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Updates to deep learning module structure get delphinID working

Browse Source 
Also sorted out some build issues with Tethys in the POM
This commit is contained in:
Jamie Mac 2024-04-22 16:59:34 +01:00
parent 85ae261a4c
commit 9b77a97a17
21 changed files with 448 additions and 314 deletions
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3
.classpath
View File

@ -6,8 +6,9 @@
<attribute name="maven.pomderived" value="true"/>
</attributes>
</classpathentry>
<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-21">
<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/jdk-21.0.2.13-hotspot">
<attributes>
<attribute name="module" value="true"/>
<attribute name="maven.pomderived" value="true"/>
</attributes>
</classpathentry>

1
.gitignore vendored
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@ -108,3 +108,4 @@ settings.xml
.classpath
.classpath
.classpath
.classpath

100
pom.xml
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@ -4,7 +4,7 @@
<modelVersion>4.0.0</modelVersion>
<groupId>org.pamguard</groupId>
<artifactId>Pamguard</artifactId>
<version>2.02.11b</version>
<version>2.02.11c</version>
<name>Pamguard</name>
<description>Pamguard using Maven to control dependencies</description>
<url>www.pamguard.org</url>
@ -19,7 +19,7 @@
<javafx.version>21</javafx.version>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<!-- Thethys version control -->
<!-- Tethys version control -->
<jaxb.runtime.version>2.4.0-b180830.0438</jaxb.runtime.version>
<jaxb.api.version>2.4.0-b180830.0359</jaxb.api.version>
<jaxb.xjc.version>2.4.0-b180830.0438</jaxb.xjc.version>
@ -74,59 +74,63 @@
</dependencies>
</plugin>
<!-- Set up javafx properly. -->
<!-- Set up javafx properly. -->
<plugin>
<groupId>org.openjfx</groupId>
<artifactId>javafx-maven-plugin</artifactId>
<version>0.0.8</version>
</plugin>
<!-- Maven Shade plugin - for creating the uberjar / fatjar -->
<!-- see http://maven.apache.org/plugins/maven-shade-plugin/index.html for details -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.2.1</version>
<configuration>
<transformers>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
</transformers>
</configuration>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<manifestEntries>
<Main-Class>pamguard.Pamguard</Main-Class>
<Class-Path>.</Class-Path> <!-- this is from the orig ant build file -->
<SplashScreen-Image>Resources/pgBlankSplash.png</SplashScreen-Image> <!-- this is from the orig ant build file -->
</manifestEntries>
</transformer>
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
</transformers>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude> <!-- get rid of manifests from library jars - also done in orig ant build file -->
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
<!-- Maven Shade plugin - for creating the uberjar / fatjar -->
<!-- see http://maven.apache.org/plugins/maven-shade-plugin/index.html for details -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.2.1</version>
<configuration>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
</transformers>
<filters>
<!-- Moved these filters up to here from where they were int <executions><execution><configurtion>
based on advice at https://stackoverflow.com/questions/34738653/maven-shade-plugin-does-not-exclude-the-manifest-signature-files
and the are now working -->
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude> <!-- get rid of manifests from library jars - also done in orig ant build file -->
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<manifestEntries>
<Main-Class>pamguard.Pamguard</Main-Class>
<Class-Path>.</Class-Path> <!-- this is from the orig ant build file -->
<SplashScreen-Image>Resources/pgBlankSplash.png</SplashScreen-Image> <!-- this is from the orig ant build file -->
</manifestEntries>
</transformer>
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
</transformers>
</configuration>
</execution>
</executions>
</plugin>
<!-- The Maven-JDEPS plugin, to analyze necessary JDK dependencies. See
this site for details: https://maven.apache.org/plugins/maven-jdeps-plugin/index.html -->
<!-- The Maven-JDEPS plugin, to analyze necessary JDK dependencies. See
this site for details: https://maven.apache.org/plugins/maven-jdeps-plugin/index.html -->
<plugin>
<groupId>com.github.marschall</groupId>
<artifactId>jdeps-maven-plugin</artifactId>

2
src/Acquisition/pamAudio/WavFileInputStream.java
View File

@ -59,7 +59,7 @@ public class WavFileInputStream extends AudioInputStream {
//wav files (this is in fact tha standard emthod for calculated blockalign)
int blockAlign = wavHeader.getNChannels() * (wavHeader.getBitsPerSample() / 8);
System.out.println("NFRAMES: " + nFrames + " "+ wavHeader.getDataSize() + " " + wavHeader.getBlockAlign() + " "+blockAlign );
// System.out.println("NFRAMES: " + nFrames + " "+ wavHeader.getDataSize() + " " + wavHeader.getBlockAlign() + " "+blockAlign );
Encoding encoding = getEncoding(wavHeader.getFmtTag());
if (encoding == null) {

