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Orbit, a versatile image analysis software for biological image-based quantification
/*
* Orbit, a versatile image analysis software for biological image-based quantification.
* Copyright (C) 2009 - 2018 Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see EncodeImageTiles(int tileChunkSize, int imageLevel, final List rdfList) throws OrbitImageServletException {
return EncodeImageTiles(null, tileChunkSize, imageLevel, rdfList.toArray(new RawDataFile[0]));
}
/**
* Encodes an (tiled) image in several rdfID#tileX1,tileY1;tileX2,tileY2;... strings.
*
* @param rdfList list of RawDataFiles
* @return
* @aram tileChunkSize defines the number of tiles per package (encoded string).
*/
public static List EncodeImageTiles(int tileChunkSize, int imageLevel, final RawDataFile... rdfList) throws OrbitImageServletException {
return EncodeImageTiles(null, tileChunkSize, imageLevel, rdfList);
}
/**
* Encodes an (tiled) image and a model filename in several rdfID#tileX1,tileY1;tileX2,tileY2;...#model strings.
* Use -1 as chunkSize for a pseudo marker to use all tiles of the image.
*
* @param rdfList list of RawDataFiles
* @param model the model filename (with or without path). Must not contain spaces.
* @return
* @aram tileChunkSize defines the number of tiles per package (encoded string).
*/
public static List EncodeImageTiles(String model, int tileChunkSize, int imageLevel, final List rdfList) throws OrbitImageServletException {
return EncodeImageTiles(model, tileChunkSize, imageLevel, rdfList.toArray(new RawDataFile[0]));
}
/**
* Encodes an (tiled) image and a model filename in several rdfID#tileX1,tileY1;tileX2,tileY2;...#model strings.
* Use -1 as chunkSize for a pseudo marker to use all tiles of the image.
*
* @param rdfList list of RawDataFiles
* @param model the model filename (with or without path). Must not contain spaces.
* @return
* @aram tileChunkSize defines the number of tiles per package (encoded string).
*/
public static List EncodeImageTiles(String model, int tileChunkSize, int imageLevel, final RawDataFile... rdfList) throws OrbitImageServletException {
return EncodeImageTiles(model, null, tileChunkSize, imageLevel, false, false, rdfList);
}
/**
* Encodes an (tiled) image and a model filename in several rdfID#tileX1,tileY1;tileX2,tileY2;...#model strings.
* Use -1 as chunkSize for a pseudo marker to use all tiles of the image.
*
* @param rdfList list of RawDataFiles
* @param model the model filename (with or without path). Must not contain spaces.
* @return
* @aram tileChunkSize defines the number of tiles per package (encoded string).
*/
public static List EncodeImageTiles(String model, OrbitModel roiModel, int parallelism, int imageLevel, boolean onlyTilesInRoi, boolean skipNonExplicitROIImages, final RawDataFile... rdfList) throws OrbitImageServletException {
Map> tileMap = new HashMap<>();
int totalCnt = 0;
for (RawDataFile rdf : rdfList) {
boolean found = false;
try {
List annos = DALConfig.getImageProvider().LoadRawAnnotationsByRawDataFile(rdf.getRawDataFileId(), RawAnnotation.ANNOTATION_TYPE_IMAGE);
for (RawAnnotation anno : annos) {
ImageAnnotation ia = new ImageAnnotation(anno);
if ((ia.getSubType() == ImageAnnotation.SUBTYPE_ROI) && (roiModel == null || roiModel.getAnnotationGroup() <= 0 || roiModel.getAnnotationGroup() == ia.getGroup())) {
found = true;
}
}
} catch (Exception e) {
e.printStackTrace();
}
if (skipNonExplicitROIImages && !found) continue;
tileMap.put(rdf, new ArrayList());
RecognitionFrame rf;
if (imageLevel <= 0) {
rf = new RecognitionFrame(rdf, false);
} else {
rf = new RecognitionFrame(null, rdf, imageLevel - 1); // -1 because imageLEvel is number, not index
}
Point[] tileIndices;
Rectangle roi = null;
if (onlyTilesInRoi && roiModel != null) {
rf.loadAnnotationROI(rdf.getRawDataFileId(), roiModel.getAnnotationGroup());
if (rf.getROI() != null) {
