随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习的基础内容。

进行识别和匹配，将提取出来的数字与模板数字（需要自己创建）进行比较，这里采用两幅图片像素相减的方法，寻找出差值最小的图片即为最匹配的数字

代码如下：

import cv2
import numpy as np

代码如下：

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(cnt) for cnt in cnts]
    (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

2.对信用卡信息进行处理，去除多余的背景信息。

cv_show(f"number1",number[3])

cardImg = cv2.imread('credit_card_03.png')
cardImg = cv2.resize(cardImg, \
                    (300, int(float(300 / cardImg.shape[1]) * cardImg.shape[0])),\
                     interpolation=cv2.INTER_AREA)

cardImg_gray = cv2.cvtColor(cardImg, cv2.COLOR_BGR2GRAY)
cv_show('cardImg_gray', cardImg_gray)

rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))

cardImg_tophat = cv2.morphologyEx(cardImg_gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('cardImg_tophat', cardImg_tophat)

3.对信用卡上的数字进行选取，对于非卡号数字进行剔除。

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)
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)

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)

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)

4.得到卡号区域后，对卡号进行数字划分。进行模板匹配，得到每个数字图像所对应的数字。

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) 

以上就是今天要讲的内容，本文仅仅简单介绍了python中数字识别的使用，而python提供了大量能使我们快速便捷地处理数据的函数和方法。