mirror of
https://github.com/PAMGuard/PAMGuard.git
synced 2024-11-21 22:52:22 +00:00
More work on delphinID classifier but not quite there yet.
This commit is contained in:
Jamie Mac
parent
ee694146c3
commit
c1f4ba9e37
@ -228,6 +228,7 @@ public class DLControl extends PamControlledUnit implements PamSettings {
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// classify the raw data segments.
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addPamProcess(dlClassifyProcess = new DLClassifyProcess(this, segmenterProcess.getSegmenterDataBlock()));
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dlClassifyProcess.addMultiPlexDataBlock(segmenterProcess.getSegmenteGrouprDataBlock());
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//manages the names assigned to different output classes.
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dlClassNameManager = new DLClassNameManager(this);
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@ -20,6 +20,7 @@ import rawDeepLearningClassifier.logging.DLAnnotation;
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import rawDeepLearningClassifier.logging.DLAnnotationType;
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import rawDeepLearningClassifier.segmenter.GroupedRawData;
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import rawDeepLearningClassifier.segmenter.SegmenterDataBlock;
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import rawDeepLearningClassifier.segmenter.SegmenterDetectionGroup;
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/**
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* The deep learning classification process. This takes a segment of raw data from the segmenter.
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@ -35,7 +36,7 @@ import rawDeepLearningClassifier.segmenter.SegmenterDataBlock;
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*/
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public class DLClassifyProcess extends PamInstantProcess {
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/**
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* Holds all model results but no other information
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*/
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@ -64,15 +65,13 @@ public class DLClassifyProcess extends PamInstantProcess {
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/**
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* The DL buffer
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*/
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private ArrayList<GroupedRawData> classificationBuffer;
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private ArrayList<PamDataUnit> classificationBuffer;
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/**
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* The DL annotation type.
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*/
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private DLAnnotationType dlAnnotationType;
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/**
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* The last parent data for grouped data. This is used to ensure that DLDetections
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* correspond to the raw chunk of data from a parent detection e.g. a click detection.
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@ -82,8 +81,8 @@ public class DLClassifyProcess extends PamInstantProcess {
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public DLClassifyProcess(DLControl dlControl, SegmenterDataBlock parentDataBlock) {
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super(dlControl);
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this.setParentDataBlock(parentDataBlock);
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// this.setParentDataBlock(parentDataBlock);
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@ -117,7 +116,7 @@ public class DLClassifyProcess extends PamInstantProcess {
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overlayGraphics.setDetectionData(true);
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dlDetectionDataBlock.setOverlayDraw(overlayGraphics);
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classificationBuffer = new ArrayList<GroupedRawData>();
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classificationBuffer = new ArrayList<PamDataUnit>();
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//the process name.
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setProcessName("Deep Learning Classifier");
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@ -140,7 +139,7 @@ public class DLClassifyProcess extends PamInstantProcess {
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System.err.println("Raw Deep Learning Classifier: The grouped source parameters were null."
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+ " A new instance has been created: Possible de-serialization error.");
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}
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//important for downstream processes such as the bearing localiser.
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dlModelResultDataBlock.setChannelMap(dlControl.getDLParams().groupedSourceParams.getChannelBitmap());
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@ -166,7 +165,7 @@ public class DLClassifyProcess extends PamInstantProcess {
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public void prepareProcess() {
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setupClassifierProcess();
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}
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/**
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* called for every process once the system model has been created.