roi = rf.getROI().getScaledInstance(100d, new Point(0, 0)).getBounds();
}
}
if (parallelism > 0) tileIndices = rf.bimg.getImage().getTileIndices(roi);
else
tileIndices = new Point[]{new Point(-1, -1)}; // dummy marker for all tiles (used for exclusion model quantification)
for (Point tile : tileIndices) {
tileMap.get(rdf).add(tile);
totalCnt++;
}
}
int chunksize = 100;
if (parallelism > 0) chunksize = totalCnt / parallelism;
List imageList = new ArrayList();
for (RawDataFile rdf : tileMap.keySet()) {
List tiles = tileMap.get(rdf);
if (tiles.size() > 0) {
Collections.shuffle(tiles); // very fast tiles (e.g. just background) and long running tiles should be mixed for average job runtime
List localList = new ArrayList();
for (Point tile : tiles) {
localList.add(tile);
if (localList.size() >= chunksize) {
imageList.add(new ImageTile(rdf.getRawDataFileId(), localList, model).toString());
localList = new ArrayList();
}
}
if (localList.size() > 0) {
imageList.add(new ImageTile(rdf.getRawDataFileId(), localList, model).toString());
}
}
}
log.debug("imageList chunks: " + imageList.size());
return imageList;
}
/********************************************************************/
/***** Cell Segmentation ********************************************/
/********************************************************************/
public static SegmentationResult Segmentation(final int rdfId, final int modelId) throws Exception {
return Segmentation(rdfId, OrbitModel.LoadFromOrbit(modelId), null, -1);
}
public static SegmentationResult Segmentation(final int rdfId, final int modelId, List tiles) throws Exception {
return Segmentation(rdfId, OrbitModel.LoadFromOrbit(modelId), tiles, -1);
}
/**
* set numThreads=-1 for availableProcessors
*/
public static SegmentationResult Segmentation(final int rdfId, final OrbitModel model, List tiles, int numThreads) throws Exception {
final RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
return Segmentation(rdf, model, tiles, numThreads);
}
public static SegmentationResult Segmentation(final RawDataFile rdf, final OrbitModel model, List tiles, int numThreads) throws OrbitImageServletException {
RecognitionFrame rf = new RecognitionFrame(rdf, false);
return Segmentation(rf, rdf.getRawDataFileId(), model, tiles, numThreads, true);
}
public static SegmentationResult Segmentation(RecognitionFrame rf, int rdfId, final OrbitModel model, List tiles, int numThreads, boolean newMipRf) throws OrbitImageServletException {
return Segmentation(rf, rdfId, model, tiles, numThreads, newMipRf, null);
}
/**
* If roi!=null the parameter roi is used, otherwise it tries to load the annotationROI.
*/
public static SegmentationResult Segmentation(RecognitionFrame rf, int rdfId, final OrbitModel model, List tiles, int numThreads, boolean newMipRf, IScaleableShape roi) throws OrbitImageServletException {
rf.setClassShapes(model.getSegmentationModel().getClassShapes()); // 22.09.2015
rf.setWindowSize(model.getFeatureDescription().getWindowSize());
rf.setBoundaryClass(model.getBoundaryClass());
rf.setFeatureDescription(model.getFeatureDescription()); // Manuel 27.06.2012
OrbitUtils.setMultiChannelFeatures(rf.bimg.getImage(),model.getSegmentationModel().getFeatureDescription());
int annoGroup = model.getAnnotationGroup();
if (annoGroup < 0 && model.isLoadAnnotationsAsInversROI()) {
annoGroup = 0;// 0 means all
}
if (roi == null) {
if (rdfId >= 0)
rf.loadAnnotationROI(rdfId, annoGroup);
} else rf.setROI(roi);
RawDataFile rdf = null;
if (rdfId > 0) {
try {
rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
} catch (Exception e) {
e.printStackTrace();
}
}
RecognitionFrame oriRf = null;
if (newMipRf) {
oriRf = rf;
int level = model.getSegmentationModel().getMipLayer();
if (level > 0) { // upd 22.09.2015 (.getSegModel)
// attention: currently miplayers only work in combination with an image server (not local files)!
