changedetection.io/changedetectionio/image_diff.py

from skimage.metrics import structural_similarity as compare_ssim
import argparse
import imutils
import cv2

# From https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/
def render_diff(fpath_imageA, fpath_imageB):

	imageA = cv2.imread(fpath_imageA)
	imageB = cv2.imread(fpath_imageB)

	# 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 = 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), 1)
		cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 1)

	#return cv2.imencode('.jpg', imageB)[1].tobytes()
	return cv2.imencode('.jpg', imageA)[1].tobytes()