Python get image color palette

I�created a web application that included screenshots of about 190 country’s�national statistics agencies website home page. I created the site to host the results of comparisons of each country’s website home page features.

One of the features I wanted to include was the top 5 colors used on each home page. For example here is an image of the Central Statistical Office of Zambia website home page.

I wanted a list of the top 5 colors used in the web page, in my case I wanted these as a list of� rgb color values that I could save to a database and use to create a color palette image.

For example the Central Statistical Office of Zambia website home page top 5 rgb colors were:

1. 138, 187, 223
2. 174, 212, 235
3. 101, 166, 216
4. 93, 92, 88
5. 242, 245, 247

These 5 colors were not equally distributed on the home page and when plotted as stacked bar plot and saved as an image, looked like this:

How to identify top 5 colors on webpage

Identifying dominant colors in an image is a common task for a variety of�use cases so there was a number of options available.

The general technique essentially involves reducing an image to a list of pixels and then identifying each pixel’s�color and the relative proportion of that color to all other colors and then taking the top 5 pixel counts to get top 5 colors.

However a web page can have many similar colors for example many shades of blue. So �identification of colors involves a statistical categorization and enumeration to group similar colors into one category, for example, group all shades of blue into one ‘blue’ category.

There are a couple of different statistical methods to do this categorization that are discussed below.

Modified Median Cut Quantization

This�method involves counting image pixels by color and charting them on a histogram from which peaks are counted to get dominant colors.

This method is used in a Python module called color-thief-py.�This module uses Pillow to process the image and modified median cut quantization

I used this module in the code below where I loop through a folder of screenshots to open image and then pass it to color-thief-py to process. Then the top 5 colors’ rgb strings are written into my database as a list so they can be used later.

    import os, os.path
    from PIL import Image
    import psycopg2
    from colorthief import ColorThief

    conn_string = \
        "host='localhost' \
        dbname='databasename' \
        user='username' \
        password='userpassword'" 
        
    conn = psycopg2.connect(conn_string)     
    cur = conn.cursor()

    ## dev paths
    screenshots_path = 'C:/screenshots/'

    screenshots_dir = os.listdir(screenshots_path)
    for screenshot in screenshots_dir:
        if screenshot != 'Thumbs.db':
            img = Image.open(screenshots_path + screenshot)
            width, height = img.size
            quantized = img.quantize(colors=5, kmeans=3)
            palette = quantized.getpalette()[:15]
            convert_rgb = quantized.convert('RGB')
            colors = convert_rgb.getcolors(width*height)
            color_str = str(sorted(colors, reverse=True))
            color_str = str([x[1] for x in colors])
            print screenshot + ' ' + str(img.size[1]) + ' ' + color_str
            
            cur.execute("UPDATE screenshots \
            set color_palette = %s,  \
            height = %s \
            WHERE filename like %s", \
            (str(color_str),img.size[1], '%' + screenshot + '%',))
            conn.commit()
        
    cur.close()
    conn.close()

K-means clustering

Another statistical method to identify dominant colors in an image is using�K-means clustering to group image pixels by rgb values into centroids. Centroid’s with highest counts are identified as dominant colors.

This�method is used in the process I found on this awesome blog post on pyimagesearch.com.

This method also uses Python module sklearn k-means to identify the dominant colors. It produced very similar results to the other method.

This code from the pyimagesearch website used Python module matplotlib�to create plot of the palette and saved these as images. I modified the code to instead simply save the rgb color values as strings to insert into database and save the matplotlib palette rendered plot as an image. I �added�plt.close()�after each loop to close the rendered plot after the plot image was saved because if they aren’t closed, they accumulate in memory and crashed the program.

# USAGE
# python color_kmeans.py --image images/jp.png --clusters 3

# import the necessary packages
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
import utils
import cv2
import os, os.path
import csv

# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required = True, help = "Path to the image")
#ap.add_argument("-c", "--clusters", required = True, type = int, help = "# of clusters")
#args = vars(ap.parse_args())

screenshots_path = 'screenshots/'

screenshot_palette = list()

## Create csv file to write results to
file = csv.writer(open('palettes.csv', 'wb'))
file.writerow(['screenshot','palette'])

screenshots_dir = os.listdir(screenshots_path)
for screenshot in screenshots_dir:
    if screenshot != 'Thumbs.db':
        print screenshot

        # load the image and convert it from BGR to RGB so that
        # we can dispaly it with matplotlib
        image = cv2.imread(screenshots_path + screenshot)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # show our image
        #plt.figure()
        #plt.axis("off")
        #plt.imshow(image)
        #plt.close()

        # reshape the image to be a list of pixels
        image = image.reshape((image.shape[0] * image.shape[1], 3))

        # cluster the pixel intensities
        clt = KMeans(n_clusters = 3)
        clt.fit(image)

        # build a histogram of clusters and then create a figure
        # representing the number of pixels labeled to each color
        hist = utils.centroid_histogram(clt)
        bar = utils.plot_colors(hist, clt.cluster_centers_)
        #color_palette = utils.plot_colors(hist, clt.cluster_centers_)
        #print color_palette
        #row = (screenshot, [tuple(x) for x in color_palette])
        #screenshot_palette.append(row)
        #print row
        
        #print row[0]
        #print row[1]
        
        # show our color bar
        plt.figure()
        plt.axis("off")
        plt.imshow(bar)
        plt.show()
        plt.savefig('palettes/' + screenshot)
        plt.close()
       
        #file.writerow([
        #        row[0].encode('utf-8', 'ignore'),
        #        row[1],
        #        ])