def rotatedView(img, angle, enlarge=True, extend=Views.extendBorder):
""" Return a rotated view of the image, around the Z axis,
with an expanded (or reduced) interval view so that all pixels are exactly included.
img: a RandomAccessibleInterval
angle: in degrees
"""
cx = img.dimension(0) / 2.0
cy = img.dimension(1) / 2.0
toCenter = AffineTransform2D()
toCenter.translate(-cx, -cy)
rotation = AffineTransform2D()
# Step 1: place origin of rotation at the center of the image
rotation.preConcatenate(toCenter)
# Step 2: rotate around the Z axis
rotation.rotate(radians(angle))
# Step 3: undo translation to the center
rotation.preConcatenate(toCenter.inverse())
rotated = RV.transform(Views.interpolate(extend(img),
NLinearInterpolatorFactory()), rotation)
if enlarge:
# Bounds:
bounds = repeat((sys.maxint, 0)) # initial upper- and lower-bound values
# for min, max to compare against
transformed = zeros(2, 'f')
for corner in product(*zip(repeat(0), Intervals.maxAsLongArray(img))):
rotation.apply(corner, transformed)
bounds = [(min(vmin, int(floor(v))), max(vmax, int(ceil(v))))
for (vmin, vmax), v in zip(bounds, transformed)]
minC, maxC = map(list, zip(*bounds)) # transpose list of 2 pairs
# into 2 lists of 2 values
imgRot = Views.zeroMin(Views.interval(rotated, minC, maxC))
else:
imgRot = Views.interval(rotated, img)
return imgRot
def transformedView(img, transform, interval=None):
""" """
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, transform)
if interval:
return Views.interval(imgT, interval)
else:
return Views.interval(
imgT, [0, 0, 0],
[img.dimension(d) - 1 for d in xrange(img.numDimensions())])
def create(self, index):
cell_dimensions = [
self.grid.cellDimension(0),
self.grid.cellDimension(1)
]
n_cols = grid.imgDimension(0) / cell_dimensions[0]
x0 = (index % n_cols) * cell_dimensions[0]
y0 = (index / n_cols) * cell_dimensions[1]
index += 1 # 1-based slice indices in ij.ImageStack
if index < 1 or index > self.stack.size():
# Return blank image: a ByteAccess that always returns 255
return Cell(
cell_dimensions, [x0, y0],
type('ConstantValue', (ByteAccess, ), {
'getValue': lambda self, index: 255
})())
else:
# ImageJ stack slice indices are 1-based
img = IL.wrap(ImagePlus("", self.stack.getProcessor(index)))
# Create extended image with the padding color value
imgE = Views.extendValue(img, self.t.copy())
# A view that includes the padding between slices
minC = [-self.cell_padding for d in xrange(img.numDimensions())]
maxC = [
img.dimension(d) - 1 + self.cell_padding
for d in xrange(img.numDimensions())
]
imgP = Views.interval(imgE, minC, maxC)
return Cell(cell_dimensions, [x0, y0],
ProxyByteAccess(imgP, self.grid))
def translatedView(img, matrix): imgE = Views.extendZero(img) imgI = Views.interpolate(imgE, NLinearInterpolatorFactory()) # In negative: the inverse t = Translation3D(-matrix[3], -matrix[7], -matrix[11]) imgT = RealViews.transform(imgI, t) return Views.interval(imgT, [0, 0, 0], [img.dimension(d) for d in xrange(3)])
def asNormalizedUnsignedByteArrayImg(interval, invert, blockRadius, n_bins,
slope, matrices, copy_threads, index,
imp):
sp = imp.getProcessor() # ShortProcessor
sp.setRoi(interval.min(0), interval.min(1),
interval.max(0) - interval.min(0) + 1,
interval.max(1) - interval.min(1) + 1)
sp = sp.crop()
if invert:
sp.invert()
CLAHE.run(
ImagePlus("", sp), blockRadius, n_bins, slope, None
) # far less memory requirements than NormalizeLocalContrast, and faster.
