After you set up your environment (see below), you can run the following commands to build a CV model and annotate a video. Everything is stored in your project directory, so you can pick up where you left off.
First, run ./src/s1setup.py to set up the project directory, and cluster the frames to find diverse representative frames.
Then run ./src/s2train.py to create a detector, and send the most interesting frames to the model. During this time, you should open the dashboard and label the images as they come in. This script will wait for confident scores.
Now run ./src/s3infer.py to run all the frames through the detector, and build a new video with the results.
There are at least a couple ways to set up your development environment. Pick one that works for you.
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
conda create -n video-annotator python=3.11
sudo apt update && apt install direnv
echo 'eval "$(direnv hook bash)"' >> ~/.bashrc
echo 'export CONDA_BASE_PATH="$(dirname "$(dirname "$(which conda)")")"' >> ~/.bashrc
direnv allow .
conda activate video-annotator # Should be automatic and unneeded
poetry install
On Ubuntu, if you get an error about libGL, you may need to install the libgl1-mesa-glx package:
sudo apt update
sudo apt install -y libgl1-mesa-glx