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@ -194,36 +193,71 @@ public class DLClassifyProcess extends PamInstantProcess {
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*/
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@Override
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public void newData(PamObservable obs, PamDataUnit pamRawData) {
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// System.out.println("NEW SEGMENTER DATA");
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//the raw data units should appear in sequential channel order
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GroupedRawData rawDataUnit = (GroupedRawData) pamRawData;
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if (checkGroupData(rawDataUnit)) {
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//check whether the classification buffer is full. If it is then run
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if (isClassificationBufferFull(classificationBuffer, rawDataUnit)) {
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//first call run model to clear out the classification buffer if needs be
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runModel();
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if (pamRawData instanceof SegmenterDetectionGroup) {
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if (classificationBuffer.size()>=1) {
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System.out.println("RUN THE MODEL FOR WHISTLES: ");
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runDetectionGroupModel();
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classificationBuffer.clear();
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}
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classificationBuffer.add(rawDataUnit);
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else {
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classificationBuffer.add(pamRawData);
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}
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}
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// System.out.println("New raw data in: chan: " + PamUtils.getSingleChannel(pamRawData.getChannelBitmap()) +
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// " Size: " + pamRawData.getSampleDuration() + " first sample: " + rawDataUnit.getRawData()[0][0]
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// + "Parent UID: " + rawDataUnit.getParentDataUnit().getUID());
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if (pamRawData instanceof GroupedRawData) {
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//the raw data units should appear in sequential channel order
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GroupedRawData rawDataUnit = (GroupedRawData) pamRawData;
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if (checkGroupData(rawDataUnit)) {
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//check whether the classification buffer is full. If it is then run
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if (isRawClassificationBufferFull(classificationBuffer, rawDataUnit)) {
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//first call run model to clear out the classification buffer if needs be
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runRawModel();
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classificationBuffer.clear();
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}
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classificationBuffer.add(rawDataUnit);
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}
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}
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// System.out.println("New raw data in: chan: " + PamUtils.getSingleChannel(pamRawData.getChannelBitmap()) +
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// " Size: " + pamRawData.getSampleDuration() + " first sample: " + rawDataUnit.getRawData()[0][0]
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// + "Parent UID: " + rawDataUnit.getParentDataUnit().getUID());
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}
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/**
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* Run a model for which the input is a detection group.
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*/
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private void runDetectionGroupModel() {
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if (classificationBuffer.size()<=0) return;
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ArrayList<PamDataUnit> classificationBufferTemp = (ArrayList<PamDataUnit>) classificationBuffer.clone();
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ArrayList<? extends PredictionResult> modelResults = this.dlControl.getDLModel().runModel(classificationBufferTemp);
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for (int i=0; i<classificationBufferTemp.size(); i++) {
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if (modelResults.get(i)!=null) {
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DLDataUnit dlDataUnit = predictionToDataUnit(classificationBuffer.get(i), modelResults.get(i));
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this.dlModelResultDataBlock.addPamData(dlDataUnit); //here
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}
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}
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}
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/**
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* Run the model if the classification buffer is full.
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*/
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private void runModel() {
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private void runRawModel() {
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if (classificationBuffer.size()<=0) return;
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//run the deep learning algorithm
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ArrayList<GroupedRawData> classificationBufferTemp = (ArrayList<GroupedRawData>) classificationBuffer.clone();
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ArrayList<? extends PredictionResult> modelResults = this.dlControl.getDLModel().runModel(classificationBuffer);
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ArrayList<? extends PredictionResult> modelResults = this.dlControl.getDLModel().runModel(classificationBufferTemp);
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if (modelResults==null) {
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return; //there has been a problem
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@ -235,18 +269,18 @@ public class DLClassifyProcess extends PamInstantProcess {
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// System.out.println("Compare Times: " + PamCalendar.formatDBDateTime(modelResults.get(i).getTimeMillis(), true) +
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// " " + PamCalendar.formatDBDateTime(classificationBufferTemp.get(i).getTimeMilliseconds(), true) + " " +
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// modelResults.get(i).getPrediction()[1]);
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newModelResult(modelResults.get(i), classificationBufferTemp.get(i));
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newRawModelResult(modelResults.get(i), classificationBufferTemp.get(i));
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}
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}
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}
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/**
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* Check whether the buffer is full and the results should be passed to the classification model.
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* Check whether the buffer is full and the results should be passed to the classification model if we are using GrpoupDataUnits
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* @param classificationBuffer2 - the classification buffer.
