array as input to warmup pipeline engine.
Args:
meta: Metadata of image data
shape: Warmup image shape in (w, h, c) format
iteration: How many times to feed in warmup image
Returns:
N/A
"""
logger.debug('Warmup shape: {}'.format(shape))
input_data = [np.zeros(shape=shape, dtype=np.uint8)] * iteration
# FIXME: get engines programatically
dl_comp_config = self.get_dl_component_config(self.pipeline_config)
for comp_config in dl_comp_config:
t_start = time.time()
comp_name = comp_config['name']
inst = self.launcher.pipeline.pipeline[comp_name]['instance']
inst.input_data = input_data
inst.main_process()
t_duration = time.time() - t_start
logger.debug('Warmup {0} costs {1} sec'.format(comp_name,
t_duration))
def duration(t):
return (datetime.now() - t).microseconds / 1000
class PipelineDummyEngine(DLEngine):
def inference(self, tensor, meta={}):
output = None
return output
class PipelineService(EngineService):
def __init__(self, service_name, engine, comm_config,
pipeline_config_path=None,
disable_engine=False,
disable_warmup=False,
warmup_size=(480, 640, 3),
output_broker_ip="localhost",
output_mqtt_topic='berrynet/engine/pipeline/result'):
super().__init__(service_name,
engine,
comm_config)
self.pipeline_config_path = pipeline_config_path
self.trainer_config_path = ''
self.warmup_size = warmup_size
self.disable_engine = disable_engine
self.output_broker_ip = output_broker_ip
self.output_mqtt_topic = output_mqtt_topic
if not os.path.exists('/tmp/dlbox-pipeline'):
os.mkdir('/tmp/dlbox-pipeline')
self.counter = 0
logger.debug('Pipeline result topic: {}'.format(
self.output_mqtt_topic))
def inference(self, pl):
if self.disable_engine:
self.dummy_inference(pl)
else:
self.dl_inference(pl)
def dl_inference(self, pl):
def empty_inference_result(count):
return [
{
'channel': i,
'annotations': []
}
for i in range(count)]
t = datetime.now()
base_name = None
logger.debug('counter #{}'.format(self.counter))
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes_list = [
payload.destringify_jpg(img['bytes']) for img in jpg_json]
metas = [img.get('meta', {}) for img in jpg_json]
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_arrays = [
payload.jpg2bgr(jpg_bytes) for jpg_bytes in jpg_bytes_list]
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_arrays)
# FIXME: Galaxy pipeline doesn't support multiple metadata for multiple
# images at the moment (which will be needed), so we provide the first
# metadata here. This commit should be revert when Galaxy pipeline
# support it: https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/120
meta_data = metas[0]
try:
logger.debug(meta_data)
output = self.engine.inference(image_data,
meta=meta_data,
base_name=base_name)
model_outputs = self.engine.process_output(output)
except IndexError as e:
# FIXME: workaround for pipeline
# Pipeline throw IndexError when there's no results, see:
# https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/86
# So we catch the exeception, and produce a dummy result
# to hook. This workaround should be removed after the issue
# has been fixed.
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'pipeline raised IndexError'))
if model_outputs is None:
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'severe error happened in pipeline'))
if isinstance(model_outputs, dict):
model_outputs = [model_outputs]
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} ms'.format(duration(t)))
# self.engine.cache_data('model_output', model_outputs)
# self.engine.cache_data('model_output_filepath', output_name)
# self.engine.save_cache()
self.send_result(self.generalize_result(jpg_json, model_outputs),
self.output_mqtt_topic)
self.counter += 1
def dummy_inference(self, pl):
logger.debug('dummy_inference is called')
def switch_mode(self, pl):
"""Switch pipeline service between inference and non-inference modes
If Pipeline service receives berrynet/data/mode topic with
"inference" in payload, service will switch to inference mode;
If "idle" or "learning" in payload, service will switch to
non-inference mode.
Pipeline service will create pipeline engine and
listen to specified topics only in inference mode.
Args:
pl: MQTT message payload
valid value: {'inference', 'idle', 'learning'}
Returns:
N/A
"""
mode = pl.decode('utf-8')
if mode == 'inference':
self.disable_engine = False
else:
self.disable_engine = True
def deploy(self, pl):
"""Deploy newly retrained model for pipeline engine
New trainer config filepath is in the payload.
Args:
pl: MQTT message payload w/ new trainer config filepath.
Returns:
N/A
"""
trainer_config_path = pl.decode('utf-8')
self.trainer_config_path = trainer_config_path
self.comm.send('berrynet/data/deployed', '')
logger.info(('New model has been deployed, '
'trainer config: {}'.format(self.trainer_config_path)))
def generalize_result(self, eng_inputs, eng_outputs):
# Pipeline returns None if any error happened
if eng_outputs is None:
eng_outputs = [{}]
if len(eng_inputs) != len(eng_outputs):
logger.warning('Input length != output length: {} != {}'.format(
len(eng_inputs), len(eng_outputs)))
# We guarantee len of inputs will always be 1 (at least now), so
# it's safer to access eng_inputs by index than to eng_outputs
c_id = int(eng_inputs[0]['meta']['channel_id'])
eng_outputs = [eng_outputs[c_id]]
# FIXME: Workaround for spec incompatibility
# DLBox spec use 'image_blob', but BerryNet use 'bytes', so we have to
# do a convert here
for eng_in, eng_out in list(zip(eng_inputs, eng_outputs)):
if isinstance(eng_out, np.ndarray):
r, result_img = cv2.imencode('.jpg', eng_out)
eng_in['bytes'] = payload.stringify_jpg(result_img)
else:
try:
eng_in.update(eng_out)
except KeyError as e:
logger.exception(
'{} ({}): {}'.format(e.__class__, e.__doc__, e))
eng_in['image_blob'] = eng_in.pop('bytes')
return eng_inputs
def send_result(
self, generalized_result, topic='berrynet/engine/pipeline/result'):
# NOTE: There are numpy float in pipeline output, so we use
# tools.dump_json instead of payload.serialize_payload
for r in generalized_result:
try:
c_id = r.get('channel', None) or r['meta']['channel_id']
topic += '/{}'.format(c_id)
except KeyError:
logger.warn(
'No channel id found, set topic to {}'.format(topic))
logger.debug('output topic: {}'.format(topic))
publish.single(topic,
payload=tools.dump_json(generalized_result),
hostname=self.output_broker_ip)
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--pipeline-config',
help='File contains the definition '
'of pipeline application')
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle')
ap.add_argument('--benchmark',
action='store_true',
help='Benchmark mode toggle')
ap.add_argument('--broker-ip', '--input-broker-ip',
default='localhost',
help='MQTT broker IP')
ap.add_argument('--output-broker-ip',
default='localhost',
help='Result output MQTT broker IP')
ap.add_argument('--output-mqtt-topic',
default='berrynet/engine/pipeline/result',
help='Result output MQTT topic')
ap.add_argument('--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.')
ap.add_argument('--topic',
nargs=2,
action='append',
default=None,
help=('Two params in "" format. '
'It can be declared multiple times.'))
