Colorgram

colorgram.md

Colorgram

Extract colors from images and attach them as swatches. See an example here.

Takes one argument: number_of_color_clusters (int), which is pretty self-explanatory. No? Okay, it means how many color clusters (e.g. reddish colors, bluish colors, etc.) to return and add to the swatches.

Usage:

input_path = "input_image.jpg"
output_path = "output_image.jpg"
colorgrammed_image = create(image_path=input_path, number_of_color_clusters=10)
cv2.imwrite(output_path, colorgrammed_image)

PS: I owe a debt to various places where I learned how to do this and copied some code snippets. I don't remember them all but now wish I did. If you recognize this as something you've worked on, and want credit, please let me know as I'm happy to give it!

colorgram.py

import cv2
import numpy as np


def create(image_path: str, number_of_color_clusters: int) -> list:

    # Read in the image.
    img = cv2.imread(image_path)

    # Get the image's height, width, and channels.
    img_height, img_width, img_channels = np.shape(img)

    # Convert the image into a list of pixels, with each pixel representing a color.
    pixels = np.reshape(img, (img_height * img_width, img_channels))

    # Turn each number in the pixel array into a float32, needed for the kmeans() function.
    pixels = np.float32(pixels)

    # Specify kmeans clustering settings.
    kmeans_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) # How clustering should work
    kmeans_flags = cv2.KMEANS_RANDOM_CENTERS # Start clustering with a random set of samples

    # Cluster colors and set pixel_labels to the cluster label for each pixel, and color_clusters to the colors we've clustered pixels into.
    _, pixel_labels, color_clusters = cv2.kmeans(pixels, number_of_color_clusters, None, kmeans_criteria, 10, kmeans_flags)

    # Calculate the percentage of pixels that each color cluster represents.
    (pixel_label_counts, _) = np.histogram(pixel_labels, bins=number_of_color_clusters)
    pixel_label_percentages = pixel_label_counts / pixel_label_counts.sum()

    # Create a canvas on which to display the colors in percentages.
    color_bar_width = img_width # Because we stack the original image on top of the color bar, so they need to be the same height
    color_bar_height = int(img_height * .2) # Make the height proportional 
    color_bar = np.zeros((color_bar_height, color_bar_width, 3), dtype = "uint8")

    # Merge the colours and percentages they represent. Then order by percentage in descending order so more prominent color clusters come first.
    colors_and_percentages = list(zip(color_clusters, pixel_label_percentages))
    colors_and_percentages.sort(key=lambda tup: tup[1], reverse=True)

    # Create a variable to represent the starting point of each color in the bar.
    start_x = 0

    # For each color and percent...
    for (color, percent) in colors_and_percentages:

        # Plot the width of the color in the bar.
        end_x = start_x + (percent * color_bar_width)

        # Create the rectangle with OpenCV.
        cv2.rectangle(color_bar, (int(start_x), 0), (int(end_x), color_bar_height), color.astype("uint8").tolist(), -1)

        # Update the starting point for the next colour.
        start_x = end_x

    # Merge the color bar and original image.
    combined = np.zeros((img_height + color_bar_height, img_width, 3), np.uint8)
    combined[:img_height, :img_width, :3] = img
    combined[img_height:img_height+color_bar_height, :img_width, :3] = color_bar

    # Return the new image.
    return combined