信用卡识别
一、环境配置
# 导入工具包
from imutils import contours
import numpy as np
import argparse
import cv2
import myutils
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,help="path to input image")
ap.add_argument("-t", "--template", required=True,help="path to template OCR-A image")
args = vars(ap.parse_args())
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175240315-132583208.png)
# 指定信用卡类型
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
# 绘图展示
def cv_show(name,img):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
二、模板处理:
# 读取一个模板图像
img = cv2.imread(args["template"])
cv_show('img',img)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175252572-969545568.png)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175301964-84434633.png)
# 二值图像,轮廓检测都需要二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref',ref)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175309562-1583052791.png)
# 计算轮廓
#cv2.findContours()函数接受的参数为二值图,即黑白的(不是灰度图),cv2.RETR_EXTERNAL只检测外轮廓#cv2.CHAIN_APPROX_SIMPLE只保留终点坐标。
#返回的list中每个元素都是图像中的一个轮廓
ref_, refCnts, hierarchy = cv2.findContours(ref.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#显示轮廓,-1表示画出所有轮廓
cv2.drawContours(img,refCnts,-1,(0,0,255),3)
cv_show('img',img)
print (np.array(refCnts).shape) #(10,),有10个轮廓
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175321792-1263003193.png)
#对轮廓按照0-10的顺序排序,根据外接矩形的x值从左到右排序。
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]
# 遍历每一个轮廓,i为轮廓索引,c为每个轮廓
digits = {}
for (i, c) in enumerate(refCnts):
# 计算每个数字的外接矩形并且resize成合适大小
(x, y, w, h) = cv2.boundingRect(c)
roi = ref[y:y + h, x:x + w]
roi = cv2.resize(roi, (57, 88))
# 每一个数字对应每一个模板
digits[i] = roi
三、处理输入图像
# 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
#读取输入图像,预处理
image = cv2.imread(args["image"])
cv_show('image',image)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175334588-1669534585.png)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175343186-646216122.png)
#礼帽操作,突出更明亮的区域
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat',tophat)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175352356-1321611419.png)
#ksize=-1相当于用3*3的,用于边缘检测
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)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175405825-1019180987.png)
#通过闭操作(先膨胀,再腐蚀)将数字连在一起
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX',gradX)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175418996-1834609973.png)
#THRESH_OTSU会自动寻找合适的阈值,适合双峰,需把阈值参数设置为0
thresh = cv2.threshold(gradX, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh',thresh)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175429911-22057382.png)
#再来一个闭操作
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
cv_show('thresh',thresh)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175439000-31598118.png)
# 计算轮廓
thresh_, 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)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175447745-689909572.png)
# 遍历轮廓,并过滤掉无用轮廓
locs = []
for (i, c) in enumerate(cnts):
# 计算外接矩形
(x, y, w, h) = cv2.boundingRect(c)
#计算长宽比例
ar = w / float(h)
# 选择合适的区域,根据实际任务来,这里的基本都是四个数字一组
if ar > 2.5 and ar < 4.0:
if (w > 40 and w < 55) and (h > 10 and h < 20):
#符合的留下来
locs.append((x, y, w, h))
# 将符合的轮廓从左到右排序
locs = sorted(locs, key=lambda x:x[0])
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175458463-1440072875.png)
# 遍历每一个轮廓中的数字
output = []
for (i, (gX, gY, gW, gH)) in enumerate(locs):
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175506675-822029882.png)
# initialize the list of group digits
groupOutput = []
# 根据坐标提取每一个组
group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
cv_show('group',group)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175514772-1861867756.png)
# 预处理
group = cv2.threshold(group, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('group',group)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175525264-805233345.png)
# 计算每一组的轮廓并排序
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:
# 找到当前数值的轮廓,resize成合适的的大小
(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)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175535809-1462672363.png)
# 进行模板匹配并计算匹配得分
scores = []
# 在模板中计算每一个得分
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)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175546641-323688871.png)
# 得到最合适的数字
groupOutput.append(str(np.argmax(scores)))
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175553646-835155535.png)
四、得出结果
# 画出来
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_SIMPLEX,0.65, (0, 0, 255), 2)
# 得到结果
output.extend(groupOutput)
cv2.imshow("Image", image)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175602575-437959112.png)
# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.waitKey(0)
![](https://img2022.cnblogs.com/blog/2745680/202203/2745680-20220304175610023-1401441316.png)