Updating deep learning module to support delphinID models

This commit is contained in:
Jamie Mac 2024-03-29 16:41:28 +00:00
parent 198071e051
commit 0eca6a1a9f
19 changed files with 338 additions and 64 deletions

View File

@ -6,7 +6,7 @@
<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/Amazon Coretto 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"/>

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@ -411,26 +411,29 @@ public class ConnectionPane extends Pane {
if (includeBranch) {
StandardConnectionSocket socket;
for (int i = 0; i < parentNode.getConnectionPlugs().size(); i++) {
socket = (StandardConnectionSocket) parentNode.getConnectionPlugs().get(i).getConnectedShape();
if (socket == null || !socket.isBranch())
continue;
// is this socket connected to child node?
if (socket.getParentConnectionPlug().getConnectedShape() != null) {
if (parentNode.getConnectionPlugs().get(i).getConnectedShape() instanceof StandardConnectionSocket) {
socket = (StandardConnectionSocket) parentNode.getConnectionPlugs().get(i).getConnectedShape();
if (socket == null || !socket.isBranch())
continue;
// is this socket connected to child node?
if (socket.getParentConnectionPlug().getConnectedShape() != null) {
if (socket.getParentConnectionPlug().getConnectedShape().getConnectionNode() == childNode) {
// return socket.getParentConnectionPlug();
node = socket.getConnectedShape();
}
else if (bypassStructure && socket.getParentConnectionPlug().getConnectedShape()
.getConnectionNode() instanceof ConnectionStructure) {
// go down another level
node = getConnectionPlug(parentNode,
socket.getParentConnectionPlug().getConnectedShape().getConnectionNode(), includeBranch,
bypassStructure);
if (socket.getParentConnectionPlug().getConnectedShape().getConnectionNode() == childNode) {
// return socket.getParentConnectionPlug();
node = socket.getConnectedShape();
}
else if (bypassStructure && socket.getParentConnectionPlug().getConnectedShape()
.getConnectionNode() instanceof ConnectionStructure) {
// go down another level
node = getConnectionPlug(parentNode,
socket.getParentConnectionPlug().getConnectedShape().getConnectionNode(), includeBranch,
bypassStructure);
}
}
if (node != null)
return node;
}
if (node != null)
return node;
}
}

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@ -12,7 +12,7 @@ public enum DLStatus {
" The model failed to load - this could be because it is incompatible with PAMGuard or an uknown file format.",
ErrorLevel.ERROR),
MODEL_LOAD_SUCCESS("The model loaded", " The model successfully load", ErrorLevel.NO_ERROR),
MODEL_LOAD_SUCCESS("The model loaded", " The model successfully loaded", ErrorLevel.NO_ERROR),
DOWNLOAD_STARTING("Download starting", "The model is downloading", ErrorLevel.NO_ERROR),

View File

@ -0,0 +1,106 @@
package rawDeepLearningClassifier;
import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStream;
import java.net.URI;
import java.net.URISyntaxException;
import java.util.Enumeration;
import java.util.zip.ZipEntry;
import java.util.zip.ZipException;
import java.util.zip.ZipFile;
import java.util.zip.ZipInputStream;
public class DLZipUtils {
/**
* Find the first file within a zip folder that matches a pattern. This peaks into the zip file instead of decompressing it.
* @param zipUri - uri to the zip file
* @param filePattern - the file pattern to match - the file must contain this string.
* @return null if no file found and the file pqth if the file is founf
* @throws ZipException
* @throws IOException
*/
public static String getZipFilePath(URI zipUri, String filePattern) throws ZipException, IOException {
return getZipFilePath(new File(zipUri), filePattern);
}
/**
* Extract a single file from a zip folder.
* @param zipPackage - URI to the the zip file
* @param fileToBeExtracted - the name of the file to be extracted
* @param - the folder path to extract the file to.
* @return - return the File that has been extracted.
* @throws IOException
* @throws URISyntaxException
*/
public static File extractFile(String zipPackage, String fileToBeExtracted, String outFolder) throws IOException, URISyntaxException {
return extractFile( new URI(zipPackage), fileToBeExtracted, outFolder) ;
}
/**
* Extract a single file from a zip folder.
* @param zipPackage - path the the zip file
* @param fileToBeExtracted - the name of the file to be extracted
* @param - the folder path to extract the file to.
* @return - return the File that has been extracted.
* @throws IOException
*/
public static File extractFile(URI zipPackage, String fileToBeExtracted, String outFolder) throws IOException {
File fileOut = new File(outFolder, fileToBeExtracted);
OutputStream out = new FileOutputStream(fileOut);
FileInputStream fileInputStream = new FileInputStream(new File(zipPackage));
BufferedInputStream bufferedInputStream = new BufferedInputStream(fileInputStream );
ZipInputStream zin = new ZipInputStream(bufferedInputStream);
ZipEntry ze = null;
while ((ze = zin.getNextEntry()) != null) {
if (ze.getName().equals(fileToBeExtracted)) {
byte[] buffer = new byte[9000];
int len;
while ((len = zin.read(buffer)) != -1) {
out.write(buffer, 0, len);
}
out.close();
break;
}
}
zin.close();
return fileOut;
}
/**
* Find the first file within a zip folder that matches a pattern.
* @param zipFile - uri to the zip file
* @param filePattern - the file pattern to match - the file must contain this string.
* @return null if no file found and the file pqth if the file is founf
* @throws ZipException
* @throws IOException
*/
public static String getZipFilePath(File zipFileIn, String filePattern) throws ZipException, IOException {
try (ZipFile zipFile = new ZipFile(zipFileIn)) {
Enumeration<? extends ZipEntry> entries = zipFile.entries();
//this iterates through all files, including in sub folders.
while (entries.hasMoreElements()) {
ZipEntry entry = entries.nextElement();
// Check if entry is a directory
if (!entry.isDirectory()) {
//System.out.println(entry);
if (entry.getName().contains(filePattern)) {
return entry.getName();
}
}
}
}
return null;
}
}

