# Create a matrix with constant intensity.
matrix = np.ones(img.shape, dtype = 'uint8') * 30

# Create brighter and darker images.
img_brighter = cv2.add(img, matrix)
img_darker   = cv2.subtract(img, matrix)

# Display the images
plt.figure(figsize = [18,5])
plt.subplot(131); plt.imshow(img_darker[:, :, ::-1]);   plt.title('Darker')
plt.subplot(132); plt.imshow(img[:, :, ::-1]);          plt.title('Original')
plt.subplot(133); plt.imshow(img_brighter[:, :, ::-1]); plt.title('Brighter');

# Create matrices with a constant scale factor.
matrix_ones = np.ones(img.shape, dtype = 'float64')

# Create lower and higher contrast images.
img_lower   = np.uint8(cv2.multiply(np.float64(img), matrix_ones, scale = 0.8))
img_higher  = np.uint8(np.clip(cv2.multiply(np.float64(img), matrix_ones, scale = 1.2) , 0, 255))

# Display the images.
plt.figure(figsize = [18,5])
plt.subplot(131); plt.imshow(img_lower[:, :, ::-1]);  plt.title('Lower Contrast')
plt.subplot(132); plt.imshow(img[:, :, ::-1]);        plt.title('Original')
plt.subplot(133); plt.imshow(img_higher[:, :, ::-1]); plt.title('Higher Contrast');

img_rec = cv2.imread('rectangle.jpg', cv2.IMREAD_GRAYSCALE)
img_cir = cv2.imread('circle.jpg', cv2.IMREAD_GRAYSCALE)

plt.figure(figsize = [20,5])
plt.subplot(121);  plt.imshow(img_rec);
plt.subplot(122);  plt.imshow(img_cir);
print(img_rec.shape)

result = cv2.bitwise_and(img_rec, img_cir, mask = None)
plt.imshow(result);

result = cv2.bitwise_or(img_rec, img_cir, mask = None)
plt.imshow(result);

result = cv2.bitwise_xor(img_rec, img_cir, mask = None)
plt.imshow(result);