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 makeInterpolatedImage(img1, img2, weight):
""" weight: float between 0 and 1 """
edge_pix1 = findEdgePixels(img1)
kdtree1 = KDTree(edge_pix1, edge_pix1)
search1 = NearestNeighborSearchOnKDTree(kdtree1)
edge_pix2 = findEdgePixels(img2)
kdtree2 = KDTree(edge_pix2, edge_pix2)
search2 = NearestNeighborSearchOnKDTree(kdtree2)
img3 = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img1))
c1 = img1.cursor()
c2 = img2.cursor()
c3 = img3.cursor()
pos = zeros(img1.numDimensions(), 'l')
while c3.hasNext():
t1 = c1.next()
t2 = c2.next()
t3 = c3.next()
sign1 = -1 if 0 == t1.get() else 1
sign2 = -1 if 0 == t2.get() else 1
search1.search(c1)
search2.search(c2)
value1 = sign1 * search1.getDistance() * weight
value2 = sign2 * search2.getDistance() * (1 - weight)
if value1 + value2 > 0:
t3.setOne()
return img3
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 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(self, dx, dy):
a = zeros(2, 'l')
self.interval.min(a)
width = self.cell_dimensions[0]
height = self.cell_dimensions[1]
x0 = max(0, min(a[0] + dx, self.img_dimensions[0] - width))
y0 = max(0, min(a[1] + dy, self.img_dimensions[1] - height))
self.interval = FinalInterval([x0, y0],
[x0 + width - 1, y0 + height - 1])
syncPrintQ(str(Intervals.dimensionsAsLongArray(self.interval)))
self.cache.clear()
def makeImg(filepaths, pixelType, loadImg, img_dimensions, matrices,
cropInterval, preload):
dims = Intervals.dimensionsAsLongArray(cropInterval)
voldims = [dims[0], dims[1], len(filepaths)]
cell_dimensions = [dims[0], dims[1], 1]
grid = CellGrid(voldims, cell_dimensions)
cellGet = TranslatedSectionGet(filepaths,
loadImg,
matrices,
img_dimensions,
cell_dimensions,
cropInterval,
preload=preload)
return LazyCellImg(grid, pixelType(), cellGet), cellGet
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 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 prepare(index):
# Prepare the img for deconvolution:
# 0. Transform in one step.
# 1. Ensure its pixel values conform to expectations (no zeros inside)
# 2. Copy it into an ArrayImg for faster recurrent retrieval of same pixels
syncPrint("Preparing %s CM0%i for deconvolution" % (tm_dirname, index))
img = klb_loader.get(filepaths[index]) # of UnsignedShortType
imgP = prepareImgForDeconvolution(
img, transforms[index], target_interval) # returns of FloatType
# Copy transformed view into ArrayImg for best performance in deconvolution
imgA = ArrayImgs.floats(Intervals.dimensionsAsLongArray(imgP))
#ImgUtil.copy(ImgView.wrap(imgP, imgA.factory()), imgA)
ImgUtil.copy(imgP, imgA, n_threads / 2) # parallel copying
syncPrint("--Completed preparing %s CM0%i for deconvolution" %
(tm_dirname, index))
imgP = None
img = None
return (index, imgA)
def makeInterpolatedImage(img1, search1, img2, search2, weight):
""" weight: float between 0 and 1 """
img3 = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img1))
c1 = img1.cursor()
c2 = img2.cursor()
c3 = img3.cursor()
while c3.hasNext():
t1 = c1.next()
t2 = c2.next()
t3 = c3.next()
sign1 = -1 if 0 == t1.get() else 1
sign2 = -1 if 0 == t2.get() else 1
search1.search(c1)
search2.search(c2)
value1 = sign1 * search1.getDistance() * (1 - weight)
value2 = sign2 * search2.getDistance() * weight
if value1 + value2 > 0:
t3.setOne()
return img3
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()
fi.nImages = 128
baseDir = "/home/albert/lab/scripts/data/cim.mcgill.ca-shape-benchmark/"
bird = IL.wrap(Raw.open(baseDir + "/birdsIm/b21.im", fi))
airplane = IL.wrap(Raw.open(baseDir + "/airplanesIm/b14.im", fi))
# Rotate bird
# Starts with posterior view
# Rotate 180 degrees around Y axis
# Set to dorsal up: 180 degrees
birdY90 = Views.rotate(bird, 2, 0) # 90
birdY180 = Views.rotate(birdY90, 2, 0) # 90 again: 180
c1 = Views.iterable(birdY180).cursor()
img1 = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(birdY180))
c2 = img1.cursor()
while c2.hasNext():
c2.next().set(c1.next())
# Rotate airplane
# Starts with dorsal view, anterior down
# Set to: coronal view, but dorsal is down
airplaneC = Views.rotate(airplane, 2, 1)
# Set to dorsal up: 180 degrees
airplaneC90 = Views.rotate(airplaneC, 0, 1) # 90
airplaneC180 = Views.rotate(airplaneC90, 0, 1) # 90 again: 180
c1 = Views.iterable(airplaneC180).cursor()
img2 = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(airplaneC180))
c2 = img2.cursor()
