Opencv实现身份证OCR识别

article/2025/8/21 10:20:10

Opencv 配置IDEA可参考：https://blog.csdn.net/zwl18210851801/article/details/81075781

opencv位置：

OpencvUtil类：

package com.x.common.utils;import org.opencv.core.*;
import org.opencv.core.Point;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.ByteArrayInputStream;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;public  class OpencvUtil {private static final int BLACK = 0;private static final int WHITE = 255;/*** 灰化处理* @return*/public static Mat gray (Mat mat){Mat gray = new Mat();Imgproc.cvtColor(mat, gray, Imgproc.COLOR_BGR2GRAY,1);return gray;}/*** 二值化处理* @return*/public static Mat binary (Mat mat){Mat binary = new Mat();Imgproc.adaptiveThreshold(mat, binary, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 25, 10);return binary;}/*** 模糊处理* @param mat* @return*/public static Mat blur (Mat mat) {Mat blur = new Mat();Imgproc.blur(mat,blur,new Size(5,5));return blur;}/***膨胀* @param mat* @return*/public static Mat dilate (Mat mat,int size){Mat dilate=new Mat();Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(size,size));//膨胀Imgproc.dilate(mat, dilate, element, new Point(-1, -1), 1);return dilate;}/*** 腐蚀* @param mat* @return*/public static Mat erode (Mat mat,int size){Mat erode=new Mat();Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(size,size));//腐蚀Imgproc.erode(mat, erode, element, new Point(-1, -1), 1);return erode;}/*** 边缘检测* @param mat* @return*/public static Mat carry(Mat mat){Mat dst=new Mat();//高斯平滑滤波器卷积降噪Imgproc.GaussianBlur(mat, dst, new Size(3,3), 0);//边缘检测Imgproc.Canny(mat, dst, 50, 150);return dst;}/*** 轮廓检测* @param mat* @return*/public static List<MatOfPoint> findContours(Mat mat){List<MatOfPoint> contours=new ArrayList<>();Mat hierarchy = new Mat();Imgproc.findContours(mat, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);return contours;}/*** 清除小面积轮廓* @param mat* @param size* @return*/public static Mat drawContours(Mat mat,int size){List<MatOfPoint> cardContours=OpencvUtil.findContours(mat);for (int i = 0; i < cardContours.size(); i++){double area=OpencvUtil.area(cardContours.get(i));if(area<size){Imgproc.drawContours(mat, cardContours, i, new Scalar( 0, 0, 0),-1 );}}return mat;}/*** 人脸识别* @param mat* @return*/public static Mat face(Mat mat){CascadeClassifier faceDetector = new CascadeClassifier(System.getProperty("user.dir")+"\\opencv\\haarcascades\\haarcascade_frontalface_alt.xml");// 在图片中检测人脸MatOfRect faceDetections = new MatOfRect();//指定人脸识别的最大和最小像素范围Size minSize = new Size(100, 100);Size maxSize = new Size(500, 500);//参数设置为scaleFactor=1.1f, minNeighbors=4, flags=0 以此来增加识别人脸的正确率faceDetector.detectMultiScale(mat, faceDetections, 1.1f, 4, 0, minSize, maxSize);Rect[] rects = faceDetections.toArray();if(rects != null && rects.length == 1){// 在每一个识别出来的人脸周围画出一个方框Rect rect = rects[0];return mat;}else{return null;}}/*** 循环进行人脸识别* */public static Mat faceLoop(Mat src){Mat face=new Mat();//默认人脸识别失败时图像旋转90度int k=90;while (k>0){for(int i=0;i<360/k;i++){//人脸识别face= OpencvUtil.face(src);if(face==null){src = rotate3(src,k);}else{break;}}if(face!