opencv图片处理与OCR识别

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经过一个月的研究、opencv、能处理图片,并半吊子识别。 暂时还是没有数据,不能实现需求护照识别,对身份证的识别,又因为中文的原因,识别率不高。其次针对护照处理图片的参数需要动态配置。
对于只熟悉java的开发,查找资料学习opencv有些困难,网上大多都是C++版本的,并且,2.4,3.4库有改动。 记录一下opencv的基本操作,一起学习。并提供一种ocr思路

一、处理身份证案例:

Opencv获取身份证号码区域的示例代码 m.jb51.net/show/144805

1、对图片进行降噪以及二值化,凸显内容区域
2、对图片进行轮廓检测
3、对轮廓结果进行分析
4、剪裁指定区域
复制代码
  • 处理过程
  1. 灰度图
  2. 高斯模糊降噪 GaussianBlur
  3. 二值化 threshold
  4. 中值滤波降噪 medianBlur
  5. 腐蚀操作 erode

二、一般处理代码

- 2.4版本
//获取图片
Mat templateImage = Highgui.imread(templateFilePath, Highgui.CV_LOAD_IMAGE_COLOR);

//灰度
Imgproc.cvtColor(img2, img2,  Imgproc.COLOR_BGR2GRAY);

//高斯滤波
Imgproc.GaussianBlur(img2, img2, new Size(3,3), 0);

//中值滤波
Imgproc.medianBlur(img2,img2,3);

//腐蚀
Imgproc.erode(originalImage, originalImage, new Mat(14, 14, 0));

//可调节阈值二值
Imgproc.adaptiveThreshold(img2, img2, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY, 33, 25);

//只过滤黑色
Mat imgHSV = new Mat(img.rows(), img.cols(), CvType.CV_8UC3);
Imgproc.cvtColor(img, imgHSV, Imgproc.COLOR_BGR2GRAY);
Scalar minValues = new Scalar(0, 0, 0);
Scalar maxValues = new Scalar(107, 107, 107);
Mat mask = new Mat();
Core.inRange(imgHSV, minValues, maxValues, mask);

//边缘检测矩形识别,并标注
 List<MatOfPoint> contours=new ArrayList<>();
Mat mat=new Mat();
Imgproc.findContours(originalImage1,contours,mat,Imgproc.RETR_LIST,Imgproc.CHAIN_APPROX_NONE);
Mat originalImage12=originalImage1;
for (int i = 0; i < contours.size(); i++) {
    Rect rect = Imgproc.boundingRect(contours.get(i));
    Core.rectangle(originalImage12, rect.tl(), rect.br(), new Scalar(255, 0, 255));
    originalImage12.submat(rect);
}
Highgui.imwrite(Imginfo.PATH_CACHE+UUID.randomUUID().toString()+Imginfo.JPG_SUFFIX, originalImage12);


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三、图形匹配,仿射变换、旋转

参考链接:基于 OpenCV 的图像匹配( Java 版)

package com.cyd.ocr.passportocr;

import com.sun.image.codec.jpeg.JPEGCodec;
import com.sun.image.codec.jpeg.JPEGEncodeParam;
import com.sun.image.codec.jpeg.JPEGImageEncoder;
import org.opencv.calib3d.Calib3d;
import org.opencv.core.*;
import org.opencv.features2d.*;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.*;
import java.util.LinkedList;
import java.util.List;

