Java tutorial
/* * This file is part of the LIRE project: http://www.semanticmetadata.net/lire * 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 * * Copyright statement: * ==================== * (c) 2002-2013 by Mathias Lux (mathias@juggle.at) * http://www.semanticmetadata.net/lire, http://www.lire-project.net * * Updated: 07.08.13 12:09 */ package net.semanticmetadata.lire.imageanalysis; import junit.framework.TestCase; import net.semanticmetadata.lire.DocumentBuilder; import net.semanticmetadata.lire.impl.ChainedDocumentBuilder; import net.semanticmetadata.lire.impl.GenericDocumentBuilder; import net.semanticmetadata.lire.indexing.parallel.ParallelIndexer; import net.semanticmetadata.lire.utils.FileUtils; import org.apache.lucene.document.Document; import org.apache.lucene.index.DirectoryReader; import org.apache.lucene.index.IndexReader; import org.apache.lucene.store.FSDirectory; import org.apache.lucene.util.BytesRef; import javax.imageio.ImageIO; import java.awt.image.BufferedImage; import java.io.File; import java.io.FileInputStream; import java.io.IOException; import java.util.*; public class ScalableColorTest extends TestCase { private String[] testFiles = new String[] { "img01.jpg", "img02.jpg", "img03.jpg", "img04.jpg", "img05.jpg", "img06.jpg", "img07.jpg", "img08.jpg", "img09.jpg", "img10.jpg" }; private String testFilesPath = "D:\\Desktop\\UCID_png"; public void testExtraction() throws IOException { List<String> results = new ArrayList<String>(); File[] files = new File(testFilesPath).listFiles(); for (File file : files) { if (file.isFile()) { results.add(file.getPath()); } } for (int i = 0; i < results.size(); i++) { BufferedImage image = null; try { image = ImageIO.read(new File(results.get(i))); } catch (IOException e) { } ScalableColor sch = new ScalableColor(); sch.extract(image); System.out.println(results.get(i) + "\t" + sch.getStringRepresentation()); } // ScalableColor sch = new ScalableColor(); // BufferedImage image = ImageIO.read(new FileInputStream(testFilesPath + testFiles[0])); // System.out.println("image = " + image.getWidth() + " x " + image.getHeight()); // sch.extract(image); // System.out.println("sch = " + sch.getStringRepresentation()); } public void testRetrieval() throws Exception { ScalableColor[] acc = new ScalableColor[testFiles.length]; LinkedList<String> vds = new LinkedList<String>(); for (int i = 0; i < acc.length; i++) { System.out.println("Extracting from number " + i); acc[i] = new ScalableColor(); acc[i].extract(ImageIO.read(new FileInputStream(testFilesPath + testFiles[i]))); vds.add(acc[i].getStringRepresentation()); } System.out.println("Calculating distance for " + testFiles[5]); for (int i = 0; i < acc.length; i++) { float distance = acc[i].getDistance(acc[5]); System.out.println(testFiles[i] + " distance = " + distance); } int count = 0; for (Iterator<String> iterator = vds.iterator(); iterator.hasNext();) { String s = iterator.next(); SimpleColorHistogram a = new SimpleColorHistogram(); a.setStringRepresentation(s); float distance = acc[count].getDistance(a); System.out.println(testFiles[count] + " distance = " + distance); count++; } } public void testSingleFile() throws IOException { ScalableColor c = new ScalableColor(); BufferedImage img = ImageIO.read(new File("C:\\Java\\Projects\\LireSVN\\testdata\\UCID\\ucid00001.jpg")); c.extract(img); String s = Arrays.toString(c.getDoubleHistogram()); System.out.println("s = " + s); byte[] b = c.getByteArrayRepresentation(); ScalableColor d = new ScalableColor(); d.setByteArrayRepresentation(b); System.out.println(d.getDistance(c)); } public void testSerialization() throws IOException { int bytes = 0; int sum = 0; ArrayList<File> files = FileUtils.getAllImageFiles(new File("testdata\\UCID"), true); for (Iterator<File> iterator = files.iterator(); iterator.hasNext();) { File next = iterator.next(); BufferedImage image = ImageIO.read(next); ScalableColor f1 = new ScalableColor(); ScalableColor f2 = new ScalableColor(); f1.extract(image); // System.out.println(Arrays.toString(f1.getDoubleHistogram())); // bytes += f1.getByteArrayRepresentation().length; // sum += 144 / 2; byte[] br = f1.getByteArrayRepresentation(); f2.setByteArrayRepresentation(br, 0, br.length); // System.out.println(Arrays.toString(f2.getDoubleHistogram())); double[] h = f2.getDoubleHistogram(); // int pos = -1; // for (int i = 0; i < h.length; i++) { // double v = h[i]; // if (pos == -1) { // if (v == 0) pos = i; // } else if (pos > -1) { // if (v != 0) pos = -1; // } // } // System.out.println("save = " + (144 - pos)); // bytes += (168 - pos); assertTrue(f2.getDistance(f1) == 0); boolean isSame = true; for (int i = 0; i < f2.getDoubleHistogram().length; i++) { if (f1.getDoubleHistogram()[i] != f2.getDoubleHistogram()[i]) isSame = false; } assertTrue(isSame); } // double save = 1d - (double) bytes / (double) sum; // System.out.println(save * 100 + "% saved"); } public void testIndexSerialization() throws IOException { ParallelIndexer pi = new ParallelIndexer(8, "test-idx", "testdata\\UCID", true) { @Override public void addBuilders(ChainedDocumentBuilder builder) { builder.addBuilder(new GenericDocumentBuilder(ScalableColor.class)); } }; pi.run(); IndexReader ir = DirectoryReader.open(FSDirectory.open(new File("test-idx"))); ScalableColor tmp = new ScalableColor(); ScalableColor idx = new ScalableColor(); for (int i = 0; i < ir.maxDoc(); i++) { Document d = ir.document(i); BytesRef ref = d.getBinaryValue(new ScalableColor().getFieldName()); idx.setByteArrayRepresentation(ref.bytes, ref.offset, ref.length); tmp.extract(ImageIO.read(new File(d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0]))); for (int j = 0; j < tmp.getDoubleHistogram().length; j++) { double v = tmp.getDoubleHistogram()[j]; if (Math.abs(v - idx.getDoubleHistogram()[j]) > 0.1) { System.err.println( d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0] + " error at position " + j); break; } } // assertEquals((double) idx.getDistance(tmp), 0d, 0.00001); } } public void testNextSerialization() throws IOException { // todo ... int tmp = 0x0004; System.out.println("tmp = " + (tmp & 0x0007)); tmp = tmp << 3; System.out.println("tmp = " + (tmp & 0x0007)); tmp = tmp | 0x003; System.out.println("tmp = " + (tmp & 0x0007)); tmp = tmp << 3; tmp = tmp | 0x007; System.out.println("tmp = " + tmp); System.out.println("tmp = " + (tmp & 0x0007)); } /** * Storing zeros run length coded: one zero is "8", two zeros are "9", ... etc. * @throws java.io.IOException */ public void testRLE() throws IOException { ArrayList<File> files = FileUtils.getAllImageFiles(new File("testdata/ferrari"), true); int sum = 0, saved = 0; for (Iterator<File> iterator = files.iterator(); iterator.hasNext();) { File next = iterator.next(); BufferedImage image = ImageIO.read(next); ScalableColor f1 = new ScalableColor(); f1.extract(image); double[] hist = f1.getDoubleHistogram(); int[] rep = new int[hist.length]; int actualLength = 0; for (int i = 0; i < hist.length; i++) { // hist[i]; if (hist[i] == 0) { int count = 0; while (i + count < hist.length && hist[i + count] == 0 && count < 8) count++; if (count == 0) rep[actualLength] = 0; else { rep[actualLength] = 7 + count; i += count - 1; } } else { rep[actualLength] = (int) hist[i]; } actualLength++; } for (int i = 0; i < actualLength; i++) { System.out.print(rep[i] + " "); } System.out.println(); // decoding ... double[] hist2 = new double[144]; int pos = 0; for (int i = 0; i < actualLength; i++) { if (rep[i] < 8) { hist2[pos] = rep[i]; pos++; } else { for (int j = 7; j < rep[i]; j++) { hist2[pos] = 0; pos++; } } } sum += 144; saved += actualLength; assertTrue(Arrays.equals(hist2, f1.getDoubleHistogram())); } System.out.println("sum of dimensions = " + sum); System.out.println("actual dimensions = " + saved); } }