rf = OrbitUtils.getMipRecognitionFrame(rf,rdf,level);
//System.out.println("mipimage: "+rf.bimg.image);
}
}
ExclusionMapGen exMap = ExclusionMapGen.constructExclusionMap(rdf, rf, model, rf.getROI());
if (exMap != null) {
exMap.setNumThreads(1);
exMap.setKeepVisualClassificationImage(false);
exMap.setDoNormalize(false);
exMap.generateMap();
}
// cellSegmentation
ObjectSegmentationWorker segWorker = new ObjectSegmentationWorker(rdf, rf, null, model.getSegmentationModel().getClassShapes(), tiles);
segWorker.setModel(model);
segWorker.setExclusionMap(exMap);
if (numThreads > 0) {
segWorker.setNumThreads(numThreads);
}
if (oriRf != null) {
segWorker.setDontClassify(false);
segWorker.setOriginalFrame(oriRf);
}
segWorker.run();
// TODO: cell features???
String taskRes = segWorker.getTaskResult() != null ? segWorker.getTaskResult().getResultStr() : "No segmentation result";
System.out.println("TaskResult:\n" + taskRes);
SegmentationResult sr = segWorker.getSegmentationResult();
if (oriRf != null) {
sr.setShapeList(oriRf.getObjectSegmentationList());
sr.setSecondaryShapeList(oriRf.getSecondaryObjectSegmentationList());
// sr.setFeatureList(...); not possible right now, features are computed on lower scale, maybe recompute them on full res ???
}
return sr;
}
/**
* Segmentaiton on resolution based on scale factor.
* Currently only works with lower resolution level images (<=10000x10000 pixel) (for WSI scale factor of around 0.02).
*/
public static SegmentationResult Segmentation(RecognitionFrame rf, int rdfId, final OrbitModel model, List tiles, int numThreads, boolean newMipRf, IScaleableShape roi, double scaleFactor) throws OrbitImageServletException {
rf.setClassShapes(model.getSegmentationModel().getClassShapes()); // 22.09.2015
rf.setWindowSize(model.getFeatureDescription().getWindowSize());
rf.setBoundaryClass(model.getBoundaryClass());
rf.setFeatureDescription(model.getFeatureDescription()); // Manuel 27.06.2012
OrbitUtils.setMultiChannelFeatures(rf.bimg.getImage(),model.getSegmentationModel().getFeatureDescription());
int annoGroup = model.getAnnotationGroup();
if (annoGroup < 0 && model.isLoadAnnotationsAsInversROI()) {
annoGroup = 0;// 0 means all
}
if (roi == null) {
if (rdfId >= 0)
rf.loadAnnotationROI(rdfId, annoGroup);
} else rf.setROI(roi);
RawDataFile rdf = null;
if (rdfId > 0) {
try {
rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
} catch (Exception e) {
e.printStackTrace();
}
}
RecognitionFrame oriRf = null;
if (newMipRf) {
oriRf = rf;
if (/*level > 0*/ true) { // upd 22.09.2015 (.getSegModel)
// attention: currently miplayers only work in combination with an image server (not local files)!