print median, max_sum
# Turns out the maximum is infinity.
# Therefore, discard all infinity values, and also any above 1.5 * median
threshold = median * 1.5
filtered = [
filename for filename, pixel_sum in sums if pixel_sum < threshold
]
n_threads = Runtime.getRuntime().availableProcessors()
threads = []
chunk_size = len(filtered) / n_threads
aimgs = []
first = klb.readFull(os.path.join(srcDir, filtered[0]))
dimensions = Intervals.dimensionsAsLongArray(first)
for i in xrange(n_threads):
m = Max(dimensions, filtered[i * chunk_size:(i + 1) * chunk_size])
m.start()
threads.append(m)
# Await completion of all
for m in threads:
m.join()
# Merge all results into a single maximum projection
max_projection = computeInto(maximum([m.aimg for m in threads]),
ArrayImgs.floats(dimensions))
max3D = writeZip(max_projection,
IL.wrap(img_sub, "LoopBuilder").show()
"""
# Example 2b: with ImgLib2 LoopBuilder using a clojure-defined TriConsumer
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.converter import Converters
from net.imglib2.img.array import ArrayImgs
from net.imglib2.util import Intervals
from net.imglib2.loops import LoopBuilder
from org.scijava.plugins.scripting.clojure import ClojureScriptEngine
img = IL.wrap(imp_rgb) # an ARGBType Img
red = Converters.argbChannel(img, 1) # a view of the ARGB red channel
green = Converters.argbChannel(img, 2) # a view of the ARGB green channel
img_sub = ArrayImgs.unsignedBytes(Intervals.dimensionsAsLongArray(img)) # the img to store the result
code = """
(deftype Consumer [^long threshold]
%s
(accept [self red green result] ; can't type-hint, doesn't find matching method
(let [^%s r red
^%s g green
^%s s result]
(.setInteger s (if (>= (.getInteger r) threshold)
(.getInteger g)
0)))))
""" % ((LoopBuilder.TriConsumer.getName(),) \
+ tuple(a.randomAccess().get().getClass().getName() for a in [red, green, img_sub]))
clj = ClojureScriptEngine()
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
def makeCropUI(imp, images, tgtDir, panel=None, cropContinuationFn=None):
""" imp: the ImagePlus to work on.
images: the list of ImgLib2 images, one per frame, not original but already isotropic.
(These are views that use a nearest neighbor interpolation using the calibration to scale to isotropy.)
tgtDir: the target directory where e.g. CSV files will be stored, for ROI, features, pointmatches.
panel: optional, a JPanel controlled by a GridBagLayout.