=null){break;}else{k=k-30;}}return src;}/*** 剪切身份证区域* @param mat*/public static Mat houghLinesP(Mat begin,Mat mat){//灰度mat=OpencvUtil.gray(mat);//二值化mat=OpencvUtil.binary(mat);//腐蚀mat=OpencvUtil.erode(mat,5);//边缘检测mat=OpencvUtil.carry(mat);//降噪mat=OpencvUtil.navieRemoveNoise(mat,1);//膨胀mat=OpencvUtil.dilate(mat,3);//轮廓检测,清除小的轮廓部分List<MatOfPoint> contours=OpencvUtil.findContours(mat);for(int i=0;i<contours.size();i++){double area=OpencvUtil.area(contours.get(i));if(area<5000){Imgproc.drawContours(mat, contours, i, new Scalar( 0, 0, 0), -1);}}Mat storage = new Mat();Imgproc.HoughLinesP(mat, storage, 1, Math.PI / 180, 10, 0, 10);double[] maxLine = new double[]{0,0,0,0};//获取最长的直线for (int x = 0; x < storage.rows(); x++){double[] vec = storage.get(x, 0);double x1 = vec[0], y1 = vec[1], x2 = vec[2], y2 = vec[3];double newLength = Math.sqrt(Math.abs((x1 - x2)* (x1 - x2)+(y1 - y2)* (y1 - y2)));double oldLength = Math.sqrt(Math.abs((maxLine[0] - maxLine[2])* (maxLine[0] - maxLine[2])+(maxLine[1] - maxLine[3])* (maxLine[1] - maxLine[3])));if(newLength>oldLength){maxLine = vec;}}//计算最长线的角度double angle = getAngle(maxLine[0],maxLine[1],maxLine[2],maxLine[3]);//旋转角度mat = rotate3( mat,angle);begin = rotate3( begin,angle);Imgproc.HoughLinesP(mat, storage, 1, Math.PI / 180, 10, 10, 10);List<double[]> lines=new ArrayList<>();//在mat上划线for (int x = 0; x < storage.rows(); x++){double[] vec = storage.get(x, 0);double x1 = vec[0], y1 = vec[1], x2 = vec[2], y2 = vec[3];Point start = new Point(x1, y1);Point end = new Point(x2, y2);//获取与图像x边缘近似平行的直线if(Math.abs(start.y-end.y)<5){if(Math.abs(x2-x1)>20){lines.add(vec);}}//获取与图像y边缘近似平行的直线if(Math.abs(start.x-end.x)<5){if(Math.abs(y2-y1)>20){lines.add(vec);}}}//获取最大的和最小的X,Y坐标double maxX=0.0,minX=10000,minY=10000,maxY=0.0;for(int i=0;i<lines.size();i++){double[] vec = lines.get(i);double x1 = vec[0], y1 = vec[1], x2 = vec[2], y2 = vec[3];maxX=maxX>x1?maxX:x1;maxX=maxX>x2?maxX:x2;minX=minX>x1?x1:minX;minX=minX>x2?x2:minX;maxY=maxY>y1?maxY:y1;maxY=maxY>y2?maxY:y2;minY=minY>y1?y1:minY;minY=minY>y2?y2:minY;}if(maxX<mat.cols()&&minX>0&&maxY<mat.rows()&&minY>0){List<Point> list=new ArrayList<>();Point point1=new Point(minX+10,minY+10);Point point2=new Point(minX+10,maxY-10);Point point3=new Point(maxX-10,minY+10);Point point4=new Point(maxX-10,maxY-10);list.add(point1);list.add(point2);list.add(point3);list.add(point4);mat=shear(begin,list);}else{mat=begin;}return mat;}/*** 计算角度* @param px1* @param py1* @param px2* @param py2* @return*/public static double  getAngle(double px1, double py1, double px2, double py2) {//两点的x、y值double x = px2-px1;double y = py2-py1;double hypotenuse = Math.sqrt(Math.pow(x, 2)+Math.pow(y, 2));//斜边长度double cos = x/hypotenuse;double radian = Math.acos(cos);//求出弧度double angle = 180/(Math.PI/radian);//用弧度算出角度if (y<0) {angle = -angle;} else if ((y == 0) && (x<0)) {angle = 180;}while (angle<0){angle = angle +90;}return angle;}/*** 累计概率hough变换直线检测* @param mat*/public static Mat houghLines(Mat mat){Mat storage = new Mat();Imgproc.HoughLines(mat, storage, 1, Math.PI / 180, 50, 0, 0, 0, 1);for (int x = 0; x < storage.rows(); x++) {double[] vec = storage.get(x, 0);double rho = vec[0];double theta = vec[1];Point pt1 = new Point();Point pt2 = new Point();double a = Math.cos(theta);double b = Math.sin(theta);double x0 = a * rho;double y0 = b * rho;pt1.x = Math.round(x0 + 1000 * (-b));pt1.y = Math.round(y0 + 1000 * (a));pt2.x = Math.round(x0 - 1000 * (-b));pt2.y = Math.round(y0 - 1000 * (a));if (theta >= 0){Imgproc.line(mat, pt1, pt2, new Scalar(255), 3);}}return mat;}/*** 根据四点坐标截取模板图片* @param mat* @param pointList* @return*/public static