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 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 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 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 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 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 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 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 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 viewTransformed(img, calibration, transform):
""" View img transformed to isotropy (via the calibration)
and transformed by the affine. """
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
if type(transform) == AffineTransform3D:
scale3d = AffineTransform3D()
scale3d.set(calibration[0], 0, 0, 0, 0, calibration[1], 0, 0, 0, 0,
calibration[2], 0)
affine = transform.copy()
affine.concatenate(scale3d)
imgT = RealViews.transform(imgI, affine)
else:
imgT = RealViews.transform(imgI, Scale3D(*calibration))
imgT = RealViews.transform(imgT, 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 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 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):
# Correct calibration
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 viewTransformed(img, calibration, matrix):
affine = AffineTransform3D()
affine.set(*matrix)
# It's a forward transform: invert
affine = affine.inverse()
# Correct calibration
scale3d = AffineTransform3D()
scale3d.set(calibration[0], 0, 0, 0,
0, calibration[1], 0, 0,
0, 0, calibration[2], 0)
affine.concatenate(scale3d)
imgE = Views.extendZero(img)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine)
# 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 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 viewTransformed(image,
transformation,
title=None,
interval=None,
show=True):
if isinstance(image, ImagePlus):
img = IL.wrap(
image
) # ImagePlus to ImgLib2 RandomAccessibleInterva & IterableInterval aka Img
elif isinstance(image, RandomAccessibleInterval):
img = image
else:
return None
# Make the image be defined anywhere by infinitely padding with zeros.
imgInfinite = Views.extendZero(img)
# Make the image be defined at arbitrarily precise subpixel coordinates
# by using n-dimensional linear interpolation
imgInterpolated = Views.interpolate(imgInfinite,
NLinearInterpolatorFactory())
# Make the image be seen as a transformed view of the source image
imgTransformed = RealViews.transform(imgInterpolated, transformation)
# Define an interval within which we want the transformed image to be defined
# (such as that of the source img itself; an img in ImgLib2 also happens to be an Interval
# and can therefore be used as an interval, which is convenient here because we
# expect the original field of view--the interval--to be where image data can still be found)
interval = interval if interval else img # every Img is also an Interval because each Img is bounded
# Make the image finite by defining it as the content within the interval
imgBounded = Views.interval(imgTransformed, interval) # same as original
# Optionally show the transformed, bounded image in an ImageJ VirtualStack
# (Note that anytime one of the VirtualStack's ImageProcessor will have to
# update its pixel data, it will incur in executing the transformation again;
# no pixel data is cached or copied anywhere other than for display purposes)
if show:
title = title if title else imp.getTitle()
imp = IL.wrap(imgBounded, title) # as an ImagePlus
imp.show() # in an ImageJ ImageWindow
return imgBounded
img1M = Views.interval(
img1E, [(dim1 - dim3) / 2 for dim1, dim3 in izip(dims1, dims3)],
[dim1 + (dim3 - dim1) / 2 - 1 for dim1, dim3 in izip(dims1, dims3)])
img2M = Views.interval(
img2E, [(dim2 - dim3) / 2 for dim2, dim3 in izip(dims2, dims3)],
[dim2 + (dim3 - dim2) / 2 - 1 for dim2, dim3 in izip(dims2, dims3)])
IL.show(img1M, "img1M")
IL.show(img2M, "img2M")
# Scale by half (too slow otherwise) -- ERROR: the smaller one (img1) doesn't remain centered.
s = [0.5 for d in xrange(img1.numDimensions())]
img1s = Views.interval(
RealViews.transform(
Views.interpolate(Views.extendValue(img1M, zero),
NLinearInterpolatorFactory()), Scale(s)),
[0 for d in xrange(img1M.numDimensions())], [
int(img1M.dimension(d) / 2.0 + 0.5) - 1
for d in xrange(img1M.numDimensions())
])
img2s = Views.interval(
RealViews.transform(
Views.interpolate(Views.extendValue(img2M, zero),
NLinearInterpolatorFactory()), Scale(s)),
[0 for d in xrange(img2M.numDimensions())], [
int(img2M.dimension(d) / 2.0 + 0.5) - 1
for d in xrange(img2M.numDimensions())
])
# simplify var names
def pyramid(img,
top_level,
min_width=32,
ViewOutOfBounds=Views.extendBorder
interpolation_factory=NLinearInterpolatorFactory()):
"""
Create an image pyramid as interpolated scaled views of the provided img.
