OpenCV7 模板匹配—信用卡卡号识别

I 整体代码

from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

FIRST_NUMBER = {
    
    
	"3": "American Express",
	"4": "Visa",
	"5": "MasterCard",
	"6": "Disco5ver Card"
}

def cv_show(name,img):
	cv2.imshow(name, img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
img = cv2.imread('1.png',cv2.IMREAD_ANYCOLOR)#导入模板
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img,refCnts,-1,(0,0,255),3) 
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] 
digits = {
    
    }
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

rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
image = cv2.imread('123.png') #导入目标
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
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")
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 
thresh = cv2.threshold(gradX, 0, 255,
	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) 
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) 
locs = []
for (i, c) in enumerate(cnts):
	(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))
locs = sorted(locs, key=lambda x:x[0])
output = []
for (i, (gX, gY, gW, gH)) in enumerate(locs):
	groupOutput = []
	group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
	group = cv2.threshold(group, 0, 255,
		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
	digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	digitCnts = contours.sort_contours(digitCnts,
		method="left-to-right")[0]
	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))
		scores = []
		for (digit, digitROI) in digits.items():
			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_SIMPLEX, 0.65, (0, 0, 255), 2)
	output.extend(groupOutput)
	print(groupOutput)
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)

II 代码解释

1.导入开发用的工具包

其中myutils是第八步所需要的工具包(在这里)：https://download.csdn.net/download/helloworld573/12329376

from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

2.定义一个信用卡名称的字典，用于识别这是什么信用卡

(在最后我们知道，看信用卡的第1位就行了)

FIRST_NUMBER = {
    
    
	"3": "American Express",
	"4": "Visa",
	"5": "MasterCard",
	"6": "Disco5ver Card"
}

3.cv_show函数的定义

def cv_show(name,img):
	cv2.imshow(name, img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

4.把模板读取进来

img = cv2.imread('1.png',cv2.IMREAD_ANYCOLOR)
cv_show('img',img)

5.转化为灰度图

ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)

6.将灰度图转化为二值图片

ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]#取第二个返回值给ref
cv_show('ref',ref)

7.绘制外侧轮廓并打印个数

refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#将ref备份，进行轮廓检测，只检测最外轮廓，只保留终点坐标(便于知道轮廓位置)
#refCnts是储存每一个轮廓的列表
cv2.drawContours(img,refCnts,-1,(0,0,255),3) 
#将所有的轮廓都画出来
cv_show('img',img)
#统计个数
print (np.array(refCnts).shape)

8.将轮廓信息以从左到右从上到下的方式排序

refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] 
#refCnts是排序完的轮廓

9.遍历排序完的轮廓并绘制固定大小的外接矩形，并把这些矩形保留为模板

digits = {
    
    }
# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
	# 计算外接矩形并且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))
	#保留为模板
	digits[i] = roi
	print(digits[i])

10.准备导入信用卡图片，先定义两个不同大小的卷积核以便形态学操作使用

rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3)) #9x3
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))   #5x5

11.导入信用卡图片，并进行预处理

image = cv2.imread('123.png')
cv_show('image',image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)

12.进行一次礼帽操作，突出更明亮的区域

tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) 
cv_show('tophat',tophat) 

13使用sobel算子进行梯度检测，这里只用了x

关于梯度检测: https://blog.csdn.net/helloworld573/article/details/105284024

gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
	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)

可以得到不明显的梯度

14.通过闭操作（先膨胀，再腐蚀）将每四个数字连在一起

关于闭操作: https://blog.csdn.net/helloworld573/article/details/105284024

gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 
cv_show('gradX',gradX)

15.进行二值化处理

进行二值化处理，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
适合我不太清楚这这个阈值是多少，这是一种自动寻找阈值的方法
关于二值化处理的基本：https://blog.csdn.net/helloworld573/article/details/105263693

thresh = cv2.threshold(gradX, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
cv_show('thresh',thresh)

这样就更清晰了

16.再次闭操作，使四个数字连接的更为清晰

关于闭操作: https://blog.csdn.net/helloworld573/article/details/105284024

thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
cv_show('thresh',thresh)

17.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)

18.遍历轮廓，过滤掉不需要的轮廓

for (i, c) in enumerate(cnts):
	# 计算轮廓的外接矩形
	(x, y, w, h) = cv2.boundingRect(c)
	# 算一下长宽比例
	ar = w / float(h)
	
	# 根据长宽比例来过滤不符合的区域。
	# 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))
			print(locs)

最后得到四个矩形

19.将符合要求的四个轮廓进行排序

locs = sorted(locs, key=lambda x:x[0])

20.(最后的迭代处理)一个大for循环来将这四个轮廓：处理-拆分-处理

output = []
# 遍历每一个轮廓中的数字
for (i, (gX, gY, gW, gH)) in enumerate(locs):
	# 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]#稍微把这个轮廓坐标+5-5把这个轮廓扩大一点给他点余缝
	cv_show('group',group)
	# 预处理
	group = cv2.threshold(group, 0, 255,
		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
	cv_show('group',group)
	# 计算每一组的轮廓并排序
	digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	digitCnts = contours.sort_contours(digitCnts,
		method="left-to-right")[0]
	# 开始计算每一组中的每一个数值：准备模板匹配
	for c in digitCnts:
		# 找到当前数值的轮廓，resize成合适的的大小（需要和模板大小一模一样）
		(x, y, w, h) = cv2.boundingRect(c)
		roi = group[y:y + h, x:x + w]
		roi = cv2.resize(roi, (57, 88))#模板大小也是57，88
		cv_show('roi',roi)

		# 计算匹配得分：开始进行模板匹配
		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)

			# 得到最合适的数字
			groupOutput.append(str(np.argmax(scores)))

		# 在图片上画出来
		cv2.rectangle(image, (gX - 5, gY - 5),
			(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
	    # 在图片上写出来
		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)
		print(groupOutput)

21.最终效果

print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)