3.2 KiB
3.2 KiB
Trajectory Prediction Video installation
Install
- Run
bash build_opencv_with_gstreamer.shto build opencv with gstreamer support - Use
uvto install
How to
See also the sibling repo traptools for camera calibration and homography tools that are needed for this repo.
These are roughly the steps to go from datagathering to training
- Make sure to have some recordings with a fixed camera. [UPDATE: not needed anymore, except for calibration & homography footage]
- Recording can be done with
ffmpeg -rtsp_transport udp -i rtsp://USER:PASS@IP:554/Streaming/Channels/1.mp4 hof2-cam-$(date "+%Y%m%d-%H%M").mp4
- Recording can be done with
- Follow the steps in the auxilary traptools repository to obtain (1) camera matrix, lens distortion, image dimensions, and (2+3) homography
- Run the tracker, e.g.
uv run tracker --detector ultralytics --homography ../DATASETS/NAME/homography.json --video-src ../DATASETS/NAME/*.mp4 --calibration ../DATASETS/NAME/calibration.json --save-for-training EXPERIMENTS/raw/NAME/- Note: You can run this right of the camera stream:
uv run tracker --eval_device cuda:0 --detector ultralytics --video-src rtsp://USER:PW@ADDRESS/STREAM --homography ../DATASETS/NAME/homography.json --calibration ../DATASETS/NAME/calibration.json --save-for-training EXPERIMENTS/raw/NAME/, each recording adding a new file to therawfolder.
- Note: You can run this right of the camera stream:
- Parse tracker data to Trajectron format:
uv run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAMEOptionally, smooth tracks:--smooth-tracks- Optionally, add a map: ideally a RGB png: 3 layers of 0-255
uv run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME --smooth-tracks --camera-fps 12 --homography ../DATASETS/NAME/homography.json --calibration ../DATASETS/NAME/calibration.json --filter-displacement 2 --map-img-path ../DATASETS/NAME/map.png
- Optionally, add a map: ideally a RGB png: 3 layers of 0-255
- Train Trajectron model
uv run trajectron_train --eval_every 10 --vis_every 1 --train_data_dict NAME_train.pkl --eval_data_dict NAME_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _NAME --train_epochs 100 --conf EXPERIMENTS/config.json --batch_size 256 --data_dir EXPERIMENTS/trajectron-data - The run!
uv run supervisord