mirror of
https://github.com/PAMGuard/PAMGuard.git
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Updates to DelphinID
This commit is contained in:
Jamie Mac
parent
61033b38ca
commit
822832f179
@ -6,7 +6,7 @@
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<attribute name="maven.pomderived" value="true"/>
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</attributes>
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</classpathentry>
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<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/Amazon Coretto 21">
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<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/jdk-21.0.2.13-hotspot">
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<attributes>
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<attribute name="module" value="true"/>
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<attribute name="maven.pomderived" value="true"/>
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2
pom.xml
2
pom.xml
@ -310,7 +310,7 @@
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<dependency>
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<groupId>io.github.macster110</groupId>
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<artifactId>jdl4pam</artifactId>
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<version>0.0.98</version>
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<version>0.0.99</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/gov.nist.math/jama -->
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@ -228,7 +228,7 @@ public class DelphinIDTest {
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String whistleContourPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_contours.mat";
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//the path to the model
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String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_4s_encounter415.zip";
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String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
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//the path to the model
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String matImageSave = "C:/Users/Jamie Macaulay/MATLAB Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistleimages.mat";
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@ -240,7 +240,7 @@ public class DelphinIDTest {
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ArrayList<AbstractWhistleDataUnit> whistleContours = getWhistleContoursMAT(whistleContourPath);
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//segment the whistle detections
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ArrayList<SegmenterDetectionGroup> segments = segmentWhsitleData(whistleContours, dataStartMillis,
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ArrayList<SegmenterDetectionGroup> segments = segmentWhsitleData(whistleContours, (long) (dataStartMillis+(9.565*1000.)),
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segLen, segHop);
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for (int i=0; i<segments.size(); i++) {
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@ -267,7 +267,7 @@ public class DelphinIDTest {
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float[] output = predicition.get(0).getPrediction();
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System.out.println();
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System.out.print("Segment: " +(aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.);
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System.out.print("Segment: " + i + " " + (aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000. + "s ");
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for (int j=0; j<output.length; j++) {
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System.out.print(" " + output[j]);
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}
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@ -12,6 +12,7 @@ import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
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import org.jamdev.jdl4pam.transforms.jsonfile.DLTransformsParser;
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import org.jamdev.jdl4pam.utils.DLMatFile;
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import org.jamdev.jdl4pam.utils.DLUtils;
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import org.jamdev.jpamutils.JamArr;
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import org.json.JSONArray;
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import org.json.JSONObject;
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@ -59,6 +60,7 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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for (int i=0; i<dlParams.numClasses; i++) {
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dlParams.binaryClassification[i]=true;
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}
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}
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@ -94,6 +96,8 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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}
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}
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//something has gone wrong if we get here.
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return null;
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}
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@ -153,6 +157,8 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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float[][][] transformedDataStack = new float[numChunks][][];
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double[][] transformedData2; //spectrogram data
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for (int j=0; j<numChunks; j++) {
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// System.out.println("Number of whistle to process: " + whistleGroups.get(j).getStartSecond() + "s " + whistleGroups.get(j).getSubDetectionsCount() + " " + whistleGroups.get(j).getSegmentStartMillis());
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@ -161,7 +167,7 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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Whistles2Image whistles2Image = new Whistles2Image(whistleGroups.get(j), whistleImageParams);
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//set the spec transform
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((FreqTransform) this.getModelTransforms().get(0)).setSpecTransfrom(whistles2Image.getSpecTransfrom());
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((FreqTransform) modelTransforms.get(0)).setSpecTransfrom(whistles2Image.getSpecTransfrom());
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//process all the transforms.
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DLTransform transform = modelTransforms.get(0);
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@ -170,6 +176,11 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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}
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transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
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//a bit ugly but works.
