pax_global_header 0000666 0000000 0000000 00000000064 13606321373 0014516 g ustar 00root root 0000000 0000000 52 comment=3edd9749699394e2961d3b9bd33d260aea5583c8
BerryNet-upstream-3.9.0/ 0000775 0000000 0000000 00000000000 13606321373 0015117 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/.eslintrc.yaml 0000664 0000000 0000000 00000000135 13606321373 0017703 0 ustar 00root root 0000000 0000000 env:
node: true
es6: true
extends: google
rules:
comma-dangle: [2, only-multiline]
BerryNet-upstream-3.9.0/.github/ 0000775 0000000 0000000 00000000000 13606321373 0016457 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/.github/FUNDING.yml 0000664 0000000 0000000 00000001357 13606321373 0020302 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-upstream-3.9.0/.gitignore 0000664 0000000 0000000 00000000200 13606321373 0017077 0 ustar 00root root 0000000 0000000 # Byte-compiled / optimized / DLL files
__pycache__
*.pyc
# Distribution / packaging
build
dist
*.egg-info
# Environments
env
BerryNet-upstream-3.9.0/.gitlab-ci.yml 0000664 0000000 0000000 00000003331 13606321373 0017553 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-upstream-3.9.0/.gitmodules 0000664 0000000 0000000 00000000153 13606321373 0017273 0 ustar 00root root 0000000 0000000 [submodule "inference/darkflow"]
path = inference/darkflow
url = https://github.com/thtrieu/darkflow.git
BerryNet-upstream-3.9.0/AUTHORS 0000664 0000000 0000000 00000000554 13606321373 0016173 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-upstream-3.9.0/BACKERS.md 0000664 0000000 0000000 00000001116 13606321373 0016512 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-upstream-3.9.0/CONTRIBUTING.md 0000664 0000000 0000000 00000000304 13606321373 0017345 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-upstream-3.9.0/LICENSE.txt 0000664 0000000 0000000 00000104513 13606321373 0016746 0 ustar 00root root 0000000 0000000 GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
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to choose that version for the Program.
Later license versions may give you additional or different
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author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
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EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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17. Interpretation of Sections 15 and 16.
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above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
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Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program 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.
This program 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 this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
Copyright (C)
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
BerryNet-upstream-3.9.0/README.md 0000664 0000000 0000000 00000020557 13606321373 0016407 0 ustar 00root root 0000000 0000000
Deep Learning Gateway on Raspberry Pi And Other Edge Devices
[Supporting BerryNet](https://github.com/DT42/BerryNet/wiki/Donation)
* [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).
# 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 [Wiki page](https://github.com/DT42/BerryNet/wiki/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 RPi3 (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 [Telegram Group](https://t.me/berrynetdev) or [Google Group](https://groups.google.com/a/dt42.io/d/forum/berrynet) for questions, suggestions, or any idea discussion.
BerryNet-upstream-3.9.0/README.md.old 0000664 0000000 0000000 00000014024 13606321373 0017154 0 ustar 00root root 0000000 0000000 # BerryNet: Deep Learning Gateway on Raspberry Pi And Other Edge Devices
This project turns edge devices such as Raspberry Pi 3 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 [Wiki page](https://github.com/DT42/BerryNet/wiki/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 is managed by [systemd](https://freedesktop.org/wiki/Software/systemd/). You can manage BerryNet via `berrynet-manager`:
```
$ berrynet-manager [start | stop | status | log]
```
# Configuration
All the configurations are in `config.js`.
* Choose AI Engine.
* Two types of AI engines currently: object classifier and object detector.
* Configure IP camera's snapshot access interface.
* Please refer to [IP camera setup](doc/ipcam.md) for more details.
* MQTT topics.
# Dashboard
## Open dashboard on RPi3 (with touch screen)
Open browser and enter the URL:
`http://localhost:8080/index.html#source=dashboard.json`
The default dashboard configuration file will be loaded.
## Open dashboard on browser from any computer
Open browser and enter the URL:
`http://:8080/index.html#source=dashboard.json`
Click the data sources, and change MQTT broker's IP address to the gateway's IP.
For more details about dashboard configuration (e.g. how to add widgets), please refer to [freeboard project](https://github.com/Freeboard/freeboard).
# Provide Image Input
To capture an image via configured IP camera
```
$ mosquitto_pub -h localhost -t berrynet/event/camera -m snapshot_ipcam
```
To capture an image via board-connected camera (RPi camera or USB webcam)
```
$ mosquitto_pub -h localhost -t berrynet/event/camera -m snapshot_boardcam
```
To provide a local image
```
$ mosquitto_pub -h localhost -t berrynet/event/localImage -m
```
To start and stop streaming from board-connected camera
```
$ mosquitto_pub -h localhost -t berrynet/event/camera -m stream_boardcam_start
$ mosquitto_pub -h localhost -t berrynet/event/camera -m stream_boardcam_stop
```
To start and stop streaming from Nest IP camera
```
$ mosquitto_pub -h localhost -t berrynet/event/camera -m stream_nest_ipcam_start
$ mosquitto_pub -h localhost -t berrynet/event/camera -m stream_nest_ipcam_stop
```
# Enable Data Collector
You might want to store the snapshot and inference results for data analysis.
To enable data collector, you can set the storage directory path in config.js:
```
config.storageDirPath = '';
```
and restart BerryNet.
# 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 [Telegram Group](https://t.me/berrynetdev) or [Google Group](https://groups.google.com/a/dt42.io/d/forum/berrynet) for questions, suggestions, or any idea discussion.
BerryNet-upstream-3.9.0/berrynet-manager 0000775 0000000 0000000 00000003412 13606321373 0020307 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-upstream-3.9.0/berrynet/ 0000775 0000000 0000000 00000000000 13606321373 0016751 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/__init__.py 0000664 0000000 0000000 00000000514 13606321373 0021062 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-upstream-3.9.0/berrynet/client/ 0000775 0000000 0000000 00000000000 13606321373 0020227 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/client/__init__.py 0000664 0000000 0000000 00000000000 13606321373 0022326 0 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/client/camera.py 0000664 0000000 0000000 00000013055 13606321373 0022035 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 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('--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('--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
if args['mode'] == 'stream':
counter = 0
# Check input stream source
if args['stream_src'].isdigit():
# source is a physically connected camera
stream_source = '/dev/video{}'.format(int(args['stream_src']))
capture = cv2.VideoCapture(int(args['stream_src']))
else:
# source is an IP camera
stream_source = args['stream_src']
capture = cv2.VideoCapture(args['stream_src'])
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 =====')
logger.debug('Stream Source: {}'.format(stream_source))
logger.debug('Camera FPS: {}'.format(cam_fps))
logger.debug('Output FPS: {}'.format(out_fps))
logger.debug('Interval: {}'.format(interval))
#logger.debug('Warmup Counter: {}'.format(warmup_counter))
logger.debug('====================================')
while True:
status, im = capture.read()
if (status is False):
logger.warn('ERROR: Failure happened when reading frame')
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)
comm.send('berrynet/data/rgbimage', mqtt_payload)
logger.debug('send: {} ms'.format(duration(t)))
else:
pass
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)
logger.debug('payload: {} ms'.format(duration(t)))
logger.debug('payload size: {}'.format(len(mqtt_payload)))
# Client publishes payload
t = datetime.now()
comm.send('berrynet/data/rgbimage', 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-upstream-3.9.0/berrynet/client/dashboard.py 0000664 0000000 0000000 00000005245 13606321373 0022536 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-upstream-3.9.0/berrynet/client/data_collector.py 0000664 0000000 0000000 00000010375 13606321373 0023566 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-upstream-3.9.0/berrynet/client/data_collector_ui.py 0000664 0000000 0000000 00000024273 13606321373 0024265 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-upstream-3.9.0/berrynet/client/fbdashboard.py 0000664 0000000 0000000 00000024424 13606321373 0023046 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-upstream-3.9.0/berrynet/client/gmail.py 0000664 0000000 0000000 00000020252 13606321373 0021673 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-upstream-3.9.0/berrynet/client/snapshot.py 0000664 0000000 0000000 00000006474 13606321373 0022453 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-upstream-3.9.0/berrynet/client/telegram_bot.py 0000664 0000000 0000000 00000016110 13606321373 0023244 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 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)
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 = []
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 == '':
logger.info("Send photo to %s" % u)
self.updater.bot.send_photo(chat_id = u, photo=jpg_file_descriptor)
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 run(self, args):
"""Infinite loop serving inference requests"""
self.comm.start_nb()
self.connect_telegram()
def connect_telegram(self):
try:
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('help', self.handler_help))
self.updater.dispatcher.add_handler(
telegram.ext.CommandHandler('hello', self.handler_hello))
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.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: help, hello, camera')
def handler_hello(self, update, context):
logger.info("Received command `hello`")
update.message.reply_text(
'Hello, {}'.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 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'
)
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-upstream-3.9.0/berrynet/comm/ 0000775 0000000 0000000 00000000000 13606321373 0017704 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/comm/__init__.py 0000664 0000000 0000000 00000003345 13606321373 0022022 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-upstream-3.9.0/berrynet/comm/payload.py 0000664 0000000 0000000 00000005742 13606321373 0021717 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 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 serialize_payload(json_object):
return json.dumps(json_object)
def serialize_jpg(jpg_bytes):
"""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)
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-upstream-3.9.0/berrynet/dlmodelmgr.py 0000664 0000000 0000000 00000003717 13606321373 0021461 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-upstream-3.9.0/berrynet/engine/ 0000775 0000000 0000000 00000000000 13606321373 0020216 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/engine/__init__.py 0000664 0000000 0000000 00000003252 13606321373 0022331 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-upstream-3.9.0/berrynet/engine/caffe_engine.py 0000664 0000000 0000000 00000004655 13606321373 0023173 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-upstream-3.9.0/berrynet/engine/darknet_engine.py 0000664 0000000 0000000 00000013453 13606321373 0023553 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)
BerryNet-upstream-3.9.0/berrynet/engine/movidius.py 0000664 0000000 0000000 00000013672 13606321373 0022440 0 ustar 00root root 0000000 0000000 #!/usr/bin/python
#
# 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 .
import os
import cv2
import numpy as np
from mvnc import mvncapi as mvnc
class MovidiusNeuralGraph(object):
def __init__(self, graph_filepath, label_filepath):
# mvnc.SetGlobalOption(mvnc.GlobalOption.LOGLEVEL, 2)
devices = mvnc.EnumerateDevices()
if len(devices) == 0:
raise Exception('No devices found')
self.device = mvnc.Device(devices[0])
self.device.OpenDevice()
# Load graph
with open(graph_filepath, mode='rb') as f:
graphfile = f.read()
self.graph = self.device.AllocateGraph(graphfile)
# Load labels
self.labels = []
with open(label_filepath, 'r') as f:
for line in f:
label = line.split('\n')[0]
if label != 'classes':
self.labels.append(label)
f.close()
def __exit__(self, exc_type, exc_value, traceback):
self.graph.DeallocateGraph()
self.device.CloseDevice()
def inference(self, data):
self.graph.LoadTensor(data.astype(np.float16), 'user object')
output, userobj = self.graph.GetResult()
return output
def get_graph(self):
return self.graph
def get_labels(self):
return self.labels
def process_inceptionv3_input(img):
image_size = 299
mean = 128
std = 1.0/128
dx, dy, dz = img.shape
delta = float(abs(dy - dx))
if dx > dy: # crop the x dimension
img = img[int(0.5*delta):dx-int(0.5*delta), 0:dy]
else:
img = img[0:dx, int(0.5*delta):dy-int(0.5*delta)]
img = cv2.resize(img, (image_size, image_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(3):
img[:, :, i] = (img[:, :, i] - mean) * std
return img
def process_inceptionv3_output(output, labels):
processed_output = {'annotations': []}
decimal_digits = 2
top_k = 5
top_inds = output.argsort()[::-1][:top_k]
for i in range(top_k):
human_string = labels[top_inds[i]]
score = round(float(output[top_inds[i]]), decimal_digits)
anno = {
'type': 'classification',
'label': human_string,
'confidence': score
}
processed_output['annotations'].append(anno)
return processed_output
#return [(labels[top_inds[i]], output[top_inds[i]]) for i in range(5)]
def print_inceptionv3_output(output, labels):
top_inds = output.argsort()[::-1][:5]
for i in range(5):
print(top_inds[i], labels[top_inds[i]], output[top_inds[i]])
def process_mobilenetssd_input(bgr_nparray):
"""Normalize MobileNet SSD input image.
Args:
img: Image nparray in BGR color model.
Returns:
Pre-processed image nparray in BGR color model.
"""
img = cv2.resize(bgr_nparray, (300, 300))
img = img - 127.5
img = img * 0.007843
return img
def process_mobilenetssd_output(output, img_w, img_h, labels, threshold=0.1):
"""
More details about inference result format:
https://github.com/movidius/ncappzoo/blob/master/caffe/SSD_MobileNet/run.py
Args:
output: Inference result returned by Graph.GetResult().
img_w: Width of input image.
img_h: Height of input image.
labels:
threshold:
Returns:
Annotations as dictionary, key is "annotations" and
value a list of parsed results.
Example:
'annotations': [
{
"label": "car",
"confidence": 0.93,
"left": 100,
"top": 100,
"right": 200,
"bottom": 200
},
...
]
"""
boxnum_index = 0
result_index = 7
result_size = 7
num_valid_boxes = int(output[boxnum_index])
annotations = []
for i in range(num_valid_boxes):
base_index = result_index + result_size * i
result_objinfo = output[base_index:(base_index + result_size)]
anno = {}
anno['label'] = labels[int(result_objinfo[1])]
anno['confidence'] = float(result_objinfo[2])
anno['left'] = int(result_objinfo[3] * img_w)
anno['top'] = int(result_objinfo[4] * img_h)
anno['right'] = int(result_objinfo[5] * img_w)
anno['bottom'] = int(result_objinfo[6] * img_h)
if anno['confidence'] >= threshold:
annotations.append(anno)
return {'annotations': annotations}
if __name__ == '__main__':
graph_filepath = '' # model filepath
label_filepath = '' # label filepath
path_to_images = '' # image dirpath
image_filenames = [os.path.join(path_to_images, image_name)
for image_name in []] # image filename list
movidius = MovidiusNeuralGraph(graph_filepath, label_filepath)
labels = movidius.get_labels()
print(''.join(['*' for i in range(79)]))
print('inception-v3 on NCS')
for image_filename in image_filenames:
img = cv2.imread(image_filename).astype(np.float32)
img = process_inceptionv3_input(img)
print(''.join(['*' for i in range(79)]))
print('Start download to NCS...')
output = movidius.inference(img)
print_inceptionv3_output(output, labels)
print(''.join(['*' for i in range(79)]))
print('Finished')
BerryNet-upstream-3.9.0/berrynet/engine/movidius_engine.py 0000664 0000000 0000000 00000004376 13606321373 0023766 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 .
"""Movidius classification inference engine.
"""
from __future__ import print_function
from berrynet.engine import DLEngine
from berrynet.engine import movidius as mv
class MovidiusEngine(DLEngine):
def __init__(self, model, label):
super(MovidiusEngine, self).__init__()
self.mvng = mv.MovidiusNeuralGraph(model, label)
def process_input(self, tensor):
return mv.process_inceptionv3_input(tensor)
def inference(self, tensor):
return self.mvng.inference(tensor)
def process_output(self, output):
return mv.process_inceptionv3_output(
output,
self.mvng.get_labels())
def save_cache(self):
with open(self.cache['model_output_filepath'], 'w') as f:
for i in self.cache['model_output']:
print("%s (score = %.5f)" % (i[0], i[1]), file=f)
class MovidiusMobileNetSSDEngine(DLEngine):
def __init__(self, model, label):
super(MovidiusMobileNetSSDEngine, self).__init__()
self.mvng = mv.MovidiusNeuralGraph(model, label)
self.labels = self.mvng.get_labels()
self.classes = len(self.labels)
def process_input(self, tensor):
self.img_w = tensor.shape[1]
self.img_h = tensor.shape[0]
return mv.process_mobilenetssd_input(tensor)
def inference(self, tensor):
return self.mvng.inference(tensor)
def process_output(self, output):
return mv.process_mobilenetssd_output(
output,
self.img_w,
self.img_h,
self.mvng.get_labels())
BerryNet-upstream-3.9.0/berrynet/engine/openvino_engine.py 0000664 0000000 0000000 00000041555 13606321373 0023764 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 .
"""OpenVINO classification inference engine.
"""
import logging
import os
import sys
from argparse import ArgumentParser
from time import time
from berrynet.engine import DLEngine
import cv2
import numpy as np
from berrynet import logger
from openvino.inference_engine import IENetwork, IEPlugin
class OpenVINOClassifierEngine(DLEngine):
def __init__(self, model, labels=None, top_k=3, device='CPU'):
"""
Args:
model: Path to an .xml file with a trained model.
device: Specify the target device to infer on; CPU, GPU, FPGA
or MYRIAD is acceptable. Sample will look for a suitable
plugin for device specified (CPU by default)
labels: Labels mapping file
top_k: Number of top results
"""
super(OpenVINOClassifierEngine, self).__init__()
model_bin = model
model_xml = os.path.splitext(model_bin)[0] + ".xml"
if labels:
with open(labels, 'r') as f:
# Allow label name with spaces. To use onlyh the 1st word,
# uncomment another labels_map implementation below.
self.labels_map = [l.strip() for l in f.readlines()]
#self.labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip()
# for x in f]
else:
self.labels_map = None
self.top_k = top_k
# Plugin initialization for specified device and
# load extensions library if specified
#
# Note: MKLDNN CPU-targeted custom layer support is not included
# because we do not use it yet.
self.plugin = IEPlugin(device=device, plugin_dirs=None)
# Read IR
logger.debug('Loading network files:'
'\n\txml: {0}\n\tbin: {1}'.format(model_xml, model_bin))
net = IENetwork.from_ir(model=model_xml, weights=model_bin)
if self.plugin.device == "CPU":
supported_layers = self.plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
logger.error("Following layers are not supported by the plugin for specified device {}:\n {}".format(self.plugin.device, ', '.join(not_supported_layers)))
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
# input_blob and and out_blob are the layer names in string format.
logger.debug("Preparing input blobs")
self.input_blob = next(iter(net.inputs))
self.out_blob = next(iter(net.outputs))
net.batch_size = 1
self.n, self.c, self.h, self.w = net.inputs[self.input_blob].shape
# Loading model to the plugin
logger.debug("Loading model to the plugin")
self.exec_net = self.plugin.load(network=net)
del net
def __delete__(self, instance):
del self.exec_net
del self.plugin
def process_input(self, tensor):
"""Resize tensor (if needed) and change layout from HWC to CHW.