4
src/rawDeepLearningClassifier/dlClassification/DLClassiferModel.java
View File

@ -9,10 +9,10 @@ import org.jamdev.jdl4pam.transforms.DLTransfromParams;
import org.jamdev.jdl4pam.transforms.SimpleTransform;
import org.jamdev.jdl4pam.transforms.SimpleTransformParams;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.layoutFX.DLCLassiferModelUI;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import warnings.PamWarning;
/**
@ -32,7 +32,7 @@ public interface DLClassiferModel {
*
* @return the deep learning model.
*/
public ArrayList<? extends PredictionResult> runModel(ArrayList<GroupedRawData> rawDataUnit);
public ArrayList<? extends PredictionResult> runModel(ArrayList<? extends PamDataUnit> rawDataUnit);
/**
* Prepare the model. This is called on PAMGuard start up.

2
src/rawDeepLearningClassifier/dlClassification/DLClassifyProcess.java
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@ -18,8 +18,8 @@ import rawDeepLearningClassifier.layoutFX.DLDetectionGraphics;
import rawDeepLearningClassifier.layoutFX.DLGraphics;
import rawDeepLearningClassifier.logging.DLAnnotation;
import rawDeepLearningClassifier.logging.DLAnnotationType;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import rawDeepLearningClassifier.segmenter.SegmenterDataBlock;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
/**
* The deep learning classification process. This takes a segment of raw data from the segmenter.

16
src/rawDeepLearningClassifier/dlClassification/DLTaskThread.java
View File

@ -5,14 +5,14 @@ import java.util.Collections;
import java.util.List;
import java.util.concurrent.atomic.AtomicBoolean;
import rawDeepLearningClassifier.dlClassification.animalSpot.SoundSpotResult;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
/**
* Creates a que for grouped data units for classiifcation.
* @author au671271
* @author Jamie Macaulay
*
*/
public abstract class DLTaskThread extends Thread {
@ -28,7 +28,7 @@ public abstract class DLTaskThread extends Thread {
/**
* Holds a list of segmented raw data units which need to be classified.
*/
private List<ArrayList<GroupedRawData>> queue = Collections.synchronizedList(new ArrayList<ArrayList<GroupedRawData>>());
private List<ArrayList<? extends PamDataUnit>> queue = Collections.synchronizedList(new ArrayList<ArrayList<? extends PamDataUnit>>());
public DLTaskThread(DLModelWorker soundSpotWorker) {
@ -50,7 +50,7 @@ public abstract class DLTaskThread extends Thread {
try {
if (queue.size()>0) {
System.out.println("DL TASK THREAD: " + "The queue size is " + queue.size());
ArrayList<GroupedRawData> groupedRawData = queue.remove(0);
ArrayList<? extends PamDataUnit> groupedRawData = queue.remove(0);
ArrayList<GenericPrediction> modelResult = dlModelWorker.runModel(groupedRawData,
groupedRawData.get(0).getParentDataBlock().getSampleRate(), 0); //TODO channel?
@ -79,17 +79,17 @@ public abstract class DLTaskThread extends Thread {
* @param soundSpotResult - the new result.
* @param groupedRawData - the grouped data unit.
*/
public abstract void newDLResult(GenericPrediction soundSpotResult, GroupedRawData groupedRawData);
public abstract void newDLResult(GenericPrediction soundSpotResult, PamDataUnit groupedRawData);
/**
* Get the grouped data queue
* @return
*/
public List<ArrayList<GroupedRawData>> getQueue() {
public List<ArrayList<? extends PamDataUnit>> getQueue() {
return queue;
}
public void setQueue(List<ArrayList<GroupedRawData>> queue) {
public void setQueue(List<ArrayList<? extends PamDataUnit>> queue) {
this.queue = queue;
}

