def test(img):
imgT = TransformView.transformView(img, aff, interval,
MultiViewDeconvolution.minValueImg,
MultiViewDeconvolution.outsideValueImg,
1) # 1: linear interpolation
imgA = ArrayImgs.floats(dimensions)
ImgUtil.copy(ImgView.wrap(imgT, imgA.factory()), imgA)
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 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 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 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 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 oneStep(index=0):
# Combining transforms into one, via a translation to account of the ROI crop
img = klb.readFull(filepaths[index]) # klb_loader.get(filepaths[index])
t1 = cmIsotropicTransforms[index]
t2 = affine3D(
[1, 0, 0, -roi[0][0], 0, 1, 0, -roi[0][1], 0, 0, 1, -roi[0][2]])
t3 = affine3D(fineTransformsPostROICrop[index]).inverse()
aff = AffineTransform3D()
aff.set(t1)
aff.preConcatenate(t2)
aff.preConcatenate(t3)
# Final interval is now rooted at 0,0,0 given that the transform includes the translation
imgP = prepareImgForDeconvolution(
img, aff,
FinalInterval([0, 0, 0],
[maxC - minC for minC, maxC in izip(roi[0], roi[1])]))
# 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, "one step index %i" % index).show()
def testJython():
ImgUtil.copy(
ImgView.wrap(
Converters.convertRandomAccessibleIterableInterval(
img1, UnsignedByteToFloatSamplerConverter()), img1.factory()),
img2)
def testASM():
ImgUtil.copy(
ImgView.wrap(
Converters.convertRandomAccessibleIterableInterval(
img1, samplerClass.newInstance()), img1.factory()), img2)
def testASMLongs():
img2 = ArrayImgs.unsignedLongs(dimensions)
ImgUtil.copy(
ImgView.wrap(
Converters.convertRandomAccessibleIterableInterval(
img1s, sampler_conv_longs), img1.factory()), img2)
def testASMDoubles():
img2 = ArrayImgs.doubles(dimensions)
ImgUtil.copy(
ImgView.wrap(
Converters.convertRandomAccessibleIterableInterval(
img1, sampler_conv_doubles), img1.factory()), img2)
def test(red, green, blue, easy=True):
saturation = let(
"red", red, "green", green, "blue", blue, "max",
maximum("red", "green", "blue"), "min",
minimum("red", "green", "blue"),
IF(EQ(0, "max"), THEN(0), ELSE(div(sub("max", "min"), "max"))))
brightness = div(maximum(red, green, blue), 255.0)
hue = IF(
EQ(0, saturation), THEN(0),
ELSE(
let(
"red", red, "green", green, "blue", blue, "max",
maximum("red", "green", "blue"), "min",
minimum("red", "green", "blue"), "range", sub("max", "min"),
"redc", div(sub("max", "red"), "range"), "greenc",
div(sub("max", "green"), "range"), "bluec",
div(sub("max", "blue"), "range"), "hue",
div(
IF(
EQ("red", "max"), THEN(sub("bluec", "greenc")),
ELSE(
IF(EQ("green", "max"),
THEN(sub(add(2, "redc"), "bluec")),
ELSE(sub(add(4, "greenc"), "redc"))))), 6),
IF(LT("hue", 0), THEN(add("hue", 1)), ELSE("hue")))))
#print hierarchy(hue)
#print "hue view:", hue.view( FloatType() ).iterationOrder()
if easy:
# About 26 ms
"""
hsb = Views.stack( hue.view( FloatType() ),
saturation.view( FloatType() ),
brightness.view( FloatType() ) )
"""
# About 13 ms: half! Still much worse than plain ImageJ,
# but the source images are iterated 4 times, rather than just once,
# and the saturation is computed twice,
# and the min, max is computed 3 and 4 times, respectively.
hsb = Views.stack(hue.viewDouble(FloatType()),
saturation.viewDouble(FloatType()),
brightness.viewDouble(FloatType()))
"""
# Even worse: ~37 ms
width, height = rgb.dimension(0), rgb.dimension(1)
h = compute(hue).into(ArrayImgs.floats([width, height]))
s = compute(saturation).into(ArrayImgs.floats([width, height]))
b = compute(brightness).into(ArrayImgs.floats([width, height]))
hsb = Views.stack( h, s, b )
"""
imp = IL.wrap(hsb, "HSB view")
else:
# Tested it: takes more time (~40 ms vs 26 ms above)
width, height = rgb.dimension(0), rgb.dimension(1)
hb = zeros(width * height, 'f')
sb = zeros(width * height, 'f')
bb = zeros(width * height, 'f')
h = ArrayImgs.floats(hb, [width, height])
s = ArrayImgs.floats(sb, [width, height])
b = ArrayImgs.floats(bb, [width, height])
#print "ArrayImg:", b.iterationOrder()
ImgUtil.copy(ImgView.wrap(hue.view(FloatType()), None), h)
ImgUtil.copy(ImgView.wrap(saturation.view(FloatType()), None), s)
ImgUtil.copy(ImgView.wrap(brightness.view(FloatType()), None), b)
stack = ImageStack(width, height)
stack.addSlice(FloatProcessor(width, height, hb, None))
stack.addSlice(FloatProcessor(width, height, sb, None))
stack.addSlice(FloatProcessor(width, height, bb, None))
imp = ImagePlus("hsb", stack)
return imp
# Write NumDirEntries as 2 bytes: the number of tags
ra.writeShort(8) # 7 in tags dict plus tag 273 added later
# Write each tag as 12 bytes each:
# First all non-changing tags, constant for all IFDs
ra.write(tags_bytes)
# Size of IFD dict in number of bytes
# Then the variable 273 tag: the offset to the image data array, just after the IFD definition
ra.write(asBytes(273, (9, 4, offset + n_bytes_IFD)))
# Write NextIFDOffset as 4 bytes
offset += n_bytes_IFD + tags[279][
2] # i.e. StripByteCounts: the size of the image data in number of bytes
ra.writeInt(0 if img.dimension(2) - 1 == z else offset)
# Write image plane
# The long[] array doesn't necessarily end sharply at image plane boundaries
# Therefore must copy plane into another, 2D ArrayImg of bit type
ImgUtil.copy(ImgView.wrap(Views.hyperSlice(img, 2, z), None),
plane_img)
# Each long stores 64 bits but from right to left, and we need left to right
# (the 8 bytes of the 64-bit long are already left to right in little endian)
longbits = array(imap(Long.reverse, plane_array), 'l')
bb.rewind() # bring mark to zero
bb.asLongBuffer().put(
longbits
) # a LongBuffer view of the ByteBuffer, writes to the ByteBuffer
ra.write(bb.array())
finally:
ra.close()
# Now read the file back as a stack using lib.io.TIFFSlices
slices = TIFFSlices(filepath,
types={1: TIFFSlices.types[64][:2] + (BitType, )})
def get(self, path):
img = self.klb_loader.load(path)
return ImgView.wrap(
viewTransformed(img, getCalibration(img),
self.path_transforms[path]), img.factory())