ap.add_argument('--disable-engine',
action='store_true',
help='Service disable engine initially')
ap.add_argument('--disable-warmup',
action='store_true',
help='Skip warming up pipeline by black image')
ap.add_argument('-v', '--verbosity',
action='count', default=0,
help='Output verbosity')
ap.add_argument('-w', '--warmup-size',
nargs=2,
type=int,
default=(640, 480),
help='Warmup image\'s size, in format "w h", '
'e.g., "640 480"')
return vars(ap.parse_args())
def main():
result = activation.verify.check_license()
if not result:
sys.exit(1)
# Process CLI arguments
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
topic_config['berrynet/data/mode'] = 'self.switch_mode'
topic_config['berrynet/data/deploy'] = 'self.deploy'
if args['topic'] is not None:
for t, h in args['topic']:
topic_config[t] = h
w, h = args['warmup_size']
# Setup pipeline service
if args['disable_engine']:
eng = PipelineDummyEngine()
else:
eng = PipelineEngine(args['pipeline_config'],
disable_warmup=args['disable_warmup'],
verbosity=args['verbosity'],
benchmark=args['benchmark'],
warmup_size=(h, w, 3))
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': 1883
}
}
engine_service = PipelineService(
'pipeline service',
eng,
comm_config,
pipeline_config_path=args['pipeline_config'],
disable_engine=args['disable_engine'],
disable_warmup=args['disable_warmup'],
warmup_size=(h, w, 3),
output_broker_ip=args['output_broker_ip'],
output_mqtt_topic=args['output_mqtt_topic'])
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/bndyda/pipeline_restarter.py 0000664 0000000 0000000 00000004243 14007662526 0022707 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Pipeline service restarter.
Restart pipeline service after received config update notification.
"""
import argparse
import logging
import subprocess
from berrynet import logger
from berrynet.comm import Communicator
class PipelineRestarterService(object):
def __init__(self, service_name, comm_config):
self.service_name = service_name
self.comm_config = comm_config
self.comm_config['subscribe']['dlboxapi/config/update'] = \
self.restart_pipeline
self.comm = Communicator(self.comm_config, debug=True)
def restart_pipeline(self, pl):
logger.debug('Restart pipeline')
subprocess.call('dlbox-manager restart dlbox-pipeline.service',
shell=True)
def run(self, args):
"""Infinite loop serving pipeline restart requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle')
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
engine_service = PipelineRestarterService(
'pipeline service restarter',
comm_config)
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/bndyda/warmup.py 0000664 0000000 0000000 00000002267 14007662526 0020326 0 ustar 00root root 0000000 0000000 #!/usr/bin/python3
"""This script will block until the specified file appears or the timeout
expires. The first argument is the timeout in seconds. The second argument
is the filepath."""
import os
import time
import sys
def logging(*args):
"""add a prefix to print() output"""
print("wait_for_warmup:", ' '.join(args), flush=True)
def wait_for_warmup(warmup_file, max_wait_sec):
"""wait for warmup_file to appear until max_wait_sec timeout"""
counter = 0
msg = warmup_file
while True:
if os.access(warmup_file, os.F_OK):
msg += " is detected!"
break
if counter >= max_wait_sec:
msg += " didn't show up!"
break
counter += 1
time.sleep(1)
msg += " Time elapsed = %d" % counter
logging(msg)
def main():
"""Program starts here"""
warmup_file = '/tmp/pipeline.warmup.done'
max_wait_sec = 60
for i in range(len(sys.argv)):
arg = sys.argv[i]
if i == 1:
max_wait_sec = int(arg)
elif i == 2:
warmup_file = arg
if max_wait_sec > 0:
wait_for_warmup(warmup_file, max_wait_sec)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/ 0000775 0000000 0000000 00000000000 14007662526 0016447 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/client/__init__.py 0000664 0000000 0000000 00000000000 14007662526 0020546 0 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/client/camera.py 0000664 0000000 0000000 00000016426 14007662526 0020262 0 ustar 00root root 0000000 0000000 #!/usr/bin/python3
#
# Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
import argparse
import json
import logging
import time
from datetime import datetime
import cv2
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--mode',
default='stream',
help='Camera creates frame(s) from stream or file. (default: stream)'
)
ap.add_argument(
'--stream-src',
type=str,
default='0',
help=('Camera stream source. '
'It can be device node ID or RTSP URL. '
'(default: 0)')
)
ap.add_argument(
'--fps',
type=float,
default=1,
help='Frame per second in streaming mode. (default: 1)'
)
ap.add_argument(
'--filepath',
default='',
help='Input image path in file mode. (default: empty)'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument('--topic',
default='berrynet/data/rgbimage',
help='The topic to send the captured frames.'
)
ap.add_argument('--display',
action='store_true',
help=('Open a window and display the sent out frames. '
'This argument is only effective in stream mode.')
)
ap.add_argument('--hash',
action='store_true',
help='Add md5sum of a captured frame into the result.'
)
ap.add_argument('--meta',
type=str,
default='{}',
help='Metadata field for stringified JSON data.'
)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
comm_config = {
'subscribe': {},
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
comm = Communicator(comm_config, debug=True)
duration = lambda t: (datetime.now() - t).microseconds / 1000
metadata = json.loads(args.get('meta', '{}'))
if args['mode'] == 'stream':
counter = 0
fail_counter = 0
# Check input stream source
if args['stream_src'].isdigit():
# source is a physically connected camera
stream_source = int(args['stream_src'])
else:
# source is an IP camera
stream_source = args['stream_src']
capture = cv2.VideoCapture(stream_source)
cam_fps = capture.get(cv2.CAP_PROP_FPS)
if cam_fps > 30 or cam_fps < 1:
logger.warn('Camera FPS is {} (>30 or <1). Set it to 30.'.format(cam_fps))
cam_fps = 30
out_fps = args['fps']
interval = int(cam_fps / out_fps)
# warmup
#t_warmup_start = time.time()
#t_warmup_now = time.time()
#warmup_counter = 0
#while t_warmup_now - t_warmup_start < 1:
# capture.read()
# warmup_counter += 1
# t_warmup_now = time.time()
logger.debug('===== VideoCapture Information =====')
if stream_source.isdigit():
stream_source_uri = '/dev/video{}'.format(stream_source)
else:
stream_source_uri = stream_source
logger.debug('Stream Source: {}'.format(stream_source_uri))
logger.debug('Camera FPS: {}'.format(cam_fps))
logger.debug('Output FPS: {}'.format(out_fps))
logger.debug('Interval: {}'.format(interval))
logger.debug('Send MQTT Topic: {}'.format(args['topic']))
#logger.debug('Warmup Counter: {}'.format(warmup_counter))
logger.debug('====================================')
while True:
status, im = capture.read()
# To verify whether the input source is alive, you should use the
# return value of capture.read(). It will not work by capturing
# exception of a capture instance, or by checking the return value
# of capture.isOpened().
#
# Two reasons:
# 1. If a dead stream is alive again, capture will not notify
# that input source is dead.
#
# 2. If you check capture.isOpened(), it will keep retruning
# True if a stream is dead afterward. So you can not use
# the capture return value (capture status) to determine
# whether a stream is alive or not.
if (status is True):
counter += 1
if counter == interval:
logger.debug('Drop frames: {}'.format(counter-1))