Args:
tensor: Input BGR tensor (OpenCV convention)
Returns:
Resized and transposed tensor
"""
if tensor.shape[:-1] != (self.h, self.w):
logger.warning("Input tensor is resized from {} to {}".format(
tensor.shape[:-1], (self.h, self.w)))
tensor = cv2.resize(tensor, (self.w, self.h))
tensor = tensor.transpose((2, 0, 1)) # Change data layout from HWC to CHW
return tensor
def inference(self, tensor):
logger.debug("Starting inference")
res = self.exec_net.infer(inputs={self.input_blob: tensor})
return res[self.out_blob]
def process_output(self, output):
logger.debug("Processing output blob")
logger.debug("Top {} results: ".format(self.top_k))
annotations = []
for i, probs in enumerate(output):
probs = np.squeeze(probs)
top_ind = np.argsort(probs)[-self.top_k:][::-1]
for id in top_ind:
det_label = self.labels_map[id] if self.labels_map else "#{}".format(id)
logger.debug("\t{:.7f} label {}".format(probs[id], det_label))
annotations.append({
'type': 'classification',
'label': det_label,
'confidence': float(probs[id])
})
return {'annotations': annotations}
class OpenVINODetectorEngine(DLEngine):
def __init__(self, model, labels=None, threshold=0.3, device='CPU'):
super(OpenVINODetectorEngine, self).__init__()
# Prepare model and labels
model_bin = model
model_xml = os.path.splitext(model_bin)[0] + ".xml"
if labels:
with open(labels, 'r') as f:
self.labels_map = [x.strip() for x in f]
else:
self.labels_map = None
self.threshold = threshold
# Plugin initialization for specified device and
# load extensions library if specified
#
# Note: MKLDNN CPU-targeted custom layer support is not included
# because we do not use it yet.
if device == 'CPU':
plugin_dirs = '/opt/intel/openvino_2019.1.144/deployment_tools/inference_engine/lib/intel64'
self.plugin = IEPlugin(device=device, plugin_dirs=plugin_dirs)
self.plugin.add_cpu_extension(plugin_dirs + '/libcpu_extension_avx2.so')
self.plugin.add_cpu_extension(plugin_dirs + '/libcpu_extension_avx512.so')
self.plugin.add_cpu_extension(plugin_dirs + '/libcpu_extension_sse4.so')
logger.debug('plugin dirs: {}'.format(plugin_dirs))
else:
plugin_dirs = None
self.plugin = IEPlugin(device=device, plugin_dirs=plugin_dirs)
# Read IR
logger.debug('Loading network files:'
'\n\txml: {0}\n\tbin: {1}'.format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
if self.plugin.device == "CPU":
supported_layers = self.plugin.get_supported_layers(net)
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
logger.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(self.plugin.device, ', '.join(not_supported_layers)))
logger.error("Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
# input_blob and and out_blob are the layer names in string format.
logger.debug("Preparing input blobs")
self.input_blob = next(iter(net.inputs))
self.out_blob = next(iter(net.outputs))
self.n, self.c, self.h, self.w = net.inputs[self.input_blob].shape
# Loading model to the plugin
self.exec_net = self.plugin.load(network=net, num_requests=2)
del net
# Initialize engine mode: sync or async
#
# FIXME: async mode does not work currently.
# process_input needs to provide two input tensors for async.
self.is_async_mode = False
self.cur_request_id = 0
self.next_request_id = 1
def __delete__(self, instance):
del self.exec_net
del self.plugin
def process_input(self, tensor, next_tensor=None):
frame = tensor
next_frame = next_tensor
# original input shape will be used in process_output
self.img_w = tensor.shape[1]
self.img_h = tensor.shape[0]
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
if self.is_async_mode:
in_frame = cv2.resize(next_frame, (self.w, self.h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
else:
in_frame = cv2.resize(frame, (self.w, self.h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
return in_frame
def inference(self, tensor):
inf_start = time()
if self.is_async_mode:
self.exec_net.start_async(request_id=self.next_request_id, inputs={self.input_blob: tensor})
else:
self.exec_net.start_async(request_id=self.cur_request_id, inputs={self.input_blob: tensor})
if self.exec_net.requests[self.cur_request_id].wait(-1) == 0:
inf_end = time()
det_time = inf_end - inf_start
if self.is_async_mode:
logger.debug('Inference time: N\A for async mode')
else:
logger.debug("Inference time: {:.3f} ms".format(det_time * 1000))
# Parse detection results of the current request
res = self.exec_net.requests[self.cur_request_id].outputs[self.out_blob]
else:
res = None
return res
# FIXME: async mode does not work currently.
# process_input needs to provide two input tensors for async.
if self.is_async_mode:
self.cur_request_id, self.next_request_id = self.next_request_id, self.cur_request_id
frame = next_frame
def process_output(self, output):
logger.debug("Processing output blob")
logger.debug("Threshold: {}".format(self.threshold))
annotations = []
for obj in output[0][0]:
# Collect objects when probability more than specified threshold
if obj[2] > self.threshold:
xmin = int(obj[3] * self.img_w)
ymin = int(obj[4] * self.img_h)
xmax = int(obj[5] * self.img_w)
ymax = int(obj[6] * self.img_h)
class_id = int(obj[1])
det_label = self.labels_map[class_id] if self.labels_map else str(class_id)
annotations.append({
'label': det_label,
'confidence': float(obj[2]),
'left': xmin,
'top': ymin,
'right': xmax,
'bottom': ymax
})
return {'annotations': annotations}
def get_distribution_info():
"""Get Debuan or Ubuntu distribution information.
"""
info = {}
with open('/etc/lsb-release') as f:
info_l = [i.strip().split('=') for i in f.readlines()]
for i in info_l:
info[i[0]] = i[1]
logger.debug('Distribution info: {}'.format(info))
return info
def set_openvino_environment():
"""The same effect as executing /bin/setupvars.sh.
"""
dist_info = get_distribution_info()
python_version = 3.5
os.environ['INSTALLDIR'] = '/opt/intel/computer_vision_sdk_2018.4.420'
os.environ['INTEL_CVSDK_DIR'] = os.environ['INSTALLDIR']
os.environ['LD_LIBRARY_PATH'] = (
'{installdir}/deployment_tools/model_optimizer/bin:'
'{ld_library_path}').format(
installdir = os.environ.get('INSTALLDIR'),
ld_library_path = os.environ.get('LD_LIBRARY_PATH' or '')
)
os.environ['InferenceEngine_DIR'] = os.path.join(
os.environ.get('INTEL_CVSDK_DIR'),
'deployment_tools/inference_engine/share'
)
os.environ['IE_PLUGINS_PATH'] = os.path.join(
os.environ.get('INTEL_CVSDK_DIR'),
'deployment_tools/inference_engine/lib/ubuntu_{}.04/intel64'.format(
dist_info['DISTRIB_RELEASE'])
)
os.environ['LD_LIBRARY_PATH'] = (
'/opt/intel/opencl:'
'{installdir}/deployment_tools/inference_engine/external/gna/lib:'
'{installdir}/deployment_tools/inference_engine/external/mkltiny_lnx/lib:'
'{installdir}/deployment_tools/inference_engine/external/omp/lib:'
'{ie_plugins_path}:'
'{ld_library_path}').format(
installdir = os.environ.get('INSTALLDIR'),
ie_plugins_path = os.environ.get('IE_PLUGINS_PATH'),
ld_library_path = os.environ.get('LD_LIBRARY_PATH' or '')
)
os.environ['PATH'] = (
'{intel_cvsdk_dir}/deployment_tools/model_optimizer:'
'{path}').format(
intel_cvsdk_dir = os.environ.get('INTEL_CVSDK_DIR'),
path = os.environ.get('PATH'),
)
os.environ['PYTHONPATH'] = (
'{intel_cvsdk_dir}/deployment_tools/model_optimizer:'
'{pythonpath}').format(
intel_cvsdk_dir = os.environ.get('INTEL_CVSDK_DIR'),
pythonpath = os.environ.get('PYTHONPATH' or '')
)
os.environ['PYTHONPATH'] = (
'{intel_cvsdk_dir}/python/python$python_version:'
'{intel_cvsdk_dir}/python/python$python_version/ubuntu16:'
'{pythonpath}').format(
intel_cvsdk_dir = os.environ.get('INTEL_CVSDK_DIR'),
pythonpath = os.environ.get('PYTHONPATH' or '')
)
def parse_argsr():
parser = ArgumentParser()
parser.add_argument(
"-e", "--engine",
help=("Classifier or Detector engine. "
"classifier, or detector is acceptable. "
"(classifier by default)"),
default="classifier",
type=str)
parser.add_argument(
"-m", "--model",
help="Path to an .xml file with a trained model.",
required=True,
type=str)
parser.add_argument(
"-l", "--labels",
help="Labels mapping file",
default=None,
type=str)
parser.add_argument(
"--top_k",
help="Number of top results",
default=10,
type=int)
parser.add_argument(
"-d", "--device",
help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified (CPU by default)",
default="CPU",
type=str)
parser.add_argument(
"-i", "--input",
help="Path to a folder with images or path to an image files",
required=True,
type=str)
parser.add_argument(
"--debug",
help="Debug mode toggle",
default=False,
action="store_true")
return parser.parse_args()
def main():
args = parse_argsr()
if args.debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args.engine == 'classifier':
engine = OpenVINOClassifierEngine(
model = args.model,
device = args.device,
labels = args.labels,
top_k = args.top_k)
elif args.engine == 'detector':
engine = OpenVINODetectorEngine(
model = args.model,
device = args.device,
labels = args.labels)
else:
raise Exception('Illegal engine {}, it should be '
'classifier or detector'.format(args.engine))
#set_openvino_environment()
#if args.debug:
# logger.debug('OpenVINO environment vars')
# target_vars = ['INSTALLDIR',
# 'INTEL_CVSDK_DIR',
# 'LD_LIBRARY_PATH',
# 'InferenceEngine_DIR',
# 'IE_PLUGINS_PATH',
# 'PATH',
# 'PYTHONPATH']
# for i in target_vars:
# logger.debug('\t{var}: {val}'.format(
# var = i,
# val = os.environ.get(i)))
bgr_array = cv2.imread(args.input)
t = time()
image_data = engine.process_input(bgr_array)
output = engine.inference(image_data)
model_outputs = engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} s'.format(time() - t))
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/engine/tensorflow_engine.py 0000664 0000000 0000000 00000007126 13606321373 0024325 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 cv2
import numpy as np
import tensorflow as tf
from berrynet import logger
#from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine import DLEngine
class TensorFlowEngine(DLEngine):
# FIXME: Get model information by model manager
def __init__(self, model, label, input_layer, output_layer, top_k=3):
super(TensorFlowEngine, self).__init__()
# Load model
with tf.gfile.FastGFile(model, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
# Load labels
self.labels = [line.rstrip() for line in tf.gfile.FastGFile(label)]
# Load other configs
self.input_layer = input_layer
self.output_layer = output_layer
self.top_k = top_k
# NOTE: Do NOT call read_tensor_from_nparray twice to prevent from
# recreating unexpected placeholders.
self.tensor_op = self.read_tensor_from_nparray(
input_height=299,
input_width=299,
input_mean=0,
input_std=255)
def create(self):
# Create session
self.sess = tf.Session()
def process_input(self, rgb_array):
return self.sess.run(self.tensor_op,
feed_dict={'inarray:0': rgb_array})
def inference(self, tensor):
return self.sess.run(self.output_layer,
{self.input_layer: tensor})
def process_output(self, output):
processed_output = {'annotations': []}
decimal_digits = 2
predictions = np.squeeze(output)
top_k_index = predictions.argsort()[-self.top_k:][::-1]
for node_id in top_k_index:
human_string = self.labels[node_id]
score = round(float(predictions[node_id]), decimal_digits)
anno = {
'type': 'classification',
'label': human_string,
'confidence': score
}
processed_output['annotations'].append(anno)
logger.debug('%s (score = %.5f)' % (human_string, score))
return processed_output
def save_cache(self):
pass
# NOTE: Copied from trainer.component
def read_tensor_from_nparray(self, input_height=192, input_width=192,
input_mean=0, input_std=255):
""" Create normalized tensor based on input numpy array """
image_reader = tf.placeholder(tf.uint8, name='inarray')
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.image.resize_bilinear(dims_expander,
[input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
return normalized
BerryNet-upstream-3.9.0/berrynet/engine/tflite_engine.py 0000664 0000000 0000000 00000023535 13606321373 0023414 0 ustar 00root root 0000000 0000000 import logging
import time
from argparse import ArgumentParser
from os import path
import cv2
import numpy as np
import tensorflow as tf
from berrynet.engine import DLEngine
from berrynet import logger
class TFLiteDetectorEngine(DLEngine):
def __init__(self, model, labels, threshold=0.5, num_threads=1):
"""
Builds Tensorflow graph, load model and labels
"""
# Load labels
self.labels = self._load_label(labels)
self.classes = len(self.labels)
# Define lite graph and Load Tensorflow Lite model into memory
self.interpreter = tf.lite.Interpreter(
model_path=model)
self.interpreter.allocate_tensors()
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
self.input_dtype = self.input_details[0]['dtype']
self.num_threads = num_threads
self.threshold = threshold
def __delete__(self, instance):
#tf.reset_default_graph()
#self.sess = tf.InteractiveSession()
del self.interpreter
def process_input(self, tensor):
"""Resize and normalize image for network input"""
self.img_w = tensor.shape[1]
self.img_h = tensor.shape[0]
frame = cv2.cvtColor(tensor, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (300, 300))
frame = np.expand_dims(frame, axis=0)
if self.input_dtype == np.float32:
frame = (2.0 / 255.0) * frame - 1.0
frame = frame.astype('float32')
else:
# default data type returned by cv2.imread is np.unit8
pass
return frame
def inference(self, tensor):
self.interpreter.set_num_threads(int(self.num_threads));
self.interpreter.set_tensor(self.input_details[0]['index'], tensor)
self.interpreter.invoke()
# get results
boxes = self.interpreter.get_tensor(
self.output_details[0]['index'])
classes = self.interpreter.get_tensor(
self.output_details[1]['index'])
scores = self.interpreter.get_tensor(
self.output_details[2]['index'])
num = self.interpreter.get_tensor(
self.output_details[3]['index'])
return {
'boxes': boxes,
'classes': classes,
'scores': scores,
'num': num
}
def process_output(self, output):
# get results
boxes = np.squeeze(output['boxes'][0])
classes = np.squeeze(output['classes'][0] + 1).astype(np.int32)
scores = np.squeeze(output['scores'][0])
num = output['num'][0]
annotations = []
number_boxes = boxes.shape[0]
for i in range(number_boxes):
box = tuple(boxes[i].tolist())
ymin, xmin, ymax, xmax = box
if scores[i] < self.threshold:
continue
annotations.append({
'label': self.labels[classes[i]],
'confidence': float(scores[i]),
'left': int(xmin * self.img_w),
'top': int(ymin * self.img_h),
'right': int(xmax * self.img_w),
'bottom': int(ymax * self.img_h)
})
return {'annotations': annotations}
def _load_label(self, path):
with open(path, 'r') as f:
labels = list(map(str.strip, f.readlines()))
return labels
class TFLiteClassifierEngine(DLEngine):
def __init__(self, model, labels, top_k=3, num_threads=1,
input_mean=127.5, input_std=127.5):
"""
Builds Tensorflow graph, load model and labels
"""
# Load labels
self.labels = self._load_label(labels)
self.classes = len(self.labels)
# Define lite graph and Load Tensorflow Lite model into memory
self.interpreter = tf.lite.Interpreter(
model_path=model)
self.interpreter.allocate_tensors()
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
self.floating_model = False
if self.input_details[0]['dtype'] == np.float32:
self.floating_model = True
self.input_mean = input_mean
self.input_std = input_std
self.top_k = int(top_k)
self.num_threads = num_threads
def __delete__(self, instance):
#tf.reset_default_graph()
#self.sess = tf.InteractiveSession()
del self.interpreter
def process_input(self, tensor):
"""Resize and normalize image for network input"""
self.img_w = tensor.shape[1]
self.img_h = tensor.shape[0]
frame = cv2.cvtColor(tensor, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (self.input_details[0]['shape'][2],
self.input_details[0]['shape'][1]))
frame = np.expand_dims(frame, axis=0)
if self.floating_model:
frame = (np.float32(frame) - self.input_mean) / self.input_std
return frame
def inference(self, tensor):
self.interpreter.set_num_threads(int(self.num_threads));
self.interpreter.set_tensor(self.input_details[0]['index'], tensor)
self.interpreter.invoke()
output_data = self.interpreter.get_tensor(self.output_details[0]['index'])
results = np.squeeze(output_data)
return {
'scores': results,
}
def process_output(self, output):
# get results
scores = output['scores']
top_k_results = scores.argsort()[-self.top_k:][::-1]
processed_output = {'annotations': []}
for i in top_k_results:
human_string = self.labels[i]
if self.floating_model:
score = float(scores[i])
else:
score = float(scores[i])/255.0
anno = {
'type': 'classification',
'label': human_string,
'confidence': score
}
processed_output['annotations'].append(anno)
return processed_output
def _load_label(self, path):
with open(path, 'r') as f:
labels = list(map(str.strip, f.readlines()))
return labels
def parse_argsr():
parser = ArgumentParser()
parser.add_argument(
"-e", "--engine",
help=("Classifier or Detector engine. "
"classifier, or detector is acceptable. "
"(classifier by default)"),
default="classifier",
type=str)
parser.add_argument(
"-m", "--model",
help="Path to an .xml file with a trained model.",
required=True,
type=str)
parser.add_argument(
"-l", "--labels",
help="Labels mapping file",
default=None,
type=str)
parser.add_argument(
"--top_k",
help="Number of top results",
default=3,
type=int)
parser.add_argument(
"--num_threads",
help="Number of threads",
default=1,
type=int)
parser.add_argument(
"-i", "--input",
help="Path to a folder with images or path to an image files",
required=True,
type=str)
parser.add_argument(
"--debug",
help="Debug mode toggle",
default=False,
action="store_true")
return parser.parse_args()
def main():
# Example command
# $ python3 tflite_engine.py -e detector \
# -m detect.tflite --labels labels.txt -i dog.jpg --debug
args = parse_argsr()
if args.debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args.engine == 'classifier':
engine = TFLiteClassifierEngine(
model = args.model,
labels = args.labels,
top_k = args.top_k,
num_threads = args.num_threads)
elif args.engine == 'detector':
engine = TFLiteDetectorEngine(
model = args.model,
labels = args.labels,
num_threads = args.num_threads)
else:
raise Exception('Illegal engine {}, it should be '
'classifier or detector'.format(args.engine))
for i in range(5):
bgr_array = cv2.imread(args.input)
t = time.time()
image_data = engine.process_input(bgr_array)
output = engine.inference(image_data)
model_outputs = engine.process_output(output)
# Reference result
# input:
# darknet/data/dog.jpg
# output:
# Inference takes 0.7533011436462402 s
# Inference takes 0.5741658210754395 s
# Inference takes 0.6120760440826416 s
# Inference takes 0.6191139221191406 s
# Inference takes 0.5809791088104248 s
# label: bicycle conf: 0.9563907980918884 (139, 116) (567, 429)
# label: car conf: 0.8757821917533875 (459, 80) (690, 172)
# label: dog conf: 0.869189441204071 (131, 218) (314, 539)
# label: car conf: 0.40003547072410583 (698, 122) (724, 152)
logger.debug('Inference takes {} s'.format(time.time() - t))
if args.engine == 'classifier':
for r in model_outputs['annotations']:
logger.debug('label: {0} conf: {1}'.format(
r['label'],
r['confidence']
))
elif args.engine == 'detector':
for r in model_outputs['annotations']:
logger.debug('label: {0} conf: {1} ({2}, {3}) ({4}, {5})'.format(
r['label'],
r['confidence'],
r['left'],
r['top'],
r['right'],
r['bottom']
))
else:
raise Exception('Can not get result '
'from illegal engine {}'.format(args.engine))
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/service/ 0000775 0000000 0000000 00000000000 13606321373 0020411 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/berrynet/service/__init__.py 0000664 0000000 0000000 00000005474 13606321373 0022534 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import os
import time
from datetime import datetime
from berrynet import logger
from berrynet.comm import Communicator
from berrynet.comm import payload
class EngineService(object):
def __init__(self, service_name, engine, comm_config):
self.service_name = service_name
self.engine = engine
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.inference
self.comm = Communicator(self.comm_config, debug=True)
def inference(self, pl):
duration = lambda t: (datetime.now() - t).microseconds / 1000
t = datetime.now()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
rgb_array = payload.jpg2rgb(jpg_bytes)
logger.debug('jpg2rgb: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(rgb_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
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.result_hook(self.generalize_result(jpg_json, model_outputs))
def generalize_result(self, eng_input, eng_output):
eng_input.update(eng_output)
return eng_input
def result_hook(self, generalized_result):
logger.debug('base result_hook')
def run(self, args):
"""Infinite loop serving inference requests"""
self.engine.create()
self.comm.run()
BerryNet-upstream-3.9.0/berrynet/service/darknet_service.py 0000664 0000000 0000000 00000010347 13606321373 0024140 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import argparse
import logging
import math
import cv2
from berrynet import logger
from berrynet.comm import payload
from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine.darknet_engine import DarknetEngine
from berrynet.service import EngineService
from berrynet.utils import generate_class_color
from berrynet.utils import draw_bb
class DarknetService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(DarknetService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
bgr_array = payload.jpg2bgr(jpg_bytes)
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
classes = self.engine.classes
labels = self.engine.labels
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes),
labels))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/darknet/result',
payload.serialize_payload(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'--service_name',
default='darknet',
help='Human-readable service name for service management.')
ap.add_argument(
'-m', '--model',
help='Model file path')
ap.add_argument(
'-l', '--label',
help='Label file path')
ap.add_argument(
'-p', '--model_package',
default='',
help='Model package name. Find model and label file paths automatically.')
ap.add_argument(
'--draw',
action='store_true',
help='Draw bounding boxes on image in result')
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)
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logger.debug('model filepath: ' + args['model'])
logger.debug('label filepath: ' + args['label'])
engine = DarknetEngine(
config=meta['config']['config'].encode('utf-8'),
model=args['model'].encode('utf-8'),
meta=meta['config']['meta'].encode('utf-8')
)
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
engine_service = DarknetService(args['service_name'],
engine,
comm_config,
args['draw'])
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/service/mockup_service.py 0000664 0000000 0000000 00000005170 13606321373 0024004 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 .
"""Mockup service with relay engine (default engine).
