def projectMax(img, minC, maxC, reduce_max): imgA = ArrayImgs.unsignedSorts( Intervals.dimensionsAsLongArray(imgC)) ImgUtil.copy( ImgView.wrap( convert( Views.collapseReal( Views.interval(img, minC, maxC)), reduce_max.newInstance(), imglibtype), img.factory()), imgA) return imgA
def translate_using_imglib2(imp, dx, dy, dz): print "imp channels",imp.getNChannels() # todo: # if multiple channels use Duplicator to translate each channel individually ## wrap # http://javadoc.imagej.net/ImgLib2/net/imglib2/img/imageplus/ImagePlusImg.html img = ImagePlusImgs.from(imp.duplicate()) print "dimensions:",img.numDimensions() print img.getChannels() ## prepare image print "img",img ddd extended = Views.extendBorder(img) #print "extended",extended #print "extended",extended.dimension(1) dims = zeros(4, 'l') img.dimensions(dims) print "dims",dims converted = Converters.convert(extended, RealFloatSamplerConverter()) composite = Views.collapseReal(converted, imp.getNChannels()) print "composite",composite interpolant = Views.interpolate(composite, NLinearInterpolatorFactory()) #print "interpolant",interpolant transformed = RealViews.affine(interpolant, Translation3D(dx, dy, dz)) print "transformed", transformed cropped = Views.interval(transformed, img) print "cropped.numDimensions()", cropped.numDimensions() print "cropped",cropped ## wrap back and return bd = imp.getBitDepth() # maybe simply wrap works? if bd==8: return(ImageJFunctions.wrapUnsignedByte(cropped,"imglib2")) elif bd == 16: return(ImageJFunctions.wrapUnsignedShort(cropped,"imglib2")) elif bd == 32: return(ImageJFunctions.wrapFloat(cropped,"imglib2")) else: return None
def projectLastDimension(img, showEarly=False): """ Project the last dimension, e.g. a 4D image becomes a 3D image, using the provided reducing function (e.g. min, max, sum). """ last_dimension = img.numDimensions() - 1 # The collapsed image imgC = ArrayImgs.unsignedShorts( [img.dimension(d) for d in xrange(last_dimension)]) if showEarly: showStack( imgC, title="projected") # show it early, will be updated progressively if img.dimension(last_dimension) > 10: # one by one print "One by one" for i in xrange(img.dimension(last_dimension)): print i compute(maximum(imgC, Views.hyperSlice(img, last_dimension, i))).into(imgC) else: # Each sample of img3DV is a virtual vector over all time frames at that 3D coordinate: imgV = Views.collapseReal(img) # Reduce each vector to a single scalar, using a Converter # The Converter class reduce_max = makeCompositeToRealConverter( reducer_class=Math, reducer_method="max", reducer_method_signature="(DD)D") img3DC = convert(imgV, reduce_max.newInstance(), img.randomAccess().get().getClass()) ImgUtil.copy(ImgView.wrap(imgV, img.factory()), imgC) return imgC
def maxProjectLastDimension(img, strategy="1by1", chunk_size=0): last_dimension = img.numDimensions() -1 if "1by1" == strategy: exe = newFixedThreadPool() try: n_threads = exe.getCorePoolSize() imgTs = [ArrayImgs.unsignedShorts(list(Intervals.dimensionsAsLongArray(img))[:-1]) for i in xrange(n_threads)] def mergeMax(img1, img2, imgT): return compute(maximum(img1, img2)).into(imgT) def hyperSlice(index): return Views.hyperSlice(img, last_dimension, index) # The first n_threads mergeMax: futures = [exe.submit(Task(mergeMax, hyperSlice(i*2), hyperSlice(i*2 +1), imgTs[i])) for i in xrange(n_threads)] # As soon as one finishes, merge it with the next available hyperSlice next = n_threads while len(futures) > 0: # i.e. not empty imgT = futures.pop(0).get() if next < img.dimension(last_dimension): futures.append(exe.submit(Task(mergeMax, imgT, hyperSlice(next), imgT))) next += 1 else: # Run out of hyperSlices to merge if 0 == len(futures): return imgT # done # Merge imgT to each other until none remain futures.append(exe.submit(Task(mergeMax, imgT, futures.pop(0).get(), imgT))) finally: exe.shutdownNow() else: # By chunks imglibtype = img.randomAccess().get().getClass() # The Converter class reduce_max = makeCompositeToRealConverter(reducer_class=Math, reducer_method="max", reducer_method_signature="(DD)D") if chunk_size > 0: # map reduce approach exe = newFixedThreadPool() try: def projectMax(img, minC, maxC, reduce_max): imgA = ArrayImgs.unsignedSorts(Intervals.dimensionsAsLongArray(imgC)) ImgUtil.copy(ImgView.wrap(convert(Views.collapseReal(Views.interval(img, minC, maxC)), reduce_max.newInstance(), imglibtype), img.factory()), imgA) return imgA # The min and max coordinates of all dimensions except the last one minCS = [0 for d in xrange(last_dimension)] maxCS = [img.dimension(d) -1 for d in xrange(last_dimension)] # Process every chunk in parallel futures = [exe.submit(Task(projectMax, img, minCS + [offset], maxCS + [min(offset + chunk_size, img.dimension(last_dimension)) -1])) for offset in xrange(0, img.dimension(last_dimension), chunk_size)] return reduce(lambda f1, f2: compute(maximum(f1.get(), f2.get())).into(f1.get(), futures)) finally: exe.shutdownNow() else: # One chunk: all at once # Each sample of img3DV is a virtual vector over all time frames at that 3D coordinate # Reduce each vector to a single scalar, using a Converter img3DC = convert(Views.collapseReal(img), reduce_max.newInstance(), imglibtype) imgA = ArrayImgs.unsignedShorts([img.dimension(d) for d in xrange(last_dimension)]) ImgUtil.copy(ImgView.wrap(imgV, img.factory()), imgA) return imgA
import sys sys.path.append("/home/albert/lab/scripts/python/imagej/IsoView-GCaMP/") from lib.converter import makeCompositeToRealConverter, convert from net.imglib2.view import Views from java.lang import Math from net.imglib2.img.display.imagej import ImageJFunctions as IL from ij import IJ # Create the Converter class reduce_max = makeCompositeToRealConverter(reducer_class=Math, reducer_method="max", reducer_method_signature="(DD)D") # Testing with the bat cochlea volume, # projecting it from 3D to 2D with a max intensity projection img3D = IL.wrap(IJ.getImage()) img3DV = Views.collapseReal(img3D) img2D = convert(img3DV, reduce_max.newInstance(), img3D.randomAccess().get().getClass()) IL.wrap(img2D, "Z projection").show()