Mat shear (Mat mat,List<Point> pointList){int x=minX(pointList);int y=minY(pointList);int xl=xLength(pointList)>mat.cols()-x?mat.cols()-x:xLength(pointList);int yl=yLength(pointList)>mat.rows()-y?mat.rows()-y:yLength(pointList);Rect re=new Rect(x,y,xl,yl);return new Mat(mat,re);}/*** 图片旋转* @param splitImage* @param angle* @return*/public static Mat rotate3(Mat splitImage, double angle){double thera = angle * Math.PI / 180;double a = Math.sin(thera);double b = Math.cos(thera);int wsrc = splitImage.width();int hsrc = splitImage.height();int wdst = (int) (hsrc * Math.abs(a) + wsrc * Math.abs(b));int hdst = (int) (wsrc * Math.abs(a) + hsrc * Math.abs(b));Mat imgDst = new Mat(hdst, wdst, splitImage.type());Point pt = new Point(splitImage.cols() / 2, splitImage.rows() / 2);// 获取仿射变换矩阵Mat affineTrans = Imgproc.getRotationMatrix2D(pt, angle, 1.0);//System.out.println(affineTrans.dump());// 改变变换矩阵第三列的值affineTrans.put(0, 2, affineTrans.get(0, 2)[0] + (wdst - wsrc) / 2);affineTrans.put(1, 2, affineTrans.get(1, 2)[0] + (hdst - hsrc) / 2);Imgproc.warpAffine(splitImage, imgDst, affineTrans, imgDst.size(),Imgproc.INTER_CUBIC | Imgproc.WARP_FILL_OUTLIERS);return imgDst;}/*** 图像直方图处理* @param mat* @return*/public static Mat equalizeHist(Mat mat){Mat dst = new Mat();List<Mat> mv = new ArrayList<>();Core.split(mat, mv);for (int i = 0; i < mat.channels(); i++){Imgproc.equalizeHist(mv.get(i), mv.get(i));}Core.merge(mv, dst);return dst;}/*** 8邻域降噪,又有点像9宫格降噪;即如果9宫格中心被异色包围，则同化* @param pNum 默认值为1*/public static Mat navieRemoveNoise(Mat mat,int pNum) {int i, j, m, n, nValue, nCount;int nWidth = mat.cols();int nHeight = mat.rows();// 如果一个点的周围都是白色的，而它确是黑色的，删除它for (j = 1; j < nHeight - 1; ++j) {for (i = 1; i < nWidth - 1; ++i) {nValue =  (int)mat.get(j, i)[0];if (nValue == 0) {nCount = 0;// 比较以(j ,i)为中心的9宫格，如果周围都是白色的，同化for (m = j - 1; m <= j + 1; ++m) {for (n = i - 1; n <= i + 1; ++n) {if ((int)mat.get(m, n)[0] == 0) {nCount++;}}}if (nCount <= pNum) {// 周围黑色点的个数小于阀值pNum,把该点设置白色mat.put(j, i, WHITE);}} else {nCount = 0;// 比较以(j ,i)为中心的9宫格，如果周围都是黑色的，同化for (m = j - 1; m <= j + 1; ++m) {for (n = i - 1; n <= i + 1; ++n) {if ((int)mat.get(m, n)[0] == 0) {nCount++;}}}if (nCount >= 7) {// 周围黑色点的个数大于等于7,把该点设置黑色;即周围都是黑色mat.put(j, i, BLACK);}}}}return mat;}/*** 连通域降噪* @param pArea 默认值为1*/public static Mat contoursRemoveNoise(Mat mat,double pArea) {//mat=floodFill(mat,mat.new Point(mat.cols()/2,mat.rows()/2),new Color(225,0,0));int i, j, color = 1;int nWidth =  mat.cols(), nHeight = mat.rows();for (i = 0; i < nWidth; ++i) {for (j = 0; j < nHeight; ++j) {if ((int) mat.get(j, i)[0] == BLACK) {//用不同颜色填充连接区域中的每个黑色点//floodFill就是把一个点x的所有相邻的点都涂上x点的颜色，一直填充下去，直到这个区域内所有的点都被填充完为止Imgproc.floodFill(mat, new Mat(), new Point(i, j), new Scalar(color));color++;}}}//统计不同颜色点的个数int[] ColorCount = new int[255];for (i = 0; i < nWidth; ++i) {for (j = 0; j < nHeight; ++j) {if ((int) mat.get(j, i)[0] != 255) {ColorCount[(int) mat.get(j, i)[0] - 1]++;}}}//去除噪点for (i = 0; i < nWidth; ++i) {for (j = 0; j < nHeight; ++j) {if (ColorCount[(int) mat.get(j, i)[0] - 1] <= pArea) {mat.put(j, i, WHITE);}}}for (i = 0; i < nWidth; ++i) {for (j = 0; j < nHeight; ++j) {if ((int) mat.get(j, i)[0] < WHITE) {mat.put(j, i, BLACK);}}}return mat;}/*** Mat转换成BufferedImage** @param matrix*            要转换的Mat* @param fileExtension*            格式为 ".jpg", ".png", etc* @return*/public