counter = 0
# Open a window and display the ready-to-send frame.
# This is useful for development and debugging.
if args['display']:
cv2.imshow('Frame', im)
cv2.waitKey(1)
t = datetime.now()
retval, jpg_bytes = cv2.imencode('.jpg', im)
mqtt_payload = payload.serialize_jpg(jpg_bytes, args['hash'], metadata)
comm.send(args['topic'], mqtt_payload)
logger.debug('send: {} ms'.format(duration(t)))
else:
pass
else:
fail_counter += 1
logger.critical('ERROR: Failure #{} happened when reading frame'.format(fail_counter))
# Re-create capture.
capture.release()
logger.critical('Re-create a capture and reconnect to {} after 5s'.format(stream_source))
time.sleep(5)
capture = cv2.VideoCapture(stream_source)
elif args['mode'] == 'file':
# Prepare MQTT payload
im = cv2.imread(args['filepath'])
retval, jpg_bytes = cv2.imencode('.jpg', im)
t = datetime.now()
mqtt_payload = payload.serialize_jpg(jpg_bytes, args['hash'], metadata)
logger.debug('payload: {} ms'.format(duration(t)))
logger.debug('payload size: {}'.format(len(mqtt_payload)))
# Client publishes payload
t = datetime.now()
comm.send(args['topic'], mqtt_payload)
logger.debug('mqtt.publish: {} ms'.format(duration(t)))
logger.debug('publish at {}'.format(datetime.now().isoformat()))
else:
logger.error('User assigned unknown mode {}'.format(args['mode']))
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/dashboard.py 0000664 0000000 0000000 00000005245 14007662526 0020756 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Dashboard agent service.
"""
import argparse
import json
from os.path import join as pjoin
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class DashboardService(object):
def __init__(self, service_name, comm_config):
self.service_name = service_name
self.comm_config = comm_config
self.comm_config['subscribe']['berrynet/engine/tensorflow/result'] = self.update
self.comm_config['subscribe']['berrynet/engine/mvclassification/result'] = self.update
self.comm = Communicator(self.comm_config, debug=True)
self.basedir = '/usr/local/berrynet/dashboard/www/freeboard'
def update(self, pl):
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(payload_json['bytes'])
inference_result = [
'{0}: {1} '.format(anno['label'], anno['confidence'])
for anno in payload_json['annotations']
]
logger.debug('inference results: {}'.format(inference_result))
with open(pjoin(self.basedir, 'snapshot.jpg'), 'wb') as f:
f.write(jpg_bytes)
self.comm.send('berrynet/dashboard/snapshot', 'snapshot.jpg')
self.comm.send('berrynet/dashboard/inferenceResult',
json.dumps(inference_result))
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--service_name', required=True,
help='Engine service name used as PID filename')
return vars(ap.parse_args())
def main():
args = parse_args()
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
dashboard_service = DashboardService(args['service_name'],
comm_config)
dashboard_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/data_collector.py 0000664 0000000 0000000 00000010375 14007662526 0022006 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Data collector service.
"""
import argparse
import json
import os
from datetime import datetime
from os.path import join as pjoin
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class DataCollectorService(object):
def __init__(self, comm_config, data_dirpath):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
self.comm_config['subscribe']['berrynet/engine/tensorflow/result'] = self.update
self.comm_config['subscribe']['berrynet/engine/mvclassification/result'] = self.update
self.comm = Communicator(self.comm_config, debug=True)
self.data_dirpath = data_dirpath
def update(self, pl):
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(payload_json['bytes'])
payload_json.pop('bytes')
logger.debug('inference text result: {}'.format(payload_json))
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def save_pipeline_result(self, pl):
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(payload_json['image_blob'])
payload_json.pop('image_blob')
logger.debug('inference text result: {}'.format(payload_json))
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--data-dirpath',
default='/tmp/berrynet-data',
help='Dirpath where to store collected data.'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
dc_service = DataCollectorService(comm_config,
args['data_dirpath'])
dc_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/data_collector_ui.py 0000664 0000000 0000000 00000024273 14007662526 0022505 0 ustar 00root root 0000000 0000000 #!/usr/bin/env python
# Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Data collector with UI showing inference result for human
"""
import argparse
import json
import os
import sys
import threading
import tkinter as tk
from datetime import datetime
from os.path import join as pjoin
import cv2
import numpy as np
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
from PIL import Image
from PIL import ImageTk
class DataCollectorService(object):
def __init__(self, comm_config, data_dirpath):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
#self.comm_config['subscribe']['berrynet/data/rgbimage'] = self.update
self.comm_config['subscribe']['berrynet/engine/pipeline/result'] = self.save_pipeline_result
self.comm = Communicator(self.comm_config, debug=True)
self.data_dirpath = data_dirpath
def update(self, pl):
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
# update UI with the latest inference result
self.ui.update(payload_json, 'bytes')
if self.data_dirpath:
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
jpg_bytes = payload.destringify_jpg(payload_json['bytes'])
payload_json.pop('bytes')
logger.debug('inference text result: {}'.format(payload_json))
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def save_pipeline_result(self, pl):
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
# update UI with the latest inference result
self.ui.update(payload_json, 'image_blob')
if self.data_dirpath:
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
jpg_bytes = payload.destringify_jpg(payload_json['image_blob'])
payload_json.pop('image_blob')
logger.debug('inference text result: {}'.format(payload_json))
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def send_snapshot_trigger(self):
payload = {}
payload['timestamp'] = datetime.now().isoformat()
mqtt_payload = json.dumps(payload)
self.comm.send('berrynet/trigger/controller/snapshot', mqtt_payload)
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
class UI(object):
def __init__(self, dc_service, dc_kwargs):
# Create data collector attributes
self.dc_service = dc_service
self.dc_kwargs = dc_kwargs
self.dc_service.ui = self
# Create UI attributes
self.window = tk.Tk()
self.window.title('BerryNet Inference Dashboard')
self.window.protocol('WM_DELETE_WINDOW', self.on_closing)
self.canvas_w = dc_kwargs['image_width']
self.canvas_h = dc_kwargs['image_height']
self.crowd_factor = 3
# Add label: inference result text
self.result = tk.Label(self.window,
text='TBD',
font=('Courier New', 10),
justify=tk.LEFT)
#self.result.pack(expand=True, side=tk.LEFT)
self.result.grid(row=0, column=0, padx=10)
#self.result.columnconfigure(1, weight=2)
# Add canvas: inference result image
#self.canvas = tk.Canvas(self.window, width=1920, height=1080)
self.canvas = tk.Canvas(self.window)
self.photo = ImageTk.PhotoImage(
image=Image.fromarray(
np.zeros((self.canvas_h, self.canvas_w, 3), dtype=np.uint8)))
self.image_id = self.canvas.create_image(
0, 0, image=self.photo, anchor=tk.NW)
#self.canvas.pack(side=tk.LEFT)
self.canvas.grid(row=0, column=1, rowspan=2, columnspan=4, sticky='nesw')
# Add button: snapshot trigger
self.snapshot_button = tk.Button(self.window,
text='Query',
command=self.snapshot)
#self.snapshot_button.pack(expand=True)
self.snapshot_button.grid(row=1, column=0)
# Add button and label: threshold controller
self.threshold = tk.Label(self.window,
text=self.crowd_factor,
font=('Courier New', 10),
justify=tk.LEFT)
self.threshold.grid(row=1, column=1)
self.snapshot_button = tk.Button(self.window,
text='+',
command=self.increase_threshold)
self.snapshot_button.grid(row=1, column=2)
self.snapshot_button = tk.Button(self.window,
text='-',
command=self.decrease_threshold)
self.snapshot_button.grid(row=1, column=3)
# Create data collector thread
t = threading.Thread(name='Data Collector',
target=self.dc_service.run,
args=(self.dc_kwargs,))
t.start()
# Start the main UI program
self.window.mainloop()
def update(self, data, imgkey='bytes'):
'''
Args:
data: Inference result loaded from JSON object
'''
# Retrieve result image
jpg_bytes = payload.destringify_jpg(data[imgkey])
img = payload.jpg2rgb(jpg_bytes)
# Retrieve result text, and update text area
data.pop(imgkey)
result_text = self.process_output(data)
if 'safely' in result_text:
text_color = 'blue'
else:
text_color = 'red'
self.result.config(text=result_text, fg=text_color)
# update image area
resized_img = Image.fromarray(img).resize((self.canvas_h, self.canvas_w))
self.photo = ImageTk.PhotoImage(image=resized_img)
win_w = self.photo.width() + self.result.winfo_width()
win_h = self.photo.height() + self.snapshot_button.winfo_height()
self.window.geometry('{}x{}'.format(win_w, win_h))
self.canvas.itemconfig(self.image_id, image=self.photo)
def snapshot(self):
self.dc_service.send_snapshot_trigger()
def increase_threshold(self):
self.crowd_factor += 1
self.threshold.config(text=self.crowd_factor)
def decrease_threshold(self):
self.crowd_factor -= 1
self.threshold.config(text=self.crowd_factor)
def process_output(self, output):
'''
Args:
output: Inference result, JSON object
Returns:
Stringified JSON data.
