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tess4

idcardocr
/
idcardocr.py

# -*- coding: utf-8 -*-
from PIL import Image
import pytesseract
import cv2
import numpy as np
import re
from multiprocessing import Pool, Queue, Lock, Process, freeze_support
import time

#pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files (x86)\\Tesseract-OCR\\tesseract.exe'
x = 1280.00 / 3840.00
pixel_x = int(x * 3840)
print(x, pixel_x)

#mode0:识别姓名，出生日期，身份证号； mode1：识别所有信息
def idcardocr(imgname, mode=1):
        print(u'进入身份证光学识别流程...')
        if mode==1:
            # generate_mask(x)
            img_data_gray, img_org = img_resize_gray(imgname)
            result_dict = dict()
            name_pic = find_name(img_data_gray, img_org)
            # showimg(name_pic)
            # print 'name'
            result_dict['name'] = get_name(name_pic)
            # print result_dict['name']

            sex_pic = find_sex(img_data_gray, img_org)
            # showimg(sex_pic)
            # print 'sex'
            result_dict['sex'] = get_sex(sex_pic)
            # print result_dict['sex']

            nation_pic = find_nation(img_data_gray, img_org)
            # showimg(nation_pic)
            # print 'nation'
            result_dict['nation'] = get_nation(nation_pic)
            # print result_dict['nation']

            address_pic = find_address(img_data_gray, img_org)
            # showimg(address_pic)
            # print 'address'
            result_dict['address'] = get_address(address_pic)
            # print result_dict['address']

            idnum_pic = find_idnum(img_data_gray, img_org)
            # showimg(idnum_pic)
            # print 'idnum'
            result_dict['idnum'], result_dict['birth'] = get_idnum_and_birth(idnum_pic)
            # print result_dict['idnum']
        elif mode==0:
            # generate_mask(x)
            img_data_gray, img_org = img_resize_gray(imgname)
            result_dict = dict()
            name_pic = find_name(img_data_gray, img_org)
            # showimg(name_pic)
            # print 'name'
            result_dict['name'] = get_name(name_pic)
            # print result_dict['name']

            idnum_pic = find_idnum(img_data_gray, img_org)
            # showimg(idnum_pic)
            # print 'idnum'

            result_dict['idnum'], result_dict['birth'] = get_idnum_and_birth(idnum_pic)
            result_dict['sex']=''
            result_dict['nation']=''
            result_dict['address']=''

        else:
            print(u"模式选择错误！")

        #showimg(img_data_gray)
        return result_dict

def generate_mask(x):
        name_mask_pic = cv2.UMat(cv2.imread('name_mask.jpg'))
        sex_mask_pic = cv2.UMat(cv2.imread('sex_mask.jpg'))
        nation_mask_pic = cv2.UMat(cv2.imread('nation_mask.jpg'))
        birth_mask_pic = cv2.UMat(cv2.imread('birth_mask.jpg'))
        year_mask_pic = cv2.UMat(cv2.imread('year_mask.jpg'))
        month_mask_pic = cv2.UMat(cv2.imread('month_mask.jpg'))
        day_mask_pic = cv2.UMat(cv2.imread('day_mask.jpg'))
        address_mask_pic = cv2.UMat(cv2.imread('address_mask.jpg'))
        idnum_mask_pic = cv2.UMat(cv2.imread('idnum_mask.jpg'))
        name_mask_pic = img_resize_x(name_mask_pic)
        sex_mask_pic = img_resize_x(sex_mask_pic)
        nation_mask_pic = img_resize_x(nation_mask_pic)
        birth_mask_pic = img_resize_x(birth_mask_pic)
        year_mask_pic = img_resize_x(year_mask_pic)
        month_mask_pic = img_resize_x(month_mask_pic)
        day_mask_pic = img_resize_x(day_mask_pic)
        address_mask_pic = img_resize_x(address_mask_pic)
        idnum_mask_pic = img_resize_x(idnum_mask_pic)
        cv2.imwrite('name_mask_%s.jpg'%pixel_x, name_mask_pic)
        cv2.imwrite('sex_mask_%s.jpg' %pixel_x, sex_mask_pic)
        cv2.imwrite('nation_mask_%s.jpg' %pixel_x, nation_mask_pic)
        cv2.imwrite('birth_mask_%s.jpg' %pixel_x, birth_mask_pic)
        cv2.imwrite('year_mask_%s.jpg' % pixel_x, year_mask_pic)
        cv2.imwrite('month_mask_%s.jpg' % pixel_x, month_mask_pic)
        cv2.imwrite('day_mask_%s.jpg' % pixel_x, day_mask_pic)
        cv2.imwrite('address_mask_%s.jpg' %pixel_x, address_mask_pic)
        cv2.imwrite('idnum_mask_%s.jpg' %pixel_x, idnum_mask_pic)

