ginbor/yolov5_rk3588_work

YOLOv5 for RK3588

Yolov5_RK3588

1. Prerequisites

• Ubuntu

  Install Ubuntu on your RK3588 device. (tested on Ubuntu 20.04 and OrangePi5/Firefly ROC RK3588S devices)

  For installing Ubuntu on Firefly you can use their manual[1][2].

  For installing Ubuntu on OrangePi you can use their manual.

  Or use ours README's for them (select the one below).

  OrangePi	Firefly

• FFMPEG

  Install ffmpeg package for WebUI:

  sudo apt-get update
  sudo apt-get install -y ffmpeg
  

  And dependencies for WebUI:

  sudo apt-get update
  # General dependencies
  sudo apt-get install -y python-dev pkg-config
  
  # Library components
  sudo apt-get install libavformat-dev libavcodec-dev libavdevice-dev \
    libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
  

  Open .bashrc in nano text editor:

  nano ~/.bashrc
  

  At the end of file add next line:

  export LD_PRELOAD=$LD_PRELOAD:/usr/lib/aarch64-linux-gnu/libffi.so.7
  

  Save and close nano with sortcuts ctrl-o, Enter, ctrl-x

• Docker (Optional)

  For installing docker on RK3588 device you can use official docker docs or check our README_DOCKER.md

2. Install docker images (Optional)

• From Docker hub

  At first you need download docker image:

  docker pull deathk9t/yolov5_rk3588:latest
  

  Then you can run container with:

  docker run --privileged --name [container-name] -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /dev/:/dev --network host -it deathk9t/yolov5_rk3588:latest
  

• Build docker image by yourself

  You can build docker image by yourself usning Dockerfile:

  docker build -t [name-docker-image:tag] .
  

  Then you can run container with:

  docker run --privileged --name [container-name] -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /dev/:/dev --network host -it [name-docker-image:tag]
  

3. Installing and configurating

Install miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
bash Miniconda3-latest-Linux-aarch64.sh

Then rerun terminal session:

source ~/.bashrc

Create conda env with python3.9

conda create -n <env-name> python=3.9

And then activate conda env

conda activate <env-name>

Clone repository:

git clone https://github.com/Applied-Deep-Learning-Lab/Yolov5_RK3588

And got into repo-dir:

cd Yolov5_RK3588

Install RKNN-Toolkit2-Lite，such as rknn_toolkit_lite2-1.4.0-cp39-cp39-linux_aarch64.whl

pip install install/rknn_toolkit_lite2-1.4.0-cp39-cp39-linux_aarch64.whl

In created conda enviroment also install requirements from the same directory

pip install -r install/requirements.txt

Then go to the install dir for building and installing cython_bbox

cd install/cython_bbox
python3 setup.py build
python3 setup.py install

4. Running Yolov5

main.py runs inference with WebUI. You can turn on/off some options in config file or using Settings page at webUI.

python3 main.py

Or run it using bash script:

source run.sh

For see WebUI write to browser address bar next (localhost - device's ip):

localhost:8080

You also can set autostart for running this.

Before it deactivate conda env:

conda deactivate

• For Orange Pi

  source install/autostart/orangepi_autostart.sh
  

• For Firefly:

  source install/autostart/firefly_autostart.sh
  

5. Convert onnx model to rknn

• Host PC

  Install Python3 and pip3

  sudo apt-get update
  sudo apt-get install python3 python3-dev python3-pip
  

  Install dependent libraries

  sudo apt-get update
  sudo apt-get install libxslt1-dev zlib1g zlib1g-dev libglib2.0-0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc git
  

  Install RKNN-Toolkit2，such as rknn_toolkit2-1.4.0_22dcfef4-cp38-cp38-linux_x86_64.whl

  pip install resources/HostPC/converter/install/rknn_toolkit2-1.4.0_22dcfef4-cp38-cp38-linux_x86_64.whl
  

  For convert your .onnx model to .rknn run onnx2rknn.py like:

  cd resources/HostPC/converter/convert/
  python3 onnx2rknn.py \
          --input <path-to-your-onnx-model> \
          --output <path-where-save-rknn-model> \
          --dataset <path-to-txt-file-with-calibration-images-names>