/**
 *
 * 根据特征点,匹配模板,旋转截取,根据比例特定区域进行识别。
 *
 * 未改进点:只过滤出黑色进行识别。
 * @author chenyd
 * @date 2018/10/7 18:34
 */
public class test_ocr {

    public static int DPI = 300;

    private float nndrRatio = 0.7f;//这里设置既定值为0.7,该值可自行调整

    private int matchesPointCount = 0;

    public float getNndrRatio() {
        return nndrRatio;
    }

    public void setNndrRatio(float nndrRatio) {
        this.nndrRatio = nndrRatio;
    }

    public int getMatchesPointCount() {
        return matchesPointCount;
    }

    public void setMatchesPointCount(int matchesPointCount) {
        this.matchesPointCount = matchesPointCount;
    }

    public void matchImage(Mat templateImage, Mat originalImage) {
        MatOfKeyPoint templateKeyPoints = new MatOfKeyPoint();
        //指定特征点算法SURF
        FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SURF);
        //获取模板图的特征点
        featureDetector.detect(templateImage, templateKeyPoints);
        //提取模板图的特征点
        MatOfKeyPoint templateDescriptors = new MatOfKeyPoint();
        DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
        System.out.println("提取模板图的特征点");
        descriptorExtractor.compute(templateImage, templateKeyPoints, templateDescriptors);

        //显示模板图的特征点图片
        Mat outputImage = new Mat(templateImage.rows(), templateImage.cols(), Highgui.CV_LOAD_IMAGE_COLOR);
        System.out.println("在图片上显示提取的特征点");
        Features2d.drawKeypoints(templateImage, templateKeyPoints, outputImage, new Scalar(255, 0, 0), 0);

        //获取原图的特征点
        MatOfKeyPoint originalDescriptors = new MatOfKeyPoint();
        MatOfKeyPoint originalKeyPoints = new MatOfKeyPoint();
        featureDetector.detect(originalImage, originalKeyPoints);
        System.out.println("提取原图的特征点");
        descriptorExtractor.compute(originalImage, originalKeyPoints, originalDescriptors);

        List<MatOfDMatch> matches = new LinkedList();
        DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
        System.out.println("寻找最佳匹配");
        /**
         * knnMatch方法的作用就是在给定特征描述集合中寻找最佳匹配
         * 使用KNN-matching算法,令K=2,则每个match得到两个最接近的descriptor,然后计算最接近距离和次接近距离之间的比值,当比值大于既定值时,才作为最终match。
         */
        descriptorMatcher.knnMatch(templateDescriptors, originalDescriptors, matches, 2);

        System.out.println("计算匹配结果");
        LinkedList<DMatch> goodMatchesList = new LinkedList();

        //对匹配结果进行筛选,依据distance进行筛选
        matches.forEach(match -> {
            DMatch[] dmatcharray = match.toArray();
            DMatch m1 = dmatcharray[0];
            DMatch m2 = dmatcharray[1];

            if (m1.distance <= m2.distance * nndrRatio) {
                goodMatchesList.addLast(m1);
            }
        });

        matchesPointCount = goodMatchesList.size();
        //当匹配后的特征点大于等于 4 个,则认为模板图在原图中,该值可以自行调整
        if (matchesPointCount >= 4) {
            System.out.println("模板图在原图匹配成功!");

            List<KeyPoint> templateKeyPointList = templateKeyPoints.toList();
            List<KeyPoint> originalKeyPointList = originalKeyPoints.toList();
            LinkedList<Point> objectPoints = new LinkedList();
            LinkedList<Point> scenePoints = new LinkedList();
            goodMatchesList.forEach(goodMatch -> {
                objectPoints.addLast(templateKeyPointList.get(goodMatch.queryIdx).pt);
                scenePoints.addLast(originalKeyPointList.get(goodMatch.trainIdx).pt);
            });
            MatOfPoint2f objMatOfPoint2f = new MatOfPoint2f();
            objMatOfPoint2f.fromList(objectPoints);
            MatOfPoint2f scnMatOfPoint2f = new MatOfPoint2f();
            scnMatOfPoint2f.fromList(scenePoints);
            //使用 findHomography 寻找匹配上的关键点的变换
            Mat homography = Calib3d.findHomography(objMatOfPoint2f, scnMatOfPoint2f, Calib3d.RANSAC, 3);

