import cv2 as cv
import numpy as np

img_cache = {}
hist_rgb_cache = {}


def readImage(img_path):
    global img_cache

    if img_path in img_cache.keys():
        pass

    else:
        # read image
        img_test_rgb = cv.imread(img_path)
        # convert to gray scale
        img_test_rgb = cv.resize(img_test_rgb, (1024, 1024))
        img_cache[img_path] = img_test_rgb

    return img_cache[img_path]


def cachedCvCalcHist(img2_path, channel, num_bins):
    global hist_rgb_cache

    img_input = readImage(img2_path)

    if tuple([img2_path, channel, num_bins]) in hist_rgb_cache.keys():
        return hist_rgb_cache[tuple([img2_path, channel, num_bins])]
    else:
        hist = cv.calcHist([img_input], [channel], None, [num_bins], [0, 255])
        hist_rgb_cache[tuple([img2_path, channel, num_bins])] = hist
        return hist


def compareImgs_hist_rgb(img1_path, img2_path, num_bins=10):
    total_diff = 0

    for channel in range(3):  # Iterate over RGB channels
        # hist1 = cv.calcHist([img1], [channel], None, [num_bins], [0, 255])
        # hist2 = cv.calcHist([img2], [channel], None, [num_bins], [0, 255])
        hist1 = cachedCvCalcHist(img1_path, channel, num_bins)
        hist2 = cachedCvCalcHist(img2_path, channel, num_bins)

        channel_diff = np.sum(np.abs(hist1 - hist2))
        total_diff += channel_diff

    return total_diff / (3 * float(1024 * 1024))