Finished testing suite for DelphinID

src/PamUtils/PamArrayUtils.java

@@ -719,6 +719,17 @@ public class PamArrayUtils {
 		}
 	}
 	
+	/**
+	 * Print an array to the console. 
+	 * @param array to print
+	 */
+	public static void printArray(float[] array) {
+		if (array==null) System.out.println("null"); 
+		for (int i=0; i<array.length; i++) {
+			System.out.println(i + ": " + array[i]);
+		}
+	}
+	
 	
 	/**
 	 * Print an array to the console with no index numbers
@@ -806,14 +817,17 @@ public class PamArrayUtils {
 	/**
 	 * Check whether there are duplicates within an array
 	 * @param the array.
-	 * @return true if there are duplicates. 
+	 * @return true if the array is unique
 	 */
 	public static boolean unique(double[] array) {
-		boolean duplicates=false;
+		boolean duplicates=true;
 		for (int j=0;j<array.length;j++)
 			for (int k=j+1;k<array.length;k++)
-				if (k!=j && array[k] == array[j])
+				if (k!=j && array[k] == array[j]) {
-					duplicates=true;
+					//if 
+					duplicates=false;
+					return duplicates;					
+				}
 		return duplicates;
 	}
 

src/rawDeepLearningClassifier/DLControl.java

@@ -234,9 +234,10 @@ public class DLControl extends PamControlledUnit implements PamSettings {
 		
 		//manages default models
 		defaultModelManager = new DLDefaultModelManager(this);
-		
+				
 		//manages downloading models
 		modelDownloadManager = new DLDownloadManager(); 
+		
 
 		// add storage options etc.
 		dlBinaryDataSource = new DLResultBinarySource(dlClassifyProcess);

src/rawDeepLearningClassifier/dlClassification/archiveModel/ArchiveModelWorker.java

@@ -128,6 +128,7 @@ public class ArchiveModelWorker extends GenericModelWorker {
 			//				}
 
 			//generate the transforms from the KetosParams objects. 
+			System.out.println(modelParams.dlTransforms);
 			ArrayList<DLTransform> transforms =	DLTransformsFactory.makeDLTransforms(modelParams.dlTransforms); 
 
 //			///HACK here for now to fix an issue with dB and Ketos transforms having zero length somehow...

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDTest.java

@@ -3,16 +3,19 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
 import java.io.IOException;
 import java.util.ArrayList;
 
+import org.jamdev.jdl4pam.utils.DLMatFile;
+
 import PamUtils.PamArrayUtils;
 import PamguardMVC.DataUnitBaseData;
+import PamguardMVC.PamDataUnit;
 import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
 import rawDeepLearningClassifier.dlClassification.genericModel.StandardPrediction;
 import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
 import us.hebi.matlab.mat.format.Mat5;
 import us.hebi.matlab.mat.format.Mat5File;
+import us.hebi.matlab.mat.types.MatFile;
 import us.hebi.matlab.mat.types.Matrix;
 import us.hebi.matlab.mat.types.Struct;
-import whistleClassifier.WhistleContour;
 import whistlesAndMoans.AbstractWhistleDataUnit;
 
 
@@ -24,10 +27,10 @@ import whistlesAndMoans.AbstractWhistleDataUnit;
  */
 public class DelphinIDTest {
 
-	public static DelphinIDWorker prepDelphinIDModel(String modelPath) {
+	public static DelphinIDWorkerTest prepDelphinIDModel(String modelPath) {
 
 		//create the delphinID worker. 
-		DelphinIDWorker delphinIDWorker = new DelphinIDWorker();
+		DelphinIDWorkerTest delphinIDWorker = new DelphinIDWorkerTest();
 
 		StandardModelParams params = new StandardModelParams();
 		params.modelPath = modelPath;
@@ -134,7 +137,7 @@ public class DelphinIDTest {
 		SegmenterDetectionGroup whistleGroup;
 		while (segStart<endTime){
 
-			whistleGroup = new SegmenterDetectionGroup(segStart, 1, segEnd, segHop);
+			whistleGroup = new SegmenterDetectionGroup(segStart, 1, segEnd, segLen);
 
 			for (AbstractWhistleDataUnit whislte: whistles) {
 				whistleStart = whislte.getTimeMilliseconds();
@@ -148,6 +151,8 @@ public class DelphinIDTest {
 			}
 
 			group.add(whistleGroup);
+			
+//			System.out.println("SegStart: " + (segStart - dataStartMillis));
 
 			segStart = (long) (segStart+segHop);
 			segEnd = (long) (segStart+segLen);
@@ -185,6 +190,27 @@ public class DelphinIDTest {
 
 
 	}
+	
+	
+	public static class DelphinIDWorkerTest extends DelphinIDWorker {
+		
+		private float[][][] lastModelInput;
+
+
+		public float[][][] dataUnits2ModelInput(ArrayList<? extends PamDataUnit> dataUnits, float sampleRate, int iChan){
+			float[][][] data = super.dataUnits2ModelInput(dataUnits, sampleRate, iChan);
+			
+			this.lastModelInput = data;
+		
+			
+			return data;
+		}
+		
+		public float[][][] getLastModelInput() {
+			return lastModelInput;
+		}
+		
+	}
 
