image_diff.py

#!/usr/bin/env python
"""based on this article
https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/

tested on python 3.6.2, ubuntu 18.10 with following package
imutils==0.5.2
matplotlib==3.0.2
opencv-python==3.4.3.18
scikit-image==0.14.1

to install package:
```console
$ pip install scikit-image opencv-python matplotlib imutils
```
"""
import argparse

# import the necessary packages
from matplotlib import pyplot as plt
from skimage.measure import compare_ssim
import cv2
import imutils


def image_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    # initialize the dimensions of the image to be resized and
    # grab the image size
    dim = None
    (h, w) = image.shape[:2]

    # if both the width and height are None, then return the
    # original image
    if width is None and height is None:
        return image

    # check to see if the width is None
    if width is None:
        # calculate the ratio of the height and construct the
        # dimensions
        r = height / float(h)
        dim = (int(w * r), height)

    # otherwise, the height is None
    else:
        # calculate the ratio of the width and construct the
        # dimensions
        r = width / float(w)
        dim = (width, int(h * r))

    # resize the image
    resized = cv2.resize(image, dim, interpolation=inter)

    # return the resized image
    return resized


# construct the argument parse and parse the arguments
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 the two input images
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
max_w, max_h = None, None
if imageA.shape[:2] != imageB.shape[:2]:
    max_w = max(imageA.shape[0], imageB.shape[0])
    max_h = max(imageA.shape[1], imageB.shape[1])

# convert the images to grayscale
if max_w or max_h:
    grayA = cv2.cvtColor(
        imutils.resize(imageA, width=max_w, height=max_h), cv2.COLOR_BGR2GRAY)
    grayB = cv2.cvtColor(
        imutils.resize(imageB, width=max_w, height=max_h), cv2.COLOR_BGR2GRAY)
    if grayA.shape != grayB.shape:
        gray_max_w = max(grayA.shape[0], grayB.shape[0])
        gray_max_h = max(grayA.shape[1], grayB.shape[1])
        right_a, bottom_a = gray_max_h-grayA.shape[1],  gray_max_w-grayA.shape[0]
        right_b, bottom_b = gray_max_h-grayB.shape[1],  gray_max_w-grayB.shape[0]
        if (bottom_a, right_a) != (0, 0):
            grayA = cv2.copyMakeBorder(
                grayA, 0, bottom_a, 0, right_a,
                cv2.BORDER_CONSTANT, value=(0, 0, 0))
        if (bottom_b, right_b) != (0, 0):
            o_grayB = grayB.copy()
            grayB = cv2.copyMakeBorder(
                grayB, 0, bottom_b, 0, right_b,
                cv2.BORDER_CONSTANT, value=(0, 0, 0))
else:
    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), 2)
    cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)

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

plt.subplot(221), plt.imshow(imutils.opencv2matplotlib(imageA)), plt.title('A')
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.subplot(222), plt.imshow(imutils.opencv2matplotlib(imageB)), plt.title('B')
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.subplot(223), plt.imshow(diff, cmap='gray'), plt.title('diff')
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.subplot(224), plt.imshow(thresh, cmap='gray'), plt.title('thresh')
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.show()