【OpenCV】银行卡号识别

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前言

本文引用其他作者代码，本文仅供记录用。详细内容可看此处

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一、环境

如果提示没有imutils模块就pip install imutils
提供一个字体模板文件和银行卡图片。可以右键保存。
字体模板文件：

银行卡图片；

二、代码

from imutils import contours
import numpy as np
import argparse
import cv2
 
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default='E:\VSCode\BankCard\credit_card_01.jpg',help="path to input image")
ap.add_argument("-t", "--template", default='E:\VSCode\BankCard\ocr_a_reference.png',help="path to template OCR-A image")
args = vars(ap.parse_args())
 
# 指定信用卡类型
FIRST_NUMBER = {
	"3": "American Express",
	"4": "Visa",
	"5": "MasterCard",
	"6": "Discover Card"
}


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] #用一个最小的矩形，把找到的形状包起来x,y,h,w
    (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 resized

# 绘图展示
def cv_show(name,img):
	cv2.imshow(name, img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
# 读取一个模板图像
img = cv2.imread(args["template"])
cv_show('img',img)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)
# 二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref',ref)
 
# 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
 
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
 
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] #排序，从左到右，从上到下
refCnts = sort_contours(refCnts, method="left-to-right")[0] #排
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))
 
	# 每一个数字对应每一个模板
	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))
 
#读取输入图像，预处理
image = cv2.imread(args["image"])
cv_show('image',image)
# image = myutils.resize(image, width=300)
image = resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)
 
#礼帽操作，突出更明亮的区域
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)
 
#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)
#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)
 
#再来闭操作
 
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
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)
 
	# 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])
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]
	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
	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)
 
		# 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)
 
		# 得到最合适的数字
		groupOutput.append(str(np.argmax(scores)))
 
	# 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)
 
# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)

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总结

细看处理流程。