cropContinuationFn: optional, a function to execute after cropping,
which is given as arguments the original images,
minC, maxC (both define a ROI), and the cropped images. """
independent = None == panel
if not panel:
panel = JPanel()
panel.setBorder(BorderFactory.createEmptyBorder(10,10,10,10))
gb = GridBagLayout()
gc = GBC()
else:
gb = panel.getLayout()
# Constraints of the last component
gc = gb.getConstraints(panel.getComponent(panel.getComponentCount() - 1))
# Horizontal line to separate prior UI components from crop UI
gc.gridx = 0
gc.gridy += 1
gc.gridwidth = 4
gc.anchor = GBC.WEST
gc.fill = GBC.HORIZONTAL
sep = JSeparator()
sep.setMinimumSize(Dimension(200, 10))
gb.setConstraints(sep, gc)
panel.add(sep)
# ROI UI header
title = JLabel("ROI controls:")
gc.gridy +=1
gc.anchor = GBC.WEST
gc.gridwidth = 4
gb.setConstraints(title, gc)
panel.add(title)
# Column labels for the min and max coordinates
gc.gridy += 1
gc.gridwidth = 1
for i, title in enumerate(["", "X", "Y", "Z"]):
gc.gridx = i
gc.anchor = GBC.CENTER
label = JLabel(title)
gb.setConstraints(label, gc)
panel.add(label)
textfields = []
rms = []
# Load stored ROI if any
roi_path = path = os.path.join(tgtDir, "crop-roi.csv")
if os.path.exists(roi_path):
with open(roi_path, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar="\"")
reader.next() # header
minC = map(int, reader.next()[1:])
maxC = map(int, reader.next()[1:])
# Place the ROI over the ImagePlus
imp.setRoi(Roi(minC[0], minC[1], maxC[0] + 1 - minC[0], maxC[1] + 1 - minC[1]))
else:
# Use whole image dimensions
minC = [0, 0, 0]
maxC = [v -1 for v in Intervals.dimensionsAsLongArray(images[0])]
# Text fields for the min and max coordinates
for rowLabel, coords in izip(["min coords: ", "max coords: "],
[minC, maxC]):
gc.gridx = 0
gc.gridy += 1
label = JLabel(rowLabel)
gb.setConstraints(label, gc)
panel.add(label)
for i in xrange(3):
gc.gridx += 1
tf = JTextField(str(coords[i]), 10)
gb.setConstraints(tf, gc)
panel.add(tf)
textfields.append(tf)
listener = RoiMaker(imp, textfields, len(textfields) -1)
rms.append(listener)
tf.addKeyListener(listener)
tf.addMouseWheelListener(listener)
# Listen to changes in the ROI of imp
rfl = RoiFieldListener(imp, textfields)
Roi.addRoiListener(rfl)
# ... and enable cleanup
ImagePlus.addImageListener(FieldDisabler(rfl, rms))
# Functions for cropping images
cropped = None
cropped_imp = None
def storeRoi(minC, maxC):
if os.path.exists(roi_path):
# Load ROI
with open(path, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar="\"")
reader.next() # header
same = True
for a, b in izip(minC + maxC, map(int, reader.next()[1:] + reader.next()[1:])):
if a != b:
same = False
# Invalidate any CSV files for features and pointmatches: different cropping
for filename in os.listdir(tgtDir):
if filename.endswith("features.csv") or filename.endswith("pointmatches.csv"):
os.remove(os.path.join(tgtDir, filename))
break
if same:
return
# Store the ROI as crop-roi.csv
with open(roi_path, 'w') as csvfile:
w = csv.writer(csvfile, delimiter=',', quotechar="\"", quoting=csv.QUOTE_NONNUMERIC)
w.writerow(["coords", "x", "y", "z"])
w.writerow(["min"] + map(int, minC))
w.writerow(["max"] + map(int, maxC))
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)
# Buttons to create a ROI and to crop to ROI,
# which when activated enables the fine registration buttons
crop_button = JButton("Crop to ROI")
crop_button.addActionListener(crop)
gc.gridx = 0
gc.gridy += 1
gc.gridwidth = 4
gc.anchor = GBC.WEST
buttons_panel = JPanel()