View File

@ -1,32 +1,91 @@
package rawDeepLearningClassifier.dlClassification;
import java.io.File;
import java.io.IOException;
import java.net.URI;
import org.codehaus.plexus.util.FileUtils;
import org.jamdev.jdl4pam.transforms.jsonfile.DLTransformsParser;
import org.json.JSONObject;
import ai.djl.repository.FilenameUtils;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLZipUtils;
/**
* Selects which type of DL classiifer to use.
* @author Jamie Macaulay
* Selects which type of DL classifier to use.
*
*
* @author Jamie Macaulay
*/
public class DLClassifierChooser {
private DLControl dlControl;
public DLClassifierChooser(DLControl dlControl) {
this.dlControl = dlControl;
}
/**
* Select the correct classifier model to use from a URI.
* @param modelURI - the URI
* @return the classifier model that runs the file.
*/
public DLClassiferModel selectClassiferModel(URI modelURI) {
//check for model compatibility.
String extension = FileUtils.getExtension(modelURI.getPath());
if (extension.equals("zip")) {
//If the model is a zip file it may contain a model and a metadata file. Want to check
//this metadata file in case it points to a particular framework.
try {
String settingsFile = DLZipUtils.getZipFilePath(modelURI, ".pdtf");
System.out.println("Settings file: " +settingsFile);
if (settingsFile!=null) {
//there's a settings file - does it contain a metadata field describing which
//type of classifier it belongs to.
String outFolder = System.getProperty("user.home") + File.separator + "PAMGuard_temp";
new File(outFolder).mkdir();
File file = DLZipUtils.extractFile(modelURI, settingsFile, outFolder);
if (file!=null) {
//now we need to open the file and get a specific JSON field which describes the model.
//read the JSON string from the the file.
String jsonString = DLTransformsParser.readJSONString(file);
JSONObject object = new JSONObject(jsonString);
JSONObject frameworkObject = object.getJSONObject("framework_info");
String frameworkString = frameworkObject.getString("framework");
for (DLClassiferModel model: dlControl.getDLModels()) {
//System.out.println("frameworkString: " + frameworkString + " " + model.getName());
if (model.getName().toLowerCase().equals(frameworkString.trim().toLowerCase())) return model;
}
}
else {
System.err.println("DLClassifierChooser: Unable to extract the metadata file even though it exists in the zip archive:??");
}
}
else {
System.err.println("DLClassifierChooser: The zip file does not contain a metadata file for the deep learning model");
}
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
// Check for model compatibility. This usually means checking for a file
// extension. Note that a koogu .kgu model. for example can cxontain a metadata
// file without the correct @koogu@ field. In this instance the model is loaded
// properly because of the file extension but would not have been loaded by
// checking the metadata as above
for (DLClassiferModel model: dlControl.getDLModels()) {
if (model.isModelType(modelURI)) return model;
}
//return the generic model.
return dlControl.getDLModels().get(dlControl.getDLModels().size()-1);
}

