基于Qt和opencv的身份证号码识别系统

#include "widget.h" #include "ui_widget.h" #include <QTimer> #include <QString> #include <QFileDialog> #include <QMessageBox> #include <string> #include <QDebug> #include <stdlib.h> widget::widget(QWidget *parent) : QWidget(parent), ui(new Ui::widget) { ui->setupUi(this); } widget::~widget() { delete ui; } void widget::on_pushButton_clicked() { QString s = QFileDialog::getOpenFileName(this , "打开图片文件","/", tr("Images (*.png *.xpm *.jpg)")); std::string str = s.toStdString(); const char* ch = str.c_str(); //路径不能包含中文，否则用imread打不开 if(s.isEmpty()) { return; } else { QImage* img=new QImage; if(! ( img->load(s) ) ) //加载图像 { QMessageBox::information(this, tr("打开图像失败"), tr("打开图像失败!")); delete img; return; } QImage* imgScaled = new QImage; //更改图片分辨率 *imgScaled = img->scaled(480, 360, Qt::KeepAspectRatio); //换为Qt::IgnoreAspectRatio，则一律变更 ui->label_display->resize(imgScaled->size()); ui->label_display->setPixmap(QPixmap::fromImage(*imgScaled)); imgSrc = imread(ch ,1); //使用打开的文件对话框获得的路径，由imread读出 } } void widget::on_pushButton_2_clicked() { //Mat imgSrc_test = imread("E:/PracticeOfQt/Id_recognition/id_cardBai.jpg" ,1); //用于测试，实际应用只需将下面替换成imgSrc Mat imgRplane = getRplane(imgSrc); //获得原始图像R分量 vector <RotatedRect> rects; posDetect(imgRplane ,rects); //获得身份证号码区域 Mat outputMat; normalPosArea(imgRplane ,rects[0],outputMat); //获得身份证号码字符矩阵 vector<Mat> char_mat; //获得切割得的字符矩阵 char_segment(outputMat , char_mat); //getAnnXML(); //该函数只需执行一次，获得训练矩阵和标签矩阵，保存于xml文件中 CvANN_MLP ann; ann_train(ann, 10, 24); //10为输出层结点,也等于输出的类别，24为隐藏层结点 vector<int> char_result; classify( ann ,char_mat ,char_result); getParityBit(char_result); //最后一位易出错，直接由前17位计算最后一位 //设置界面信息 if(char_result[16]%2 == 1) //根据倒数第2位区分男女 { ui->radioButton->setChecked(true); } else ui->radioButton_2->setChecked(true); QString id = ""; for(int i = 0; i < char_result.size();++i) { if (char_result[i] == 10) id += "X"; else id += QString::number(char_result[i], 10); //将int转换为Qstring } ui->lineEdit_2->setText(id); QString birthday; for(int i = 6; i < 14;++i) birthday += id[i]; ui->lineEdit->setText(birthday); } Mat widget::getRplane(const Mat &in) { vector<Mat> splitBGR(in.channels()); //容器大小为通道数3 split(in,splitBGR); //return splitBGR[2]; //R分量 if(in.cols > 700 |in.cols >600) { Mat resizeR( 450,600 , CV_8UC1); cv::resize( splitBGR[2] ,resizeR ,resizeR.size()); return resizeR; } else return splitBGR[2]; } void widget::OstuBeresenThreshold(const Mat &in, Mat &out) //输入为单通道 { double ostu_T = threshold(in , out, 0,255 ,CV_THRESH_OTSU); //otsu获得全局阈值 double min; double max; minMaxIdx(in,&min,&max); const double CI = 0.12; double beta = CI*(max - min +1)/128; double beta_lowT = (1-beta)*ostu_T; double beta_highT = (1+beta)*ostu_T; Mat doubleMatIn; in.copyTo(doubleMatIn); int rows = doubleMatIn.rows; int cols = doubleMatIn.cols; double Tbn; for( int i = 0; i < rows; ++i) { //获取第 i行首像素指针 uchar * p = doubleMatIn.ptr<uchar>(i); uchar *outPtr = out.ptr<uchar>(i); //对第i 行的每个像素(byte)操作 for( int j = 0; j < cols; ++j ) { if(i <2 | i>rows - 3 | j<2 | j>rows - 3) { if( p[j] <= beta_lowT ) outPtr[j] = 0; else outPtr[j] = 255; } else { Tbn = sum(doubleMatIn(Rect(i-2,j-2,5,5)))[0]/25 ; //窗口大小25*25 if( p[j] < beta_lowT | (p[j] < Tbn && (beta_lowT <= p[j] && p[j] >= beta_highT))) outPtr[j] = 0; if( p[j] > beta_highT | (p[j] >= Tbn && (beta_lowT <= p[j] && p[j] >= beta_highT))) outPtr[j] = 255; } } } } void widget::posDetect(const Mat &in, vector<RotatedRect> & rects) { Mat threshold_R; OstuBeresenThreshold(in ,threshold_R ); //二值化 Mat imgInv(in.size(),in.type(),cv::Scalar(255)); Mat threshold_Inv = imgInv - threshold_R; //黑白色反转，即背景为黑色 Mat element = getStructuringElement(MORPH_RECT ,Size(15 ,3)); //闭形态学的结构元素 morphologyEx(threshold_Inv ,threshold_Inv,CV_MOP_CLOSE,element); vector< vector <Point> > contours; findContours(threshold_Inv ,contours,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);//只检测外轮廓 //对候选的轮廓进行进一步筛选 vector< vector <Point> > ::iterator itc = contours.begin(); while( itc != contours.end()) { RotatedRect mr = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域 if(!isEligible(mr)) //判断矩形轮廓是否符合要求 { itc = contours.erase(itc); } else { rects.push_back(mr); ++itc; } } //测试是否找到了号码区域 // Mat out; // in.copyTo(out); // Point2f vertices[4]; // rects[0].points(vertices); // for (int i = 0; i < 4; i++) // line(out, vertices[i], vertices[(i+1)%4], Scalar(0,0,0));//画黑色线条 // imshow("Test_Rplane" ,out); // waitKey(0); } bool widget::isEligible(const RotatedRect &candidate) { float error = 0.2; const float aspect = 4.5/0.3; //长宽比 int min = 10*aspect*10; //最小区域 int max = 50*aspect*50; //最大区域 float rmin = aspect - aspect*error; //考虑误差后的最小长宽比 float rmax = aspect + aspect*error; //考虑误差后的最大长宽比 int area = candidate.size.height * candidate.size.width; float r = (float)candidate.size.width/(float)candidate.size.height; if(r <1) r = 1/r; if( (area < min || area > max) || (r< rmin || r > rmax) ) //满足该条件才认为该candidate为车牌区域 return false; else return true; } void widget::normalPosArea(const Mat &intputImg, RotatedRect &rects_optimal, Mat& output_area) { float r,angle; angle = rects_optimal.angle; r = (float)rects_optimal.size.width / (float) (float)rects_optimal.size.height; if(r<1) angle = 90 + angle; Mat rotmat = getRotationMatrix2D(rects_optimal.center , angle,1);//获得变形矩阵对象 Mat img_rotated; warpAffine(intputImg ,img_rotated,rotmat, intputImg.size(),CV_INTER_CUBIC); //裁剪图像 Size rect_size = rects_optimal.size; if(r<1) std::swap(rect_size.width, rect_size.height); Mat img_crop; getRectSubPix(img_rotated ,rect_size,rects_optimal.center , img_crop ); //用光照直方图调整所有裁剪得到的图像，使具有相同宽度