com.actelion.research.orbit.imageAnalysis.tasks.ClassificationTaskTiled Maven / Gradle / Ivy
Show all versions of orbit-image-analysis Show documentation
/*
* Orbit, a versatile image analysis software for biological image-based quantification.
* Copyright (C) 2009 - 2017 Actelion Pharmaceuticals Ltd., Gewerbestrasse 16, 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
* Like ClassificationTask, but can handle set of tiles.
* Fires CLASSIFICATION_PROGRESS [0..100] propertyChangeEvents.
*
* Normalization is done in RecognitionWorker, this task outputs only sums.
*/
public class ClassificationTaskTiled extends PropertyChangeEmitter implements Callable {
public final static String CLASSIFICATION_PROGRESS = "classificationProgress";
private final static Logger logger = LoggerFactory.getLogger(ClassificationTaskTiled.class);
private ClassifierWrapper classifier = null;
private Instances dataSet = null;
private List tileList = null;
private double progress = 0d;
private FeatureDescription featureDescription = null;
private int windowSize = 4;
private List classShapes = null;
private TiledImagePainter bimg = null;
private TiledImageWriter classImage = null;
private boolean canceled = false;
private boolean paused = false;
private boolean writeClassificationImage = true;
private Shape ROI = null;
private ExclusionMapGen exclusionMapGen = null;
private Random rand = new Random();
private double pixelFuzzyness = 0d;
private boolean executed = false;
private Histogram[] histograms = null; // histograms per channel
public ClassificationTaskTiled(final OrbitModel model, final Shape ROI, final TiledImagePainter bimg, final TiledImageWriter classImage, final List tileList, final boolean writeClassificationImage) {
this(model.getClassifier(), model.getStructure(), model.getFeatureDescription(), model.getClassShapes(), ROI, bimg, classImage, tileList, writeClassificationImage);
}
/**
* @param classifier (has to be build before)
* @param dataSet
* @param classShapes
* @param ROI (can be null)
* @param bimg
* @param classImage
* @param tileList
* @param writeClassificationImage
*/
public ClassificationTaskTiled(final ClassifierWrapper classifier, final Instances dataSet, final FeatureDescription featureDescription, final List classShapes, final Shape ROI, final TiledImagePainter bimg, final TiledImageWriter classImage, final List tileList, final boolean writeClassificationImage) {
this.classifier = classifier;
this.dataSet = dataSet;
this.tileList = tileList;
this.featureDescription = featureDescription;
this.windowSize = featureDescription.getWindowSize();
this.classShapes = classShapes;
this.ROI = ROI;
this.bimg = bimg;
this.classImage = classImage;
this.writeClassificationImage = writeClassificationImage;
if (writeClassificationImage && (classImage == null))
throw new IllegalArgumentException("writeClassificationImage is true but classImage is null, please either deactivate writeClassificationImage or provide a classImage (e.g. TilesImage).");
}
public Long[] call() throws Exception {
if (executed) throw new RuntimeException("only one execution allowed"); // only one execution!
executed = true;
Long[] ratio = new Long[classShapes.size()];
for (int i = 0; i < ratio.length; i++) ratio[i] = new Long(0);
Color c;
Instance inst = null;
TissueFeatures tissueFeatures = OrbitUtils.createTissueFeatures(featureDescription, bimg);
double[] feats = new double[(windowSize * 2 + 1) * (windowSize * 2 + 1) * 3 + 1]; // +1 for contextclassification???
TiledImage classImg = null;
if (writeClassificationImage) classImg = classImage.getImage();
PlanarImage image = bimg.getImage();
int numDone = 0;
double oldProgress = 0d;
if ((tileList == null) || (tileList.size() == 0)) {
logger.trace("Warning: tileList.size()==0 (e.g. too many threads, but not problem)"); // happens if to many threads, but no problem
firePropertyChangeEvent(new PropertyChangeEvent(this, CLASSIFICATION_PROGRESS, new Double(0), new Double(100)));
return ratio;
}
if (tileList.size() == 1 && tileList.get(0).x == -1 && tileList.get(0).y == -1) {
tileList = Arrays.asList(image.getTileIndices(null));
}
HashSet featureClassSet = null;
if (featureDescription.getFeatureClasses() != null && featureDescription.getFeatureClasses().length > 0) {
featureClassSet = new HashSet(featureDescription.getFeatureClasses().length);
for (int i = 0; i < featureDescription.getFeatureClasses().length; i++) {
featureClassSet.add(featureDescription.getFeatureClasses()[i]);
}
}
//System.out.println("tileList: "+tileList);
for (Point tileNum : tileList) {
//logger.trace("before getting raster "+tileNum.x+"/"+tileNum.y);
Raster readRaster;
if (OrbitUtils.TILEMODE) {
