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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 java.util.List;
33  
34  import org.openimaj.image.FImage;
35  import org.openimaj.image.Image;
36  import org.openimaj.image.model.ImageClassificationModel;
37  import org.openimaj.util.pair.IndependentPair;
38  
39  /**
40   * Simple model for classifying pixels. When learning assumes ALL provided
41   * sample pixels are positive exemplars, and that anything not given is
42   * negative.
43   * 
44   * @author Jonathon Hare
45   * @param <Q>
46   *            Type of pixel
47   * @param <T>
48   *            Type of image
49   * 
50   */
51  public abstract class PixelClassificationModel<Q, T extends Image<Q, T>> implements ImageClassificationModel<T> {
52  	private static final long serialVersionUID = 1L;
53  
54  	protected abstract float classifyPixel(Q pix);
55  
56  	@Override
57  	public FImage classifyImage(T im) {
58  		final FImage out = new FImage(im.getWidth(), im.getHeight());
59  
60  		for (int y = 0; y < im.getHeight(); y++) {
61  			for (int x = 0; x < im.getWidth(); x++) {
62  				out.pixels[y][x] = classifyPixel(im.getPixel(x, y));
63  			}
64  		}
65  
66  		return out;
67  	}
68  
69  	protected abstract T[] getArray(int length);
70  
71  	@Override
72  	public boolean estimate(List<? extends IndependentPair<T, FImage>> data) {
73  		final T[] samples = getArray(data.size());
74  		for (int i = 0; i < data.size(); i++) {
75  			samples[i] = data.get(i).firstObject();
76  		}
77  		learnModel(samples);
78  		return true;
79  	}
80  
81  	@Override
82  	public int numItemsToEstimate() {
83  		return 1; // need a minimum of 1 sample
84  	}
85  
86  	@Override
87  	public FImage predict(T data) {
88  		return classifyImage(data);
89  	}
90  
91  	@Override
92  	public abstract PixelClassificationModel<Q, T> clone();
93  }