Filtering-and-Segmentation

A simple Python project for experimenting with classic image filtering and segmentation techniques using OpenCV and NumPy.

Features

• Applies low-pass (smoothing) and median filters to images
• Performs edge detection using the Canny algorithm
• Segments images using:
  • K-means clustering
  • Region growing
  • Global and adaptive thresholding

Requirements

• Python 3.x
• OpenCV (opencv-python)
• NumPy

Usage

1. Install dependencies:

   pip install opencv-python numpy

2. Set your image path:

   Each script contains a placeholder line such as:

   img = cv2.imread('c:/path/to/image.png')  # <-- Placeholder path

   Update this path to point to your own image file.

3. Run a script:

   For example, to run the k-means segmentation:

   python Segmentation/k-means.py

   Or to apply a median filter:

   python Filtering/medium-filter.py

   Each script will display the processed image in a window.

Scripts Overview

• Filtering/

  • low-pass-filter.py: Applies a 5x5 averaging filter
  • medium-filter.py: Applies a 5x5 median filter
  • edge-detection.py: Detects edges using Canny

• Segmentation/

  • k-means.py: Segments image colours using k-means clustering
  • region-growth.py: Segments regions based on intensity similarity
  • thresholding.py: Performs global and adaptive thresholding

Notes

• Ensure you update the image path in each script before running.
• All scripts open a window to display results; press any key to close.