def drop_over(over_canny, over_width):
over_canny_width = np.shape(over_canny)[1]
over_left = over_width / 2
over_right = over_width - over_left
eliminate_pic_01 = fn.deal_pixel_01(over_canny)
shadow_x = fn.picshadow_x(eliminate_pic_01)
shadow_y = fn.picshadow_y(eliminate_pic_01)
# cv2.imshow('canny' + str(over_left), over_canny)
for i in range(over_canny_width):
if shadow_x[i] > height - 4:
over_canny[:, i] = 0
for i in range(height):
if shadow_y[i] > over_canny_width - 1:
over_canny[i, :] = 0
eliminate_pic_01 = fn.deal_pixel_01(over_canny)
shadow_x = fn.picshadow_x(eliminate_pic_01)
# print(int(np.sum(shadow_x[: over_width])), int(np.sum(shadow_x[-over_width :])))
# print((height - 1) * over_width)
if np.sum(shadow_x) > (height - 1.5) * over_canny_width: # 去除线段后接近黑图
return 0
if int(np.sum(shadow_x[:over_width])) < (height - 1) * over_width and int(
np.sum(shadow_x[-over_width:])) > (height -
2) * over_width: # 右边黑色像素远大出左边
return 1
if int(np.sum(shadow_x[-over_width:])) < (height - 1) * over_width and int(
np.sum(shadow_x[:over_width])) > (height -
2) * over_width: # 左边黑色像素远大出右边
return 2
if over_canny_width > 1.5 * thre:
eliminate_pic_01 = fn.deal_pixel_01(over_canny)
again_list = etp.find_singledigital(shadow_x, over_canny_width)
return again_list
else:
return 3
def drop_little(eliminate_pic):
eliminate_pic_01 = fn.deal_pixel_01(eliminate_pic)
shadow_x = fn.picshadow_x(eliminate_pic_01)
thre = height - 3
for i in range(width):
if shadow_x[i] > thre:
eliminate_pic[:, i] = 0
return eliminate_pic
if shadow_pic[i] < height:
single_start = i
is_start = False
else:
if shadow_pic[i] == height:
single_end = i
is_start = True
if single_end - single_start > thre:
single_digital_list.append([single_start, single_end])
return single_digital_list
num_img2gray = cv2.cvtColor(num_img, cv2.COLOR_RGB2GRAY)
threshold = cv2.adaptiveThreshold(num_img2gray, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 5, 5)
for i in range(np.shape(threshold)[1]):
for j in range(np.shape(threshold)[0]):
if threshold[j, i] == 0:
threshold[j, i] = 255
else:
threshold[j, i] = 0
cv2.imshow('threshold', threshold)
drop_num = drop_little(threshold)
cv2.imshow('drop_num', drop_num)
eliminate_pic_01 = fn.deal_pixel_01(drop_num)
shadow_x = fn.picshadow_x(eliminate_pic_01)
digital_list = find_singledigital(shadow_x)
cv2.waitKey()