Image Tiling

This projects contains a Python script for reducing the number of colors in an image and transforming it into a pixelated version using predefined tile colors. This project uses KMeans clustering to identify dominant colors in the image and maps them to a set of predefined colors.

Demo

Step 0 - Original Image

Step 1 - Pixelate image

The image is pixelated to 25×25 pixels by downscaling using the PIL resize function.

Step 2 - Reduce Colors

The image is reduced to 4 colors using KMeans clustering to find the dominant colors in the image.

Step 3 - Remap Colors

The four found colors are remapped to a set of predefined colors - red, white, black, and gray.
This is accomplished by calculating the Euclidean distances in RGB space between the found and predefined colors.

How to Run

Option 1 (easiest): Web Interface

Go to https://anna-st-40.github.io/image_tiling/ to use the web interface for this project.

Option 2: Run locally

To run the Python script and generate your own image locally, simply use server\pixelator.py. Make sure to put your own parameters under

if __name__ == "__main__":

and save the file to your desired location.

The requirements to run this script are:

joblib==1.4.2
numpy==2.2.1
pillow==11.1.0
scikit-learn==1.6.1
scipy==1.15.1
threadpoolctl==3.5.0

Option 3: Call API

You can also send an API request to https://image-tiling-api.onrender.com/, which runs the same Python file on a Flask server. It deletes your image as soon as you receive it.

Request

• Content-Type: multipart/form-data
• Body Parameters:
  • image (required): The image file to be processed.
  • tile_colors (optional): A JSON list of hex color codes to use for the tiles. If not provided, the default colors are used.
  • pixel_dimensions (optional): The side length of the final dimension in tiles. Default value is 50.

Response

If the request is successful, you will receive the processed image in PNG format.