Python ImageJFunctions.wrapReal Examples

Example #1

def find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue):
	# FIND PEAKS
	# sigmaSmaller ==> Size of the smaller dots (in pixels)
	# sigmaLarger ==> Size of the bigger dots (in pixels)
	# minPeakValue ==> Intensity above which to look for dots
	# Preparation Neuron channel
	ip1_1 = IL.wrapReal(imp1)
	ip1E = Views.extendMirrorSingle(ip1_1)
	imp1.show()

	#Preparation Glioma channel
	ip2_1 = IL.wrapReal(imp2)
	ip2E = Views.extendMirrorSingle(ip2_1)
	imp2.show()

	calibration = [1.0 for i in range(ip1_1.numDimensions())]
	extremaType = DogDetection.ExtremaType.MINIMA
	normalizedMinPeakValue = False

	dog_1 = DogDetection(ip1E, ip1_1, calibration, sigmaSmaller, sigmaLarger,
	  				   extremaType, minPeakValue, normalizedMinPeakValue)

	dog_2 = DogDetection(ip2E, ip2_1, calibration, sigmaSmaller, sigmaLarger,
	  				   extremaType, minPeakValue, normalizedMinPeakValue)

	peaks_1 = dog_1.getPeaks()
	peaks_2 = dog_2.getPeaks()

	return ip1_1, ip2_1, peaks_1, peaks_2

Example #2

from net.imglib2.realtransform import RealViews as RV
from net.imglib2.realtransform import AffineTransform3D
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from ij import IJ
from net.imglib2.view import Views
from net.imglib2.interpolation.randomaccess import NLinearInterpolatorFactory
from net.imglib2.util import Intervals
from math import radians, floor, ceil
from jarray import zeros
from pprint import pprint

# Load an image (of any dimensions)
imp = IJ.getImage()

# Access its pixel data as an ImgLib2 RandomAccessibleInterval
img = IL.wrapReal(imp)

# View as an infinite image, with value 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())

# 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