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40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import cv2
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import numpy as np
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# 找图 返回最近似的点
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def search_returnPoint(img, template, template_size):
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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template_ = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
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result = cv2.matchTemplate(img_gray, template_, cv2.TM_CCOEFF_NORMED)
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threshold = 0.7
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# res大于70%
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loc = np.where(result >= threshold)
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# 使用灰度图像中的坐标对原始RGB图像进行标记
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point = ()
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for pt in zip(*loc[::-1]):
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cv2.rectangle(img, pt, (pt[0] + template_size[1], pt[1] + + template_size[0]), (7, 249, 151), 2)
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point = pt
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if point == ():
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return None, None, None
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return img, point[0] + template_size[1] / 2, point[1]
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def run(target_image_path, image_path):
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scale = 1
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img = cv2.imread(target_image_path) # 要找的大图
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img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
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template = cv2.imread(image_path) # 图中的小图
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template = cv2.resize(template, (0, 0), fx=scale, fy=scale)
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template_size = template.shape[:2]
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img, x_, y_ = search_returnPoint(img, template, template_size)
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if img is None:
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return False
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else:
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print(f"找到图片位置:{x_, y_}")
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return True
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if __name__ == '__main__':
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run('../target_img/image_6.jpg', '../img/cma.jpg')
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