ssim_index.py

#Load Data
imageA = cv2.imread('/content/2_Prediction.png')
imageB = cv2.imread('/content/2_GroundTruth.png')

# convert the images to grayscale
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)

# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM: {}".format(score))

# threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255,
	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]

# loop over the contours
for c in cnts:
	# compute the bounding box of the contour and then draw the
	# bounding box on both input images to represent where the two
	# images differ
	(x, y, w, h) = cv2.boundingRect(c)
	cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2)
	cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)

# show the output images
cv2_imshow(imageA) #print original
cv2_imshow(imageB)  #print modified
cv2_imshow(diff)  #print diff
cv2_imshow(thresh)  #print thresh

cv2_imshow(imageA) #print original