from imutils import build_montages
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
def image_colorfulness(image):
# split the image into its respective RGB components
(B, G, R) = cv2.split(image.astype("float"))
# compute rg = R - G
rg = np.absolute(R - G)
# compute yb = 0.5 * (R + G) - B
yb = np.absolute(0.5 * (R + G) - B)
# compute the mean and standard deviation of both `rg` and `yb`
(rbMean, rbStd) = (np.mean(rg), np.std(rg))
(ybMean, ybStd) = (np.mean(yb), np.std(yb))
# combine the mean and standard deviations
stdRoot = np.sqrt((rbStd ** 2) + (ybStd ** 2))
meanRoot = np.sqrt((rbMean ** 2) + (ybMean ** 2))
# derive the "colorfulness" metric and return it
return stdRoot + (0.3 * meanRoot)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", required=True,
help="path to input directory of images")
args = vars(ap.parse_args())
# initialize the results list
print("[INFO] computing colorfulness metric for dataset...")
results = []
# loop over the image paths
for imagePath in paths.list_images(args["images"]):
# load the image, resize it (to speed up computation), and
# compute the colorfulness metric for the image
image = cv2.imread(imagePath)
image = imutils.resize(image, width=250)
C = image_colorfulness(image)
# display the colorfulness score on the image
cv2.putText(image, "{:.2f}".format(C), (40, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 255, 0), 3)
# add the image and colorfulness metric to the results list
results.append((image, C))
# sort the results with more colorful images at the front of the
# list, then build the lists of the *most colorful* and *least
# colorful* images
print("[INFO] displaying results...")
allColors = results
allColor = [a[0] for a in allColors[:64]]
allColorMontage = build_montages(allColor, (150, 84), (8, 8))
results = sorted(results, key=lambda x: x[1], reverse=True)
cv2.imshow("ALL", allColorMontage[0])
cv2.waitKey(0)
