附代码 | OpenCV实现银行卡号识别,字符识别算法你知多少?
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实验前的准备
程序的搭建
1、参考图像的读取和处理:
#定义了一个字典 FIRST_NUMBER ,它将第一个数字映射到相应的信用卡类型。
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
#参考数字图像,用于匹配
#灰度化及二值化
ref=cv2.imread("1.png")
ref = cv2.cvtColor(ref, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
#查找轮廓,从左往右排序
refCnts = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
refCnts = imutils.grab_contours(refCnts)
refCnts = contours.sort_contours(refCnts, method="left-to-right")[0]
digits = {}
#对于其中每一个轮廓进行提循环,i为数字名称,c为轮廓,我们将每个数字0-9(字典键)与第30行的每个roi 图像(字典值)相关联 。
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
#初始化几个结构化内核,构造了两个这样的内核 - 一个矩形和一个正方形。我们将使用矩形的一个用于Top-hat形态运算符,将方形一个用于关闭操作。
rectKernel=cv2.getStructuringElement(cv2.MORPH_RECT,(9,3))
sqKernel=cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
#加载信用卡图像
image=cv2.imread("3.jpg")
image=imutils.resize(image,width=300)
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
#执行Top-hat形态操作,将结果存储为 tophat,Top-hat操作显示了深色背景下的亮区(即信用卡号)
tophat=cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)
#计算沿x方向的渐变在计算gradX 数组中每个元素的绝对值之后 ,我们采取一些步骤将值缩放到范围[0-255](因为图像当前是浮点数据类型)。要做到这一点,我们计算 MINVAL
# 和 MAXVAL 的 gradX (线72),然后由我们的缩放方程上显示 线73(即,最小/最大归一化)。最后一步是将gradX转换 为 uint8 ,其范围为[0-255]
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")
#执行gradX 图像的Otsu和二进制阈值,然后是另一个关闭操作,对数字分段
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)
#找到轮廓并初始化数字分组位置列表
cnts=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts=imutils.grab_contours(cnts)
locs = []
#循环遍历轮廓,同时根据每个的宽高比进行过滤,允许我们从信用卡的其他不相关区域修剪数字组位置
for (i, c) in enumerate(cnts):
# compute the bounding box of the contour, then use the
# bounding box coordinates to derive the aspect ratio
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
# since credit cards used a fixed size fonts with 4 groups
# of 4 digits, we can prune potential contours based on the
# aspect ratio
if ar > 2.5 and ar < 4.0:
# contours can further be pruned on minimum/maximum width
# and height
if (w > 40 and w < 55) and (h > 10 and h < 20):
# append the bounding box region of the digits group
# to our locations list
locs.append((x, y, w, h))
#从左到右对分组进行排序,并初始化信用卡数字列表
locs = sorted(locs, key=lambda x:x[0])
output = []
#遍历四个排序的分组并确定其中的数字,循环的第一个块中,我们在每一侧提取并填充组5个像素(第125行)
# ,应用阈值处理(第126和127行),并查找和排序轮廓(第129-135行)。
for (i, (gX, gY, gW, gH)) in enumerate(locs):
# initialize the list of group digits
groupOutput = []
# extract the group ROI of 4 digits from the grayscale image,
# then apply thresholding to segment the digits from the
# background of the credit card
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]
# detect the contours of each individual digit in the group,
# then sort the digit contours from left to right
digitCnts = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
digitCnts = imutils.grab_contours(digitCnts)
digitCnts = contours.sort_contours(digitCnts,
method="left-to-right")[0]
# loop over the digit contours
for c in digitCnts:
# compute the bounding box of the individual digit, extract
# the digit, and resize it to have the same fixed size as
# the reference OCR-A images
(x, y, w, h) = cv2.boundingRect(c)
roi = group[y:y + h, x:x + w]
roi = cv2.resize(roi, (57, 88))
# initialize a list of template matching scores
scores = []
# loop over the reference digit name and digit ROI
for (digit, digitROI) in digits.items():
# apply correlation-based template matching, take the
# score, and update the scores list
result = cv2.matchTemplate(roi, digitROI,
cv2.TM_CCOEFF)
(_, score, _, _) = cv2.minMaxLoc(result)
scores.append(score)
# the classification for the digit ROI will be the reference
# digit name with the *largest* template matching score
groupOutput.append(str(np.argmax(scores)))
# draw the digit classifications around the group
cv2.rectangle(image, (gX - 5, gY - 5),
(gX + gW + 5, gY + gH + 5), (0, 0, 255), 2)
cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
# update the output digits list
output.extend(groupOutput)
# display the output credit card information to the screen
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)
作者简介:
李秋键,CSDN博客专家,CSDN达人课作者。硕士在读于中国矿业大学,开发有taptap竞赛获奖等等。
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