java+opencv 目标图像调整

java+opencv 进行图像匹配，FeatureDetector ，DescriptorExtractor
DescriptorMatcher 配合使用。分别进行关键点检测，提取描述向量，特征匹配。
废话少说直接上代码。有问题可留言！！！opencv+java 论坛

import java.util.ArrayList;import java.util.Iterator;import java.util.LinkedList;import java.util.List;import org.opencv.core.Core;import org.opencv.core.CvType;import org.opencv.core.Mat;import org.opencv.core.MatOfDMatch;import org.opencv.core.MatOfKeyPoint;import org.opencv.core.MatOfPoint2f;import org.opencv.core.Point;import org.opencv.core.Rect;import org.opencv.core.Size;import org.opencv.highgui.Highgui;import org.opencv.imgproc.Imgproc;import org.opencv.features2d.DMatch;import org.opencv.features2d.DescriptorExtractor;import org.opencv.features2d.DescriptorMatcher;import org.opencv.features2d.FeatureDetector;import org.opencv.features2d.Features2d;import org.opencv.calib3d.*;public class testFeatureDetector{    /**     *      * @param scene 参考图     * @param object 目标需要调整图     */    static void tiaozheng2(Mat scene,Mat object)     {        FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF);        DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);        // DETECTION        // first image        Mat descriptors_scene= new Mat();        MatOfKeyPoint keypoints_scene= new MatOfKeyPoint();        detector.detect(scene, keypoints_scene);        descriptor.compute(scene, keypoints_scene, descriptors_scene);        // second image        Mat descriptors_object= new Mat();        MatOfKeyPoint keypoints_object = new MatOfKeyPoint();        detector.detect(object, keypoints_object);        descriptor.compute(object, keypoints_object,descriptors_object);        // MATCHING        // match these two keypoints sets        List<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();        matcher.knnMatch(descriptors_object, descriptors_scene,matches, 5);        /////////////////////////////////////////////////////////////        // ratio test        LinkedList<DMatch> good_matches = new LinkedList<DMatch>();        for (Iterator<MatOfDMatch> iterator = matches.iterator(); iterator.hasNext();) {            MatOfDMatch matOfDMatch = (MatOfDMatch) iterator.next();            if (matOfDMatch.toArray()[0].distance / matOfDMatch.toArray()[1].distance < 0.9) {                good_matches.add(matOfDMatch.toArray()[0]);            }        }        // get keypoint coordinates of good matches to find homography and remove outliers using ransac        List<Point> pts_object = new ArrayList<Point>();        List<Point> pts_scene = new ArrayList<Point>();        for(int i = 0; i<good_matches.size(); i++){            pts_object.add(keypoints_object.toList().get(good_matches.get(i).queryIdx).pt);            pts_scene.add(keypoints_scene.toList().get(good_matches.get(i).trainIdx).pt);        }        // convertion of data types - there is maybe a more beautiful way        Mat outputMask = new Mat();        MatOfPoint2f pts_objectMat = new MatOfPoint2f();        pts_objectMat.fromList(pts_object);        MatOfPoint2f pts_sceneMat = new MatOfPoint2f();        pts_sceneMat.fromList(pts_scene);        Mat Homog = Calib3d.findHomography(pts_objectMat, pts_sceneMat, Calib3d.RANSAC, 10, outputMask);        Mat resultMat=new Mat(new Size(object.cols(),object.rows()),object.type());           Imgproc.warpPerspective(object,object,  Homog, resultMat.size());//      // outputMask contains zeros and ones indicating which matches are filtered        LinkedList<DMatch> better_matches = new LinkedList<DMatch>();        for (int i = 0; i < good_matches.size(); i++)         {        //  System.out.println(outputMask.get(i, 0)[0]);            if (outputMask.get(i, 0)[0] != 0.0) {                better_matches.add(good_matches.get(i));            }        } //      System.out.println(better_matches.toString());       ///////////////////////////////////////////////////////////////      // DRAWING OUTPUT        Mat outputImg = new Mat();        // this will draw all matches, works fine        MatOfDMatch better_matches_mat = new MatOfDMatch();        better_matches_mat.fromList(better_matches);     //   System.out.println(better_matches_mat.toString());        Features2d.drawMatches( object, keypoints_object, scene, keypoints_scene,better_matches_mat, outputImg);        Mat outputImg2 = new Mat();        Features2d.drawMatches2( object, keypoints_object, scene, keypoints_scene,matches, outputImg2);        Highgui.imwrite("result.jpg", outputImg);        Highgui.imwrite("result2.jpg", outputImg2);    }    /**     *      * @param scene 参考图     * @param object 目标需要调整图     */    static void tiaozheng(Mat scene,Mat object)    {        FeatureDetector  detector =FeatureDetector.create(FeatureDetector.SURF);          MatOfKeyPoint keypoints_object = new MatOfKeyPoint();        MatOfKeyPoint keypoints_scene = new MatOfKeyPoint();        detector.detect(object, keypoints_object);        detector.detect(scene, keypoints_scene);        Mat descriptors_object=new Mat();        Mat descriptors_scene=new Mat();        DescriptorExtractor extractor=DescriptorExtractor.create(DescriptorExtractor.ORB);         extractor.compute( object, keypoints_object, descriptors_object );        extractor.compute( scene, keypoints_scene, descriptors_scene );        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);        MatOfDMatch matches = new MatOfDMatch();        matcher.match(descriptors_object, descriptors_scene, matches);        DMatch[] tmmpDMatchs=matches.toArray();        double max_dist = 0; double min_dist =400;        for( int i = 0; i < descriptors_object.rows(); i++ )        {             double dist =tmmpDMatchs[i].distance;            if( dist < min_dist ) min_dist = dist;            if( dist > max_dist ) max_dist = dist;        }        LinkedList<DMatch> good_matches = new LinkedList<DMatch>();        for (int i = 0; i < descriptors_object.rows();++i)         {            double dist =tmmpDMatchs[i].distance;            if (dist < 3*min_dist )             {                good_matches.add(tmmpDMatchs[i]);            }        }        List<Point> pts_scene = new ArrayList<Point>();        List<Point> pts_object = new ArrayList<Point>();//        for(int i = 0; i<good_matches.size(); i++)        {            pts_object.add(keypoints_object.toList().get(good_matches.get(i).queryIdx).pt);            pts_scene.add(keypoints_scene.toList().get(good_matches.get(i).trainIdx).pt);        }        Mat outputMask = new Mat();        MatOfPoint2f pts_sceneMat = new MatOfPoint2f();        pts_sceneMat.fromList(pts_scene);        MatOfPoint2f pts_objectMat = new MatOfPoint2f();        pts_objectMat.fromList(pts_object);        Mat Homog = Calib3d.findHomography(pts_objectMat, pts_sceneMat, Calib3d.RANSAC, 10, outputMask);        Mat resultMat=new Mat(new Size(scene.cols(),scene.rows()),scene.type());        Imgproc.warpPerspective(object,object,  Homog, resultMat.size());    }    public  static void main(String args[])    {        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);        Mat scene=Highgui.imread("00.jpg");//参考          Mat object=Highgui.imread("11.jpg");//调整目标        object.convertTo(object, CvType.CV_8UC3);// 目标图        scene.convertTo(scene, CvType.CV_8UC3);// 参考图        Mat img2=object.submat(new Rect(0,0,object.width(),object.height()*9/10));        Mat img1=scene.submat(new Rect(0,0,object.width(),object.height()*9/10));        Highgui.imwrite("scene.jpg",img1);//调整目标        Highgui.imwrite("object.jpg",img2);//调整目标    //  tiaozheng(img1,img2);        tiaozheng2(img1,img2);        Highgui.imwrite("object2.jpg",img2);    }}

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