buttons_panel.add(crop_button)
gb.setConstraints(buttons_panel, gc)
panel.add(buttons_panel)
if independent:
frame = JFrame("Crop by ROI")
frame.getContentPane().add(panel)
frame.pack()
frame.setDefaultCloseOperation(JFrame.DO_NOTHING_ON_CLOSE)
frame.addWindowListener(CloseControl(destroyables=rms + [rfl]))
frame.setVisible(True)
else:
# Re-pack the JFrame
parent = panel.getParent()
while not isinstance(parent, JFrame) and parent is not None:
parent = parent.getParent()
if parent:
frame = parent
frame.pack()
found = False
for wl in frame.getWindowListeners():
if isinstance(wl, CloseControl):
wl.addDestroyables(rms + [rfl])
found = True
break
if not found:
frame.addWindowListener(CloseControl(destroyables=rms + [rfl]))
frame.setVisible(True)
return panel
from net.imglib2.algorithm.math.ImgMath import compute, gen
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.type.numeric.integer import UnsignedByteType
from net.imglib2.img.array import ArrayImgs
from net.imglib2.util import Intervals
from net.imglib2 import KDTree, Point
locations = [(10, 15), (25, 40), (30, 75), (80, 60)]
points = [
Point.wrap([x, y]) for x, y in [(10, 15), (25, 40), (30, 75), (80, 60)]
]
values = [UnsignedByteType(v) for v in [128, 164, 200, 255]]
kt = KDTree(values, points)
dimensions = [100, 100]
op = gen(kt, 10)
target = ArrayImgs.unsignedBytes(
Intervals.dimensionsAsLongArray(op.getInterval()))
compute(op).into(target)
IL.wrap(target, "KDTree").show()
def get(self, index):
img = self.asArrayImg(index, self.loadFn(self.filepaths[index]))
dims = Intervals.dimensionsAsLongArray(img)
return Cell(
list(dims) + [1], [0] * img.numDimensions() + [index],
img.update(None))
bytes = zeros(width * height * depth, 'b')
ra.read(bytes)
return ArrayImgs.unsignedBytes(bytes, [width, height, depth])
finally:
ra.close()
bird = readBinaryMaskImg(os.path.join(baseDir, "birdsIm/b21.im"), 128, 128, 128, 1024)
airplane = readBinaryMaskImg(os.path.join(baseDir, "airplanesIm/b14.im"), 128, 128, 128, 1024)
# Rotate bird: starts with posterior view, dorsal down
# Rotate 180 degrees around Y axis
birdY90 = Views.rotate(bird, 2, 0) # 90
birdY180 = Views.rotate(birdY90, 2, 0) # 90 again: 180
# Copy rotated bird into ArrayImg
dims = Intervals.dimensionsAsLongArray(birdY90)
img1 = compute(ImgSource(birdY180)).into(ArrayImgs.unsignedBytes(dims))
# Rotate airplane: starts with dorsal view, anterior down
# Set to: coronal view, but dorsal is still down
airplaneC = Views.rotate(airplane, 2, 1)
# Set to dorsal up: rotate 180 degrees
airplaneC90 = Views.rotate(airplaneC, 0, 1) # 90
airplaneC180 = Views.rotate(airplaneC90, 0, 1) # 90 again: 180
# Copy rotated airplace into ArrayImg
img2 = compute(ImgSource(airplaneC180)).into(ArrayImgs.unsignedBytes(dims))
# Find edges
def findEdgePixels(img):
def get(self, index):
img = self.asArrayImg(index, self.loadFn(self.filepaths[index]))
dims = Intervals.dimensionsAsLongArray(img)
return Cell(list(dims) + [1], # cell dimensions
[0] * img.numDimensions() + [index], # position in the grid: 0, 0, 0, Z-index
img.update(None)) # get the underlying DataAccess
def export8bitN5(filepaths,
img_dimensions,
matrices,
name,
exportDir,
interval,
gzip_compression=6,
block_size=[128, 128, 128]):
"""
Export into an N5 volume, in parallel, in 8-bit.
name: name to assign to the N5 volume.
img3D: the serial sections to export.
exportDir: the directory into which to save the N5 volume.
interval: for cropping.
gzip_compression: defaults to 6 as suggested by Saalfeld.
block_size: defaults to 128x128x128 px.