View File

@ -87,6 +87,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
return modelResult; //returns to the classifier.
}
else {
//REAL TIME
//add to a buffer if in real time.
if (workerThread.getQueue().size()>DLModelWorker.MAX_QUEUE_SIZE) {
//we are not doing well - clear the buffer

View File

@ -62,9 +62,7 @@ public class SoundSpotClassifier extends StandardClassifierModel {
this.soundSpotUI= new SoundSpotUI(this);
//load the previous settings
PamSettingManager.getInstance().registerSettings(this);
// System.out.println("LOADED CLASS NAMES: currParams.classNames: " + soundSpotParmas.classNames);
}

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@ -201,6 +201,7 @@ public abstract class StandardModelPane extends SettingsPane<StandardModelParams
//speciesIDBox.setMaxWidth(100);
// speciesIDBox.setPrefWidth(100);
speciesIDBox.prefHeightProperty().bind(detectionSpinner.heightProperty());
speciesIDBox.setMaxWidth(200); //otherwise expands too much if multiple classes selected
validator = new PamValidator();

View File

@ -177,7 +177,6 @@ public abstract class ArchiveModelClassifier extends StandardClassifierModel {
@Override
public boolean isModelType(URI uri) {
//Ketos is easy because there are not many files with a .ktpb extension.
return super.isModelExtensions(uri);
}

View File

@ -256,33 +256,6 @@ public class ArchiveModelWorker extends GenericModelWorker {
public boolean isModelNull() {
return dlModel==null;
}
/**
* Find the first file within a zip folder that matches a pattern.
* @param zipFile - uri to the zip file
* @param filePattern - the file pattern to match - the file must contain this string.
* @return null if no file found and the file pqth if the file is founf
* @throws ZipException
* @throws IOException
*/
static String getZipFilePath(File zipFileIn, String filePattern) throws ZipException, IOException {
try (ZipFile zipFile = new ZipFile(zipFileIn)) {
Enumeration<? extends ZipEntry> entries = zipFile.entries();
//this iterates through all files, including in sub folders.
while (entries.hasMoreElements()) {
ZipEntry entry = entries.nextElement();
// Check if entry is a directory
if (!entry.isDirectory()) {
//System.out.println(entry);
if (entry.getName().contains(filePattern)) {
return entry.getName();
}
}
}
}
return null;
}
}

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@ -6,6 +6,7 @@ import java.net.URI;
import java.util.zip.ZipException;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.DLZipUtils;
/**
* Loads a zip file and checks for a saved model alongside a pamguard settings file
@ -72,8 +73,8 @@ public class PamZipModelClassifier extends ArchiveModelClassifier {
* @throws ZipException
* @throws IOException
*/
private static String getZipFilePath(URI zipUri, String filePattern) throws ZipException, IOException {
return ArchiveModelWorker.getZipFilePath(new File(zipUri), filePattern);
public static String getZipFilePath(URI zipUri, String filePattern) throws ZipException, IOException {
return DLZipUtils.getZipFilePath(new File(zipUri), filePattern);
}
/**

View File

@ -58,9 +58,14 @@ public class SimpleArchiveModel extends ArchiveModel {
return "zip_model";
}
/**
* Gte the relative path of file within a zip folder.
* @param zipFolder
* @param fileEnd
* @return
* @throws IOException
*/
private static String getRelFilePath(String zipFolder, String fileEnd) throws IOException {
// find files matched `png` file extension from folder C:\\test
try (Stream<Path> walk = Files.walk(Paths.get(zipFolder))) {
List<String> result = walk
.filter(p -> !Files.isDirectory(p)) // not a directory

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@ -0,0 +1,41 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelClassifier;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
/**
* A classifier based on the delphinID method which uses whistle contours to predict
* dolphin species.
*
* @author Jamie Macaulay
*
*/
public class DelphinIDClassifier extends ArchiveModelClassifier{
private static final String MODEL_NAME = "delphinID";
/**
* Reference to the worker
*/
private DelphinIDWorker delphinIDWorker;
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;
}
}

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@ -0,0 +1,12 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
public class DelphinIDParams extends StandardModelParams {
/**
*
*/
private static final long serialVersionUID = 1L;
}

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@ -0,0 +1,10 @@
package rawDeepLearningClassifier.dlClassification.delphinID;
import rawDeepLearningClassifier.dlClassification.archiveModel.ArchiveModelWorker;
public class DelphinIDWorker extends ArchiveModelWorker{
}