"""
import argparse
import json
import logging
import os
from berrynet import logger
from berrynet.comm import payload
from berrynet.engine import DLEngine
from berrynet.service import EngineService
class MockupEngine(DLEngine):
def inference(self, tensor):
return {
'annotations': {
'label': 'dt42',
'confidence': 0.99
}
}
class MockupService(EngineService):
def __init__(self, service_name, engine, comm_config):
super().__init__(service_name,
engine,
comm_config)
if not os.path.exists('/tmp/mockup'):
os.mkdir('/tmp/mockup')
#def generalize_result(self, eng_input, eng_output):
# logger.debug()
# eng_input.update(eng_output)
# return eng_input
def result_hook(self, generalized_result):
gr = generalized_result
jpg_bytes = payload.destringify_jpg(gr.pop('bytes'))
logger.debug('generalized result (readable only): {}'.format(gr))
with open('/tmp/mockup/{}.jpg'.format(gr['timestamp']), 'wb') as f:
f.write(jpg_bytes)
with open('/tmp/mockup/{}.json'.format(gr['timestamp']), 'w') as f:
f.write(json.dumps(gr, indent=4))
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)
eng = MockupEngine()
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
engine_service = MockupService('mockup service', eng, comm_config)
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/service/movidius_service.py 0000664 0000000 0000000 00000013547 13606321373 0024354 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import argparse
import logging
from datetime import datetime
from berrynet import logger
from berrynet.comm import payload
from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine.movidius_engine import MovidiusEngine
from berrynet.engine.movidius_engine import MovidiusMobileNetSSDEngine
from berrynet.service import EngineService
from berrynet.utils import draw_bb
from berrynet.utils import generate_class_color
class MovidiusClassificationService(EngineService):
def __init__(self, service_name, engine, comm_config):
super(MovidiusClassificationService, self).__init__(service_name,
engine,
comm_config)
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/mvclassification/result',
payload.serialize_payload(generalized_result))
class MovidiusMobileNetSSDService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(MovidiusMobileNetSSDService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
duration = lambda t: (datetime.now() - t).microseconds / 1000
t = datetime.now()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(duration(t)))
classes = self.engine.classes
labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes),
labels))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/mvmobilenetssd/result',
payload.serialize_payload(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model',
help='Model file path')
ap.add_argument('--label',
help='Label file path')
ap.add_argument('--model_package',
default='',
help='Model package name')
ap.add_argument('--service_name', required=True,
help='Valid value: Classification, MobileNetSSD')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
ap.add_argument('--draw',
action='store_true',
help='Draw bounding boxes on image in result')
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle')
return vars(ap.parse_args())
def main():
# Test Movidius engine
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logger.debug('model filepath: ' + args['model'])
logger.debug('label filepath: ' + args['label'])
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
if args['service_name'] == 'Classification':
mvng = MovidiusEngine(args['model'], args['label'])
service_functor = MovidiusClassificationService
elif args['service_name'] == 'MobileNetSSD':
mvng = MovidiusMobileNetSSDEngine(args['model'], args['label'])
service_functor = MovidiusMobileNetSSDService
else:
logger.critical('Legal service names are Classification, MobileNetSSD')
engine_service = service_functor(args['service_name'],
mvng,
comm_config,
draw=args['draw'])
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/service/openvino_service.py 0000664 0000000 0000000 00000020232 13606321373 0024337 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import argparse
import logging
from datetime import datetime
from berrynet import logger
from berrynet.comm import payload
from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine.openvino_engine import OpenVINOClassifierEngine
from berrynet.engine.openvino_engine import OpenVINODetectorEngine
from berrynet.service import EngineService
from berrynet.utils import draw_bb
from berrynet.utils import generate_class_color
class OpenVINOClassifierService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(OpenVINOClassifierService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
duration = lambda t: (datetime.now() - t).microseconds / 1000
t = datetime.now()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(duration(t)))
#classes = self.engine.classes
#labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
#self.result_hook(
# draw_bb(bgr_array,
# self.generalize_result(jpg_json, model_outputs),
# generate_class_color(class_num=classes),
# labels))
self.result_hook(self.generalize_result(jpg_json, model_outputs))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/ovclassifier/result',
payload.serialize_payload(generalized_result))
class OpenVINODetectorService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(OpenVINODetectorService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
duration = lambda t: (datetime.now() - t).microseconds / 1000
t = datetime.now()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(duration(t)))
classes = len(self.engine.labels_map)
labels = self.engine.labels_map
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes),
labels))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/ovdetector/result',
payload.serialize_payload(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'-s', '--service',
help=('Classifier or Detector service. '
'classifier, or detector is acceptable. '
'(classifier by default)'),
default='classifier',
type=str)
ap.add_argument(
'--service_name',
default='openvino_classifier',
help='Human-readable service name for service management.')
ap.add_argument(
'-m', '--model',
help='Model file path')
ap.add_argument(
'-l', '--label',
help='Label file path')
ap.add_argument(
'-p', '--model_package',
default='',
help='Model package name. Find model and label file paths automatically.')
ap.add_argument(
'--top_k',
help='Display top K classification results.',
default=3,
type=int)
ap.add_argument(
'-d', '--device',
help='Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified (CPU by default)',
default='CPU',
type=str)
ap.add_argument(
'--draw',
action='store_true',
help='Draw bounding boxes on image in result')
ap.add_argument(
'--debug',
action='store_true',
help='Debug mode toggle')
return vars(ap.parse_args())
def main():
# Test OpenVINO classifier engine
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logger.debug('model filepath: ' + args['model'])
logger.debug('label filepath: ' + args['label'])
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
if args['service'] == 'classifier':
engine = OpenVINOClassifierEngine(
model = args['model'],
labels = args['label'],
top_k = args['top_k'],
device = args['device'])
service_functor = OpenVINOClassifierService
elif args['service'] == 'detector':
engine = OpenVINODetectorEngine(
model = args['model'],
labels = args['label'],
device = args['device'])
service_functor = OpenVINODetectorService
else:
raise Exception('Illegal service {}, it should be '
'classifier or detector'.format(args['service']))
engine_service = service_functor(args['service_name'],
engine,
comm_config,
draw=args['draw'])
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/service/tensorflow_service.py 0000664 0000000 0000000 00000007713 13606321373 0024715 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import argparse
import logging
from berrynet import logger
from berrynet.comm import payload
from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine.tensorflow_engine import TensorFlowEngine
from berrynet.service import EngineService
class TensorFlowService(EngineService):
def __init__(self, service_name, engine, comm_config):
super(TensorFlowService, self).__init__(service_name,
engine,
comm_config)
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/tensorflow/result',
payload.serialize_payload(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model',
help='Model file path')
ap.add_argument('--label',
help='Label file path')
ap.add_argument('--model_package',
default='',
help='Model package name. Find model and label file paths automatically.')
ap.add_argument('--service_name',
default='tensorflow',
help='Human-readable service name for service management.')
ap.add_argument('--num_top_predictions',
help='Display this many predictions',
default=3,
type=int)
ap.add_argument('--debug',
action='store_true',
help='Debug mode toggle')
return vars(ap.parse_args())
def main():
# Test TensorFlow engine
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
logger.debug('model filepath: ' + args['model'])
logger.debug('label filepath: ' + args['label'])
model = 'berrynet/engine/inception_v3_2016_08_28_frozen.pb'
label = 'berrynet/engine/imagenet_slim_labels.txt'
jpg_filepath = 'berrynet/engine/grace_hopper.jpg'
input_layer = 'input:0'
output_layer = 'InceptionV3/Predictions/Reshape_1:0'
tfe = TensorFlowEngine(model, label, input_layer, output_layer)
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
engine_service = TensorFlowService(args['service_name'],
tfe,
comm_config)
engine_service.run(args)
if __name__ == '__main__':
# Test Movidius engine
#import movidius as mv
#logging.basicConfig(level=logging.DEBUG)
#args = parse_args()
#if args['model_package'] != '':
# dlmm = DLModelManager()
# meta = dlmm.get_model_meta(args['model_package'])
# args['model'] = meta['model']
# args['label'] = meta['label']
#logging.debug('model filepath: ' + args['model'])
#logging.debug('label filepath: ' + args['label'])
#logging.debug('image_dir: ' + args['image_dir'])
#mvng = mv.MovidiusNeuralGraph(args['model'], args['label'])
#engine_service = EngineService(args['service_name'], mvng)
#engine_service.run(args)
main()
BerryNet-upstream-3.9.0/berrynet/service/tflite_service.py 0000664 0000000 0000000 00000017543 13606321373 0024004 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
import argparse
import logging
import time
from berrynet import logger
from berrynet.comm import payload
from berrynet.dlmodelmgr import DLModelManager
from berrynet.engine.tflite_engine import TFLiteClassifierEngine
from berrynet.engine.tflite_engine import TFLiteDetectorEngine
from berrynet.service import EngineService
from berrynet.utils import draw_bb
from berrynet.utils import generate_class_color
class TFLiteClassifierService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(TFLiteClassifierService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
t0 = time.time()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(time.time() - t0))
t1 = time.time()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(time.time() - t1))
t2 = time.time()
image_data = self.engine.process_input(bgr_array)
logger.debug('Input processing takes {} ms'.format(time.time() - t2))
t3 = time.time()
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} ms'.format(time.time() - t3))
classes = self.engine.classes
labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_label(bgr_array,
self.generalize_result(jpg_json, model_outputs),
color,
labels))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/tfliteclassifier/result',
payload.serialize_payload(generalized_result))
class TFLiteDetectorService(EngineService):
def __init__(self, service_name, engine, comm_config, draw=False):
super(TFLiteDetectorService, self).__init__(service_name,
engine,
comm_config)
self.draw = draw
def inference(self, pl):
t0 = time.time()
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 = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(time.time() - t0))
t1 = time.time()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(time.time() - t1))
t2 = time.time()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(time.time() - t2))
classes = self.engine.classes
labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes),
labels))
def result_hook(self, generalized_result):
logger.debug('result_hook, annotations: {}'.format(generalized_result['annotations']))
self.comm.send('berrynet/engine/tflitedetector/result',
payload.serialize_payload(generalized_result))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument(
'-s', '--service',
help=('Classifier or Detector service. '
'classifier, or detector is acceptable. '
'(classifier by default)'),
default='classifier',
type=str)
ap.add_argument(
'--service_name',
default='tflite_classifier',
help='Human-readable service name for service management.')
ap.add_argument(
'-m', '--model',
help='Model file path')
ap.add_argument(
'-l', '--label',
help='Label file path')
ap.add_argument(
'-p', '--model_package',
default='',
help='Model package name. Find model and label file paths automatically.')
ap.add_argument(
'--top_k',
help='Display top K classification results.',
default=3,
type=int)
ap.add_argument(
'--num_threads',
default=1,
help="Number of threads for running inference.")
ap.add_argument(
'--draw',
action='store_true',
help='Draw bounding boxes on image in result')
ap.add_argument(
'--debug',
action='store_true',
help='Debug mode toggle')
return vars(ap.parse_args())
def main():
# Test TFLite engines
args = parse_args()
if args['debug']:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logger.debug('model filepath: ' + args['model'])
logger.debug('label filepath: ' + args['label'])
comm_config = {
'subscribe': {},
'broker': {
'address': 'localhost',
'port': 1883
}
}
if args['service'] == 'classifier':
engine = TFLiteClassifierEngine(
model = args['model'],
labels = args['label'],
top_k = args['top_k'],
num_threads = args['num_threads'])
service_functor = TFLiteClassifierService
elif args['service'] == 'detector':
engine = TFLiteDetectorEngine(
model = args['model'],
labels = args['label'],
num_threads = args['num_threads'])
service_functor = TFLiteDetectorService
else:
raise Exception('Illegal service {}, it should be '
'classifier or detector'.format(args['service']))
engine_service = service_functor(args['service_name'],
engine,
comm_config,
draw=args['draw'])
engine_service.run(args)
if __name__ == '__main__':
main()
BerryNet-upstream-3.9.0/berrynet/utils.py 0000664 0000000 0000000 00000014052 13606321373 0020465 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 .
"""Utility Functions.
"""
import math
import cv2
from berrynet.comm import payload
def generate_class_color(class_num=20):
"""Generate a RGB color set based on given class number.
Args:
class_num: Default is VOC dataset class number.
Returns:
A tuple containing RGB colors.
"""
colors = [(1, 0, 1), (0, 0, 1), (0, 1, 1),
(0, 1, 0), (1, 1, 0), (1, 0, 0)]
const = 1234567 # only for offset calculation
colorset = []
for cls_i in range(class_num):
offset = cls_i * const % class_num
ratio = (float(offset) / class_num) * (len(colors) - 1)
i = math.floor(ratio)
j = math.ceil(ratio)
ratio -= i
rgb = []
for ch_i in range(3):
r = (1 - ratio) * colors[i][ch_i] + ratio * colors[j][ch_i]
rgb.append(math.ceil(r * 255))
colorset.append(tuple(rgb[::-1]))
return tuple(colorset)
def draw_bb(bgr_nparr, infres, class_colors, labels):
"""Draw bounding boxes on an image.
Args:
bgr_nparr: image data in numpy array format
infres: Darkflow inference results
class_colors: Bounding box color candidates, list of RGB tuples.
Returens:
Generalized result whose image data is drew w/ bounding boxes.
"""
for res in infres['annotations']:
left = int(res['left'])
top = int(res['top'])
right = int(res['right'])
bottom = int(res['bottom'])
label = res['label']
color = class_colors[labels.index(label)]
confidence = res['confidence']
imgHeight, imgWidth, _ = bgr_nparr.shape
thick = int((imgHeight + imgWidth) // 300)
cv2.rectangle(bgr_nparr,(left, top), (right, bottom), color, thick)
cv2.putText(bgr_nparr, label, (left, top - 12), 0, 1e-3 * imgHeight,
color, thick//3)
#cv2.imwrite('prediction.jpg', bgr_nparr)
infres['bytes'] = payload.stringify_jpg(
cv2.imencode('.jpg', bgr_nparr)[1])
return infres
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,
(anno['left'], anno['top']),
(anno['right'], 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,
(label_left - 1, label_top - 1),
(label_right + 1, label_bottom + 1),
label_background_color,
-1)
cv2.putText(img,
label_text,
(label_left, 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 draw_label(bgr_nparr, infres, class_color, save_image_path=None):
"""Draw bounding boxes on an image.
Args:
bgr_nparr: image data in numpy array format
infres: Inference results followed generic format specification.
class_color: Label color, a RGB tuple.
Returens:
Generalized result whose image data is drew w/ labels.
"""
left = 0
top = 0
for res in infres['annotations']:
imgHeight, imgWidth, _ = bgr_nparr.shape
thick = int((imgHeight + imgWidth) // 300)
# putText can not handle newline char yet,
# so we have to put multiple texts manually.
cv2.putText(bgr_nparr,
'{0}: {1}'.format(res['label'], res['confidence']),
(left + 10, top + 20), # bottom-left corner of text
0, # fontFace
1e-3 * imgHeight, # fontScale
class_color,
thick // 3)
top += 20
infres['bytes'] = payload.stringify_jpg(
cv2.imencode('.jpg', bgr_nparr)[1])
if save_image_path:
cv2.imwrite(save_image_path, bgr_nparr)
return infres
BerryNet-upstream-3.9.0/broker.js 0000664 0000000 0000000 00000002704 13606321373 0016744 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 .
'use strict';
const mosca = require('mosca');
// ascoltatore: https://github.com/mcollina/ascoltatori
const ascoltatore = {
type: 'mongo',
url: 'mongodb://localhost:27017/mqtt',
pubsubCollection: 'ascoltatori',
mongo: {}
};
const settings = {
port: 1883,
backend: ascoltatore,
http: {
port: 3000,
bundle: true,
static: './'
}
};
const server = new mosca.Server(settings);
server.on('clientConnected', (client) => {
console.log('client connected', client.id);
});
// fired when a message is received
server.on('published', (packet, client) => {
console.log('Server published. Avoid showing package content here.');
});
// fired when the mqtt server is ready
server.on('ready', () => {
console.log('Mosca server is up and running');
});
BerryNet-upstream-3.9.0/camera.js 0000664 0000000 0000000 00000012620 13606321373 0016706 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 .
'use strict';
const fs = require('fs');
const mqtt = require('mqtt');
const request = require('request');
const spawnsync = require('child_process').spawnSync;
const config = require('./config');
const cv = require('opencv');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicActionInference = config.topicActionInference;
const topicEventCamera = config.topicEventCamera;
const cameraURI = config.ipcameraSnapshot;
const snapshotFile = '/tmp/snapshot.jpg';
const snapshotWidth = config.boardcameraImageWidth;
const snapshotHeight = config.boardcameraImageHeight;
const cameraCmd = '/usr/bin/raspistill';
const cameraArgs = ['-vf', '-hf',
'-w', snapshotWidth,
'-h', snapshotHeight,
'-o', snapshotFile];
const usbCameraCmd = '/usr/bin/fswebcam';
const usbCameraArgs = ['-r', snapshotWidth + 'x' + snapshotHeight,
'--no-banner', '-D', '0.5', snapshotFile];
const fps = 30;
var cameraIntervalID = null;
var cameraInterval = 1000.0 / fps;
var cameraCV = null;
var frameCounter = 0;
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
client.on('connect', () => {
client.subscribe(topicEventCamera);
log(`camera client: connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
log(`camera client: on topic ${t}, received message ${m}.`);
const action = m.toString();
if (action == 'snapshot_picam') {
/* NOTE: We use V4L2 to support RPi camera, so RPi camera's usage is
* the same as USB camera. Both RPi and USB cameras are called
* "board camera".
*
* This action is obsoleted and will be removed in the future.
*/
// Take a snapshot from RPi3 camera. The snapshot will be displayed
// on dashboard.
spawnsync(cameraCmd, cameraArgs);
fs.readFile(snapshotFile, function(err, data) {
if (err) {
log('camera client: cannot get image.');
} else {
log('camera client: publishing image.');
client.publish(topicActionInference, data);
}
});
} else if (action == 'snapshot_ipcam') {
// Take a snapshot from IP camera. The snapshot will be sent to
// configured email address.
request.get(
{uri: cameraURI, encoding: null},
(e, res, body) => {
if (!e && res.statusCode == 200) {
log('camera client: publishing image.');
client.publish(topicActionInference, body);
} else {
log('camera client: cannot get image.');
}
}
);
} else if (action == 'snapshot_boardcam') {
// Take a snapshot from USB camera.
spawnsync(usbCameraCmd, usbCameraArgs);
fs.readFile(snapshotFile, function(err, data) {
if (err) {
log('camera client: cannot get image.');
} else {
log('camera client: publishing image.');
client.publish(topicActionInference, data);
}
});
} else if (action == 'stream_boardcam_start') {
if ((!cameraCV) && (!cameraIntervalID)) {
cameraCV = new cv.VideoCapture(0);
cameraCV.setWidth(snapshotWidth);
cameraCV.setHeight(snapshotHeight);
cameraIntervalID = setInterval(function() {
cameraCV.read(function(err, im) {
if (err) {
throw err;
}
if (frameCounter < fps * 2) {
frameCounter++;
} else {
frameCounter = 0;
im.save(snapshotFile);
fs.readFile(snapshotFile, function(err, data) {
if (err) {
log('camera client: cannot get image.');
} else {
log('camera client: publishing image.');
client.publish(topicActionInference, data);
}
});
}
im.release();
});
}, cameraInterval);
}
} else if (action == 'stream_boardcam_stop') {
if (cameraCV) {
cameraCV.release();
cameraCV = null;
}
if (cameraIntervalID) {
clearInterval(cameraIntervalID);
cameraIntervalID = null;
}
} else if (action == 'stream_nest_ipcam_start') {
if (!cameraIntervalID) {
cameraIntervalID = setInterval(function() {
request.get(
{uri: cameraURI, encoding: null},
(e, res, body) => {
if (!e && res.statusCode == 200) {
log('camera client: publishing image.');
client.publish(topicActionInference, body);
} else {
log('camera client: cannot get image.');
}
}
);
}, cameraInterval);
}
} else if (action == 'stream_nest_ipcam_stop') {
if (cameraIntervalID) {
clearInterval(cameraIntervalID);
cameraIntervalID = null;
}
} else {
log('camera client: unkown action.');
}
});
BerryNet-upstream-3.9.0/client.js 0000664 0000000 0000000 00000000577 13606321373 0016744 0 ustar 00root root 0000000 0000000 var mqtt = require('mqtt');
//var client = mqtt.connect('mqtt://test.mosquitto.org');
var client = mqtt.connect('mqtt://localhost:1883');
client.on('connect', function () {
client.subscribe('presence');
client.publish('presence', 'Hello mqtt');
});
client.on('message', function (topic, message) {
// message is Buffer
console.log(message.toString());
client.end();
});
BerryNet-upstream-3.9.0/config.js 0000664 0000000 0000000 00000004701 13606321373 0016724 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 .
