# Trajectory Prediction Video installation

## Install

* Run `bash build_opencv_with_gstreamer.sh` to build opencv with gstreamer support
* Use pyenv + poetry to install

## How to

> See also the sibling repo [traptools](https://git.rubenvandeven.com/security_vision/traptools) for camera calibration and homography tools that are needed for this repo.

These are roughly the steps to go from datagathering to training

1. 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`
2. Follow the steps in the auxilary  [traptools](https://git.rubenvandeven.com/security_vision/traptools) repository to obtain (1) camera matrix, lens distortion, image dimensions, and (2+3) homography
3. Run the tracker, e.g. `poetry 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: `poetry 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 the `raw` folder.
4. Parse tracker data to Trajectron format: `poetry run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME`
5. Train Trajectron model `poetry 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 `
6. The run!
    * On a video file (you can use a wildcard) `DISPLAY=:1 poetry run trapserv --remote-log-addr 100.69.123.91 --eval_device cuda:0 --detector ultralytics  --homography ../DATASETS/NAME/homography.json   --video-src ../DATASETS/NAME/*.mp4 --model_dir EXPERIMENTS/models/models_DATE_NAME/--smooth-predictions  --num-samples 3  --render-window --calibration ../DATASETS/NAME/calibration.json` (the DISPLAY environment variable is used here to running over SSH connection and display on local monitor)
    * or on the RTSP stream. Which uses gstreamer to substantially reduce latency compared to the default ffmpeg bindings in OpenCV.