Foreword: The development of credit card number recognition technology is conducive to improving the business level and efficiency of the banking system. I believe that by learning the image processing method in OpenCV to realize the credit card number recognition project, everyone can clearly understand the general methods and steps of image processing technology and how to use the OpenCV library.
1. Design idea
Credit cards issued by different banks have slightly different appearances and shapes of numbers in their card numbers. Since the author uses template matching to identify credit card numbers, since it is template matching, there must be a set of digital templates that are exactly the same as those in credit cards. , by comparing the difference between the numbers in the credit card and the numbers in the template to determine the recognition result, the distribution of the template image and the credit card image is shown in Figure 1 and Figure 2.
figure 1
figure 2
Before template matching, it is necessary to use image processing methods, such as morphology, to find the domain of interest of this project from the credit card image—that is, the area where the card number is located, and segment the area, and then extract the area. The numbers of are compared with the 10 numbers in the template, and the number is considered to be the same as the number with the highest matching score in the template.
2. Code implementation
The project implementation includes two parts of code, main.py and myutils.py. If you want to observe the experimental results, just run main.py. myutils.py mainly includes the outline sorting method and the resize method, and main.py will call the myutils.py module. function.
The code in the main.py file is as follows:
# 导入工具包
from imutils import contours
import numpy as np
import cv2
import myutils
# 指定信用卡类型
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
# 绘图函数
def cv_show(name, img1):
cv2.imshow(name, img1)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 读取模板
img = cv2.imread("template.png")
cv_show('img', img)
# 转化为灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref', ref)
# 转化为二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref', ref)
# 计算轮廓
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#print(np.size(refCnts))返回值refcnts返回的是10组轮廓及其每个轮廓所有组成点的坐标
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show('img', img)
print(np.array(refCnts).shape)
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]
digits = {}
# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
(x, y, w, h) = cv2.boundingRect(c)
roi = ref[y:y + h, x:x + w]
roi = cv2.resize(roi, (57, 88))
digits[i] = roi
# 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
# 预处理
image = cv2.imread('object.png')
cv_show('image', image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)
# 礼貌操作，突出高亮
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat', tophat)
gradx = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
gradx = np.absolute(gradx)
(minVal, maxVal) = (np.min(gradx), np.max(gradx))
gradx = (255 * ((gradx - minVal) / (maxVal - minVal)))
gradx = gradx.astype("uint8")
print(np.array(gradx).shape)
cv_show('gradx', gradx)
# 闭操作
gradx = cv2.morphologyEx(gradx, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradx', gradx)
thresh = cv2.threshold(gradx, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh', thresh)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)
cv_show('thresh', thresh)
# 计算轮廓
threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = threshCnts
cur_img = image.copy()
cv2.drawContours(cur_img, cnts, -1, (0, 0, 255), 3)
cv_show('img', cur_img)
locs = []
# 遍历轮廓
for (i, c) in enumerate(cnts):
# 计算矩形
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
if ar > 2.5 and ar < 4.0:
if (w > 40 and w < 55) and (h > 10 and h < 20):
locs.append((x, y, w, h))
# 排序
locs = sorted(locs, key=lambda x: x[0])
output = []
# 遍历数字
for (i, (gx, gy, gw, gh)) in enumerate(locs):
groupOutput = []
group = gray[gy - 5:gy + gh + 5, gx - 5:gx + gw + 5]
cv_show('group', group)
# 预处理
group = cv2.threshold(group, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('group', group)
digitCnts, hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
digitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0]
# 计算每一组中的每一个数值
for c in digitCnts:
(x, y, w, h) = cv2.boundingRect(c)
roi = group[y:y + h, x:x + w]
roi = cv2.resize(roi, (57, 88))
cv_show('roi', roi)
scores = []
for (digit, digitROI) in digits.items():
# 模板匹配
result = cv2.matchTemplate(roi, digitROI, cv2.TM_CCOEFF)
(_, score, _, _) = cv2.minMaxLoc(result)
scores.append(score)
groupOutput.append(str(np.argmax(scores)))
cv2.rectangle(image, (gx - 5, gy - 5), (gx + gw + 5, gy + gh + 5), (0, 0, 255), 1)
cv2.putText(image, "".join(groupOutput), (gx, gy - 15), cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, 0.65, (0, 0, 255), 2)
output.extend(groupOutput)
print("Credit Card Type:{}".format(FIRST_NUMBER[output[0]]))
print("Credit Card # : {}".format("".join(output)))
cv2.imshow("image", image)
cv2.waitKey(0)
The code in myutils.py is as follows:
import cv2
def sort_contours(cnts, method="left-to-right"):
reverse = False
i = 0
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
if method == "top-to-bottom" or method == "bottom-to-top":
i = 1
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
return cnts, boundingBoxes
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height/float(h)
dim = (int(w*r), height)
else:
r = width/float(w)
dim = (width, int(h*r))
resized = cv2.resize(image, dim, interpolation=inter)
return resizedThe template.png that appears in the code is Figure 1, and the object.png is Figure 2. The final recognition results of the credit card number recognition project are shown in Figure 3 and Figure 4.
image 3
Figure 4