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环境
提示：硬件环境Mac m1 python:3.8 opencv-python: 3.4.8.29 pytesseract: 0.3.9 cnocr: 2.1.2.1
文章目录
环境前言正面识别背面识别源码下载
前言
这里利用opencv 做身份证照片处理，为识别增加准确度。利用pytesseract 做文字提取，由于是识别身份证，需要提前下载好中文字体包。
正面识别
代码如下（示例）：
# -*- coding: utf-8 -*-
import os, cv2
import sys, numpy as np
import math
import include.binaryzation as bz
import include.functions as func
import copy
import fileutil
DEBUG = False
CARD_NAME = ''
CARD_SEX = ''
CARD_ETHNIC = ''
CARD_YEAR = ''
CARD_MON = ''
CARD_DAY = ''
CARD_ADDR = ''
# 身份证号码
CARD_NUM = ''
# from imutils.perspective import four_point_transform
# parser = argparse.ArgumentParser()
# parser.add_argument('image', help='path to image file')
# args = parser.parse_args()
#
_localDir = os.path.dirname(__file__)
_curpath = os.path.normpath(os.path.join(os.getcwd(), _localDir))
curpath = _curpath
def show(image, window_name):
cv2.namedWindow(window_name, 0)
cv2.imshow(window_name, image)
# 0任意键终止窗口
cv2.waitKey(0)
cv2.destroyAllWindows()
def getCardNum(img, kenalRect):
"""
识别并提取身份证号码
:return:
"""
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
thr = bz.myThreshold().getMinimumThreshold(gray)
ret, binary = cv2.threshold(gray, thr, 255, cv2.THRESH_BINARY)
# 3. 膨胀和腐蚀操作的核函数
kenal = cv2.getStructuringElement(cv2.MORPH_RECT, (kenalRect[0], kenalRect[1]))
dilation = cv2.dilate(binary, kenal, iterations=1)
erosion = cv2.erode(dilation, kenal, iterations=1)
# OCR识别
cardNum = func.ocr(erosion)
cardNum = func.is_identi_number(cardNum)
if cardNum:
return cardNum
return False
def getChineseChar(img, kenalRect):
"""
分析汉字区域，并识别提取
:return:
"""
global CARD_NAME
global CARD_SEX
global CARD_ETHNIC
global CARD_YEAR
global CARD_MON
global CARD_DAY
global CARD_ADDR
global CARD_NUM
CARD_NAME = CARD_SEX = CARD_ETHNIC = CARD_YEAR = CARD_MON = CARD_DAY = CARD_ADDR = ''
# 图片大小比例缩小处理，为了加快性能
h, w, _ = img.shape
min_w = 200
scale = 1 # min(1., w * 1. / min_w)
h_proc = int(h * 1. / scale)
w_proc = int(w * 1. / scale)
im_dis = cv2.resize(img, (w_proc, h_proc))
# 灰度处理
gray = cv2.cvtColor(im_dis, cv2.COLOR_BGR2GRAY)
# 2. 形态学变换的预处理，得到可以查找矩形的图片
mybz = bz.myThreshold()
algos = mybz.getAlgos()
for i in algos:
# 获取阈值
thr = getattr(mybz, algos[i])(gray)
# thr = mybz.getMinimumThreshold(gray)
# func.showImg(gray, 'gray')
# 膨胀和腐蚀
ret, binary = cv2.threshold(gray, thr, 255, cv2.THRESH_BINARY)
# 获取行起始坐标
boundaryCoors = func.horizontalProjection(binary)
if not boundaryCoors:
continue
# print(boundaryCoors)
# 垂直投影对行内字符进行切割
erosion = cb = copy.copy(binary)
# show(binary, 'binary')
coors = {} # 信息所对应的坐标
textLine = 0 # 有效文本行序号
for LineNum, boundaryCoor in enumerate(boundaryCoors):
if textLine == 2:
kenal1 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
kenal2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
dilation = cv2.dilate(cb, kenal1, iterations=1)
erosion = cv2.erode(dilation, kenal2, iterations=1)
vertiCoors, text = func.verticalProjection(erosion, boundaryCoor, textLine, img)
if len(vertiCoors) == 0:
continue
if textLine == 0:
CARD_NAME = text
elif textLine == 1:
if text[0] != '男' and text[0] != '女':
CARD_SEX = func.getSexByCardNum(CARD_NUM)
else:
CARD_SEX = text[0]
CARD_ETHNIC = text[1]
elif textLine == 2:
# 为了获取更加精准的值，通过身份证号码规则直接取得出生年月
CARD_YEAR = text[0]
CARD_MON = text[1]
CARD_DAY = text[2]
pass
else:
CARD_ADDR += text
if DEBUG:
fator = 2
for verticoo in vertiCoors:
box = [[verticoo[0] * scale - fator, boundaryCoor[0] * scale - fator],
[verticoo[1] * scale + fator, boundaryCoor[0] * scale - fator],
[verticoo[1] * scale + fator, boundaryCoor[1] * scale + fator],
[verticoo[0] * scale - fator, boundaryCoor[1] * scale + fator],
]
cv2.drawContours(img, [np.int0(box)], 0, (0, 255, 0), 2)
textLine += 1
