# -*- coding: utf-8 -*- import cv2 file=r'D:/Setting.png' #大图 temp=r'D:/Battery.png' #小图 # 弹出图片后 CTRL+S保存图片到本地 method = cv2.TM_SQDIFF_NORMED # method = cv2.TM_CCOEFF_NORMED # Read the images from the file small_image = cv2.imread(temp) large_image = cv2.imread(file) result = cv2.matchTemplate(small_image, large_image, method) # 需要最小平方差 mn,_,mnLoc,_ = cv2.minMaxLoc(result) # 开始画矩形: # Extract the coordinates of our best match MPx,MPy = mnLoc #获得最小坐标的 print(MPx, MPy) # MPx1,MPy1 = mxLoc #获得最大坐标的 # print(MPx1, MPy1) # Step 2: Get the size of the template. This is the same size as the match. trows,tcols = small_image.shape[:2] #获得图片的宽度 # Step 3: Draw the rectangle on large_image # 将小图片用红线在大图片圈出来 cv2.rectangle(large_image, (MPx,MPy),(MPx+tcols,MPy+trows),(0,0,255),2) # cv2.rectangle(large_image, (MPx+169,MPy+76),(MPx+719,MPy+117),(0,0,255),2) # Display the original image with the rectangle around the match. cv2.imshow('output',large_image) # The image is only displayed if we call this cv2.waitKey(0) #方法不同，获得的坐标不同 # print(MPx, MPy) # print(MPx1, MPy1) # 注意看一下两种方法有一个结果是相同的 #cv2.TM_SQDIFF_NORMED # 结果: # 96 179 # 70 1122 # cv2.TM_CCOEFF_NORMED # 结果: # 72 1631 # 96 179
如下图所示，在背景图片中找到的? 搜索的 图标，并用红线圈出来?
