Dominant Colour Extraction

Deployment Link: Dominant Colour Extraction

This Streamlit application allows users to upload an image and extract the dominant colors using KMeans clustering. Here's a guide to understand and use the application effectively.

Setup Instructions

1. Clone the Repository

   git clone https://github.com/DevanshChhabra/Dominant-Colour-Extraction.git
   cd Dominant-Colour-Extraction

2. Install Dependencies Ensure you have Python installed. Install required packages using pip:

   pip install streamlit scikit-learn numpy matplotlib Pillow

3. Run the Application
   Execute the following command to start the Streamlit application:

   streamlit run master.py

   This will open a local server where you can interact with the application in your web browser.

Usage Instructions

1. Upload Image

• Click on the "Choose an image" button to upload an image from your local machine.
• Supported formats include JPG, PNG, and others compatible with PIL.

2. Select Number of Colors

• Use the slider to select the number of dominant colors to extract from the image (from 1 to 10).

3. View Results

• The application displays the original image and below it, the modified image with reduced colors based on your selection.

Customization

• CSS Styling

The application uses custom CSS for styling. You can modify the designing.css file to change the appearance of various elements like backgrounds, text colors, and gradients.

Project Structure

• master.py: Contains the main Streamlit application code, including image processing and KMeans clustering.
• designing.css: Custom CSS file for styling elements within the Streamlit app.

Dependencies

• Streamlit: Frontend framework for building interactive web applications with Python.
• scikit-learn: Used for KMeans clustering.
• numpy: Fundamental package for scientific computing with Python.
• matplotlib: Library for creating static, animated, and interactive visualizations in Python.
• Pillow: Python Imaging Library (PIL fork) for opening, manipulating, and saving many different image file formats.

Notes

• Ensure that the Python environment where you run the application has all necessary packages installed.
• For production deployment, consider hosting the application on platforms like Heroku or AWS after adjusting configurations as necessary.

Example

For a live demonstration, visit Streamlit and learn how to deploy Streamlit apps in the documentation.

Credits

• This project utilizes Streamlit, scikit-learn, numpy, matplotlib, and Pillow, all of which are open-source libraries. Thanks to their contributors!

License

• This project is licensed under MIT License - feel free to modify and distribute the code.