return
return False
def findChineseCharArea(cardNumPoint1, width, hight):
"""
根据身份证号码的位置推断姓名、性别、名族、出生年月、住址的位置
:param cardNumPoint1: tuple 身份证号码所处的矩形的左上角坐标
:param width: int 身份证号码所处的矩形的宽
:param hight: int 身份证号码所处的矩形的高
:return:
"""
# new_x = int(cardNumPoint1[0] - (width / 18) * 6)
new_x = cardNumPoint1[0] - (width / 18) * 5.5
new_width = int(width / 5 * 4)
box = []
# new_y = cardNumPoint1[1] - hight * 6.5
card_hight = hight / (0.9044 - 0.7976) # 身份证高度
card_y_start = cardNumPoint1[1] - card_hight * 0.7976 # 粗略算出图像中身份证上边界的y坐标
# 为了保证不丢失文字区域，姓名的相对位置保留，以身份证上边界作为起始切割点
# new_y = card_y_start# + card_hight * 0.0967
# 容错因子，防止矩形存在倾斜导致区域重叠
factor = 20
new_y = card_y_start if card_y_start > factor else factor
new_hight = card_hight * (0.7616 - 0.0967) + card_hight * 0.0967
# 文字下边界坐标
new_y_low = (new_y + new_hight) if (new_y + new_hight) b:
width = a
hight = b
pts2 = np.float32([[0, hight], [0, 0], [width, 0], [width, hight]])
else:
width = b
hight = a
angle = 90 + angle
pts2 = np.float32([[width, hight], [0, hight], [0, 0], [width, 0]])
# 透视变换
box = cv2.boxPoints(rect)
pts1 = np.float32(box)
M = cv2.getPerspectiveTransform(pts1, pts2)
cropImg = cv2.warpPerspective(img, M, (int(width), int(hight)))
# show(cropImg, 'cropImg')
# 计算核大小
kenalx = kenaly = int(math.ceil((hight / 100.0)))
CARD_NUM = getCardNum(cropImg, (kenalx, kenaly))
if CARD_NUM:
notFound = False
# 找到身份证号码，然后根据号码区域的倾斜角度，对原图进行旋转变换
if abs(angle) > 10:
sp = img.shape
H = sp[0]
W = sp[1]
M = cv2.getRotationMatrix2D((W / 2, H / 2), angle, 1)
cropImg = cv2.warpAffine(img, M, (W, H))
# cv2.namedWindow("倾斜矫正", cv2.WINDOW_NORMAL)
# cv2.imshow("倾斜矫正", cropImg)
# cv2.waitKey(0)
# 矫正图片地址
global curpath
path = 'tilt_correction.jpg'
newFile = os.path.join(curpath, path)
cv2.imwrite(newFile, cropImg)
return True, '', newFile
# 寻找汉字区域
# 裁剪后的图片
box = cv2.boxPoints(rect)
box = np.int0(box)
cropImg, point, width, hight = func.cropImgByBox(img, box)
box = findChineseCharArea(point, width, hight)
# cv2.drawContours(img, [box], 0, (0, 255, 0), 3)
chiCharArea, point, width, hight = func.cropImgByBox(img, box)
getChineseChar(chiCharArea, (kenalx, kenaly))
# if DEBUG:
# show(cropImg,'cropImg')
# winname = "身份证号码： %s" % (CARD_NUM)
# cv2.namedWindow(winname, cv2.WINDOW_NORMAL)
# cv2.imshow(winname, cropImg)
# cv2.waitKey(0)
break
if notFound:
continue
else:
break
if notFound:
# win32api.MessageBox(0, "无法识别，请换一个分辨率高点的照片~", "错误提示")
return False, '无法识别，请换一个分辨率高点的照片~", "错误提示', ''
# 带轮廓的图片
if DEBUG:
# cv2.namedWindow("img", cv2.WINDOW_NORMAL)
# cv2.imshow("img", img)
key = cv2.waitKey(0)
#
# cv2.destroyAllWindows()
if key != 32:
sys.exit()
if CARD_NUM != '':
# 为了获取更加精准的值，通过身份证号码规则直接取得出生年月
CARD_YEAR, CARD_MON, CARD_DAY = func.getBirthByCardNum(CARD_NUM)
if DEBUG:
info = """
姓名：%s
性别：%s 民族：%s
出生：%s 年 %s 月 %s 日
住址：%s
公民身份号码：%s
""" % (CARD_NAME, CARD_SEX, CARD_ETHNIC, CARD_YEAR, CARD_MON, CARD_DAY, CARD_ADDR, CARD_NUM)
# ret = [CARD_NAME, CARD_SEX, CARD_ETHNIC, CARD_YEAR, CARD_MON, CARD_DAY, CARD_ADDR, CARD_NUM]
ret = {"card_name": CARD_NAME, "CARD_SEX": CARD_SEX, "card_ethnic": CARD_ETHNIC,
"date_time": CARD_YEAR + "-" + CARD_MON + "-" + CARD_DAY, "card_addr": CARD_ADDR, "card_num": CARD_NUM}
return True, ret, ''
else:
return True, '', ''
def calculateElement(img):
# 根据图片大小粗略计算腐蚀 或膨胀所需核的大小
sp = img.shape
width = sp[1] # width(colums) of image
kenaly = math.ceil((width / 400.0) * 12)
kenalx = math.ceil((kenaly / 5.0) * 4)
a = (int(kenalx), int(kenaly))
return a
def preprocess(gray, algoFunc):
# 1. Sobel算子，x方向求梯度
# sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize = 3)
# 获取二值化阈值
thr = bz.myThreshold()
# threshold = thr.get1DMaxEntropyThreshold(gray)
threshold = getattr(thr, algoFunc)(gray)
if threshold |