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* @param rawDataUnit - the next raw data unit to add to the buffer.
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* @return true if the buffer is full.
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*/
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private boolean isClassificationBufferFull(ArrayList<GroupedRawData> classificationBuffer2, GroupedRawData rawDataUnit) {
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private boolean isRawClassificationBufferFull(ArrayList<PamDataUnit> classificationBuffer2, GroupedRawData rawDataUnit) {
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if (classificationBuffer2.size()==0) return false;
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@ -254,16 +288,16 @@ public class DLClassifyProcess extends PamInstantProcess {
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//1) It's over a max time
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//2) Contains different parent data units (if not from raw data).
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GroupedRawData lastUnit = classificationBuffer2.get(classificationBuffer2.size()-1);
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GroupedRawData lastUnit = (GroupedRawData) classificationBuffer2.get(classificationBuffer2.size()-1);
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if (!(lastUnit.getParentDataUnit() instanceof RawDataUnit) && lastUnit.getParentDataUnit()!=rawDataUnit.getParentDataUnit()) {
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//there is a new parent data unit.
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return true;
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}
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//get the start time. Use min value instead of first data just in case units ar enot in order.
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//get the start time. Use min value instead of first data just in case units are not in order.
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long min = Long.MAX_VALUE;
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for (GroupedRawData groupedRawData: classificationBuffer2) {
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for (PamDataUnit groupedRawData: classificationBuffer2) {
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if (groupedRawData.getTimeMilliseconds()<min) {
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min=groupedRawData.getTimeMilliseconds();
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}
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@ -299,18 +333,7 @@ public class DLClassifyProcess extends PamInstantProcess {
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return true;
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}
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/**
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* Create a data unit form a model result. This is called whenever data passes a prediction threshold.
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*
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* @param modelResult - the model result.
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* @param pamRawData - the raw data unit which the model result came from.
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*/
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public void newModelResult(PredictionResult modelResult, GroupedRawData pamRawData) {
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//the model result may be null if the classifier uses a new thread.
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//System.out.println("New segment: parent UID: " + pamRawData.getParentDataUnit().getUID() + " Prediciton: " + modelResult.getPrediction()[0]+ " " + getSourceParams().countChannelGroups());
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private DLDataUnit predictionToDataUnit(PamDataUnit pamRawData, PredictionResult modelResult) {
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//create a new data unit - always add to the model result section.
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DLDataUnit dlDataUnit = new DLDataUnit(pamRawData.getTimeMilliseconds(), pamRawData.getChannelBitmap(),
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pamRawData.getStartSample(), pamRawData.getSampleDuration(), modelResult);
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@ -319,17 +342,35 @@ public class DLClassifyProcess extends PamInstantProcess {
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dlDataUnit.setFrequency(new double[] {0, dlControl.getDLClassifyProcess().getSampleRate()/2});
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dlDataUnit.setDurationInMilliseconds(pamRawData.getDurationInMilliseconds());
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return dlDataUnit;
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}
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/**
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* Create a data unit form a model result. This is called whenever data passes a prediction threshold.
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*
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* @param modelResult - the model result.
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* @param pamRawData - the raw data unit which the model result came from.
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*/
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public void newRawModelResult(PredictionResult modelResult, GroupedRawData pamRawData) {
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//the model result may be null if the classifier uses a new thread.
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//System.out.println("New segment: parent UID: " + pamRawData.getParentDataUnit().getUID() + " Prediciton: " + modelResult.getPrediction()[0]+ " " + getSourceParams().countChannelGroups());
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//create a new data unit - always add to the model result section.
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DLDataUnit dlDataUnit = predictionToDataUnit(pamRawData, modelResult);
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this.dlModelResultDataBlock.addPamData(dlDataUnit); //here
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//need to implement multiple groups.