#用于生成模板
def img_resize_x(imggray):
        # print 'dheight:%s' % dheight
        crop = imggray
        size = crop.get().shape
        dheight = int(size[0]*x)
        dwidth = int(size[1]*x)
        crop = cv2.resize(src=crop, dsize=(dwidth, dheight), interpolation=cv2.INTER_CUBIC)
        return crop

#idcardocr里面resize以高度为依据, 用于get部分
def img_resize(imggray, dheight):
        # print 'dheight:%s' % dheight
        crop = imggray
        size = crop.get().shape
        height = size[0]
        width = size[1]
        width = width * dheight / height
        crop = cv2.resize(src=crop, dsize=(int(width), dheight), interpolation=cv2.INTER_CUBIC)
        return crop

def img_resize_gray(imgorg):

        #imgorg = cv2.imread(imgname)
        crop = imgorg
        size = cv2.UMat.get(crop).shape
        # print size
        height = size[0]
        width = size[1]
        # 参数是根据3840调的
        height = int(height * 3840 * x / width)
        # print height
        crop = cv2.resize(src=crop, dsize=(int(3840 * x), height), interpolation=cv2.INTER_CUBIC)
        return hist_equal(cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)), crop

def find_name(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('name_mask_%s.jpg'%pixel_x, 0))
        # showimg(crop_org)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        # print(max_loc)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(700*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        # showimg(result)
        return cv2.UMat(result)

def find_sex(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('sex_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(300*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result)

def find_nation(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('nation_mask_%s.jpg'%pixel_x, 0))
        #showimg(template)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w - int(20*x), max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(500*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result)

# def find_birth(crop_gray, crop_org):
#         template = cv2.UMat(cv2.imread('birth_mask_%s.jpg'%pixel_x, 0))
#         # showimg(template)
#         w, h = cv2.UMat.get(template).shape[::-1]
#         res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
#         #showimg(crop_gray)
#         min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
#         top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
#         bottom_right = (top_left[0] + int(1500*x), top_left[1] + int(300*x))
#         # 提取result需要在rectangle之前
#         date_org = cv2.UMat.get(crop_org)[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
#         date = cv2.cvtColor(date_org, cv2.COLOR_BGR2GRAY)
#         cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
#         # cv2.imwrite('date.png',date)
#
#         # 提取年份
#         template = cv2.UMat(cv2.imread('year_mask_%s.jpg'%pixel_x, 0))
#         year_res = cv2.matchTemplate(date, template, cv2.TM_CCOEFF_NORMED)
#         min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(year_res)
#         bottom_right = (max_loc[0]+int(20*x), int(300*x))
#         top_left = (0, 0)
#         year = date_org[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
#         # cv2.imwrite('year.png',year)
#         cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
#
#         # 提取月
#         template = cv2.UMat(cv2.imread('month_mask_%s.jpg'%pixel_x, 0))
#         month_res = cv2.matchTemplate(date, template, cv2.TM_CCOEFF_NORMED)
#         min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(month_res)
#         bottom_right = (max_loc[0]+int(40*x), int(300*x))
#         top_left = (max_loc[0] - int(220*x), 0)
#         month = date_org[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
#         # cv2.imwrite('month.png',month)
#         cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
#
#         # 提取日
#         template = cv2.UMat(cv2.imread('day_mask_%s.jpg'%pixel_x, 0))
#         day_res = cv2.matchTemplate(date, template, cv2.TM_CCOEFF_NORMED)
#         min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(day_res)
#         bottom_right = (max_loc[0]+int(20*x), int(300*x))
#         top_left = (max_loc[0] - int(220*x), 0)
#         day = date_org[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
#         # cv2.imwrite('day.png',day)
#         cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
#         showimg(crop_gray)
#         return cv2.UMat(year), cv2.UMat(month), cv2.UMat(day)