'''
if 'annotations' in output.keys():
count = 0
for obj in output['annotations']:
if obj['label'] == 'person':
count += 1
#logger.info('label = {}'.format(k))
msg = '{} persons at the corner\n\n'.format(count)
if count > self.crowd_factor:
msg += 'Too crowded,\nsuggest to go straight'
else:
msg += 'You can turn right safely'
return msg
else:
return json.dumps(output, indent=4)
def on_closing(self):
self.dc_service.comm.disconnect()
self.window.destroy()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--data-dirpath',
default=None,
help='Dirpath where to store collected data.'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
ap.add_argument(
'--image-width',
type=int,
default=300,
help='Image display width in pixel.'
)
ap.add_argument(
'--image-height',
type=int,
default=300,
help='Image display height in pixel.'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
dc_service = DataCollectorService(comm_config,
args['data_dirpath'])
UI(dc_service, args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/dyda_config_update.py 0000664 0000000 0000000 00000006127 14007662526 0022637 0 ustar 00root root 0000000 0000000 #!/usr/bin/env python3
#
# Copyright 2019 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
import argparse
import json
import io
import logging
import os
import tempfile
import tarfile
import time
import sys
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class DydaConfigUpdateClient(object):
def __init__(self, comm_config, debug=False):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = self.handleResult
self.comm = Communicator(self.comm_config, debug=True)
def sendConfig(self, payloadID):
self.comm.send(self.comm_config['publish'], payloadID)
def handleResult(self, pl):
try:
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
print(payload_json)
self.comm.stop_nb()
sys.exit(0)
except Exception as e:
logger.info(e)
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.start_nb()
self.sendConfig(args['payload'])
time.sleep(1)
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle'
)
ap.add_argument(
'--payload',
required=True,
help='payload ID'
)
ap.add_argument(
'--topic',
default='berrynet/manager/aikea/config',
help='topic to listen for the result'
)
ap.add_argument(
'--publish',
default='berrynet/config/aikea/update',
help='topic to publish'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
comm_config = {
'subscribe': {
args['topic']: None
},
'publish': args['publish'],
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
config_client = DydaConfigUpdateClient(comm_config,
args['debug'])
config_client.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/fbdashboard.py 0000664 0000000 0000000 00000024424 14007662526 0021266 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Framebuffer dashboard.
"""
import argparse
import json
import logging
import os
import random
import sys
import time
from datetime import datetime
from os.path import join as pjoin
import cv2
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
from OpenGL.GL import *
from OpenGL.GLU import *
from OpenGL.GLUT import *
class FBDashboardService(object):
def __init__(self, comm_config, data_dirpath=None, no_decoration=False,
debug=False, save_frame=False):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
self.comm = Communicator(self.comm_config, debug=True)
self.data_dirpath = data_dirpath
self.no_decoration = no_decoration
self.frame = None
self.debug = debug
self.save_frame = save_frame
def update(self, pl):
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
if 'bytes' in payload_json.keys():
img_k = 'bytes'
elif 'image_blob' in payload_json.keys():
img_k = 'image_blob'
else:
raise Exception('No image data in MQTT payload')
jpg_bytes = payload.destringify_jpg(payload_json[img_k])
payload_json.pop(img_k)
logger.debug('inference text result: {}'.format(payload_json))
img = payload.jpg2rgb(jpg_bytes)
if self.no_decoration:
self.frame = img
else:
try:
res = payload_json['annotations']
except KeyError:
res = [
{
'label': 'hello',
'confidence': 0.42,
'left': random.randint(50, 60),
'top': random.randint(50, 60),
'right': random.randint(300, 400),
'bottom': random.randint(300, 400)
}
]
self.frame = overlay_on_image(img, res)
# Save frames for analysis or debugging
if self.debug and self.save_frame:
if not os.path.exists(self.data_dirpath):
try:
os.mkdir(self.data_dirpath)
except Exception as e:
logger.warn('Failed to create {}'.format(self.data_dirpath))
raise(e)
timestamp = datetime.now().isoformat()
with open(pjoin(self.data_dirpath, timestamp + '.jpg'), 'wb') as f:
f.write(jpg_bytes)
with open(pjoin(self.data_dirpath, timestamp + '.json'), 'w') as f:
f.write(json.dumps(payload_json, indent=4))
def update_fb(self):
if self.frame is not None:
gl_draw_fbimage(self.frame)
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.start_nb()
def gl_draw_fbimage(rgbimg):
h, w = rgbimg.shape[:2]
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, w, h, 0, GL_RGB, GL_UNSIGNED_BYTE, rgbimg)
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
glColor3f(1.0, 1.0, 1.0)
glEnable(GL_TEXTURE_2D)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
glBegin(GL_QUADS)
glTexCoord2d(0.0, 1.0)
glVertex3d(-1.0, -1.0, 0.0)
glTexCoord2d(1.0, 1.0)
glVertex3d( 1.0, -1.0, 0.0)
glTexCoord2d(1.0, 0.0)
glVertex3d( 1.0, 1.0, 0.0)
glTexCoord2d(0.0, 0.0)
glVertex3d(-1.0, 1.0, 0.0)
glEnd()
glFlush()
glutSwapBuffers()
def init():
glClearColor(0.7, 0.7, 0.7, 0.7)
def idle():
glutPostRedisplay()
def keyboard(key, x, y):
key = key.decode('utf-8')
if key == 'q':
print("\n\nFinished\n\n")
sys.exit()
def opencv_frame(src, w=None, h=None, fps=30):
vidcap = cv2.VideoCapture(src)
if not vidcap.isOpened():
print('opened failed')
sys.exit(errno.ENOENT)
# set frame w/h if indicated
if w and h:
vidcap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
vidcap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
# set FPS
rate = int(vidcap.get(cv2.CAP_PROP_FPS))
if rate > fps or rate < 1:
print('Illegal data rate {} (1-30)'.format(rate))
rate = fps
print('fps: {}'.format(rate))
# frame generator
while True:
success, image = vidcap.read()
if not success:
print('Failed to read frame')
break
yield image
#Vcap = opencv_frame(0, w=320, h=240)
Vcap = opencv_frame(0)
def draw_box(image, annotations):
"""Draw information of annotations onto image.