static BufferedImage Mat2BufImg (Mat matrix, String fileExtension) {MatOfByte mob = new MatOfByte();Imgcodecs.imencode(fileExtension, matrix, mob);byte[] byteArray = mob.toArray();BufferedImage bufImage = null;try {InputStream in = new ByteArrayInputStream(byteArray);bufImage = ImageIO.read(in);} catch (Exception e) {e.printStackTrace();}return bufImage;}/*** BufferedImage转换成Mat** @param original*            要转换的BufferedImage* @param imgType*            bufferedImage的类型 如 BufferedImage.TYPE_3BYTE_BGR* @param matType*            转换成mat的type 如 CvType.CV_8UC3*/public static Mat BufImg2Mat (BufferedImage original, int imgType, int matType) {if (original == null) {throw new IllegalArgumentException("original == null");}if (original.getType() != imgType) {BufferedImage image = new BufferedImage(original.getWidth(), original.getHeight(), imgType);Graphics2D g = image.createGraphics();try {g.setComposite(AlphaComposite.Src);g.drawImage(original, 0, 0, null);} finally {g.dispose();}}DataBufferByte dbi =(DataBufferByte)original.getRaster().getDataBuffer();byte[] pixels = dbi.getData();Mat mat = Mat.eye(original.getHeight(), original.getWidth(), matType);mat.put(0, 0, pixels);return mat;}/*** 人眼识别* @param mat* @return*/public static List<Point> eye(Mat mat){List<Point> eyeList=new ArrayList<>();CascadeClassifier eyeDetector = new CascadeClassifier(System.getProperty("user.dir")+"\\opencv\\haarcascades\\haarcascade_eye.xml");// 在图片中检测人眼MatOfRect eyeDetections = new MatOfRect();//指定人脸识别的最大和最小像素范围Size minSize = new Size(20, 20);Size maxSize = new Size(30, 30);eyeDetector.detectMultiScale(mat, eyeDetections, 1.1f, 3, 0, minSize, maxSize);Rect[] rects = eyeDetections.toArray();if(rects != null && rects.length == 2){Point point1=new Point(rects[0].x,rects[0].y);eyeList.add(point1);Point point2=new Point(rects[1].x,rects[1].y);eyeList.add(point2);}else{return null;}return eyeList;}/*** 获取轮廓的顶点坐标* @param contour* @return*/public static List<Point> getPointList(MatOfPoint contour){MatOfPoint2f mat2f=new MatOfPoint2f();contour.convertTo(mat2f,CvType.CV_32FC1);RotatedRect rect=Imgproc.minAreaRect(mat2f);Mat points=new Mat();Imgproc.boxPoints(rect,points);return getPoints(points.dump());}/*** 获取轮廓的面积* @param contour* @return*/public static double area (MatOfPoint contour){MatOfPoint2f mat2f=new MatOfPoint2f();contour.convertTo(mat2f,CvType.CV_32FC1);RotatedRect rect=Imgproc.minAreaRect(mat2f);return rect.boundingRect().area();}/*** 获取点坐标集合* @param str* @return*/public  static List<Point> getPoints(String str){List<Point> points=new ArrayList<>();str=str.replace("[","").replace("]","");String[] pointStr=str.split(";");for(int i=0;i<pointStr.length;i++){double x=Double.parseDouble(pointStr[i].split(",")[0]);double y=Double.parseDouble(pointStr[i].split(",")[1]);Point po=new Point(x,y);points.add(po);}return points;}/*** 获取最小的X坐标* @param points* @return*/public  static int minX(List<Point> points){Collections.sort(points, new XComparator(false));return (int)(points.get(0).x>0?points.get(0).x:-points.get(0).x);}/*** 获取最小的Y坐标* @param points* @return*/public  static int minY(List<Point> points){Collections.sort(points, new YComparator(false));return (int)(points.get(0).y>0?points.get(0).y:-points.get(0).y);}/*** 获取最长的X坐标距离* @param points* @return*/public static int xLength(List<Point> points){Collections.sort(points, new