20
src/rawDeepLearningClassifier/dlClassification/StandardClassifierModel.java
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@ -12,6 +12,7 @@ import PamController.PamSettings;
import PamDetection.RawDataUnit;
import PamUtils.PamArrayUtils;
import PamUtils.PamCalendar;
import PamguardMVC.PamDataUnit;
import javafx.stage.FileChooser.ExtensionFilter;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
@ -20,13 +21,13 @@ import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericDLClassifier;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.layoutFX.DLSettingsPane;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import warnings.PamWarning;
import warnings.WarningSystem;
/**
* A useful abstract class for standard models which are a file or URL that is loaded, have a UI and
* utilise PAMSettings to save settings state.
* utilise PAMSettings to save settings state. These models only accept raw sound data segments.
*/
public abstract class StandardClassifierModel implements DLClassiferModel, PamSettings {
@ -56,12 +57,15 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
@Override
public ArrayList<? extends PredictionResult> runModel(ArrayList<GroupedRawData> groupedRawData) {
@SuppressWarnings("rawtypes")
public ArrayList<? extends PredictionResult> runModel( ArrayList<? extends PamDataUnit> groupedRawData) {
if (getDLWorker().isModelNull()) return null;
// System.out.println("SoundSpotClassifier: PamCalendar.isSoundFile(): "
// + PamCalendar.isSoundFile() + " " + (PamCalendar.isSoundFile() && !forceQueue));
/**
* If a sound file is being analysed then Ketos can go as slow as it wants. if used in real time
* then there is a buffer with a maximum queue size.
@ -165,7 +169,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
return DLStatus.NO_MODEL_LOADED;
}
// if continous data is selected and all classes are false then this is a
// if continuous data is selected and all classes are false then this is a
// potential mistake...
if (dlControl.getSettingsPane().getSelectedParentDataBlock().getUnitClass() == RawDataUnit.class
&& (getDLParams().binaryClassification==null || PamArrayUtils.isAllFalse(getDLParams().binaryClassification))){
@ -200,7 +204,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
}
@Override
public void newDLResult(GenericPrediction soundSpotResult, GroupedRawData groupedRawData) {
public void newDLResult(GenericPrediction soundSpotResult, PamDataUnit groupedRawData) {
soundSpotResult.setClassNameID(GenericDLClassifier.getClassNameIDs(getDLParams()));
soundSpotResult.setBinaryClassification(GenericDLClassifier.isBinaryResult(soundSpotResult, getDLParams()));
newResult(soundSpotResult, groupedRawData);
@ -220,8 +224,10 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
* @param modelResult - the model result;
* @param groupedRawData - the grouped raw data.
*/
protected void newResult(GenericPrediction modelResult, GroupedRawData groupedRawData) {
this.dlControl.getDLClassifyProcess().newModelResult(modelResult, groupedRawData);
protected void newResult(GenericPrediction modelResult, PamDataUnit groupedRawData) {
if (groupedRawData instanceof GroupedRawData) {
this.dlControl.getDLClassifyProcess().newModelResult(modelResult, (GroupedRawData) groupedRawData);
}
}
//
// @Override

106
src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDClassifier.java
View File

@ -1,8 +1,24 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import java.io.Serializable;
import java.net.URI;
import java.util.ArrayList;
import PamController.PamControlledUnitSettings;
import PamController.PamSettings;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.dlClassification.DLClassName;
import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
import rawDeepLearningClassifier.dlClassification.PredictionResult;
import rawDeepLearningClassifier.dlClassification.StandardClassifierModel;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelClassifier;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.layoutFX.DLCLassiferModelUI;
/**
* A classifier based on the delphinID method which uses whistle contours to predict
@ -11,31 +27,77 @@ import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorke
* @author Jamie Macaulay
*
*/
public class DelphinIDClassifier extends ArchiveModelClassifier{
private static final String MODEL_NAME = "delphinID";
/**
* Reference to the worker
*/
private DelphinIDWorker delphinIDWorker;
public class DelphinIDClassifier extends StandardClassifierModel {
public DelphinIDClassifier(DLControl dlControl) {
super(dlControl);
}
@Override
public String getName() {
return MODEL_NAME;
}
@Override
public ArchiveModelWorker getModelWorker() {
if (delphinIDWorker==null) {
delphinIDWorker= new DelphinIDWorker();
}
return delphinIDWorker;
// TODO Auto-generated constructor stub
}
@Override
public boolean isModelType(URI model) {
// TODO Auto-generated method stub
return false;
}
@Override
public String getName() {
// TODO Auto-generated method stub
return null;
}
@Override
public DLCLassiferModelUI getModelUI() {
// TODO Auto-generated method stub
return null;
}
@Override
public Serializable getDLModelSettings() {
// TODO Auto-generated method stub
return null;
}
@Override
public String getUnitType() {
// TODO Auto-generated method stub
return null;
}
@Override
public Serializable getSettingsReference() {
// TODO Auto-generated method stub
return null;
}
@Override
public long getSettingsVersion() {
// TODO Auto-generated method stub
return 0;
}
@Override
public boolean restoreSettings(PamControlledUnitSettings pamControlledUnitSettings) {
// TODO Auto-generated method stub
return false;
}
@Override
public String getUnitName() {
// TODO Auto-generated method stub
return null;
}
@Override
public DLModelWorker<GenericPrediction> getDLWorker() {
// TODO Auto-generated method stub
return null;
}
@Override
public StandardModelParams getDLParams() {
// TODO Auto-generated method stub
return null;
}
}
}