            /**
             * 透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane),也称作投影映射(Projective Mapping)。
             */
            Mat templateCorners = new Mat(4, 1, CvType.CV_32FC2);
            Mat templateTransformResult = new Mat(4, 1, CvType.CV_32FC2);
            templateCorners.put(0, 0, new double[]{0, 0});
            templateCorners.put(1, 0, new double[]{templateImage.cols(), 0});
            templateCorners.put(2, 0, new double[]{templateImage.cols(), templateImage.rows()});
            templateCorners.put(3, 0, new double[]{0, templateImage.rows()});
            //使用 perspectiveTransform 将模板图进行透视变以矫正图象得到标准图片
            Core.perspectiveTransform(templateCorners, templateTransformResult, homography);

            //矩形四个顶点
            double[] pointA = templateTransformResult.get(0, 0);
            double[] pointB = templateTransformResult.get(1, 0);
            double[] pointC = templateTransformResult.get(2, 0);
            double[] pointD = templateTransformResult.get(3, 0);
//            System.out.println(String.format("【%s,%s】,【%s,%s】,【%s,%s】,【%s,%s】",pointA[0],pointA[1],pointB[0],pointB[1], pointC[0],pointC[1],pointD[0],pointD[1]));

            //左上,右上点之间的距离
            double range=getDistance(new Point(pointA),new Point(pointB));
            double sina=Math.abs(pointA[1]-pointB[1]);
            double jd=Math.asin(sina/range)/Math.PI*180;
            System.out.println("旋转角度:"+jd);
            Mat jdmat=rotate3(originalImage,-jd);
            String xz="C:\\Users\\chenyd\\Desktop\\img\\idcode\\jdmat.jpg";
            Highgui.imwrite(xz, jdmat);
            if(jd > 1){
                System.out.println("匹配旋转之后的图片");
                matchImage(templateImage,jdmat);
                return;
            }

            //指定取得数组子集的范围
            int rowStart = (int) pointA[1];
            int rowEnd = (int) pointC[1];
            int colStart = (int) pointD[0];
            int colEnd = (int) pointB[0];
            int temp=0;
            if(rowStart>rowEnd){
                temp=rowStart;
                rowStart=rowEnd;
                rowEnd=temp;
            }
            if(colStart>colEnd){
                temp=colStart;
                colStart=colEnd;
                colEnd=temp;
            }

            //TODO 大于零
            System.out.println(String.format("%s,%s,%s,%s",rowStart, rowEnd, colStart, colEnd));
            Mat subMat = originalImage.submat(rowStart, rowEnd, colStart, colEnd);
            Highgui.imwrite("C:\\Users\\chenyd\\Desktop\\img\\idcode\\match.jpg", subMat);

            subTarget(subMat);


            //将匹配的图像用用四条线框出来
            Core.line(originalImage, new Point(pointA), new Point(pointB), new Scalar(0, 255, 0), 4);//上 A->B
            Core.line(originalImage, new Point(pointB), new Point(pointC), new Scalar(0, 255, 0), 4);//右 B->C
            Core.line(originalImage, new Point(pointC), new Point(pointD), new Scalar(0, 255, 0), 4);//下 C->D
            Core.line(originalImage, new Point(pointD), new Point(pointA), new Scalar(0, 255, 0), 4);//左 D->A

            MatOfDMatch goodMatches = new MatOfDMatch();
            goodMatches.fromList(goodMatchesList);
            Mat matchOutput = new Mat(originalImage.rows() * 2, originalImage.cols() * 2, Highgui.CV_LOAD_IMAGE_COLOR);
            Features2d.drawMatches(templateImage, templateKeyPoints, originalImage, originalKeyPoints, goodMatches, matchOutput, new Scalar(0, 255, 0), new Scalar(255, 0, 0), new MatOfByte(), 2);