'use strict';
const path = require('path');
let config = {};
// system configs
config.projectDir = __dirname;
config.inferenceEngine = 'detector'; // {classifier, detector}
// gateway configs
function padTopicBase(topic) {
return path.join(config.topicBase, topic);
}
config.snapshot = path.join(
config.projectDir,
'dashboard/www/freeboard/snapshot.jpg');
config.brokerHost = 'mqtt://localhost';
config.topicBase = 'berrynet';
config.topicActionLog = padTopicBase('action/log');
config.topicActionInference = padTopicBase('action/inference');
config.topicEventCamera = padTopicBase('event/camera');
config.topicEventLocalImage = padTopicBase('event/localImage');
config.topicNotifyEmail = padTopicBase('notify/email');
config.topicNotifySMS = padTopicBase('notify/sms');
config.topicNotifyLINE = padTopicBase('notify/line');
config.topicDashboardLog = padTopicBase('dashboard/log');
config.topicDashboardSnapshot = padTopicBase('dashboard/snapshot');
config.topicDashboardInferenceResult = padTopicBase('dashboard/inferenceResult');
config.topicJSONInferenceResult = padTopicBase('data/jsonInferenceResult');
// IP camera
config.ipcameraSnapshot = '';
// Board camera, e.g. USB and RPi cameras
config.boardcameraImageWidth = 640;
config.boardcameraImageHeight = 480;
// data collector configs
config.storageDirPath = '';
// email notification
config.senderEmail = '';
config.senderPassword = '';
config.receiverEmail = '';
// for compatibility
config.sender_email = config.senderEmail;
config.sender_password = config.senderPassword;
config.receiver_email = config.receiverEmail;
// Authentication and channel information for LINE
config.LINETargetUserID = '';
config.LINEChannelSecret = '';
config.LINEChannelAccessToken = '';
// make config importable
module.exports = config;
BerryNet-upstream-3.9.0/config/ 0000775 0000000 0000000 00000000000 13606321373 0016364 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/bcm2835-v4l2.conf 0000664 0000000 0000000 00000000112 13606321373 0021075 0 ustar 00root root 0000000 0000000 # BerryNet supports accessing RPi camera access via OpenCV.
bcm2835_v4l2
BerryNet-upstream-3.9.0/config/berrynet-bionic.list 0000664 0000000 0000000 00000000226 13606321373 0022354 0 ustar 00root root 0000000 0000000 # For details, please refer to https://github.com/DT42/BerryNet-repo
deb http://repo.berrynet.org/ bionic/
deb-src http://repo.berrynet.org/ bionic/
BerryNet-upstream-3.9.0/config/berrynet-buster.list 0000664 0000000 0000000 00000000226 13606321373 0022415 0 ustar 00root root 0000000 0000000 # For details, please refer to https://github.com/DT42/BerryNet-repo
deb http://repo.berrynet.org/ buster/
deb-src http://repo.berrynet.org/ buster/
BerryNet-upstream-3.9.0/config/berrynet-xenial.list 0000664 0000000 0000000 00000000226 13606321373 0022371 0 ustar 00root root 0000000 0000000 # For details, please refer to https://github.com/DT42/BerryNet-repo
deb http://repo.berrynet.org/ xenial/
deb-src http://repo.berrynet.org/ xenial/
BerryNet-upstream-3.9.0/config/berrynet.list 0000664 0000000 0000000 00000000230 13606321373 0021106 0 ustar 00root root 0000000 0000000 # For details, please refer to https://github.com/DT42/BerryNet-repo
deb http://repo.berrynet.org/ stretch/
deb-src http://repo.berrynet.org/ stretch/
BerryNet-upstream-3.9.0/config/dashboard-darknet-official.json 0000664 0000000 0000000 00000002544 13606321373 0024413 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Detection Result\"][\"msg\"][\"bytes\"]\ns = \"\"\nreturn s",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Detection Result\"][\"msg\"][\"annotations\"].map(function(r) {\n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s\n",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Detection Result",
"type": "paho_mqtt",
"settings": {
"server": "localhost",
"port": 3000,
"use_ssl": false,
"client_id": "freeboard_darknetres",
"username": "",
"password": "",
"topic": "berrynet/engine/darknet/result",
"json_data": true,
"name": "Detection Result"
}
}
],
"columns": 3
} BerryNet-upstream-3.9.0/config/dashboard-darknet.json 0000664 0000000 0000000 00000002577 13606321373 0022647 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Detection Result\"][\"msg\"][\"bytes\"]\ns = \"\"\nreturn s\n\n",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Detection Result\"][\"msg\"][\"annotations\"].map(function(r) { \n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Detection Result",
"type": "paho_mqtt",
"settings": {
"server": "localhost",
"port": 3000,
"use_ssl": false,
"client_id": "freeboard_darknetres",
"topic": "berrynet/engine/darknet/result",
"json_data": true,
"username": "",
"password": "",
"name": "Detection Result"
}
}
],
"columns": 3
}
BerryNet-upstream-3.9.0/config/dashboard-movidius.json 0000664 0000000 0000000 00000002530 13606321373 0023043 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Detection Result\"][\"berrynet/engine/mvmobilenetssd/result\"][\"bytes\"]\ns = \"\"\nreturn s\n\n",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Detection Result\"][\"berrynet/engine/mvmobilenetssd/result\"][\"annotations\"].map(function(r) { \n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Detection Result",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_darknetres",
"topics": "berrynet/engine/mvmobilenetssd/result"
}
}
],
"columns": 3
} BerryNet-upstream-3.9.0/config/dashboard-mqtt.json 0000664 0000000 0000000 00000005166 13606321373 0022201 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "picture",
"settings": {
"src": "\n\n\n",
"refresh": 30
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "datasources[\"Inference Result\"][\"berrynet/dashboard/inferenceResult\"]",
"height": 4
}
}
]
},
{
"title": "MQTT Client Logs",
"width": 1,
"row": {
"2": 11,
"3": 11
},
"col": {
"2": 1,
"3": 1
},
"col_width": "2",
"widgets": [
{
"type": "html",
"settings": {
"html": "datasources[\"MQTT Client Logs\"][\"berrynet/dashboard/log\"]",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Inference Result",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_ifresult",
"topics": "berrynet/dashboard/inferenceResult",
"name": "Inference Result"
}
},
{
"name": "MQTT Client Logs",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_aaaaalog",
"topics": "berrynet/dashboard/log",
"name": "MQTT Client Logs"
}
}
],
"columns": 3
} BerryNet-upstream-3.9.0/config/dashboard-ovclassifier.json 0000664 0000000 0000000 00000002610 13606321373 0023674 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Classifier Result\"][\"msg\"][\"bytes\"]\ns = \"\"\nreturn s\n\n",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Classifier Result\"][\"msg\"][\"annotations\"].map(function(r) { \n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Classifier Result",
"type": "paho_mqtt",
"settings": {
"server": "localhost",
"port": 3000,
"use_ssl": false,
"client_id": "freeboard_darknetres",
"topic": "berrynet/engine/ovclassifier/result",
"json_data": true,
"username": "",
"password": "",
"name": "Classifier Result"
}
}
],
"columns": 3
}
BerryNet-upstream-3.9.0/config/dashboard-ovdetector.json 0000664 0000000 0000000 00000002576 13606321373 0023374 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Detector Result\"][\"msg\"][\"bytes\"]\ns = \"\"\nreturn s\n\n",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Detector Result\"][\"msg\"][\"annotations\"].map(function(r) { \n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Detector Result",
"type": "paho_mqtt",
"settings": {
"server": "localhost",
"port": 3000,
"use_ssl": false,
"client_id": "freeboard_darknetres",
"topic": "berrynet/engine/ovdetector/result",
"json_data": true,
"username": "",
"password": "",
"name": "Detector Result"
}
}
],
"columns": 3
}
BerryNet-upstream-3.9.0/config/dashboard-tflitedetector.json 0000664 0000000 0000000 00000002606 13606321373 0024231 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"2": 1,
"3": 1,
"4": 1
},
"col": {
"2": 2,
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "html",
"settings": {
"html": "imgstr = datasources[\"Detection Result\"][\"msg\"][\"bytes\"]\ns = \"\"\nreturn s\n\n",
"height": 4
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"2": 1,
"3": 1
},
"col": {
"2": 1,
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "s = datasources[\"Detection Result\"][\"msg\"][\"annotations\"].map(function(r) { \n return (r[\"label\"] + \": \" + r[\"confidence\"] + \" \")\n}).join().replace(/,/g, \"\")\nconsole.log(s)\nreturn s",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Detection Result",
"type": "paho_mqtt",
"settings": {
"server": "localhost",
"port": 3000,
"use_ssl": false,
"client_id": "freeboard_darknetres",
"topic": "berrynet/engine/tflitedetector/result",
"json_data": true,
"username": "",
"password": "",
"name": "Detection Result"
}
}
],
"columns": 3
}
BerryNet-upstream-3.9.0/config/dashboard.json 0000664 0000000 0000000 00000003447 13606321373 0021216 0 ustar 00root root 0000000 0000000 {
"version": 1,
"allow_edit": true,
"plugins": [],
"panes": [
{
"title": "Camera Snapshot",
"width": 1,
"row": {
"3": 1,
"4": 1
},
"col": {
"3": 1,
"4": 3
},
"col_width": "1",
"widgets": [
{
"type": "picture",
"settings": {
"src": "datasources[\"Camera Snapshot\"][\"berrynet/dashboard/snapshot\"]",
"refresh": 30
}
}
]
},
{
"title": "Inference Result",
"width": 1,
"row": {
"3": 1
},
"col": {
"3": 2
},
"col_width": 1,
"widgets": [
{
"type": "html",
"settings": {
"html": "datasources[\"Inference Result\"][\"berrynet/dashboard/inferenceResult\"]",
"height": 4
}
}
]
},
{
"title": "MQTT Client Logs",
"width": 1,
"row": {
"3": 11
},
"col": {
"3": 1
},
"col_width": "2",
"widgets": [
{
"type": "html",
"settings": {
"html": "datasources[\"MQTT Client Logs\"][\"berrynet/dashboard/log\"]",
"height": 4
}
}
]
}
],
"datasources": [
{
"name": "Camera Snapshot",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_snapshot",
"topics": "berrynet/dashboard/snapshot",
"name": "Camera Snapshot"
}
},
{
"name": "Inference Result",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_ifresult",
"topics": "berrynet/dashboard/inferenceResult",
"name": "Inference Result"
}
},
{
"name": "MQTT Client Logs",
"type": "paho_mqtt_js",
"settings": {
"server": "localhost",
"port": 3000,
"client_id": "freeboard_aaaaalog",
"topics": "berrynet/dashboard/log",
"name": "MQTT Client Logs"
}
}
],
"columns": 3
}
BerryNet-upstream-3.9.0/config/etc/ 0000775 0000000 0000000 00000000000 13606321373 0017137 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/etc/mosquitto/ 0000775 0000000 0000000 00000000000 13606321373 0021203 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/etc/mosquitto/conf.d/ 0000775 0000000 0000000 00000000000 13606321373 0022352 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/etc/mosquitto/conf.d/berrynet.conf 0000664 0000000 0000000 00000000167 13606321373 0025057 0 ustar 00root root 0000000 0000000 # Listen on port 1883 and websockets
# default listener
port 1883
# extra listener
listener 3000
protocol websockets
BerryNet-upstream-3.9.0/config/intel-openvino-2019.list 0000664 0000000 0000000 00000000070 13606321373 0022615 0 ustar 00root root 0000000 0000000 deb https://apt.repos.intel.com/openvino/2019/ all main
BerryNet-upstream-3.9.0/config/supervisor/ 0000775 0000000 0000000 00000000000 13606321373 0020605 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/supervisor/conf.d/ 0000775 0000000 0000000 00000000000 13606321373 0021754 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/config/supervisor/conf.d/berrynet-darknet.conf 0000664 0000000 0000000 00000001213 13606321373 0026100 0 ustar 00root root 0000000 0000000 [program:darknet-service]
command=python3 /usr/lib/python3/dist-packages/berrynet/service/darknet_service.py --model_package tinyyolovoc --service_name detector --draw --debug
stdout_logfile=/var/log/berrynet/darknet-service-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/darknet-service-stderr.log
stderr_logfile_maxbytes=1048576
priority=10
[program:camera]
command=python3 /usr/lib/python3/dist-packages/berrynet/client/camera.py --fps 0.5
stdout_logfile=/var/log/berrynet/camera-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/camera-stderr.log
stderr_logfile_maxbytes=1048576
priority=30
BerryNet-upstream-3.9.0/config/supervisor/conf.d/berrynet-movidius.conf 0000664 0000000 0000000 00000001723 13606321373 0026315 0 ustar 00root root 0000000 0000000 [program:movidius-service]
command=su -c "python3 /home/pi/codes/BerryNet/berrynet/service/movidius_service.py --service_name MobileNetSSD --model_package mobilenet-ssd-movidius-1.0.0 --debug" pi
stdout_logfile=/var/log/berrynet/movidius-service-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/movidius-service-stderr.log
stderr_logfile_maxbytes=1048576
priority=10
[program:fbdashboard]
command=su -c "DISPLAY=:0 python3 /home/pi/codes/BerryNet/berrynet/client/fbdashboard.py" pi
stdout_logfile=/var/log/berrynet/fbdashboard-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/fbdashboard-stderr.log
stderr_logfile_maxbytes=1048576
priority=30
[program:camera]
command=su -c "python3 /home/pi/codes/BerryNet/berrynet/client/camera.py --fps 7" pi
stdout_logfile=/var/log/berrynet/camera-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/camera-stderr.log
stderr_logfile_maxbytes=1048576
priority=40
BerryNet-upstream-3.9.0/config/supervisor/conf.d/berrynet-tflite.conf 0000664 0000000 0000000 00000001113 13606321373 0025736 0 ustar 00root root 0000000 0000000 [program:tflite-service]
command=bn_tflite --service detector --service_name tflitedetector --model_package mobilenet-ssd-coco-tflite-2.0.0 --num_threads 4 --draw --debug
stdout_logfile=/var/log/berrynet/tflite-service-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/tflite-service-stderr.log
stderr_logfile_maxbytes=1048576
priority=10
[program:camera]
command=bn_camera --fps 6
stdout_logfile=/var/log/berrynet/camera-stdout.log
stdout_logfile_maxbytes=1048576
stderr_logfile=/var/log/berrynet/camera-stderr.log
stderr_logfile_maxbytes=1048576
priority=30
BerryNet-upstream-3.9.0/configure 0000775 0000000 0000000 00000025330 13606321373 0017031 0 ustar 00root root 0000000 0000000 #!/bin/bash
#
# 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 .
# One-click IoT gateway deployment script.
set -e
LOG="$PWD/berrynet-install.log"
DISTRIBUTIONID=`lsb_release -i -s`
CODENAME=`lsb_release -c -s`
INSTALL_TENSORFLOW="Y"
INSTALL_OPENVINO="N"
INSTALL_DARKNET="N"
INSTALL_CAFFE2="N"
install_opencv() {
# OpenCV sources
# x86 Ubuntu 16.04: PPA
# Raspbian: BerryNet repo
if [ "$DISTRIBUTIONID" = "Ubuntu" ]; then
sudo add-apt-repository -yu ppa:timsc/opencv-3.4
fi
sudo apt install -y python3-opencv
}
install_nodejs() {
# v10.x is LTS, if you want the latest feature, change to "setup_11.x".
curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -
sudo apt install -y nodejs
}
install_system_dependencies() {
sudo apt update
if [ "$CODENAME" = "buster" ]; then
sudo apt install -y \
supervisor
else
sudo apt install -y \
apache2 \
curl \
dirmngr \
fswebcam \
git \
imagemagick \
libkrb5-dev \
libyaml-dev \
libzmq3-dev \
lsb-release \
mosquitto \
mosquitto-clients \
python3-dev \
python3-opengl \
python3-pip \
supervisor \
wget
sudo -H pip3 install --timeout 60 cython
sudo -H pip3 install --timeout 60 logzero
sudo -H pip3 install --timeout 60 paho-mqtt
sudo -H pip3 install --timeout 60 PyOpenGL
sudo -H pip3 install --timeout 60 PyOpenGL-accelerate
sudo -H pip3 install --timeout 60 watchdog
install_opencv
install_nodejs
fi
}
install_berrynet_repository() {
curl -sL https://raw.githubusercontent.com/DT42/BerryNet-repo/master/setup.sh | sudo -E bash -
}
install_configs() {
# supervisor
sudo cp config/supervisor/conf.d/berrynet-tflite.conf /etc/supervisor/conf.d/
sudo mkdir -p /var/log/berrynet
# mosquitto
sudo cp config/etc/mosquitto/conf.d/berrynet.conf /etc/mosquitto/conf.d
if [ "$DISTRIBUTIONID" = "Raspbian" ]; then
# Make RPi camera to show at /dev/videoN
sudo cp config/bcm2835-v4l2.conf /etc/modules-load.d
fi
}
install_berrynet_py() {
sudo apt install -y berrynet
# FIXME: Freeboard dependency
#sudo npm install -g mime
}
install_berrynet_js() {
local working_dir="/usr/local/berrynet"
sudo mkdir -p $working_dir
sudo cp -a \
broker.js \
camera.js \
config.js \
data_collector.js \
inference \
journal.js \
localimg.js \
mail.js \
line.js \
package.json \
$working_dir
sudo cp berrynet-manager /usr/local/bin
# FIXME: image dir should be created by program
mkdir -p /usr/local/berrynet/inference/image
# install npm dependencies
pushd $working_dir > /dev/null
sudo npm install --unsafe-perm
popd > /dev/null
}
install_model() {
local model_name="$1"
echo "Install model $model_name"
sudo apt install -y $model_name
}
download_classifier_model_caffe2() {
sh ./utils/install-caffe2-models.sh
}
install_tensorflow() {
if [ "$DISTRIBUTIONID" = "Ubuntu" ]; then
sudo -H pip3 install tensorflow
elif [ "$DISTRIBUTIONID" = "Raspbian" ]; then
case $CODENAME in
"buster")
# Unsatisfied dependencies in Buster, and wheels will be installed
# python3-tensorboard none
# python3-wrapt 1.10.11-1+b1
# python3-gast none
# python3-tensorflow-estimator none
# python3-astor 0.5-1
# python3-absl-py none
# python3-google-pasta none
# python3-setuptools 40.8.0-1
sudo apt install -y \
python3-numpy \
python3-termcolor \
python3-six \
python3-keras-applications \
python3-protobuf \
python3-keras-preprocessing \
python3-wheel \
python3-grpcio \
python3-werkzeug \
python3-markdown \
python3-h5py
TENSORFLOW_PKGNAME="tensorflow-1.15.0-cp37-cp37m-linux_armv7l.whl"
if [ ! -e "/tmp/$TENSORFLOW_PKGNAME" ]; then
wget -O /tmp/$TENSORFLOW_PKGNAME \
https://github.com/PINTO0309/Tensorflow-bin/raw/master/$TENSORFLOW_PKGNAME
else
echo "$TENSORFLOW_PKGNAME has existed, skip to download it."
fi
sudo -H pip3 install /tmp/$TENSORFLOW_PKGNAME
;;
"stretch")
TENSORFLOW_PKGNAME="tensorflow-1.15.0-cp35-cp35m-linux_armv7l.whl"
if [ ! -e "/tmp/$TENSORFLOW_PKGNAME" ]; then
wget -O /tmp/$TENSORFLOW_PKGNAME \
https://github.com/PINTO0309/Tensorflow-bin/raw/master/$TENSORFLOW_PKGNAME
else
echo "$TENSORFLOW_PKGNAME has existed, skip to download it."
fi
sudo -H pip3 install /tmp/$TENSORFLOW_PKGNAME
;;
*)
echo "ERROR: Fail to install TensorFlow, unknown Raspbian codename $CODENAME"
exit 1
esac
fi
}
install_openvino_repository() {
echo 'Adding the OpenVINO GPG key to your keyring...'
curl -sL https://apt.repos.intel.com/openvino/2019/GPG-PUB-KEY-INTEL-OPENVINO-2019 | sudo apt-key add -
sudo curl -sL -o /etc/apt/sources.list.d/intel-openvino-2019.list \
https://raw.githubusercontent.com/DT42/BerryNet/master/config/intel-openvino-2019.list
sudo apt update
}
install_openvino() {
# OpenVINO 2019R2.1
if [ "$DISTRIBUTIONID" = "Ubuntu" ]; then
if [ "$CODENAME" = "xenial" ]; then
sudo apt install intel-openvino-runtime-ubuntu16-2019.2.242
elif [ "$CODENAMe" = "bionic" ]; then
sudo apt install intel-openvino-runtime-ubuntu18-2019.2.242
else
echo "ERROR: OpenVINO on Ubuntu $CODENAME is not supported by Intel."
