Computer Vision Explorer

A simple computer vision application that allows users to apply various image processing techniques and face detection to uploaded images.

Features

• Image Processing Techniques
  • Grayscale conversion
  • Gaussian blur with adjustable kernel size
  • Edge detection with adjustable thresholds
• Face Detection
  • Detects faces in images using Haar Cascade classifier
  • Highlights detected faces with blue rectangles

Getting Started

Prerequisites

• Python 3.7+
• pip

Installation

1. Clone the repository

   git clone https://github.com/yourusername/computer-vision-project.git
   cd computer-vision-project

2. Create and activate a virtual environment

   python -m venv venv

   On Windows:

   venv\Scripts\activate

   On macOS/Linux:

   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Usage

Running the Basic Image Processing Script

python image_processing.py

Running the Face Detection Script

python face_detection.py

Running the Streamlit App Locally

streamlit run app.py

Project Structure

computer-vision-project/
├── app.py                  # Streamlit web application
├── image_processing.py     # Basic image processing script
├── face_detection.py       # Face detection script
├── requirements.txt        # Project dependencies
├── assets/                 # Screenshots and images
├── README.md               # This file
└── .gitignore              # Git ignore file

Technologies Used

• OpenCV - Open Source Computer Vision Library
• NumPy - Numerical computing library
• Matplotlib - Visualization library
• Streamlit - Web application framework

Blog Post

For a detailed walkthrough of how this project was built, check out my blog post: Make Your First Computer Vision Project: A Step-by-Step Guide

Future Enhancements

• Add more image processing techniques
• Implement object detection
• Create a real-time webcam version
• Add image classification capabilities

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenCV Documentation
• Streamlit Documentation
• Python Pillow Library