            Highgui.imwrite("C:\\Users\\chenyd\\Desktop\\img\\idcode\\matchOutput.jpg", matchOutput);
            Highgui.imwrite("C:\\Users\\chenyd\\Desktop\\img\\idcode\\originalImage.jpg", originalImage);
        } else {
            System.out.println("模板图不在原图中!");
        }

        Highgui.imwrite("C:\\Users\\chenyd\\Desktop\\img\\idcode\\outputImage.jpg", outputImage);
    }

    private void subTarget(Mat originalImage) {

        //Imgproc.GaussianBlur(originalImage, originalImage, new Size(3,3), 0);
        //Imgproc.medianBlur(originalImage,originalImage,3);
        Imgproc.cvtColor(originalImage, originalImage,  Imgproc.COLOR_BGR2GRAY);
        Imgproc.adaptiveThreshold(originalImage, originalImage, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 45, 55);
        Highgui.imwrite("C:\\Users\\chenyd\\Desktop\\img\\idcode\\medianBlurMat.jpg", originalImage);

        int imgrow=originalImage.rows();
        int imgcol=originalImage.cols();

        double[] tarT={930,550};//模板中A点坐标
        //PR
//        double[] tarA={301,108};
//        double[] tarC={365,138};

        //NAME
        double[] tarA={307,163};
        double[] tarC={492,193};

        //DATE
//        double[] tarA={300,422};
//        double[] tarC={660,460};

      /*  double[] tarT={1054,553};
        //NAME
        double[] tarA={314,173};
        double[] tarC={607,216};
        //NAME
//        double[] tarA={515,116};
//        double[] tarC={680,145};*/

        //识别区左上和右下的相对距离
        int targRowS=(int) (tarA[1]/tarT[1]*imgrow);//列
        int targRowE=(int) (tarC[1]/tarT[1]*imgrow);
        int targColS=(int) (tarA[0]/tarT[0]*imgcol);//行
        int targColE=(int) (tarC[0]/tarT[0]*imgcol);
        Mat subMat1 = originalImage.submat( targRowS,targRowE, targColS, targColE);
        String file ="C:\\Users\\chenyd\\Desktop\\img\\idcode\\subTarget.jpg";
        Highgui.imwrite(file, subMat1);
        //BufferedImage bi=Mat2Img(subMat1,".jpg");
        handleDpi( new File(file), DPI, DPI);
        tesseract(file);
    }


    public static void handleDpi(File file, int xDensity, int yDensity) {
        FileOutputStream out=null;
        try {
            BufferedImage image = ImageIO.read(file);
            out=new FileOutputStream(file);
            JPEGImageEncoder jpegEncoder = JPEGCodec.createJPEGEncoder(out);
            JPEGEncodeParam jpegEncodeParam = jpegEncoder.getDefaultJPEGEncodeParam(image);
            jpegEncodeParam.setDensityUnit(JPEGEncodeParam.DENSITY_UNIT_DOTS_INCH);
            jpegEncoder.setJPEGEncodeParam(jpegEncodeParam);
            //jpegEncodeParam.setQuality(0.75f, false);
            //jpegEncodeParam.setQuality(2f, false);
            jpegEncodeParam.setXDensity(xDensity);
            jpegEncodeParam.setYDensity(yDensity);
            jpegEncoder.encode(image, jpegEncodeParam);
            image.flush();
        } catch (IOException e) {
            e.printStackTrace();
        }finally {
            if(out!=null){
                try {
                    out.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

    public double getDistance(Point p,Point p2){
        double _x = Math.abs(p.x - p2.x);
        double _y = Math.abs(p.y - p2.y);
        return Math.sqrt(_x*_x+_y*_y);
    }

    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);

