def popup_sub_crop():
util.log_title('弹窗判断')
shape_dict = {}
for i in range(len(c.popup_flag_img_paths)):
shape,score = template_match(c.popup_flag_img_paths[i],c.img_sc_path)
shape_dict[shape] = (score,i)
print(shape_dict)
max_shape = max(shape_dict, key=shape_dict.get)
score,i = shape_dict[max_shape]
print(f'最大区域 {max_shape} 最终得分为 {score}' )
if score >=3 :
sub_shape = (
max_shape[0]+c.popup_move_shapes[i][0],
max_shape[1]+c.popup_move_shapes[i][1],
max_shape[2]+c.popup_move_shapes[i][2],
max_shape[3]+c.popup_move_shapes[i][3]
)
print(f'弹框区域 {sub_shape}')
return crop(c.img_sc_path,c.popup_sub_img_path,sub_shape)
print(f'没有弹框')
return False
def template_match(template_path,src_path):
img = cv.imread(src_path,0)
img2 = img.copy()
template = cv.imread(template_path,0)
w, h = template.shape[::-1]
methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED','cv.TM_CCORR',
'cv.TM_CCORR_NORMED', 'cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED']
shape_dict = {}
for meth in methods:
img = img2.copy()
method = eval(meth)
# Apply template Matching
res = cv.matchTemplate(img,template,method)
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
# cv.rectangle(img,top_left, bottom_right, 255, 2)
# plt.subplot(121),plt.imshow(res,cmap = 'gray')
# plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
# plt.subplot(122),plt.imshow(img,cmap = 'gray')
# plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
# plt.suptitle(meth)
# plt.show()
shape = (top_left[0],top_left[1],bottom_right[0],bottom_right[1])
# print(shape)
if shape_dict.get(shape) == None:
shape_dict[shape] = 1;
else:
shape_dict[shape] = shape_dict[shape]+1
max_shape = max(shape_dict, key=shape_dict.get)
return max_shape,shape_dict[max_shape]
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