minimum, maximum = autoAdjust(sp)
# Transform and convert image to 8-bit, mapping to display range
img = ArrayImgs.unsignedShorts(
sp.getPixels(), [sp.getWidth(), sp.getHeight()])
sp = None
affine = AffineTransform2D()
affine.set(matrices[index])
imgI = Views.interpolate(Views.extendZero(img),
NLinearInterpolatorFactory())
imgA = RealViews.transform(imgI, affine)
imgT = Views.zeroMin(Views.interval(imgA, img))
imgMinMax = convert(imgT, RealUnsignedByteConverter(minimum, maximum),
UnsignedByteType)
aimg = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img))
ImgUtil.copy(ImgView.wrap(imgMinMax, aimg.factory()), aimg,
copy_threads)
img = imgI = imgA = imgT = imgMinMax = None
return aimg
def test(iraf):
# Test dimensions: should be the same as the one input image
print "Dimensions:", Intervals.dimensionsAsLongArray(iraf)
# Test Cursor
c = iraf.cursor()
pos = zeros(2, 'l')
while c.hasNext():
c.fwd()
c.localize(pos)
print "Cursor:", pos, "::", c.get()
# Test RandomAccess
ra = iraf.randomAccess()
c = iraf.cursor()
while c.hasNext():
c.fwd()
ra.setPosition(c)
c.localize(pos)
print "RandomAccess:", pos, "::", ra.get()
# Test source img: should be untouched
c = img.cursor()
while c.hasNext():
print "source:", c.next()
# Test interval view: the middle 2x2 square
v = Views.interval(iraf, [1, 1], [2, 2])
IL.wrap(v, "+2 view").show()
def translate_single_stack_using_imglib2(imp, dx, dy, dz):
# wrap into a float imglib2 and translate
# conversion into float is necessary due to "overflow of n-linear interpolation due to accuracy limits of unsigned bytes"
# see: https://github.com/fiji/fiji/issues/136#issuecomment-173831951
img = ImagePlusImgs.from(imp.duplicate())
extended = Views.extendBorder(img)
converted = Converters.convert(extended, RealFloatSamplerConverter())
interpolant = Views.interpolate(converted, NLinearInterpolatorFactory())
# translate
if imp.getNDimensions()==3:
transformed = RealViews.affine(interpolant, Translation3D(dx, dy, dz))
elif imp.getNDimensions()==2:
transformed = RealViews.affine(interpolant, Translation2D(dx, dy))
else:
IJ.log("Can only work on 2D or 3D stacks")
return None
cropped = Views.interval(transformed, img)
# wrap back into bit depth of input image and return
bd = imp.getBitDepth()
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 translate_single_stack_using_imglib2(imp, dx, dy, dz):
# wrap into a float imglib2 and translate
# conversion into float is necessary due to "overflow of n-linear interpolation due to accuracy limits of unsigned bytes"
# see: https://github.com/fiji/fiji/issues/136#issuecomment-173831951
img = ImagePlusImgs.from(imp.duplicate())
extended = Views.extendZero(img)
converted = Converters.convert(extended, RealFloatSamplerConverter())
interpolant = Views.interpolate(converted, NLinearInterpolatorFactory())
# translate
if imp.getNDimensions()==3:
transformed = RealViews.affine(interpolant, Translation3D(dx, dy, dz))
elif imp.getNDimensions()==2:
transformed = RealViews.affine(interpolant, Translation2D(dx, dy))
else:
IJ.log("Can only work on 2D or 3D stacks")
return None
cropped = Views.interval(transformed, img)
# wrap back into bit depth of input image and return
bd = imp.getBitDepth()
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 findEdgePixels(img):
edge_pix = []
zero = img.firstElement().createVariable()
zero.setZero()
imgE = Views.extendValue(img, zero)
pos = zeros(img.numDimensions(), 'l')
inc = partial(operator.add, 1)
dec = partial(operator.add, -1)
cursor = img.cursor()
while cursor.hasNext():
t = cursor.next()
# A pixel is on the edge of the binary mask
# if it has a non-zero value
if 0 == t.getByte():
