python笔记 | 身份证识别实例

实现步骤

1、使用人脸识别分类器定位脸的位置

重点语句：
classfier = cv2.CascadeClassifier("data/haarcascade_frontalface_alt.xml")
faceRects = classfier.detectMultiScale

2、通过头像的位置算出身份证号码位置

重点语句：
(x1, y1, x2, y2) = CalcIdRectByFaceRect_normal(x, y, w, h)

3、调整获取身份证号码位置

重点语句：
def GetRegionString(img, region):

4、解析身份证号码的详细步骤（pytesseract.image_to_string）

• 转化为灰度图
• 把图片变成二值图像
• 调用解析图片转文字的接口
  （code = pytesseract.image_to_string(dImg)）
• 修正部分不正确的值

import os
import re
import cv2
import pytesseract
import common.common as co
from PIL import Image

# 身份证号
r = r'^([1-9]\d{5}[12]\d{3}(0[1-9]|1[012])(0[1-9]|[12][0-9]|3[01])\d{3}[0-9xX])$'


# 根据比例和偏移算出号码位置
# 一般面部比例
def CalcIdRectByFaceRect_normal(x, y, w, h):
    scale = float(w) / 95
    x1 = int(x + ((0 - 159)) * scale)
    y1 = int(y + (0 + (149)) * scale)
    x2 = int(x + (0 - 159 + (275)) * scale)
    y2 = int(y + (0 + (149) + (45)) * scale)
    return (x1, y1, x2, y2)


# 较大面部比例
def CalcIdRectByFaceRect_big(x, y, w, h):
    scale = float(w) / 95
    x1 = int(x + ((0 - 159) + 10) * scale)
    y1 = int(y + (0 + (149 - 3)) * scale)
    x2 = int(x + (0 - 159 + (275 - 10)) * scale)
    y2 = int(y + (0 + (149 - 3) + (45 - 10)) * scale)
    return (x1, y1, x2, y2)


# 较小面部比例
def CalcIdRectByFaceRect_small(x, y, w, h):
    scale = float(w) / 95
    x1 = int(x + ((0 - 159) - 10) * scale)
    y1 = int(y + (0 + (149 + 3)) * scale)
    x2 = int(x + (0 - 159 + (275 + 10)) * scale)
    y2 = int(y + (0 + (149 + 5) + (45 + 10)) * scale)
    return (x1, y1, x2, y2)


# 二值化算法
def binarizing(img, threshold):
    pixdata = img.load()
    w, h = img.size
    for y in range(h):
        for x in range(w):
            if pixdata[x, y] < threshold:
                pixdata[x, y] = 0
            else:
                pixdata[x, y] = 255
    return img


# 去除干扰线算法
def depoint(img):  # input: gray image
    pixdata = img.load()
    w, h = img.size
    for y in range(1, h - 1):
        for x in range(1, w - 1):
            count = 0
            if pixdata[x, y - 1] > 245:
                count = count + 1
            if pixdata[x, y + 1] > 245:
                count = count + 1
            if pixdata[x - 1, y] > 245:
                count = count + 1
            if pixdata[x + 1, y] > 245:
                count = count + 1
            if count > 2:
                pixdata[x, y] = 255
    return img


#  通过头像的位置 身份证号码识别
def identity_OCR_byFaceRect(oImg, faceRect):
    (x, y, w, h) = faceRect
    iw, ih = oImg.size
    # 将身份证放大3倍
    largeImg = oImg.resize((iw * 3, ih * 3), Image.ANTIALIAS)
    # largeImg.save('1_large.png')

    (x1, y1, x2, y2) = CalcIdRectByFaceRect_normal(x, y, w, h)
    region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3)
    code = GetRegionString(largeImg, region)
    if not re.match(r, code):
        (x1, y1, x2, y2) = CalcIdRectByFaceRect_small(x, y, w, h)
        region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3)
        code = GetRegionString(largeImg, region)
    if not re.match(r, code):
        (x1, y1, x2, y2) = CalcIdRectByFaceRect_big(x, y, w, h)
        region = (x1 * 3, y1 * 3, x2 * 3, y2 * 3)
        code = GetRegionString(largeImg, region)
    if not re.match(r, code):
        code = 'NONE'
    return code, (x1, y1, x2, y2)


def GetRegionString(img, region):
    # 裁切身份证号码图片
    cropImg = img.crop(region)
    cropImg.save('tmp/2_crop.png')
    # 转化为灰度图
    grayImg = cropImg.convert('L')
    # grayImg.save('3_grey.png')
    # 把图片变成二值图像。
    bImg = binarizing(grayImg, 100)
    # bImg.save('4_bin.png')
    dImg = depoint(bImg)
    # dImg.save('5_depoint.png')
    code = pytesseract.image_to_string(dImg)
    code = PostProc(code)
    return code


#  号码后处理
def PostProc(s):
    res = s
    res = res.replace(" ", "")
    res = res.replace("O", "0")
    res = res.replace("U", "0")
    res = res.replace("D", "0")
    res = res.replace("Z", "2")
    res = res.replace("S", "5")
    res = res.replace("s", "5")
    res = res.replace("o", "6")
    res = res.replace("f", "7")
    res = res.replace("H", "11")
    return res


#  检测身份证
def DetectFacesAndIDs(pic_path):
    frame = cv2.imread(pic_path)
    oImg = Image.open(pic_path)

    ih, iw = frame.shape[:2]
    # 人脸识别分类器
    classfier = cv2.CascadeClassifier("data/haarcascade_frontalface_alt.xml")

    # 识别出人脸后要画的边框的颜色，RGB格式
    color = (0, 255, 0)
    color2 = (255, 0, 0)

    # 将当前帧转换成灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
    faceRects = classfier.detectMultiScale(
        gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
    if len(faceRects) > 0:  # 大于0则检测到人脸
        for faceRect in faceRects:  # 单独框出每一张人脸
            x, y, w, h = faceRect

            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            code, (x1, y1, x2, y2) = identity_OCR_byFaceRect(oImg, faceRect)
            cv2.rectangle(frame, (x1, y1), (x2, y2), color2, 2)
            # cv2.imwrite("%s.iddet.png" % pic_path, frame)
            print("CODE", code)
            return code
    print("ERROR : NONE")
    return 'NONE'


if __name__ == '__main__':
    if not os.path.isdir("tmp"):
        os.mkdir("tmp")
    co.printTime("START")

    DetectFacesAndIDs('src/ids.png')
    co.printTime("MIDDLE")
    DetectFacesAndIDs('src/lina2.jpg')
    co.printTime("END")
    input("Waitting for end\n")

以上代码调试通过，

本代码缺点，图片必须正才能识别出，如果拍照歪了，需要手动校正后才能识别出对应的图片。

待日后找到，纠正图片角度的函数再放上来。
co.printTime()函数代码如下：

def printTime(s):
    localtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
    print ("-- ",s," TIME：", localtime)

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