def find_address(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('address_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        #showimg(crop_gray)
        w, h = cv2.UMat.get(template).shape[::-1]
        #t1 = round(time.time()*1000)
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        #t2 = round(time.time()*1000)
        #print 'time:%s'%(t2-t1)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(1700*x), top_left[1] + int(550*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result)

def find_idnum(crop_gray, crop_org):
        template = cv2.UMat(cv2.imread('idnum_mask_%s.jpg'%pixel_x, 0))
        # showimg(template)
        #showimg(crop_gray)
        w, h = cv2.UMat.get(template).shape[::-1]
        res = cv2.matchTemplate(crop_gray, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        top_left = (max_loc[0] + w, max_loc[1] - int(20*x))
        bottom_right = (top_left[0] + int(2300*x), top_left[1] + int(300*x))
        result = cv2.UMat.get(crop_org)[top_left[1]-10:bottom_right[1], top_left[0]-10:bottom_right[0]]
        cv2.rectangle(crop_gray, top_left, bottom_right, 255, 2)
        #showimg(crop_gray)
        return cv2.UMat(result)


def showimg(img):
        cv2.namedWindow("contours", 0);
        cv2.resizeWindow("contours", 1280, 720);
        cv2.imshow("contours", img)
        cv2.waitKey()

#psm model:
#  0    Orientation and script detection (OSD) only.
#  1    Automatic page segmentation with OSD.
#  2    Automatic page segmentation, but no OSD, or OCR.
#  3    Fully automatic page segmentation, but no OSD. (Default)
#  4    Assume a single column of text of variable sizes.
#  5    Assume a single uniform block of vertically aligned text.
#  6    Assume a single uniform block of text.
#  7    Treat the image as a single text line.
#  8    Treat the image as a single word.
#  9    Treat the image as a single word in a circle.
#  10    Treat the image as a single character.
#  11    Sparse text. Find as much text as possible in no particular order.
#  12    Sparse text with OSD.
#  13    Raw line. Treat the image as a single text line,
# 			bypassing hacks that are Tesseract-specific

def get_name(img):
        #    cv2.imshow("method3", img)
        #    cv2.waitKey()
        print('name')
        _, _, red = cv2.split(img) #split 会自动将UMat转换回Mat
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 151, 50)
        #    red = cv2.medianBlur(red, 3)
        red = img_resize(red, 150)
        img = img_resize(img, 150)
        # showimg(red)
        # cv2.imwrite('name.png', red)
        #    img2 = Image.open('address.png')
        # img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return get_result_vary_length(red, 'chi_sim', img, '-psm 7')
        return get_result_vary_length(red, 'chi_sim', img, '--psm 7')
        # return punc_filter(pytesseract.image_to_string(img, lang='chi_sim', config='-psm 13').replace(" ",""))

def get_sex(img):
        _, _, red = cv2.split(img)
        print('sex')
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        #    red = cv2.medianBlur(red, 3)
        #    cv2.imwrite('address.png', img)
        #    img2 = Image.open('address.png')
        red = img_resize(red, 150)
        # cv2.imwrite('sex.png', red)
        # img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return get_result_fix_length(red, 1, 'sex', '-psm 10')
        return get_result_fix_length(red, 1, 'chi_sim', '--psm 10')
        # return pytesseract.image_to_string(img, lang='sex', config='-psm 10').replace(" ","")

def get_nation(img):
        _, _, red = cv2.split(img)
        print('nation')
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        red = img_resize(red, 150)
        # cv2.imwrite('nation.png', red)
        # img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return get_result_fix_length(red, 1, 'nation', '-psm 10')
        return get_result_fix_length(red, 1, 'chi_sim', '--psm 10')
        # return pytesseract.image_to_string(img, lang='nation', config='-psm 13').replace(" ","")

