Skip to content

README.md

Latest commit

 

History

History
93 lines (73 loc) · 2.19 KB

README.md

File metadata and controls

93 lines (73 loc) · 2.19 KB

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

📌 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

📂 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)

⚙️ 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.

▶️ Running the Application

python app.py

🖼️ 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.

📌 Requirements

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

📜 License

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