"""
imgR = Views.interpolate(ViewOutOfBounds(img), interpolation_factory)
# Create levels of a pyramid as interpolated views
width = img.dimension(0)
pyramid = [img]
scale = 1.0
level_index = 1
while width > min_width and level_index <= top_level:
scale /= 2.0
width /= 2
s = [scale for d in xrange(img.numDimensions())]
scaled = Views.interval(RealViews.transform(imgR, Scale(s)),
FinalInterval([int(img.dimension(d) * scale)
for d in xrange(img.numDimensions())]))
pyramid.append(scaled)
level_index += 1 # for next iteration
return pyramid
# TODO pyramidGauss a la Saalfeld
sliceInterval = FinalInterval([interval2.dimension(0), interval2.dimension(1)])
slices2 = []
for index in xrange(img1.dimension(2)):
# One single 2D RGB slice
imgSlice1 = Views.hyperSlice(img1, 2, index)
# Views of the 3 color channels, as extended and interpolatable
channels = [
Views.interpolate(
Views.extendZero(Converters.argbChannel(imgSlice1, i)),
NLinearInterpolatorFactory()) for i in [1, 2, 3]
]
# ARGBType 2D view of the transformed color channels
imgSlice2 = Converters.mergeARGB(
Views.stack(
Views.interval(RealViews.transform(channel, transform),
sliceInterval)
for channel in channels), ColorChannelOrder.RGB)
slices2.append(imgSlice2)
# Transformed view
viewImg2 = Views.stack(slices2)
# Materialized image
img2 = ArrayImgs.argbs(Intervals.dimensionsAsLongArray(interval2))
ImgUtil.copy(viewImg2, img2)
imp4 = IL.wrap(img2, "imglib2-transformed RGB (pull)")
imp4.show()
# Fourth approach: pull (CORRECT!), and much faster (delegates pixel-wise operations
# to java libraries and delegates RGB color handling altogether)
def maxCoords(img):
return [
int(img.dimension(d) * calibration[d] - 1)
for d in xrange(img.numDimensions())
]
# Identity transform for CM00, scaled to isotropy
affine0 = AffineTransform3D()
affine0.identity()
affine0.concatenate(scale3D)
# Expand camera CM00 to isotropy
imgE = Views.extendZero(img0)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine0)
imgB0 = Views.interval(imgT, [0, 0, 0], maxCoords(img0))
# Transform camera CM01 to CM00: 180 degrees on Y axis, plus a translation in X
affine1 = AffineTransform3D()
affine1.set(-1.0, 0.0, 0.0, img1.dimension(0), 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0)
affine1.concatenate(scale3D)
imgE = Views.extendZero(img1)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine1)
imgB1 = Views.interval(imgT, [0, 0, 0], maxCoords(img1))
# Transform camera CM02 to CM00: 90 degrees on Y axis, plus a translation in Z
affine2 = AffineTransform3D()
affine2.set(0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, -1.0, 0.0, 0.0,
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 )
img0 = klb.readFull(paths[0])
img1 = klb.readFull(paths[1])
img2 = klb.readFull(paths[2])
img3 = klb.readFull(paths[3])
# Calibration: [1.0, 1.0, 5.0]
scale3D = AffineTransform3D()
scale3D.set(1.0, 0.0, 0.0, 0.0,
0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 5.0, 0.0)
# Expand camera CM00 to isotropy
imgE = Views.extendZero(img0)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, scale3D)
imgB0 = Views.interval(imgT, [0, 0, 0], [img0.dimension(0) -1, img0.dimension(1) -1, img0.dimension(2) * 5 - 1])
# Transform camera CM01 to CM00: 180 degrees on Y axis, plus a translation
dx = -195
dy = 54
dz = 8
affine = AffineTransform3D()
affine.set(-1.0, 0.0, 0.0, img1.dimension(0) + dx,
0.0, 1.0, 0.0, 0.0 + dy,
0.0, 0.0, 1.0, 0.0 + dz)
affine.concatenate(scale3D)
imgE = Views.extendZero(img1)
imgI = Views.interpolate(imgE, NLinearInterpolatorFactory())
imgT = RealViews.transform(imgI, affine)
affine.rotate(angle_rad)
affine.translate(center)
# Get the interpolator
interpolator = LanczosInterpolatorFactory()
# Iterate over all frame in the stack
axis = Axes.TIME
output = []
for d in range(data.dimension(axis)):
# Get the current frame
frame = crop_along_one_axis(ops, data, [d, d], "TIME")
# Get the interpolate view of the frame
extended = ops.run("transform.extendZeroView", frame)
interpolant = ops.run("transform.interpolateView", extended, interpolator)
# Apply the transformation to it
rotated = RealViews.affine(interpolant, affine)
# Set the intervals
rotated = ops.transform().offset(rotated, frame)
output.append(rotated)
output = Views.stack(output)
# Create output Dataset
output = ds.create(output)
# imp = IJ.getImage()
# Access its pixel data as an ImgLib2 RandomAccessibleInterval
img = IL.wrapReal(imp2)
# View as an infinite image, with a value of zero beyond the image edges
imgE = Views.extendZero(img)
# View the pixel data as a RealRandomAccessible
# (that is, accessible with sub-pixel precision)
# by using an interpolator
imgR = Views.interpolate(imgE, NLinearInterpolatorFactory())