"""
dims = Intervals.dimensionsAsLongArray(interval)
voldims = [dims[0], dims[1], len(filepaths)]
cell_dimensions = [dims[0], dims[1], 1]
def asNormalizedUnsignedByteArrayImg(blockRadius, stds, center, stretch,
imp):
sp = imp.getProcessor() # ShortProcessor
NormalizeLocalContrast().run(sp, blockRadius, blockRadius, stds,
center, stretch)
return ArrayImgs.unsignedBytes(
sp.convertToByte(True).getPixels(),
[sp.getWidth(), sp.getHeight()])
loader = SectionCellLoader(filepaths,
asArrayImg=partial(
asNormalizedUnsignedByteArrayImg, 400, 3,
True, True))
# TODO: how to preload 128 at a time? Or at least as many as numCPUs()?
# One possibility is to query the SoftRefLoaderCache.map for its entries, using a ScheduledExecutorService,
# and preload sections ahead for the whole blockSize[2] dimension.
cachedCellImg = lazyCachedCellImg(loader,
voldims,
cell_dimensions,
UnsignedByteType,
BYTE,
returnCache=True)
def preload(cachedCellImg, loader):
"""
Find which is the last cell index in the cache, identify to which block
(given the blockSize[2] AKA Z dimension) that index belongs to,
and concurrently load all cells (sections) that the Z dimension of the blockSize will need.
If they are already loaded, these operations are insignificant.
"""
# The SoftRefLoaderCache.map is a ConcurrentHashMap with Long keys, aka numbers
cache = cachedCellImg.getCache()
f1 = cache.getClass().getDeclaredField(
"cache") # LoaderCacheAsCacheAdapter.cache
f1.setAccessible(True)
softCache = f1.get(cache)
f2 = softCache.getClass().getDeclaredField(
"map") # SoftRefLoaderCache.map
f2.setAccessible(True)
keys = sorted(f2.get(softCache).getKeys())
if 0 == len(keys):
return
first = keys[-1] - (keys[-1] % blockSize[2])
syncPrint("Preloading %i-%i" % (first, first + blockSize[2] - 1))
exe = newFixedThreadPool(n_threads=-1, name="preloader")
try:
for index in xrange(first, first + blockSize[2]):
exe.submit(Task, softCache.get, index, loader)
except:
syncPrint(sys.exc_info())
finally:
exe.shutdown()
preloader = Executors.newSingleThreadScheduledExecutor()
preloader.scheduleWithFixedDelay(partial(preload, cachedCellImg, loader),
0, 60, TimeUnit.SECONDS)
writeN5(cachedCellImg,
exportDir,
name,
block_size,
gzip_compression=gzip_compression,
n_threads=0)
preloader.shutdown()
img1 = IL.wrap(IJ.openImage("/home/albert/Desktop/bat-cochlea-volume.zip"))
img2 = IL.wrap(IJ.openImage("/home/albert/Desktop/mri-stack-mask.zip"))
# Make any non-zero pixel be 1
for t in img1:
if 0 != t.getIntegerLong():
t.setOne()
for t in img2:
if 0 != t.getIntegerLong():
t.setOne()
# Make both fit within the same window, centered
dims1 = Intervals.dimensionsAsLongArray(img1)
dims2 = Intervals.dimensionsAsLongArray(img2)
dims3 = [max(a, b) for a, b in izip(dims1, dims2)]
zero = UnsignedByteType(0)
img1E = Views.extendValue(img1, zero)
img2E = Views.extendValue(img2, zero)
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)])
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)