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for (int i=0; i<getSourceParams().countChannelGroups(); i++) {
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// System.out.println("RawDataIn: chan: " + pamRawData.getChannelBitmap()+ " " +
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// PamUtils.hasChannel(getSourceParams().getGroupChannels(i), pamRawData.getChannelBitmap()) +
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// " grouped source: " +getSourceParams().getGroupChannels(i) + " Channels OK? "
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// +PamUtils.hasChannel(getSourceParams().getGroupChannels(i), PamUtils.getSingleChannel(pamRawData.getChannelBitmap()))
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// + " groupchan: " + getSourceParams().getGroupChannels(i) + " " + PamUtils.getLowestChannel(pamRawData.getChannelBitmap())
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// + " chan bitmap: " + pamRawData.getChannelBitmap());
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// System.out.println("RawDataIn: chan: " + pamRawData.getChannelBitmap()+ " " +
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// PamUtils.hasChannel(getSourceParams().getGroupChannels(i), pamRawData.getChannelBitmap()) +
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// " grouped source: " +getSourceParams().getGroupChannels(i) + " Channels OK? "
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// +PamUtils.hasChannel(getSourceParams().getGroupChannels(i), PamUtils.getSingleChannel(pamRawData.getChannelBitmap()))
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// + " groupchan: " + getSourceParams().getGroupChannels(i) + " " + PamUtils.getLowestChannel(pamRawData.getChannelBitmap())
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// + " chan bitmap: " + pamRawData.getChannelBitmap());
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if (PamUtils.hasChannel(getSourceParams().getGroupChannels(i), PamUtils.getLowestChannel(pamRawData.getChannelBitmap()))) {
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@ -375,7 +416,7 @@ public class DLClassifyProcess extends PamInstantProcess {
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//Need to go by the parent data unit for merging data not the segments. Note that we may still add multiple
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//predicitions to a single data unit depending on how many segments it contains.
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//System.out.println("New model data " + pamRawData.getParentDataUnit().getUID() + " " + groupDataBuffer[i].size() + " " + modelResultDataBuffer[i].size());
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//System.out.println("New model data " + pamRawData.getParentDataUnit().getUID() + " " + groupDataBuffer[i].size() + " " + modelResultDataBuffer[i].size());
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if (pamRawData.getParentDataUnit()!=lastParentDataUnit[i]) {
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//save any data
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@ -422,25 +463,25 @@ public class DLClassifyProcess extends PamInstantProcess {
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*
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*/
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public void forceRunClassifier(PamDataUnit dataUnit) {
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//first call run model to clear out the classification buffer if needs be
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runModel();
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runRawModel();
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classificationBuffer.clear();
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//need to implement multiple groups.
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for (int i=0; i<getSourceParams().countChannelGroups(); i++) {
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// System.out.println("Nummber segments " + groupDataBuffer[i].size() + " data unit len: " + dataUnit.getSampleDurationAsInt() + " samples UID: " + dataUnit.getUID());
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// System.out.println("RawDataIn: chan: " + dataUnit.getChannelBitmap()+ " " +
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// PamUtils.hasChannel(getSourceParams().getGroupChannels(i), dataUnit.getChannelBitmap()) +
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// " grouped source: " +getSourceParams().getGroupChannels(i));
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// System.out.println("Nummber segments " + groupDataBuffer[i].size() + " data unit len: " + dataUnit.getSampleDurationAsInt() + " samples UID: " + dataUnit.getUID());
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// System.out.println("RawDataIn: chan: " + dataUnit.getChannelBitmap()+ " " +
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// PamUtils.hasChannel(getSourceParams().getGroupChannels(i), dataUnit.getChannelBitmap()) +
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// " grouped source: " +getSourceParams().getGroupChannels(i));
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if (PamUtils.hasChannel(getSourceParams().getGroupChannels(i), PamUtils.getSingleChannel(dataUnit.getChannelBitmap()))) {
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if (groupDataBuffer[i].size()>0) {
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//System.out.println("Save click annotation to " + lastParentDataUnit[i].getUID());
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//System.out.println("Save click annotation to " + lastParentDataUnit[i].getUID());
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addDLAnnotation(dataUnit,groupDataBuffer[i],modelResultDataBuffer[i]);
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lastParentDataUnit[i]=null;
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clearBuffer(i);
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@ -532,15 +573,22 @@ public class DLClassifyProcess extends PamInstantProcess {
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@Override
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public void pamStart() {
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// TODO Auto-generated method stub
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System.out.println("PREP MODEL:");
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// System.out.println("PREP MODEL:");
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this.dlControl.getDLModel().prepModel();
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}
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@Override
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public void pamStop() {
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runModel(); //make sure to run the last data in the buffer.
