DelphinID training script

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDTest.java

@@ -65,7 +65,9 @@ public class DelphinIDTest {
 		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_model_2/whistle_4s_415.zip";
+//		String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
+		String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_3/whistle_4s_415_f5.zip";
+
 		
 		//the path to the model
 		String matImageSave = "C:/Users/Jamie Macaulay/MATLAB Drive/MATLAB/PAMGUARD/deep_learning/delphinID/whistleimages.mat";

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDUtils.java

@@ -224,28 +224,34 @@ public class DelphinIDUtils {
 
 		//segment the whistle detections
 		ArrayList<SegmenterDetectionGroup> segments =  DelphinIDUtils.segmentWhsitleData(whistles,  whistles.get(0).getTimeMilliseconds(), segLen, segHop);
-
+		
 		float[][][] images = worker.dataUnits2ModelInput(segments,  (float) sampleRate,  0);
 
 		float[][] image;
 		BufferedImage bfImage;
+		double density;
 		for (int k=0; k<images.length; k++) {
+			
+			if (segments.get(k).getSubDetectionsCount()<1) {
+				continue;
+			}
 			image = images[k];
 
-			bfImage = new BufferedImage(image.length, image[0].length, BufferedImage.TYPE_INT_RGB);
+			bfImage = new BufferedImage(image[0].length, image.length, BufferedImage.TYPE_INT_RGB);
-			
+
-//			System.out.println("Max of image: " + PamArrayUtils.minmax(image)[1]);
+			//			System.out.println("Max of image: " + PamArrayUtils.minmax(image)[1]);
 
 			for(int i = 0; i < image.length; i++) {
 				for(int j = 0; j < image[0].length; j++) {
 					Color myRGB = new Color(image[i][j], image[i][j], image[i][j]);
 					int rgb = myRGB.getRGB();
-					bfImage.setRGB(i, j, rgb);
+					bfImage.setRGB(j,i, rgb);
 				}
 			}
 
+			density = getDensity(segments.get(k)); 
 			//now save the image 
-			String outputPath = outName + "_" + k + ".png";
+			String outputPath = String.format("%s_d%.2f_%d.png", outName, density, k);
 
 			File outputfile = new File(outputPath);
 
@@ -255,30 +261,51 @@ public class DelphinIDUtils {
 				// TODO Auto-generated catch block
 				e.printStackTrace();
 			}
-
 		}
-
+	}
-
+	
-
+	/**
+	 * Calculate the density of whistles for a segmenter group in the absence of a known fft length and hop. 
+	 * @param group - the group
+	 * @return
+	 */
+	private static double getDensity(SegmenterDetectionGroup group) {
+		//number of whistle bins/number of time bins
+		ArrayList<double[][]> contour = Whistles2Image.whistContours2Points(group);
+		
+		//time bin length from the first contour
+		double[] times = new double[contour.get(0).length-1];
+		for (int i=0; i<times.length; i++) {
+			times[i]=1000.*(contour.get(0)[i+1][0] - contour.get(0)[i][0]);
+		}
+		
+		double timebinMillis = PamArrayUtils.mean(times);
+		
+		double nBins = group.getSegmentDuration()/timebinMillis;
+		
+		double nwhistleBins = 0;
+		for (int i=0; i<contour.size(); i++) {
+			nwhistleBins+=contour.get(i).length;
+		}
+		
+//		System.out.println("nwhistleBins: " +nwhistleBins + "nBins: " + nBins + " timebinMillis: " + timebinMillis);
+			
+		return nwhistleBins/nBins;
 	}
 
-	public static void main(String[] args) {
+	/**
-
+	 * Generate training images for DelphinID
-		//the whsitle contours as csv files.
+	 * @param modelPath
-		String whistlefolder = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD";
+	 * @param whistlefolder
-
+	 * @param imageFolder
-		//the image folder to save to.
+	 * @param lineWidth - the line width in pixels to use
-		String imageFolder = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD_Images";
+	 */
-
+	private static void generateTrainingData(String modelPath, String whistlefolder, String imageFolder, double lineWidth) {
-		//the path to the model
+		DelphinIDWorkerTest model = DelphinIDUtils.prepDelphinIDModel(modelPath);
-		String modelPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
-
-		//prepare the model - this loads the zip file and loads the correct transforms. 
-		DelphinIDWorkerTest model;
-		
-		model = DelphinIDUtils.prepDelphinIDModel(modelPath);
 		model.setEnableSoftMax(false);
 