# Defines a list of views (recipes, really) for transforming every stack slice
# and then materializes the view by copying it in a multi-threaded way into an ArrayImg.
# Now without separating the color channels: will use the NLinearInterpolatorARGB
# In practice, it's a tad slower than the third approach: also processes the alpha channel in ARGB
# even though we know it is empty. Its conciseness adds clarity and is a win.
"""
# Procedural code:
slices3 = []
ui.show(normalized)
# convolution
kernel = ops.run("create.img", [3, 3], FloatType())
for p in kernel:
p.set(1.0 / kernel.size())
convoluted = ops.create().img(normalized)
ops.filter().convolve(convoluted, Views.extendMirrorSingle(normalized), kernel)
ui.show(convoluted)
# min filter
minfiltered = ops.create().img(convoluted)
ops.filter().min(minfiltered, input, RectangleShape(3, False))
ui.show(minfiltered)
# threshold
thresholded = ops.create().img(minfiltered, BitType())
ops.threshold().apply(thresholded, minfiltered, UnsignedByteType(128))
ui.show(thresholded)
# project
dims = Intervals.dimensionsAsLongArray(thresholded)
projected_dims = dims[:-1]
projected = ops.create().img(projected_dims)
ops.transform().project(IterableRandomAccessibleInterval(projected),
thresholded, ops.op(IterableMax, input),
len(projected_dims))
ui.show(projected)
scifio.datasetIO().save(datasets.create(projected), output_path)
print("OK")
def export8bitN5(
filepaths,
img_dimensions,
matrices,
name,
exportDir,
interval,
gzip_compression=6,
invert=True,
CLAHE_params=[400, 256, 3.0],
copy_threads=2,
n5_threads=0, # 0 means as many as CPU cores
block_size=[128, 128, 128]):
"""
Export into an N5 volume, in parallel, in 8-bit.
name: name to assign to the N5 volume.
img3D: the serial sections to export.
exportDir: the directory into which to save the N5 volume.
interval: for cropping.
gzip_compression: defaults to 6 as suggested by Saalfeld.
block_size: defaults to 128x128x128 px.
"""
dims = Intervals.dimensionsAsLongArray(interval)
voldims = [dims[0], dims[1], len(filepaths)]
cell_dimensions = [dims[0], dims[1], 1]
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
blockRadius, n_bins, slope = CLAHE_params
loader = SectionCellLoader(
filepaths,
asArrayImg=partial(asNormalizedUnsignedByteArrayImg, interval, invert,
blockRadius, n_bins, slope, matrices, copy_threads))
# How to preload block_size[2] files at a time? Or at least as many as numCPUs()?
# One possibility is to query the SoftRefLoaderCache.map for its entries, using a ScheduledExecutorService,
# and preload sections ahead for the whole blockSize[2] dimension.
cachedCellImg = lazyCachedCellImg(loader, voldims, cell_dimensions,
UnsignedByteType, BYTE)
def preload(cachedCellImg, loader, block_size, filepaths):
"""
Find which is the last cell index in the cache, identify to which block
(given the blockSize[2] AKA Z dimension) that index belongs to,
and concurrently load all cells (sections) that the Z dimension of the blockSize will need.
If they are already loaded, these operations are insignificant.
"""
exe = newFixedThreadPool(n_threads=min(block_size[2], numCPUs()),
name="preloader")
try:
# The SoftRefLoaderCache.map is a ConcurrentHashMap with Long keys, aka numbers
cache = cachedCellImg.getCache()
f1 = cache.getClass().getDeclaredField(
"cache") # LoaderCacheAsCacheAdapter.cache
f1.setAccessible(True)
softCache = f1.get(cache)
cache = None
f2 = softCache.getClass().getDeclaredField(
"map") # SoftRefLoaderCache.map
f2.setAccessible(True)
keys = sorted(f2.get(softCache).keySet())
if 0 == len(keys):
return
first = keys[-1] - (keys[-1] % block_size[2])
last = max(len(filepaths), first + block_size[2] - 1)
keys = None
msg = "Preloading %i-%i" % (first, first + block_size[2] - 1)
futures = []
for index in xrange(first, first + block_size[2]):
futures.append(
exe.submit(TimeItTask(softCache.get, index, loader)))
softCache = None
# Wait for all
count = 1
while len(futures) > 0:
r, t = futures.pop(0).get()
# t in miliseconds
if t > 500:
if msg:
syncPrint(msg)
msg = None
syncPrint("preloaded index %i in %f ms" %
(first + count, t))
count += 1
if not msg: # msg was printed
syncPrint("Completed preloading %i-%i" %
(first, first + block_size[2] - 1))
except:
syncPrint(sys.exc_info())
finally:
exe.shutdown()
preloader = Executors.newSingleThreadScheduledExecutor()
preloader.scheduleWithFixedDelay(
RunTask(preload, cachedCellImg, loader, block_size), 10, 60,
TimeUnit.SECONDS)
try:
syncPrint("N5 directory: " + exportDir + "\nN5 dataset name: " + name +
"\nN5 blockSize: " + str(block_size))
writeN5(cachedCellImg,
exportDir,
name,
block_size,
gzip_compression_level=gzip_compression,
n_threads=n5_threads)
finally:
preloader.shutdown()
def export8bitN5(
filepaths,
loadFn,
img_dimensions,
matrices,
name,
exportDir,
interval,
gzip_compression=6,
invert=True,
CLAHE_params=[400, 256, 3.0],
n5_threads=0, # 0 means as many as CPU cores
block_size=[128, 128, 128]):
"""
Export into an N5 volume, in parallel, in 8-bit.
