https://github.com/xgagandeep/image-segmentation-using-kmeans

This project demonstrates image segmentation using the KMeans clustering algorithm.
https://github.com/xgagandeep/image-segmentation-using-kmeans

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This project demonstrates image segmentation using the KMeans clustering algorithm.

• Host: GitHub
• URL: https://github.com/xgagandeep/image-segmentation-using-kmeans
• Owner: xgagandeep
• Created: 2024-09-11T18:24:45.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-11T18:27:30.000Z (2 months ago)
• Last Synced: 2024-09-12T04:46:18.873Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 132 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


# Image Segmentation using KMeans

**Repository:** [xgagandeep/Image-Segmentation-using-Kmeans](https://github.com/xgagandeep/Image-Segmentation-using-Kmeans)  

**Date:** 2020  

**Language:** Python  

**Libraries:** NumPy, OpenCV, Matplotlib, Scikit-learn

## Description

This project demonstrates image segmentation using the KMeans clustering algorithm. The notebook performs color-based segmentation on an image by clustering pixel colors and then reconstructing the image based on the dominant colors identified by KMeans.

## Features

- **Image Loading:** Reads and preprocesses the image.

- **Pixel Flattening:** Reshapes the image into a 2D array where each row represents a pixel's RGB values.

- **KMeans Clustering:** Applies KMeans to segment the image into a specified number of color clusters.

- **Color Visualization:** Displays the dominant colors identified by KMeans.

- **Image Reconstruction:** Reconstructs and visualizes the segmented image based on the dominant colors.

## Files

- **`Image Segmentation.ipynb`:** Jupyter Notebook containing the implementation of image segmentation using KMeans.

## Installation

To run this project, you need Python and the required libraries installed. Follow these steps:

1. **Clone the repository:**

   ```bash

   git clone https://github.com/xgagandeep/Image-Segmentation-using-Kmeans.git

   ```

2. **Navigate to the project directory:**

   ```bash

   cd Image-Segmentation-using-Kmeans

   ```

3. **Install the required libraries:**

   ```bash

   pip install numpy opencv-python matplotlib scikit-learn

   ```

4. **Run the Jupyter Notebook:**

   ```bash

   jupyter notebook Image\ Segmentation.ipynb

   ```

## Usage

1. **Image Reading:** The image is loaded and converted to RGB format for processing.

2. **Pixel Flattening:** The image is reshaped into a 2D array of RGB values.

3. **KMeans Clustering:**

   - Define the number of dominant colors (clusters).

   - Apply KMeans clustering to the pixel data.

   - Retrieve cluster centers (dominant colors).

4. **Color Visualization:** Display the dominant colors as color swatches.

5. **Image Reconstruction:**

   - Assign each pixel to the nearest cluster center.

   - Reconstruct the image using the dominant colors.

   - Display the segmented image.

## Functions

- **`cv2.imread(path)`**: Reads the image from the specified path.

- **`cv2.cvtColor(img, cv2.COLOR_BGR2RGB)`**: Converts the image from BGR to RGB format.

- **`reshape((-1, 3))`**: Flattens the image into a 2D array of RGB values.

- **`KMeans(n_clusters)`**: Applies KMeans clustering to the pixel data.

- **`plt.imshow(image)`**: Displays images and color swatches.

## Example

The notebook demonstrates segmentation on a sample image. You can modify the `dominant_colors` variable to change the number of clusters for segmentation and visualize how different numbers of colors affect the segmentation results.

## Contribution

Feel free to contribute to this project by submitting issues or pull requests. For any questions or feedback, please open an issue on the GitHub repository.