相关图片示​​​​​​​例:

import base64import numpy as npimport cv2def get_pic_location(target, template):"""找出图像中最佳匹配位置:param target: 目标即背景图:param template: 模板即需要找到的图:return: 返回最佳匹配及其最差匹配和对应的坐标,实例:-0.11801975220441818 ,  0.3789277970790863 ,  (215, 49) ,  (78, 52)"""# 导入图片,灰度化img_rgb = cv2.imdecode(np.asarray(bytearray(target), dtype=np.uint8), cv2.IMREAD_COLOR)template_rgb = cv2.imdecode(np.asarray(bytearray(template), dtype=np.uint8), cv2.IMREAD_COLOR)img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)tm_gray = cv2.cvtColor(template_rgb, cv2.COLOR_BGR2GRAY)# 自适应阈值img_thresh = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 0)tm_thresh = cv2.adaptiveThreshold(tm_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 0)# 边缘检测img_canny = cv2.Canny(img_thresh, 0, 500)tm_canny = cv2.Canny(tm_thresh, 0, 500)# 模板匹配res = cv2.matchTemplate(img_canny, tm_canny, cv2.TM_CCOEFF_NORMED)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)return min_val, max_val, min_loc, max_locif __name__ == '__main__':cutout_image = "data:image/png;base64,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"shade_image = 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"image_content = base64.b64decode(cutout_image.replace("data:image/png;base64,", ''))image_bg_content = base64.b64decode(shade_image.replace('data:image/png;base64,', ''))min_val, max_val, min_loc, max_loc = get_pic_location(image_content, image_bg_content)print(min_val, ", ", max_val, ", ", min_loc, ", ", max_loc)""">> -0.11801975220441818 ,  0.3789277970790863 ,  (215, 49) ,  (78, 52)"""

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