filepaths: the ordered list of filepaths, one per serial section.
loadFn: a function to load a filepath into an ImagePlus.
name: name to assign to the N5 volume.
matrices: the list of transformation matrices (each one is an array), one per section
exportDir: the directory into which to save the N5 volume.
interval: for cropping.
gzip_compression: defaults to 6 as suggested by Saalfeld. 0 means no compression.
invert: Defaults to True (necessary for FIBSEM). Whether to invert the images upon loading.
CLAHE_params: defaults to [400, 256, 3.0]. If not None, the a list of the 3 parameters needed for a CLAHE filter to apply to each image.
n5_threads: defaults to 0, meaning as many as CPU cores.
block_size: defaults to 128x128x128 px. A list of 3 integer numbers, the dimensions of each individual block.
"""
dims = Intervals.dimensionsAsLongArray(interval)
voldims = [dims[0], dims[1], len(filepaths)]
cell_dimensions = [dims[0], dims[1], 1]
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
blockRadius, n_bins, slope = CLAHE_params
# A CacheLoader that interprets the list of filepaths as a 3D volume: a stack of 2D slices
loader = SectionCellLoader(
filepaths,
asArrayImg=partial(asNormalizedUnsignedByteArrayImg, interval, invert,
blockRadius, n_bins, slope, matrices),
loadFn=loadFn)
# How to preload block_size[2] files at a time? Or at least as many as numCPUs()?
# One possibility is to query the SoftRefLoaderCache.map for its entries, using a ScheduledExecutorService,
# and preload sections ahead for the whole blockSize[2] dimension.
cachedCellImg = lazyCachedCellImg(loader, voldims, cell_dimensions,
UnsignedByteType, BYTE)
exe_preloader = newFixedThreadPool(n_threads=min(
block_size[2], n5_threads if n5_threads > 0 else numCPUs()),
name="preloader")
def preload(cachedCellImg, loader, block_size, filepaths, exe):
"""
Find which is the last cell index in the cache, identify to which block
(given the blockSize[2] AKA Z dimension) that index belongs to,
and concurrently load all cells (sections) that the Z dimension of the blockSize will need.
If they are already loaded, these operations are insignificant.