+		model.getWhistleImageParams().lineWidth=lineWidth;
+
 		FileList filelist = new FileList();
 
 		File folder = new File(whistlefolder);
@@ -291,12 +318,17 @@ public class DelphinIDUtils {
 
 					System.out.println("Directory " + listOfFiles[i].getName());
 
-					File outFolder = new File(imageFolder + File.separator + listOfFiles[i].getName());
+
-					outFolder.mkdir();//make the out folder directory
 
 					try {
 
 						File file  = new File(listOfFiles[i].getPath() + File.separator + "whistles.mat");
+
+						if (!file.exists()) {
+							System.out.println("No whistles.mat for " + listOfFiles[i].getName()); 
+							continue;
+						}
+
 						Mat5File matFile = Mat5.readFromFile(file);
 
 						Struct whistlesStruct = matFile.getStruct("whistles");
@@ -307,11 +339,15 @@ public class DelphinIDUtils {
 
 						List<String> fieldNames = whistlesStruct.getFieldNames();
 
+						File outFolder = new File(imageFolder + File.separator + listOfFiles[i].getName());
+						outFolder.mkdir();//make the out folder directory
+
 						Struct whistecontours;
 						for (String name: fieldNames) {
-							System.out.println("Generating images for recording " + name + " from " + listOfFiles[i].getName());
+							System.out.println("Generating images for recording " + name + " from " + listOfFiles[i].getName() + " " + lineWidth);
 							if (!name.equals("fftlen") && !name.equals("ffthop") && !name.equals("samplerate")) {
 								whistecontours = whistlesStruct.get(name);
+
 								generateImages( whistecontours,  (outFolder + File.separator + name) , model, fftLen, fftHop, sampleRate);
 							}
 						}
@@ -327,4 +363,32 @@ public class DelphinIDUtils {
 		}
 	}
 
+	public static void main(String[] args) {
+
+		//		double[] density = new double[] {0.15 - 1.5}; 
+
+		//number of whistle bins/number of time bins; either 16 or 21
+		//the e contours as csv files.
+		//		String whistlefolder = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD";
+//		String whistlefolder = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD_examples/contours";
+		String whistlefolder = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD/contours";
+
+		//the image folder to save to.
+		//		String imageFolder = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD_Images";
+//		String imageFolder = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/training/WMD_examples/images";
+		String imageFolder = "C:/Users/Jamie Macaulay/Desktop/Tristan_training_images/contour_images";
+
+		//the path to the model
+		//		String modelPath = "/Users/au671271/Library/CloudStorage/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
+		String modelPath = "D:/Dropbox/PAMGuard_dev/Deep_Learning/delphinID/testencounter415/whistle_model_2/whistle_4s_415.zip";
+
+		double[] lineWidths = new double[] {6, 7, 10, 15, 20};
+
+		for (double lineWidth:lineWidths) {
+			String imageFolderWidth = (imageFolder + "_"+ String.format("%d",(int)lineWidth));
+			new File(imageFolderWidth).mkdir();
+			generateTrainingData( modelPath,  whistlefolder,  imageFolderWidth, lineWidth);
+		}
+	}
+
 }

src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWorker.java

@@ -42,6 +42,15 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 	 */
 	private Whistle2ImageParams whistleImageParams;
 
+	/**
+	 * Get the whislte to image parameters. 
+	 * 
+	 * @return
+	 */
+	public Whistle2ImageParams getWhistleImageParams() {
+		return whistleImageParams;
+	}
+
 
 	@Override
 	public void prepModel(StandardModelParams dlParams, DLControl dlControl) {
@@ -87,10 +96,12 @@ public class DelphinIDWorker extends  ArchiveModelWorker {
 				freqLimits[1] = jsonObjectParams.getFloat("maxfreq"); 
 				size[0] = jsonObjectParams.getInt("widthpix"); 
 				size[1] = jsonObjectParams.getInt("heightpix"); 
+				double lineWidth = jsonObjectParams.getDouble("linewidthpix"); 
 
 				Whistle2ImageParams whistle2ImageParmas = new Whistle2ImageParams();
 				whistle2ImageParmas.freqLimits = freqLimits;
 				whistle2ImageParmas.size = size;
+				whistle2ImageParmas.lineWidth = lineWidth;
 
 				return whistle2ImageParmas;
 			}

src/rawDeepLearningClassifier/dlClassification/delphinID/Whistles2Image.java

@@ -36,7 +36,7 @@ public class Whistles2Image extends FreqTransform {
 		//		double[] freqLimits = 	new double[] {params[0].doubleValue(), params[1].doubleValue()};
 		//		double[] size =			new double[] {params[2].doubleValue(), params[3].doubleValue()};
 
