身份证识别

安装环境

tar leptonica-1.77.0
./configure
make && make install

1. centos yum install epel-release yum install tesseract-devel leptonica-devel
2. debian apt-get install libtesseract-dev libtesseract3 tesseract-ocr

pip install opencv-python pytesseract tesseract tesseract-ocr

代码

import re
import cv2
import pytesseract
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("/usr/local/lib/python3.6/site-packages/cv2/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)
return code
return 'NONE'

if __name__ == '__main__':
DetectFacesAndIDs('1.jpg')

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