import cv2
def show(name):
'''展示圈出来的位置'''
cv2.imshow('Show', name)
cv2.waitKey(0)
cv2.destroyAllWindows()
def _tran_canny(image):
"""消除噪声"""
image = cv2.GaussianBlur(image, (3, 3), 0)
return cv2.Canny(image, 50, 150)
def detect_displacement(img_slider_path, image_background_path):
"""detect displacement"""
# # 参数0是灰度模式
image = cv2.imread(img_slider_path, 0)
# 缩放0.616
image = cv2.resize(image, None, fx=0.616, fy=0.616, interpolation=cv2.INTER_AREA)
template = cv2.imread(image_background_path, 0)
# 缩放0.616
template = cv2.resize(template, None, fx=0.616,fy=0.616,interpolation=cv2.INTER_AREA)
# 寻找最佳匹配
res = cv2.matchTemplate(_tran_canny(image), _tran_canny(template), cv2.TM_CCOEFF_NORMED)
# 最小值,最大值,并得到最小值, 最大值的索引
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
top_left = max_loc[0] # 横坐标
# 展示圈出来的区域
x, y = max_loc # 获取x,y位置坐标
w, h = image.shape[::-1] # 宽高
cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)
show(template)
return top_left
if __name__ == '__main__':
top_left = detect_displacement("ddd.png", "ccc.png")
print(top_left)
python逆向计算滑块距离
猜你喜欢
转载自blog.csdn.net/weixin_47723549/article/details/129621013
今日推荐
周排行