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.ArrayList;
33 import java.util.List;
34
35 import org.openimaj.image.MBFImage;
36 import org.openimaj.math.statistics.distribution.CachingMultivariateGaussian;
37
38 /**
39 * An {@link MBFPixelClassificationModel} that classifies an individual pixel by
40 * comparing it to a {@link CachingMultivariateGaussian}. The Gaussian is learnt
41 * from the values of the positive pixel samples given in training. The
42 * probability returned by the classification is determined from the PDF of the
43 * Gaussian at the given pixel.
44 *
45 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
46 */
47 public class SingleGaussianPixelModel extends MBFPixelClassificationModel {
48 private static final long serialVersionUID = 1L;
49 protected CachingMultivariateGaussian gauss;
50
51 /**
52 * Construct with the given number of dimensions. This should be equal to
53 * the number of bands in the {@link MBFImage}s you wish to classify.
54 *
55 * @param ndims
56 * the number of dimensions.
57 */
58 public SingleGaussianPixelModel(int ndims) {
59 super(ndims);
60 }
61
62 @Override
63 protected float classifyPixel(Float[] pix) {
64 return (float) gauss.estimateProbability(pix);
65 }
66
67 @Override
68 public void learnModel(MBFImage... images) {
69 final List<float[]> data = new ArrayList<float[]>();
70
71 for (int i = 0; i < images.length; i++) {
72
73 for (int y = 0; y < images[i].getHeight(); y++) {
74 for (int x = 0; x < images[i].getWidth(); x++) {
75 final float[] d = new float[ndims];
76
77 for (int j = 0; j < ndims; j++) {
78 d[j] = images[i].getBand(j).pixels[y][x];
79 }
80
81 data.add(d);
82 }
83 }
84 }
85
86 final float[][] arraydata = data.toArray(new float[data.size()][ndims]);
87
88 gauss = CachingMultivariateGaussian.estimate(arraydata);
89 }
90
91 @Override
92 public SingleGaussianPixelModel clone() {
93 final SingleGaussianPixelModel model = new SingleGaussianPixelModel(ndims);
94 model.gauss = new CachingMultivariateGaussian(gauss.getMean().copy(), gauss.getCovariance().copy());
95
96 return null;
97 }
98 }