fi
elif [ "$DISTRIBUTIONID" = "Raspbian" ]; then
sudo apt install -y openvino-rpi
fi
}
install_darknet () {
# For x86, we use pure Darknet.
# For Raspberry Pi, we use pure Darknet + NNPACK.
#
# To understand how Darknet NNPACK packages are built, please refer to
# https://github.com/DT42/BerryNet/blob/master/doc/darknet-nnpack.md
sudo apt install -y darknet libdarknet
}
#install_darknet() {
# local peachpy_version="0.0.1"
# local confu_version="cc90068"
# local ninja_version="v1.8.2"
# local nnpack_darknet_version="1ecda10"
# local darknet_nnpack_version="fa5bddc"
#
# # build dependencies
# pip3 install --user PeachPy==$peachpy_version
# pip3 install --user git+https://github.com/Maratyszcza/confu@$confu_version
#
# pushd /tmp > /dev/null
# git clone https://github.com/ninja-build/ninja.git
# pushd ninja > /dev/null
# git reset --hard $ninja_version
# ./configure.py --bootstrap
# popd > /dev/null
# popd > /dev/null
#
# sudo apt install -y clang
#
# pushd /tmp > /dev/null
# git clone https://github.com/thomaspark-pkj/NNPACK-darknet.git
# pushd NNPACK-darknet > /dev/null
# git reset --hard $nnpack_darknet_version
# $HOME/.local/bin/confu setup
# python ./configure.py --backend auto
# /tmp/ninja/ninja
# sudo cp lib/{libgoogletest-core.a,libnnpack.a,libpthreadpool.a} /usr/lib/
# sudo cp include/nnpack.h /usr/include/
# sudo cp deps/pthreadpool/include/pthreadpool.h /usr/include/
# popd > /dev/null
# popd > /dev/null
#
# # build detection backend (darknet)
# pushd inference > /dev/null
# git clone https://github.com/thomaspark-pkj/darknet-nnpack.git darknet
# pushd darknet > /dev/null
# git reset --hard $darknet_nnpack_version
# patch -p 1 < ../../patch/01-detection-backend.patch
# make -j
# popd > /dev/null
# popd > /dev/null
#
# cp utils/darknet/detectord.py inference/darknet
# mkdir inference/darknet/utils
# cp utils/darknet/utils/localrun.sh inference/darknet/utils
#}
install_caffe2() {
if [ "$DISTRIBUTIONID" = "Ubuntu" ]; then
sh ./utils/install-caffe2-ubuntu.sh
elif [ "$DISTRIBUTIONID" = "Raspbian" ]; then
sh ./utils/install-caffe2-raspbian.sh
fi
}
update_system_service() {
sudo service mosquitto restart
sudo supervisorctl reload
sudo a2enconf berrynet-dashboard
}
install_system_dependencies 2>&1 | tee -a $LOG
install_berrynet_repository 2>&1 | tee -a $LOG
install_berrynet_py 2>&1 | tee -a $LOG
install_configs 2>&1 | tee -a $LOG
# FIXME: Raspbian 20190408 breaks some dependencies of BerryNet JS.
# Disable temporaily before this issue is fixed.
#install_berrynet_js 2>&1 | tee -a $LOG
if [ "$INSTALL_TENSORFLOW" = "Y" ]; then
install_tensorflow 2>&1 | tee -a $LOG
#install_model inception
install_model mobilenet-1.0-224-quantized-tflite 2>&1 | tee -a $LOG
install_model mobilenet-ssd-coco-tflite 2>&1 | tee -a $LOG
else
echo "Not install TensorFlow" >> $LOG
fi
if [ "$INSTALL_OPENVINO" = "Y" ]; then
install_openvino_repository 2>&1 | tee -a $LOG
install_openvino 2>&1 | tee -a $LOG
install_model mobilenet-1.0-224-fp16-openvino 2>&1 | tee -a $LOG
install_model mobilenet-ssd-openvino 2>&1 | tee -a $LOG
else
echo "Not install OpenVINO" >> $LOG
fi
if [ "$INSTALL_DARKNET" = "Y" ]; then
install_darknet 2>&1 | tee -a $LOG
install_model tinyyolovoc 2>&1 | tee -a $LOG
else
echo "Not install Darknet NNPACK" >> $LOG
fi
if [ "$INSTALL_CAFFE2" = "Y" ]; then
install_caffe2 2>&1 | tee -a $LOG
download_classifier_model_caffe2 2>&1 | tee -a $LOG
else
echo "Not install Caffe2" >> $LOG
fi
update_system_service 2>&1 | tee -a $LOG
{
echo "Installation is completed successfully!"
echo ""
echo "If there is any issue, please open an issue on"
echo ""
echo " https://github.com/DT42/BerryNet/issues"
echo ""
echo "and attach these logs:"
echo ""
echo " $LOG"
echo " /usr/local/berrynet/npm-debug.log"
echo ""
echo "We are happy to check and fix it, thank you!"
} | tee -a $LOG
BerryNet-upstream-3.9.0/data_collector.js 0000664 0000000 0000000 00000007465 13606321373 0020450 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 .
'use strict';
const fs = require('fs');
const mqtt = require('mqtt');
const path = require('path');
const config = require('./config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicActionInference = config.topicActionInference;
const topicDashboardSnapshot = config.topicDashboardSnapshot;
const topicJSONInferenceResult = config.topicJSONInferenceResult;
const storageDirPath = config.storageDirPath;
/**
* Log wrapper to publish log message via MQTT and display on console.
* @param {string} m Log message.
*/
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
/**
* Save published MQTT binary data as an image file.
* @param {object} b The binary data published via MQTT.
* @param {string} filepath The file path of the saved image.
*/
function saveBufferToImage(b, filepath) {
fs.writeFile(filepath, b, (e) => {
if (e)
log(`log client: cannot save buffer to image.`);
else
log(`log client: saved buffer to image successfully.`);
});
}
/**
* Get time string in ISO format.
* @return {string} Time string.
*/
function getTimeString() {
const d = new Date();
return d.toISOString();
}
/**
* Save snapshot, detection image, and detection JSON to data directory.
* @param {string} topic Subscribed MQTT topic.
* @param {object} message Snapshot binary | 'snapshot.jpg' | detection JSON.
*/
function callbackSaveData(topic, message) {
if (topic == topicActionInference) {
console.log('Get ' + topicActionInference);
// NOTE: topicActionInference always happens prior other topics.
callbackSaveData.timeString = getTimeString();
console.log(callbackSaveData.timeString);
const snapshotImage = path.join(
storageDirPath,
callbackSaveData.timeString + '.jpg');
saveBufferToImage(message, snapshotImage);
} else if (topic == topicDashboardSnapshot) {
console.log('Get ' + topicDashboardSnapshot);
const detectionImage = path.join(
storageDirPath,
callbackSaveData.timeString + '-detection.jpg');
/*
fs.readFile(config.snapshot, (err, data) => {
fs.writeFile(detectionImage, data, (e) => {
if (e)
log('Failed to save detection image.');
});
});
*/
fs.createReadStream(config.snapshot)
.pipe(fs.createWriteStream(detectionImage));
} else if (topic == topicJSONInferenceResult) {
console.log('Get ' + topicJSONInferenceResult);
const detectionJSON = path.join(
storageDirPath,
callbackSaveData.timeString + '-detection.json');
fs.writeFile(detectionJSON, message, (e) => {
if (e)
log('Failed to save detection JSON.');
});
} else {
console.log('Unsubscribed topic ' + topic);
}
}
fs.mkdir(storageDirPath, (e) => {
if (e)
log('Failed to create data storage dir.');
});
client.on('connect', () => {
client.subscribe(topicActionLog);
client.subscribe(topicActionInference);
client.subscribe(topicDashboardSnapshot);
client.subscribe(topicJSONInferenceResult);
log(`log client: connected to ${broker} successfully.`);
});
client.on('message', callbackSaveData);
BerryNet-upstream-3.9.0/doc/ 0000775 0000000 0000000 00000000000 13606321373 0015664 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/doc/STYLE_GUIDE.md 0000664 0000000 0000000 00000000121 13606321373 0020015 0 ustar 00root root 0000000 0000000 [We follow the Node.js Style Guide](https://github.com/felixge/node-style-guide)
BerryNet-upstream-3.9.0/doc/darknet-nnpack.md 0000664 0000000 0000000 00000004105 13606321373 0021106 0 ustar 00root root 0000000 0000000 # Get darknet-nnpack and NNPack-darknet
Sources
* https://github.com/digitalbrain79/darknet-nnpack
* https://github.com/digitalbrain79/NNPACK-darknet
* When running ninja and make on RPi3, add `-j 3` parameter to prevent system from being blocked.
# Link NNPack and pthreadpool libraries
Because NNPACK-darknet only generates static libraries (.a).
1. Modify build.ninja and add -fPIC (and -shared?) to cflags, still only create .a.
1. But it's okay because the key is `-fPIC`. Compiler can combine
static libraries and object files if static libraries contain
PIC object files.
1. [Patch](https://github.com/DT42/darknet-nnpack/blob/debian/debian/patches/01-use-nnpack-rpi3.patch) darknet-nnpack source and Makefile.
* I patched C code directly because the Darknet library users will have consistent API, and do not need to initialize NNPack and pthreadpool by themselves.
* You can initialize NNPack and pthreadpool in Python ([example](https://github.com/NTUEE-ESLab/2017Fall-IntelligentSecurityGuard/blob/master/replace/darknet.py#L110) to initialize NNPack only).
* Simpler than adding `-Wl,--whole-archive /usr/lib/libnnpack.a /usr/lib/libpthreadpool.a -Wl,--no-whole-archive`
to darknet-nnpack Makefile in NNPACK section.
References
* Issue discussion
* https://github.com/digitalbrain79/darknet-nnpack/issues/17
* https://github.com/Maratyszcza/NNPACK/issues/70
* Link .a to .so
* https://stackoverflow.com/questions/2649735/how-to-link-static-library-into-dynamic-library-in-gcc
* ftp://ftp.gnu.org/old-gnu/Manuals/ld-2.9.1/html_node/ld_3.html
# Verification
Run [darknet_npinput_rpi.py](https://github.com/DT42/darknet-numpy/blob/master/darknet_npinput_rpi.py) which loads libdarknet and model from system filepaths.
# Further Optimization
Sources
* https://github.com/shizukachan/darknet-nnpack
* https://github.com/shizukachan/NNPACK
* Known issues: https://gitlab.com/bafu/nnpack-bin/issues/1
# Upstream NNPACK (drop)
Steps
1. $ pip3 install --user --upgrade confu # update from v0.0.1 to v0.0.3, use pip3 instead of pip
1. $ sudo apt install clog
BerryNet-upstream-3.9.0/doc/ipcam.md 0000664 0000000 0000000 00000001767 13606321373 0017312 0 ustar 00root root 0000000 0000000 # Setup General Purpose IP Camera
Steps to connect your IP camera to BerryNet:
1. Get snapshot URL of the IP camera.
The [camera connection database](https://www.ispyconnect.com/sources.aspx) can generate snapshot URL if your IP camera is supported.
The key is that the IP camera supports retrieving snapshots via HTTP. For example, my IP camera provides the interface to retrieve snapshot:
```
http:///cgi-bin/encoder?USER=&PWD=&Channel=1&SNAPSHOT
```
1. Configure BerryNet
1. Edit `config.ipcameraSnapshot` in `/usr/local/berrynet/config.js`.
2. Restart BerryNet.
# Get Nest Camera's Snapshot URL
Follow the [quick start guide](https://codelabs.developers.google.com/codelabs/wwn-api-quickstart/#0) to get snapshot URL. The high-level steps are:
1. Create a "product" (the concept is like a project, we will use it for personal usage).
1. Get access token from Nest cloud service.
1. Get snapshot URL with the access token Nest cloud service.
BerryNet-upstream-3.9.0/docker/ 0000775 0000000 0000000 00000000000 13606321373 0016366 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/docker/Dockerfile 0000664 0000000 0000000 00000001132 13606321373 0020355 0 ustar 00root root 0000000 0000000 # We start from Debian Stretch. Can be rebase to Raspbian because they are
# similar
FROM debian:stretch
LABEL maintainer="dev@dt42.io"
LABEL project="Berrynet"
LABEL version="3.5.1"
# Update apt
RUN apt-get update
# Install dependencies
RUN apt-get install -y git sudo wget lsb-release software-properties-common
# Install build-essential
RUN apt-get install -y build-essential
# Install systemd
RUN apt-get install -y systemd systemd-sysv
# Install python
RUN apt-get install -y python python3
# Install BerryNet
RUN git clone https://github.com/DT42/BerryNet.git
RUN cd BerryNet; ./configure
BerryNet-upstream-3.9.0/examples/ 0000775 0000000 0000000 00000000000 13606321373 0016735 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/examples/run-darknet-detector.sh 0000664 0000000 0000000 00000004423 13606321373 0023335 0 ustar 00root root 0000000 0000000 #!/bin/bash
#
# 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 .
# Example: Run Darknet detector.
set -e
SCRIPT_NAME=$0
COMMAND=$1
usage() {
echo -e "Usage:\n\t$ bash $SCRIPT_NAME start|stop"
exit 1
}
kill_script() {
local script_name=$1
local ext=${script_name##*.}
local interpreter=""
if [ "$ext" == "py" ]; then
interpreter="python3"
elif [ "$ext" == "js" ]; then
interpreter="nodejs"
else
interpreter="bash"
fi
local process_id=$(ps aux | grep $script_name | grep $interpreter | awk '{print $2}')
echo "Terminate $script_name (pid $process_id)"
kill -9 $process_id
}
example_start() {
# Run Freeboard
echo -n "Run Dashboard..."
pushd /usr/lib/berrynet/dashboard > /dev/null
nodejs server.js >> /tmp/berrynet-example.log &
popd > /dev/null
echo "done"
# Run Darknet detector
echo -n "Run Darknet detector..."
sleep 1
python3 /usr/lib/python3/dist-packages/berrynet/service/darknet_service.py \
--service_name detector \
--model_package tinyyolovoc-20170816 \
--draw \
--debug >> /tmp/berrynet-example.log 2>&1 &
echo "done"
# Run camera client
echo -n "Run Camera..."
sleep 1
python3 /usr/lib/python3/dist-packages/berrynet/client/camera.py \
--fps 0.5 >> /tmp/berrynet-example.log 2>&1 &
echo "done"
}
example_stop() {
kill_script "camera.py"
kill_script "darknet_service.py"
kill_script "server.js"
}
main() {
local cmd=$1
if [ "$cmd" == "start" ]; then
example_start
elif [ "$cmd" == "stop" ]; then
example_stop
else
usage
fi
}
main $COMMAND
BerryNet-upstream-3.9.0/examples/run-openvino-classifier.sh 0000664 0000000 0000000 00000004562 13606321373 0024061 0 ustar 00root root 0000000 0000000 #!/bin/bash
#
# 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 .
# Example: Run OpenVINO classifier on general x86 notebook.
#
# For more details, please refer to Medium XXX.
set -e
SCRIPT_NAME=$0
COMMAND=$1
usage() {
echo -e "Usage:\n\t$ bash $SCRIPT_NAME start|stop"
exit 1
}
kill_script() {
local script_name=$1
local process_id=$(ps aux | grep $script_name | grep Sl | awk '{print $2}')
echo "Terminate $script_name (pid $process_id)"
kill -9 $process_id
}
example_start() {
# Run Freeboard
echo -n "Run Dashboard..."
pushd /usr/lib/berrynet/dashboard > /dev/null
nodejs server.js >> /tmp/berrynet-example.log &
popd > /dev/null
echo "done"
# Run OpenVINO classifier
echo -n "Run OpenVINO classifier..."
MODELPKG_DIRPATH="/usr/share/dlmodels/mobilenet-1.0-224-openvino-1"
source /opt/intel/computer_vision_sdk_2018.5.445/bin/setupvars.sh
sleep 1
python3 /usr/lib/python3/dist-packages/berrynet/service/openvino_service.py \
--model $MODELPKG_DIRPATH/mobilenet_v1_1.0_224_frozen.xml \
--label $MODELPKG_DIRPATH/imagenet_slim_labels.txt \
--service_name ovclassifier \
--num_top_predictions 3 \
--debug >> /tmp/berrynet-example.log 2>&1 &
echo "done"
# Run camera client
echo -n "Run Camera..."
sleep 1
python3 /usr/lib/python3/dist-packages/berrynet/client/camera.py \
--fps 5 >> /tmp/berrynet-example.log 2>&1 &
echo "done"
}
example_stop() {
kill_script "camera.py"
kill_script "openvino_service.py"
kill_script "server.js"
}
main() {
local cmd=$1
if [ "$cmd" == "start" ]; then
example_start
elif [ "$cmd" == "stop" ]; then
example_stop
else
usage
fi
}
main $COMMAND
BerryNet-upstream-3.9.0/inference/ 0000775 0000000 0000000 00000000000 13606321373 0017055 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/inference/agent.js 0000664 0000000 0000000 00000013144 13606321373 0020514 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 .
'use strict';
const fs = require('fs');
const path = require('path');
const moment = require('moment');
const mqtt = require('mqtt');
const im = require('imagemagick');
const config = require('../config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicActionInference = config.topicActionInference;
const topicDashboardSnapshot = config.topicDashboardSnapshot;
const topicDashboardInferenceResult = config.topicDashboardInferenceResult;
const topicJSONInferenceResult = config.topicJSONInferenceResult;
const topicNotifyLINE = config.topicNotifyLINE;
const inferenceEngine = config.inferenceEngine;
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
function saveBufferToImage(b, filepath) {
fs.writeFile(filepath, b, (e) => {
if (e)
log(`inference client: cannot save buffer to image.`);
else
log(`inference client: saved buffer to image ${filepath} successfully.`);
});
}
const parseDarknet = function(str) {
const elements = str.split(' ');
// label might consists of multiple words, e.g. cell phone.
const label = elements.slice(0, elements.length - 5).join(' ');
let [confidence, x, y, width, height] = elements.slice(elements.length - 5);
let result = {
label: label,
confidence: parseFloat(confidence),
top: parseInt(y),
bottom: parseInt(y) + parseInt(height),
left: parseInt(x),
right: parseInt(x) + parseInt(width)
};
return result
}
const darknetToJSON = function(data) {
let dataStrList = data.toString().replace(/\n$/, '').split('\n');
let jsonResult = [];
for (let i in dataStrList) {
let item = `${dataStrList[i]}`;
jsonResult.push(parseDarknet(item));
}
return jsonResult;
};
client.on('connect', () => {
client.subscribe(topicActionInference);
log(`inference client: connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
const size = m.length;
const now = moment().format('YYYYMMDD-HHmmss');
const inference_server_img_dir = __dirname + '/image';
const snapshot = `snapshot-${now}.jpg`;
const snapshot_path = path.join(inference_server_img_dir, snapshot);
const donefile_path = snapshot_path + '.done';
const resultfile_path = snapshot_path + '.txt';
const resultdonefile_path = snapshot_path + '.txt.done';
const dashboard_image_path = __dirname + '/../dashboard/www/freeboard/snapshot.jpg';
log(`inference client: on topic ${t}, received ${size} bytes.`);
// Save snapshot and create its done file. Classifier/detector will
// be triggered after snapshot done file is created.
saveBufferToImage(m, snapshot_path);
fs.closeSync(fs.openSync(donefile_path, 'w'));
log('Image done file ' + donefile_path + ' is ready.');
// Listen to classifier/detector's result done file. When result done
// file (.txt.done) is created, result is available.
var watcher = fs.watch(inference_server_img_dir, (eventType, filename) => {
if (eventType === 'rename') {
if (filename === (snapshot + '.txt.done')) {
fs.readFile(resultfile_path, (err, result) => {
if (err) throw err
watcher.close();
if (inferenceEngine === 'classifier') {
fs.writeFile(dashboard_image_path, m, (err, written, buffer) => {
console.log('Written snapshot to dashboard image directory: ' + dashboard_image_path);
client.publish(topicDashboardSnapshot, 'snapshot.jpg');
})
client.publish(topicDashboardInferenceResult,
result.toString().replace(/(\n)+/g, ' '));
} else if (inferenceEngine === 'detector') {
console.log('Snapshot is created by detector, only notify dashboard to update.');
client.publish(topicDashboardSnapshot, 'snapshot.jpg');
client.publish(topicDashboardInferenceResult,
result.toString().replace(/(\n)+/g, ' '));
client.publish(topicJSONInferenceResult,
JSON.stringify(darknetToJSON(result)));
// Delete intermediate files.