 	/**
 	 * Main class for running the test. 
@@ -199,15 +225,20 @@ public class DelphinIDTest {
 		long dataStartMillis = 1340212413000L;
 
 		//path to the .mat containing whistle contours. 
-		String whistleContourPath = "D:\\Dropbox\\PAMGuard_dev\\Deep_Learning\\delphinID\\testencounter415\\whistle_contours.mat";
+		String whistleContourPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_contours.mat";
 
 		//the path to the model
 		String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_4s_encounter415.zip";
+		
+		//the path to the model
+		String matImageSave = "C:/Users/Jamie Macaulay/MATLAB Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistleimages.mat";
+		
+		//create MatFile for saving the image data to. 
+		MatFile matFile = Mat5.newMatFile();
 
 		//get the whislte contours form a .mat file. 
 		ArrayList<AbstractWhistleDataUnit> whistleContours = getWhistleContoursMAT(whistleContourPath);
 
-
 		//segment the whistle detections
 		ArrayList<SegmenterDetectionGroup> segments =  segmentWhsitleData(whistleContours,  dataStartMillis, 
 				segLen,  segHop);
@@ -216,7 +247,13 @@ public class DelphinIDTest {
 			System.out.println("Segment " + i + " contains " + segments.get(i).getSubDetectionsCount() + " whistles"); 
 		}
 
-		DelphinIDWorker model = prepDelphinIDModel(modelPath);
+		//prepare the model - this loads the zip file and loads the correct transforms. 
+		DelphinIDWorkerTest model = prepDelphinIDModel(modelPath);
+		model.setEnableSoftMax(false);
+
+		
+		//initialise strcuture for image data
+		Struct imageStruct = Mat5.newStruct(segments.size(), 1);
 
 		for (int i=0; i<segments.size(); i++) {
 
@@ -228,16 +265,35 @@ public class DelphinIDTest {
 			ArrayList<StandardPrediction> predicition = model.runModel(aSegment, sampleRate, 1);		
 
 			float[] output =  predicition.get(0).getPrediction();
+		
+			System.out.println();
+			System.out.print("Segment: " +(aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.);
+			for (int j=0; j<output.length; j++) {
+				System.out.print("  " + output[j]); 
+			}
+
+			Matrix image = DLMatFile.array2Matrix(PamArrayUtils.float2Double(model.getLastModelInput()[0]));
+			imageStruct.set("image", i, image);
+			imageStruct.set("startmillis", i, Mat5.newScalar(aSegment.get(0).getSegmentStartMillis()));
+			imageStruct.set("startseconds", i, Mat5.newScalar((aSegment.get(0).getSegmentStartMillis()-dataStartMillis)/1000.));
+			imageStruct.set("prediction", i, DLMatFile.array2Matrix(PamArrayUtils.float2Double(output)));
 
 		}
 
+		matFile.addArray("whistle_images", imageStruct);
+		// Serialize to disk using default configurations
+		try {
+			Mat5.writeToFile(matFile,matImageSave);
+		} catch (IOException e) {
+			// TODO Auto-generated catch block
+			e.printStackTrace();
+		}
+		
 		//		for (int i=0; i<whistleContours.size(); i++) {
 		//			System.out.println("Whislte: " + i);
 		//			PamArrayUtils.printArray(whistleContours.get(i).getFreqsHz());
 		//		}
 
-
-
 	}
 
 }

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWorker.java

@@ -109,8 +109,8 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 			//contained within the SegmenterDetectionGroup unit. 
 			Whistles2Image whistles2Image = new Whistles2Image(whistleGroups.get(j), whistleImageParams);
 
-			//add the transform to the current list.
+			//set the spec transform
-			this.getModelTransforms().add(0, whistles2Image);
+			((FreqTransform) this.getModelTransforms().get(0)).setSpecTransfrom(whistles2Image.getSpecTransfrom());
 
 			//process all the transforms. 
 			DLTransform transform = modelTransforms.get(0); 

src/rawDeepLearningClassifier/dlClassification/delphinID/Whistles2Image.java

@@ -2,11 +2,13 @@ package rawDeepLearningClassifier.dlClassification.delphinID;
 
 import java.awt.Color;
 import java.awt.image.BufferedImage;
+import java.awt.image.Raster;
 import java.util.ArrayList;
 
 import org.jamdev.jdl4pam.transforms.FreqTransform;
 import org.jamdev.jpamutils.spectrogram.SpecTransform;
 
+import PamUtils.PamArrayUtils;
 import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
 import whistlesAndMoans.AbstractWhistleDataUnit;
 
@@ -61,17 +63,21 @@ public class Whistles2Image extends FreqTransform {
 		ArrayList<double[][]> points = whistContours2Points(whistleGroup);
 
 		//does not work becaue it has to be on the AWT thread. 
-		BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getDurationInMilliseconds()}, freqLimits,  5.);
+		BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getSegmentDuration()/1000.}, freqLimits,  5.);
 
 		double[][] imaged = new double[(int) size[0]][(int) size[1]];
 
