Image Pattern Analysis and Modification

This repository contains the code and example outputs for a project focused on image pattern recognition and manipulation using advanced computer vision techniques.

Project Overview

The core of this project is the Jupyter notebook which contains the implementation of the Patchmatch algorithm an efficient random search algorithm to find approximate nearest neighbor matches between image patches.

This Jupyter notebook includes the following:

• Detailed explanation of the Patchmatch algorithm.
• Implementation of the algorithm in Python.
• Visualization of the algorithm's process and results.
• Optimizations and modifications to the original algorithm for improved performance.

512x512_squares.png

An example image provided to showcase the result of the algorithm. The image contains a noisy background with clearly marked white squares, used for testing the pattern recognition capabilities of the Patchmatch implementation more focused on time consumption.

How to Run the Project

To run the project, you will need Jupyter or an equivalent service that can execute .ipynb notebook files. Ensure you have all the necessary dependencies installed by running:

pip install -r requirements.txt

Open the Jupyter notebook and execute the cells in sequence to see the algorithm in action.

Dependencies

• Python 3.x
• NumPy
• OpenCV
• Matplotlib
• Jupyter

A requirements.txt file should be included that lists the precise versions of these dependencies.