XComparator(false));return (int)(points.get(3).x-points.get(0).x);}/*** 获取最长的Y坐标距离* @param points* @return*/public  static int yLength(List<Point> points){Collections.sort(points, new YComparator(false));return (int)(points.get(3).y-points.get(0).y);}//集合排序规则（根据X坐标排序）public static class XComparator implements Comparator<Point> {private boolean reverseOrder; // 是否倒序public XComparator(boolean reverseOrder) {this.reverseOrder = reverseOrder;}public int compare(Point arg0, Point arg1) {if(reverseOrder)return (int)arg1.x - (int)arg0.x;elsereturn (int)arg0.x - (int)arg1.x;}}//集合排序规则（根据Y坐标排序）public static class YComparator implements Comparator<Point> {private boolean reverseOrder; // 是否倒序public YComparator(boolean reverseOrder) {this.reverseOrder = reverseOrder;}public int compare(Point arg0, Point arg1) {if(reverseOrder)return (int)arg1.y - (int)arg0.y;elsereturn (int)arg0.y - (int)arg1.y;}}}

OCRUtil类：

package com.xinjian.x.common.ocr;import net.sourceforge.tess4j.ITesseract;
import net.sourceforge.tess4j.Tesseract;
import net.sourceforge.tess4j.util.LoadLibs;import java.awt.image.BufferedImage;
import java.io.File;public class OCRUtil {/*** 识别图片信息* @param img* @return*/public static String getImageMessage(BufferedImage img,String language){String result="end";try{ITesseract instance = new Tesseract();File tessDataFolder = LoadLibs.extractTessResources("tessdata");instance.setLanguage(language);instance.setDatapath(tessDataFolder.getAbsolutePath());result = instance.doOCR(img);//System.out.println(result);}catch(Exception e){System.out.println(e.getMessage());}return result;}
}

language为语言包名称eng或者chi_sim,chi_sim语言包可能与jar包不匹配需要注意

语言包下载地址：https://download.csdn.net/download/psdnfu/5187836

<!--OCR  Tesseract-->
<dependency><groupId>net.java.dev.jna</groupId><artifactId>jna</artifactId><version>4.1.0</version>
</dependency>
<dependency><groupId>net.sourceforge.tess4j</groupId><artifactId>tess4j</artifactId><version>2.0.1</version><exclusions><exclusion><groupId>com.sun.jna</groupId><artifactId>jna</artifactId></exclusion></exclusions>
</dependency>

Main 方法：

package com.xinjian.x.modules.orc;import com.xinjian.x.common.ocr.OCRUtil;
import com.xinjian.x.common.utils.OpencvUtil;
import org.opencv.core.*;
import org.opencv.core.Point;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import java.awt.image.BufferedImage;
import java.util.ArrayList;
import java.util.List;
import static com.xinjian.x.common.utils.OpencvUtil.rotate3;
import static com.xinjian.x.common.utils.OpencvUtil.shear;public class OrcTest {static {System.loadLibrary(Core.NATIVE_LIBRARY_NAME);//注意程序运行的时候需要在VM option添加该行 指明opencv的dll文件所在路径//-Djava.library.path=$PROJECT_DIR$\opencv\x64}public static void main(String[] args){String path="D:/Users/xinjian09/Desktop/c.jpg";Mat mat= Imgcodecs.imread(path);cardUp(mat);}/*** 身份证反面识别*/public static void cardDown(Mat mat){//灰度mat=OpencvUtil.gray(mat);//二值化mat=OpencvUtil.binary(mat);//腐蚀mat=OpencvUtil.erode(mat,3);//膨胀mat=OpencvUtil.dilate(mat,3);//检测是否有居民身份证字体，若有为正向，若没有则旋转图片for(int i=0;i<4;i++){String temp=temp(mat);if(!temp.contains("居")&&!temp.contains("民")){mat= rotate3(mat,90);}else{break;}}Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/result.jpg", mat);String organization=organization (mat);System.out.print("签发机关是："+organization);String time=time (mat);System.out.print("有效期限是："+time);}public static String temp (Mat mat){Point point1=new Point(mat.cols()*0.30,mat.rows()*0.25);Point point2=new Point(mat.cols()*0.30,mat.rows()*0.25);Point point3=new Point(mat.cols()*0.90,mat.rows()*0.45);Point point4=new Point(mat.cols()*0.90,mat.rows()*0.45);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat temp= shear(mat,list);List<MatOfPoint> nameContours=OpencvUtil.findContours(temp);for (int i = 0; i < nameContours.size(); i++){double area=OpencvUtil.area(nameContours.get(i));if(area<100){Imgproc.drawContours(temp, nameContours, i, new Scalar( 0, 0, 0), -1);}}Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/temp.jpg", temp);BufferedImage nameBuffer=OpencvUtil.Mat2BufImg(temp,".jpg");String nameStr=OCRUtil.getImageMessage(nameBuffer,"chi_sim");nameStr=nameStr.replace("\n","");return nameStr;}public static String organization (Mat mat){Point point1=new Point(mat.cols()*0.36,mat.rows()*0.68);Point point2=new Point(mat.cols()*0.36,mat.rows()*0.68);Point point3=new Point(mat.cols()*0.80,mat.rows()*0.80);Point point4=new Point(mat.cols()*0.80,mat.rows()*0.80);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat name= shear(mat,list);List<MatOfPoint> nameContours=OpencvUtil.findContours(name);for (int i = 0; i < nameContours.size(); i++){double area=OpencvUtil.area(nameContours.get(i));if(area<100){Imgproc.drawContours(name, nameContours, i, new Scalar( 0, 0, 0), -1);}}Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/organization.jpg", name);BufferedImage nameBuffer=OpencvUtil.Mat2BufImg(name,".jpg");String nameStr=OCRUtil.getImageMessage(nameBuffer,"chi_sim");nameStr=nameStr.replace("\n","");return nameStr+"\n";}public static String time (Mat mat){Point point1=new Point(mat.cols()*0.38,mat.rows()*0.82);Point point2=new Point(mat.cols()*0.38,mat.rows()*0.82);Point point3=new Point(mat.cols()*0.85,mat.rows()*0.92);Point point4=new Point(mat.cols()*0.85,mat.rows()*0.92);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat time= shear(mat,list);List<MatOfPoint> timeContours=OpencvUtil.findContours(time);for (int i = 0; i < timeContours.size(); i++){double area=OpencvUtil.area(timeContours.get(i));if(area<100){Imgproc.drawContours(time, timeContours, i, new Scalar( 0, 0, 0), -1);}}Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/time.jpg", time);//起始日期Point startPoint1=new Point(0,0);Point startPoint2=new Point(0,time.rows());Point startPoint3=new Point(time.cols()*0.47,0);Point startPoint4=new Point(time.cols()*0.47,time.rows());List<Point> startList=new ArrayList<>();startList.add(startPoint1);startList.add(startPoint2);startList.add(startPoint3);startList.add(startPoint4);Mat start= shear(time,startList);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/start.jpg", start);BufferedImage yearBuffer=OpencvUtil.Mat2BufImg(start,".jpg");String startStr=OCRUtil.getImageMessage(yearBuffer,"eng");startStr=startStr.replace("-","");startStr=startStr.replace(" ","");startStr=startStr.replace("\n","");//截止日期Point endPoint1=new Point(time.cols()*0.47,0);Point endPoint2=new Point(time.cols()*0.47,time.rows());Point endPoint3=new Point(time.cols(),0);Point endPoint4=new Point(time.cols(),time.rows());List<Point> endList=new ArrayList<>();endList.add(endPoint1);endList.add(endPoint2);endList.add(endPoint3);endList.add(endPoint4);Mat