In [1]:

    

%matplotlib inline
from matplotlib import pyplot as plt, cm
import cv2
from skimage.measure import structural_similarity as ssim
import numpy as np


    

In [4]:

    

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 = 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")


    

In [5]:

    

# the MSE has increased and the SSIM decreased, implying that the images are less similar.

# load the images -- the original, the original + contrast,
# and the original + photoshop
original = cv2.imread("./data/jp_gates_original.png")
contrast = cv2.imread("./data/jp_gates_contrast.png")
shopped = cv2.imread("./data//jp_gates_photoshopped.png")
 
# 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")
 
 
# compare the images
compare_images(original, original, "Original vs. Original")
compare_images(original, contrast, "Original vs. Contrast")
compare_images(original, shopped, "Original vs. Photoshopped")