python版本为3.7.7，OpenCV版本为4.2.1，源码如下：
# -*- coding: utf-8 -*-
from skimage.metricsimport structural_similarity
import imutils
import cv2
# 加载两张图片并将他们转换为灰度
imageA = cv2.imread(r"D:\Software\PythonProject\image\11.png")
imageB = cv2.imread(r"D:\Software\PythonProject\image\22.png")
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# 计算两个灰度图像之间的结构相似度指数
(score,diff) = structural_similarity(grayA,grayB,full =True)
diff = (diff *255).astype("uint8")
print("SSIM:{}".format(score))
#找到不同点的轮廓以致于我们可以在被标识为“不同”的区域周围放置矩形
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]
#找到一系列区域，在区域周围放置矩形
for cin cnts:
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(imageA,(x,y),(x+w,y+h),(0,255,0),2)
cv2.rectangle(imageB,(x,y),(x+w,y+h),(0,255,0),2)
#用cv2.imshow 展现最终对比之后的图片， cv2.imwrite 保存最终的结果图片
cv2.imshow("Modified", imageB)
cv2.imwrite(r"D:\Software\PythonProject\image\result.png", imageB)
cv2.waitKey(0)
执行完后会报错，报错信息如下：
Traceback (most recent call last):
File "D:/Software/PythonProject/yangtest.py", line 24, in <module>
(x, y, w, h) = cv2.boundingRect(c)
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgproc\src\shapedescr.cpp:784: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'cv::pointSetBoundingRect'
经过各种查验，发现是OpenCV版本问题，因为当前环境版本过高，所以需将“#找到不同点的轮廓以致于我们可以在被标识为“不同”的区域周围放置矩形”下的代码修改如下：
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[1]if imutils.is_cv3()else cnts[0]
最终代码如下：
# -*- coding: utf-8 -*-
from skimage.metricsimport structural_similarity
import imutils
import cv2
# 加载两张图片并将他们转换为灰度
imageA = cv2.imread(r"D:\Software\PythonProject\image\11.png")
imageB = cv2.imread(r"D:\Software\PythonProject\image\22.png")
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
# 计算两个灰度图像之间的结构相似度指数
(score,diff) = structural_similarity(grayA,grayB,full =True)
diff = (diff *255).astype("uint8")
print("SSIM:{}".format(score))
#找到不同点的轮廓以致于我们可以在被标识为“不同”的区域周围放置矩形
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[1]if imutils.is_cv3()else cnts[0]
#找到一系列区域，在区域周围放置矩形
for cin cnts:
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(imageA,(x,y),(x+w,y+h),(0,255,0),2)
cv2.rectangle(imageB,(x,y),(x+w,y+h),(0,255,0),2)
#用cv2.imshow 展现最终对比之后的图片， cv2.imwrite 保存最终的结果图片
cv2.imshow("Modified", imageB)
cv2.imwrite(r"D:\Software\PythonProject\image\result.png", imageB)
cv2.waitKey(0)
