This is NTU BME MS thesis project. The purpose of this project is using edge device (Rpi) to monitoring dairy cow feeding behavior. The code is focus on streaming also recognizing individual cow faces by edge computing (with Tensorflow Lite etc).
This repo contains c++ and python version. The python version is deprecated, but still
usable(in python branch). The main program uses c++.
$ ntu-node -h
NTU BBLAB cow face monitoring system
Usage:
ntu-node [OPTION...]
-s, --stream Start streaming
-i, --image arg image directory to recognize (default: )
-D, --detect arg detect model path (default:
./model/yolov4-tiny-f16.tflite)
-C, --classify arg classify model path (default:
./model/mobilenetv2.tflite)
-n, --node arg node number (default: )
-r, --ref arg class reference (default: ./cfg/ref.8f.tsv)
-d, --ref_dict arg class id (default: ./cfg/ref_dict.csv)
-f, --fence arg fence bbox (default: )
-m, --mqtt_ip arg mqtt broker ip (default: )
-u, --user arg mqtt username (default: )
-p, --pwd arg mqtt password (default: )
-v, --version ntu-node version
-h, --help Print usage- os: Raspbian GNU/Linux 10 (buster)
- kernel: 5.4.51-v7l+ / 5.10.60-v8+
All libraries are built in static and default location is placed in /opt/. Or, modify
CMakeLists.txt to the correct folder you built.
| Library | armv7 | arm64 |
|---|---|---|
| OpenCV: 4.5.3 | ✔️ | ✔️ |
| Tensorflow Lite: 2.4.2 | ✔️ | ✔️ |
| CXXopts: 2.2.1 | ✔️ | ✔️ |
| MQTT: 1.2.0 | ✔️ | ✔️ |
| Boost: 1.75.0 | ✔️ | ✔️ |
use download_xxx.sh to download and extract.
For OpenCV, extract and cp opencv/lib/pkgconfig/opencv4.pc to your $PKG_CONFIG_PATH (default: /usr/lib/pkgconfig/). Then type pkg-config --list-all|grep opencv and if you see
$ pkg-config --list-all|grep opencv
opencv4 OpenCV - Open Source Computer Vision Library
OpenCV is set up.
Finally, go to /opt/ and extract all then good to go.
Clone the project to the Raspberry Pi. And cd to cc/recognition/ then compile. Make sure your prerequisites are built.
git clone https://github.com/WesleyCh3n/NTU-IoT-Node && cd NTU-IoT-Node
mkdir build && cd build
cmake ../src/you will see something like
-- The C compiler identification is GNU 8.3.0
-- The CXX compiler identification is GNU 8.3.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/local/aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/local/aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found PkgConfig: /usr/bin/pkg-config (found version "0.29.2")
-- Checking for module 'opencv4'
-- Found opencv4, version 4.5.0
-- NTU IOT NODE:
-- VERSION = beta
-- FOUND ARCH: arm64v8
-- Configuring done
-- Generating done
-- Build files have been written to: /work/arm64/ntu-nodemake sure ARCH is the architecture you want to perform (in this example, aarch64 is built)
After cmake complete, start compile by (-j)
make -j4once finished, you will get following output
[ 20%] Building CXX object cow_monitor/yolov4-tiny_lib/CMakeFiles/yolov4-tiny.dir/yolov4-tiny.cc.o
[ 20%] Building CXX object cow_monitor/network_lib/CMakeFiles/network.dir/network.cc.o
[ 30%] Building CXX object cow_monitor/mobilenetv2_lib/CMakeFiles/mobilenetv2.dir/mobilenetv2.cc.o
[ 40%] Linking CXX static library libnetwork.a
[ 40%] Built target network
[ 50%] Linking CXX static library libmobilenetv2.a
[ 50%] Built target mobilenetv2
[ 60%] Linking CXX static library libyolov4-tiny.a
[ 60%] Built target yolov4-tiny
[ 70%] Building CXX object cow_monitor/CMakeFiles/cow_monitor.dir/cow_monitor.cpp.o
[ 80%] Linking CXX static library libcow_monitor.a
[ 80%] Built target cow_monitor
[ 90%] Building CXX object CMakeFiles/ntu-node.dir/main.cc.o
[100%] Linking CXX executable ntu-node
[100%] Built target ntu-nodeThen the folder should have ntu-node binary.
Use pi-cross docker image to build. Follow pi-cross instruction to install helper script on your x64 host machine. In this example, armv7 target was used.
git clone https://github.com/WesleyCh3n/NTU-IoT-Node && cd NTU-IoT-Node
mkdir -p ./build/opt && cd build/opt/
# download all lib
../../3rdparty/armv7/download_mqtt.sh
../../3rdparty/armv7/download_boost.sh
../../3rdparty/armv7/download_opencv.sh
../../3rdparty/armv7/download_tflite.sh
../../3rdparty/armv7/download_cxxopts.sh
# Start pi-cross container instance
cd ../../ # Back to root dir of NTU-IoT-Node
# Start container with mounting opencv.pc and lib folder
armv7-8.3.0 -a "-v `pwd`/build/opt/opencv/lib/pkgconfig/opencv4.pc:/usr/lib/pkgconfig/opencv4.pc:ro \
-v `pwd`/build/opt:/opt/" bash
# (following is in container)
cd build && cmake ../src/
make -j4
# Then the `build` directory should have `ntu-node` binary.
exitAfter compiling complete, you can use the docker image to verify on RPi.
For arm32v7: docker image
$ docker run -it --rm --privileged=true \
-w /home/ \
-v `pwd`:/home/ \
--device=/dev/video0
arm32v7/debian:stable-slim bashFor arm64v8: docker image
$ docker run -it --rm --privileged=true \
-w /home/ \
-v `pwd`:/home/ \
--device=/dev/video0
arm64v8/debian:stable-slim bashThen
./ntu-node -h$ ./upload.sh \
-u rsync_user \
-p rsync_ip \
-P rsync_port \
-d remote_folder \ # must have NODE+02d in the path to know which node is it
-t backup_time_interval(s)for example
$ ./upload.sh \
-u root \
-p 255.255.255.255 \
-P 22 \
-d /data/dir/NODE00/ \
-t 600
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent