Find the difference between the two images and mark the python version & c++ version

Python version verified code reference: 

# -*- coding: utf-8 -*-
from skimage.metrics import 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"1.jpg")
imageB = cv2.imread(r"2.jpg")

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 c in 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"result.bmp", imageB)
cv2.waitKey(0)

operation result:

 

Disadvantages: not suitable for industrial defect detection, only suitable for pictures of the same size

operation result:

C++ version code reference:

void imageSubtract(Mat &image1, Mat &image2)
{
	if ((image1.rows != image2.rows) || (image1.cols != image2.cols))
	{
		if (image1.rows > image2.rows)
		{
			resize(image1, image1, image2.size(), 0, 0, INTER_LINEAR);
		}
		else if (image1.rows < image2.rows)
		{
			resize(image2, image2, image1.size(), 0, 0, INTER_LINEAR);
		}
	}

	Mat image1_gary, image2_gary;
	if (image1.channels() != 1)
	{
		cvtColor(image1, image1_gary, COLOR_BGR2GRAY);
	}
	if (image2.channels() != 1)
	{
		cvtColor(image2, image2_gary, COLOR_BGR2GRAY);
	}

	Mat frameDifference, absFrameDifferece;
	Mat previousGrayFrame = image2_gary.clone();
	//图1减图2
	subtract(image1_gary, image2_gary, frameDifference, Mat(), CV_16SC1);

	//取绝对值
	absFrameDifferece = abs(frameDifference);

	//位深的改变
	absFrameDifferece.convertTo(absFrameDifferece, CV_8UC1, 1, 0);
	imshow("absFrameDifferece", absFrameDifferece);
	Mat segmentation;
	
	//阈值处理（这一步很关键，要调好二值化的值）
	threshold(absFrameDifferece, segmentation,100, 255, THRESH_BINARY);

	//中值滤波
	medianBlur(segmentation, segmentation, 3);

	//形态学处理(开闭运算)
	//形态学处理用到的算子
	Mat morphologyKernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	morphologyEx(segmentation, segmentation, MORPH_CLOSE, morphologyKernel, Point(-1, -1), 2, BORDER_REPLICATE);

	//显示二值化图片
	imshow("segmentation", segmentation);

	//找边界
	vector< vector<Point> > contours;
	vector<Vec4i> hierarchy;
	findContours(segmentation, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));//CV_RETR_TREE
	vector< vector<Point> > contours_poly(contours.size());

	vector<Rect> boundRect;
	boundRect.clear();

	for (int index = 0; index < contours.size(); index++)
	{
		approxPolyDP(Mat(contours[index]), contours_poly[index], 3, true);
		Rect rect = boundingRect(Mat(contours_poly[index]));
		rectangle(image2, rect, Scalar(0, 255, 0), 2);
	}
	imshow("效果图", image2);
}

Reference link:

https://blog.csdn.net/matt45m/article/details/89294950