https://github.com/brettfazio/cvballtracking
Computer Vision Basketball Tracking
https://github.com/brettfazio/cvballtracking
Last synced: about 2 months ago
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Computer Vision Basketball Tracking
- Host: GitHub
- URL: https://github.com/brettfazio/cvballtracking
- Owner: brettfazio
- Created: 2020-11-23T22:03:57.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-10T03:24:20.000Z (about 1 year ago)
- Last Synced: 2025-03-28T13:21:44.023Z (2 months ago)
- Language: Python
- Homepage:
- Size: 78.6 MB
- Stars: 43
- Watchers: 4
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Basketball Tracking 🏀⛹🏻♀️⛹🏿♂️
Created by [Brett Fazio](http://linkedin.com/in/brett-fazio/) and [William Chen](https://www.linkedin.com/in/william-chen-6474a216b/)
## Overview
Read our paper [here](https://github.com/brettfazio/CVBallTracking/blob/main/assets/Ball_Tracking.pdf).
## Requirements
The libraries to run the code are [cv2](https://pypi.org/project/opencv-python/), [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [h5py](https://www.h5py.org/) (if trying to run/evaluate on the A2D dataset).
An extended version of cv2, ```opencv-contrib-python```, is required. Make sure this is the only cv2 package installed. ```opencv-python``` is a different package that does not include support for the trackers. Do not install multiple different opencv packages in the same environment.
```
pip install opencv-contrib-python
```
Additionally access to the YOLO tracker is required but this is already included in the `/src/yolo` folder. However, you must download the weights for the YOLO model. It can be done as follows:
```
cd src/yolo/weights/
bash download_weights.sh
```
If you wish to use the GOTURN tracker instead of the CSRT tracker (we recommend CSRT) you must download the GOTURN model [here](https://github.com/Mogball/goturn-files) and place it within the `src` folder.
To run on the A2D dataset, the Release of the dataset itself is also required. It is available [here](https://web.eecs.umich.edu/~jjcorso/r/a2d/) and the unzipped folder entitled `Release` should be placed in the `/a2d` directory.
## Usage
The main entry point for this project is `main.py`. To avoid errors, please run it from the `src` directory.
The most basic usage for the project would be to run on a single input video. It can be done as follows:
```
python3 main.py --video PATH
```
Where `PATH` is a path to a video file, for example:
```
python3 main.py --video ../sample_data/lebron_on_court.mp4
```
Adding the `--fast` flag only tracks the ball in frames after the first detection.
```
python3 main.py --video ../sample_data/lebron_on_court.mp4 --fast
```
Adding the `--live` flag allows for real-time tracking. Live tracking is only available when used with the `--fast` flag.
Note: performance may be vary depending on CPU/GPU.
```
python3 main.py --video ../sample_data/lebron_on_court.mp4 --fast --live
```
To run A2D add the `--a2d` flag, and then to specify the number of samples use `--a2d_amt`. For example:
```
python3 main.py --a2d --a2d_amt 5
```
### Forward Pass Only
### Track Backwards + Forwards
## References / Credit
This project builds on the work of eriklindernoren's PyTorch Yolo implementation as a base, specifically the pre-trained model. The repository can be found [here](https://github.com/eriklindernoren/PyTorch-YOLOv3).