readRaster = bimg.getModifiedImage(featureDescription).getTile(tileNum.x, tileNum.y);
} else
readRaster = bimg.getData(new Rectangle(image.tileXToX(tileNum.x) - windowSize, image.tileYToY(tileNum.y) - windowSize, image.getTileWidth() + (windowSize * 2 + 1), image.getTileHeight() + (windowSize * 2 + 1)), featureDescription);
if (readRaster == null) {
throw new OrbitImageServletException("error getting image raster");
}
// apply raster modifications like color deconvolution
readRaster = OrbitUtils.getModifiedRaster(readRaster, featureDescription, bimg.getImage().getColorModel(), true, tileNum.x, tileNum.y, "modifiedRaster", bimg.getImage().getLevel());
WritableRaster writeRaster = null;
if (writeClassificationImage) {
try {
writeRaster = classImg.getWritableTile(tileNum.x, tileNum.y);
} catch (Throwable ex) {
writeRaster = null;
logger.error("error getting writable tile: " + ex.getMessage()); // batch mode error: unable to create new native thread
}
}
int samples = featureDescription.getSampleSize();
int[] rgb = null;
if (histograms != null) {
if (histograms.length != samples)
throw new IllegalArgumentException("Histogram size must match sample size. Histogram.length=" + histograms.length + " but samplesize=" + samples);
rgb = new int[samples];
}
final boolean doHistogram = (histograms != null && featureClassSet != null);
int minX = image.tileXToX(tileNum.x);
int maxX = image.tileXToX(tileNum.x) + image.getTileWidth();
int minY = image.tileYToY(tileNum.y);
int maxY = image.tileYToY(tileNum.y) + image.getTileHeight();
if (ROI != null) {
Rectangle bb = ROI.getBounds();
if (bb != null) {
if (bb.getMinX() > minX) minX = (int) bb.getMinX();
if (bb.getMinY() > minY) minY = (int) bb.getMinY();
if (bb.getMaxX() < maxX) maxX = (int) bb.getMaxX();
if (bb.getMaxY() < maxY) maxY = (int) bb.getMaxY();
}
}
for (int x = minX; x < maxX; x++)
for (int y = minY; y < maxY; y++) {
if (canceled) continue;
if (OrbitUtils.TILEMODE) {
if (!((x > readRaster.getMinX() + windowSize) && (y > readRaster.getMinY() + windowSize) && (x < readRaster.getMinX() + readRaster.getWidth() - windowSize) && (y < readRaster.getMinY() + readRaster.getHeight() - windowSize)))
continue;
}
if (OrbitUtils.isInROI(x, y, ROI, exclusionMapGen)) {
if ((pixelFuzzyness > 0) && (rand.nextDouble() < pixelFuzzyness)) continue;
feats = tissueFeatures.buildFeatures(readRaster, x, y, Double.NaN);
inst = new DenseInstance(1.0d, feats);
inst.setDataset(dataSet);
try {
double classVal;
classVal = classifier.classifyInstance(inst);
if (classVal > ratio.length - 1)
logger.warn("classVal > image ratios (class numbers and rations don't match anymore!) ratioLength: " + ratio.length + " classVal: " + classVal);
ratio[(int) classVal]++;
if (doHistogram) {
if (featureClassSet.contains((int) classVal)) {
rgb = readRaster.getPixel(x, y, rgb);
for (int i = 0; i < rgb.length; i++) {
histograms[i].inc(rgb[i]);
}
}
}
if (writeClassificationImage) {
c = classShapes.get((byte) classVal).getColor();
writeRaster.setPixel(x, y, new int[]{c.getRed(), c.getGreen(), c.getBlue(), 255});
}
} catch (Exception e) {
e.printStackTrace();
logger.error("error classifying image", e);
}
} // ROI check
} // x,y
if (writeClassificationImage) {
classImg.releaseWritableTile(tileNum.x, tileNum.y);
//DiskMemImage.getCommonTileCache().flush(); // really needed? -> NO!
}
if (canceled) break;
numDone++;
progress = (numDone / (double) tileList.size()) * 100d;
firePropertyChangeEvent(new PropertyChangeEvent(this, CLASSIFICATION_PROGRESS, oldProgress, progress));
oldProgress = progress;
// pause
while (paused) {
try {
//this.wait();
Thread.sleep(200);
} catch (Exception ex) {
}
}
} // tileNum
return ratio;
}
public double getProgress() {
return progress;
}
public boolean isCanceled() {
return canceled;
}
public void setCanceled(boolean canceled) {
this.canceled = canceled;
}
public void cancel() {
setCanceled(true);
}
public boolean isPaused() {
return paused;
}
public void setPaused(boolean paused) {
this.paused = paused;
}
public void pause() {
setPaused(true);
}
public void resume() {
setPaused(false);
}
public boolean isWriteClassificationImage() {
return writeClassificationImage;
}
public ExclusionMapGen getExclusionMapGen() {
return exclusionMapGen;
}
public void setExclusionMapGen(ExclusionMapGen exclusionMapGen) {
this.exclusionMapGen = exclusionMapGen;
}
public double getPixelFuzzyness() {
return pixelFuzzyness;
}
public void setPixelFuzzyness(double pixelFuzzyness) {
this.pixelFuzzyness = pixelFuzzyness;
}
public Histogram[] getHistograms() {
return histograms;
}
public void setHistograms(Histogram[] histograms) {
this.histograms = histograms;
}
}