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transformedData2 = JamArr.transposeMatrix(transformedData2);
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// System.out.println("DelphinID input image: " + transformedData2.length + " x " + transformedData2[0].length );
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transformedDataStack[j] = DLUtils.toFloatArray(transformedData2);
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// //TEMP
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@ -65,7 +65,7 @@ public class Whistles2Image extends FreqTransform {
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ArrayList<double[][]> points = whistContours2Points(whistleGroup);
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//does not work becaue it has to be on the AWT thread.
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BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getSegmentDuration()/1000.}, freqLimits, 5.);
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BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getSegmentDuration()/1000.}, freqLimits, 10.);
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double[][] imaged = new double[(int) size[0]][(int) size[1]];
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@ -4,6 +4,7 @@ import static org.junit.jupiter.api.Assertions.assertEquals;
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import java.awt.image.BufferedImage;
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import java.awt.image.Raster;
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import java.io.File;
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import java.io.IOException;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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@ -14,8 +15,15 @@ import org.jamdev.jdl4pam.transforms.FreqTransform;
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import org.jamdev.jdl4pam.transforms.DLTransform.DLTransformType;
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import org.jamdev.jdl4pam.utils.DLMatFile;
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import org.jamdev.jdl4pam.utils.DLUtils;
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import org.jamdev.jpamutils.JamArr;
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import org.jamdev.jpamutils.interpolation.Bicubic;
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import org.jamdev.jpamutils.interpolation.Bilinear;
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import org.jamdev.jpamutils.interpolation.NearestNeighbor;
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import org.jamdev.jpamutils.spectrogram.SpecTransform;
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import org.junit.jupiter.api.Test;
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import ai.djl.MalformedModelException;
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import rawDeepLearningClassifier.dlClassification.archiveModel.SimpleArchiveModel;
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import rawDeepLearningClassifier.dlClassification.delphinID.Whistles2Image;
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import us.hebi.matlab.mat.format.Mat5;
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import us.hebi.matlab.mat.types.MatFile;
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@ -24,6 +32,55 @@ import us.hebi.matlab.mat.types.Matrix;
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public class DelphinIDTest {
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private static double[][] whistleScatter2Image(double[][] whistleValues) {
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//now perform the image transform in Java
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double[] freqLimits = new double[] {2000., 20000.};
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double[] size = new double[] {680., 480.};
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ArrayList<double[][]> whistleImageArr = new ArrayList<double[][]>();
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whistleImageArr.add(whistleValues);
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BufferedImage canvas = Whistles2Image.makeScatterImage(whistleImageArr, size, new double[]{50, 50. + 4.}, freqLimits, 6.);
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double[][] imaged = new double[(int) size[0]][(int) size[1]];
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float[] color = new float[3];
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Raster raster = canvas.getData();
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for (int i=0; i<imaged.length; i++) {
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for (int j=0; j<imaged[0].length; j++) {
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color = raster.getPixel(i,j, color);
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imaged[i][j] = (255-color[0])/255.; //normalize
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}
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}
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//create the model transforms
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ArrayList<DLTransform> modelTransforms = new ArrayList<DLTransform>();
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modelTransforms.add(new FreqTransform(DLTransformType.SPECFLIP));
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// modelTransforms.add(new FreqTransform(DLTransformType.SPECNORMALISE_MINIMAX));
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modelTransforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(60), Integer.valueOf(80), SpecTransform.RESIZE_BICUBIC}));
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modelTransforms.add(new FreqTransform(DLTransformType.GAUSSIAN_FILTER, new Number[] {Double.valueOf(0.5)}));
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SpecTransform specTransform = new SpecTransform();
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specTransform.setSpecData(imaged);
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specTransform.setSampleRate((float) (freqLimits[1]*2));
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//set the spec transform
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((FreqTransform) modelTransforms.get(0)).setSpecTransfrom(specTransform);
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//process all the transforms.
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DLTransform transform = modelTransforms.get(0);
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for (int i =0; i<modelTransforms.size(); i++) {
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transform = modelTransforms.get(i).transformData(transform);
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}
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double[][] transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
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return transformedData2;
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}
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@Test
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public void whistle2ImageTest() {
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@ -44,22 +101,22 @@ public class DelphinIDTest {
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//the values for the whistle detector.