rf = OrbitUtils.getMipRecognitionFrameScaleFactor(rf,rdf,scaleFactor);
//System.out.println("mipimage: "+rf.bimg.image);
}
}
ExclusionMapGen exMap = ExclusionMapGen.constructExclusionMap(rdf, rf, model, rf.getROI());
if (exMap != null) {
exMap.setNumThreads(1);
exMap.setKeepVisualClassificationImage(false);
exMap.setDoNormalize(false);
exMap.generateMap();
}
if (rf.bimg.getWidth()*(long)rf.bimg.getHeight() > 10000L*10000L) {
throw new IllegalArgumentException("The scaled segmentation currently only works with lower resolution level images (<=10000x10000 pixel)");
}
BufferedImage bi = new BufferedImage(rf.bimg.getImage().getWidth(),rf.bimg.getImage().getHeight(),BufferedImage.TYPE_INT_RGB);
Graphics2D g = (Graphics2D) bi.getGraphics();
g.drawImage(rf.bimg.getModifiedImage(model.getSegmentationModel().getFeatureDescription()).getAsBufferedImage(),0,0,null);
g.dispose();
// apply image modifications, here: intensity normalization
//bi = ImageNormalizer.normalize(bi,0.98d,0.99d, Band.BAND_1);
// TODO: do the scaling here (scaleFactor), getMipRecognitionFrameScaleFactor only selects the right level
RecognitionFrame rf2 = new RecognitionFrame(new TiledImagePainter(PlanarImage.wrapRenderedImage(bi),""));
rf2.setROI(rf.getROI());
// PlanarImage pi = scalePlanarImage(rf.bimg.getImage(),0.99d);
// pi.getWidth();
// RecognitionFrame rf3 = new RecognitionFrame(new TiledImagePainter(pi,"pi"));
// rf3.setROI(rf.getROI());
// rf3.setMipScale(rf.getMipScale());
// works with rf2, not with rf or rf3
// difference: shapes in segmentationResult have scale 1600 instead of 100. Why?
// cellSegmentation
ObjectSegmentationWorker segWorker = new ObjectSegmentationWorker(rdf, rf2, null, model.getSegmentationModel().getClassShapes(), tiles);
segWorker.setModel(model);
segWorker.setExclusionMap(exMap);
if (numThreads > 0) {
segWorker.setNumThreads(numThreads);
}
if (oriRf != null) {
segWorker.setDontClassify(false);
segWorker.setOriginalFrame(oriRf);
}
//if (!(rf.bimg.getImage() instanceof OrbitTiledImageIOrbitImage)) System.out.println("not an OrbitTiledImageIOrbitImage image!");
segWorker.run();
// debugSegmentation(rf2,segWorker.getSegmentationResult().getShapeList());
// TODO: cell features???
String taskRes = segWorker.getTaskResult() != null ? segWorker.getTaskResult().getResultStr() : "No segmentation result";
System.out.println("TaskResult:\n" + taskRes);
SegmentationResult sr = segWorker.getSegmentationResult();
if (oriRf != null) {
sr.setShapeList(oriRf.getObjectSegmentationList());
sr.setSecondaryShapeList(oriRf.getSecondaryObjectSegmentationList());
// sr.setFeatureList(...); not possible right now, features are computed on lower scale, maybe recompute them on full res ???
