Real-time object detection using a Raspberry Pi with a Pi Camera Module V2 and Coral USB Accelerator. Streams video over Flask. Training and model prep is done on a laptop, and inference runs on the Pi.
sudo apt update && sudo apt install -y \
python3-picamera2 v4l-utils libusb-1.0-0-dev libedgetpu1-std \
make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev \
libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev gitcurl https://pyenv.run | bashThen add to your ~/.bashrc:
export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv virtualenv-init -)"Restart your shell:
exec "$SHELL"pyenv install 3.9.18
python3.9 -m venv ~/coral-venv --system-site-packages
source ~/coral-venv/bin/activatepip install -r requirements.txt
pip install --extra-index-url https://google-coral.github.io/py-repo/ tflite-runtime
pip install https://github.com/google-coral/pycoral/releases/download/v2.0.0/pycoral-2.0.0-cp39-cp39-linux_aarch64.whlpython stream.pyView at:
http://<your-pi-ip>:5000/video_feed
pyenv install 3.9.18
'''
#### Create a virtual environment using Python 3.9
```bash
python3.9 -m venv coral-train
source coral-train/bin/activatepyenv virtualenv 3.9.18 coral-train
pyenv activate coral-train
'''
#### Install training dependencies
```bash
pip install tensorflow-macos==2.13.0 numpy pandas opencv-pythontflite_convert --output_file=model.tflite ...
edgetpu_compiler model.tflitescp model_edgetpu.tflite pi@<pi-ip>:~/models/- Uses
picamera2for fast streaming libedgetpu1-stdis installed (not max)- Tested on Raspberry Pi OS Bookworm 64-bit + Coral USB Accelerator