https://github.com/akanshjain12mg/imagexplorica

This project enables you interact with digital image processing algorithms which enhances and filter your images
https://github.com/akanshjain12mg/imagexplorica

digital-image-processing googlecolab javascript opencv-python opencv2 python react

Last synced: 3 months ago
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This project enables you interact with digital image processing algorithms which enhances and filter your images

• Host: GitHub
• URL: https://github.com/akanshjain12mg/imagexplorica
• Owner: AkanshJain12mg
• Created: 2025-04-02T13:34:05.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-04-02T14:08:40.000Z (3 months ago)
• Last Synced: 2025-04-02T14:42:29.626Z (3 months ago)
• Topics: digital-image-processing, googlecolab, javascript, opencv-python, opencv2, python, react
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1WceZGl_5rFR65R-pNsbJPXTz0rkEeZ1d?usp=sharing
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Image Processing Algorithms Project

This project demonstrates several image processing techniques implemented in Python using OpenCV. The algorithms implemented include **Histogram Equalization**, **Gradient Edge Detection**, **Image Sharpening**, and **Template Matching**. The project was developed using Google Colab and is intended to enhance image quality and feature extraction.

## Algorithms Implemented:

- **Histogram Equalization**: Enhances the contrast of an image by adjusting the intensity distribution.

- **Gradient Edge Detection**: Identifies edges in the image using gradient-based methods.

- **Image Sharpening**: Enhances image details and removes blurriness.

- **Template Matching**: Finds a template image within a larger image.

## Screenshots:

Here are some visualizations of the results from the implemented algorithms:

### 1. **Histogram Equalization**:

The following image shows the result of applying **Histogram Equalization** to an image, which enhances the contrast:

![Histogram Equalization Result](histogram_equalization_result.png)

### 2. **Template Matching**:

This image shows the result of applying **Template Matching** where the template was successfully identified within the larger image:

![Template Matching Result](template_matching_result.png)

## Installation:

To run the code locally, you'll need to have **Python 3.x** installed along with the following libraries:

- OpenCV

- NumPy

- Matplotlib

- PIL (Pillow)

You can install these dependencies by running:

```bash

pip install opencv-python numpy matplotlib pillow