Object Detection with YOLO

This project demonstrates object detection using the YOLO model with OpenCV and PyTorch. The project processes both images and videos, saving the results in specified output directories.

Project Structure

/D:/Perso/Project_IA/Object_Detection_Computer_Vison/
│
├── main.py
├── video_detection.py
├── utils.py
├── yolo/
│   ├── yolov3.cfg
│   ├── yolov3.weights
│   └── coco.names
├── data/
│   ├── images/
│   └── videos/
└── output/
    ├── images/
    └── videos/

Setup

1. Clone the repository.
2. Download the YOLO configuration, weights, and class names files and place them in the yolo/ directory.
3. Install the required Python packages:

   pip install opencv-python-headless numpy torch matplotlib

Usage

Detect Objects in Images

1. Place your images in the data/images directory.
2. Run the main.py script:

   python main.py

3. Processed images will be saved in the output/images directory.

Detect Objects in Videos

1. Place your videos in the data/videos directory.
2. Run the video_detection.py script:

   python video_detection.py

3. Processed videos will be saved in the output/videos directory.

Example Results

Image Detection

Original Image:

Processed Image:

Video Detection

Original Video:

Processed Video:

License

This project is licensed under the MIT License.