# Obtain a view of the 2D image twice as big
s = [2.0
for d in range(img.numDimensions())] # as many 2.0 as image dimensions
bigger = RV.transform(imgR, Scale(s))
# Define the interval we want to see: the original image, enlarged by 2X
# E.g. from 0 to 2*width, from 0 to 2*height, etc. for every dimension
minC = [0 for d in range(img.numDimensions())]
maxC = [int(img.dimension(i) * scale) for i, scale in enumerate(s)]
imgI = Views.interval(bigger, minC, maxC)
# Visualize the bigger view
imp2x = IL.wrap(imgI, imp.getTitle() + " - 2X") # an ImagePlus
imp2x.show()
FileSaver(imp2x).saveAsPng(str_out_png)
# create an empty image
phantom = ops.create().img([xSize, ySize, zSize])
# make phantom an ImgPlus
phantom = ops.create().imgPlus(phantom)
location = Point(phantom.numDimensions())
location.setPosition([xSize / 2, ySize / 2, zSize / 2])
hyperSphere = HyperSphere(phantom, location, 10)
for value in hyperSphere:
value.setReal(100)
phantom.setName("phantom")
affine = AffineTransform3D()
affine.scale(1, 1, 0.4)
interpolatedImg = Views.interpolate(Views.extendZero(phantom),
NLinearInterpolatorFactory())
phantom = Views.interval(
Views.raster(RealViews.affine(interpolatedImg, affine)),
Intervals.createMinMax(0, 0, 18, 255, 255, 82))
# make phantom an ImgPlus
phantom = ops.create().imgPlus(ops.copy().iterableInterval(
Views.zeroMin(phantom)))
phantom.setName('phantom')
affine.rotate(angle_rad)
affine.translate(center)
# Get the interpolator
interpolator = LanczosInterpolatorFactory()
# Iterate over all frame in the stack
axis = Axes.TIME
output = []
for d in range(data.dimension(axis)):
# Get the current frame
frame = crop_along_one_axis(ops, data, [d, d], "TIME")
# Get the interpolate view of the frame
extended = ops.run("transform.extendZeroView", frame)
interpolant = ops.run("transform.interpolateView", extended, interpolator)
# Apply the transformation to it
rotated = RealViews.affine(interpolant, affine)
# Set the intervals
rotated = ops.transform().offsetView(rotated, frame)
output.append(rotated)
output = Views.stack(output)
# Create output Dataset
output = ds.create(output)
# Define a rotation by +30 degrees relative to the image center in the XY axes angle = radians(30) toCenter = AffineTransform3D() cx = img.dimension(0) / 2.0 # X axis cy = img.dimension(1) / 2.0 # Y axis toCenter.setTranslation(-cx, -cy, 0.0) # no translation in the Z axis rotation = AffineTransform3D() # Step 1: place origin of rotation at the center of the image rotation.preConcatenate(toCenter) # Step 2: rotate around the Z axis rotation.rotate(2, angle) # 2 is the Z axis, or 3rd dimension # Step 3: undo translation to the center rotation.preConcatenate(toCenter.inverse()) # undo translation to the center # Define a rotated view of the image rotated = RV.transform(imgR, rotation) # View the image rotated, without enlarging the canvas # so we define the interval (here, the field of view of an otherwise infinite image) # as the original image dimensions by using "img", which in itself is an Interval. imgRot2d = IL.wrap(Views.interval(rotated, img), imp.getTitle() + " - rot2d") imgRot2d.show() # View the image rotated, enlarging the interval to fit it. # (This is akin to enlarging the canvas.) # We define each corner of the nth-dimensional volume as a combination, # namely the 'product' (think nested loop) of the pairs of possible values # that each dimension can take in every corner coordinate, zipping each # with the value zero (hence the repeat(0) to provide as many as necessary), # and then unpacking the list of pairs by using the * in front of 'zip'
#imgE = Views.extendValue(img, t) # Easier: imgE = Views.extendZero(img) # Or view mirroring the data beyond the edges #imgE = Views.extendMirrorSingle(img) # View the pixel data as a RealRandomAccessible with the help of an interpolator imgR = Views.interpolate(imgE, NLinearInterpolatorFactory()) print type(imgR) print dir(imgR) # Obtain a view of the 2D image twice as big s = [2.0 for d in range(img.numDimensions())] # as many 2.0 as dimensions the image has bigger = RV.transform(imgR, Scale(s)) # Obtain a rasterized view (with integer coordinates for its pixels) # NOT NEEDED #imgRA = Views.raster(bigger) # Define the interval we want to see: the original image, enlarged by 2X # E.g. from 0 to 2*width, from 0 to 2*height, etc. for every dimension # Notice the -1 in maxC: the interval is inclusive of the largest coordinate. minC = [0 for d in range(img.numDimensions())] maxC = [int(img.dimension(i) * scale) -1 for i, scale in enumerate(s)] imgI = Views.interval(bigger, minC, maxC) # Visualize the bigger view imp2x = IL.wrap(imgI, imp.getTitle() + " - 2X") # an ImagePlus imp2x.show()
