深度学习从入门到精通——Opencv模板匹配完成信用卡识别

轮廓模板

识别出来的数字需要比对，需要找个模板对应一下。

# 导入工具包
import argparse

import cv2
import numpy as np
from imutils import contours
import myutils
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
def mainargs():
    # 设置参数
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", default="./images/credit_card_01.png",
                    help="input image")
    ap.add_argument("-t", "--template", default="ocr_a_reference.png",
                    help="template  image")
    args = vars(ap.parse_args())
    return args
    # 绘图展示


def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

读取模板图像

   # 读取一个模板图像
    img = cv2.imread(args["template"])

显示模板图像

    cv_show('img', img)

模板转灰度图转阈值

 # 灰度图
    ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
    cv_show('ref', ref)

计算轮廓

    refCnts, hierarchy = cv2.findContours(ref.copy(), 		  cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # 数据、全部、颜色、线厚
    cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
    cv_show('img', img)

10个轮廓

轮廓排序并且保留

refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]  # 排序，从左到右，从上到下
    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

• 接下来处理我们需要的比对的图像

显示图像

  	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)

转灰度图

	image = myutils.resize(image, width=300)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv_show('gray', gray)

进行礼帽操作

原始 - 开操作

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

这里面背景色是主题部分，利用礼帽操作特性，可以获取到白色噪点。

Sobel边缘算子

 	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))
    # 归一化
    gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
    gradX = gradX.astype("uint8")

    print(np.array(gradX).shape)
    cv_show('gradX', gradX)

闭操作 补洞

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)
  ```
![在这里插入图片描述](https://img-blog.csdnimg.cn/cb6da869493544c3a7f00b14b299fc7d.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5bCP6ZmIcGhk,size_11,color_FFFFFF,t_70,g_se,x_16)
再次闭操作
![在这里插入图片描述](https://img-blog.csdnimg.cn/62dba347791a497bbaecc420138d1753.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5bCP6ZmIcGhk,size_11,color_FFFFFF,t_70,g_se,x_16)

### 计算轮廓
```python
# 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)

轮廓排序

这里知道我们需要的银行卡数字在中心，大小长度宽度，我们可以进行筛选。

    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])

结果依次识别

# 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)))
   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)

完整识别代码

# 导入工具包
import argparse

import cv2
import numpy as np
from imutils import contours

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
def mainargs():
    # 设置参数
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", default="./images/credit_card_01.png",
                    help="input image")
    ap.add_argument("-t", "--template", default="ocr_a_reference.png",
                    help="template  image")
    args = vars(ap.parse_args())
    return args
    # 绘图展示


def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def main(args):
    # 指定信用卡类型
    FIRST_NUMBER = {
        "3": "American Express",
        "4": "Visa",
        "5": "MasterCard",
        "6": "Discover Card"
    }
    # 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, dtype=object).shape)

    refCnts = sort_contours(refCnts, method="left-to-right")[0]  # 排序，从左到右，从上到下
    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 = 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)

    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)

    # 再来一个闭操作

    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)


if __name__ == '__main__':
    args = mainargs()
    main(args)