Args:
image: Image nparray.
annotations: List of detected object information.
Returns: Image nparray containing object information on it.
"""
print('draw_box, annotations: {}'.format(annotations))
img = image.copy()
for anno in annotations:
# draw bounding box
box_color = (0, 0, 255)
box_thickness = 1
cv2.rectangle(img,
(int(anno['left']), int(anno['top'])),
(int(anno['right']), int(anno['bottom'])),
box_color,
box_thickness)
# draw label
label_background_color = box_color
label_text_color = (255, 255, 255)
if 'track_id' in anno.keys():
label = 'ID:{} {}'.format(anno['track_id'], anno['label'])
else:
label = anno['label']
label_text = '{} ({} %)'.format(label,
int(anno['confidence'] * 100))
label_size = cv2.getTextSize(label_text,
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
1)[0]
label_left = anno['left']
label_top = anno['top'] - label_size[1]
if (label_top < 1):
label_top = 1
label_right = label_left + label_size[0]
label_bottom = label_top + label_size[1]
cv2.rectangle(img,
(int(label_left - 1), int(label_top - 1)),
(int(label_right + 1), int(label_bottom + 1)),
label_background_color,
-1)
cv2.putText(img,
label_text,
(int(label_left), int(label_bottom)),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
label_text_color,
1)
return img
def overlay_on_image(display_image, object_info):
"""Modulized version of overlay_on_image function
"""
if isinstance(object_info, type(None)):
print('WARNING: object info is None')
return display_image
return draw_box(display_image, object_info)
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--data-dirpath',
default='/tmp/berrynet-data',
help='Dirpath where to store collected data.'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic',
nargs='*',
default=['berrynet/engine/tflitedetector/result'],
help='The topic to listen, and can be indicated multiple times.'
)
ap.add_argument(
'--topic-action',
default='self.update',
help='The action for the indicated topics.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
ap.add_argument(
'--no-decoration',
action='store_true',
help='Display image in payload without applying result information.'
)
ap.add_argument(
'--no-full-screen',
action='store_true',
help='Display fbdashboard in a window.'
)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle'
)
ap.add_argument('--debug-save-frame',
action='store_true',
help='Save frames for debugging. --debug also needs to be set.'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Topics and actions can come from two sources: CLI and config file.
# Setup topic_config by parsing values from the two sources.
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
topic_config.update({t:args['topic_action'] for t in args['topic']})
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
fbd_service = FBDashboardService(comm_config,
args['data_dirpath'],
args['no_decoration'],
args['debug'],
args['debug_save_frame'])
fbd_service.run(args)
glutInitWindowPosition(0, 0)
glutInit(sys.argv)
glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE)
glutCreateWindow("BerryNet Result Dashboard, q to quit")
glutDisplayFunc(fbd_service.update_fb)
glutKeyboardFunc(keyboard)
init()
glutIdleFunc(idle)
if args['no_full_screen']:
pass
else:
glutFullScreen()
glutMainLoop()
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/gmail.py 0000664 0000000 0000000 00000020252 14007662526 0020113 0 ustar 00root root 0000000 0000000 # Copyright 2019 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
# Reference
# https://www.geeksforgeeks.org/send-mail-attachment-gmail-account-using-python/
"""Gmail client sends an email with inference result.
The email will contain two attachments: image and text.
"""
import argparse
import json
import logging
import os
import smtplib
from datetime import datetime
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
from os.path import join as pjoin
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
def create_mime_attachment(filepath):
filename = os.path.basename(filepath)
attachment = open(filepath, "rb")
# instance of MIMEBase and named as p
p = MIMEBase('application', 'octet-stream')
# To change the payload into encoded form
p.set_payload(attachment.read())
# encode into base64
encoders.encode_base64(p)
p.add_header('Content-Disposition',
"attachment; filename= %s" % filename)
return p
def send_email_text(sender_address,
sender_password,
receiver_address,
body='',
subject='BerryNet mail client notification',
attachments=None):
# instance of MIMEMultipart
msg = MIMEMultipart()
msg['From'] = sender_address
msg['To'] = receiver_address
msg['Subject'] = subject
logger.debug('Sender: {}'.format(msg['From']))
logger.debug('Receiver: {}'.format(msg['To']))
logger.debug('Subject: {}'.format(msg['Subject']))
# attach the body with the msg instance
msg.attach(MIMEText(body, 'plain'))
for fpath in attachments:
logger.debug('Attachment: {}'.format(fpath))
msg.attach(create_mime_attachment(fpath))
# creates SMTP session
s = smtplib.SMTP('smtp.gmail.com', 587)
# start TLS for security
s.starttls()
# Authentication
s.login(sender_address, sender_password)
# Converts the Multipart msg into a string
text = msg.as_string()
# sending the mail
s.sendmail(sender_address, receiver_address, text)
# terminating the session
s.quit()
class GmailService(object):
def __init__(self, comm_config):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
self.comm = Communicator(self.comm_config, debug=True)
self.email = comm_config['email']
self.pipeline_compatible = comm_config['pipeline_compatible']
self.target_label = comm_config['target_label']
def find_target_label(self, target_label, generalized_result):
label_list = [i['label'] for i in generalized_result['annotations']]
logger.debug('Result labels: {}'.format(label_list))
return target_label in label_list
def update(self, pl):
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
if self.pipeline_compatible:
b64img_key = 'image_blob'
else:
b64img_key = 'bytes'
jpg_bytes = payload.destringify_jpg(payload_json[b64img_key])
payload_json.pop(b64img_key)
logger.debug('inference text result: {}'.format(payload_json))
match_target_label = self.find_target_label(self.target_label,
payload_json)
logger.debug('Find target label {0}: {1}'.format(
self.target_label, match_target_label))
if match_target_label:
timestamp = datetime.now().isoformat()
notification_image = pjoin('/tmp', timestamp + '.jpg')
notification_text = pjoin('/tmp', timestamp + '.json')
with open(notification_image, 'wb') as f:
f.write(jpg_bytes)
with open(notification_text, 'w') as f:
f.write(json.dumps(payload_json, indent=4))
try:
send_email_text(
self.email['sender_address'],
self.email['sender_password'],
self.email['receiver_address'],
body=('Target label {} is found. '
'Please check the attachments.'
''.format(self.target_label)),
subject='BerryNet mail client notification',
attachments=set([notification_image, notification_text]))
except Exception as e:
logger.warn(e)
os.remove(notification_image)
os.remove(notification_text)
else:
# target label is not in generalized result, do nothing
pass
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--sender-address',
required=True,
help='Email address of sender. Ex: foo@email.org'
)
ap.add_argument(
'--sender-password',
required=True,
help='Password of sender email address.'
)
ap.add_argument(
'--receiver-address',
required=True,
help='Email address of receiver. Ex: bar@email.org'
)
ap.add_argument(
'--target-label',
required=True,
help='Send notification email if the label is in inference result.'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic',
nargs='*',
default=['berrynet/engine/tflitedetector/result'],
help='The topic to listen, and can be indicated multiple times.'
)
ap.add_argument(
'--topic-action',
default='self.update',
help='The action for the indicated topics.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
ap.add_argument(
'--pipeline-compatible',
action='store_true',
help=(
'Change key of b64 image string in generalized result '
'from bytes to image_blob. '
'Note: This is an experimental parameter.'