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//make sure to run the last data in the buffer.
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if (this.classificationBuffer.size()>0) {
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if (classificationBuffer.get(0) instanceof GroupedRawData) {
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runRawModel(); //raw data or raw data units
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}
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if (classificationBuffer.get(0) instanceof SegmenterDetectionGroup) {
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runDetectionGroupModel(); //any other data units.
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}
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}
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//21/11/2022 - it seems like this causes a memory leak when models are reopened and closed every file...
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//this.dlControl.getDLModel().closeModel();
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}
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@ -271,7 +271,7 @@ public abstract class StandardClassifierModel implements DLClassiferModel, PamSe
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*/
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protected void newResult(StandardPrediction modelResult, PamDataUnit groupedRawData) {
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if (groupedRawData instanceof GroupedRawData) {
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this.dlControl.getDLClassifyProcess().newModelResult(modelResult, (GroupedRawData) groupedRawData);
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this.dlControl.getDLClassifyProcess().newRawModelResult(modelResult, (GroupedRawData) groupedRawData);
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}
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}
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//
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@ -91,7 +91,7 @@ public class DelphinIDClassifier extends StandardClassifierModel {
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@Override
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public boolean isDecision(StandardPrediction modelResult, StandardModelParams modelParmas) {
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//TODO
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//DelphinID uses a different decision making process to most of the standard classifiers which just pass a binary threhsoild.
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//DelphinID uses a different decision making process to most of the standard classifiers which just pass a binary threshold.
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return false;
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}
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@ -49,8 +49,8 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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this.setModel(null); // set model to null to make sure nothing works and errors are thrown
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}
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dlParams.binaryClassification = new boolean[dlParams.classNames.length];
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for (int i=0; i<dlParams.classNames.length; i++) {
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dlParams.binaryClassification = new boolean[dlParams.numClasses];
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for (int i=0; i<dlParams.numClasses; i++) {
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dlParams.binaryClassification[i]=true;
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}
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}
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@ -110,6 +110,7 @@ public class DelphinIDWorker extends ArchiveModelWorker {
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double[][] transformedData2; //spectrogram data
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for (int j=0; j<numChunks; j++) {
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System.out.println("Number of whisltes to process: " + whistleGroups.get(j).getSubDetectionsCount() + " " + whistleGroups.get(j).getSegmentStartMillis());
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//create the first transform and set then whistle data. Note that the absolute time limits are
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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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@ -185,7 +185,7 @@ public class GenericDLClassifier extends StandardClassifierModel {
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* @param groupedRawData - the grouped raw data.