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double[][] whistleValues = DLMatFile.matrix2array(array);
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//the image after compression
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array = matFile.getArray("image1compressedgrayscale");
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double[][] compressedWhistleImage = DLMatFile.matrix2array(array);
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//the whistle2Image transform image
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array = matFile.getArray("image1originalgrayscalenorm");
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double[][] whislteImage = DLMatFile.matrix2array(array);
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// //the image after compression
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// array = matFile.getArray("image1compressedgrayscale");
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// double[][] compressedWhistleImage = DLMatFile.matrix2array(array);
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//
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// //the whistle2Image transform image
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// array = matFile.getArray("image1originalgrayscalenorm");
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// double[][] whislteImage = DLMatFile.matrix2array(array);
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//now perform the image transform in Java
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double[] freqLimits = new double[] {0., 20000.};
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double[] freqLimits = new double[] {2000., 20000.};
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double[] size = new double[] {680., 480.};
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ArrayList<double[][]> whistleImageArr = new ArrayList<double[][]>();
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whistleImageArr.add(whistleValues);
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BufferedImage canvas = Whistles2Image.makeScatterImage(whistleImageArr, size, new double[]{48, 48. + 4.}, freqLimits, 5.);
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BufferedImage canvas = Whistles2Image.makeScatterImage(whistleImageArr, size, new double[]{50, 50. + 4.}, freqLimits, 10.);
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double[][] imaged = new double[(int) size[0]][(int) size[1]];
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@ -67,33 +124,53 @@ public class DelphinIDTest {
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Raster raster = canvas.getData();
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for (int i=0; i<imaged.length; i++) {
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for (int j=0; j<imaged[0].length; j++) {
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color = raster.getPixel(i, j, color);
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color = raster.getPixel(i,j, color);
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imaged[i][j] = (255-color[0])/255.; //normalize
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}
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}
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ArrayList<DLTransform> transforms = new ArrayList<DLTransform>();
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transforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(64), Integer.valueOf(48)}));
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//create the model transforms
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ArrayList<DLTransform> modelTransforms = new ArrayList<DLTransform>();
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modelTransforms.add(new FreqTransform(DLTransformType.SPECFLIP));
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// modelTransforms.add(new FreqTransform(DLTransformType.SPECNORMALISE_MINIMAX));
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modelTransforms.add(new FreqTransform(DLTransformType.SPECRESIZE, new Number[] {Integer.valueOf(60), Integer.valueOf(80), SpecTransform.RESIZE_BICUBIC}));
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modelTransforms.add(new FreqTransform(DLTransformType.GAUSSIAN_FILTER, new Number[] {Double.valueOf(0.5)}));
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//
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// //set the spec transform
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// ((FreqTransform) transforms.get(0)).setSpecTransfrom(whistles2Image.getSpecTransfrom());
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//
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// //process all the transforms.
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// DLTransform transform = modelTransforms.get(0);
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// for (int i =0; i<modelTransforms.size(); i++) {
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// transform = modelTransforms.get(i).transformData(transform);
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// }
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//
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// transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
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// transformedDataStack[j] = DLUtils.toFloatArray(transformedData2);
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//
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SpecTransform specTransform = new SpecTransform();
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specTransform.setSpecData(imaged);
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specTransform.setSampleRate((float) (freqLimits[1]*2));
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//set the spec transform
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((FreqTransform) modelTransforms.get(0)).setSpecTransfrom(specTransform);
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//process all the transforms.