12
src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDPrediction.java Normal file
View File

@ -0,0 +1,12 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
public class DelphinIDPrediction extends GenericPrediction{
public DelphinIDPrediction(float[] prob) {
super(prob);
// TODO Auto-generated constructor stub
}
}

35
src/rawDeepLearningClassifier/dlClassification/delphinID/DelphinIDWorker.java
View File

@ -1,8 +1,39 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
import rawDeepLearningClassifier.dlClassification.genericModel.DLModelWorker;
public class DelphinIDWorker extends ArchiveModelWorker{
public class DelphinIDWorker extends DLModelWorker<DelphinIDPrediction>{
@Override
public float[] runModel(float[][][] transformedDataStack) {
// TODO Auto-generated method stub
return null;
}
@Override
public boolean isModelNull() {
// TODO Auto-generated method stub
return false;
}
@Override
public DelphinIDPrediction makeModelResult(float[] prob, double time) {
// TODO Auto-generated method stub
return null;
}
@Override
public void prepModel(StandardModelParams delphinIDParams, DLControl dlControl) {
// TODO Auto-generated method stub
}
@Override
public void closeModel() {
// TODO Auto-generated method stub
}

7
src/rawDeepLearningClassifier/dlClassification/dummyClassifier/DummyClassifier.java
View File

@ -4,14 +4,14 @@ import java.io.Serializable;
import java.net.URI;
import java.util.ArrayList;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.dlClassification.DLClassName;
import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
import rawDeepLearningClassifier.dlClassification.PredictionResult;
import rawDeepLearningClassifier.layoutFX.DLCLassiferModelUI;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import warnings.PamWarning;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
/**
* Classifier which returns a random results. Used for debugging and testing.
@ -62,8 +62,7 @@ public class DummyClassifier implements DLClassiferModel{
return null;
}
@Override
public ArrayList<PredictionResult> runModel(ArrayList<GroupedRawData> rawDataUnit) {
public ArrayList<? extends PredictionResult> runModel(ArrayList<? extends PamDataUnit> rawDataUnit) {
ArrayList<PredictionResult> modelResults = new ArrayList<PredictionResult>();
for (int i=0; i<rawDataUnit.size(); i++) {

120
src/rawDeepLearningClassifier/dlClassification/genericModel/DLModelWorker.java
View File

