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1   /**
2    * Copyright (c) 2011, The University of Southampton and the individual contributors.
3    * All rights reserved.
4    *
5    * Redistribution and use in source and binary forms, with or without modification,
6    * are permitted provided that the following conditions are met:
7    *
8    *   * 	Redistributions of source code must retain the above copyright notice,
9    * 	this list of conditions and the following disclaimer.
10   *
11   *   *	Redistributions in binary form must reproduce the above copyright notice,
12   * 	this list of conditions and the following disclaimer in the documentation
13   * 	and/or other materials provided with the distribution.
14   *
15   *   *	Neither the name of the University of Southampton nor the names of its
16   * 	contributors may be used to endorse or promote products derived from this
17   * 	software without specific prior written permission.
18   *
19   * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
20   * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
21   * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
22   * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
23   * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
24   * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
25   * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
26   * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
27   * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
28   * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
29   */
30  package org.openimaj.image.model.pixel;
31  
32  import org.openimaj.image.MBFImage;
33  import org.openimaj.image.pixel.statistics.HistogramModel;
34  
35  /**
36   * An {@link MBFPixelClassificationModel} that classifies an individual pixel by
37   * comparing it to a joint (colour) histogram. The histogram is learnt from the
38   * positive pixel samples given in training. The probability returned by the
39   * classification is determined from the value of the histogram bin in which the
40   * pixel being classified falls.
41   * 
42   * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
43   */
44  public class HistogramPixelModel extends MBFPixelClassificationModel {
45  	private static final long serialVersionUID = 1L;
46  
47  	/**
48  	 * The model histogram; public for speed.
49  	 */
50  	public HistogramModel model;
51  
52  	/**
53  	 * Construct with the given number of histogram bins per dimension.
54  	 * 
55  	 * @param nbins
56  	 *            number of bins per dimension.
57  	 */
58  	public HistogramPixelModel(int... nbins) {
59  		super(nbins.length);
60  		model = new HistogramModel(nbins);
61  	}
62  
63  	@Override
64  	protected float classifyPixel(Float[] pix) {
65  		int bin = 0;
66  
67  		for (int i = 0; i < ndims; i++) {
68  			int b = (int) (pix[i] * (model.histogram.nbins[i]));
69  			if (b >= model.histogram.nbins[i])
70  				b = model.histogram.nbins[i] - 1;
71  
72  			int f = 1;
73  			for (int j = 0; j < i; j++)
74  				f *= model.histogram.nbins[j];
75  
76  			bin += f * b;
77  		}
78  
79  		return (float) model.histogram.values[bin];
80  	}
81  
82  	@Override
83  	public String toString() {
84  		return model.toString();
85  	}
86  
87  	@Override
88  	public HistogramPixelModel clone() {
89  		final HistogramPixelModel newmodel = new HistogramPixelModel();
90  		newmodel.model = model.clone();
91  		newmodel.ndims = ndims;
92  		return newmodel;
93  	}
94  
95  	@Override
96  	public void learnModel(MBFImage... images) {
97  		model.estimateModel(images);
98  	}
99  }