}
return sr;
}
@Deprecated
private static PlanarImage scalePlanarImage(PlanarImage sourceImage, double factor) {
ParameterBlockJAI pb = new ParameterBlockJAI("scale", RenderedRegistryMode.MODE_NAME);
pb.setSource("source0", sourceImage);
pb.setParameter("xScale", new Float(factor));
pb.setParameter("yScale", new Float(factor));
PlanarImage pi = JAI.create("scale", pb);
return pi;
}
private static void debugSegmentation(RecognitionFrame rf, List segments) {
BufferedImage bi = new BufferedImage(rf.bimg.getImage().getWidth(),rf.bimg.getImage().getHeight(),BufferedImage.TYPE_INT_RGB);
Graphics2D g = (Graphics2D) bi.getGraphics();
g.drawImage(rf.bimg.getImage().getAsBufferedImage(),0,0,null);
g.setColor(Color.yellow);
for (Shape shape: segments) {
if (shape instanceof PolygonExt)
g.drawPolygon((Polygon) shape);
}
g.dispose();
try {
ImageIO.write(bi,"jpeg",new File("d:/test.jpg"));
} catch (IOException e) {
e.printStackTrace();
}
}
private static void debugSegmentation(BufferedImage biOri, List segments, int minX, int minY) {
BufferedImage bi = new BufferedImage(biOri.getWidth(),biOri.getHeight(),BufferedImage.TYPE_INT_RGB);
Graphics2D g = (Graphics2D) bi.getGraphics();
g.drawImage(biOri,0,0,null);
g.setColor(Color.yellow);
for (Shape shape: segments) {
if (shape instanceof PolygonExt)
//((PolygonExt) shape).translate(-minX,-minY);
g.drawPolygon((Polygon) shape);
}
g.dispose();
try {
ImageIO.write(bi,"jpeg",new File("d:/test.jpg"));
} catch (IOException e) {
e.printStackTrace();
}
}
/********************************************************************/
/***** Cell Classification ******************************************/
/********************************************************************/
/**
* set numThreads=-1 for availableProcessors
*/
public static ClassificationResult CellClassification(final int rdfId, final OrbitModel model, List tiles, int numThreads) throws Exception {
final RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
return CellClassification(rdf, model, tiles, numThreads);
}
public static ClassificationResult CellClassification(final RawDataFile rdf, final OrbitModel model, List tiles, int numThreads) throws OrbitImageServletException {
RecognitionFrame rf = new RecognitionFrame(rdf, false);
if (model.getMipLayer() > 0) {
rf = new RecognitionFrame(rf, rdf, model.getMipLayer() - 1); // mip number, not index
}
SegmentationResult segmentationResult = Segmentation(rf, rdf.getRawDataFileId(), model, tiles, numThreads, false); // sets objectSegmentationList list in rf
ClassificationResult classificationResult = getClassificationResult(rf, model, tiles, segmentationResult);
return classificationResult;
}
public static ClassificationResult CellClassification(final RecognitionFrame rf, final OrbitModel model, List tiles, int numThreads) throws OrbitImageServletException {
SegmentationResult segmentationResult = Segmentation(rf, -1, model, tiles, numThreads, false); // sets objectSegmentationList list in rf
ClassificationResult classificationResult = getClassificationResult(rf, model, tiles, segmentationResult);
return classificationResult;
}
private static ClassificationResult getClassificationResult(RecognitionFrame rf, OrbitModel model, List tiles, SegmentationResult segmentationResult) {
log.debug("{} cells segmented", segmentationResult==null?0: segmentationResult.getObjectCount());
OrbitModel modelNoMip = new OrbitModel(model);
modelNoMip.setMipLayer(0);
Map classCount = null;
if (segmentationResult==null || segmentationResult.getObjectCount()==0) {
// no objects available, create dummy result with 0 counts per class
classCount = new HashMap(model.getClassShapes().size());
for (int idx=0; idx classIdxCount = worker.getClassCount();
classCount = new HashMap(classIdxCount.keySet().size());
for (Integer idx : classIdxCount.keySet()) {
String className = "ignored objects";
if (idx < model.getClassShapes().size())
className = model.getClassShapes().get(idx).getName(); // else count is ignoredCount
int cnt = classIdxCount.get(idx);
classCount.put(className, (long) cnt);
}
}
return new ClassificationResult(classCount, null, model);
}
/********************************************************************/
/***** Tissue Classification ****************************************/
/********************************************************************/
public static ClassificationResult Classify(final int rdfId, final OrbitModel model) throws Exception {
final RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
return Classify(rdf, model, Collections.singletonList(new Point(-1, -1)), -1);
}
/**
* set numThreads=-1 for availableProcessors
*/
public static ClassificationResult Classify(final int rdfId, final OrbitModel model, List tiles, int numThreads) throws Exception {
final RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
return Classify(rdf, model, tiles, numThreads);
}
public static ClassificationResult Classify(final RawDataFile rdf, final OrbitModel _model, final List tiles, int numThreads) throws Exception {
return Classify(rdf, _model, tiles, numThreads, null);
}
public static ClassificationResult Classify(final RawDataFile rdf, final OrbitModel _model, final List tiles, int numThreads, IScaleableShape overrideROI) throws Exception {
return Classify(rdf, null, _model, tiles, numThreads, overrideROI);
}
/**
* Rdf is used for mip-level and exclusion model, but can be null (if mip and exclusion model is not used).