continue
# ... and its immediate neighbors ...
cursor.localize(pos)
minimum = array(imap(dec, pos), 'l') # map(dec, pos) also works, less performance
maximum = array(imap(inc, pos), 'l') # map(inc, pos) also works, less performance
neighborhood = Views.interval(imgE, minimum, maximum)
# ... have at least one zero value:
# Good performance: the "if x in <iterable>" approach stops upon finding the first x
if 0 in imap(UnsignedByteType.getByte, neighborhood):
edge_pix.append(RealPoint(array(list(pos), 'f')))
return edge_pix
def viewTransformed(img, affine):
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine)
minC = [0, 0, 0]
maxC = [img.dimension(d) - 1 for d in xrange(img.numDimensions())]
imgB = Views.interval(imgT, minC, maxC)
return imgB
def peakData():
""" A generator function that returns all peaks and their pixel sum, one by one. """
for peak in peaks:
peak.localize(p)
minCoords, maxCoords = centerAt(p, minC, maxC)
fov = Views.interval(img, minCoords, maxCoords)
s = sum(t.getInteger() for t in fov)
yield p, s
def roi(mask, image): # Convert ROI from R^n to Z^n. #discreteROI = Views.raster(Masks.toRealRandomAccessible(mask)) # Apply finite bounds to the discrete ROI. boundedDiscreteROI = Views.interval(mask, image) # Create an iterable version of the finite discrete ROI. iterableROI = Regions.iterable(boundedDiscreteROI) return iterableROI
def transformedView(img, matrix): imgE = Views.extendZero(img) imgI = Views.interpolate(imgE, NLinearInterpolatorFactory()) aff = AffineTransform3D() aff.set(*matrix) aff = aff.inverse() imgT = RealViews.transform(imgI, aff) return Views.interval(imgT, [0, 0, 0], [img.dimension(d) for d in xrange(3)])
def getViewFromImp(imp, r=None):
# r is a java.awt.rectangle
im = IL.wrapByte(imp)
if r is None:
r = Rectangle(0, 0, imp.getWidth(), imp.getHeight())
v = Views.zeroMin(
Views.interval(im, [r.x, r.y],
[r.x + r.width - 1, r.y + r.height - 1]))
return v
def updatePixels(self):
# Copy interval into pixels
view = Views.interval(
Views.extendZero(Views.hyperSlice(self.img3D, 2, self.indexZ)),
self.interval2D)
aimg = ArrayImgs.floats(
self.getPixels(),
[self.interval2D.dimension(0),
self.interval2D.dimension(1)])
ImgUtil.copy(view, aimg)
def get(self, path): transform = self.transformsDict[path] img = self.loader.get(path) imgE = Views.extendZero(img) imgI = Views.interpolate(imgE, NLinearInterpolatorFactory()) imgT = RealViews.transform(imgI, transform) minC = self.roi[0] if self.roi else [0] * img.numDimensions() maxC = self.roi[1] if self.roi else [img.dimension(d) -1 for d in xrange(img.numDimensions())] imgO = Views.zeroMin(Views.interval(imgT, minC, maxC)) return ImgView.wrap(imgO, img.factory()) if self.asImg else imgO
def getFFTFromView(v, extension, extSize, paddedDimensions, fftSize):
FFTMethods.dimensionsRealToComplexFast(extSize, paddedDimensions, fftSize)
fft = ArrayImgFactory(ComplexFloatType()).create(fftSize,
ComplexFloatType())
FFT.realToComplex(
Views.interval(
PhaseCorrelation2Util.extendImageByFactor(v, extension),
FFTMethods.paddingIntervalCentered(
v, FinalInterval(paddedDimensions))), fft, exe)
return fft
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 viewTransformed(img, matrix):
affine = AffineTransform3D()
affine.set(*matrix)
# It's a forward transform: invert
affine = affine.inverse()
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine)
# Same dimensions
imgB = Views.interval(imgT, img)
return imgB
def makeCell(self, index):
self.preloadCells(index) # preload others in the background
img = self.loadImg(self.filepaths[index])
affine = AffineTransform2D()
affine.set(self.matrices[index])