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*/
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protected void newResult(StandardPrediction modelResult, GroupedRawData groupedRawData) {
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this.dlControl.getDLClassifyProcess().newModelResult(modelResult, groupedRawData);
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this.dlControl.getDLClassifyProcess().newRawModelResult(modelResult, groupedRawData);
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}
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@ -9,6 +9,7 @@ import org.jamdev.jdl4pam.transforms.FreqTransform;
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import PamModel.PamModel;
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import PamModel.PamModel.PluginClassloader;
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import PamUtils.PamArrayUtils;
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import rawDeepLearningClassifier.DLControl;
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import rawDeepLearningClassifier.dlClassification.animalSpot.StandardModelParams;
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@ -32,7 +33,7 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
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@Override
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public float[] runModel(float[][][] transformedDataStack) {
|
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//System.out.println("RUN GENERIC MODEL: " + transformedDataStack.length + " " + transformedDataStack[0].length + " " + transformedDataStack[0][0].length);
|
||||
System.out.println("RUN GENERIC MODEL: " + transformedDataStack.length + " " + transformedDataStack[0].length + " " + transformedDataStack[0][0].length);
|
||||
// System.out.println("RUN GENERIC MODEL: " + transformedDataStack[0][0][0]);
|
||||
float[] results;
|
||||
if (freqTransform)
|
||||
@ -47,7 +48,8 @@ public class GenericModelWorker extends DLModelWorker<StandardPrediction> {
|
||||
//System.out.println("RUN GENERIC MODEL WAVE: " + waveStack.length + " " + waveStack[0].length + " " + waveStack[0][0]);
|
||||
results = getModel().runModel(waveStack);
|
||||
}
|
||||
//System.out.println("GENERIC MODEL RESULTS: " + results== null ? null : results.length);
|
||||
System.out.println("GENERIC MODEL RESULTS: " + (results== null ? null : results.length));
|
||||
PamArrayUtils.printArray(results);
|
||||
return results;
|
||||
}
|
||||
|
||||
|
||||
@ -28,7 +28,7 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
|
||||
* @param timeMilliseconds - this is the start of the SEGMENT - Note that the
|
||||
* @param channelBitmap - channels of all detections
|
||||
* @param startSample - the stratSample of the SEGMENT.
|
||||
* @param duration - the duration of the SEGMENT.
|
||||
* @param duration - the duration of the SEGMENT in milliseconds.
|
||||
*/
|
||||
public SegmenterDetectionGroup(long timeMilliseconds, int channelBitmap, long startSample, double duration) {
|
||||
super(timeMilliseconds, channelBitmap, startSample, (long) duration);
|
||||
@ -52,5 +52,9 @@ public class SegmenterDetectionGroup extends GroupDetection<PamDataUnit> {
|
||||
return segDuration;
|
||||
}
|
||||
|
||||
public long getSegmentEndMillis() {
|
||||
return (long) (segMillis+segDuration);
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
@ -12,6 +12,8 @@ public class SegmenterGroupDataBlock extends PamDataBlock<SegmenterDetectionGrou
|
||||
|
||||
public SegmenterGroupDataBlock(String dataName, PamProcess parentProcess, int channelMap) {
|
||||
super(SegmenterDetectionGroup.class, dataName, parentProcess, channelMap);
|
||||
this.setNaturalLifetimeMillis(15000); //do not want to keep the data for very long - it's raw data segmnents so memory intensive
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -6,6 +6,7 @@ import java.util.Arrays;
|
||||
|
||||
import PamController.PamController;
|
||||
import PamDetection.RawDataUnit;
|
||||
import PamUtils.PamArrayUtils;
|
||||
import PamUtils.PamUtils;
|
||||
import PamView.GroupedSourceParameters;
|
||||
import PamView.PamDetectionOverlayGraphics;
|
||||
@ -70,7 +71,7 @@ public class SegmenterProcess extends PamProcess {
|
||||
/**
|
||||
* The current segmenter detection group.
|
||||
*/
|
||||
private SegmenterDetectionGroup segmenterDetectionGroup = null;
|
||||
private SegmenterDetectionGroup[] segmenterDetectionGroup = null;
|
||||
|
||||
|
||||
public SegmenterProcess(DLControl pamControlledUnit, PamDataBlock parentDataBlock) {
|
||||
@ -110,6 +111,8 @@ public class SegmenterProcess extends PamProcess {
|
||||
public void prepareProcess() {
|
||||
setupSegmenter();
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* A list of data block class types which are compatible as parent data blocks
|
||||
@ -159,6 +162,7 @@ public class SegmenterProcess extends PamProcess {
|
||||
if (chanGroups!=null) {
|
||||
currentRawChunks = new GroupedRawData[chanGroups.length];
|
||||
nextRawChunks = new GroupedRawData[chanGroups.length][];
|
||||
segmenterDetectionGroup = new SegmenterDetectionGroup[chanGroups.length];
|
||||
}
|
||||
|
||||
|
||||
@ -236,33 +240,118 @@ public class SegmenterProcess extends PamProcess {
|
||||
|
||||
|
||||
/**
|
||||
* A new whistle data unit.