end= shear(time,endList);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/end.jpg", end);BufferedImage endBuffer=OpencvUtil.Mat2BufImg(end,".jpg");String endStr=OCRUtil.getImageMessage(endBuffer,"chi_sim");if(!endStr.contains("长")&&!endStr.contains("期")){endStr=OCRUtil.getImageMessage(endBuffer,"eng");endStr=endStr.replace("-","");endStr=endStr.replace(" ","");}return startStr+"-"+endStr;}/*** 身份证正面识别*/public static void cardUp (Mat mat){Mat begin=mat.clone();//截取身份证区域，并校正旋转角度mat = OpencvUtil.houghLinesP(begin,mat);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/houghLinesP.jpg", mat);//循环进行人脸识别,校正图片方向mat=OpencvUtil.faceLoop(mat);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/face.jpg", mat);//灰度mat=OpencvUtil.gray(mat);//二值化mat=OpencvUtil.binary(mat);//腐蚀mat=OpencvUtil.erode(mat,1);//膨胀mat=OpencvUtil.dilate(mat,1);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/bbb.jpg", mat);//获取名称String name=name(mat);System.out.print("姓名是："+name);//获取性别String sex=sex(mat);System.out.print("性别是："+sex);//获取民族String nation=nation(mat);System.out.print("民族是："+nation);//获取出生日期String birthday=birthday(mat);System.out.print("出生日期是："+birthday);//获取住址String address=address(mat);System.out.print("住址是："+address);//获取身份证String card=card(mat);System.out.print("身份证号是："+card);}public static String name(Mat mat){Point point1=new Point(mat.cols()*0.18,mat.rows()*0.11);Point point2=new Point(mat.cols()*0.18,mat.rows()*0.24);Point point3=new Point(mat.cols()*0.4,mat.rows()*0.11);Point point4=new Point(mat.cols()*0.4,mat.rows()*0.24);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat name= shear(mat,list);name=OpencvUtil.drawContours(name,50);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/name.jpg", name);BufferedImage nameBuffer=OpencvUtil.Mat2BufImg(name,".jpg");String nameStr=OCRUtil.getImageMessage(nameBuffer,"chi_sim");nameStr=nameStr.replace("\n","");return nameStr+"\n";}public static String sex(Mat mat){Point point1=new Point(mat.cols()*0.18,mat.rows()*0.25);Point point2=new Point(mat.cols()*0.18,mat.rows()*0.35);Point point3=new Point(mat.cols()*0.25,mat.rows()*0.25);Point point4=new Point(mat.cols()*0.25,mat.rows()*0.35);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat sex= shear(mat,list);sex=OpencvUtil.drawContours(sex,50);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/sex.jpg", sex);BufferedImage sexBuffer=OpencvUtil.Mat2BufImg(sex,".jpg");String sexStr=OCRUtil.getImageMessage(sexBuffer,"chi_sim");sexStr=sexStr.replace("\n","");return sexStr+"\n";}public static String nation(Mat mat){Point point1=new Point(mat.cols()*0.39,mat.rows()*0.25);Point point2=new Point(mat.cols()*0.39,mat.rows()*0.36);Point point3=new Point(mat.cols()*0.55,mat.rows()*0.25);Point point4=new Point(mat.cols()*0.55,mat.rows()*0.36);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat nation= shear(mat,list);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/nation.jpg", nation);BufferedImage nationBuffer=OpencvUtil.Mat2BufImg(nation,".jpg");String nationStr=OCRUtil.getImageMessage(nationBuffer,"chi_sim");nationStr=nationStr.replace("\n","");return nationStr+"\n";}public static String birthday(Mat mat){Point point1=new Point(mat.cols()*0.18,mat.rows()*0.35);Point point2=new Point(mat.cols()*0.18,mat.rows()*0.35);Point point3=new Point(mat.cols()*0.55,mat.rows()*0.48);Point point4=new