-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path08信用卡数字识别.py
146 lines (111 loc) · 4.63 KB
/
08信用卡数字识别.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
120
121
122
123
124
125
126
127
128
129
130
131
132
import cv2
import matplotlib.pyplot as plt
import numpy as np
import myutils
# 使用cv.read展示图像函数
def show_img(img_name, img_src):
cv2.imshow(img_name, img_src)
cv2.waitKey(0)
cv2.destroyAllWindows()
template = cv2.imread("./images/ocr_a_reference.png")
# 灰度图
ref = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
# 二值化
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
show_img("template_threshold:", ref)
print(ref.shape)
# 寻找各个数字轮廓(轮廓是乱序的)
contours, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 画出展示所有数字轮廓
res = cv2.drawContours(template.copy(), contours, -1, (0, 0, 255), 3)
show_img("contours", res)
# 调用myutils文件对contours进行排序(从左到右)，第一个维度代表是轮廓返回
refCnts = myutils.sort_contours(contours, method="left-to-right")[0]
# 存放模板的数字ROI
digits = {}
for (i, c) in enumerate(refCnts):
# cv2.imread()返回的是(height, width, channels),
#x，y是矩阵左上点的坐标，w，h是矩阵的宽和高
# 但是ref是hxwxc，所以提取模板数字roi为如下：
(x, y, w, h) = cv2.boundingRect(c)
roi = ref[y:y+h, x:x+w]
roi = cv2.resize(roi, (57, 88))
digits[i] = roi
# 初始卷积核，定义开闭操作的卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
# 读取银行卡，首先resize，灰度化
img = cv2.imread("./images/credit_card_01.png")
img = myutils.resize(img, width=300)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# show_img("gray:", gray)
# 礼帽操作：原始输入-开运算
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
# 单独x方向，将梯度变大
gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
# 绝对化梯度x
gradX = np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
# 归一化操作
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
# 变为uint8类型
gradX = gradX.astype("uint8")
print(np.array(gradX).shape)
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
thresold = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]
# 闭操作
gradX = cv2.morphologyEx(thresold, cv2.MORPH_CLOSE, sqKernel)
# 得出闭操作后的轮廓
threCnts, hierarchy = cv2.findContours(gradX.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = threCnts
cur_img = img.copy()
# 画轮廓
res = cv2.drawContours(cur_img, cnts, -1, (0, 0, 255), 3)
show_img("locate digts ：", res)
# 放4个数组区域
locs = []
for (i, c) in enumerate(cnts):
(x, y, w, h) = cv2.boundingRect(c)
# 根据长宽比来废除银行卡其他区域
ar = w / float(h)
if ar > 2.5 and ar < 4.0 :
if(w > 40 and w < 55) and (h >10 and h < 20):
# 将银行卡图片数字区域块存放
locs.append((x, y, w, h))
# 数字区域升序排放，以第一个维度x来指标
locs = sorted(locs, key=lambda x:x[0])
outputs = []
# 大区域数字遍历
for (i, (gX, gY, gW, gH)) in enumerate(locs):
# 数字区域输出
groupOutput = []
group = gray[gY-5:gY+gH+5, gX-5:gX+gW+5]
# show_img("group", group)
# 在只有两种颜色情侣下，0代表自动查找阈值
group = cv2.threshold(group, 0, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]
groupCnts, hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 每一个大块数字区域有4个数字，这4个数字得排序一次
digitCnts = myutils.sort_contours(groupCnts, method="left-to-right")[0]
for c in digitCnts:
(x, y, w, h) = cv2.boundingRect(c)
roi = group[y:y+h, x:x+w]
# 找出单独数字并且seize
roi = cv2.resize(roi, (57, 88))
# show_img("roi",roi)
# 4个数字的具体得分
scores = []
# digits是模板数字
for (digit, digitROI) in digits.items():
result = cv2.matchTemplate(roi, digitROI, cv2.TM_CCOEFF)
# minval， maxval，minloc，maxloc
# 把maxval当成匹配，因为TM_CCOEFF是越相关，系数越大
(_, score, _, _) = cv2.minMaxLoc(result)
scores.append(score)
# 将一个数字与10个模板数字匹配，取最大值
groupOutput.append(str(np.argmax(scores)))
print(groupOutput)
# 画框，左上角x，y，右下角x，y
cv2.rectangle(img, (gX-5, gY-5), (gX+gW+5, gY+gH+5), (0, 0, 255), 2)
cv2.putText(img, "".join(groupOutput), (gX, gY-15), cv2.FONT_HERSHEY_SIMPLEX, 0.65,(0, 0, 255),2)
outputs.extend(groupOutput)
show_img("img",img)