"""
try:
# The SoftRefLoaderCache.map is a ConcurrentHashMap with Long keys, aka numbers
cache = cachedCellImg.getCache()
f1 = cache.getClass().getDeclaredField(
"cache") # LoaderCacheAsCacheAdapter.cache
f1.setAccessible(True)
softCache = f1.get(cache)
cache = None
f2 = softCache.getClass().getDeclaredField(
"map") # SoftRefLoaderCache.map
f2.setAccessible(True)
keys = sorted(f2.get(softCache).keySet())
if 0 == len(keys):
return
first = max(0, keys[-1] - (keys[-1] % block_size[2]))
last = min(len(filepaths), first + block_size[2]) - 1
keys = None
syncPrintQ("### Preloading %i-%i ###" % (first, last))
futures = []
for index in xrange(first, last + 1):
futures.append(
exe.submit(TimeItTask(softCache.get, index, loader)))
softCache = None
# Wait for all
loaded_any = False
count = 0
while len(futures) > 0:
r, t = futures.pop(0).get() # waits for the image to load
if t > 1000: # in miliseconds. Less than this is for sure a cache hit, more a cache miss and reload
loaded_any = True
r = None
# t in miliseconds
syncPrintQ("preloaded index %i in %f ms" % (first + count, t))
count += 1
if not loaded_any:
syncPrintQ("Completed preloading %i-%i" %
(first, first + block_size[2] - 1))
except:
syncPrintQ(sys.exc_info())
preloader = Executors.newSingleThreadScheduledExecutor()
preloader.scheduleWithFixedDelay(
RunTask(preload, cachedCellImg, loader, block_size, filepaths,
exe_preloader), 10, 60, TimeUnit.SECONDS)
try:
syncPrint("N5 directory: " + exportDir + "\nN5 dataset name: " + name +
"\nN5 blockSize: " + str(block_size))
writeN5(cachedCellImg,
exportDir,
name,
block_size,
gzip_compression_level=gzip_compression,
n_threads=n5_threads)
finally:
preloader.shutdown()
exe_preloader.shutdown()
from net.imglib2.img.array import ArrayImgFactory
from net.imglib2.type.numeric.integer import UnsignedByteType, UnsignedShortType
from net.imglib2.util import Intervals
# An 8-bit 256x256x256 volume
img = ArrayImgFactory(UnsignedByteType()).create([256, 256, 256])
# Another image of the same type and dimensions, but empty
img2 = img.factory().create(
[img.dimension(d) for d in xrange(img.numDimensions())])
# Same, but easier reading of the image dimensions
img3 = img.factory().create(Intervals.dimensionsAsLongArray(img))
# Same, but use an existing img as an Interval from which to read out the dimensions
img4 = img.factory().create(img)
# Now we change the type: same kind of image and same dimensions,
# but crucially a different pixel type (16-bit) via a new ImgFactory
imgShorts = img.factory().imgFactory(UnsignedShortType()).create(img)
#@ OpService ops
#@ SCIFIO scifio
#@ DatasetService datasetService
#@ String input_path
#@ String output_path
#@ UIService ui
import os
from net.imglib2.util import Intervals
from net.imglib2.view import IterableRandomAccessibleInterval
try:
os.unlink(output_path)
except OSError:
pass
input = scifio.datasetIO().open(input_path)
dims = Intervals.dimensionsAsLongArray(input)
output_dims = dims[:-1]
output = ops.create().img(output_dims)
ops.transform().project(IterableRandomAccessibleInterval(output), input,
ops.op('stats.max', input.getImgPlus()),
len(output_dims))
scifio.datasetIO().save(datasetService.create(output), output_path)
ui.show(output)
print("OK")
from net.imglib2.algorithm.math.ImgMath import compute, block, div, offset, add
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.img.array import ArrayImgs
from net.imglib2.type.numeric.real import FloatType
from net.imglib2.view import Views
from net.imglib2.util import Intervals
from ij import IJ
imp = IJ.getImage() # an 8-bit image
img = IL.wrap(imp)
# Create the integral image of an 8-bit input, stored as 64-bit
target = ArrayImgs.unsignedLongs(Intervals.dimensionsAsLongArray(img))
# Copy input onto the target image
compute(img).into(target)
# Extend target with zeros, so that we can read at coordinate -1
imgE = Views.extendZero(target)
# Integrate every dimension, cummulatively by writing into
# a target image that is also the input
for d in xrange(img.numDimensions()):
coord = [0] * img.numDimensions() # array of zeros
coord[d] = -1
# Cummulative sum along the current dimension
# Note that instead of the ImgMath offset op,
# we could have used Views.translate(Views.extendZero(target), [1, 0]))
# (Notice though the sign change in the translation)
integral = add(target, offset(imgE, coord))
compute(integral).into(target)
# The target is the integral image
integralImg = target