* _rf can be null, the rf is created based on rdf.
*
* @param rdf can be null, then set _rf
* @param _rf can be null, then set rdf
* @param _model
* @param tiles can be null - then all tiles are used
* @param numThreads currently not used (only one thread is used)
* @param overrideROI
* @return
* @throws Exception
*/
public static ClassificationResult Classify(final RawDataFile rdf, final RecognitionFrame _rf, final OrbitModel _model, List tiles, int numThreads, IScaleableShape overrideROI) throws Exception {
RecognitionFrame maskFrame = null;
if (rdf!=null) {
maskFrame = OrbitUtils.createMaskRecognitionFrame(rdf,_model);
}
return Classify(rdf,_rf,maskFrame,_model,tiles,numThreads,overrideROI);
}
/**
* Rdf is used for mip-level and exclusion model, but can be null (if mip and exclusion model is not used).
* _rf can be null, the rf is created based on rdf.
*
* @param rdf can be null, then set _rf
* @param _rf can be null, then set rdf
* @param _model
* @param tiles can be null - then all tiles are used
* @param numThreads currently not used (only one thread is used)
* @param overrideROI
* @return
* @throws Exception
*/
public static ClassificationResult Classify(final RawDataFile rdf, final RecognitionFrame _rf, final RecognitionFrame _maskFrame, final OrbitModel _model, List tiles, int numThreads, IScaleableShape overrideROI) throws Exception {
final double pixelFuzzyness = 0d;
final double tileFuzzyness = 0d;
OrbitModel model = new OrbitModel(_model);
RecognitionFrame rf = _rf;
if (rf == null) rf = new RecognitionFrame(rdf, false);
rf.setClassShapes(model.getClassShapes());
rf.setWindowSize(model.getFeatureDescription().getWindowSize());
rf.setBoundaryClass(model.getBoundaryClass());
rf.setFeatureDescription(model.getFeatureDescription()); // Manuel 27.06.2012
OrbitUtils.setMultiChannelFeatures(rf.bimg.getImage(),model.getFeatureDescription()); // TODO: create rf for mask and set fluo features
// mask image
RecognitionFrame maskFrame = _maskFrame;
if (maskFrame==null) {
maskFrame = rf;
}
int annoGroup = model.getAnnotationGroup();
if (annoGroup < 0 && model.isLoadAnnotationsAsInversROI()) {
annoGroup = 0;// 0 means all
}
if (overrideROI == null && rdf != null) {
rf.loadAnnotationROI(rdf.getRawDataFileId(), annoGroup);
} else {
if (overrideROI != null) {
overrideROI = (IScaleableShape) overrideROI.getScaledInstance(100d, new Point(0, 0));
}
rf.setROI(overrideROI);
}
if (model.getMipLayer() > 0) {
rf = new RecognitionFrame(rf, rdf, model.getMipLayer() - 1); // mip number, not index
}
RecognitionFrame exclusionFrame = null;
ExclusionMapGen exMap = ExclusionMapGen.constructExclusionMap(rdf, rf, model, rf.getROI());
if (exMap != null) {
exMap.setNumThreads(1);
exMap.setDoNormalize(false);
exMap.setKeepVisualClassificationImage(false);
exMap.generateMap();
exclusionFrame = exMap.getRecognitionFrame();