//
// Note: Data collector will not be affected. It retrieves data from
// * topicActionInference: contains snapshot raw data
// * topicDashboardSnapshot: to copy snapshot with bounding boxes
// * topicDashboardInferenceResult: contains inference result string
fs.unlink(snapshot_path, (e) => {});
fs.unlink(resultfile_path, (e) => {});
fs.unlink(resultdonefile_path, (e) => {});
} else {
console.log('Unknown owner ' + inferenceEngine);
}
client.publish(topicNotifyLINE, dashboard_image_path);
})
} else {
console.log('rename event for ' +
filename +
', but it is not inference result done file.');
}
} else {
console.log(eventType + ' event for ' + filename + ', ignore it.');
}
});
});
BerryNet-upstream-3.9.0/inference/classify_caffe2_server.py 0000664 0000000 0000000 00000016766 13606321373 0024060 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import os
import sys
import time
from caffe2.proto import caffe2_pb2
import numpy as np
import skimage.io
import skimage.transform
import threading
import multiprocessing
import Queue
import signal
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
from caffe2.python import core, workspace
import urllib2
image_dir = '/run/image_dir'
image_queue = Queue.Queue()
sess = None
threads = []
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def crop_center(img,cropx,cropy):
y,x,c = img.shape
startx = x//2-(cropx//2)
starty = y//2-(cropy//2)
return img[starty:starty+cropy,startx:startx+cropx]
def rescale(img, input_height, input_width):
aspect = img.shape[1]/float(img.shape[0])
if(aspect>1):
# landscape orientation - wide image
res = int(aspect * input_height)
imgScaled = skimage.transform.resize(img, (input_width, res))
if(aspect<1):
# portrait orientation - tall image
res = int(input_width/aspect)
imgScaled = skimage.transform.resize(img, (res, input_height))
if(aspect == 1):
imgScaled = skimage.transform.resize(img, (input_width, input_height))
return imgScaled
def server(labels):
"""Infinite loop serving inference requests"""
global image_queue, sess
CAFFE2_ROOT = "/caffe2"
CAFFE_MODELS = "/caffe2/caffe2/python/models"
MODEL = 'squeezenet', 'exec_net.pb', 'predict_net.pb', 'ilsvrc_2012_mean.npy', 227
codes = "https://gist.githubusercontent.com/aaronmarkham/cd3a6b6ac071eca6f7b4a6e40e6038aa/raw/9edb4038a37da6b5a44c3b5bc52e448ff09bfe5b/alexnet_codes"
logging(threading.current_thread().getName(), "is running")
CAFFE2_ROOT = os.path.expanduser(CAFFE2_ROOT)
CAFFE_MODELS = os.path.expanduser(CAFFE_MODELS)
MEAN_FILE = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[3])
if not os.path.exists(MEAN_FILE):
mean = 128
else:
mean = np.load(MEAN_FILE).mean(1).mean(1)
mean = mean[:, np.newaxis, np.newaxis]
INPUT_IMAGE_SIZE = MODEL[4]
INIT_NET = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[1])
PREDICT_NET = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[2])
while True:
input_name = image_queue.get()
img = skimage.img_as_float(skimage.io.imread(input_name)).astype(np.float32)
img = rescale(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
img = crop_center(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
img = img.swapaxes(1, 2).swapaxes(0, 1)
img = img[(2, 1, 0), :, :]
img = img * 255 - mean
img = img[np.newaxis, :, :, :].astype(np.float32)
with open(INIT_NET, 'rb') as f:
init_net = f.read()
with open(PREDICT_NET, 'rb') as f:
predict_net = f.read()
p = workspace.Predictor(init_net, predict_net)
# run the net and return prediction
results = p.run([img])
results = np.asarray(results)
results = np.delete(results, 1)
index = 0
highest = 0
arr = np.empty((0,2), dtype=object)
arr[:,0] = int(10)
arr[:,1:] = float(10)
for i, r in enumerate(results):
# imagenet index begins with 1!
i=i+1
arr = np.append(arr, np.array([[i,r]]), axis=0)
if (r > highest):
highest = r
index = i
response = urllib2.urlopen(codes)
output_name = input_name+'.txt'
output_done_name = output_name+'.done'
output = open(output_name, 'w')
for line in response:
code, result = line.partition(":")[::2]
if (code.strip() == str(index)):
human_string = result.strip()[1:-2]
score = highest
print("%s (score = %.5f)" % (human_string, score), file=output)
output.close()
touch(output_done_name)
logging(input_name, " classified!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
def main(_):
"""Called by Tensorflow"""
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in xrange(cpu_count/4):
threads.append(threading.Thread(target=server,
name='Server thread %d' % i,
args=({},)))
for t in threads: t.start()
for t in threads: t.join()
if __name__ == '__main__':
global sess, threads
pid = str(os.getpid())
pidfile = "/tmp/classify_server.pid"
if os.path.isfile(pidfile):
logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0:
# child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), path=image_dir)
observer.start()
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in xrange(cpu_count/4):
threads.append(threading.Thread(target=server,
name='Server thread %d' % i,
args=({},)))
for t in threads: t.start()
for t in threads: t.join()
else:
# parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/classify_caffe_server.py 0000664 0000000 0000000 00000022642 13606321373 0023764 0 ustar 00root root 0000000 0000000 #!/usr/bin/env 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import os
import sys
import time
import numpy as np
import threading
import multiprocessing
import queue
import signal
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
import caffe
import hashlib
import urllib.request
import tempfile
import shutil
image_queue = queue.Queue()
sess = None
threads = []
image_dir = '/run/image_dir'
caffe_classifier = None
caffe_labels = []
model_meta_file = '/usr/share/doc/caffe-doc/models/bvlc_reference_caffenet/readme.md'
label_file = '/tmp/synset_words.txt'
pretrained_model = None
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def load_labels(filename):
"""Read in labels, one label per line."""
return [line.rstrip() for line in open(filename)]
def read_model_meta_file(meta_file):
"""Read model meta file. The meta file is inside caffe-doc package"""
# We believe we shouldn't read this file for downloading and checking
# model. Instead we should package some model if there is free one.
url = None
sha1sum = None
filename = None
for line in open(meta_file):
l1 = line.rstrip()
if (l1.startswith("sha1:")):
sha1sum = l1[len("sha1:"):].strip()
if (l1.startswith("caffemodel_url:")):
url = l1[len("caffemodel_url:"):].strip()
if (l1.startswith("caffemodel:")):
filename = l1[len("caffemodel:"):].strip()
if ((sha1sum != None) and (url != None) and (filename != None)):
break
if ((url != None) and (sha1sum != None) and (filename != None)):
return {'url': url, 'sha1sum': sha1sum, 'filename': filename}
return None
def sha1sum(filename):
"""calculate sha1sum"""
BUF_SIZE=1024
sha1 = hashlib.sha1()
with open(filename, 'rb') as f:
while True:
data = f.read(BUF_SIZE)
if not data:
break
sha1.update(data)
return sha1.hexdigest()
def download_model():
"""Download pretrained model"""
# Downloading model from network isn't good for Debian. We need to package
# the model.
global pretrained_model
meta_data = read_model_meta_file(model_meta_file)
if (meta_data is None):
logging('Cannot load %s'%(meta_data))
return None
# FIXME: using /tmp/ will be in-secure.
pretrained_model = os.path.join('/','tmp',meta_data['filename'])
if (os.path.isfile(pretrained_model)):
sha1 = sha1sum(pretrained_model)
if (sha1 != meta_data['sha1sum']):
logging('Model %s SHA1 is not equal to %s'%(pretrained_model, meta_data['sha1sum']))
pretrained_model = None
return None
else:
logging('Model already exists')
pass
else:
logging('Downloading model file from %s'%(meta_data['url']))
urllib.request.urlretrieve(meta_data['url'], pretrained_model)
logging('Checking SHA1...')
sha1 = sha1sum(pretrained_model)
if (sha1 != meta_data['sha1sum']):
logging('Model %s SHA1 is not equal to %s'%(pretrained_model, meta_data['sha1sum']))
pretrained_model = None
return None
else:
logging('Model downloaded')
pass
return None
def download_label():
"""Download label file"""
# Using the scripts inside caffe Debian package to download label file.
# This could also be wrong. Why we don't package the label file?
global label_file
if (os.path.isfile(label_file)):
logging("Label file exists");
pass
else:
logging("Label file not exists. Downloading...");
tmpdir = tempfile.mkdtemp()
s1 = shutil.copy2(os.path.join('/', 'usr', 'share', 'doc', 'caffe-doc',
'data', 'ilsvrc12',
'get_ilsvrc_aux.sh'),
tmpdir)
os.system('sh \'%s\''%(s1));
# FIXME: using /tmp/ will be in-secure.
shutil.copy2(os.path.join(tmpdir, 'synset_words.txt'), '/tmp')
def create_classifier(pretrained_model):
"""Creates a model from saved caffemodel file and returns a classifier."""
# Creates model from saved .caffemodel.
# The following file are shipped inside caffe-doc Debian package
model_def = os.path.join("/", "usr", "share", "doc", "caffe-doc",
"models","bvlc_reference_caffenet",
"deploy.prototxt")
image_dims = [ 256, 256 ]
# The following file are shipped inside python3-caffe-cpu Debian package
mean = np.load(os.path.join('/', 'usr', 'lib', 'python3',
'dist-packages', 'caffe', 'imagenet',
'ilsvrc_2012_mean.npy'))
channel_swap = [2, 1, 0]
raw_scale = 255.0
caffe.set_mode_cpu()
classifier = caffe.Classifier(model_def, pretrained_model,
image_dims=image_dims, mean=mean,
raw_scale=raw_scale,
channel_swap=channel_swap)
return classifier
def server(labels):
"""Infinite loop serving inference requests"""
global image_queue, sess
logging(threading.current_thread().getName(), "is running")
while True:
input_name = image_queue.get()
if (input_name.endswith('npy')):
inputs = np.load(input_name)
else:
inputs = [caffe.io.load_image(input_name)]
predictions = caffe_classifier.predict(inputs, False)
# make tuples
predictions_list = predictions[0].tolist()
data = zip(predictions_list, caffe_labels)
output_name = input_name+'.txt'
output_done_name = output_name+'.done'
output = open(output_name, 'wt')
for d in sorted(data, reverse=True):
human_string = d[1]
score = d[0]
print("%s (score = %.5f)" % (human_string, score), file=output)
if (score < 0.00001):
break
output.close()
touch(output_done_name)
logging(input_name, " classified!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
if __name__ == '__main__':
pid = str(os.getpid())
pidfile = "/tmp/classify_server.pid"
if os.path.isfile(pidfile):
logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
# Please read /usr/share/doc/caffe-doc/models/bvlc_reference_caffenet/readme.md
download_model()
download_label()
caffe_labels = load_labels(label_file)
caffe_classifier = create_classifier(pretrained_model)
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0:
# child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), path=image_dir)
observer.start()
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in range(1):
threads.append(threading.Thread(target=server,
name='Server thread %d' % i,
args=({},)))
for t in threads: t.start()
for t in threads: t.join()
else:
# parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/classify_movidius_server.py 0000664 0000000 0000000 00000013521 13606321373 0024553 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import argparse
import json
import multiprocessing
import os
import signal
import sys
import threading
import time
import cv2
import movidius as mv
import numpy as np
import Queue
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
image_queue = Queue.Queue()
threads = []
mvng = None
graph_filepath = ''
labels_filepath = ''
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def generalize_inference_result(ncs_classifications):
"""Result format conversion from NCS classification to generic format.
param list ncs_classifications: [(label, confidence), ...]
return dict r: generic inference result format
"""
r = {
'annotations': []
}
conf_digits = 2
for label, confidence in ncs_classifications:
r['annotations'].append({
'type': 'classification',
'label': label,
'confidence': round(float(confidence), conf_digits)
})
return r
def server():
"""Infinite loop serving inference requests"""
global image_queue
global mvng
logging(threading.current_thread().getName(), "is running")
while True:
input_name = image_queue.get()
image_data = cv2.imread(input_name).astype(np.float32)
image_data = mv.process_inceptionv3_input(image_data)
output = mvng.inference(image_data)
inceptionv3_outputs = mv.process_inceptionv3_output(output,
mvng.get_labels())
output_name = input_name + '.txt'
output_done_name = output_name + '.done'
inference_result = generalize_inference_result(inceptionv3_outputs)
logging(json.dumps(inference_result, indent=4))
with open(output_name, 'w') as f:
json.dump(inference_result, f, indent=4)
#with open(output_name, 'w') as f:
# for i in inceptionv3_outputs:
# print("%s (score = %.5f)" % (i[0], i[1]), file=f)
touch(output_done_name)
logging(input_name, " classified!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
def main(args):
global threads
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in xrange(cpu_count/4):
threads.append(
threading.Thread(target=server,
name='Server thread %d' % i))
for t in threads:
t.start()
for t in threads:
t.join()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model', required=True, help='Model file path')
ap.add_argument('--label', required=True, help='Label file path')
ap.add_argument('--image_dir', required=True, help='Path to image file')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
args = parse_args()
mvng = mv.MovidiusNeuralGraph(args['model'], args['label'])
pid = str(os.getpid())
pidfile = "/tmp/classify_movidius_server.pid"
if os.path.isfile(pidfile):
logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
logging("model filepath: ", args['model'])
logging("label filepath: ", args['label'])
logging("image_dir: ", args['image_dir'])
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0: # child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), path=args['image_dir'])
observer.start()
main(args)
else: # parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/classify_server.py 0000664 0000000 0000000 00000015326 13606321373 0022641 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import os
import sys
import time
import numpy as np
import threading
import multiprocessing
import tensorflow as tf
import Queue
import signal
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
FLAGS = tf.app.flags.FLAGS
image_queue = Queue.Queue()
sess = None
threads = []
# classify_image_graph_def.pb:
# Binary representation of the GraphDef protocol buffer.
# imagenet_synset_to_human_label_map.txt:
# Map from synset ID to a human readable string.
# imagenet_2012_challenge_label_map_proto.pbtxt:
# Text representation of a protocol buffer mapping a label to synset ID.
tf.app.flags.DEFINE_string(
'model_dir', 'model',
"""Path to output_graph.pb and output_labels.txt.""")
tf.app.flags.DEFINE_string('image_dir', 'image',
"""Path to image file.""")
tf.app.flags.DEFINE_string('output_layer', 'softmax:0',
"""Name of the result operation""")
tf.app.flags.DEFINE_string('input_layer', 'DecodeJpeg/contents:0',
"""Name of the input operation""")
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
"""Display this many predictions.""")
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def load_labels(filename):
"""Read in labels, one label per line."""
return [line.rstrip() for line in tf.gfile.FastGFile(filename)]
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
def server(labels):
"""Infinite loop serving inference requests"""
global image_queue, sess
logging(threading.current_thread().getName(), "is running")
with sess.as_default():
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
while True:
input_name = image_queue.get()
image_data = tf.gfile.FastGFile(input_name, 'rb').read()
predictions = sess.run(FLAGS.output_layer,
{FLAGS.input_layer: image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
output_name = input_name+'.txt'
output_done_name = output_name+'.done'
output = open(output_name, 'w')
for node_id in top_k:
human_string = labels[node_id]
score = predictions[node_id]
print("%s (score = %.5f)" % (human_string, score), file=output)
output.close()
touch(output_done_name)
logging(input_name, " classified!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
def main(_):
"""Called by Tensorflow"""
global sess, threads
# Creates graph from saved GraphDef.
create_graph()
# Reuse the same session for all threads processing requests
sess = tf.Session()
# Creates node ID --> English string lookup.
labels = load_labels(os.path.join(FLAGS.model_dir, 'output_labels.txt'))
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in xrange(cpu_count/4):
threads.append(threading.Thread(target=server,
name='Server thread %d' % i,
args=(labels,)))
for t in threads: t.start()
for t in threads: t.join()
if __name__ == '__main__':
pid = str(os.getpid())
pidfile = "/tmp/classify_server.pid"
if os.path.isfile(pidfile):
logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
logging("model_dir: ", FLAGS.model_dir)
logging("image_dir: ", FLAGS.image_dir)
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0:
# child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), path=FLAGS.image_dir)
observer.start()
tf.app.run()
else:
# parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/darkflow_engine.py 0000664 0000000 0000000 00000011306 13606321373 0022566 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 .
"""Darkflow inference engine.
"""
from __future__ import print_function
import argparse
import logging
import cv2
from darkflow.net.build import TFNet
from engineservice import EngineService
from engineservice import DLEngine
from dlmodelmgr import DLModelManager
# FIXME: Make these variables configurable
SystemSnapshot = '/usr/local/berrynet/dashboard/www/freeboard/snapshot.jpg'
class DarkflowEngine(DLEngine):
def __init__(self, model, label, config):
super(DarkflowEngine, self).__init__()
self.engine_options = {
'model': config,
'load': model,
#'model': "cfg/tiny-yolo.cfg",
#'load': "bin/tiny-yolo.weights",
'verbalise': True,
"threshold": 0.1
}
def create(self):
self.tfnet = TFNet(self.engine_options)
def inference(self, tensor):
return self.tfnet.return_predict(tensor)
def save_cache(self):
#with open(self.cache['model_output_filepath'], 'w') as f:
# f.write(str(self.cache['model_output']))
drawBoundingBoxes(self.cache['model_input'],
#self.cache['model_output_filepath'] + '.jpg',
SystemSnapshot,
self.cache['model_output'],
self.tfnet.meta['colors'])
def drawBoundingBoxes(imageData, imageOutputPath, inferenceResults, colorMap):
"""Draw bounding boxes on an image.
imageData: image data in numpy array format
imageOutputPath: output image file path
inferenceResults: Darkflow inference results
colorMap: Bounding box color candidates, list of RGB tuples.
"""
# TODO: return raw data instead of save image
for res in inferenceResults:
left = res['topleft']['x']
top = res['topleft']['y']
right = res['bottomright']['x']
bottom = res['bottomright']['y']
colorIndex = res['coloridx']
color = colorMap[colorIndex]
label = res['label']
confidence = res['confidence']
imgHeight, imgWidth, _ = imageData.shape
thick = int((imgHeight + imgWidth) // 300)
cv2.rectangle(imageData,(left, top), (right, bottom), color, thick)
cv2.putText(imageData, label, (left, top - 12), 0, 1e-3 * imgHeight,
color, thick//3)
cv2.imwrite(imageOutputPath, imageData)
logging.debug('Save bounding box result image to {}'.format(imageOutputPath))
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model',
help='Model file path')
ap.add_argument('--label',
help='Label file path')
ap.add_argument('--model_package',
default='',
help='Model package "name-version" naming')
ap.add_argument('--image_dir', required=True,
help='Path to image file')
ap.add_argument('--service_name', required=True,
help='Engine service name used as PID filename')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG)
args = parse_args()
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
args['config'] = meta['config']['graph']
logging.debug('model filepath: ' + args['model'])
logging.debug('label filepath: ' + args['label'])
logging.debug('image_dir: ' + args['image_dir'])
darkflow_engine = DarkflowEngine(args['model'], args['label'], args['config'])
engine_service = EngineService(args['service_name'], darkflow_engine)
engine_service.run(args)
# this code block works
#import cv2
#input_tensor = cv2.imread('/tmp/berrynet/dog.jpg')
#tensor = darkflow_engine.process_input(input_tensor)
#output = darkflow_engine.inference(tensor)
#output = darkflow_engine.process_output(output)
BerryNet-upstream-3.9.0/inference/detect_movidius_server.py 0000664 0000000 0000000 00000012674 13606321373 0024216 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import argparse
import multiprocessing
import os
import signal
import sys
import threading
import time
from datetime import datetime
import cv2
import movidius as mv
import numpy as np
import Queue
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
image_queue = Queue.Queue()
threads = []
mvng = None
SystemSnapshot = '/usr/local/berrynet/dashboard/www/freeboard/snapshot.jpg'
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def server():
"""Infinite loop serving inference requests"""
global image_queue
global mvng
logging(threading.current_thread().getName(), "is running")
while True:
input_name = image_queue.get()
image_data = cv2.imread(input_name)
processed_data = mv.process_yolo_input(image_data)
t_start = datetime.now()
output = mvng.inference(processed_data)
t_end = datetime.now()
t_inference = t_end - t_start
logging('inference time: {} ms'.format(t_inference.total_seconds() * 1000))
interpreted_output = mv.interpret_yolo_output(output,
image_data.shape[1],
image_data.shape[0])
yolo_outputs = mv.process_yolo_output(image_data,
interpreted_output,
SystemSnapshot)
output_name = input_name + '.txt'
output_done_name = output_name + '.done'
mv.save_yolo_output_text(output_name, yolo_outputs)
touch(output_done_name)
logging(input_name, " detected!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
def main(args):
global threads
# Create a server thread for each CPU core
cpu_count = multiprocessing.cpu_count()
for i in xrange(cpu_count/4):
threads.append(
threading.Thread(target=server,
name='Server thread %d' % i))
for t in threads:
t.start()
for t in threads:
t.join()
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model', required=True, help='Model file path')
ap.add_argument('--label', required=True, help='Label file path')
ap.add_argument('--image_dir', required=True, help='Path to image file')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
args = parse_args()
mvng = mv.MovidiusNeuralGraph(args['model'], args['label'])
pid = str(os.getpid())
pidfile = "/tmp/detect_movidius_server.pid"
if os.path.isfile(pidfile):
logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
logging("model filepath: ", args['model'])
logging("label filepath: ", args['label'])
logging("image_dir: ", args['image_dir'])
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0: # child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), path=args['image_dir'])
observer.start()
main(args)
else: # parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/detect_movidius_server_cv.py 0000664 0000000 0000000 00000010144 13606321373 0024674 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 .
"""Simple image classification server with Inception.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import argparse
import os
import sys
import time
from datetime import datetime
import cv2
import movidius as mv
import numpy as np
mvng = None
def logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def run(mvng):
"""Infinite loop serving inference requests"""
logging('detection service is running')
capture = cv2.VideoCapture(0)
while True:
t_start = datetime.now()
status, image_data = capture.read()
t_end = datetime.now()
t_capture = t_end - t_start
logging('capture time: {} ms'.format(t_capture.total_seconds() * 1000))
t_start = datetime.now()
processed_data = mv.process_yolo_input(image_data)
t_end = datetime.now()
t_preprocess = t_end - t_start
logging('preprocess time: {} ms'.format(t_preprocess.total_seconds() * 1000))
t_start = datetime.now()
output = mvng.inference(processed_data)
t_end = datetime.now()
t_inference = t_end - t_start
logging('inference time: {} ms'.format(t_inference.total_seconds() * 1000))
t_start = datetime.now()
interpreted_output = mv.interpret_yolo_output(output,
image_data.shape[1],
image_data.shape[0])
yolo_outputs = mv.process_yolo_output(image_data,
interpreted_output,
'/tmp/yolo_resutl.jpg')
t_end = datetime.now()
t_postprocess = t_end - t_start
logging('postprocess time: {} ms'.format(t_postprocess.total_seconds() * 1000))
#output_name = input_name + '.txt'
#mv.save_yolo_output_text(output_name, yolo_outputs)
#logging(input_name, " detected!")