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@ -39,6 +39,7 @@ import rawDeepLearningClassifier.DLControl;
import rawDeepLearningClassifier.DLStatus;
import rawDeepLearningClassifier.RawDLParams;
import warnings.PamWarning;
import whistlesAndMoans.ConnectedRegionDataUnit;
/**
* The settings pane.
@ -159,6 +160,7 @@ public class DLSettingsPane extends SettingsPane<RawDLParams>{
sourcePane = new GroupedSourcePaneFX("Raw Sound Data", RawDataUnit.class, true, false, true);
sourcePane.addSourceType(ClickDetection.class, false);
sourcePane.addSourceType(ClipDataUnit.class, false);
sourcePane.addSourceType(ConnectedRegionDataUnit.class, false);
vBox.getChildren().add(sourcePane);

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@ -0,0 +1,20 @@
package rawDeepLearningClassifier.segmenter;
import Localiser.detectionGroupLocaliser.GroupDetection;
import PamguardMVC.PamDataUnit;
/**
* A group of detection which are within a particular segment. This is used to pass detection groups straight to
*
*
* * @author Jamie Macaulay
*
*/
public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
public SegmenterDetectionGroup(long timeMilliseconds, int channelBitmap, long startSample, long duration) {
super(timeMilliseconds, channelBitmap, startSample, duration);
// TODO Auto-generated constructor stub
}
}

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@ -0,0 +1,18 @@
package rawDeepLearningClassifier.segmenter;
import PamguardMVC.PamDataBlock;
import PamguardMVC.PamProcess;
/**
*
* @author Jamie Macaulay
*
*/
public class SegmenterGroupDataBlock extends PamDataBlock<SegmenterDetectionGroup> {
public SegmenterGroupDataBlock(String dataName, PamProcess parentProcess, int channelMap) {
super(SegmenterDetectionGroup.class, dataName, parentProcess, channelMap);
// TODO Auto-generated constructor stub
}
}

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@ -21,6 +21,7 @@ import PamguardMVC.debug.Debug;
import clickDetector.ClickDetection;
import clipgenerator.ClipDataUnit;
import rawDeepLearningClassifier.DLControl;
import whistlesAndMoans.ConnectedRegionDataUnit;
import PamUtils.PamCalendar;
@ -58,7 +59,12 @@ public class SegmenterProcess extends PamProcess {
private SegmenterDataBlock segmenterDataBlock;
PamSymbol defaultSymbol = new PamSymbol(PamSymbolType.SYMBOL_DIAMOND, 10, 12, false,
Color.CYAN, Color.CYAN);
Color.CYAN, Color.CYAN);
/**
* Holds groups of data units which are within a defined segment.
*/
private SegmenterGroupDataBlock segmenterGroupDataBlock;
public SegmenterProcess(DLControl pamControlledUnit, PamDataBlock parentDataBlock) {
@ -76,7 +82,12 @@ public class SegmenterProcess extends PamProcess {
segmenterDataBlock = new SegmenterDataBlock("Segmented Raw Data", this,
dlControl.getDLParams().groupedSourceParams.getChanOrSeqBitmap());
segmenterGroupDataBlock = new SegmenterGroupDataBlock("Segmented Raw Data", this,
dlControl.getDLParams().groupedSourceParams.getChanOrSeqBitmap());
addOutputDataBlock(segmenterDataBlock);
addOutputDataBlock(segmenterGroupDataBlock);
setProcessName("Segmenter");
@ -208,11 +219,25 @@ public class SegmenterProcess extends PamProcess {
else if (pamRawData instanceof ClipDataUnit) {
newClipData(pamRawData);
}
else if (pamRawData instanceof ConnectedRegionDataUnit) {
newWhistleData(pamRawData);
}
}
}
/**
* A new whistle data unit.
* @param dataUnit - the whistle data unit.
*/
private void newWhistleData(PamDataUnit dataUnit) {
ConnectedRegionDataUnit rawDataUnit = (ConnectedRegionDataUnit) dataUnit;
}
/**
* A new raw data unit.
* @param obs - the PAM observable
@ -344,7 +369,7 @@ public class SegmenterProcess extends PamProcess {
*
* @param unit - the data unit which contains relevant metadata on time
* etc.
* @param rawDataChunk - the sound chunk to segment extracted form the data
* @param rawDataChunk - the sound chunk to segment extracted from the data
* unit.
* @param iChan - the channel that is being segmented
* @param forceSave - make sure that all data is passed into the buffers and