-		int color;
+		float[] color = new float[3];
+		Raster raster = canvas.getData();
 		for (int i=0; i<imaged.length; i++) {
 			for (int j=0; j<imaged[0].length; j++) {
-				color = canvas.getRGB(i, j);
+				color = raster.getPixel(i, j, color);
-				imaged[i][j] = color;
+				imaged[i][j] = color[0]/255.; //normalize
 			}
 		}
+//		
+//		System.out.println("Original image: "); 
+//		PamArrayUtils.printArray(imaged);
 
 		specTransform.setSpecData(imaged);
 		specTransform.setSampleRate((float) (freqLimits[1]*2)); 
@@ -90,15 +96,36 @@ public class Whistles2Image extends FreqTransform {
 
 		AbstractWhistleDataUnit whistleContour;
 
-		long segStart = whistleGroup.getTimeMilliseconds();
+		long segStart = whistleGroup.getSegmentStartMillis();
+		long segEnd = (long) (whistleGroup.getSegmentStartMillis() + whistleGroup.getSegmentDuration());
+
+		
+//		for (int i=0; i<whistleGroup.getSubDetectionsCount(); i++) {
+//			whistleContour = (AbstractWhistleDataUnit) whistleGroup.getSubDetection(i);
+//
+//			long whistleStart = whistleContour.getTimeMilliseconds();
+//			long whistleEnd = (long) (whistleContour.getTimeMilliseconds() + whistleContour.getDurationInMilliseconds());
+//
+//			if ((whistleStart>=segStart && whistleStart<segEnd) || ((whistleEnd>=segStart && whistleEnd<segEnd))){
+//				//some part of the whistle is in the segment. 
+//				System.out.println("Whistle in group?  true");
+//
+//			}
+//			else {
+//				System.out.println("Whistle in group?  false!!!");
+//			}
+//
+//		}
 
 		for (int i=0; i<whistleGroup.getSubDetectionsCount(); i++) {
 
 			whistleContour = (AbstractWhistleDataUnit) whistleGroup.getSubDetection(i);
+//			System.out.println("Whistle start time: " + (segStart - whistleContour.getTimeMilliseconds())/1000. +  " end: " + (segStart - whistleContour.getTimeMilliseconds() + whistleContour.getDurationInMilliseconds())/1000.);
+
 
 			double[][] contourD = new double[whistleContour.getSliceCount()][2];
 			for (int j=0; j<whistleContour.getSliceCount(); j++) {
-				contourD[j][0] = (segStart - whistleContour.getTimeMilliseconds())/1000. + whistleContour.getTimesInSeconds()[i];
+				contourD[j][0] = (whistleContour.getTimeMilliseconds()-segStart)/1000. + whistleContour.getTimesInSeconds()[j];
 				contourD[j][1] = whistleContour.getFreqsHz()[j];
 			}
 			contours.add(contourD);
@@ -158,7 +185,9 @@ public class Whistles2Image extends FreqTransform {
 
 				//Calculate x and y in pixels. 
 				x = ((points.get(j)[i][0]-xlims[0])/(xlims[1]-xlims[0]))*size[0];
-				y = ((points.get(j)[i][0]-xlims[0])/(xlims[1]-xlims[0]))*size[0];
+				y = ((points.get(j)[i][1]-ylims[0])/(ylims[1]-ylims[0]))*size[1];
+				
+//				System.out.println("Fill oval: x" + x + " y: " + y + " time: " + points.get(j)[i][0]);
 
 				canvas.getGraphics().fillOval((int) (x+markerSize/2),(int) (y-markerSize/2), (int) markerSize,(int) markerSize);
 			}

src/rawDeepLearningClassifier/segmenter/SegmenterDataBlock.java

@@ -21,5 +21,5 @@ public class SegmenterDataBlock extends PamDataBlock<GroupedRawData> {
 		//need this to notify classifier in viewer mode. 
 		return true;
 	}
-
+	
 }

src/rawDeepLearningClassifier/segmenter/SegmenterDetectionGroup.java

@@ -10,6 +10,17 @@ import PamguardMVC.PamDataUnit;
  */
 public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 
+	
+	/**
+	 * The duration of the segment in millis. 
+	 */
+	private double segDuration;
+	
+	/**
+	 * The start time fo the segment in millis.
+	 */
+	private long segMillis;
+
 	/**
 	 * Constructor for a group of detections within a detection. Note that some
 	 * longer detections (e.g. whistles) may have sections outside the segment.
@@ -22,6 +33,8 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 	public SegmenterDetectionGroup(long timeMilliseconds, int channelBitmap, long startSample, double duration) {
 		super(timeMilliseconds, channelBitmap, startSample, (long) duration);
 		this.setDurationInMilliseconds(duration);
+		this.segMillis =timeMilliseconds;
+		this.segDuration = duration;
 	}
 	
 	@Override
@@ -29,6 +42,15 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 		//segmetns share sub detections
 		return true;
 	}
+	
+	
+	public long getSegmentStartMillis() {
+		return segMillis;
+	}
+	
+	public double getSegmentDuration() {
+		return segDuration;
+	}
 
 
 }