|
||||
* A new detection data unit i.e. this is only if we have detection data which is being grouped into segments.
|
||||
* @param dataUnit - the whistle data unit.
|
||||
*/
|
||||
private void newWhistleData(PamDataUnit 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 algorithms
|
||||
|
||||
if (segmenterDetectionGroup==null) {
|
||||
//iterate until we find the correct time
|
||||
long segmentStart = firstClockUpdate;
|
||||
while() {
|
||||
|
||||
}
|
||||
|
||||
int[] chanGroups = dlControl.getDLParams().groupedSourceParams.getChannelGroups();
|
||||
|
||||
int index = -1;
|
||||
for (int i=0; i<chanGroups.length; i++) {
|
||||
if (dlControl.getDLParams().groupedSourceParams.getGroupChannels(chanGroups[i])==dataUnit.getChannelBitmap())
|
||||
index=i;
|
||||
}
|
||||
|
||||
//System.out.println("Whiste data: " + dataUnit + " " + chanGroups.length + " " + index + " " + dataUnit.getChannelBitmap());
|
||||
// PamArrayUtils.printArray(chanGroups);
|
||||
|
||||
if (index<0) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (segmenterDetectionGroup[index] == null || !detectionInSegment(dataUnit, segmenterDetectionGroup[index])) {
|
||||
|
||||
//System.out.println("Whiste not in segment");
|
||||
//iterate until we find the correct time for this detection. This keeps the segments consist no matter
|
||||
//the data units. What we do not want is the first data unit defining the start of the first segment.
|
||||
long segmentStart = firstClockUpdate;
|
||||
long segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
|
||||
|
||||
while(!detectionInSegment(dataUnit, segmentStart, segmenterEnd)) {
|
||||
segmentStart = segmenterEnd;
|
||||
segmenterEnd = (long) (segmentStart + getSegmentLenMillis());
|
||||
}
|
||||
|
||||
long startSample = this.absMillisecondsToSamples(segmenterEnd);
|
||||
|
||||
//now we need to create a new data unit.
|
||||
if (segmenterDetectionGroup[index]!=null) {
|
||||
System.out.println("SAVE WHISTLE SEGMENT!: " + segmenterDetectionGroup[index]);
|
||||
|
||||
//save the data unit
|
||||
this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[index]);
|
||||
}
|
||||
|
||||
//TODO There is no good way to get the start sample?
|
||||
segmenterDetectionGroup[index] = new SegmenterDetectionGroup(segmentStart, chanGroups[index], startSample, getSegmentLenMillis());
|
||||
}
|
||||
|
||||
//System.out.println("Add whistle to existing segment");
|
||||
segmenterDetectionGroup[index].addSubDetection(whistle);
|
||||
|
||||
}
|
||||
|
||||
private boolean detectionInSegment(PamDataUnit dataUnit, SegmenterDetectionGroup segmenterDetectionGroup2) {
|
||||
return detectionInSegment(dataUnit, segmenterDetectionGroup2.getSegmentStartMillis(),
|
||||
(long) (segmenterDetectionGroup2.getSegmentStartMillis()+segmenterDetectionGroup2.getSegmentDuration()));
|
||||
}
|
||||
|
||||
|
||||
private boolean detectionInSegment(PamDataUnit dataUnit, long segStart, long segEnd) {
|
||||
//TODO - this is going to fail for very small segments.
|
||||
long whistleStart = dataUnit.getTimeMilliseconds();
|
||||
long whistleEnd = dataUnit.getDurationInMilliseconds().longValue();
|
||||
|
||||
if ((whistleStart>=segStart && whistleStart<segEnd) || ((whistleEnd>=segStart && whistleEnd<segEnd))){
|
||||
//some part of the whistle is in the segment.
