-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdealnumber.py
119 lines (95 loc) · 3.27 KB
/
dealnumber.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
import cv2
import numpy as np
# 读取图像
# image = cv2.imread('errgameimg/game2.jpg')
# image = cv2.imread('game2.jpg')
image = cv2.imread('gameimg/99/99_huang_jianke.jpg')
# 获取图像宽度和高度
height, width, _ = image.shape
# 定义白色像素的阈值
white_threshold = 0.8 * height
# 初始化白色竖线宽度总和
white_line_width_sum = 0
# 从右到左，从上到下遍历图像
for x in range(width-1, width-150, -1):
white_pixel_count = 0
for y in range(height):
if (image[y, x] > 250).all():
white_pixel_count += 1
if white_pixel_count >= white_threshold:
white_line_width_sum += 1
print("白色竖线总宽度：", white_line_width_sum)
if white_line_width_sum == 132:
white_line_width_sum -= 12
width = 2400
height = 1080
# 107 21
# 13 2
# 15 2
# 132 21
# 左边120 右边147 下面黑条21
# 小图236 113
# 小图236 114
start_x = 120
start_y = 23
# end_x = 132
end_x = white_line_width_sum
end_y = 21
roi_width = width - start_x - end_x
roi_height = height - start_y - end_y
split_count = 9
# one_w = 710
# one_h = 344
one_w = (roi_width) // split_count
one_h = (roi_height) // split_count
image = image[start_y:start_y + roi_height, start_x:start_x + roi_width]
ori_image = image
cv2.imwrite('temp/roi.jpg', image)
# 将图像转换为灰度图像
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 或保存结果图像
cv2.imwrite('temp/gray.jpg', image)
# 对图像进行二值化处理
ret, image = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY )
# ret, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# 或保存结果图像
cv2.imwrite('temp/thresh.jpg', image)
# 对图像进行形态学操作（可选）
kernel = np.ones((1, 1), np.uint8)
image = cv2.morphologyEx(image, cv2.MORPH_ERODE, kernel, iterations=2)
cv2.imwrite('temp/closing.jpg', image)
little_images = []
for i in range(split_count):
for j in range(split_count):
# 计算当前小区域的起始坐标
start_x = j * (one_w)
start_y = i * (one_h)
# 提取中心数字图像
number_image = image[start_y: start_y + one_h, start_x: start_x + one_w]
# 存储提取的中心数字图像
little_images.append(number_image)
for idx, num_img in enumerate(little_images):
cv2.imwrite(f'temp/little_{idx}.jpg', num_img) # 保存图像
number_images = []
for i in range(split_count):
for j in range(split_count):
# 计算当前小区域的起始坐标
start_x = j * one_w
start_y = i * one_h
# 计算当前小区域的中心坐标
center_x = start_x + one_w // 2
center_y = start_y + one_h // 2
# 提取当前小区域的图像
half_size = 28 # 因为我们想要20像素，所以取一半的大小
# 提取中心数字图像
number_image = image[center_y: center_y + half_size, center_x -14: center_x + 28]
# 计算分割位置
split_point = 21
# 分割number_image
part1 = number_image[:, :split_point]
part2 = number_image[:, split_point:]
# 存储提取的中心数字图像
number_images.append(part1)
number_images.append(part2)
for idx, num_img in enumerate(number_images):
cv2.imwrite(f'temp/number_{idx}.jpg', num_img) # 保存图像