-		SpecTransform specTransform = whistleGroupToImage( whistleGroup,  params.freqLimits, params.size);
+		SpecTransform specTransform = whistleGroupToImage( whistleGroup,  params.freqLimits, params.size, params.lineWidth);
 
 		this.setSpecTransfrom(specTransform);
 		this.setFreqlims(params.freqLimits);
@@ -50,7 +50,7 @@ public class Whistles2Image extends FreqTransform {
 	 * @param freqLimits - the frequency limits
 	 * @return the spectrogram transform. 
 	 */
-	private SpecTransform whistleGroupToImage(SegmenterDetectionGroup whistleGroup, double[] freqLimits, double[] size) {
+	private SpecTransform whistleGroupToImage(SegmenterDetectionGroup whistleGroup, double[] freqLimits, double[] size, double lineWidth) {
 
 		SpecTransform specTransform = new SpecTransform(); 
 
@@ -65,7 +65,7 @@ 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.getSegmentDuration()/1000.}, freqLimits,   10.);
+		BufferedImage canvas = makeScatterImage(points, size, new double[]{0, whistleGroup.getSegmentDuration()/1000.}, freqLimits, lineWidth);
 
 		double[][] imaged = new double[(int) size[0]][(int) size[1]];
 
@@ -90,9 +90,9 @@ public class Whistles2Image extends FreqTransform {
 	/**
 	 * Convert a list of whistle contours to a list of time and frequency points. 
 	 * @param whistleGroup - list of whistle contours within a detection group. 
-	 * @return an array with time (milliseconds from start of group) and frequency (Hz)
+	 * @return an array with time (seconds from start of group) and frequency (Hz)
 	 */
-	private ArrayList<double[][]> whistContours2Points(SegmenterDetectionGroup whistleGroup) {
+	public static ArrayList<double[][]> whistContours2Points(SegmenterDetectionGroup whistleGroup) {
 
 		ArrayList<double[][]> contours = new ArrayList<double[][]>();
 
@@ -212,7 +212,12 @@ public class Whistles2Image extends FreqTransform {
 		 */
 		public double[] freqLimits;
 
-		public double[] size;	
+		public double[] size;
+		
+		/**
+		 * The line width to draw in pixels
+		 */
+		public double lineWidth = 10;
 
 	}
 	

src/rawDeepLearningClassifier/layoutFX/defaultModels/DefaultModelPane.java

@@ -35,6 +35,9 @@ import rawDeepLearningClassifier.defaultModels.DLModel;
  */
 public class DefaultModelPane extends PamBorderPane{
 	
+	private static final double PREF_WIDTH = 200;
+
+
 	/**
 	 * Reference to the deafult model manager that contains the default models. 
 	 */
@@ -74,7 +77,7 @@ public class DefaultModelPane extends PamBorderPane{
 //		vBox.setPrefWidth(120);
 
 		hidingPaneContent= new PamBorderPane(); 
-		hidingPaneContent.setPrefWidth(150);
+		hidingPaneContent.setPrefWidth(PREF_WIDTH);
 		 hidingPane = new HidingPane(Side.RIGHT,  hidingPaneContent,  vBox, true, 0); 
 	
 		PamButton button;
@@ -101,12 +104,15 @@ public class DefaultModelPane extends PamBorderPane{
 		}
 				
 		hidingPane.setStyle("-fx-background-color: -fx-base");
+		hidingPane.setPrefWidth(PREF_WIDTH);
 //		this.setStyle("-fx-background-color: -fx-base");
 
 		PamStackPane mainHolder = new PamStackPane(); 
 		mainHolder.getChildren().addAll(vBox, hidingPane); 
 		StackPane.setAlignment(hidingPane, Pos.TOP_RIGHT);
 		
+		mainHolder.setPrefWidth(PREF_WIDTH);
+		
 		return mainHolder;
 		
 	}

src/rawDeepLearningClassifier/segmenter/SegmenterDetectionGroup.java

@@ -50,6 +50,10 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
 		return segMillis;
 	}
 	
+	/**
+	 * Get the segment duration in milliseconds. 
+	 * @return the segment duration in millis. 
+	 */
 	public double getSegmentDuration() {
 		return segDuration;
 	}