)
)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Topics and actions can come from two sources: CLI and config file.
# Setup topic_config by parsing values from the two sources.
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
topic_config.update({t:args['topic_action'] for t in args['topic']})
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
},
'email': {
'sender_address': args['sender_address'],
'sender_password': args['sender_password'],
'receiver_address': args['receiver_address']
},
'pipeline_compatible': args['pipeline_compatible'],
'target_label': args['target_label']
}
dc_service = GmailService(comm_config)
dc_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/snapshot.py 0000664 0000000 0000000 00000006474 14007662526 0020673 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""
Snapshot service will listen to a trigger event (MQTT topic),
and send a snapshot retrieved from camera.
"""
import argparse
import json
from datetime import datetime
import cv2
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class SnapshotService(object):
def __init__(self, comm_config):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
self.comm_config['subscribe']['berrynet/trigger/controller/snapshot'] = self.snapshot
self.comm = Communicator(self.comm_config, debug=True)
def snapshot(self, pl):
'''Send camera snapshot.
The functionality is the same as using camera client in file mode.
The difference is that snapshot client retrieves image from camera
instead of given filepath.
'''
duration = lambda t: (datetime.now() - t).microseconds / 1000
# WORKAROUND: Prevent VideoCapture from buffering frames.
# VideoCapture will buffer frames automatically, and we need
# to find a way to disable it.
self.capture = cv2.VideoCapture(0)
status, im = self.capture.read()
if (status is False):
logger.warn('ERROR: Failure happened when reading frame')
t = datetime.now()
retval, jpg_bytes = cv2.imencode('.jpg', im)
mqtt_payload = payload.serialize_jpg(jpg_bytes)
self.comm.send('berrynet/data/rgbimage', mqtt_payload)
logger.debug('send: {} ms'.format(duration(t)))
self.capture.release()
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.run()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
dc_service = SnapshotService(comm_config)
dc_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/client/telegram_bot.py 0000664 0000000 0000000 00000025340 14007662526 0021471 0 ustar 00root root 0000000 0000000 #!/usr/bin/env python3
#
# Copyright 2019 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
import argparse
import json
import io
import logging
import os
import tempfile
import tarfile
import time
import telegram.ext
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class TelegramBotService(object):
def __init__(self, comm_config, token, target_label='', debug=False):
self.comm_config = comm_config
for topic, functor in self.comm_config['subscribe'].items():
self.comm_config['subscribe'][topic] = eval(functor)
# NOTE: Maybe change the hard-coding topic to parameter in the future.
self.comm_config['subscribe']['berrynet/data/rgbimage'] = self.single_shot
self.comm = Communicator(self.comm_config, debug=True)
if os.path.isfile(token):
self.token = self.get_token_from_config(token)
else:
self.token = token
self.target_label = target_label
self.debug = debug
# Telegram Updater employs Telegram Dispatcher which dispatches
# updates to its registered handlers.
self.updater = telegram.ext.Updater(self.token,
use_context=True)
self.cameraHandlers = []
self.shot = False
self.single_shot_chat_id = None
def get_token_from_config(self, config):
with open(config) as f:
cfg = json.load(f)
return cfg['token']
def match_target_label(self, target_label, bn_result):
labels = [r['label'] for r in bn_result['annotations']]
if target_label in labels:
logger.debug('Find {0} in inference result {1}'.format(target_label, labels))
return True
else:
logger.debug('Not find {0} in inference result {1}'.format(target_label, labels))
return False
def update(self, pl):
try:
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(payload_json["bytes"])
jpg_file_descriptor = io.BytesIO(jpg_bytes)
for u in self.cameraHandlers:
if self.updater is None:
continue
if self.target_label == '':
if len(payload_json['annotations']) > 0:
logger.debug("Send photo to %s" % u)
self.updater.bot.send_photo(chat_id = u, photo=jpg_file_descriptor)
else:
logger.debug("Does not detect any object, no action")
elif self.match_target_label(self.target_label, payload_json):
logger.info("Send notification photo with result to %s" % u)
self.updater.bot.send_photo(chat_id = u, photo=jpg_file_descriptor)
else:
pass
except Exception as e:
logger.info(e)
def single_shot(self, pl):
"""Capture an image from camera client and send to the client.
"""
if self.shot is True:
try:
payload_json = payload.deserialize_payload(pl.decode('utf-8'))
# WORKAROUND: Support customized camera client.
#
# Original camera client sends an `obj` in payload,
# Customized camera client sends an `[obj]` in payload.
#
# We are unifying the rules. Before that, checking the type
# as workaround.
if type(payload_json) is list:
logger.debug('WORDAROUND: receive and unpack [obj]')
payload_json = payload_json[0]
jpg_bytes = payload.destringify_jpg(payload_json["bytes"])
jpg_file_descriptor = io.BytesIO(jpg_bytes)
logger.info('Send single shot')
self.updater.bot.send_photo(chat_id=self.single_shot_chat_id,
photo=jpg_file_descriptor)
except Exception as e:
logger.info(e)
self.shot = False
else:
logger.debug('Single shot is disabled, do nothing.')
def run(self, args):
"""Infinite loop serving inference requests"""
self.comm.start_nb()
self.connect_telegram(args)
def connect_telegram(self, args):
try:
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('help', self.handler_help))
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('hi', self.handler_hi))
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('camera', self.handler_camera))
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('stop', self.handler_stop))
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('shot', self.handler_shot))
if (args["has_getlog"]):
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('getlog', self.handler_getlog))
self.updater.start_polling()
except Exception as e:
logger.critical(e)
def handler_help(self, update, context):
logger.info("Received command `help`")
update.message.reply_text((
'I support these commands:\n\n'
'help - Display help message.\n'
'hi - Test Telegram client.\n'
'camera - Start camera.\n'
'stop - Stop camera.\n'
'shot - Take a shot from camera.'))
def handler_hi(self, update, context):
logger.info("Received command `hi`")
update.message.reply_text(
'Hi, {}'.format(update.message.from_user.first_name))
def handler_camera(self, update, context):
logger.info("Received command `camera`, chat id: %s" % update.message.chat_id)
# Register the chat-id for receiving images
if (update.message.chat_id not in self.cameraHandlers):
self.cameraHandlers.append (update.message.chat_id)
update.message.reply_text('Dear, I am ready to help send notification')
def handler_stop(self, update, context):
logger.info("Received command `stop`, chat id: %s" % update.message.chat_id)
# Register the chat-id for receiving images
while (update.message.chat_id in self.cameraHandlers):
self.cameraHandlers.remove (update.message.chat_id)
update.message.reply_text('Bye')
def handler_shot(self, update, context):
logger.info("Received command `shot`, chat id: %s" % update.message.chat_id)
# Register the chat-id for receiving images
self.shot = True
self.single_shot_chat_id = update.message.chat_id
logger.debug('Enable single shot.')
def handler_getlog(self, update, context):
logger.info("Received command `getlog`, chat id: %s" % update.message.chat_id)
# Create temporary tar.xz file
tmpTGZ1 = tempfile.NamedTemporaryFile(suffix=".tar.xz")
tmpTGZ = tarfile.open(fileobj=tmpTGZ1, mode="w:xz")
tmpTGZPath = tmpTGZ1.name
# Traverse /var/log
varlogDir = os.path.abspath(os.path.join(os.sep, "var", "log"))
for root, dirs, files in os.walk(varlogDir):
for file in files:
fullPath = os.path.join(root, file)
# Check if the file is a regular file
if not os.path.isfile(fullPath):
continue
# Check if the file is accessable
if not os.access(fullPath, os.R_OK):
continue
# Pack the file
tmpTGZ.add(name = fullPath, recursive=False)
tmpTGZ.close()
self.updater.bot.send_document(chat_id = update.message.chat_id, document = open(tmpTGZPath, 'rb'), filename=time.strftime('berrynet-varlog_%Y%m%d_%H%M%S.tar.xz'))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--token',
help=('Telegram token got from BotFather, '
'or filepath of a JSON config file with token.')
