# REFERENCE: https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/
from skimage.metrics import structural_similarity
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True, help="first input image")
ap.add_argument("-s", "--second", required=True, help="second")

args = vars(ap.parse_args())

# load two input images
imageA = cv2.imread(args["first"])
print(imageA.shape)
imageB = cv2.imread(args["second"])
print(imageB.shape)
# (h, w) = imageA.shape[0], imageA.shape[1]
# imageB = cv2.resize(imageB, (w, h))


# convert image 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) = structural_similarity(grayA, grayB, full=True)
# diff contains actual image differences between the two input images
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 = imutils.grab_contours(cnts)

# 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 output images
cv2.imshow("Original", imageA)
cv2.imshow("Modified", imageB)
cv2.imshow("Diff", diff)
cv2.imshow("Thresh", thresh)
cv2.waitKey(0)