Point(mat.cols()*0.55,mat.rows()*0.48);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat birthday= shear(mat,list);birthday=OpencvUtil.drawContours(birthday,50);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/birthday.jpg", birthday);//年份Point yearPoint1=new Point(0,0);Point yearPoint2=new Point(0,birthday.rows());Point yearPoint3=new Point(birthday.cols()*0.29,0);Point yearPoint4=new Point(birthday.cols()*0.29,birthday.rows());List<Point> yearList=new ArrayList<>();yearList.add(yearPoint1);yearList.add(yearPoint2);yearList.add(yearPoint3);yearList.add(yearPoint4);Mat year= shear(birthday,yearList);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/year.jpg", year);BufferedImage yearBuffer=OpencvUtil.Mat2BufImg(year,".jpg");String yearStr=OCRUtil.getImageMessage(yearBuffer,"eng");//月份Point monthPoint1=new Point(birthday.cols()*0.44,0);Point monthPoint2=new Point(birthday.cols()*0.44,birthday.rows());Point monthPoint3=new Point(birthday.cols()*0.55,0);Point monthPoint4=new Point(birthday.cols()*0.55,birthday.rows());List<Point> monthList=new ArrayList<>();monthList.add(monthPoint1);monthList.add(monthPoint2);monthList.add(monthPoint3);monthList.add(monthPoint4);Mat month= shear(birthday,monthList);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/month.jpg", month);BufferedImage monthBuffer=OpencvUtil.Mat2BufImg(month,".jpg");String monthStr=OCRUtil.getImageMessage(monthBuffer,"eng");//日期Point dayPoint1=new Point(birthday.cols()*0.69,0);Point dayPoint2=new Point(birthday.cols()*0.69,birthday.rows());Point dayPoint3=new Point(birthday.cols()*0.80,0);Point dayPoint4=new Point(birthday.cols()*0.80,birthday.rows());List<Point> dayList=new ArrayList<>();dayList.add(dayPoint1);dayList.add(dayPoint2);dayList.add(dayPoint3);dayList.add(dayPoint4);Mat day= shear(birthday,dayList);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/day.jpg", day);BufferedImage dayBuffer=OpencvUtil.Mat2BufImg(day,".jpg");String dayStr=OCRUtil.getImageMessage(dayBuffer,"eng");String birthdayStr=yearStr+"年"+monthStr+"月"+dayStr+"日";birthdayStr=birthdayStr.replace("\n","");return birthdayStr+"\n";}public static String address(Mat mat){Point point1=new Point(mat.cols()*0.17,mat.rows()*0.47);Point point2=new Point(mat.cols()*0.17,mat.rows()*0.47);Point point3=new Point(mat.cols()*0.61,mat.rows()*0.76);Point point4=new Point(mat.cols()*0.61,mat.rows()*0.76);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat address= shear(mat,list);address=OpencvUtil.drawContours(address,50);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/address.jpg", address);BufferedImage addressBuffer=OpencvUtil.Mat2BufImg(address,".jpg");return OCRUtil.getImageMessage(addressBuffer,"chi_sim")+"\n";}public static String card(Mat mat){Point point1=new Point(mat.cols()*0.34,mat.rows()*0.75);Point point2=new Point(mat.cols()*0.34,mat.rows()*0.75);Point point3=new Point(mat.cols()*0.89,mat.rows()*0.91);Point point4=new Point(mat.cols()*0.89,mat.rows()*0.91);List<Point> list=new ArrayList<>();list.add(point1);list.add(point2);list.add(point3);list.add(point4);Mat card= shear(mat,list);card=OpencvUtil.drawContours(card,50);Imgcodecs.imwrite("D:/Users/xinjian09/Desktop/card.jpg", card);BufferedImage cardBuffer=OpencvUtil.Mat2BufImg(card,".jpg");return OCRUtil.getImageMessage(cardBuffer,"eng")+"\n";}}

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