)
ap.add_argument(
'--target-label',
default='',
help='Send a notification if the target label is in the result.'
)
ap.add_argument(
'--broker-ip',
default='localhost',
help='MQTT broker IP.'
)
ap.add_argument(
'--broker-port',
default=1883,
type=int,
help='MQTT broker port.'
)
ap.add_argument(
'--topic',
nargs='*',
default=['berrynet/engine/tflitedetector/result'],
help='The topic to listen, and can be indicated multiple times.'
)
ap.add_argument(
'--topic-action',
default='self.update',
help='The action for the indicated topics.'
)
ap.add_argument(
'--topic-config',
default=None,
help='Path of the MQTT topic subscription JSON.'
)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle'
)
ap.add_argument('--has-getlog',
action='store_true',
help='Enable getlog command'
)
return vars(ap.parse_args())
def main():
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Topics and actions can come from two sources: CLI and config file.
# Setup topic_config by parsing values from the two sources.
if args['topic_config']:
with open(args['topic_config']) as f:
topic_config = json.load(f)
else:
topic_config = {}
topic_config.update({t:args['topic_action'] for t in args['topic']})
comm_config = {
'subscribe': topic_config,
'broker': {
'address': args['broker_ip'],
'port': args['broker_port']
}
}
telbot_service = TelegramBotService(comm_config,
args['token'],
args['target_label'],
args['debug'])
telbot_service.run(args)
if __name__ == '__main__':
main()
BerryNet-3.10.2/berrynet/comm/ 0000775 0000000 0000000 00000000000 14007662526 0016124 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/comm/__init__.py 0000664 0000000 0000000 00000003345 14007662526 0020242 0 ustar 00root root 0000000 0000000 #!/usr/bin/python3
import paho.mqtt.client as mqtt
import paho.mqtt.publish as publish
from berrynet import logger
from logzero import setup_logger
def on_connect(client, userdata, flags, rc):
logger.debug('Connected with result code ' + str(rc))
for topic in client.comm_config['subscribe'].keys():
logger.debug('Subscribe topic {}'.format(topic))
client.subscribe(topic)
def on_message(client, userdata, msg):
"""Dispatch received message to its bound functor.
"""
logger.debug('Receive message from topic {}'.format(msg.topic))
#logger.debug('Message payload {}'.format(msg.payload))
client.comm_config['subscribe'][msg.topic](msg.payload)
class Communicator(object):
def __init__(self, comm_config, debug=False):
self.client = mqtt.Client()
self.client.comm_config = comm_config
self.client.on_connect = on_connect
self.client.on_message = on_message
def run(self):
self.client.connect(
self.client.comm_config['broker']['address'],
self.client.comm_config['broker']['port'],
60)
self.client.loop_forever()
def start_nb(self):
self.client.connect(
self.client.comm_config['broker']['address'],
self.client.comm_config['broker']['port'],
60)
self.client.loop_start()
def stop_nb(self):
self.client.loop_stop()
def send(self, topic, payload):
logger.debug('Send message to topic {}'.format(topic))
#logger.debug('Message payload {}'.format(payload))
publish.single(topic, payload,
hostname=self.client.comm_config['broker']['address'])
def disconnect(self):
self.client.disconnect()
BerryNet-3.10.2/berrynet/comm/payload.py 0000664 0000000 0000000 00000006365 14007662526 0020141 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
import base64
import hashlib
import json
from datetime import datetime
import cv2
import numpy as np
def stringify_jpg(jpg_bytes):
return base64.b64encode(jpg_bytes).decode('utf-8')
def destringify_jpg(stringified_jpg):
"""
:return: JPEG bytes
:rtype: bytes
"""
return base64.b64decode(stringified_jpg.encode('utf-8'))
def jpg2bgr(jpg_bytes):
"""
:return: BGR bytes
:rtype: numpy array
"""
array = np.frombuffer(jpg_bytes, dtype=np.uint8)
return cv2.imdecode(array, flags=1)
def jpg2rgb(jpg_bytes):
"""
:return: RGB bytes
:rtype: numpy array
"""
return cv2.cvtColor(jpg2bgr(jpg_bytes), cv2.COLOR_BGR2RGB)
def bgr2rgb(bgr_nparray):
"""Convert image nparray from BGR to RGB.
Args:
bgr_nparray: Image nparray in BGR color model.
Returns:
Image nparray in RGB color model.
"""
return cv2.cvtColor(bgr_nparray, cv2.COLOR_BGR2RGB)
def rgb2bgr(rgb_nparray):
"""Convert image nparray from RGB to BGR.
Args:
rgb_nparray: Image nparray in RGB color model.
Returns:
Image nparray in BGR color model.
"""
return cv2.cvtColor(rgb_nparray, cv2.COLOR_RGB2BGR)
def generate_bytes_md5sum(content_bytes):
content_b64 = base64.b64encode(content_bytes)
return hashlib.md5(content_b64).hexdigest()
def serialize_payload(json_object):
return json.dumps(json_object)
def serialize_jpg(jpg_bytes, md5sum=False, meta={}):
"""Create Serialized JSON object consisting of image bytes and meta
:param imarray: JPEG bytes
:type imarray: bytes
:return: serialized image JSON
:rtype: string
"""
obj = {}
obj['timestamp'] = datetime.now().isoformat()
obj['bytes'] = stringify_jpg(jpg_bytes)
obj['meta'] = meta
if md5sum:
obj['md5sum'] = generate_bytes_md5sum(jpg_bytes)
return json.dumps(obj)
def deserialize_payload(payload):
return json.loads(payload)
#def deserialize_jpg(jpg_json):
# """Deserialized JSON object created by josnify_image.
#
# :param string :
# :return:
# :rtype:
# """
# return json.loads(jpg_json)
if __name__ == '__main__':
im = cv2.imread('/home/debug/codes/darknet/data/dog.jpg')
retval, jpg_bytes = cv2.imencode('.jpg', im)
# size of stringified dog.jpg is 1.33x larger than original
s_jpg = serialize_jpg(jpg_bytes)
d_jpg = deserialize_payload(s_jpg)
# TODO: Can we write JPEG bytes into file directly to prevent
# bytes -> numpy array -> decode RGB -> write encoded JPEG
cv2.imwrite('/tmp/dog.jpg', jpg2bgr(destringify_jpg(d_jpg['bytes'])))
BerryNet-3.10.2/berrynet/dlmodelmgr.py 0000664 0000000 0000000 00000003717 14007662526 0017701 0 ustar 00root root 0000000 0000000 # Copyright 2017 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""
DL Model Manager, following the DLModelBox model package
speccification.