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DLTransform transform = modelTransforms.get(0);
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for (int i =0; i<modelTransforms.size(); i++) {
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transform = modelTransforms.get(i).transformData(transform);
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}
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double[][] transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
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// Bilinear interpolation1 = new Bilinear(JamArr.doubleToFloat(transformedData2));
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// Bicubic interpolation2 = new Bicubic(JamArr.doubleToFloat(imaged));
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//
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// System.out.println("Len input: " + imaged.length);
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//
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// float[][] resizeArr = interpolation2.resize(Integer.valueOf(80), Integer.valueOf(60));
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//
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// System.out.println("Len resize: " + resizeArr.length);
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System.out.println("Size Java: " + transformedData2.length + " x " + transformedData2[0].length);
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//now save this image to a MATFILE
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// Create MAT file with a scalar in a nested struct
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MatFile matFileWrite = Mat5.newMatFile()
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.addArray("image1originalgrayscalenorm",DLMatFile.array2Matrix(imaged));
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.addArray("image1originalgrayscalenorm",DLMatFile.array2Matrix(imaged))
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.addArray("imagecompressedgrayscalenorm",DLMatFile.array2Matrix(transformedData2));
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// .addArray("imagecompressedgrayscalenorm_nearest",DLMatFile.array2Matrix(JamArr.floatToDouble(resizeArr)));
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// Serialize to disk using default configurations
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Mat5.writeToFile(matFileWrite, "C:\\Users\\Jamie Macaulay\\MATLAB Drive\\MATLAB\\PAMGUARD\\deep_learning\\delphinID\\whistle_image_example_java.mat");
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@ -105,9 +182,81 @@ public class DelphinIDTest {
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assertEquals(false, false);
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}
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}
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@Test
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public void modelInputTest() {
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System.out.println("DelphinID mode test start");
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//ttest the model
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String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
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String relMatPath = "./src/test/resources/rawDeepLearningClassifier/DelphinID/whistle_image_example.mat";
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try {
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//load the model
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SimpleArchiveModel model = new SimpleArchiveModel(new File(modelPath));
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Path path = Paths.get(relMatPath);
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// Create MAT file with a scalar in a nested struct
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MatFile matFile = Mat5.readFromFile(path.toString());
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Matrix array = matFile.getArray("tfvalues");
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//the values for the whistle detector.
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double[][] whistleValues = DLMatFile.matrix2array(array);
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//the image after compression
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array = matFile.getArray("image1compressedgrayscalenorm");
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double[][] compressedWhistleImage = DLMatFile.matrix2array(array);
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//the raw whistle frequency time scatter values
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array = matFile.getArray("tfvalues");
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//the values for the whistle detector.
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double[][] pamguardWhistleImage = whistleScatter2Image(whistleValues);
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System.out.println("Size python: " + compressedWhistleImage.length + " x " + compressedWhistleImage[0].length);
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float[][][] input = new float[1][][];
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input[0] = JamArr.doubleToFloat(compressedWhistleImage);
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System.out.println("Model output: ");
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float[] outputPython = model.runModel(input);
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input[0] = JamArr.doubleToFloat(JamArr.transposeMatrix(pamguardWhistleImage));
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System.out.println("Size Java: " + input[0].length + " x " + input[0][0].length);
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//a bit ugly but works.
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// transformedData2 = JamArr.transposeMatrix(transformedData2);
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float[] outputJava = model.runModel(input);
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for (int i=0; i<outputPython.length; i++) {
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System.out.println(String.format("Output Python: %.4f Java: %.4f",outputPython[i],outputJava[i] ));
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}
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MatFile matFileWrite = Mat5.newMatFile()
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.addArray("imagePython",DLMatFile.array2Matrix(compressedWhistleImage))
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.addArray("imageJava",DLMatFile.array2Matrix(JamArr.transposeMatrix(pamguardWhistleImage)));
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Mat5.writeToFile(matFileWrite, "C:\\Users\\Jamie Macaulay\\MATLAB Drive\\MATLAB\\PAMGUARD\\deep_learning\\delphinID\\whistle_image_inout_test.mat");
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// JamArr.printArray(output);
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} catch (MalformedModelException | IOException e) {
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// TODO Auto-generated catch block
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e.printStackTrace();
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}
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System.out.println("DelphinID mode test end");
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}
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}
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