This repository contains the code for the Tennis Analysis project, a sophisticated tool designed to analyze tennis gameplay. The project leverages advanced computer vision and machine learning techniques to provide detailed insights into tennis matches.
The Tennis Analysis project is built using Python, with OpenCV for video processing, YOLO for object detection, and custom tracking algorithms for player movements. It processes tennis match videos to detect court lines, track players, and overlay analytical data onto the video.
- Court Line Detection: Accurately detects the tennis court lines.
- Player Tracking: Tracks player movements during the match.
- YOLO Inference: Uses YOLO for real-time object detection in videos.
- Video Processing: Analyzes input videos and generates output videos with analysis overlays.
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Clone the repository:
git clone https://github.com/your-username/tennis-analysis.git cd tennis-analysis -
Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install the dependencies:
pip install -r requirements.txt
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Run the application:
python main.py
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Analyze Videos: Place your videos in the
input_videos/directory and retrieve analyzed videos from theoutput_videos/directory.
- Input Videos: Place video files in the
input_videos/directory. - Run Analysis: Execute the main script to process the videos.
- Output Videos: The analyzed videos will be saved in the
output_videos/directory.
tennis-analysis/
│
├── analysis/
├── constants/
├── court_line_detector/
├── input_videos/
├── output_videos/
├── trackers/
├── training/
├── utils/
├── main.py
├── requirements.txt
└── README.md
- Computer Vision: Using OpenCV for video and image processing.
- Object Detection: YOLO for real-time object detection.
- Machine Learning: Custom algorithms for player tracking and analysis.
- Python Programming: Developing a cohesive project with various libraries and frameworks.
This project is licensed under the MIT License - see the LICENSE file for details.