@ -11,16 +11,16 @@ import org.jamdev.jdl4pam.transforms.DLTransformsFactory;
import org.jamdev.jdl4pam.utils.DLUtils;
import org.jamdev.jpamutils.wavFiles.AudioData;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
/**
*
* Runs the deep learning model and performs feature extraction.
* <p>
*
*
* @author Jamie Macaulay
*
@ -41,71 +41,83 @@ public abstract class DLModelWorker<T> {
* True to enable normalisation of results using softmax;
*/
private boolean enableSoftMax = true;
/**
* Convert a list of data units to a stack if images.
* @param dataUnits - the data units.
* @param sampleRate - the sample rate
* @param iChan - the channels
* @return a stack of images for input into a deep learning model.
*/
public float[][][] dataUnits2ModelInput(ArrayList<? extends PamDataUnit> dataUnits, float sampleRate, int iChan){
@SuppressWarnings("unchecked")
ArrayList<GroupedRawData> rawDataUnits = ( ArrayList<GroupedRawData>) dataUnits;
//the number of chunks.
int numChunks = rawDataUnits.size();
//data input into the model - a stack of spectrogram images.
float[][][] transformedDataStack = new float[numChunks][][];
//generate the spectrogram stack.
AudioData soundData;
double[][] transformedData2; //spec data
double[] transformedData1; //waveform data
for (int j=0; j<numChunks; j++) {
soundData = new AudioData(rawDataUnits.get(j).getRawData()[iChan], sampleRate);
// for (int i=0; i<modelTransforms.size(); i++) {
// System.out.println("Transfrom type: " + modelTransforms.get(i).getDLTransformType());
// }
//set the sound in the first transform.
((WaveTransform) modelTransforms.get(0)).setWaveData(soundData);
// System.out.println("Model transforms:no. " + modelTransforms.size()+ " input sounds len: " + soundData.getLengthInSeconds()
// + " Decimate Params: " + ((WaveTransform) modelTransforms.get(0)).getParams()[0] + "max amplitude sound: " + PamArrayUtils.max(soundData.samples));
DLTransform transform = modelTransforms.get(0);
for (int i =0; i<modelTransforms.size(); i++) {
transform = modelTransforms.get(i).transformData(transform);
// //TEMP
// if (transform instanceof FreqTransform) {
// transformedData = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
// System.out.println("DLModelWorker: transform : " + modelTransforms.get(i).getDLTransformType() + " "+ i + transformedData.length + " " + transformedData[0].length + " minmax: " + PamArrayUtils.minmax(transformedData)[0] + " " + PamArrayUtils.minmax(transformedData)[1]);
// }
}
if (transform instanceof FreqTransform) {
//add a spectrogram to the stacl
transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
transformedDataStack[j] = DLUtils.toFloatArray(transformedData2);
}
else {
//add wavefrom to the stack = we make the 2nd dimesnion 1.
transformedData1 = ((WaveTransform) transform).getWaveData().getScaledSampleAmplitudes();
transformedDataStack[j] = new float[1][transformedData1.length];
transformedDataStack[j][0] = DLUtils.toFloatArray(transformedData1);
}
}
return transformedDataStack;
}
/**
* Run the initial data feature extraction and the model
* @param rawDataUnit - the raw data unit.
* @param rawDataUnit - the raw data unit. This is a stack of data units to be classified either together or seperately.
* @param iChan - the channel to run the data on.
* @return the model to run.
*/
public synchronized ArrayList<T> runModel(ArrayList<GroupedRawData> rawDataUnits, float sampleRate, int iChan) {
public synchronized ArrayList<T> runModel(ArrayList<? extends PamDataUnit> dataUnits, float sampleRate, int iChan) {
try {
//the number of chunks.
int numChunks = rawDataUnits.size();
//PamCalendar.isSoundFile();
//create an audio data object from the raw data chunk
long timeStart = System.nanoTime();
//data input into the model - a stack of spectrogram images.
float[][][] transformedDataStack = new float[numChunks][][];
//generate the spectrogram stack.
AudioData soundData;
double[][] transformedData2; //spec data
double[] transformedData1; //waveform data
for (int j=0; j<numChunks; j++) {
soundData = new AudioData(rawDataUnits.get(j).getRawData()[iChan], sampleRate);
// for (int i=0; i<modelTransforms.size(); i++) {
// System.out.println("Transfrom type: " + modelTransforms.get(i).getDLTransformType());
// }
//set the sound in the first transform.
((WaveTransform) modelTransforms.get(0)).setWaveData(soundData);
// System.out.println("Model transforms:no. " + modelTransforms.size()+ " input sounds len: " + soundData.getLengthInSeconds()
// + " Decimate Params: " + ((WaveTransform) modelTransforms.get(0)).getParams()[0] + "max amplitude sound: " + PamArrayUtils.max(soundData.samples));
DLTransform transform = modelTransforms.get(0);
for (int i =0; i<modelTransforms.size(); i++) {
transform = modelTransforms.get(i).transformData(transform);
// //TEMP
// if (transform instanceof FreqTransform) {
// transformedData = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
// System.out.println("DLModelWorker: transform : " + modelTransforms.get(i).getDLTransformType() + " "+ i + transformedData.length + " " + transformedData[0].length + " minmax: " + PamArrayUtils.minmax(transformedData)[0] + " " + PamArrayUtils.minmax(transformedData)[1]);
// }
}
if (transform instanceof FreqTransform) {
//add a spectrogram to the stacl
transformedData2 = ((FreqTransform) transform).getSpecTransfrom().getTransformedData();
transformedDataStack[j] = DLUtils.toFloatArray(transformedData2);
}
else {
//add wavefrom to the stack = we make the 2nd dimesnion 1.
transformedData1 = ((WaveTransform) transform).getWaveData().getScaledSampleAmplitudes();
transformedDataStack[j] = new float[1][transformedData1.length];
transformedDataStack[j][0] = DLUtils.toFloatArray(transformedData1);
}
}
float[][][] transformedDataStack = dataUnits2ModelInput(dataUnits, sampleRate, iChan);
//run the model.
float[] output = null;

2
src/rawDeepLearningClassifier/dlClassification/genericModel/GenericDLClassifier.java
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@ -12,7 +12,7 @@ import rawDeepLearningClassifier.dlClassification.DLClassName;
import rawDeepLearningClassifier.dlClassification.DLClassiferModel;
import rawDeepLearningClassifier.dlClassification.StandardClassifierModel;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import warnings.PamWarning;

