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import cv2 | |
import numpy as np | |
# load and find the dimension attritbutes of the template image | |
template_img = cv2.imread('./gray-template.png', 0) | |
w, h = template_img.shape[::-1] | |
# OpenCV's template matching method | |
res_img = cv2.matchTemplate( | |
screen2, template_img, cv2.TM_CCOEFF_NORMED | |
) | |
# find the screen image section where the template is matching | |
# with the given threshold range | |
threshold = 0.75 | |
loc = np.where(res_img >= threshold) | |
for pt in zip(*loc[::-1]): | |
# crop rectangle section around the selected template to run OCR | |
cropped_image = screen_img_gray[pt[1]:pt[1] + h + 5, pt[0] + w:pt[0] + w + 205] | |
# draw a rectangle around the found template | |
cv2.rectangle(screen2, pt, (pt[0] + w, pt[1] + h), 255, 1) |
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