OpenCV图像处理技术(Python)——信用卡数字识别

    import cv2
    import numpy as np
    def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    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

    # 读取模板图片
    template = cv2.imread('ocr_a_reference.png')
    cv_show('template', template)
    # 模板图片灰度化。这里的模板图片本身就是二值化的因此没有明显区别。
    template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    cv_show('templage_gray', template_gray)
    # 二值化，转化为数字为白色，背景为黑色的图片。
    template_binary = cv2.threshold(template_gray, 127, 255, cv2.THRESH_BINARY_INV)[1]
    cv_show('template_binary', template_binary)

    # 根据二值化的模板图，进行轮廓检测
    cnts, hierarchy = cv2.findContours(template_binary
                                                   , cv2.RETR_EXTERNAL
                                                   , cv2.CHAIN_APPROX_SIMPLE)
    # 画出每个数字的轮廓
    template_rect = cv2.drawContours(template.copy(), cnts, -1, (0,0,255), 2)
    cv_show('template_rect', template_rect)
    # 对十个数字根据左上角的位置进行排序，这样数字按照从小到大的顺序排列出来。
    cnts = sort_contours(cnts, method="left-to-right")[0]
    number = {}
    # 根据排列的结果，将每个数字截取出来。将每个数字图片所对应的数字对应起来。
    # 这里要注意，对像素值进行取值时，数组的行对应的是图片的y轴，列对应的图片的x轴。
    for (i, cnt) in enumerate(cnts):
    (x,y,w,h) = cv2.boundingRect(cnt)
    roi = template_binary[y:y+h, x:x+w]
    roi = cv2.resize(roi, (57,88))
    
    number[i] = roi

    # 读取信用卡图片
    cardImg = cv2.imread('credit_card_01.png')
    cardImg = cv2.resize(cardImg
                          , (300, int(float(300 / cardImg.shape[1]) * cardImg.shape[0]))
                          , interpolation=cv2.INTER_AREA)
    cv_show('cardImg', cardImg)
    cardImg_gray = cv2.cvtColor(cardImg, cv2.COLOR_BGR2GRAY)
    cv_show('cardImg_gray', cardImg_gray)
    
    # 指定卷积核大小
    rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
    sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    # 进行礼帽操作
    cardImg_tophat = cv2.morphologyEx(cardImg_gray, cv2.MORPH_TOPHAT, rectKernel)
    cv_show('cardImg_open', cardImg_tophat)

    # 使用sobel算子进行边缘检测，这里仅适用x方向的梯度。因为经过实验，使用x,y混合的梯度，效果并不理想。
    sobelx = cv2.Sobel(cardImg_tophat, cv2.CV_64F, 1, 0, ksize=3)
    sobelx = cv2.convertScaleAbs(sobelx)
    (minX, maxX) = (np.min(sobelx), np.max(sobelx))
    sobelx = (255 * ((sobelx - minX) / (maxX - minX)))
    sobelx = sobelx.astype('uint8')
    cv_show('sobelx', sobelx)
    #进行闭运算，使相邻的数字连接起来，这样便于筛选。
    cardImg_close = cv2.morphologyEx(sobelx, cv2.MORPH_CLOSE, rectKernel)
    cv_show('cardImg_close', cardImg_close)
    
    cardImg_binary = cv2.threshold(cardImg_close, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY)[1]
    cv_show('cardImg_binary', cardImg_binary)
    
    cardImg_close = cv2.morphologyEx(cardImg_binary, cv2.MORPH_CLOSE, sqKernel)
    cv_show('cardImg_close', cardImg_close)
    # 轮廓检测，检测出每一个数字区块
    cnts, hierarchy = cv2.findContours(cardImg_close
                                             , cv2.RETR_EXTERNAL
                                             , cv2.CHAIN_APPROX_SIMPLE)
    cardImg_cnts = cv2.drawContours(cardImg.copy(), cnts, -1, (0,0,255), 2)
    cv_show('cardImg_cnts', cardImg_cnts)
    
    # 对轮廓进行筛选， 根据边框的尺寸仅保留卡号区域
    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])
    for (x, y, w, h) in locs:
    cv2.rectangle(cardImg_cnts, (x,y),(x+w,y+h),(0,0,255),3)
    cv_show('cardImg_cnts', cardImg_cnts)

    output = []
    # 对每个4数字块进行处理
    for (i, (x, y, w, h)) in enumerate(locs):
    group_output = []
    group = cardImg_gray[y-5:y + h + 5, x-5:x + w + 5]
    group = cv2.threshold(group, 0, 255, cv2.THRESH_OTSU|cv2.THRESH_BINARY)[1]
    cv_show('group', group)
    group_cnts, group_hierarchy = cv2.findContours(group
                                                           , cv2.RETR_EXTERNAL
                                                           , cv2.CHAIN_APPROX_SIMPLE)
    group_cnts = sort_contours(group_cnts, method="left-to-right")[0]
    # 分割每个数字
    for cnt in group_cnts:
        (nx,ny,nw,nh) = cv2.boundingRect(cnt)
        roi = group[ny:ny+nh, nx:nx+nw]
        roi = cv2.resize(roi, (57, 88))
        cv_show('roi', roi)
    
        score = []
        # 对每个数字进行模板匹配
        for (number_i, number_roi) in number.items():
            result = cv2.matchTemplate(roi, number_roi, cv2.TM_CCOEFF)
            score_ = cv2.minMaxLoc(result)[1]
    
            score.append(score_)
    
        group_output.append(str(np.argmax(score)))
    # 绘制每个数字
    cv2.rectangle(cardImg, (x - 5, y - 5),
                  (x + w + 5, y + h + 5), (0, 0, 255), 1)
    cv2.putText(cardImg, "".join(group_output), (x, y - 15),
                cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
    output.append(group_output)
    
    cv_show('cardImg', cardImg)