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https://github.com/PAMGuard/PAMGuard.git
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Finished testing suite for DelphinID
This commit is contained in:
Jamie Mac
parent
9ba3e7e3f7
commit
fa6991eb80
@ -719,6 +719,17 @@ public class PamArrayUtils {
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}
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}
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/**
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* Print an array to the console.
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* @param array to print
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*/
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public static void printArray(float[] array) {
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if (array==null) System.out.println("null");
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for (int i=0; i<array.length; i++) {
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System.out.println(i + ": " + array[i]);
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}
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}
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/**
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* Print an array to the console with no index numbers
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@ -806,14 +817,17 @@ public class PamArrayUtils {
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/**
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* Check whether there are duplicates within an array
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* @param the array.
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* @return true if there are duplicates.
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* @return true if the array is unique
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*/
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public static boolean unique(double[] array) {
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boolean duplicates=false;
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boolean duplicates=true;
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for (int j=0;j<array.length;j++)
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for (int k=j+1;k<array.length;k++)
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if (k!=j && array[k] == array[j])
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duplicates=true;
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if (k!=j && array[k] == array[j]) {
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//if
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duplicates=false;
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return duplicates;
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}
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return duplicates;
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}
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@ -238,6 +238,7 @@ public class DLControl extends PamControlledUnit implements PamSettings {
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//manages downloading models
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modelDownloadManager = new DLDownloadManager();
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// add storage options etc.
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dlBinaryDataSource = new DLResultBinarySource(dlClassifyProcess);
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dlClassifyProcess.getDLPredictionDataBlock().setBinaryDataSource(dlBinaryDataSource);
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@ -128,6 +128,7 @@ public class ArchiveModelWorker extends GenericModelWorker {
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// }
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//generate the transforms from the KetosParams objects.
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System.out.println(modelParams.dlTransforms);
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ArrayList<DLTransform> transforms = DLTransformsFactory.makeDLTransforms(modelParams.dlTransforms);
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// ///HACK here for now to fix an issue with dB and Ketos transforms having zero length somehow...
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@ -3,16 +3,19 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
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import java.io.IOException;
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import java.util.ArrayList;
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import org.jamdev.jdl4pam.utils.DLMatFile;
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import PamUtils.PamArrayUtils;
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import PamguardMVC.DataUnitBaseData;
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import PamguardMVC.PamDataUnit;
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import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
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import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
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import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
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import us.hebi.matlab.mat.format.Mat5;
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import us.hebi.matlab.mat.format.Mat5File;
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import us.hebi.matlab.mat.types.MatFile;
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import us.hebi.matlab.mat.types.Matrix;
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import us.hebi.matlab.mat.types.Struct;
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import whistleClassifier.WhistleContour;
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import whistlesAndMoans.AbstractWhistleDataUnit;
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@ -24,10 +27,10 @@ import whistlesAndMoans.AbstractWhistleDataUnit;
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*/
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public class DelphinIDTest {
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public static DelphinIDWorker prepDelphinIDModel(String modelPath) {
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public static DelphinIDWorkerTest prepDelphinIDModel(String modelPath) {
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//create the delphinID worker.
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DelphinIDWorker delphinIDWorker = new DelphinIDWorker();
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DelphinIDWorkerTest delphinIDWorker = new DelphinIDWorkerTest();
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StandardModelParams params = new StandardModelParams();
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params.modelPath = modelPath;
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@ -134,7 +137,7 @@ public class DelphinIDTest {
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SegmenterDetectionGroup whistleGroup;
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while (segStart<endTime){
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whistleGroup = new SegmenterDetectionGroup(segStart, 1, segEnd, segHop);
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whistleGroup = new SegmenterDetectionGroup(segStart, 1, segEnd, segLen);
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for (AbstractWhistleDataUnit whislte: whistles) {
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whistleStart = whislte.getTimeMilliseconds();
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@ -149,6 +152,8 @@ public class DelphinIDTest {
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group.add(whistleGroup);
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// System.out.println("SegStart: " + (segStart - dataStartMillis));
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segStart = (long) (segStart+segHop);
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segEnd = (long) (segStart+segLen);
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}
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@ -186,6 +191,27 @@ public class DelphinIDTest {
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}
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public static class DelphinIDWorkerTest extends DelphinIDWorker {
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private float[][][] lastModelInput;
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public float[][][] dataUnits2ModelInput(ArrayList<? extends PamDataUnit> dataUnits, float sampleRate, int iChan){
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float[][][] data = super.dataUnits2ModelInput(dataUnits, sampleRate, iChan);
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this.lastModelInput = data;
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return data;
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}
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public float[][][] getLastModelInput() {
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return lastModelInput;
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}
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}
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/**
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* Main class for running the test.
