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The Player Productivity Tracker (PPT) is a Python-based tool designed to track the productivity of players in various sports. It uses computer vision and machine learning techniques to analyze video footage of players and provide insights into their performance.
- Video Analysis: PPT can analyze video footage of players and detect their movements, speed, and direction.
- Productivity Tracking: PPT can track the productivity of players based on their movements and provide insights into their performance.
- PDF Report Generation: PPT can generate PDF reports of the analysis, including images and statistics.
- Programming Language: Python 3.x
- Libraries: OpenCV, Pillow, NumPy, SciPy
- Machine Learning: YOLOv3 object detection model
- Computer Vision: Image processing and analysis techniques
- Clone the repository:
git clone https://github.com/your-username/player-productivity-tracker.git - Install the required libraries:
pip install -r requirements.txt - Run the script:
python main.py - Follow the prompts to select the video file and output directory.
- Analyze a video of a badminton player's game to track their productivity and provide insights into their performance.
- Generate a PDF report of the analysis, including images and statistics.
Contributions are welcome! If you'd like to contribute to the project, please fork the repository and submit a pull request.
The Player Productivity Tracker is licensed under the MIT License.
- The YOLOv3 object detection model was developed by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi.
- The OpenCV library was developed by the OpenCV Team.
- The Pillow library was developed by the Python Imaging Library (PIL) Team.
This README file provides a comprehensive overview of the Player Productivity Tracker project, including its features, technical details, usage instructions, and acknowledgments. It is designed to help users understand the project and get started quickly.