27
src/rawDeepLearningClassifier/dlClassification/orcaSpot/OrcaSpotClassifier.java
View File

@ -10,6 +10,7 @@ import java.util.concurrent.atomic.AtomicBoolean;
import PamController.PamControlledUnitSettings;
import PamController.PamSettingManager;
import PamController.PamSettings;
import PamguardMVC.PamDataUnit;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.dlClassification.DLClassName;
@ -18,7 +19,7 @@ import rawDeepLearningClassifier.dlClassification.DLDataUnit;
import rawDeepLearningClassifier.dlClassification.DLDetection;
import rawDeepLearningClassifier.dlClassification.PredictionResult;
import rawDeepLearningClassifier.layoutFX.DLCLassiferModelUI;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import warnings.PamWarning;
/**
@ -99,18 +100,18 @@ public class OrcaSpotClassifier implements DLClassiferModel, PamSettings {
}
@Override
public ArrayList<PredictionResult> runModel(ArrayList<GroupedRawData> rawDataUnits) {
public ArrayList<? extends PredictionResult> runModel(ArrayList<? extends PamDataUnit> rawDataUnits) {
for (GroupedRawData groupedRawData: rawDataUnits){
if (queue.size()>MAX_QUEUE_SIZE) {
//we are not doing well - clear the buffer
queue.clear();
}
queue.add(groupedRawData);
}
this.orcaSpotUI.notifyUpdate(-1);
// for (PamDataUnit groupedRawData: rawDataUnits){
// if (queue.size()>MAX_QUEUE_SIZE) {
// //we are not doing well - clear the buffer
// queue.clear();
// }
// queue.add(groupedRawData);
//
//
// }
// this.orcaSpotUI.notifyUpdate(-1);
return null;
}
@ -363,4 +364,6 @@ public class OrcaSpotClassifier implements DLClassiferModel, PamSettings {
return null;
}
}

145
src/rawDeepLearningClassifier/segmenter/GroupedRawData.java Normal file
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@ -0,0 +1,145 @@
package rawDeepLearningClassifier.segmenter;
import java.util.Arrays;
import PamDetection.PamDetection;
import PamUtils.PamUtils;
import PamguardMVC.PamDataUnit;
/**
*
* Temporary holder for raw data with a pre defined size. This holds one channel group of raw
* sound data.
*
* @author Jamie Macaulay
*
*/
public class GroupedRawData extends PamDataUnit implements PamDetection, Cloneable {
/*
* Raw data holder
*/
protected double[][] rawData;
/**
* Current position in the rawData;
*/
protected int[] rawDataPointer;
/**
* The data unit associated with this raw data chunk.
*/
private PamDataUnit rawDataUnit;
/**
* Create a grouped raw data unit. This contains a segment of sound data.
* @param timeMilliseconds - the time in milliseconds.
* @param channelBitmap - the channel bitmap of the raw data.
* @param startSample - the start sample of the raw data.
* @param duration - the duration of the raw data in samples.
* @param samplesize - the total sample size of the raw data unit chunk in samples.
*/
public GroupedRawData(long timeMilliseconds, int channelBitmap, long startSample, long duration, int samplesize) {
super(timeMilliseconds, channelBitmap, startSample, duration);
rawData = new double[PamUtils.getNumChannels(channelBitmap)][];
rawDataPointer = new int[PamUtils.getNumChannels(channelBitmap)];
// rawDataStartMillis = new long[PamUtils.getNumChannels(channelBitmap)];
for (int i =0; i<rawData.length; i++) {
rawData[i] = new double[samplesize];
}
}
/**
* Set the parent data unit.
* @param unit - the raw data unit.
*/
public void setParentDataUnit(PamDataUnit rawDataUnit) {
this.rawDataUnit=rawDataUnit;
}
/**
* Get the data unit that this raw sound segment is associated with.
* @Return unit - the raw data unit
*/
public PamDataUnit getParentDataUnit() {
return rawDataUnit;
}
/**
* Copy raw data from an array to another.
* @param src - the array to come from
* @param srcPos - the raw source position
* @param copyLen - the copy length.
* @groupChan - the channel (within the group)
* @return overflow - the number of raw data points left at the end which were not copied.
*/
public int copyRawData(Object src, int srcPos, int copyLen, int groupChan) {
//how much of the chunk should we copy?
int lastPos = rawDataPointer[groupChan] + copyLen;
int dataOverflow = 0;
int arrayCopyLen;
//make sure the copy length
if (lastPos>=rawData[groupChan].length) {
arrayCopyLen=copyLen-(lastPos-rawData[groupChan].length)-1;
dataOverflow = copyLen - arrayCopyLen;
}
else {
arrayCopyLen= copyLen;
}
arrayCopyLen = Math.max(arrayCopyLen, 0);
//update the current grouped raw data unit with new raw data.
System.arraycopy(src, srcPos, rawData[groupChan], rawDataPointer[groupChan], arrayCopyLen);
rawDataPointer[groupChan]=rawDataPointer[groupChan] + arrayCopyLen;
return dataOverflow;
}
/**
* Get the raw data grouped by channel.
* @return the raw acoustic data.
*/
public double[][] getRawData() {
return rawData;
}
/**
* Get the current pointer for rawData.
* @return the data pointer per channel.
*/
public int[] getRawDataPointer() {
return rawDataPointer;
}
@Override
protected GroupedRawData clone() {
try {
GroupedRawData groupedRawData = (GroupedRawData) super.clone();
//hard clone the acoustic data
groupedRawData.rawData = new double[this.rawData.length][];
for (int i=0; i<groupedRawData.rawData.length; i++) {
groupedRawData.rawData[i] = Arrays.copyOf(this.rawData[i], this.rawData[i].length);
}
return groupedRawData;
} catch (CloneNotSupportedException e) {
e.printStackTrace();
return null;
}
}
}

