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This article mainly shows you how to use Python+OpenCV for image template matching (Match Template), the content is simple and easy to understand, organized clearly, I hope to help you solve doubts, let Xiaobian lead you to study and learn "how to use Python+OpenCV for image template matching (Match Template)" this article bar.

First import the required library files, numpy and cv2.

#import cv2 import numpy as np

Then load the original image and the image template to be searched. OpenCV processes the original image, creates a grayscale version, processes and finds matches in the grayscale image. Then restore and output the original image using the same coordinates.

#Load the original RGB image img_rgb = cv2.imread("photo.jpg") #Create a grayscale version of the original image, all operations processed in grayscale, then restore img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) using the same coordinates in the RGB image #Load image template to be searched template = cv2.imread ('face.jpg ',0) #Record image template size w, h = template.shape[::-1]

Here we output and view the original image, the grayscale version of the original image, and the image template separately.

#View three sets of images (image tag name, file name) cv2.imshow ('rgb ',img_rgb) cv2.imshow ('gray',img_gray) cv2.imshow ('template ',template) cv2.waitKey(0) cv2.destroyAllWindows()

Use matchTemplate to find and match the content in the image template in the original image and set thresholds.

#Match the original grayscale image to the image template using matchTemplate res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEF_NORMED) #Set threshold threshold = 0.7 #res greater than 70% loc = np.where( res >= threshold)

After matching, the original image is marked with the coordinates of the gray image.

#Mark the original RGB image with coordinates from the grayscale image for pt in zip(*loc[::-1]): cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (7,249,151), 2) #Display image cv2.imshow('Detected',img_rgb) cv2.waitKey(0) cv2.destroyAllWindows()

The following is the complete code:

def mathc_img(image,Target,value): import cv2 import numpy as np img_rgb = cv2.imread(image) img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) template = cv2.imread(Target,0) w, h = template.shape[::-1] res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED) threshold = value loc = np.where( res >= threshold) for pt in zip(*loc[::-1]): cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (7,249,151), 2) cv2.imshow('Detected',img_rgb) cv2.waitKey(0) cv2.destrucyAllWindows ()image=("photo.jpg") Target=('face.jpg ') value=0.9 mathc_img(image,Target,value) Above is "How to use Python+OpenCV for image template matching (Match Template)" All the content of this article, thank you for reading! I believe that everyone has a certain understanding, hope to share the content to help everyone, if you still want to learn more knowledge, welcome to pay attention to the industry information channel!

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