/*
* This file is part of the LIRE project: http://lire-project.net
* LIRE is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* LIRE is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LIRE; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* We kindly ask you to refer the any or one of the following publications in
* any publication mentioning or employing Lire:
*
* Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval -
* An Extensible Java CBIR Library. In proceedings of the 16th ACM International
* Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
* URL: http://doi.acm.org/10.1145/1459359.1459577
*
* Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
* 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
* Arizona, USA, 2011
* URL: http://dl.acm.org/citation.cfm?id=2072432
*
* Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
* Morgan & Claypool, 2013
* URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
*/
package net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures;
import net.semanticmetadata.lire.imageanalysis.features.LocalFeature;
import net.semanticmetadata.lire.imageanalysis.features.LocalFeatureExtractor;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.FeatureDetector;
import org.opencv.features2d.KeyPoint;
import org.opencv.imgproc.Imgproc;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
/**
* Created by Nektarios on 1/10/2014.
*
* @author Nektarios Anagnostopoulos, nek.anag@gmail.com
*/
public class CvSiftExtractor implements LocalFeatureExtractor {
//Default: int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6
private int nfeatures=0;
private int nOctaveLayers=3;
private double contrastThreshold=0.04;
private double edgeThreshold=10;
private double sigma=1.6;
LinkedList<CvSiftFeature> features = null;
FeatureDetector detector;
DescriptorExtractor extractor;
// private boolean passingParams = false;
public CvSiftExtractor(){
init();
}
public CvSiftExtractor(int features, int OctaveLayers, double contrastThres, double edgeThres, double sgm){
this.nfeatures=features;
this.nOctaveLayers=OctaveLayers;
this.contrastThreshold=contrastThres;
this.edgeThreshold=edgeThres;
this.sigma=sgm;
// this.passingParams = true;
init();
}
private void init(){
System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
detector = FeatureDetector.create(FeatureDetector.SIFT);
extractor = DescriptorExtractor.create(DescriptorExtractor.SIFT);
// if (passingParams) {
try {
File temp = File.createTempFile("tempFile", ".tmp");
//int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6
//String settings = "%YAML:1.0\nnfeatures: 0\nnOctaveLayers: 3\ncontrastThreshold: 0.04\nedgeThreshold: 10\nsigma: 1.6";
String settings = "%YAML:1.0\nnfeatures: " + nfeatures + "\nnOctaveLayers: " + nOctaveLayers + "\ncontrastThreshold: " + contrastThreshold + "\nedgeThreshold: " + edgeThreshold + "\nsigma: " + sigma;
FileWriter writer = new FileWriter(temp, false);
writer.write(settings);
writer.close();
extractor.read(temp.getPath());
detector.read(temp.getPath());
temp.deleteOnExit();
} catch (IOException e) {
e.printStackTrace();
}
// }
}
@Override
public LinkedList<CvSiftFeature> getFeatures() {
return features;
}
@Override
public Class<? extends LocalFeature> getClassOfFeatures() {
return CvSiftFeature.class;
}
@Override
public void extract(BufferedImage img) {
MatOfKeyPoint keypoints = new MatOfKeyPoint();
Mat descriptors = new Mat();
List<KeyPoint> myKeys;
// Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
// detector.detect(img_object, keypoints);
byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
matRGB.put(0, 0, data);
Mat matGray = new Mat(img.getHeight(),img.getWidth(),CvType.CV_8UC1);
Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
byte[] dataGray = new byte[matGray.rows()*matGray.cols()*(int)(matGray.elemSize())];
matGray.get(0, 0, dataGray);
detector.detect(matGray, keypoints);
extractor.compute(matGray, keypoints, descriptors);
myKeys = keypoints.toList();
features = new LinkedList<CvSiftFeature>();
KeyPoint key;
CvSiftFeature feat;
double[] desc;
int cols, rows = myKeys.size();
for (int i=0; i<rows; i++) {
cols = (descriptors.row(i)).cols();
desc = new double[cols];
key = myKeys.get(i);
for(int j=0; j < cols; j++)
{
desc[j]=descriptors.get(i, j)[0];
}
feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, desc);
features.add(feat);
}
}
public LinkedList<CvSiftFeature> computeSiftKeypoints(BufferedImage img) {
MatOfKeyPoint keypoints = new MatOfKeyPoint();
List<KeyPoint> myKeys;
// Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE
// detector.detect(img_object, keypoints);
byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData();
Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3);
matRGB.put(0, 0, data);
Mat matGray = new Mat(img.getHeight(),img.getWidth(),CvType.CV_8UC1);
Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR?
byte[] dataGray = new byte[matGray.rows()*matGray.cols()*(int)(matGray.elemSize())];
matGray.get(0, 0, dataGray);
detector.detect(matGray, keypoints);
myKeys = keypoints.toList();
LinkedList<CvSiftFeature> myKeypoints = new LinkedList<CvSiftFeature>();
KeyPoint key;
CvSiftFeature feat;
for (Iterator<KeyPoint> iterator = myKeys.iterator(); iterator.hasNext(); ) {
key = iterator.next();
feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, null);
myKeypoints.add(feat);
}
return myKeypoints;
}
public String getParameters()
{
return "nfeatures: "+nfeatures+" nOctaveLayers: "+nOctaveLayers+" contrastThreshold: "+contrastThreshold+" edgeThreshold: "+edgeThreshold+" sigma: "+sigma;
}
}