# def get_birth(year, month, day):
#         _, _, red = cv2.split(year)
#         red = cv2.UMat(red)
#         red = hist_equal(red)
#         red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
#         red = img_resize(red, 150)
#         # cv2.imwrite('year_red.png', red)
#         year_red = red
#
#         _, _, red = cv2.split(month)
#         red = cv2.UMat(red)
#         red = hist_equal(red)
#         red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
#         #red = cv2.erode(red,kernel,iterations = 1)
#         red = img_resize(red, 150)
#         # cv2.imwrite('month_red.png', red)
#         month_red = red
#
#         _, _, red = cv2.split(day)
#         red = cv2.UMat(red)
#         red = hist_equal(red)
#         red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
#         red = img_resize(red, 150)
#         # cv2.imwrite('day_red.png', red)
#         day_red = red
#         # return pytesseract.image_to_string(img, lang='birth', config='-psm 7')
#         return get_result_fix_length(year_red, 4, 'eng', '-c tessedit_char_whitelist=0123456789 -psm 13'), \
#                get_result_vary_length(month_red, 'eng', '-c tessedit_char_whitelist=0123456789 -psm 13'), \
#                get_result_vary_length(day_red, 'eng', '-c tessedit_char_whitelist=0123456789 -psm 13')


def get_address(img):
        #_, _, red = cv2.split(img)
        #red = cv2.medianBlur(red, 3)
        print('address')
        _, _, red = cv2.split(img)
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        red = img_resize(red, 300)
        #img = img_resize(img, 300)
        #cv2.imwrite('address_red.png', red)
        img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        #return punc_filter(get_result_vary_length(red,'chi_sim', img, '-psm 6'))
        return punc_filter(get_result_vary_length(red, 'chi_sim', img, '--psm 6'))
        #return punc_filter(pytesseract.image_to_string(img, lang='chi_sim', config='-psm 3').replace(" ",""))

def get_idnum_and_birth(img):
        _, _, red = cv2.split(img)
        print('idnum')
        red = cv2.UMat(red)
        red = hist_equal(red)
        red = cv2.adaptiveThreshold(red, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 151, 50)
        red = img_resize(red, 150)
        # cv2.imwrite('idnum_red.png', red)
        #idnum_str = get_result_fix_length(red, 18, 'idnum', '-psm 8')
        # idnum_str = get_result_fix_length(red, 18, 'eng', '--psm 8 ')
        img = Image.fromarray(cv2.UMat.get(red).astype('uint8'))
        idnum_str = get_result_vary_length(red, 'eng', img, '--psm 8 ')
        return idnum_str, idnum_str[6:14]

def get_result_fix_length(red, fix_length, langset, custom_config=''):
    red_org = red
    cv2.fastNlMeansDenoising(red, red, 4, 7, 35)
    rec, red = cv2.threshold(red, 127, 255, cv2.THRESH_BINARY_INV)
    image, contours, hierarchy = cv2.findContours(red, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    # print(len(contours))
    # 描边一次可以减少噪点
    cv2.drawContours(red, contours, -1, (0, 255, 0), 1)
    color_img = cv2.cvtColor(red, cv2.COLOR_GRAY2BGR)
    # for x, y, w, h in contours:
    #     imgrect = cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
    # showimg(imgrect)

    h_threshold = 54
    numset_contours = []
    calcu_cnt = 1
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        if h > h_threshold:
            numset_contours.append((x, y, w, h))
    while len(numset_contours) != fix_length:
        if calcu_cnt > 50:
            print(u'计算次数过多！目前阈值为：', h_threshold)
            break
        numset_contours = []
        calcu_cnt += 1
        if len(numset_contours) > fix_length:
            h_threshold += 1
            contours_cnt = 0
            for cnt in contours:
                x, y, w, h = cv2.boundingRect(cnt)
                if h > h_threshold:
                    contours_cnt += 1
                    numset_contours.append((x, y, w, h))
        if len(numset_contours) < fix_length:
            h_threshold -= 1
            contours_cnt = 0
            for cnt in contours:
                x, y, w, h = cv2.boundingRect(cnt)
                if h > h_threshold:
                    contours_cnt += 1
                    numset_contours.append((x, y, w, h))
    result_string = ''
    numset_contours.sort(key=lambda num: num[0])
    for x, y, w, h in numset_contours:
        result_string += pytesseract.image_to_string(cv2.UMat.get(red_org)[y-10:y + h + 10, x-10:x + w + 10], lang=langset, config=custom_config)
    # print(new_r)
    # cv2.imwrite('fixlengthred.png', cv2.UMat.get(red_org)[y-10:y + h +10 , x-10:x + w + 10])
    print(result_string)
    return result_string

