opencv的信用卡数字识别项目

![这是模板数字]

这是信用卡
看看最后的效果图

import cv2
import numpy as np
from imutils import contours

#指定信用卡类型
FIRST_NUMBER = {
    "3":"American Express",
    "4":"Visa",
    "5":"MasterCard",
    "6":"Discover Card"

}
#绘制展示
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('img.png')
cv_show("img",img)
#灰度图
ref = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)
#【1】为返回处理后的二值图像，【0】为返回阈值10
ref = cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1]
cv_show('ref',ref)
#计算轮廓
#cv2.findContous()函数接受的参数为二值图，即黑白的（不是灰度图），
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)

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先，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









refCnts = sort_contours(refCnts,method = "left-to-right")[0]#排序，从左到右，从上到下
digits= {}
#年里每一个轮廓
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))

    #每一个数字对应一个模板
    digits[i] = roi


#初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT,(9,3))
sqKeernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))

#读取输入图像、
image= cv2.imread('image.png')
cv_show('image',image)
image = resize(image,width=300)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)


#礼帽操作，使得突出更明亮的区域
tophat = cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)
cv_show('tophat',tophat)


#将信用卡上的边缘提取出来
gradX = cv2.Sobel(tophat,ddepth=cv2.CV_32F,dx = 1,dy = 0,ksize = -1)#ksize = -1相当于用3*3的
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)

8，将靠近的数字连载一起，从而可以划分出4个数字区域
通过闭操作（先膨胀，再腐蚀）将数字连载一起

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，需要把阈值 参数设置为0
thresh = cv2.threshold(gradX,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh',thresh)
#在来一个闭操作
thresh = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,sqKeernel)
cv_show('thresh',thresh)

 9 计算轮廓，并将轮廓画在原始图片上

#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("cur_img",cur_img)

#10 遍历所有的轮廓，按照宽高比将需要的四个数字轮廓提取出来
locs =[]
#便利轮廓
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，这里的基本都睡四个数字一组
    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])


#11 用模板进行匹配
output = []
#遍历每一个轮廓的数字
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)

        #计算匹配的分
        socres = []
        #在模板中计算每一个得分
        for (digitCnts,digitROI) in digits.items():
            #模板匹配
            result = cv2.matchTemplate(roi,digitROI,cv2.TM_CCOEFF)
            (_,score,_,_) = cv2.minMaxLoc(result)
            socres.append(score)

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

    #画出来
    cv2.rectangle(img,(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)




#12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)
cv2.destroyAllWindows()