📌 Project Overview

Color-Based Ball Tracking With OpenCV is a computer vision project that uses color segmentation to track the movement of balls in a video. It detects the color of each ball, determines its position in frame quadrants, logs entry and exit times, and generates an annotated output video showing tracking details.

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🎥 Demo & Files

• Input Video: AI Assignment video.mp4
• Output Video: processed_video.avi
• Event Log: events.csv

The output video contains overlays indicating the ball's position, direction, and quadrant transitions. All events are timestamped and saved for review in the CSV file.

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🗂️ Project Structure

📁 Color-Based-Ball-Tracking-With-OpenCV/
├── .gitattributes                  # Git settings
├── AI Assignment video.mp4        # Input video file for tracking
├── LICENSE                        # License file (MIT)
├── README.md                      # Project documentation
├── events.csv                     # Output log of events (entry/exit times)
├── processed\_video.avi            # Annotated output video
└── trani model.ipynb              # Main Jupyter notebook (typo: 'trani' → 'train' or 'tracking')

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⚙️ Installation

1. Clone the repository

git clone https://github.com/your-username/Color-Based-Ball-Tracking-With-OpenCV.git
cd Color-Based-Ball-Tracking-With-OpenCV

2. Install dependencies

Make sure you have Python 3.7+ and Jupyter installed. Then run:

pip install opencv-python numpy imutils

Or install from within the notebook.

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🚀 Getting Started

1. Open the Jupyter notebook:

jupyter notebook "trani model.ipynb"

2. Run the cells in sequence:

   • Load and preprocess the video
   • Apply HSV-based color masking
   • Detect ball contours and calculate positions
   • Log entry/exit events based on quadrant transitions
   • Write results to processed_video.avi and events.csv

3. Review the output files in the root directory.

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📊 Sample Event Log (events.csv)

Timestamp	Color	Event	Quadrant
00:00:05.12	Red	Entered	Q1
00:00:06.45	Red	Exited	Q2
00:00:07.21	Green	Entered	Q3

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🧪 Features

• 🎯 Color Detection using HSV thresholding
• 🧭 Quadrant Detection in a 2x2 frame split
• 📝 Event Logging (entry & exit per quadrant)
• 🎥 Processed Video Output with overlays

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🛠️ Customization

You can easily customize the project:

• 🎨 Change tracked colors by modifying HSV ranges
• 📐 Adjust quadrant logic to fit other layouts
• 🖼️ Change video resolution or format
• 📄 Log additional metrics like speed or trajectory

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📚 Use Cases

• 🏀 Sports video analysis
• 🤖 Robotics vision systems
• 🎓 Educational demos for OpenCV and tracking
• 📹 Surveillance and area monitoring

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🤝 Contributing

Feel free to fork this repo and submit pull requests. For any major changes, please open an issue first.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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✏️ Notes

• The file name trani model.ipynb contains a typo. Consider renaming it to tracking_model.ipynb for clarity.
• You can convert this notebook to a Python script using:

jupyter nbconvert --to script "trani model.ipynb"