Live-Object-Detection-with-Camera

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Live-Object-Detection-with-Camera

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Real-time object detection using YOLO model and OpenCV on live camera feed.

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# Live-Object-Detection-with-Camera

Real-time object detection using **YOLO model** and OpenCV on live camera feed.

![Demo](https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png)

## Overview
The "Live Object Detection with YOLO and OpenCV" project is a real-time object detection system that utilizes the YOLO (You Only Look Once) model and the OpenCV library to perform live object detection on a camera feed. This project aims to showcase the potential of real-time object detection and inspire developers, researchers, and hobbyists to explore the exciting world of computer vision applications.

**Main Objectives:**

- Real-Time Object Detection: Detect and identify various objects in a live video stream in real-time.
- YOLO Model Integration: Utilize the speed and accuracy of the YOLO model for efficient object detection.
- OpenCV Visualization: Visualize the detected objects with bounding box annotations using OpenCV.

## Key Features
- Efficiency: The YOLO model enables fast and reliable object detection in real-time, suitable for various applications.
- Customization: Users can adjust the confidence threshold, modify annotation styles, and select specific object classes for detection.
- Ease of Use: The code is user-friendly, with comprehensive documentation to guide users in setup and customization.
- Versatility: The live object detection system can be used for security, surveillance, traffic monitoring, and interactive installations.

## Installation and Setup
To get started with the project, follow the installation and setup instructions in the [Installation Guide](installation_guide.md). This will help you set up the necessary environment and install required dependencies.

## Usage
1. Make sure you have followed the installation instructions.
2. Run the Jupyter notebook "demo.ipynb" to start the live object detection demo.
3. Your webcam or connected camera will display a live video stream with object detection annotations.
4. Press the 'q' key to stop the program and close the video window.
5. or **you can run app.py file directly just make sure that yolov8n.pt file path is currently mentioned.To stop the program hit ctrl+C**
## Customization Options
The project allows you to customize various aspects of the live object detection system:
- Adjust the confidence threshold for object detection.
- Modify the appearance of bounding box annotations.
- Select specific object classes to detect or exclude.

Refer to the [Customization Guide](customization_guide.md) for detailed instructions on how to customize the project.

## Examples and Use Cases
Explore the [Examples](examples.md) section to discover practical use cases and scenarios for the live object detection system.

## Troubleshooting
If you encounter any issues during setup or execution, refer to the [Troubleshooting Guide](troubleshooting_guide.md) for common solutions to potential problems.

## Contributing
Contributions to the project are welcome! If you find a bug, have a feature request, or want to contribute improvements, please check out our [Contributing Guidelines](CONTRIBUTING.md).

## Acknowledgments
I would like to acknowledge the contributions of the open-source community, the creators of YOLO, and the developers of OpenCV.

## License
This project is licensed under the [MIT License](LICENSE).

Thank You 
-Tinny Robot

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