        // 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());
        return imgDst;
    }

    public static BufferedImage Mat2Img(Mat mat, String fileExtension) {
        MatOfByte mob = new MatOfByte();
        Highgui.imencode(fileExtension, mat, 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;
    }

    public String tesseract(String file1) {
        String result = "";
        String imgPath=file1;
        BufferedReader bufReader = null;
        try {
            String outPath = imgPath.substring(0, imgPath.lastIndexOf("."));
            Runtime runtime = Runtime.getRuntime();
            String command = "tesseract" + " " + imgPath + " " + outPath +"  -l eng --psm 7 ";
            System.out.println(command);
            Process ps = runtime.exec(command);
            ps.waitFor();
            // 读取文件
            File file = new File(outPath + ".txt");
            bufReader = new BufferedReader(new FileReader(file));
            String temp = "";
            StringBuffer sb = new StringBuffer();
            while ((temp = bufReader.readLine()) != null) {
                sb.append(temp);
            }
            // 文字结果
            result = sb.toString();
            //if (!StringUtils.isEmpty(result))
            //  result = result.replaceAll(" ", "");

            System.out.println("识别结果>>>>>>>>>: "+result);

        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }




    public static void imshow(Mat image, String windowName){
//        try {
//            UIManager.setLookAndFeel(UIManager.getSystemLookAndFeelClassName());
//        } catch (ClassNotFoundException e) {
//            e.printStackTrace();
//        } catch (InstantiationException e) {
//            e.printStackTrace();
//        } catch (IllegalAccessException e) {
//            e.printStackTrace();
//        } catch (UnsupportedLookAndFeelException e) {
//            e.printStackTrace();
//        }
//
//        JFrame jFrame = new JFrame(windowName);
//        JLabel imageView = new JLabel();
//        final JScrollPane imageScrollPane = new JScrollPane(imageView);
//        imageScrollPane.setPreferredSize(new Dimension(500, 500));  // set window size
//        jFrame.add(imageScrollPane, BorderLayout.CENTER);
//        jFrame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
//
//        Image loadedImage = Mat2BufferedImage(image);
//        imageView.setIcon(new ImageIcon(loadedImage));
//        jFrame.pack();
//        jFrame.setLocationRelativeTo(null);
//        jFrame.setVisible(true);
    }



    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

//        String templateFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\gbc_1.jpg";
//        String originalFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\GRC.jpg";
        String templateFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\bhs_4.jpg";
        String originalFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\1539152678458.jpg";
//        String originalFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\BHS_3.jpg";
//        String templateFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\SGP_M_1.jpg";
//        String originalFilePath = "C:\\Users\\chenyd\\Desktop\\img\\idcode\\SGP_.jpg";
        //读取图片文件
        Mat templateImage = Highgui.imread(templateFilePath, Highgui.CV_LOAD_IMAGE_COLOR);
        Mat originalImage = Highgui.imread(originalFilePath, Highgui.CV_LOAD_IMAGE_COLOR);


        test_ocr imageRecognition = new test_ocr();
        imageRecognition.matchImage(templateImage, originalImage);

        System.out.println("匹配的像素点总数:" + imageRecognition.getMatchesPointCount());
    }


}

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四、自定义图像识别思路

  1. 识别

识别文字使用开源技术:tesseract 中文识别率不高,需要下载chi文件,可对中文进行训练

  1. 图片处理

使用opencv,大多数都是C++语言,支持java 2.4版本可以使用图片匹配,3.4版本不行,报错

  1. opencv处理图片,之后由tesseract识别。
  2. opencv处理流程
  • 将需要识别的图片制作成模板,扣去需要识别的信息,保留不变的元素。
  • 获取base64图片。
  • 与模板匹配、并扣取匹配的原图。
  • 将匹配的原图,过滤黑色,只保留文字
  • 将过滤之后的图,矩形匹配,并增加矩形过滤规则,保留符合要求的矩形
  • 订制模板中信息的大概矩形位置,矩形匹配大致都落入到模板信息区域,得到目标矩形坐标
  • 将扣取的原图进行灰度、滤波、中值、二值化、并截取目标矩形坐标。

护照模板识别,git地址

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