使用Python提取身份证上的信息

import pytesseract
import cv2
import matplotlib.pyplot as plt
import dlib
import matplotlib.patches as mpatches
from skimage import io,draw,transform,color
import numpy as np
import pandas as pd
import re

detector = dlib.get_frontal_face_detector()
image = io.imread("img-0.png")
dets = detector(image, 2) #使用detector进行人脸检测 dets为返回的结果
## 将识别的图像可视化
plt.figure()
ax = plt.subplot(111)
# ax.imshow(image)
plt.axis("off")
for i, face in enumerate(dets):
    # 在图片中标注人脸，并显示
    left = face.left()
    top = face.top()
    right = face.right()
    bottom = face.bottom()
    rect = mpatches.Rectangle((left,bottom), right - left, top - bottom,
                                  fill=False, edgecolor='red', linewidth=1)
    ax.add_patch(rect)
plt.show()

predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
detected_landmarks = predictor(image, dets[0]).parts()
landmarks = np.array([[p.x, p.y] for p in detected_landmarks])
## 将眼睛位置可视化
# plt.figure()
# ax = plt.subplot(111)
# ax.imshow(image)
# plt.axis("off")
# plt.plot(landmarks[0:4,0],landmarks[0:4,1],'ro')
# for ii in np.arange(4):
#     plt.text(landmarks[ii,0]-10,landmarks[ii,1]-15,ii)
# plt.show()

## 计算眼睛的倾斜角度,逆时针角度
def twopointcor(point1,point2):
    """point1 = (x1,y1),point2 = (x2,y2)"""
    deltxy = point2 - point1
    corner = np.arctan(deltxy[1] / deltxy[0]) * 180 / np.pi
    return corner

## 计算多个角度求均值
corner10 =  twopointcor(landmarks[1,:],landmarks[0,:])
corner23 =  twopointcor(landmarks[3,:],landmarks[2,:])
corner20 =  twopointcor(landmarks[2,:],landmarks[0,:])
corner = np.mean([corner10,corner23,corner20])
# print(corner10)
# print(corner23)
# print(corner20)
# print(corner)

## 计算图像的身份证倾斜的角度
def IDcorner(landmarks):
    """landmarks:检测的人脸5个特征点
       经过测试使用第0个和第2个特征点计算角度较合适
    """
    corner20 =  twopointcor(landmarks[2,:],landmarks[0,:])
    corner = np.mean([corner20])
    return corner
corner = IDcorner(landmarks)
# print(corner)

## 将照片转正
def rotateIdcard(image):
    "image :需要处理的图像"
    ## 使用dlib.get_frontal_face_detector识别人脸
    detector = dlib.get_frontal_face_detector()
    dets = detector(image, 2) #使用detector进行人脸检测 dets为返回的结果
    ## 检测人脸的眼睛所在位置
    predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")
    detected_landmarks = predictor(image, dets[0]).parts()
    landmarks = np.array([[p.x, p.y] for p in detected_landmarks])
    corner = IDcorner(landmarks)
    ## 旋转后的图像
    image2 = transform.rotate(image,corner,clip=False)
    image2 = np.uint8(image2*255)
    ## 旋转后人脸位置
    det = detector(image2, 2)
    return image2,det

## 转正身份证：
image = io.imread("img-0.png")
image2,dets = rotateIdcard(image)

## 可视化修正后的结果
plt.figure()
ax = plt.subplot(111)
# ax.imshow(image2)
plt.axis("off")
# 在图片中标注人脸，并显示
left = dets[0].left()
top = dets[0].top()
right = dets[0].right()
bottom = dets[0].bottom()
rect = mpatches.Rectangle((left,bottom), (right - left), (top - bottom),
                          fill=False, edgecolor='red', linewidth=1)
ax.add_patch(rect)

## 照片的位置（不怎么精确）
width = right - left
high = top - bottom
left2 = np.uint(left - 0.5*width)
bottom2 = np.uint(bottom + 0.5*width)
rect = mpatches.Rectangle((left2,bottom2), 1.8*width, 2.2*high,
                          fill=False, edgecolor='blue', linewidth=1)
ax.add_patch(rect)
plt.show()

## 身份证上人的照片
top2 = np.uint(bottom2+2.2*high)
right2 = np.uint(left2+1.8*width)
image3 = image2[top2:bottom2,left2:right2,:]
# plt.imshow(image3)
plt.axis("off")
plt.show()
# cv2.imshow('image3',image3)
# cv2.waitKey()

# ## 对图像进行处理，转化为灰度图像=>二值图像
# imagegray = cv2.cvtColor(image2,cv2.COLOR_RGB2GRAY)
# cv2.imshow('imagegray',imagegray)
#
# cv2.waitKey()
# retval, imagebin = cv2.threshold(imagegray, 120, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
# ## 将照片去除
# imagebin[0:bottom2,left2:-1] = 255
# # 高斯双边滤波
# img_bilateralFilter = cv2.bilateralFilter(imagebin, 40, 75, 75)
#
# cv2.imshow('img_bilateralFilter',img_bilateralFilter)
# cv2.waitKey()
# # plt.imshow(img_bilateralFilter,cmap=plt.cm.gray)
# #
# # plt.axis("off")
# # plt.show()


img=cv2.imread('img-0.png') #打开图片
gray=cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY) #灰度处理
# cv2.imshow('gray', gray)
retval, imagebin = cv2.threshold(gray, 50, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
## 将照片去除
imagebin[0:bottom2,left2:-1] = 255
img_bilateralFilter = cv2.bilateralFilter(imagebin, 40, 100, 100) # 高斯双边滤波

cv2.namedWindow("img_bilateralFilter", cv2.WINDOW_NORMAL)
cv2.imshow('img_bilateralFilter', img_bilateralFilter)

cv2.waitKey(0)