OPENCV特征点java提取与匹配与比较

opencv的features2d包中提供了surf,sift和orb等特征点算法，用于图像查找图像对象，搜索对象，分析对象，识别对象，合成全景等场合。

研究这些算法的原理和实现，是图像识别基础，OPENCV库使用2.413

通过一些代码研究三种特征点算法，我有意把原始图像转为灰度并放置90与照处中人物比较，以研究三种算法对人脸识别的优点和局限。辅助使用了人脸查找获取待查找图像中人脸子矩阵。上代码。

import java.util.ArrayList;import java.util.LinkedList;import java.util.List;import org.opencv.core.Core;import org.opencv.core.Mat;import org.opencv.core.MatOfDMatch;import org.opencv.core.MatOfKeyPoint;import org.opencv.core.MatOfRect;import org.opencv.core.Size;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.highgui.Highgui;import org.opencv.imgproc.Imgproc;import org.opencv.objdetect.CascadeClassifier;public class ExtractSIFT2 {public static void main(String[] args) {System.loadLibrary(Core.NATIVE_LIBRARY_NAME);Mat src = Highgui.imread("E:/work/qqq/Y9.jpg");Mat dst = Highgui.imread("E:/work/qqq/psb.jpg");MatOfRect mr = getFace(dst);Mat sub = dst.submat(mr.toArray()[0]);Highgui.imwrite("E:/work/qqq/Y4.jpg", FeatureSurfBruteforce(src.t(), sub));Highgui.imwrite("E:/work/qqq/Y5.jpg", FeatureSiftLannbased(src.t(), sub));Highgui.imwrite("E:/work/qqq/Y6.jpg", FeatureOrbLannbased(src.t(), sub));}public static Mat FeatureSurfBruteforce(Mat src, Mat dst){FeatureDetector fd = FeatureDetector.create(FeatureDetector.SURF);DescriptorExtractor de = DescriptorExtractor.create(DescriptorExtractor.SURF);//DescriptorMatcher Matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);DescriptorMatcher Matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);MatOfKeyPoint mkp = new MatOfKeyPoint();fd.detect(src, mkp);Mat desc = new Mat();de.compute(src, mkp, desc);Features2d.drawKeypoints(src, mkp, src);MatOfKeyPoint mkp2 = new MatOfKeyPoint();fd.detect(dst, mkp2);Mat desc2 = new Mat();de.compute(dst, mkp2, desc2);Features2d.drawKeypoints(dst, mkp2, dst);// Matching featuresMatOfDMatch Matches = new MatOfDMatch();Matcher.match(desc, desc2, Matches);double maxDist = Double.MIN_VALUE;double minDist = Double.MAX_VALUE;DMatch[] mats = Matches.toArray();for (int i = 0; i < mats.length; i++) {double dist = mats[i].distance;if (dist < minDist) {minDist = dist;}if (dist > maxDist) {maxDist = dist;}}System.out.println("Min Distance:" + minDist);System.out.println("Max Distance:" + maxDist);List<DMatch> goodMatch = new LinkedList<>();for (int i = 0; i < mats.length; i++) {double dist = mats[i].distance;if (dist < 3 * minDist && dist < 0.2f) {goodMatch.add(mats[i]);}}Matches.fromList(goodMatch);// Show resultMat OutImage = new Mat();Features2d.drawMatches(src, mkp, dst, mkp2, Matches, OutImage);return OutImage;}public static Mat FeatureSiftLannbased(Mat src, Mat dst){FeatureDetector fd = FeatureDetector.create(FeatureDetector.SIFT);DescriptorExtractor de = DescriptorExtractor.create(DescriptorExtractor.SIFT);DescriptorMatcher Matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);MatOfKeyPoint mkp = new MatOfKeyPoint();fd.detect(src, mkp);Mat desc = new Mat();de.compute(src, mkp, desc);Features2d.drawKeypoints(src, mkp, src);MatOfKeyPoint mkp2 = new MatOfKeyPoint();fd.detect(dst, mkp2);Mat desc2 = new Mat();de.compute(dst, mkp2, desc2);Features2d.drawKeypoints(dst, mkp2, dst);// Matching featuresMatOfDMatch Matches = new MatOfDMatch();Matcher.match(desc, desc2, Matches);List<DMatch> l = Matches.toList();List<DMatch> goodMatch = new ArrayList<DMatch>();for (int i = 0; i < l.size(); i++) {DMatch dmatch = l.get(i);if (Math.abs(dmatch.queryIdx - dmatch.trainIdx) < 10f) {goodMatch.add(dmatch);}}Matches.fromList(goodMatch);// Show resultMat OutImage = new Mat();Features2d.drawMatches(src, mkp, dst, mkp2, Matches, OutImage);return OutImage;}public static Mat FeatureOrbLannbased(Mat src, Mat dst){FeatureDetector fd = FeatureDetector.create(FeatureDetector.ORB);DescriptorExtractor de = DescriptorExtractor.create(DescriptorExtractor.ORB);DescriptorMatcher Matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);MatOfKeyPoint mkp = new MatOfKeyPoint();fd.detect(src, mkp);Mat desc = new Mat();de.compute(src, mkp, desc);Features2d.drawKeypoints(src, mkp, src);MatOfKeyPoint mkp2 = new MatOfKeyPoint();fd.detect(dst, mkp2);Mat desc2 = new Mat();de.compute(dst, mkp2, desc2);Features2d.drawKeypoints(dst, mkp2, dst);// Matching featuresMatOfDMatch Matches = new MatOfDMatch();Matcher.match(desc, desc2, Matches);double maxDist = Double.MIN_VALUE;double minDist = Double.MAX_VALUE;DMatch[] mats = Matches.toArray();for (int i = 0; i < mats.length; i++) {double dist = mats[i].distance;if (dist < minDist) {minDist = dist;}if (dist > maxDist) {maxDist = dist;}}System.out.println("Min Distance:" + minDist);System.out.println("Max Distance:" + maxDist);List<DMatch> goodMatch = new LinkedList<>();for (int i = 0; i < mats.length; i++) {double dist = mats[i].distance;if (dist < 3 * minDist && dist < 0.2f) {goodMatch.add(mats[i]);}}Matches.fromList(goodMatch);// Show resultMat OutImage = new Mat();Features2d.drawMatches(src, mkp, dst, mkp2, Matches, OutImage);//Highgui.imwrite("E:/work/qqq/Y4.jpg", OutImage);return OutImage;}public static MatOfRect getFace(Mat src) {Mat result = src.clone();if (src.cols() > 1000 || src.rows() > 1000) {Imgproc.resize(src, result, new Size(src.cols() / 3, src.rows() / 3));}CascadeClassifier faceDetector = new CascadeClassifier("./resource/haarcascade_frontalface_alt2.xml");MatOfRect objDetections = new MatOfRect();faceDetector.detectMultiScale(result, objDetections);return objDetections;}}

人脸灰度图，待处理的图片和处理后三种方法对比，做了一些简单的取优。结果来看，orb算法似乎优于其他两种。

参考：

http://blog.csdn.net/liufanghuangdi/article/details/52957094?locationNum=2&fps=1

http://blog.csdn.net/shuzhe66/article/details/40824883