}
if (tiles == null) {
tiles = new ArrayList<>();
tiles.add(new Point(-1, -1));
}
Map ratioMain = null;
if (model.getClassifier() != null && model.getClassifier().isBuild()) { // otherwise e.g. only exclusion model
double[] ratio = new double[model.getClassShapes().size()];
rf.setRatio(ratio);
rf.setClassImage(new TiledImageWriter(rf.bimg.getWidth(), rf.bimg.getHeight(), rf.bimg.getTileWidth(), rf.bimg.getTileHeight()));
// TODO multithread version
ClassificationTaskTiled classificationTask = new ClassificationTaskTiled(ClassifierWrapper.makeCopy(model.getClassifier()), model.getStructure(), model.getFeatureDescription(),/*rf.getClassShapes()*/model.getClassShapes(), rf.getROI(), model.getMask()!=null?model.getMask().clone():null, maskFrame.bimg, rf.bimg, rf.getClassImage(), tiles, false);
classificationTask.setPixelFuzzyness(pixelFuzzyness);
if (exMap != null) classificationTask.setExclusionMapGen(exMap);
Long[] vals = classificationTask.call();
// results
ratioMain = new HashMap();
for (int i = 0; i < model.getClassShapes().size(); i++) {
ratioMain.put(model.getClassShapes().get(i).getName(), vals[i]);
}
}
Map ratioExcl = new HashMap();
if ((model.getExclusionModel() != null) && (exclusionFrame != null)) {
for (int i = 0; i < model.getExclusionModel().getClassShapes().size(); i++) {
ratioExcl.put(model.getExclusionModel().getClassShapes().get(i).getName(), (long) exclusionFrame.getRatio()[i]);
}
}
ClassificationResult result = new ClassificationResult(ratioMain, ratioExcl, model);
return result;
}
/***
* Histogram
***/
public static Histogram[] Histogram(final int rdfId, final OrbitModel model, List tiles, int numThreads) throws Exception {
final RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(rdfId);
return Histogram(rdf, model, tiles, numThreads);
}
public static Histogram[] Histogram(final RawDataFile rdf, final OrbitModel _model, final List tiles, int numThreads) throws Exception {
final double pixelFuzzyness = 0d;
final double tileFuzzyness = 0d;
OrbitModel model = new OrbitModel(_model);
RecognitionFrame rf = new RecognitionFrame(null);
rf.loadImage(rdf, false);
rf.setClassShapes(model.getClassShapes());
rf.setWindowSize(model.getFeatureDescription().getWindowSize());
rf.setBoundaryClass(model.getBoundaryClass());
rf.setFeatureDescription(model.getFeatureDescription()); // Manuel 27.06.2012
OrbitUtils.setMultiChannelFeatures(rf.bimg.getImage(),model.getFeatureDescription());
int annoGroup = model.getAnnotationGroup();
if (annoGroup < 0 && model.isLoadAnnotationsAsInversROI()) {
annoGroup = 0;// 0 means all
}
rf.loadAnnotationROI(rdf.getRawDataFileId(), annoGroup);
// mask image
RecognitionFrame maskFrame = null;
if (model.getMask()!=null && model.getMask() instanceof OrbitMaskClassificationModel) {
maskFrame = new RecognitionFrame(rdf); // or from parent rf?
