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()