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Computer Vision Techniques

This repository contains a collection of computer vision techniques implemented using various libraries and frameworks.

Introduction

In this repository, you will find implementations of fundamental computer vision techniques, ranging from basic image processing operations to more advanced methods like object detection and image segmentation.

The goal of this repository is to provide clear and concise examples of how these techniques can be implemented using popular libraries such as OpenCV, TensorFlow, and PyTorch. Each technique is implemented in a separate script or notebook, along with necessary explanations and references to relevant resources.

Installation

To run the scripts and notebooks in this repository, you'll need to have the following dependencies installed:

  • Python (version >= 3.6)
  • OpenCV
  • TensorFlow
  • PyTorch
  • NumPy
  • Matplotlib

You can install these dependencies using pip:

pip install opencv-python tensorflow torch numpy matplotlib

Usage

Each technique is implemented in its own script or notebook file within the repository. To use a specific technique, follow these steps:

  1. Clone this repository to your local machine:

    git clone https://github.com/tejasOnGit/Computer-Vision-Techniques.git

  2. Navigate to the directory of the technique you are interested in.

  3. Run the script or open the notebook using Python or Jupyter Notebook respectively.

  4. Follow the instructions and comments provided in the script/notebook to understand the implementation and parameters.

Contributing

Contributions are welcome! If you'd like to add more techniques, improve existing implementations, or fix issues, please follow these steps:

  1. Fork the repository and create your branch from main.

  2. Make your changes and ensure the code follows the repository's style and conventions.

  3. Test your changes thoroughly.

  4. Create a pull request describing your changes and improvements.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.