t1 = System.currentTimeMillis()
for peak in peaks:
sphere = sphereFactory.create(
[int(peak[0]), int(peak[1]), int(peak[2])], radius, ra)
s = sum(imap(FloatType.getRealFloat, sphere.cursor()))
intensities1.append(float(s) / sphere.size())
t2 = System.currentTimeMillis()
print "Elapsed time:", (t2 - t1), "ms"
#print intensities1
# Try now with HyperSphereCursor
hs = HyperSphere(copy2, Point(3), radius)
size = hs.size()
intensities2 = []
hsc = hs.cursor()
t3 = System.currentTimeMillis()
for peak in peaks:
hsc.updateCenter([int(peak[0]), int(peak[1]), int(peak[2])])
s = sum(imap(FloatType.getRealFloat, hsc))
intensities2.append(float(s) / size)
t4 = System.currentTimeMillis()
print "Elapsed time:", (t4 - t3), "ms"
from net.imglib2 import Point;
from net.imglib2.algorithm.region.hypersphere import HyperSphere;
# create an empty image
phantom=ops.create().img(array([xSize, ySize], 'l'))
# use the randomAccess interface to place points in the image
randomAccess= phantom.randomAccess();
randomAccess.setPosition(array([xSize/2, ySize/2], 'l'));
randomAccess.get().setReal(255.0);
randomAccess.setPosition(array([xSize/4, ySize/4], 'l'));
randomAccess.get().setReal(255.0);
location = Point(phantom.numDimensions())
location.setPosition(array([3*xSize/4, 3*ySize/4], 'l'));
hyperSphere = HyperSphere(phantom, location, 5);
for value in hyperSphere:
value.setReal(16);
display.createDisplay("phantom", phantom)
# create psf using the gaussian kernel op (alternatively PSF could be an input to the script)
psf=ops.create().kernelGauss(array([10, 10], 'd'));
# convolve psf with phantom
convolved=ops.filter().convolve(phantom, psf);
display.createDisplay("convolved", convolved)
from net.imglib2 import Point
from net.imglib2.algorithm.region.hypersphere import HyperSphere
# create an empty image
phantom = ops.create().img(array([xSize, ySize], 'l'))
# use the randomAccess interface to place points in the image
randomAccess = phantom.randomAccess()
randomAccess.setPosition(array([xSize / 2, ySize / 2], 'l'))
randomAccess.get().setReal(255.0)
randomAccess.setPosition(array([xSize / 4, ySize / 4], 'l'))
randomAccess.get().setReal(255.0)
location = Point(phantom.numDimensions())
location.setPosition(array([3 * xSize / 4, 3 * ySize / 4], 'l'))
hyperSphere = HyperSphere(phantom, location, 5)
for value in hyperSphere:
value.setReal(16)
display.createDisplay("phantom", phantom)
# create psf using the gaussian kernel op (alternatively PSF could be an input to the script)
psf = ops.create().kernelGauss(array([10, 10], 'd'))
# convolve psf with phantom
convolved = ops.filter().convolve(phantom, psf)
display.createDisplay("convolved", convolved)
# create an empty image
phantom=ops.create().img([xSize, ySize, zSize])
# make phantom an ImgPlus
phantom=ops.create().imgPlus(phantom);
# use the randomAccess interface to place points in the image
randomAccess= phantom.randomAccess()
randomAccess.setPosition([xSize/2, ySize/2, zSize/2])
randomAccess.get().setReal(255.0)
randomAccess.setPosition([xSize/4, ySize/4, zSize/4])
randomAccess.get().setReal(255.0)
location = Point(phantom.numDimensions())
location.setPosition([3*xSize/4, 3*ySize/4, 3*zSize/4])
hyperSphere = HyperSphere(phantom, location, 5)
for value in hyperSphere:
value.setReal(16)
phantom.setName("phantom")
# create psf using the gaussian kernel op (alternatively PSF could be an input to the script)
psf=ops.create().kernelGauss([5, 5, 5])
# convolve psf with phantom
convolved=ops.filter().convolve(phantom, psf)
#@ UIService ui
#@OUTPUT ImgPlus phantom
#@OUTPUT ImgPlus convolved
#@OUTPUT ImgPlus deconvolved1
#@OUTPUT ImgPlus deconvolved2
from net.imglib2 import Point
from net.imglib2.algorithm.region.hypersphere import HyperSphere
xSize = 200
ySize = 200
# create an empty image
phantom = ops.create().img([xSize, ySize])
location = Point(phantom.numDimensions())
location.setPosition([xSize / 2, ySize / 2])
hyperSphere = HyperSphere(phantom, location, 20)
for value in hyperSphere:
value.setReal(16)
ui.show("phantom", phantom)
psf1 = ops.create().kernelGauss([2, 2])
psf2 = ops.create().kernelGauss([4, 4])
# convolve psf with phantom
convolved = ops.filter().convolve(phantom, psf1)
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()