"""
from __future__ import print_function
import argparse
import json
import os
from berrynet import logger
class DLModelManager(object):
def __init__(self):
self.basedir = '/usr/share/dlmodels'
def get_model_names(self):
return os.listdir(self.basedir)
def get_model_meta(self, modelname):
meta_filepath = os.path.join(self.basedir, modelname, 'meta.json')
with open(meta_filepath, 'r') as f:
meta = json.load(f)
meta['model'] = os.path.join(self.basedir, modelname, meta['model'])
meta['label'] = os.path.join(self.basedir, modelname, meta['label'])
for k, v in meta['config'].items():
meta['config'][k] = os.path.join(self.basedir, modelname,
meta['config'][k])
return meta
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--modelname',
help='Model package name (without version)')
return vars(ap.parse_args())
if __name__ == '__main__':
args = parse_args()
logger.debug('model package name: ', args['modelname'])
dlmm = DLModelManager()
for name in dlmm.get_model_names():
print(dlmm.get_model_meta(name))
BerryNet-3.10.2/berrynet/engine/ 0000775 0000000 0000000 00000000000 14007662526 0016436 5 ustar 00root root 0000000 0000000 BerryNet-3.10.2/berrynet/engine/__init__.py 0000664 0000000 0000000 00000003252 14007662526 0020551 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""
Deep learning engine template provides unified interfaces for
different backends (e.g. TensorFlow, Caffe2, etc.)
"""
class DLEngine(object):
def __init__(self):
self.model_input_cache = []
self.model_output_cache = []
self.cache = {
'model_input': [],
'model_output': '',
'model_output_filepath': ''
}
def create(self):
# Workaround to posepone TensorFlow initialization.
# If TF is initialized in __init__, and pass an engine instance
# to engine service, TF session will stuck in run().
pass
def process_input(self, tensor):
return tensor
def inference(self, tensor):
output = None
return output
def process_output(self, output):
return output
def cache_data(self, key, value):
self.cache[key] = value
def save_cache(self):
with open(self.cache['model_output_filepath'], 'w') as f:
f.write(str(self.cache['model_output']))
BerryNet-3.10.2/berrynet/engine/caffe_engine.py 0000664 0000000 0000000 00000004655 14007662526 0021413 0 ustar 00root root 0000000 0000000 # Copyright 2017 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""TensorFlow inference engine.
"""
from __future__ import print_function
import argparse
import json
import numpy as np
import caffe
from berrynet import logger
#from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine import DLEngine
class CaffeEngine(DLEngine):
# FIXME: Get model information by model manager
def __init__(self, model_def, pretrained_model, mean_file, label, image_dims = [256,256], channel_swap=[2,1,0], raw_scale=255.0, top_k=5):
super(CaffeEngine, self).__init__()
# Load model
caffe.set_mode_cpu()
self.classifier = caffe.Classifier(model_def, pretrained_model, image_dims=image_dims, mean=mean_file, raw_scale=raw_scale, channel_swap=channel_swap)
# Load labels
self.labels = [line.rstrip() for line in open(label)]
self.top_k = top_k
def create(self):
pass
def process_input(self, rgb_array):
self.inputs = rgb_array
return self.inputs
def inference(self, tensor):
self.predictions = self.classifier.predict(self.inputs, False)
return self.predictions
def process_output(self, output):
predictions_list = self.predictions[0].tolist()
data = zip(predictions_list, caffe_labels)
processed_output = {'annotations': []}
i=0
for d in sorted(data, reverse=True):
human_string = d[1]
score = d[0]
anno = {
'type': 'classification',
'label': human_string,
'confidence': score
}
processed_output['annotations'].append(anno)
i = i + 1
if (i >= self.top_k):
break
return processed_output
def save_cache(self):
pass
BerryNet-3.10.2/berrynet/engine/darknet_engine.py 0000664 0000000 0000000 00000013453 14007662526 0021773 0 ustar 00root root 0000000 0000000 # Copyright 2018 DT42
#
# This file is part of BerryNet.
#
# BerryNet 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.
#
# BerryNet 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 BerryNet. If not, see .
"""Darknet inference engine.
"""
from __future__ import print_function
import argparse
import json
import math
import time
import cv2
import numpy as np
from berrynet import logger
#from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine import DLEngine
from ctypes import *
class BOX(Structure):
_fields_ = [("x", c_float),
("y", c_float),
("w", c_float),
("h", c_float)]
class IMAGE(Structure):
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]
class METADATA(Structure):
_fields_ = [("classes", c_int),
("names", POINTER(c_char_p))]
lib = CDLL("/usr/lib/libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
make_boxes = lib.make_boxes
make_boxes.argtypes = [c_void_p]
make_boxes.restype = POINTER(BOX)
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
num_boxes = lib.num_boxes
num_boxes.argtypes = [c_void_p]
num_boxes.restype = c_int
make_probs = lib.make_probs
make_probs.argtypes = [c_void_p]
make_probs.restype = POINTER(POINTER(c_float))
reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
free_image = lib.free_image
free_image.argtypes = [IMAGE]
letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE
load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]
predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
network_detect = lib.network_detect
network_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
def c_array(ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr
def nparray_to_image(arr):
"""Convert nparray to Darknet image struct.
Args:
arr: nparray containing source image in BGR color model.
Returns:
Darknet image struct, whose data is a C array
containing flatten image in BGR color model.
"""
arr = arr.transpose(2,0,1)
c = arr.shape[0]
h = arr.shape[1]
w = arr.shape[2]
arr = (arr/255.0).flatten()
data = c_array(c_float, arr)
im = IMAGE(w, h, c, data)
rgbgr_image(im)
return im
def detect_np(net, meta, np_img, thresh=.3, hier_thresh=.5, nms=.45):
im = nparray_to_image(np_img)
boxes = make_boxes(net)
probs = make_probs(net)
num = num_boxes(net)
t_start = time.time()
network_detect(net, im, thresh, hier_thresh, nms, boxes, probs)
t_end = time.time()
logger.debug('inference time: {} s'.format(t_end - t_start))
res = []
for j in range(num):
for i in range(meta.classes):
if probs[j][i] > 0:
res.append(
{
'type': 'detection',
'label': meta.names[i].decode('utf-8'),
'confidence': probs[j][i],
'left': boxes[j].x - (boxes[j].w / 2),
'top': boxes[j].y - (boxes[j].h / 2),
'right': boxes[j].x + (boxes[j].w / 2),
'bottom': boxes[j].y + (boxes[j].h / 2),
'id': -1
}
)
free_ptrs(cast(probs, POINTER(c_void_p)), num)
return res
class DarknetEngine(DLEngine):
# FIXME: Get model information by model manager
def __init__(self, config, model, meta=''):
super(DarknetEngine, self).__init__()
self.net = load_net(config, model, 0)
self.meta = load_meta(meta)
self.classes = self.meta.classes
self.labels = [self.meta.names[i].decode('utf-8')
for i in range(self.classes)]
# Warmup
zero_image = np.zeros(shape=(416, 416, 3), dtype=np.uint8)
detect_np(self.net, self.meta, zero_image)
def process_input(self, rgb_array):
return rgb_array
def inference(self, tensor):
return detect_np(self.net, self.meta, tensor)
def process_output(self, output):
return {'annotations': output}
if __name__ == '__main__':
engine = DarknetEngine(
config=b'/usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.cfg',
model=b'/usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.weights',
meta=b'/usr/share/dlmodels/tinyyolovoc-20170816/voc.data'
)
im = cv2.imread('data/dog.jpg')
for i in range(3):
r = engine.inference(im)
print(r)
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