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* @param args - the arguments
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@ -199,15 +225,20 @@ public class DelphinIDTest {
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long dataStartMillis = 1340212413000L;
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//path to the .mat containing whistle contours.
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String whistleContourPath = "D:\\Dropbox\\PAMGuard_dev\\Deep_Learning\\delphinID\\testencounter415\\whistle_contours.mat";
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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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//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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//create MatFile for saving the image data to.
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MatFile matFile = Mat5.newMatFile();
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//get the whislte contours form a .mat file.
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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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segLen, segHop);
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@ -216,7 +247,13 @@ public class DelphinIDTest {
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System.out.println("Segment " + i + " contains " + segments.get(i).getSubDetectionsCount() + " whistles");
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}
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DelphinIDWorker model = prepDelphinIDModel(modelPath);
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//prepare the model - this loads the zip file and loads the correct transforms.
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DelphinIDWorkerTest model = prepDelphinIDModel(modelPath);
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model.setEnableSoftMax(false);
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//initialise strcuture for image data
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Struct imageStruct = Mat5.newStruct(segments.size(), 1);
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for (int i=0; i<segments.size(); i++) {
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@ -229,6 +266,27 @@ 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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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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Matrix image = DLMatFile.array2Matrix(PamArrayUtils.float2Double(model.getLastModelInput()[0]));
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imageStruct.set("image", i, image);
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imageStruct.set("startmillis", i, Mat5.newScalar(aSegment.get(0).getSegmentStartMillis()));
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imageStruct.set("startseconds", i, Mat5.newScalar((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.));
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imageStruct.set("prediction", i, DLMatFile.array2Matrix(PamArrayUtils.float2Double(output)));
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}
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matFile.addArray("whistle_images", imageStruct);
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// Serialize to disk using default configurations
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try {
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Mat5.writeToFile(matFile,matImageSave);
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} catch (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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// for (int i=0; i<whistleContours.size(); i++) {
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@ -236,8 +294,6 @@ public class DelphinIDTest {
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// PamArrayUtils.printArray(whistleContours.get(i).getFreqsHz());
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// }
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}
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}
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@ -109,8 +109,8 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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//contained within the SegmenterDetectionGroup unit.
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Whistles2Image whistles2Image = new Whistles2Image(whistleGroups.get(j), whistleImageParams);
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//add the transform to the current list.
