Image Processing Exploration Suite

🌐 Live Demo: https://image-processing-exploration-suite.streamlit.app/

Overview

Image Processing Exploration Suite is an interactive web-based application developed using Streamlit that allows users to explore and visualize fundamental and advanced Digital Image Processing (DIP) techniques.

The application provides a user-friendly interface where users can upload images and immediately observe the effects of various image processing operations. It is particularly suitable for students, educators, and beginners studying image processing concepts.

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Features

The application is organized into multiple modules accessible via the sidebar:

• Resizing & Interpolations
  Resize images using different interpolation techniques such as nearest neighbor, bilinear, and bicubic interpolation.

• Channels & Histogram Analysis
  Visualize individual RGB channels and analyze image intensity distribution using histograms.

• Arithmetic Operations
  Perform image addition, subtraction, multiplication, and division.

• Geometric Transformations
  Apply translation, rotation, scaling, and flipping transformations.

• Spatial Filtering
  Implement smoothing and sharpening filters in the spatial domain.

• Edge Detection
  Detect edges using classical operators such as Sobel, Prewitt, and Canny.

• Convolution
  Understand convolution using predefined and custom kernels.

• Morphological Operations
  Perform erosion, dilation, opening, and closing on images.

• Color Space Conversions
  Convert images between RGB, Grayscale, HSV, and other color spaces.

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Tech Stack

• Python
• Streamlit – Interactive web framework
• OpenCV – Image processing operations
• NumPy – Numerical computations
• Matplotlib – Visualization

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Installation and Local Setup

Follow the steps below to run the project locally or use it live on https://image-processing-exploration-suite.streamlit.app/

1. Clone the Repository

git clone https://github.com/mabdulre9/Image-Processing-Exploration-Suite.git
cd Image-Processing-Exploration-Suite

2. Install Dependencies

pip install numpy opencv-python matplotlib streamlit

3. Run the Streamlit App

streamlit run app.py

License

This project is intended for academic and educational use only.