Object Detection using OpenCV, Python, and YOLO
Project for object detection using OpenCV, Python, and YOLO (You Only Look Once) technology. The project utilizes the powerful capabilities of YOLO for real-time object detection and recognition within images or video streams.
- Object Detection: Utilizes YOLO pre-trained models for detecting a wide range of objects in images or video streams.
- Real-time Detection: Implements real-time object detection, enabling users to process video streams in real-time.
- Customizable: Offers flexibility for users to fine-tune parameters such as confidence thresholds, input size, and model architecture.
- User Interface: Provides a user-friendly interface for easy interaction and visualization of the detected objects.
- ROI-based Detection: Supports defining regions of interest (ROIs) for focused object detection within specific areas of the input frame.
- OpenCV: A popular computer vision library used for image and video processing tasks.
- Python: A high-level programming language known for its simplicity and versatility.
- YOLO (You Only Look Once): State-of-the-art object detection system known for its speed and accuracy.
- Clone the repository to your local machine.
- Install the necessary dependencies.
- Run the main Python script to start the object detection process.
- Customize the parameters as needed, such as confidence thresholds, input source (image or video), and ROI settings.
Contributions to the project are welcome! Feel free to submit pull requests for bug fixes, feature enhancements, or optimizations.
This project is licensed under the MIT License, allowing for free use, modification, and distribution.
This project builds upon the work of the OpenCV, Python, and YOLO communities, whose contributions have made advanced object detection accessible to developers worldwide.