imgI = Views.interpolate(Views.extendZero(img),
NLinearInterpolatorFactory())
imgA = RealViews.transform(imgI, affine)
imgT = Views.zeroMin(Views.interval(imgA, self.interval))
aimg = img.factory().create(self.interval)
ImgUtil.copy(ImgView.wrap(imgT, aimg.factory()), aimg)
return Cell(self.cell_dimensions, [0, 0, index], aimg.update(None))
def intoSlice(img, xOffset, yOffset):
stack_slice = ArrayImgFactory(
img.randomAccess().get().createVariable()).create(
[canvas_width, canvas_height])
target = Views.interval(
stack_slice, [xOffset, yOffset],
[xOffset + img.dimension(0) - 1, yOffset + img.dimension(1) - 1])
c1 = target.cursor()
c2 = img.cursor()
while c1.hasNext():
c1.next().set(c2.next())
return stack_slice
def scale3D(img, x=1.0, y=1.0, z=1.0):
scale3d = AffineTransform3D()
scale3d.set(x, 0, 0, 0,
0, y, 0, 0,
0, 0, z, 0)
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, scale3d)
# dimensions
minC = [0, 0, 0]
maxC = [int(img.dimension(d) * k + 0.5) -1 for d, k in enumerate([x, y, z])]
imgB = Views.interval(imgT, minC, maxC)
return imgB
def scale(img, calibration):
scale3d = AffineTransform3D()
scale3d.set(calibration[0], 0, 0, 0, 0, calibration[1], 0, 0, 0, 0,
calibration[2], 0)
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, scale3d)
# dimensions
minC = [0, 0, 0]
maxC = [
int(img.dimension(d) * cal) - 1 for d, cal in enumerate(calibration)
]
imgB = Views.interval(imgT, minC, maxC)
return imgB
def crop(event):
global cropped, cropped_imp
coords = [int(float(tf.getText())) for tf in textfields]
minC = [max(0, c) for c in coords[0:3]]
maxC = [min(d -1, c) for d, c in izip(Intervals.dimensionsAsLongArray(images[0]), coords[3:6])]
storeRoi(minC, maxC)
print "ROI min and max coordinates"
print minC
print maxC
cropped = [Views.zeroMin(Views.interval(img, minC, maxC)) for img in images]
cropped_imp = showAsStack(cropped, title="cropped")
cropped_imp.setDisplayRange(imp.getDisplayRangeMin(), imp.getDisplayRangeMax())
if cropContinuationFn:
cropContinuationFn(images, minC, maxC, cropped, cropped_imp)
def keyPressed(self, ke):
keyCode = ke.getKeyCode()
if ke.isControlDown() and keyCode in Navigator.moves:
d, sign = Navigator.moves[keyCode]
inc = 200 if ke.isShiftDown() else 20
mins[d] += sign * inc
maxs[d] += sign * inc
# Replace source with shifted cropped volume
fsource.set(stack, Views.zeroMin(Views.interval(imgE, FinalInterval(mins, maxs))))
imp.updateVirtualSlice()
return
# Else, pass the event onto other listeners
for kl in kls:
kl.keyPressed(ke)
def populateInstances(instances, synth_imgs, class_index, mins, maxs):
# Populate the training data: create the filter bank for each feature image
# by reading values from the interval defined by mins and maxs
target = ArrayImgs.floats([width, height])
interval = FinalInterval(mins, maxs)
n_samples = Intervals.numElements(interval)
for img in synth_imgs:
vectors = [zeros(len(attributes), 'd') for _ in xrange(n_samples)]
for k, op in enumerate(filterBank(img, sumType=DoubleType())):
imgOp = compute(op).into(target)
for i, v in enumerate(Views.interval(imgOp, interval)):
vectors[i][k] = v.getRealDouble()
for vector in vectors:
vector[-1] = class_index
instances.add(DenseInstance(1.0, vector))
def pyramidAreaAveraging(img,
top_level,
min_width=32,
sumType=UnsignedLongType,
mathType=UnsignedLongType,
converter=Util.genericIntegerTypeConverter()):
""" Return a list of image views, one per scale level of the image pyramid,
except for level zero (the first image) which is the provided img.
All images are of the same type as the source img.
Based on an integral image for fast computation.