1
src/rawDeepLearningClassifier/segmenter/SegmenterDataBlock.java
View File

@ -3,7 +3,6 @@ package rawDeepLearningClassifier.segmenter;
import PamguardMVC.PamDataBlock;
import PamguardMVC.PamProcess;
import rawDeepLearningClassifier.dlClassification.ModelResultDataUnit;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
/**
* Holds raw data segments which will be classified.

151
src/rawDeepLearningClassifier/segmenter/SegmenterProcess.java
View File

@ -5,9 +5,7 @@ import java.util.ArrayList;
import java.util.Arrays;
import PamController.PamController;
import PamDetection.PamDetection;
import PamDetection.RawDataUnit;
import PamUtils.PamArrayUtils;
import PamUtils.PamUtils;
import PamView.GroupedSourceParameters;
import PamView.PamDetectionOverlayGraphics;
@ -17,12 +15,10 @@ import PamguardMVC.PamDataBlock;
import PamguardMVC.PamDataUnit;
import PamguardMVC.PamObservable;
import PamguardMVC.PamProcess;
import PamguardMVC.debug.Debug;
import clickDetector.ClickDetection;
import clipgenerator.ClipDataUnit;
import rawDeepLearningClassifier.DLControl;
import whistlesAndMoans.ConnectedRegionDataUnit;
import PamUtils.PamCalendar;
/**
@ -83,7 +79,7 @@ public class SegmenterProcess extends PamProcess {
segmenterDataBlock = new SegmenterDataBlock("Segmented Raw Data", this,
dlControl.getDLParams().groupedSourceParams.getChanOrSeqBitmap());
segmenterGroupDataBlock = new SegmenterGroupDataBlock("Segmented Raw Data", this,
segmenterGroupDataBlock = new SegmenterGroupDataBlock("Segmented data units", this,
dlControl.getDLParams().groupedSourceParams.getChanOrSeqBitmap());
addOutputDataBlock(segmenterDataBlock);
@ -232,8 +228,12 @@ public class SegmenterProcess extends PamProcess {
* @param dataUnit - the whistle data unit.
*/
private void newWhistleData(PamDataUnit dataUnit) {
ConnectedRegionDataUnit rawDataUnit = (ConnectedRegionDataUnit) dataUnit;
ConnectedRegionDataUnit whistle = (ConnectedRegionDataUnit) dataUnit;
//TODO
//this contains no raw data so we are branching off on a completely different processing path here.
//Whislte data units are saved to a buffer and then fed to the deep learning algorohtm
}
@ -671,145 +671,6 @@ public class SegmenterProcess extends PamProcess {
}
/**
*
* Temporary holder for raw data with a pre defined size. This holds one channel group of raw
* sound data.
*
* @author Jamie Macaulay
*
*/
public static class GroupedRawData extends PamDataUnit implements PamDetection, Cloneable {
/*
* Raw data holder
*/
protected double[][] rawData;
/**
* Current position in the rawData;
*/
protected int[] rawDataPointer;
/**
* The data unit associated with this raw data chunk.
*/
private PamDataUnit rawDataUnit;
/**
* Create a grouped raw data unit. This contains a segment of sound data.
* @param timeMilliseconds - the time in milliseconds.
* @param channelBitmap - the channel bitmap of the raw data.
* @param startSample - the start sample of the raw data.
* @param duration - the duration of the raw data in samples.
* @param samplesize - the total sample size of the raw data unit chunk in samples.
*/
public GroupedRawData(long timeMilliseconds, int channelBitmap, long startSample, long duration, int samplesize) {
super(timeMilliseconds, channelBitmap, startSample, duration);
rawData = new double[PamUtils.getNumChannels(channelBitmap)][];
rawDataPointer = new int[PamUtils.getNumChannels(channelBitmap)];
// rawDataStartMillis = new long[PamUtils.getNumChannels(channelBitmap)];
for (int i =0; i<rawData.length; i++) {
rawData[i] = new double[samplesize];
}
}
/**
* Set the parent data unit.
* @param unit - the raw data unit.
