これまで，Java + OpenCVを使って，2枚の画像の特徴点検出（SRUFやSIFT）やWebカメラの画像表示をしてきました．
今回はそれを合わせて，Webカメラの画像を使って特徴点検出です．
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import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.core.MatOfDMatch; import org.opencv.core.MatOfKeyPoint; import org.opencv.core.DMatch; import org.opencv.features2d.DescriptorExtractor; import org.opencv.features2d.DescriptorMatcher; import org.opencv.features2d.FeatureDetector; import org.opencv.features2d.Features2d; import org.opencv.imgcodecs.Imgcodecs; import org.opencv.imgproc.Imgproc; import java.awt.*; import java.awt.image.BufferedImage; import javax.swing.*; import org.opencv.core.Size; import org.opencv.videoio.VideoCapture; public class imagematch extends JPanel{ private static final long serialVersionUID = 1L; private BufferedImage image; // Create a constructor method public imagematch(){ super(); } private BufferedImage getimage(){ return image; } private void setimage(BufferedImage newimage){ image=newimage; return; } /** * Converts/writes a Mat into a BufferedImage. * * @param matrix Mat of type CV_8UC3 or CV_8UC1 * @return BufferedImage of type TYPE_3BYTE_BGR or TYPE_BYTE_GRAY */ public static BufferedImage matToBufferedImage(Mat matrix) { int cols = matrix.cols(); int rows = matrix.rows(); int elemSize = (int)matrix.elemSize(); byte[] data = new byte[cols * rows * elemSize]; int type; matrix.get(0, 0, data); switch (matrix.channels()) { case 1: type = BufferedImage.TYPE_BYTE_GRAY; break; case 3: type = BufferedImage.TYPE_3BYTE_BGR; // bgr to rgb byte b; for(int i=0; i<data.length; i=i+3) { b = data[i]; data[i] = data[i+2]; data[i+2] = b; } break; default: return null; } BufferedImage image2 = new BufferedImage(cols, rows, type); image2.getRaster().setDataElements(0, 0, cols, rows, data); return image2; } public void paintComponent(Graphics g){ BufferedImage temp=getimage(); if(temp!=null){ g.drawImage(temp,10,10,temp.getWidth(),temp.getHeight(), this); } } public static void main(String[] args) { System.loadLibrary(Core.NATIVE_LIBRARY_NAME); JFrame frame = new JFrame("BasicPanel"); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); frame.setSize(1200,600); imagematch panel = new imagematch(); frame.setContentPane(panel); frame.setVisible(true); // 比較画像01 Mat image01 = Imgcodecs.imread("./resources/test01s.jpg"); // 比較画像02 Mat webcam_image=new Mat(); BufferedImage temp; VideoCapture capture =new VideoCapture(0); if( capture.isOpened()) { while( true ) { capture.read(webcam_image); if( !webcam_image.empty() ) { Mat image02 = webcam_image; Mat grayImage01 = new Mat(image01.rows(), image01.cols(), image01.type()); Imgproc.cvtColor(image01, grayImage01, Imgproc.COLOR_BGRA2GRAY); Core.normalize(grayImage01, grayImage01, 0, 255, Core.NORM_MINMAX); Mat grayImage02 = new Mat(image02.rows(), image02.cols(), image02.type()); Imgproc.cvtColor(image02, grayImage02, Imgproc.COLOR_BGRA2GRAY); Core.normalize(grayImage02, grayImage02, 0, 255, Core.NORM_MINMAX); // ------ SIFTの処理 ここから ------ FeatureDetector siftDetector = FeatureDetector.create(FeatureDetector.SIFT); DescriptorExtractor siftExtractor = DescriptorExtractor.create(DescriptorExtractor.SIFT); MatOfKeyPoint keyPoint01 = new MatOfKeyPoint(); siftDetector.detect(grayImage01, keyPoint01); MatOfKeyPoint keyPoint02 = new MatOfKeyPoint(); siftDetector.detect(grayImage02, keyPoint02); Mat descripters01 = new Mat(image01.rows(), image01.cols(), image01.type()); siftExtractor.compute(grayImage01, keyPoint01, descripters01); Mat descripters02 = new Mat(image02.rows(), image02.cols(), image02.type()); siftExtractor.compute(grayImage02, keyPoint02, descripters02); MatOfDMatch matchs = new MatOfDMatch(); DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE); matcher.match(descripters01, descripters02, matchs); // 上位50点以外の点を除去する int N = 50; DMatch[] tmp01 = matchs.toArray(); DMatch[] tmp02 = new DMatch[N]; for (int i=0; i<tmp02.length; i++) { tmp02[i] = tmp01[i]; } matchs.fromArray(tmp02); Mat matchedImage = new Mat(image01.rows(), image01.cols()*2, image01.type()); Features2d.drawMatches(image01, keyPoint01, image02, keyPoint02, matchs, matchedImage); // 出力画像 at SIFT //Imgcodecs.imwrite("./resources/descriptedImageBySIFT-" + now + ".jpg", matchedImage); BufferedImage matchedImage02 = matToBufferedImage(matchedImage); // ------ SIFTの処理 ここまで ------ // Webカメラ画像出力 Imgproc.resize(webcam_image, webcam_image, new Size(webcam_image.size().width*0.5,webcam_image.size().height*0.5)); frame.setSize(matchedImage.width()+300,matchedImage.height()+60); temp=matToBufferedImage(webcam_image); panel.setimage(matchedImage02); panel.repaint(); } else { System.out.println(" --(!) No captured frame -- "); } } } return; } } |
特徴点検出のプロトコルを変更したい場合は，113行目114行目のSIFTをその他のプロトコル（SURFやAKAZEなど）に変更してください．
結果
SIFT
SURF
AKAZE
動画からの検出になるとAKAZEのハイスピードが顕著でした．検出結果も比較もかなり正確だし，AKAZEいいですね．