"""
img_type = img.randomAccess().get().createVariable()
# Create an integral image in longs
alg = IntegralImg(img, sumType(), converter)
alg.process()
integralImg = alg.getResult()
# Create an image pyramid as views, with ImgMath and imglib2,
# which amounts to scale area averaging sped up by the integral image
# and generated on demand whenever each pyramid level is read.
width = img.dimension(0)
imgE = Views.extendBorder(integralImg)
blockSide = 1
level_index = 1
# Corners for level 1: a box of 2x2
corners = [[0, 0], [1, 0], [0, 1], [1, 1]]
pyramid = [img]
while width > min_width and level_index <= top_level:
blockSide *= 2
width /= 2
# Scale the corner coordinates to make the block larger
cs = [[c * blockSide for c in corner] for corner in corners]
blockRead = div(block(imgE, cs), pow(blockSide, 2)) # the op
# a RandomAccessibleInterval view of the op, computed with shorts but seen as bytes
view = blockRead.view(mathType(), img_type.createVariable())
# Views.subsample by 2 will turn a 512-pixel width to a 257 width,
# so crop to proper interval 256
level = Views.interval(Views.subsample(view, blockSide),
[0] * img.numDimensions(), # min
[img.dimension(d) / blockSide -1
for d in xrange(img.numDimensions())]) # max
pyramid.append(level)
level_index += 1 # for next iteration
return pyramid
def asNormalizedUnsignedByteArrayImg(interval, invert, blockRadius, n_bins,
slope, matrices, index, imp):
sp = imp.getProcessor() # ShortProcessor
# Crop to interval if needed
x = interval.min(0)
y = interval.min(1)
width = interval.max(0) - interval.min(0) + 1
height = interval.max(1) - interval.min(1) + 1
if 0 != x or 0 != y or sp.getWidth() != width or sp.getHeight(
) != height:
sp.setRoi(x, y, width, height)
sp = sp.crop()
if invert:
sp.invert()
CLAHE.run(
ImagePlus("", sp), blockRadius, n_bins, slope, None
) # far less memory requirements than NormalizeLocalContrast, and faster.
minimum, maximum = autoAdjust(sp)
# Transform and convert image to 8-bit, mapping to display range
img = ArrayImgs.unsignedShorts(
sp.getPixels(), [sp.getWidth(), sp.getHeight()])
sp = None
imp = None
# Must use linear interpolation for subpixel precision
affine = AffineTransform2D()
affine.set(matrices[index])
imgI = Views.interpolate(Views.extendZero(img),
NLinearInterpolatorFactory())
imgA = RealViews.transform(imgI, affine)
imgT = Views.zeroMin(Views.interval(imgA, img))
# Convert to 8-bit
imgMinMax = convert2(imgT,
RealUnsignedByteConverter(minimum, maximum),
UnsignedByteType,
randomAccessible=False) # use IterableInterval
aimg = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img))
# ImgUtil copies multi-threaded, which is not appropriate here as there are many other images being copied too
#ImgUtil.copy(ImgView.wrap(imgMinMax, aimg.factory()), aimg)
# Single-threaded copy
copier = createBiConsumerTypeSet(UnsignedByteType)
LoopBuilder.setImages(imgMinMax, aimg).forEachPixel(copier)
img = imgI = imgA = imgMinMax = imgT = None
return aimg
def viewTransformed(img, calibration, affine):
""" View img transformed to isotropy (via the calibration)
and transformed by the affine. """
scale3d = AffineTransform3D()
scale3d.set(calibration[0], 0, 0, 0,
0, calibration[1], 0, 0,
0, 0, calibration[2], 0)
transform = affine.copy()
transform.concatenate(scale3d)
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, transform)
# dimensions
minC = [0, 0, 0]
maxC = [int(img.dimension(d) * cal) -1 for d, cal in enumerate(calibration)]
imgB = Views.interval(imgT, minC, maxC)
return imgB
def get(self, path):
img = self.klb.readFull(path)
