# by movie on 2019/12/18

import matplotlib.pyplot as plt

import numpy as np

from skimage import measure

import cv2

# import the necessary packages

def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])

    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err

def compare_images(imageA, imageB, title):

    # compute the mean squared error and structural similarity

    # index for the images

    m = mse(imageA, imageB)

    s = measure.compare_ssim(imageA, imageB)

    # setup the figure

    fig = plt.figure(title)

    plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s))

    # show first image

    ax = fig.add_subplot(1, 2, 1)

    plt.imshow(imageA, cmap=plt.cm.gray)

    plt.axis("off")

    # show the second image

    ax = fig.add_subplot(1, 2, 2)

    plt.imshow(imageB, cmap=plt.cm.gray)

    plt.axis("off")

    # show the images

    plt.show()

# load the images -- the original, the original + contrast,

# and the original + photoshop

original = cv2.imread("images/trumpA689.jpg")

contrast = cv2.imread("images/trumpA690.jpg")

shopped = cv2.imread("images/trumpA748.jpg")

# convert the images to grayscale

original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)

contrast = cv2.cvtColor(contrast, cv2.COLOR_BGR2GRAY)

shopped = cv2.cvtColor(shopped, cv2.COLOR_BGR2GRAY)

# initialize the figure

fig = plt.figure("Images")

images = ("Original", original), ("Contrast", contrast), ("Photoshopped", shopped)

# loop over the images

for (i, (name, image)) in enumerate(images):

    # show the image

    ax = fig.add_subplot(1, 3, i + 1)

    ax.set_title(name)

    plt.imshow(image, cmap=plt.cm.gray)

    plt.axis("off")

# show the figure

plt.show()

# compare the images

compare_images(original, original, "Original vs. Original")

compare_images(original, contrast, "Original vs. Contrast")

compare_images(original, shopped, "Original vs. Photoshopped")