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this.getModelTransforms().add(0, whistles2Image);
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//set the spec transform
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((FreqTransform) this.getModelTransforms().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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@ -2,11 +2,13 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
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import java.awt.Color;
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import java.awt.image.BufferedImage;
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import java.awt.image.Raster;
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import java.util.ArrayList;
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import org.jamdev.jdl4pam.transforms.FreqTransform;
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import org.jamdev.jpamutils.spectrogram.SpecTransform;
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import PamUtils.PamArrayUtils;
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import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
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import whistlesAndMoans.AbstractWhistleDataUnit;
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@ -61,17 +63,21 @@ 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.getDurationInMilliseconds()}, freqLimits, 5.);
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BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getSegmentDuration()/1000.}, freqLimits, 5.);
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double[][] imaged = new double[(int) size[0]][(int) size[1]];
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int color;
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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 = canvas.getRGB(i, j);
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imaged[i][j] = color;
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color = raster.getPixel(i, j, color);
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imaged[i][j] = color[0]/255.; //normalize
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}
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}
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//
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// System.out.println("Original image: ");
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// PamArrayUtils.printArray(imaged);
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specTransform.setSpecData(imaged);
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specTransform.setSampleRate((float) (freqLimits[1]*2));
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@ -90,15 +96,36 @@ public class Whistles2Image extends FreqTransform {
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AbstractWhistleDataUnit whistleContour;
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long segStart = whistleGroup.getTimeMilliseconds();
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long segStart = whistleGroup.getSegmentStartMillis();
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long segEnd = (long) (whistleGroup.getSegmentStartMillis() + whistleGroup.getSegmentDuration());
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// for (int i=0; i<whistleGroup.getSubDetectionsCount(); i++) {
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// whistleContour = (AbstractWhistleDataUnit) whistleGroup.getSubDetection(i);
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//
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// long whistleStart = whistleContour.getTimeMilliseconds();
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// long whistleEnd = (long) (whistleContour.getTimeMilliseconds() + whistleContour.getDurationInMilliseconds());
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//
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// if ((whistleStart>=segStart && whistleStart<segEnd) || ((whistleEnd>=segStart && whistleEnd<segEnd))){
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// //some part of the whistle is in the segment.
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// System.out.println("Whistle in group? true");
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//
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// }
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// else {
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// System.out.println("Whistle in group? false!!!");
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// }
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//
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// }
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for (int i=0; i<whistleGroup.getSubDetectionsCount(); i++) {
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whistleContour = (AbstractWhistleDataUnit) whistleGroup.getSubDetection(i);
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// System.out.println("Whistle start time: " + (segStart - whistleContour.getTimeMilliseconds())/1000. + " end: " + (segStart - whistleContour.getTimeMilliseconds() + whistleContour.getDurationInMilliseconds())/1000.);
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double[][] contourD = new double[whistleContour.getSliceCount()][2];
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for (int j=0; j<whistleContour.getSliceCount(); j++) {
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contourD[j][0] = (segStart - whistleContour.getTimeMilliseconds())/1000. + whistleContour.getTimesInSeconds()[i];
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contourD[j][0] = (whistleContour.getTimeMilliseconds()-segStart)/1000. + whistleContour.getTimesInSeconds()[j];
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contourD[j][1] = whistleContour.getFreqsHz()[j];
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}
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contours.add(contourD);
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@ -158,7 +185,9 @@ public class Whistles2Image extends FreqTransform {
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//Calculate x and y in pixels.
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x = ((points.get(j)[i][0]-xlims[0])/(xlims[1]-xlims[0]))*size[0];
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y = ((points.get(j)[i][0]-xlims[0])/(xlims[1]-xlims[0]))*size[0];
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y = ((points.get(j)[i][1]-ylims[0])/(ylims[1]-ylims[0]))*size[1];
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// System.out.println("Fill oval: x" + x + " y: " + y + " time: " + points.get(j)[i][0]);
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canvas.getGraphics().fillOval((int) (x+markerSize/2),(int) (y-markerSize/2), (int) markerSize,(int) markerSize);
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}
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@ -10,6 +10,17 @@ import PamguardMVC.PamDataUnit;
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*/
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public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
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/**
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* The duration of the segment in millis.
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*/
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private double segDuration;
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/**
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* The start time fo the segment in millis.
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*/
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private long segMillis;
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/**
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* Constructor for a group of detections within a detection. Note that some
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* longer detections (e.g. whistles) may have sections outside the segment.
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@ -22,6 +33,8 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
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public SegmenterDetectionGroup(long timeMilliseconds, int channelBitmap, long startSample, double duration) {
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super(timeMilliseconds, channelBitmap, startSample, (long) duration);
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this.setDurationInMilliseconds(duration);
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this.segMillis =timeMilliseconds;
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this.segDuration = duration;
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}
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@Override
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@ -31,4 +44,13 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
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}
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public long getSegmentStartMillis() {
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return segMillis;
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}
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public double getSegmentDuration() {
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return segDuration;
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}
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}
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