add rknn conversion - install and use scripts, auto convert to rknn after AI training is done and put pt and rknn models to /azaion/models directory

orangepi5/convert-install.sh

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+mkdir rknn-convert
+cd rknn-convert
+
+# Install converter PT to ONNX
+git clone https://github.com/airockchip/ultralytics_yolov8
+cd ultralytics_yolov8
+sudo apt install python3.12-venv
+python3 -m venv env
+source env/bin/activate
+pip install .
+pip install onnx
+cp ultralytics/cfg/default.yaml ultralytics/cfg/default_backup.yaml
+sed -i -E "s/(model: ).+( #.+)/\1azaion.pt\2/" ultralytics/cfg/default.yaml
+cd ..
+deactivate
+
+# Install converter ONNX to RKNN
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+chmod +x miniconda.sh
+bash miniconda.sh -b -p $HOME/miniconda
+
+source ~/miniconda/bin/activate
+conda create -n toolkit2 -y python=3.11
+conda activate toolkit2
+git clone https://github.com/rockchip-linux/rknn-toolkit2.git
+cd rknn-toolkit2/rknn-toolkit2/packages
+pip install -r requirements_cp311-1.6.0.txt
+pip install rknn_toolkit2-1.6.0+81f21f4d-cp311-cp311-linux_x86_64.whl
+pip install "numpy<2.0"
+cd ../../../
+git clone https://github.com/airockchip/rknn_model_zoo.git
+conda deactivate
+conda deactivate

orangepi5/convert.sh

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+# Use converter PT to ONNX
+cd rknn-convert/ultralytics_yolov8/
+cp --verbose /azaion/models/azaion.pt .
+source env/bin/activate
+pip install onnx
+export PYTHONPATH=./
+python ./ultralytics/engine/exporter.py
+cp --verbose azaion.onnx ../
+cd ..
+deactivate
+
+# Use converter ONNX to RKNN
+
+source ~/miniconda/bin/activate
+conda activate toolkit2
+cd rknn_model_zoo/examples/yolov8/python
+python convert.py ../../../../azaion.onnx rk3588 i8
+cp --verbose ../model/yolov8.rknn /azaion/models/azaion.rknn
+conda deactivate
+conda deactivate

orangepi5/install-os.md

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+1. Download latest release from here https://joshua-riek.github.io/ubuntu-rockchip-download/boards/orangepi-5.html
+   f.e. https://github.com/Joshua-Riek/ubuntu-rockchip/releases/download/v2.3.2/ubuntu-22.04-preinstalled-desktop-arm64-orangepi-5.img.xz
+   but look to the more recent version on ubuntu 22.04
+   
+2. Write the image to the microsd using https://bztsrc.gitlab.io/usbimager/ (sudo ./usbimager on linux) (or use BalenaEtcher)