Colorfulness using scikit-image's regionprops

colorfulness.py

from skimage import data, segmentation, measure, color, img_as_float

import numpy as np
import matplotlib.pyplot as plt


image = img_as_float(data.chelsea())
# or use io.imread(filename) to read your own

segments = segmentation.slic(image, slic_zero=True)
regions = measure.regionprops(segments)
colorfulness = np.zeros(image.shape[:2])

for region in regions:
    # Grab all the pixels from this region
    # Return an (N, 3) array
    coords = tuple(region.coords.T)
    values = image[coords]

    R, G, B = values.T

    # Calculate colorfulness
    rg = np.abs(R - G)
    yb = np.abs(0.5 * (R + G) - B)

    std_root = np.sqrt(np.std(rg) ** 2 + np.std(yb) ** 2)
    mean_root = np.sqrt(np.mean(rg) ** 2 + np.mean(yb) ** 2)

    colorfulness[coords] = std_root + (0.3 * mean_root)

hsv = color.rgb2hsv(image)
hsv[..., 2] *= colorfulness / colorfulness.max()

plt.imshow(color.hsv2rgb(hsv))
plt.show()