Real-time Object Recognition with TensorFlow and OpenCV

Overview

This project enables real-time object recognition using a webcam, powered by TensorFlow and OpenCV. The system identifies and classifies objects in live video streams, making it versatile for applications like security systems, robotics, and interactive installations.

Check out the GitHub repository for more details.

Features

• Real-time Processing: The system processes video frames in real-time for instantaneous object recognition.

• TensorFlow Integration: Leverage TensorFlow's deep learning capabilities for accurate and efficient object classification.

• OpenCV Webcam Interaction: Utilize OpenCV to seamlessly interact with the webcam, making it easy to integrate into various applications.

Usage

1. Clone the Repository:

   git clone https://github.com/SandeepVashishtha/Image-Recognition.git
   cd Image-Recognition

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the Real-time Object Recognition:

   python main.py

   The code will automatically initiate the webcam feed, allowing you to experience real-time object recognition directly.

Dependencies

This project requires Python 3.10 or 3.11 and the following Python libraries installed:

• opencv-python==4.5.3.56
• torch==1.9.0
• yolov5==5.0.9

You can install these dependencies using the requirements.txt file as follows:

pip install -r requirements.txt

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License. See LICENSE.md for details.