test_dct

import cv2
import numpy

def convert_to_dct(image_fp, skip_status_bar_fraction=1.0):
    dct_obj = None
    try:
        img_obj = cv2.imread(image_fp)
        height, width, channel = img_obj.shape
        height = int(height * skip_status_bar_fraction) - int(height * skip_status_bar_fraction) % 2
        img_obj = img_obj[:height][:][:]
        img_gray = numpy.zeros((height, width))
        for channel in range(channel):
            img_gray += img_obj[:, :, channel]
        img_gray /= channel
        img_dct = img_gray / 255.0
        dct_obj = cv2.dct(img_dct)
    except Exception as e:
        print e
    return dct_obj


def compare_dct(dct_obj_1, dct_obj_2, threshold=0.0003):
    match = False
    try:
        row1, cols1 = dct_obj_1.shape
        row2, cols2 = dct_obj_2.shape
        if (row1 == row2) and (cols1 == cols2):
            mismatch_rate = numpy.sum(numpy.absolute(numpy.subtract(dct_obj_1, dct_obj_2))) / (row1 * cols1)
            if mismatch_rate <= threshold:
                match = True
    except Exception as e:
        print e
    return match




a1 = convert_to_dct('temp/img00401.bmp')
a2 = convert_to_dct('temp/img02060.bmp')

print compare_dct(a1, a2)