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
affine = AffineTransform3D()
affine.set(self.transforms[path])
affine = affine.inverse() # it's a forward transform: must invert
affine.concatenate(scale3d) # calibrated space: isotropic
imgT = RealViews.transform(imgI, affine)
minC = [0, 0, 0]
maxC = [
int(img.dimension(d) * cal) - 1
for d, cal in enumerate(calibration)
]
imgB = Views.interval(imgT, minC, maxC)
# View a RandomAccessibleInterval as an Img, required by Load.lazyStack
return ImgView.wrap(imgB, img.factory())
def twoStep(index=0):
# The current way:
img = klb.readFull(filepaths[index]) # klb_loader.get(filepaths[index])
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, cmIsotropicTransforms[index])
imgB = Views.zeroMin(Views.interval(imgT, roi[0],
roi[1])) # bounded: crop with ROI
imgBA = ArrayImgs.unsignedShorts(Intervals.dimensionsAsLongArray(imgB))
ImgUtil.copy(ImgView.wrap(imgB, imgBA.factory()), imgBA)
imgP = prepareImgForDeconvolution(
imgBA,
affine3D(fineTransformsPostROICrop[index]).inverse(),
FinalInterval([0, 0, 0], [imgB.dimension(d) - 1 for d in xrange(3)]))
# Copy transformed view into ArrayImg for best performance in deconvolution
imgA = ArrayImgs.floats(Intervals.dimensionsAsLongArray(imgP))
ImgUtil.copy(ImgView.wrap(imgP, imgA.factory()), imgA)
IL.wrap(imgA, "two step").show()
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
resultFileName = '%s/result.tif' % home.rstrip('/')
imp = ImageJFunctions.wrap( result, 'result' )
IJ.saveAsTiff(imp.duplicate(), resultFileName)
relativeResult = result.copy()
c = relativeResult.cursor()
while c.hasNext():
c.fwd()
cur = c.get()
val = cur.get()
cur.set( val - c.getDoublePosition( 2 ) )
relativeResultFileName = '%s/relativeResult.tif' % home.rstrip('/')
imp = ImageJFunctions.wrap( relativeResult, 'relative result' )
IJ.saveAsTiff(imp.duplicate(), relativeResultFileName)
ratio = [ wrappedImage.dimension( 0 )*1.0/result.dimension( 0 ), wrappedImage.dimension( 1 )*1.0/result.dimension( 1 ) ]
shift = [ 0.0, 0.0 ]
lutField = SingleDimensionLUTGrid(3, 3, result, 2, ratio, shift )
transformed = Views.interval( Views.raster( RealViews.transformReal( Views.interpolate( Views.extendBorder( wrappedImage ), NLinearInterpolatorFactory() ), lutField ) ), wrappedImage )
imp = ImageJFunctions.wrap( transformed, 'transformed' )
transformedFileName = '%s/transformed.tif' % home.rstrip('/')
IJ.saveAsTiff( imp.duplicate(), transformedFileName )
# result = inference.estimateZCoordinates( 0, 0, startingCoordinates, matrixTracker, options )
threads = []
upper = start
while upper < stop:
correlationRange = int(c)
lower = max( 0, upper - overlap )
upper = lower + interval
if upper + step >= stop:
upper = min( stop, upper + step )
home = root.rstrip('/') + '/range=%d_%s/lower=%d_upper=%d'
home = home % ( correlationRange, timestamp, lower, upper )
make_sure_path_exists( home.rstrip('/') + '/' )
options.comparisonRange = int(c)
subStrip = ConvertedRandomAccessibleInterval( Views.interval( wholeStrip, [long(0), long(lower)], [long(wholeStrip.dimension(0)-1), long(upper-1)] ), RealDoubleConverter(), DoubleType() )
gitCommitInfoFile = '%s/commitHash' % home.rstrip('/')
#with open( gitCommitInfoFile, 'w' ) as f:
# f.write( '%s\n' % utility.gitcommit.getCommit( thickness_estimation_repo_dir ) )
gitDiffFile = '%s/gitDiff' % home.rstrip('/')
#with open( gitDiffFile, 'w' ) as f:
# f.write( '%s\n' % utility.gitcommit.getDiff( thickness_estimation_repo_dir ) )
optionsFile = '%s/options' % home.rstrip('/')
with open( optionsFile, 'w' ) as f:
f.write( '%s\n' % options.toString() )
