def classify(img, classifier, class_names, ops=None, distribution_class_index=-1):
""" img: a 2D RandomAccessibleInterval.
classifier: a WEKA Classifier instance, like SMO or FastRandomForest, etc. Any.
If it's a string, interprets it as a file path and attempts to deserialize
a previously saved trained classifier.
class_names: the list of names of each class to learn.
ops: the filter bank of ImgMath ops for the img.
distribution_class_index: defaults to -1, meaning return the class index for each pixel.
When larger than -1, it's interpreted as a class index, and
returns instead the floating-point value of each pixel in
the distribution of that particular class index. """
if type(classifier) == str:
classifier = SerializationHelper.read(classifier)
ops = ops if ops else filterBank(img)
attributes = ArrayList()
for i in xrange(len(ops)):
attributes.add(Attribute("attr-%i" % i))
#for name in classifier.attributeNames()[0][1]:
# attributes.add(Attribute(name))
attributes.add(Attribute("class", class_names))
info = Instances("structure", attributes, 1)
info.setClassIndex(len(attributes) -1)
opImgs = [compute(op).into(ArrayImgs.floats([img.dimension(0), img.dimension(1)])) for op in ops]
cs_opImgs = Views.collapse(Views.stack(opImgs))
result = ArrayImgs.floats([img.dimension(0), img.dimension(1)])
cr = result.cursor()
cop = Views.iterable(cs_opImgs).cursor()
while cr.hasNext():
tc = cop.next()
vector = array((tc.get(i).getRealDouble() for i in xrange(len(opImgs))), 'd')
vector += array([0], 'd')
di = DenseInstance(1.0, vector)
di.setDataset(info) # the list of attributes
if distribution_class_index > -1:
cr.next().setReal(classifier.distributionForInstance(di)[distribution_class_index])
else:
cr.next().setReal(classifier.classifyInstance(di))
return result
def getManders(self, imp, cell): ### Crop channels according to cell mask channels = self.getCroppedChannels(imp, cell) if channels is None: return None ### Calculate channel thresholds thrs = [] thrimps = [] for c, method in enumerate(self.methods): if method != "None": thr, thrimp = self.getThreshold(channels[c], method) else: thr, thrimp = None, None thrs.append(thr) thrimps.append(thrimp) ### Calculate manders colocalization manders = MandersColocalization() raws = [] thrds = [] for chA, chB in self.pairs: container = self.getContainer(channels[chA - 1], channels[chB - 1]) img1 = container.getSourceImage1() img2 = container.getSourceImage2() mask = container.getMask() cursor = TwinCursor(img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor()) rtype = img1.randomAccess().get().createVariable() raw = manders.calculateMandersCorrelation(cursor, rtype) rthr1 = rtype.copy() rthr2 = rtype.copy() rthr1.set(thrs[chA - 1]) rthr2.set(thrs[chB - 1]) cursor.reset() thrd = manders.calculateMandersCorrelation(cursor, rthr1, rthr2, ThresholdMode.Above) raws.append(raw) thrds.append(thrd) return (channels, thrimps, thrs, raws, thrds)
from net.imglib2.img.array import ArrayImgs
from net.imglib2.view import Views
from net.imglib2.type.numeric.integer import UnsignedByteType
img1 = ArrayImgs.unsignedBytes([10, 10, 10])
img2 = ArrayImgs.unsignedBytes([10, 10, 10])
stack = Views.stack([img1, img2])
for t in Views.iterable(stack):
t.setReal(1)
assert 1000 + 1000 == sum(t.get() for t in Views.iterable(stack))
title = title[:title.rfind('.')]
image.close()
preview.close()
ch1 = ImagePlusAdapter.wrap(imp1)
ch2 = ImagePlusAdapter.wrap(imp2)
for roi in rm.getRoisAsArray():
container = createContainer(roi, ch1, ch2)
img1 = container.getSourceImage1()
img2 = container.getSourceImage2()
mask = container.getMask()
thr1, thrimp1 = calculateThreshold(imp1, roi, methods[0])
thr2, thrimp2 = calculateThreshold(imp2, roi, methods[1])
cursor = TwinCursor(img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor())
rtype = img1.randomAccess().get().createVariable()
raw = manders.calculateMandersCorrelation(cursor, rtype)
rthr1 = rtype.copy()
rthr2 = rtype.copy()
rthr1.set(thr1)
rthr2.set(thr2)
cursor.reset()
thrd = manders.calculateMandersCorrelation(cursor, rthr1, rthr2, ThresholdMode.Above)
print "Results are: %f %f %f %f" % (raw.m1, raw.m2, thrd.m1, thrd.m2)
results.incrementCounter()
rowno = results.getCounter() - 1
results.setValue("Cell", rowno, int(rowno))
results.setValue("Threshold 1", rowno, int(thr1))
results.setValue("Threshold 2", rowno, int(thr2))
fi.height = 128
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))
def iterableChannel(imgARGB, i): return Views.iterable(Converters.argbChannel(imgARGB, i))
# Get each step, waiting until all are built
steps = [f.get() for f in futures] # list: eager, for concatenation below in Views.stack
finally:
# This 'finally' block executes even in the event of an error
# guaranteeing that the executing threads will be shut down no matter what.
exe.shutdown()
# ISSUE: Does not work with IntervalView from View.rotate,
# so img1 and img2 were copied into ArrayImg
# (The error would occur when iterating vol4d pixels beyond the first element in the 4th dimension.)
vol4d = Views.stack([img1] + steps + [img2])
# Convert 1 -> 255 for easier volume rendering in 3D Viewer
#compute(mul(vol4d, 255)).into(vol4d)
for t in Views.iterable(vol4d):
if 0 != t.getByte():
t.setReal(255)
# Construct an ij.VirtualStack from vol4d
virtualstack = IL.wrap(vol4d, "interpolations").getStack()
imp = ImagePlus("interpolations", virtualstack)
imp.setDimensions(1, vol4d.dimension(2), vol4d.dimension(3))
imp.setDisplayRange(0, 255)
# Show as a hyperstack with 4 dimensions, 1 channel
com = CompositeImage(imp, CompositeImage.GRAYSCALE)
com.show()
# Show rendered volumes in 4D viewer: 3D + time axis
univ = Image3DUniverse()
univ.show()