*/
public void setParentDataUnit(PamDataUnit rawDataUnit) {
this.rawDataUnit=rawDataUnit;
}
/**
* Get the data unit that this raw sound segment is associated with.
* @Return unit - the raw data unit
*/
public PamDataUnit getParentDataUnit() {
return rawDataUnit;
}
/**
* Copy raw data from an array to another.
* @param src - the array to come from
* @param srcPos - the raw source position
* @param copyLen - the copy length.
* @groupChan - the channel (within the group)
* @return overflow - the number of raw data points left at the end which were not copied.
*/
public int copyRawData(Object src, int srcPos, int copyLen, int groupChan) {
//how much of the chunk should we copy?
int lastPos = rawDataPointer[groupChan] + copyLen;
int dataOverflow = 0;
int arrayCopyLen;
//make sure the copy length
if (lastPos>=rawData[groupChan].length) {
arrayCopyLen=copyLen-(lastPos-rawData[groupChan].length)-1;
dataOverflow = copyLen - arrayCopyLen;
}
else {
arrayCopyLen= copyLen;
}
arrayCopyLen = Math.max(arrayCopyLen, 0);
//update the current grouped raw data unit with new raw data.
System.arraycopy(src, srcPos, rawData[groupChan], rawDataPointer[groupChan], arrayCopyLen);
rawDataPointer[groupChan]=rawDataPointer[groupChan] + arrayCopyLen;
return dataOverflow;
}
/**
* Get the raw data grouped by channel.
* @return the raw acoustic data.
*/
public double[][] getRawData() {
return rawData;
}
/**
* Get the current pointer for rawData.
* @return the data pointer per channel.
*/
public int[] getRawDataPointer() {
return rawDataPointer;
}
@Override
protected GroupedRawData clone() {
try {
GroupedRawData groupedRawData = (GroupedRawData) super.clone();
//hard clone the acoustic data
groupedRawData.rawData = new double[this.rawData.length][];
for (int i=0; i<groupedRawData.rawData.length; i++) {
groupedRawData.rawData[i] = Arrays.copyOf(this.rawData[i], this.rawData[i].length);
}
return groupedRawData;
} catch (CloneNotSupportedException e) {
e.printStackTrace();
return null;
}
}
}
@Override
public void pamStart() {
// TODO Auto-generated method stub

2
src/test/rawDeepLearningClassifier/GenericDLClassifierTest.java
View File

@ -23,7 +23,7 @@ import rawDeepLearningClassifier.defaultModels.RightWhaleModel1;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelParams;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericModelWorker;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
/**
* Test the generic classifier.

2
src/test/rawDeepLearningClassifier/KetosDLClassifierTest.java
View File

@ -16,7 +16,7 @@ import org.jamdev.jpamutils.wavFiles.AudioData;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.dlClassification.ketos.KetosDLParams;
import rawDeepLearningClassifier.dlClassification.ketos.KetosWorker2;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import org.junit.jupiter.api.Test;

4
src/test/rawDeepLearningClassifier/KooguDLClassifierTest.java
View File

@ -17,10 +17,8 @@ import org.junit.jupiter.api.Test;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
import rawDeepLearningClassifier.dlClassification.genericModel.GenericPrediction;
import rawDeepLearningClassifier.dlClassification.ketos.KetosDLParams;
import rawDeepLearningClassifier.dlClassification.ketos.KetosWorker2;
import rawDeepLearningClassifier.dlClassification.koogu.KooguModelWorker;
import rawDeepLearningClassifier.segmenter.SegmenterProcess.GroupedRawData;
import rawDeepLearningClassifier.segmenter.GroupedRawData;
import us.hebi.matlab.mat.format.Mat5;
import us.hebi.matlab.mat.format.Mat5File;
import us.hebi.matlab.mat.types.Matrix;