bright_contr.py

import cv2
import numpy as np

# Open a typical 24 bit color image. For this kind of image there are
# 8 bits (0 to 255) per color channel
img = cv2.imread(r'D:\computer_vision\data\Mandrill-k-means.png')  # mandrill reference image from USC SIPI

s = 128
# img = cv2.resize(img, (s, s), 0, 0, cv2.INTER_AREA)


def apply_brightness_contrast(input_img, brightness=0, contrast=0):
    if brightness != 0:
        if brightness > 0:
            shadow = brightness
            highlight = 255
        else:
            shadow = 0
            highlight = 255 + brightness
        alpha_b = (highlight - shadow) / 255
        gamma_b = shadow

        buf = cv2.addWeighted(input_img, alpha_b, input_img, 0, gamma_b)
    else:
        buf = input_img.copy()

    if contrast != 0:
        f = 131 * (contrast + 127) / (127 * (131 - contrast))
        alpha_c = f
        gamma_c = 127 * (1 - f)

        buf = cv2.addWeighted(buf, alpha_c, buf, 0, gamma_c)

    return buf

font = cv2.FONT_HERSHEY_SIMPLEX
fcolor = (0, 0, 0)

blist = [0, -127, 127, 0, 0, 64]  # list of brightness values
clist = [0, 0, 0, -64, 64, 64]  # list of contrast values

result = apply_brightness_contrast(img,brightness=127,contrast=-64)
cv2.imshow('result',result)
cv2.waitKey(0)
# out = np.zeros((s * 2, s * 3, 3), dtype=np.uint8)
#
# for i, b in enumerate(blist):
#     c = clist[i]
#     print('b, c:  ', b, ', ', c)
#     row = s * int(i / 3)
#     col = s * (i % 3)
#
#     print('row, col:   ', row, ', ', col)
#
#     out[row:row + s, col:col + s] = apply_brightness_contrast(img, b, c)
#     msg = 'b %d' % b
#     cv2.putText(out, msg, (col, row + s - 22), font, .7, fcolor, 1, cv2.LINE_AA)
#     msg = 'c %d' % c
#     cv2.putText(out, msg, (col, row + s - 4), font, .7, fcolor, 1, cv2.LINE_AA)
#
#     cv2.putText(out, 'OpenCV', (260, 30), font, 1.0, fcolor, 2, cv2.LINE_AA)
#
# cv2.imwrite('out.png', out)