def get_result_vary_length(red, langset, org_img, custom_config=''):
    red_org = red
    # cv2.fastNlMeansDenoising(red, red, 4, 7, 35)
    rec, red = cv2.threshold(red, 127, 255, cv2.THRESH_BINARY_INV)
    image, contours, hierarchy = cv2.findContours(red, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    # print(len(contours))
    # 描边一次可以减少噪点
    cv2.drawContours(red, contours, -1, (255, 255, 255), 1)
    color_img = cv2.cvtColor(red, cv2.COLOR_GRAY2BGR)
    numset_contours = []
    height_list=[]
    width_list=[]
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        height_list.append(h)
        # print(h,w)
        width_list.append(w)
    height_list.remove(max(height_list))
    width_list.remove(max(width_list))
    height_threshold = 0.70*max(height_list)
    width_threshold = 1.4 * max(width_list)
    # print('height_threshold:'+str(height_threshold)+'width_threshold:'+str(width_threshold))
    big_rect=[]
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        if h > height_threshold and w < width_threshold:
            # print(h,w)
            numset_contours.append((x, y, w, h))
            big_rect.append((x, y))
            big_rect.append((x + w, y + h))
    big_rect_nparray = np.array(big_rect, ndmin=3)
    x, y, w, h = cv2.boundingRect(big_rect_nparray)
    # imgrect = cv2.rectangle(color_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
    # showimg(imgrect)
    # showimg(cv2.UMat.get(org_img)[y:y + h, x:x + w])

    result_string = ''
    result_string += pytesseract.image_to_string(cv2.UMat.get(red_org)[y-10:y + h + 10, x-10:x + w + 10], lang=langset,
                                                 config=custom_config)
    print(result_string)
    # cv2.imwrite('varylength.png', cv2.UMat.get(org_img)[y:y + h, x:x + w])
    # cv2.imwrite('varylengthred.png', cv2.UMat.get(red_org)[y:y + h, x:x + w])
    # numset_contours.sort(key=lambda num: num[0])
    # for x, y, w, h in numset_contours:
    #     result_string += pytesseract.image_to_string(cv2.UMat.get(color_img)[y:y + h, x:x + w], lang=langset, config=custom_config)
    return punc_filter(result_string)


def punc_filter(str):
        temp = str
        xx      =   u"([\u4e00-\u9fff0-9A-Z]+)"
        pattern =   re.compile(xx)
        results =   pattern.findall(temp)
        string = ""
        for result in results:
            string += result
        return string

#这里使用直方图拉伸，不是直方图均衡
def hist_equal(img):
        # clahe_size = 8
        # clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(clahe_size, clahe_size))
        # result = clahe.apply(img)
        #test

        #result = cv2.equalizeHist(img)

        image = img.get() #UMat to Mat
        # result = cv2.equalizeHist(image)
        lut = np.zeros(256, dtype = image.dtype )#创建空的查找表
        #lut = np.zeros(256)
        hist= cv2.calcHist([image], #计算图像的直方图
                            [0], #使用的通道
                            None, #没有使用mask
                           [256], #it is a 1D histogram
                           [0,256])
        minBinNo, maxBinNo = 0, 255
        #计算从左起第一个不为0的直方图柱的位置
        for binNo, binValue in enumerate(hist):
            if binValue != 0:
                minBinNo = binNo
                break
        #计算从右起第一个不为0的直方图柱的位置
        for binNo, binValue in enumerate(reversed(hist)):
            if binValue != 0:
                maxBinNo = 255-binNo
                break
        #print minBinNo, maxBinNo
        #生成查找表
        for i,v in enumerate(lut):
            if i < minBinNo:
                lut[i] = 0
            elif i > maxBinNo:
                lut[i] = 255
            else:
                lut[i] = int(255.0*(i-minBinNo)/(maxBinNo-minBinNo)+0.5)
        #计算,调用OpenCV cv2.LUT函数,参数 image --  输入图像，lut -- 查找表
        #print lut
        result = cv2.LUT(image, lut)
        #print type(result)
        #showimg(result)
        return cv2.UMat(result)

if __name__=="__main__":
    idocr = idcardocr(cv2.UMat(cv2.imread('testimages/zrh.jpg')))
    print(idocr)
    # for i in range(15):
    #     idocr = idcardocr(cv2.UMat(cv2.imread('testimages/%s.jpg'%(i+1))))
    #     print(idocr['idnum'])