Mercurial > hg > orthanc-java

annotate Samples/MammographyDeepLearning/src/main/java/ImageProcessing.java @ 28:43923934e934

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added sample: deep learning for mammography
author Sebastien Jodogne <s.jodogne@gmail.com>
date Wed, 12 Jun 2024 13:58:29 +0200
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1 /**
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2 * SPDX-FileCopyrightText: 2023-2024 Sebastien Jodogne, UCLouvain, Belgium
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3 * SPDX-License-Identifier: GPL-3.0-or-later
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4 **/
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5
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6 /**
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7 * Java plugin for Orthanc
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8 * Copyright (C) 2023-2024 Sebastien Jodogne, UCLouvain, Belgium
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9 *
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10 * This program is free software: you can redistribute it and/or
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11 * modify it under the terms of the GNU General Public License as
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12 * published by the Free Software Foundation, either version 3 of the
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13 * License, or (at your option) any later version.
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14 *
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15 * This program is distributed in the hope that it will be useful, but
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16 * WITHOUT ANY WARRANTY; without even the implied warranty of
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17 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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18 * General Public License for more details.
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19 *
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20 * You should have received a copy of the GNU General Public License
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21 * along with this program. If not, see <http://www.gnu.org/licenses/>.
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22 **/
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25 import ai.djl.ndarray.NDArray;
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26 import ai.djl.ndarray.NDManager;
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27 import ai.djl.ndarray.types.DataType;
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28 import ai.djl.ndarray.types.Shape;
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30 import java.awt.*;
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31 import java.awt.image.BufferedImage;
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32 import java.awt.image.DataBuffer;
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34 public class ImageProcessing {
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35 static BufferedImage resizeImage(BufferedImage originalImage, int targetWidth, int targetHeight) {
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36 BufferedImage resizedImage = new BufferedImage(targetWidth, targetHeight, originalImage.getType());
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37 Graphics2D graphics2D = resizedImage.createGraphics();
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38 graphics2D.setRenderingHint(RenderingHints.KEY_INTERPOLATION, RenderingHints.VALUE_INTERPOLATION_BILINEAR);
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39 graphics2D.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON);
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40 graphics2D.drawImage(originalImage, 0, 0, targetWidth, targetHeight, null);
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41 graphics2D.dispose();
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42 return resizedImage;
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43 }
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45 static NDArray imageToTensor(NDManager manager,
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46 BufferedImage image) {
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47 if (image.getType() != image.TYPE_USHORT_GRAY /* 16 bpp */ &&
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48 image.getType() != image.TYPE_BYTE_GRAY /* 8 bpp */) {
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49 throw new IllegalArgumentException();
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50 }
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52 float pixels[] = new float[image.getHeight() * image.getWidth()];
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54 DataBuffer db = image.getData().getDataBuffer();
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56 int pos = 0;
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57 for (int y = 0; y < image.getHeight(); y++) {
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58 for (int x = 0; x < image.getWidth(); x++, pos++) {
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59 pixels[pos] = db.getElemFloat(pos);
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60 }
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61 }
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63 return manager.create(pixels, new Shape(1, image.getHeight(), image.getWidth()));
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64 }
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66 static NDArray standardize(NDArray image) {
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67 if (image.getDataType() != DataType.FLOAT32 ||
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68 image.getShape().dimension() != 3 ||
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69 (image.getShape().get(0) != 1 &&
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70 image.getShape().get(0) != 3)) {
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71 throw new IllegalArgumentException();
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72 }
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74 // Standardize the image to zero mean and 1 standard deviation
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75 NDArray doubleImage = image.toType(DataType.FLOAT64, false);
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76 NDArray squared = doubleImage.mul(doubleImage);
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78 double asum = doubleImage.sum().getDouble();
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79 double asumOfSquares = squared.sum().getDouble();
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80 double n = doubleImage.getShape().size();
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81 double amean = asum / n;
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82 double astd = Math.sqrt((asumOfSquares - asum * asum / n) / n);
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83
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84 return image.add(-amean).div(astd);
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85 }
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86 }