学习Opencv+Python之银行卡卡号识别

学习Opencv+Python之银行卡卡号识别

思路：

1. 获取模板轮廓
2. 获取模板中每个数字的轮廓
3. 获取银行卡卡号轮廓
4. 分别提取卡号中的每个数字的轮廓
5. 对比识别

代码：

# 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())

# 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()

# 读取一个模板图像，灰度化，取二值图像
img = cv2.imread(args["template"])
cv_show('img', img)
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref', ref)
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref', ref)

# 寻找轮廓
# cv2.findContours()函数：接受的参数为二值图, cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
# 返回的refCnts为一个列表，其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()在img中绘制refCnts列表中的所有轮廓，-1表示所有，轮廓颜色为红色，线宽为3
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show('img', img)
print (np.array(refCnts).shape)

# 排序，从左到右，从上到下
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]
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
	# cv2.boundingRect(): 矩形边框，c是轮廓点集，可由函数cv2.findContour()获得，(x, y)为矩阵的左上角，w, h分别为矩阵的高宽
	(x, y, w, h) = cv2.boundingRect(c)
	# roi: 将每个矩形轮廓即每个数字区域提取出来，像素范围为[y : y + h, x : x + w]
	roi = ref[y:y + h, x:x + w]
	roi = cv2.resize(roi, (57, 88))

	#每个数字依次保存在集合digits中
	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)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)

# 礼帽操作，突出更明亮的区域
# 函数cv2.morphologyEx()执行各类形态学操作，cv2.MORPH_TOPHAT礼帽操作
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)

#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
#返回两个值，第一个是bool值，所以这里[1]
thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh',thresh)

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

#计算轮廓
#返回两个值，一个索引，一个轮廓列表
threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cnts = threshCnts
cur_img = image.copy()
# 在cur_img上绘制所有轮廓，颜色为红色，线宽为3
cv2.drawContours(cur_img, cnts, -1, (0, 0, 255), 3)
cv_show('img',cur_img)
locs = []

# 遍历轮廓
# 枚举，i为轮廓的编号或者键，c为轮廓
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():
			# cv2.matchTemplate(image, templ, method, result=None, mask=None)
			result = cv2.matchTemplate(roi, digitROI, cv2.TM_CCOEFF)
			# (min_val,max_val,min_indx,max_indx)=cv2.minMaxLoc(a)依次返回最小值、最大值，以及相应的索引值
			(_, score, _, _) = cv2.minMaxLoc(result)
			# 依次添加到列表scores中
			scores.append(score)
		# 将最大的值添加到列表groupOutput中
		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)

imutils模块：

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] #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