main.py

import cv2
import numpy as np

def load_image(im_path):
    try:
        return cv2.imdecode(np.fromfile(im_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
    except cv2.error:
        return None
    
def image_colorfulness(image):
    if isinstance(image, str):
        image = load_image(image)
    if image is None:
        return 0
    try:
        if image.shape[2] > 3:
            # If more than 3 channels, use only the first three
            image = image[:, :, :3]
    except (IndexError, ValueError):
        return 0
    # 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)