OrbitUtils.setMultiChannelFeatures(maskFrame.bimg.getImage(),((OrbitMaskClassificationModel) model.getMask()).getModel().getFeatureDescription());
}
if (model.getMipLayer() > 0) {
rf = new RecognitionFrame(rf, rdf, model.getMipLayer() - 1); // mip number, not index
}
RecognitionFrame exclusionFrame = null;
ExclusionMapGen exMap = ExclusionMapGen.constructExclusionMap(rdf, rf, model, rf.getROI());
if (exMap != null) {
exMap.setNumThreads(1);
exMap.setKeepVisualClassificationImage(false);
exMap.setDoNormalize(false);
exMap.generateMap();
exclusionFrame = exMap.getRecognitionFrame();
}
Histogram[] histograms = Histogram(rdf, rf, model, exMap, tiles, pixelFuzzyness);
return histograms;
}
public static Histogram[] Histogram(RawDataFile rdf, RecognitionFrame rf, OrbitModel model, ExclusionMapGen exMap, List tiles, double pixelFuzzyness) throws Exception {
Histogram[] histograms = new Histogram[model.getFeatureDescription().getSampleSize()];
// mask image
RecognitionFrame maskFrame = OrbitUtils.createMaskRecognitionFrame(rdf, model);
if (maskFrame==null) {
maskFrame = rf;
}
for (int i = 0; i < histograms.length; i++) {
histograms[i] = new Histogram(model);
}
if (model.getClassifier() != null && model.getClassifier().isBuild()) { // otherwise e.g. only exclusion model
double[] ratio = new double[model.getClassShapes().size()];
OrbitUtils.setMultiChannelFeatures(rf.bimg.getImage(),model.getFeatureDescription());
rf.setRatio(ratio);
rf.setClassImage(new TiledImageWriter(rf.bimg.getWidth(), rf.bimg.getHeight(), rf.bimg.getTileWidth(), rf.bimg.getTileHeight()));
ClassificationTaskTiled classificationTask = new ClassificationTaskTiled(ClassifierWrapper.makeCopy(model.getClassifier()), model.getStructure(), model.getFeatureDescription(), model.getClassShapes(), rf.getROI(), model.getMask()!=null?model.getMask().clone():null, maskFrame.bimg, rf.bimg, rf.getClassImage(), tiles, false);
classificationTask.setPixelFuzzyness(pixelFuzzyness);
classificationTask.setHistograms(histograms);
if (exMap != null) classificationTask.setExclusionMapGen(exMap);
Long[] vals = classificationTask.call();
} else {
throw new IllegalArgumentException("model not trained");
}
return histograms;
}
/**
* Opens an image browser and returns a list of selected images.
* Null might be returned if no images have been selected.
*
* @param parent
* @return list of selected images or null
* @throws Exception
*/
public static List browseImagesDefault(Object parent) throws Exception {
DefaultRDFChooser rdfChooser = new DefaultRDFChooser();
rdfChooser.setMultiSelectionEnabled(true);
Component parentComponent = null;
if (parent instanceof Component) parentComponent = (Component) parent;
int returnVal = rdfChooser.showOpenDialog(parentComponent);
if (returnVal == DefaultRDFChooser.APPROVE_OPTION) {
final List rdfList = rdfChooser.getSelectedFiles();
return rdfList;
} else return null;
}
public static List searchImages(String search, int limit) throws Exception {
return DALConfig.getImageProvider().LoadRawDataFilesSearchFast(search, limit, Arrays.asList(RawUtilsCommon.fileTypesImage));
}
public static List searchImages(String search) throws Exception {
return searchImages(search, 1000);
}
public static void main(String[] args) throws Exception {
//InputStream inStream = OrbitHelper.class.getResourceAsStream("/resource/cellSegmentationWorking.cell");
InputStream inStream = new FileInputStream("d:/OrbitModels/test.apar");
OrbitModel model = OrbitModel.LoadFromInputStream(inStream);
RawDataFile rdf = DALConfig.getImageProvider().LoadRawDataFile(7683); // 21065
// int cellCount = Segmentation(rdf, model, null, -1).getObjectCount();
// System.out.println("counted cells: "+cellCount);
// List tiles = new ArrayList();
// tiles.add(new Point(10,20));
// ClassificationResult res = CellClassification(rdf,model,tiles,1);
// for (String s: res.getRatio().keySet()) {
// System.out.println("Class "+s+": "+res.getRatio().get(s));
// }
int[] featureClasses = new int[2];
featureClasses[0] = 0;
featureClasses[1] = 1;
model.getFeatureDescription().setFeatureClasses(featureClasses);
List tiles = new ArrayList();
tiles.add(new Point(-1, -1));
Histogram[] histograms = Histogram(rdf, model, tiles, 1);
long numPixel = 0;
for (int i = 0; i < histograms.length; i++) {
System.out.println(histograms[i].toString());
numPixel += histograms[i].getNumPixel();
}
System.out.println("#pixel: " + numPixel / histograms.length);
}
}