🧠 Real-Time Object Detection using YOLOv4 + OpenCV (Local Version)

This project implements real-time object detection using YOLOv4 and OpenCV, running completely locally via your webcam. It includes a utility script to auto-download YOLOv4 weights and displays detection results in a live OpenCV window.

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🔍 Features

• ✅ Real-time object detection using YOLOv4 (Darknet)
• 🧠 Uses OpenCV's DNN module (no Darknet compilation or Flask needed)
• 🔁 Auto-downloads yolov4.weights if not present
• 💻 Runs entirely locally using your webcam
• 🚫 No web deployment, no Flask, no external servers

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🗂️ Project Structure

Object-Detection/
├── app.py                # Main object detection script (local OpenCV)
├── coco.names            # COCO class labels
├── yolov4.cfg            # YOLOv4 model configuration
├── yolov4.weights        # YOLOv4 pre-trained weights (auto-downloaded)
├── download_weights.py   # Script to auto-download yolov4.weights
├── requirements.txt      # Python dependencies
├── .gitignore            # Prevents large/binary files from being tracked
└── README.md             # Project documentation (this file)

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⚙️ Installation (Local)

1. Clone the repository

git clone https://github.com/Mayank-01x/Object-Detection.git
cd Object-Detection

2. Install dependencies

pip install -r requirements.txt

3. Download YOLOv4 weights (if not already present)

python download_weights.py

4. Run the object detection app

python app.py

💡 The window will automatically close when you press q or manually close it.

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🎥 Camera Input Options

You can modify cv2.VideoCapture() in app.py to use different sources:

# Use default webcam
cv2.VideoCapture(0)

# IP camera (replace with your IP)
cv2.VideoCapture("http://your-ip-address:port/video")

# Pre-recorded video file
cv2.VideoCapture("your_video.mp4")

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🙋 Author

Mayank Aggarwal
GitHub: @Mayank-01x

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📄 License

This project is licensed under the MIT License.