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model', required=True, help='Model file path')
ap.add_argument('--label', required=True, help='Label file path')
ap.add_argument('--image_dir', required=True, help='Path to image file')
#ap.add_argument('--num_top_predictions', default=5,
# help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
args = parse_args()
mvng = mv.MovidiusNeuralGraph(args['model'], args['label'])
logging("model filepath: ", args['model'])
logging("label filepath: ", args['label'])
logging("image_dir: ", args['image_dir'])
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0: # child
run(mvng)
else: # parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/detection_server.py 0000664 0000000 0000000 00000013333 13606321373 0022776 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 .
"""Simple image detection server with Tiny-YOLO.
The server monitors image_dir and run inferences on new images added to the
directory. Every image file should come with another empty file with '.done'
suffix to signal readiness. Inference result of a image can be read from the
'.txt' file of that image after '.txt.done' is spotted.
This is an example the server expects clients to do. Note the order.
# cp cat.jpg /run/image_dir
# touch /run/image_dir/cat.jpg.done
Clients should wait for appearance of 'cat.jpg.txt.done' before getting
result from 'cat.jpg.txt'.
"""
from __future__ import print_function
import logging
import multiprocessing
import os
import Queue
import signal
import sys
import threading
import time
import cv2
import numpy as np
from os.path import join as pjoin
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
from darkflow.net.build import TFNet
image_queue = Queue.Queue()
# FIXME: Make these variables configurable
ImageDir = '../image'
SystemSnapshot = '../../dashboard/www/freeboard/snapshot.jpg'
def _logging(*args):
print("[%08.3f]" % time.time(), ' '.join(args))
def touch(fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def drawBoundingBoxes(imageData, imageOutputPath, inferenceResults, colorMap):
"""Draw bounding boxes on an image.
imageData: image data in numpy array format
imageOutputPath: output image file path
inferenceResults: Darkflow inference results
colorMap: Bounding box color candidates, list of RGB tuples.
"""
# TODO: return raw data instead of save image
for res in inferenceResults:
left = res['topleft']['x']
top = res['topleft']['y']
right = res['bottomright']['x']
bottom = res['bottomright']['y']
colorIndex = res['coloridx']
color = colorMap[colorIndex]
label = res['label']
confidence = res['confidence']
imgHeight, imgWidth, _ = imageData.shape
thick = int((imgHeight + imgWidth) // 300)
cv2.rectangle(imageData,(left, top), (right, bottom), color, thick)
cv2.putText(imageData, label, (left, top - 12), 0, 1e-3 * imgHeight,
color, thick//3)
cv2.imwrite(imageOutputPath, imageData)
logging.debug('Save bounding box result image to {}'.format(imageOutputPath))
def server(tfnet):
"""Infinite loop serving inference requests"""
global image_queue
_logging(threading.current_thread().getName(), "is running")
while True:
input_name = image_queue.get()
_logging('input image ' + input_name)
imgcv = cv2.imread(input_name)
_logging('start inference')
result = tfnet.return_predict(imgcv)
_logging('inference result: {}'.format(result))
# overwrite existing input snapshot by the result image with
# bounding boxes.
drawBoundingBoxes(imgcv, SystemSnapshot, result, tfnet.meta['colors'])
logging.debug('System snapshot path: %s' % pjoin(os.getcwd(), SystemSnapshot))
output_name = input_name+'.txt'
output_done_name = output_name+'.done'
with open(output_name, 'w') as f:
f.write(str(result))
touch(output_done_name)
_logging(input_name, " classified!")
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
global image_queue
_msg = event.src_path
image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
_logging(_msg, event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
def main():
options = {
"model": "cfg/tiny-yolo.cfg",
"load": "bin/tiny-yolo.weights",
'verbalise': True,
#"threshold": 0.1
}
tfnet = TFNet(options)
_logging('model dir: {}'.format(options['load']))
_logging('config dir: {}'.format(options['model']))
server(tfnet)
if __name__ == '__main__':
logging.basicConfig(filename='/tmp/dlDetector.log', level=logging.DEBUG)
pid = str(os.getpid())
pidfile = "/tmp/detection_server.pid"
if os.path.isfile(pidfile):
_logging("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0:
# child
# observer handles event in a different thread
observer = Observer()
observer.schedule(EventHandler(['*.jpg.done']), ImageDir)
observer.start()
main()
else:
# parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
os.unlink(pidfile)
BerryNet-upstream-3.9.0/inference/dlmodelmgr.py 0000664 0000000 0000000 00000003700 13606321373 0021555 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 logging
import os
class DLModelManager(object):
def __init__(self):
self.basedir = '/var/lib/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()
logging.debug('model package name: ', args['modelname'])
dlmm = DLModelManager()
for name in dlmm.get_model_names():
print(dlmm.get_model_meta(name))
BerryNet-upstream-3.9.0/inference/engineservice.py 0000664 0000000 0000000 00000014640 13606321373 0022262 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 .
"""Engine service is a bridge between incoming data and inference engine.
"""
from __future__ import print_function
import argparse
import logging
import multiprocessing
import os
import signal
import sys
import threading
import time
import cv2
import numpy as np
import Queue
from dlmodelmgr import DLModelManager
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
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']))
class EventHandler(PatternMatchingEventHandler):
def process(self, event):
"""
event.event_type
'modified' | 'created' | 'moved' | 'deleted'
event.is_directory
True | False
event.src_path
path/to/observed/file
"""
# the file will be processed there
_msg = event.src_path
self.image_queue.put(_msg.rstrip('.done'))
os.remove(_msg)
logging.debug(_msg + ' ' + event.event_type)
# ignore all other types of events except 'modified'
def on_created(self, event):
self.process(event)
class EngineService(object):
def __init__(self, service_name, engine):
self.service_name = service_name
self.engine = engine
self.image_queue = Queue.Queue()
self.event_handler = EventHandler(['*.jpg.done'])
# NOTE: Increase object reference count (share memory)
# instead of object creation.
self.event_handler.image_queue = self.image_queue
def touch(self, fname, times=None):
with open(fname, 'a'):
os.utime(fname, times)
def server(self):
"""Infinite loop serving inference requests"""
logging.info(threading.current_thread().getName() + " is running")
self.engine.create()
while True:
input_name = self.image_queue.get()
image_data = cv2.imread(input_name).astype(np.float32)
self.engine.cache_data('model_input', image_data)
image_data = self.engine.process_input(image_data)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
self.engine.cache_data('model_output', model_outputs)
output_name = input_name + '.txt'
output_done_name = output_name + '.done'
self.engine.cache_data('model_output_filepath', output_name)
self.engine.save_cache()
self.touch(output_done_name)
logging.debug(input_name + " classified!")
def run(self, args):
self.record_pid()
# workaround the issue that SIGINT cannot be received (fork a child to
# avoid blocking the main process in Thread.join()
child_pid = os.fork()
if child_pid == 0: # child
# observer handles event in a different thread
observer = Observer()
observer.schedule(self.event_handler, path=args['image_dir'])
observer.start()
self.server()
else: # parent
try:
os.wait()
except KeyboardInterrupt:
os.kill(child_pid, signal.SIGKILL)
self.erase_pid()
def record_pid(self):
"""Write a PID pidfile /tmp/.pid.
"""
pid = str(os.getpid())
pidfile = '/tmp/{}.pid'.format(self.service_name)
if os.path.isfile(pidfile):
logging.critical("%s already exists, exiting" % pidfile)
sys.exit(1)
with open(pidfile, 'w') as f:
f.write(pid)
def erase_pid(self):
pidfile = '/tmp/{}.pid'.format(self.service_name)
os.unlink(pidfile)
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model',
help='Model file path')
ap.add_argument('--label',
help='Label file path')
ap.add_argument('--model_package',
default='',
help='Model package name')
ap.add_argument('--image_dir', required=True,
help='Path to image file')
ap.add_argument('--service_name', required=True,
help='Engine service name used as PID filename')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
import movidius as mv
logging.basicConfig(level=logging.DEBUG)
args = parse_args()
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logging.debug('model filepath: ' + args['model'])
logging.debug('label filepath: ' + args['label'])
logging.debug('image_dir: ' + args['image_dir'])
mvng = mv.MovidiusNeuralGraph(args['model'], args['label'])
engine_service = EngineService(args['service_name'], mvng)
engine_service.run(args)
BerryNet-upstream-3.9.0/inference/movidius.py 0000664 0000000 0000000 00000023752 13606321373 0021277 0 ustar 00root root 0000000 0000000 #!/usr/bin/python
#
# 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 .
import os
import cv2
import numpy as np
from mvnc import mvncapi as mvnc
from skimage.transform import resize
class MovidiusNeuralGraph(object):
def __init__(self, graph_filepath, label_filepath):
# mvnc.SetGlobalOption(mvnc.GlobalOption.LOGLEVEL, 2)
devices = mvnc.EnumerateDevices()
if len(devices) == 0:
raise Exception('No devices found')
self.device = mvnc.Device(devices[0])
self.device.OpenDevice()
# Load graph
with open(graph_filepath, mode='rb') as f:
graphfile = f.read()
self.graph = self.device.AllocateGraph(graphfile)
# Load labels
self.labels = []
with open(label_filepath, 'r') as f:
for line in f:
label = line.split('\n')[0]
if label != 'classes':
self.labels.append(label)
f.close()
def __exit__(self, exc_type, exc_value, traceback):
self.graph.DeallocateGraph()
self.device.CloseDevice()
def inference(self, data):
self.graph.LoadTensor(data.astype(np.float16), 'user object')
output, userobj = self.graph.GetResult()
return output
def get_graph(self):
return self.graph
def get_labels(self):
return self.labels
def process_inceptionv3_input(img):
image_size = 299
mean = 128
std = 1.0/128
dx, dy, dz = img.shape
delta = float(abs(dy - dx))
if dx > dy: # crop the x dimension
img = img[int(0.5*delta):dx-int(0.5*delta), 0:dy]
else:
img = img[0:dx, int(0.5*delta):dy-int(0.5*delta)]
img = cv2.resize(img, (image_size, image_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(3):
img[:, :, i] = (img[:, :, i] - mean) * std
return img
def process_inceptionv3_output(output, labels):
top_inds = output.argsort()[::-1][:5]
return [(labels[top_inds[i]], output[top_inds[i]]) for i in range(5)]
def print_inceptionv3_output(output, labels):
top_inds = output.argsort()[::-1][:5]
for i in range(5):
print(top_inds[i], labels[top_inds[i]], output[top_inds[i]])
def interpret_yolo_output(output, img_width, img_height):
output = output.astype(np.float32)
classes = [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train','tvmonitor'
]
threshold = 0.2
iou_threshold = 0.5
num_class = 20
num_box = 2
grid_size = 7
probs = np.zeros((7, 7, 2, 20))
class_probs = (np.reshape(output[0:980], (7, 7, 20))) #.copy()
scales = (np.reshape(output[980:1078], (7, 7, 2))) #.copy()
boxes = (np.reshape(output[1078:], (7, 7, 2, 4))) #.copy()
offset = np.transpose(
np.reshape(np.array([np.arange(7)] * 14), (2, 7, 7)),
(1, 2, 0)
)
#boxes.setflags(write=1)
boxes[:, :, :, 0] += offset
boxes[:, :, :, 1] += np.transpose(offset, (1, 0, 2))
boxes[:, :, :, 0:2] = boxes[:, :, :, 0:2] / 7.0
boxes[:, :, :, 2] = np.multiply(boxes[:, :, :, 2], boxes[:, :, :, 2])
boxes[:, :, :, 3] = np.multiply(boxes[:, :, :, 3], boxes[:, :, :, 3])
boxes[:, :, :, 0] *= img_width
boxes[:, :, :, 1] *= img_height
boxes[:, :, :, 2] *= img_width
boxes[:, :, :, 3] *= img_height
for i in range(2):
for j in range(20):
probs[:, :, i, j] = np.multiply(class_probs[:, :, j],
scales[:, :, i])
#print (probs)
filter_mat_probs = np.array(probs >= threshold, dtype='bool')
filter_mat_boxes = np.nonzero(filter_mat_probs)
boxes_filtered = boxes[filter_mat_boxes[0],
filter_mat_boxes[1],
filter_mat_boxes[2]]
probs_filtered = probs[filter_mat_probs]
classes_num_filtered = np.argmax(probs, axis=3)[filter_mat_boxes[0],
filter_mat_boxes[1],
filter_mat_boxes[2]]
argsort = np.array(np.argsort(probs_filtered))[::-1]
boxes_filtered = boxes_filtered[argsort]
probs_filtered = probs_filtered[argsort]
classes_num_filtered = classes_num_filtered[argsort]
for i in range(len(boxes_filtered)):
if probs_filtered[i] == 0:
continue
for j in range(i + 1, len(boxes_filtered)):
if iou(boxes_filtered[i], boxes_filtered[j]) > iou_threshold:
probs_filtered[j] = 0.0
filter_iou = np.array(probs_filtered > 0.0, dtype='bool')
boxes_filtered = boxes_filtered[filter_iou]
probs_filtered = probs_filtered[filter_iou]
classes_num_filtered = classes_num_filtered[filter_iou]
result = []
for i in range(len(boxes_filtered)):
result.append([classes[classes_num_filtered[i]],
boxes_filtered[i][0],
boxes_filtered[i][1],
boxes_filtered[i][2],
boxes_filtered[i][3],
probs_filtered[i]])
return result
def iou(box1, box2):
tb = (min(box1[0] + 0.5 * box1[2], box2[0] + 0.5 * box2[2]) -
max(box1[0] - 0.5 * box1[2], box2[0] - 0.5 * box2[2]))
lr = (min(box1[1] + 0.5 * box1[3], box2[1] + 0.5 * box2[3]) -
max(box1[1] - 0.5 * box1[3], box2[1] - 0.5 * box2[3]))
if tb < 0 or lr < 0:
intersection = 0
else:
intersection = tb*lr
return intersection / (box1[2] * box1[3] + box2[2] * box2[3] - intersection)
def process_yolo_output(img, results, img_filepath):
img_width = img.shape[1]
img_height = img.shape[0]
img_cp = img.copy()
print_yolo_output(results)
# draw bounding boxes on input image
for i in range(len(results)):
x = int(results[i][1])
y = int(results[i][2])
w = int(results[i][3]) // 2
h = int(results[i][4]) // 2
xmin = x - w
xmax = x + w
ymin = y - h
ymax = y + h
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax > img_width:
xmax = img_width
if ymax > img_height:
ymax = img_height
cv2.rectangle(img_cp,
(xmin, ymin),
(xmax, ymax),
(0, 255, 0),
2)
cv2.rectangle(img_cp,
(xmin, ymin - 20),
(xmax, ymin),
(125, 125, 125),
-1)
cv2.putText(img_cp,results[i][0] + ' : %.2f' % results[i][5],
(xmin + 5, ymin - 7),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 0),
1)
save_yolo_output_image(img_filepath, img_cp)
return results
def process_yolo_input(rgb_data):
from datetime import datetime
input_dim = (448, 448)
t_start = datetime.now()
tmp_data = rgb_data.copy()
t_end = datetime.now()
t_pass = t_end - t_start
print('copy: {} ms'.format(t_pass.total_seconds() * 1000))
t_start = datetime.now()
#tmp_data = resize(tmp_data / 255.0, input_dim, 1) # ~270 ms
tmp_data = cv2.resize(tmp_data / 255.0, input_dim) # ~65 ms
t_end = datetime.now()
t_pass = t_end - t_start
print('resize: {} ms'.format(t_pass.total_seconds() * 1000))
t_start = datetime.now()
tmp_data[:, :, (2, 1, 0)] # BGR2RGB
t_end = datetime.now()
t_pass = t_end - t_start
print('BGR2RGB: {} ms'.format(t_pass.total_seconds() * 1000))
t_start = datetime.now()
input_data = tmp_data.astype(np.float16)
t_end = datetime.now()
t_pass = t_end - t_start
print('astype: {} ms'.format(t_pass.total_seconds() * 1000))
return input_data
def save_yolo_output_text(text_filepath, output):
with open(text_filepath, 'w') as f:
f.write(str(output))
def save_yolo_output_image(image_filepath, image_data):
cv2.imwrite(image_filepath, image_data)
def print_yolo_output(output):
for i in range(len(output)):
x = int(output[i][1])
y = int(output[i][2])
#w = int(output[i][3]) // 2
#h = int(output[i][4]) // 2
print('\tclass = {label}'.format(label=output[i][0]))
print('\t[x, y, w, h] = [{x}, {y}, {w}, {h}]'.format(
x=str(x),
y=str(y),
w=str(int(output[i][3])),
h=str(int(output[i][4]))))
print('\tconfidence = {conf}'.format(conf=str(output[i][5])))
if __name__ == '__main__':
graph_filepath = '' # model filepath
label_filepath = '' # label filepath
path_to_images = '' # image dirpath
image_filenames = [os.path.join(path_to_images, image_name)
for image_name in []] # image filename list
movidius = MovidiusNeuralGraph(graph_filepath, label_filepath)
labels = movidius.get_labels()
print(''.join(['*' for i in range(79)]))
print('inception-v3 on NCS')
for image_filename in image_filenames:
img = cv2.imread(image_filename).astype(np.float32)
img = process_inceptionv3_input(img)
print(''.join(['*' for i in range(79)]))
print('Start download to NCS...')
output = movidius.inference(img)
print_inceptionv3_output(output, labels)
print(''.join(['*' for i in range(79)]))
print('Finished')
BerryNet-upstream-3.9.0/inference/movidius_engine.py 0000664 0000000 0000000 00000005627 13606321373 0022625 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 .
"""Movidius inference engine.