|
||||
System.out.println("Whsitle in segment: " + whistleStart + " " + whistleEnd);
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
private double getSegmentLenMillis() {
|
||||
double millis = (dlControl.getDLParams().rawSampleSize/this.getSampleRate())*1000.;
|
||||
return millis;
|
||||
}
|
||||
|
||||
|
||||
int count=0;
|
||||
public void masterClockUpdate(long milliSeconds, long sampleNumber) {
|
||||
super.masterClockUpdate(milliSeconds, sampleNumber);
|
||||
if (firstClockUpdate<0) {
|
||||
firstClockUpdate = milliSeconds;
|
||||
}
|
||||
|
||||
//want to make sure that a segment isn't saved if we suddenly lose
|
||||
// a steady stream of data units. This ensure that the segments are saved properly
|
||||
//after the master clock has gone past the end of the current segment.
|
||||
if (segmenterDetectionGroup!=null && count%20==0) {
|
||||
for (int i=0; i<segmenterDetectionGroup.length; i++) {
|
||||
if (segmenterDetectionGroup[i]!=null && segmenterDetectionGroup[i].getSegmentEndMillis()<milliSeconds) {
|
||||
//add the segment to the datablock
|
||||
this.segmenterGroupDataBlock.addPamData(segmenterDetectionGroup[i]);
|
||||
|
||||
//create a new segment
|
||||
SegmenterDetectionGroup aSegmenterDetectionGroup =
|
||||
new SegmenterDetectionGroup(segmenterDetectionGroup[i].getSegmentEndMillis(),
|
||||
segmenterDetectionGroup[i].getChannelBitmap(),
|
||||
this.absMillisecondsToSamples(segmenterDetectionGroup[i].getSegmentEndMillis()),
|
||||
getSegmentLenMillis());
|
||||
segmenterDetectionGroup[i] = aSegmenterDetectionGroup;
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
count++;
|
||||
}
|
||||
|
||||
|
||||
@ -376,7 +465,7 @@ public class SegmenterProcess extends PamProcess {
|
||||
|
||||
/**
|
||||
* Take a raw sound chunk of data and segment into discrete groups. This handles
|
||||
* much situations e.g. where the segment is much larger than the raw data or
|
||||
* many situations e.g. where the segment is much larger than the raw data or
|
||||
* where the segment is much small than each rawDataChunk returning multiple
|
||||
* segments.
|
||||
*
|
||||
@ -392,7 +481,7 @@ public class SegmenterProcess extends PamProcess {
|
||||
|
||||
/**
|
||||
* Take a raw sound chunk of data and segment into discrete groups. This handles
|
||||
* much situations e.g. where the segment is much larger than the raw data or
|
||||
* many situations e.g. where the segment is much larger than the raw data or
|
||||
* where the segment is much small than each rawDataChunk returning multiple
|
||||
* segments.
|
||||
*
|
||||
@ -624,7 +713,7 @@ public class SegmenterProcess extends PamProcess {
|
||||
//Need to copy a section of the old data into the new
|
||||
if (nextRawChunks[i]!=null) {
|
||||
/**
|
||||
* It's very important to clone this as otherwise some very weird things happnen as the units are
|
||||
* It's very important to clone this as otherwise some very weird things happen as the units are
|
||||
* passed to downstream processes.
|
||||
*/
|
||||
currentRawChunks[i] = nextRawChunks[i][nextRawChunks[i].length-1].clone(); //in an unlikely situation this could be null should be picked up by the first null check.
|
||||
@ -721,4 +810,9 @@ public class SegmenterProcess extends PamProcess {
|
||||
return segmenterDataBlock;
|
||||
}
|
||||
|
||||
|
||||
public SegmenterGroupDataBlock getSegmenteGrouprDataBlock() {
|
||||
return this.segmenterGroupDataBlock;
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user