image_structural_similarity.py

from skimage.measure import compare_ssim as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2

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")
	# show the images
	plt.show()

#open the two images
im1 = cv2.imread("output/crop.mp4/08-01-21/car2_2.jpg")
im2 = cv2.imread("output/crop.mp4/08-01-21/car2_3.jpg")

#openCV required both images to have the same size
if im2.shape != im1.shape:
    im2 = cv2.resize(im2, (im1.shape[1], im1.shape[0]))

# convert the images to grayscale
im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)

'''
# initialize the figure
fig = plt.figure("Images")
images = ("Image 1", im1), ("Image 2", im2)

# 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(im1, im2, "Image comparison")