YOLO Object Detection App

This is a Python Tkinter application for real-time object detection using the YOLOv3 deep learning model.
It allows you to:

• Detect objects in uploaded images
• Detect objects in uploaded videos
• Detect objects live through your laptop camera

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📌 Features

• YOLOv3 deep learning model for object detection
• Image and video upload support
• Live camera feed detection
• Full-screen responsive interface
• Displays total object count and detailed counts per object
• Easy to exit with a dedicated Exit button

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📂 Project Structure

yolo_object_detection/
│
├── app.py             # Main application file
├── yolov3.cfg         # YOLOv3 model configuration
├── yolov3.weights     # YOLOv3 pretrained weights
├── coco.names         # Object class names
├── README.md          # Project documentation
└── env/               # Python virtual environment (optional)

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://https://github.com/Cherish01-spec/YOLO-Object-Detection.git
cd YOLO-Object-Detection

2️⃣ Create Virtual Environment (Recommended)

python -m venv env
.\env\Scripts\activate     # For Windows
# source env/bin/activate  # For Mac/Linux

3️⃣ Install Dependencies

pip install -r requirements.txt

If you don’t have a requirements.txt, install manually:

pip install opencv-python pillow numpy

4️⃣ Download YOLO Files

• yolov3.weights
• yolov3.cfg
• coco.names

Place these files in the same directory as app.py.

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▶️ Running the Application

python app.py

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🖼️ Usage

1. Upload Image → Detects objects in selected image file.
2. Upload Video → Detects objects in a video file.
3. Live Video → Detects objects in real-time using laptop camera.
4. Exit → Closes the application.

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📌 Requirements

• Python 3.7+
• OpenCV
• Pillow
• NumPy
• YOLOv3 Model files (yolov3.cfg, yolov3.weights, coco.names)

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📜 License

This project is licensed under the MIT License – feel free to use and modify.