"""
from __future__ import print_function
import argparse
import logging
import movidius as mv
from engineservice import EngineService
from engineservice import DLEngine
from dlmodelmgr import DLModelManager
class MovidiusEngine(DLEngine):
def __init__(self, model, label):
super(MovidiusEngine, self).__init__()
self.mvng = mv.MovidiusNeuralGraph(model, label)
def process_input(self, tensor):
return mv.process_inceptionv3_input(tensor)
def inference(self, tensor):
return self.mvng.inference(tensor)
def process_output(self, output):
return mv.process_inceptionv3_output(
output,
self.mvng.get_labels())
def save_cache(self):
with open(self.cache['model_output_filepath'], 'w') as f:
for i in self.cache['model_output']:
print("%s (score = %.5f)" % (i[0], i[1]), file=f)
def parse_args():
ap = argparse.ArgumentParser()
ap.add_argument('--model',
help='Model file path')
ap.add_argument('--label',
help='Label file path')
ap.add_argument('--model_package',
default='',
help='Model package "name-version" naming')
ap.add_argument('--image_dir', required=True,
help='Path to image file')
ap.add_argument('--service_name', required=True,
help='Engine service name used as PID filename')
ap.add_argument('--num_top_predictions', default=5,
help='Display this many predictions')
return vars(ap.parse_args())
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG)
args = parse_args()
if args['model_package'] != '':
dlmm = DLModelManager()
meta = dlmm.get_model_meta(args['model_package'])
args['model'] = meta['model']
args['label'] = meta['label']
logging.debug('model filepath: ' + args['model'])
logging.debug('label filepath: ' + args['label'])
logging.debug('image_dir: ' + args['image_dir'])
movidius_engine = MovidiusEngine(args['model'], args['label'])
engine_service = EngineService(args['service_name'], movidius_engine)
engine_service.run(args)
BerryNet-upstream-3.9.0/inference/yoloutils.py 0000664 0000000 0000000 00000016025 13606321373 0021476 0 ustar 00root root 0000000 0000000 import os
import sys
import time
from datetime import datetime
import cv2
import numpy as np
from mvnc import mvncapi as mvnc
from skimage.transform import resize
def interpret_yolo_output(output, img_width, img_height):
output = output.astype(np.float32)
classes = [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train','tvmonitor'
]
threshold = 0.2
iou_threshold = 0.5
num_class = 20
num_box = 2
grid_size = 7
probs = np.zeros((7, 7, 2, 20))
class_probs = (np.reshape(output[0:980], (7, 7, 20))) #.copy()
scales = (np.reshape(output[980:1078], (7, 7, 2))) #.copy()
boxes = (np.reshape(output[1078:], (7, 7, 2, 4))) #.copy()
offset = np.transpose(
np.reshape(np.array([np.arange(7)] * 14), (2, 7, 7)),
(1, 2, 0)
)
#boxes.setflags(write=1)
boxes[:, :, :, 0] += offset
boxes[:, :, :, 1] += np.transpose(offset, (1, 0, 2))
boxes[:, :, :, 0:2] = boxes[:, :, :, 0:2] / 7.0
boxes[:, :, :, 2] = np.multiply(boxes[:, :, :, 2], boxes[:, :, :, 2])
boxes[:, :, :, 3] = np.multiply(boxes[:, :, :, 3], boxes[:, :, :, 3])
boxes[:, :, :, 0] *= img_width
boxes[:, :, :, 1] *= img_height
boxes[:, :, :, 2] *= img_width
boxes[:, :, :, 3] *= img_height
for i in range(2):
for j in range(20):
probs[:, :, i, j] = np.multiply(class_probs[:, :, j],
scales[:, :, i])
#print (probs)
filter_mat_probs = np.array(probs >= threshold, dtype='bool')
filter_mat_boxes = np.nonzero(filter_mat_probs)
boxes_filtered = boxes[filter_mat_boxes[0],
filter_mat_boxes[1],
filter_mat_boxes[2]]
probs_filtered = probs[filter_mat_probs]
classes_num_filtered = np.argmax(probs, axis=3)[filter_mat_boxes[0],
filter_mat_boxes[1],
filter_mat_boxes[2]]
argsort = np.array(np.argsort(probs_filtered))[::-1]
boxes_filtered = boxes_filtered[argsort]
probs_filtered = probs_filtered[argsort]
classes_num_filtered = classes_num_filtered[argsort]
for i in range(len(boxes_filtered)):
if probs_filtered[i] == 0:
continue
for j in range(i + 1, len(boxes_filtered)):
if iou(boxes_filtered[i], boxes_filtered[j]) > iou_threshold:
probs_filtered[j] = 0.0
filter_iou = np.array(probs_filtered > 0.0, dtype='bool')
boxes_filtered = boxes_filtered[filter_iou]
probs_filtered = probs_filtered[filter_iou]
classes_num_filtered = classes_num_filtered[filter_iou]
result = []
for i in range(len(boxes_filtered)):
result.append([classes[classes_num_filtered[i]],
boxes_filtered[i][0],
boxes_filtered[i][1],
boxes_filtered[i][2],
boxes_filtered[i][3],
probs_filtered[i]])
return result
def iou(box1, box2):
tb = (min(box1[0] + 0.5 * box1[2], box2[0] + 0.5 * box2[2]) -
max(box1[0] - 0.5 * box1[2], box2[0] - 0.5 * box2[2]))
lr = (min(box1[1] + 0.5 * box1[3], box2[1] + 0.5 * box2[3]) -
max(box1[1] - 0.5 * box1[3], box2[1] - 0.5 * box2[3]))
if tb < 0 or lr < 0:
intersection = 0
else:
intersection = tb*lr
return intersection / (box1[2] * box1[3] + box2[2] * box2[3] - intersection)
def process_yolo_output(img, results):
img_width = img.shape[1]
img_height = img.shape[0]
img_cp = img.copy()
print_yolo_output(results)
# draw bounding boxes on input image
for i in range(len(results)):
x = int(results[i][1])
y = int(results[i][2])
w = int(results[i][3]) // 2
h = int(results[i][4]) // 2
xmin = x - w
xmax = x + w
ymin = y - h
ymax = y + h
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax > img_width:
xmax = img_width
if ymax > img_height:
ymax = img_height
cv2.rectangle(img_cp,
(xmin, ymin),
(xmax, ymax),
(0, 255, 0),
2)
cv2.rectangle(img_cp,
(xmin, ymin - 20),
(xmax, ymin),
(125, 125, 125),
-1)
cv2.putText(img_cp,results[i][0] + ' : %.2f' % results[i][5],
(xmin + 5, ymin - 7),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 0),
1)
cv2.imwrite('/tmp/yolo_result.jpg', img_cp)
def process_yolo_input(rgb_data):
input_dim = (448, 448)
tmp_data = rgb_data.copy()
tmp_data = resize(tmp_data / 255.0, input_dim, 1)
tmp_data[:, :, (2, 1, 0)] # BGR2RGB
input_data = tmp_data.astype(np.float16)
return input_data
def print_yolo_output(output):
for i in range(len(output)):
x = int(output[i][1])
y = int(output[i][2])
#w = int(output[i][3]) // 2
#h = int(output[i][4]) // 2
print('\tclass = {label}'.format(label=output[i][0]))
print('\t[x, y, w, h] = [{x}, {y}, {w}, {h}]'.format(
x=str(x),
y=str(y),
w=str(int(output[i][3])),
h=str(int(output[i][4]))))
print('\tconfidence = {conf}'.format(conf=str(results[i][5])))
if __name__ == '__main__':
if len(sys.argv) != 2:
print ("YOLOv1 Tiny example: python3 py_examples/yolo_example.py images/dog.jpg")
sys.exit()
network_blob='/home/pi/codes/yoloNCS/graph'
# configuration NCS
mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2)
devices = mvnc.EnumerateDevices()
if len(devices) == 0:
print('No devices found')
quit()
device = mvnc.Device(devices[0])
device.OpenDevice()
opt = device.GetDeviceOption(mvnc.DeviceOption.OPTIMISATION_LIST)
# load blob
with open(network_blob, mode='rb') as f:
blob = f.read()
graph = device.AllocateGraph(blob)
graph.SetGraphOption(mvnc.GraphOption.ITERATIONS, 1)
iterations = graph.GetGraphOption(mvnc.GraphOption.ITERATIONS)
# image preprocess
img = cv2.imread(sys.argv[1])
input_data = process_yolo_input(img)
# start MOD
start = datetime.now()
graph.LoadTensor(input_data, 'user object')
out, userobj = graph.GetResult()
end = datetime.now()
elapsedTime = end-start
print('total time is " milliseconds', elapsedTime.total_seconds()*1000)
# fc27 instead of fc12 for yolo_small
results = interpret_yolo_output(out,
img.shape[1],
img.shape[0])
#print (results)
#cv2.imshow('YOLO detection',img_cv)
process_yolo_output(img, results)
#cv2.waitKey(10000)
graph.DeallocateGraph()
device.CloseDevice()
BerryNet-upstream-3.9.0/journal.js 0000664 0000000 0000000 00000004171 13606321373 0017132 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 .
'use strict';
const fs = require('fs');
const moment = require('moment');
const mqtt = require('mqtt');
const config = require('./config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicNotifyEmail = config.topicNotifyEmail;
const topicDashboardLog = config.topicDashboardLog;
const topicDashboardSnapshot = config.topicDashboardSnapshot;
const snapshot = config.snapshot;
let logs = [];
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
function saveBufferToImage(b, filepath) {
fs.writeFile(filepath, b, (e) => {
if (e)
log(`log client: cannot save buffer to image.`);
else
log(`log client: saved buffer to image successfully.`);
});
}
client.on('connect', () => {
client.subscribe(topicActionLog);
client.subscribe(topicNotifyEmail);
log(`log client: connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
// secretly save a copy of the image
if (t === topicNotifyEmail) {
const filename = 'snapshot.jpg';
saveBufferToImage(m, snapshot);
client.publish(topicDashboardSnapshot, filename);
return;
}
// less stackoverflowy
if (String(m).match(/^log/))
return;
const now = moment().format('YYYY-MM-DD HH:mm:ss');
logs.push(`[${now}] ` + m);
if (logs.length > 10)
logs.unshift();
client.publish(topicDashboardLog, [].concat(logs).reverse().join(' '));
});
BerryNet-upstream-3.9.0/line.js 0000664 0000000 0000000 00000005351 13606321373 0016410 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 .
'use strict';
const mqtt = require('mqtt');
const line = require('@line/bot-sdk');
const imgur = require('imgur');
const config = require('./config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicNotifyLINE = config.topicNotifyLINE;
const topicDashboardInferenceResult = config.topicDashboardInferenceResult;
const targetUserID = config.LINETargetUserID;
// create LINE SDK config
const LINEConfig = {
channelAccessToken: config.LINEChannelAccessToken,
channelSecret: config.LINEChannelSecret,
};
// create LINE SDK client
const LINEClient = new line.Client(LINEConfig);
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
client.on('connect', () => {
client.subscribe(topicNotifyLINE);
client.subscribe(topicDashboardInferenceResult);
log(`client connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
const size = m.length;
log(`client on topic ${t}, received ${size} bytes.`)
if (t === topicDashboardInferenceResult) {
const result = m.toString();
LINEClient.pushMessage(targetUserID, { type: 'text', text: result });
return;
}
// save image to file and upload it to imgur for display in LINE message
const snapshot_path = m.toString();
imgur.uploadFile(snapshot_path)
.then((json) => {
var imgurLink = json.data.link;
imgurLink = imgurLink.replace('http:\/\/', 'https:\/\/');
log(`An image has been uploaded to imgur. link: ${imgurLink}`);
// Image can only be delivered via 'https://' URL, 'http://' doesn't work
LINEClient.pushMessage(targetUserID, { type: 'image',
originalContentUrl: imgurLink,
previewImageUrl: imgurLink })
.then((v) => {
log(`A message sent to ${targetUserID} successfully.`);
})
.catch((err) => {
log(`An error occurred, ${err}.`);
});
})
.catch((err) => {
log(`An error occurred. ${err}`);
});
});
BerryNet-upstream-3.9.0/linev3.js 0000664 0000000 0000000 00000006654 13606321373 0016670 0 ustar 00root root 0000000 0000000 // Copyright 2017 DT42 Inc.
//
// 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 .
'use strict';
const mqtt = require('mqtt');
const line = require('@line/bot-sdk');
const imgur = require('imgur');
const config = require('./config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicActionLog = config.topicActionLog;
const topicInferenceResult = 'berrynet/engine/darknet/result'
//const topicInferenceResult = 'berrynet/data/rgbimage'
const targetUserID = config.LINETargetUserID;
// create LINE SDK config
const LINEConfig = {
channelAccessToken: config.LINEChannelAccessToken,
channelSecret: config.LINEChannelSecret,
};
// create LINE SDK client
const LINEClient = new line.Client(LINEConfig);
function log(m) {
client.publish(topicActionLog, m)
console.log(m)
}
/**
* Debugging utility to display inference result content.
*
* @param {Object} result Inference result in JSON format
*/
function debugPrintInferenceResult(result) {
// modify result will cause side effect because of calling by reference
const b64str = result["bytes"]
delete result["bytes"]
result["annotations"] = {
"type": "detection",
"label": "dog",
"confidence": 0.95,
"left": 10,
"top": 10,
"right": 50,
"bottom": 50
}
console.log(result)
result["bytes"] = b64str
}
/**
* Send text and image in the inference result to LINE client.
*
* @param {Object} result Inference result in JSON format
*/
function notifyLine(result) {
//debugPrintInferenceResult(result)
// Send base64-encoded image in result
imgur.uploadBase64(result["bytes"])
.then((json) => {
var imgurLink = json.data.link;
imgurLink = imgurLink.replace('http:\/\/', 'https:\/\/')
log(`An image has been uploaded to imgur. link: ${imgurLink}`)
// Image can only be delivered via 'https://' URL, 'http://' doesn't work
LINEClient.pushMessage(targetUserID, { type: 'image',
originalContentUrl: imgurLink,
previewImageUrl: imgurLink })
.then((v) => {
log(`A message sent to ${targetUserID} successfully.`)
})
.catch((err) => {
log(`An error occurred, ${err}.`)
});
})
.catch((err) => {
log(`An error occurred. ${err}`)
})
// Send annotations in result
LINEClient.pushMessage(targetUserID,
{
type: 'text',
text: JSON.stringify(result["annotations"])
})
}
client.on('connect', () => {
client.subscribe(topicInferenceResult)
log(`client connected to ${broker} successfully.`)
})
client.on('message', (t, m) => {
const size = m.length
log(`client on topic ${t}, received ${size} bytes.`)
notifyLine(JSON.parse(m.toString()))
})
BerryNet-upstream-3.9.0/localimg.js 0000664 0000000 0000000 00000003620 13606321373 0017245 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 .
// Read a local image and send it to inference server.
//
// subscribe
// dt42/localimg
// publish
// dt42/inference
// dt42/log
//
// $ node localimg.js
// $ mosquitto_pub -h localhost -t dt42/localimg -m
'use strict';
const mqtt = require('mqtt');
const fs = require('fs');
const config = require('./config');
const broker = config.brokerHost;
const client = mqtt.connect(broker);
const topicEventLocalImage = config.topicEventLocalImage;
const topicActionLog = config.topicActionLog;
const topicActionInference = config.topicActionInference;
function log(m) {
client.publish(topicActionLog, m);
console.log(m);
}
client.on('connect', () => {
client.subscribe(topicEventLocalImage);
log(`localimg client: connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
log(`camera client: on topic ${t}, received message ${m}.`);
const imgurl = m.toString();
// Take a local image as snapshot. The snapshot will be displayed
// on dashboard.
fs.readFile(imgurl, function(err, data) {
if (err) {
log('localimg client: cannot get image.');
} else {
log('localimg client: publishing image.');
client.publish(topicActionInference, data);
}
});
});
BerryNet-upstream-3.9.0/mail.js 0000664 0000000 0000000 00000004256 13606321373 0016406 0 ustar 00root root 0000000 0000000 // Copyright 2017 DT42
//
// This file is part of BerryNet.
//
// BerryNet is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// BerryNet is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with BerryNet. If not, see .
// Usage:
// ./mail.js
// This app assumes the user uses gmail.
// You may need to configure "Allow Less Secure Apps" in your Gmail account.
'use strict';
const assert = require('assert');
const emailjs = require('emailjs');
const moment = require('moment');
const mqtt = require('mqtt');
assert(process.argv.length == 5);
const broker = 'mqtt://localhost';
const client = mqtt.connect(broker);
const mail_topic = 'dt42/mail';
const log_topic = 'dt42/log';
const args = process.argv.slice(2);
const account = args[0];
const password = args[1];
const recipient = args[2];
function log(m) {
client.publish(log_topic, m);
console.log(m);
}
const server = emailjs.server.connect({
user: `${account}`,
password: password,
host: 'smtp.gmail.com',
ssl: true,
});
client.on('connect', () => {
client.subscribe(mail_topic);
log(`mail client: connected to ${broker} successfully.`);
});
client.on('message', (t, m) => {
const size = m.length;
log(`mail client: on topic ${t}, received ${size} bytes.`)
const now = moment().format('YYYY-MM-DD-HH-mm-ss');
server.send({
from: `<${account}>`,
to: `<${recipient}>`,
subject: `DT42 MQTT Snapshot at ${now}`,
text: ' ',
attachment: [{
name: 'snapshot.jpg',
data: m,
}],
}, (e, m) => {
if (e) {
log(`mail client: an error occurred, ${e}.`);
return;
}
if (m)
log(`mail client: mail sent to ${recipient} successfully.`);
});
});
BerryNet-upstream-3.9.0/package.json 0000664 0000000 0000000 00000001110 13606321373 0017376 0 ustar 00root root 0000000 0000000 {
"name": "BerryNet",
"version": "2.0.0",
"description": "Deep learning gateway on Raspberry Pi",
"main": "index.js",
"author": "DT42",
"license": "GPLv3",
"dependencies": {
"ascoltatori": "^3.1.0",
"emailjs": "^1.0.8",
"@line/bot-sdk": "^1.0.0",
"imgur": "^0.2.1",
"imagemagick": "^0.1.3",
"mocha": "^3.2.0",
"mosca": "^2.2.0",
"mqtt": "^2.0.1",
"opencv": "^6.0.0",
"pino": "^2.13.0",
"prompt": "^1.0.0",
"request": "^2.79.0"
},
"devDependencies": {
"eslint": "^6.5.1",
"eslint-config-google": "^0.7.1"
}
}
BerryNet-upstream-3.9.0/patch/ 0000775 0000000 0000000 00000000000 13606321373 0016216 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/patch/01-detection-backend.patch 0000664 0000000 0000000 00000016345 13606321373 0023031 0 ustar 00root root 0000000 0000000 diff --git a/Makefile b/Makefile
index 7ba6b25..31950ce 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,6 @@
GPU=0
CUDNN=0
-OPENCV=0
+OPENCV=1
NNPACK=1
ARM_NEON=1
DEBUG=0
diff --git a/examples/coco.c b/examples/coco.c
index a07906e..170af71 100644
--- a/examples/coco.c
+++ b/examples/coco.c
@@ -342,7 +342,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80, 0);
save_image(im, "prediction");
show_image(im, "predictions");
free_image(im);
diff --git a/examples/detector.c b/examples/detector.c
index 3c4a107..f2de3cc 100644
--- a/examples/detector.c
+++ b/examples/detector.c
@@ -581,6 +581,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
+ char done[256];
+ FILE *done_signal = NULL;
+ memset(done, 0, 256);
image **alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
@@ -621,6 +624,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
//resize_network(&net, sized.w, sized.h);
#endif
layer l = net.layers[net.n-1];
+ sprintf(done, "%s.txt.done", input);
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
@@ -634,7 +638,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, 0, 0, hier_thresh, 1);
if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
//else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
- draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
+ draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes, input);
if(outfile){
save_image(im, outfile);
}
@@ -650,11 +654,13 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
cvDestroyAllWindows();
#endif
}
+ done_signal = fopen(done, "w");
free_image(im);
free_image(sized);
free(boxes);
free_ptrs((void **)probs, l.w*l.h*l.n);
+ fclose(done_signal);
if (filename) break;
}
#ifdef NNPACK
diff --git a/examples/yolo.c b/examples/yolo.c
index 5b3fd16..9e74736 100644
--- a/examples/yolo.c
+++ b/examples/yolo.c
@@ -309,7 +309,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
//draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20, 0);
save_image(im, "predictions");
show_image(im, "predictions");
diff --git a/include/darknet.h b/include/darknet.h
index b6b9402..2de7cc0 100644
--- a/include/darknet.h
+++ b/include/darknet.h
@@ -695,7 +695,7 @@ float box_iou(box a, box b);
void do_nms(box *boxes, float **probs, int total, int classes, float thresh);
data load_all_cifar10();
box_label *read_boxes(char *filename, int *n);
-void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes);
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes, char* result_file);
matrix network_predict_data(network net, data test);
image **load_alphabet();
diff --git a/src/demo.c b/src/demo.c
index 9dc4946..0030d0d 100644
--- a/src/demo.c
+++ b/src/demo.c
@@ -77,7 +77,7 @@ void *detect_in_thread(void *ptr)
printf("\nFPS:%.1f\n",fps);
printf("Objects:\n\n");
image display = buff[(buff_index+2) % 3];
- draw_detections(display, demo_detections, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes);
+ draw_detections(display, demo_detections, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes, 0);
demo_index = (demo_index + 1)%demo_frame;
running = 0;
diff --git a/src/image.c b/src/image.c
index 83ed382..c1b5b2a 100644
--- a/src/image.c
+++ b/src/image.c
@@ -190,24 +190,33 @@ image **load_alphabet()
return alphabets;
}
-void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes, char* result_file)
{
int i;
-
+ FILE *predict_result = NULL;
+ char result_txt[256];
+ memset(result_txt, 0, 256);
+ if (result_file != NULL) {
+ sprintf(result_txt, "%s.txt", result_file);
+ predict_result = fopen(result_txt, "wa");
+ if (!predict_result) {
+ printf("%s: Predict result file opened error\n", result_txt);
+ return;
+ }
+ }
for(i = 0; i < num; ++i){
int class = max_index(probs[i], classes);
float prob = probs[i][class];
if(prob > thresh){
- int width = im.h * .006;
+ int width = im.h * .012;
if(0){
width = pow(prob, 1./2.)*10+1;
alphabet = 0;
}
- //printf("%d %s: %.0f%%\n", i, names[class], prob*100);
- printf("%s: %.0f%%\n", names[class], prob*100);
+ printf("%s %.0f%%\n", names[class], prob*100);
int offset = class*123457 % classes;
float red = get_color(2,offset,classes);
float green = get_color(1,offset,classes);
@@ -232,6 +241,12 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
if(bot > im.h-1) bot = im.h-1;
draw_box_width(im, left, top, right, bot, width, red, green, blue);
+ // output: label, accuracy, x, y, width, height
+ if (predict_result)
+ fprintf(predict_result, "%s %.2f %d %d %d %d\n",
+ names[class], prob, left, top, right - left, bot - top);
+ printf("%s %.2f %d %d %d %d\n",
+ names[class], prob, left, top, right - left, bot - top);
if (alphabet) {
image label = get_label(alphabet, names[class], (im.h*.03)/10);
draw_label(im, top + width, left, label, rgb);
@@ -239,6 +254,8 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
}
}
}
+ if (predict_result)
+ fclose(predict_result);
}
void transpose_image(image im)
BerryNet-upstream-3.9.0/patch/darkflow/ 0000775 0000000 0000000 00000000000 13606321373 0020027 5 ustar 00root root 0000000 0000000 BerryNet-upstream-3.9.0/patch/darkflow/darkflow-app-example.patch 0000664 0000000 0000000 00000001261 13606321373 0025070 0 ustar 00root root 0000000 0000000 diff --git a/darkflow/net/flow.py b/darkflow/net/flow.py
index a5b8ceb..bb60704 100644
--- a/darkflow/net/flow.py
+++ b/darkflow/net/flow.py
@@ -90,7 +90,8 @@ def return_predict(self, im):
"y": tmpBox[2]},
"bottomright": {
"x": tmpBox[1],
- "y": tmpBox[3]}
+ "y": tmpBox[3]},
+ "coloridx": tmpBox[5]
})
return boxesInfo
@@ -142,4 +143,4 @@ def predict(self):
# Timing
self.say('Total time = {}s / {} inps = {} ips'.format(
- last, len(inp_feed), len(inp_feed) / last))
\ No newline at end of file
+ last, len(inp_feed), len(inp_feed) / last))
BerryNet-upstream-3.9.0/patch/ui-berrynet-theme.patch 0000664 0000000 0000000 00000002567 13606321373 0022616 0 ustar 00root root 0000000 0000000 diff --git a/index.html b/index.html
index b5877e9..196aaba 100755
--- a/index.html
+++ b/index.html
@@ -8,9 +8,12 @@
+
