targetfilename = sys.argv[1]
outputfilename = sys.argv[2]
outputtargetfilename = sys.argv[3]
useWhiten = True
maskBeforeSmooth = True
smoothMask = False
greyBackground = True
smoothAtlasAgain = True
blackVentricles = True
multicolorMask = False
target = sitk.ReadImage(targetfilename)
refImg = sitk.ReadImage(targetfilename)
origRefImg = sitk.ReadImage(targetfilename)
inflectionpoint = histFunctions.getInflectionPoint(target)
mymask = ndreg3D.imgMakeMask(target, threshold=inflectionpoint)
mymaskarray = sitk.GetArrayFromImage(mymask)
refImg = ndreg3D.imgMask(refImg, mymask)
# now go slice by slice to remove the ventricles from the mask
mymaskarray = sitk.GetArrayFromImage(mymask)
structure = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
#241 - might need to add a zero onto zc1trunc for the case where nothing needs to be cut
labelarray = -1 * np.ones(mymaskarray.shape)
for i in range(0, origRefImg.GetSize()[1]):
if np.unique(sitk.GetArrayFromImage(refImg[:, i, :])).shape[0] < 2:
temparray = np.ones((labelarray.shape[0], labelarray.shape[2]))
temparray[2:temparray.shape[0] - 2,
2:temparray.shape[1] - 2] = np.zeros(
import SimpleITK as sitk
import histFunctions
import ndreg3D
import sys
sys.path.insert(
0, '/sonas-hs/mitra/hpc/home/kram/Marmoset_Pipeline_2019/code/numpy')
import numpy as np
targetfilename = sys.argv[1]
#outputfilename = sys.argv[2]
outputslicefilename = sys.argv[2]
transformfilename = sys.argv[3]
img = sitk.ReadImage(targetfilename)
inflectionpoint = histFunctions.getInflectionPoint(img)
mymask = ndreg3D.imgMakeMask(img, threshold=inflectionpoint)
xmax = np.min((np.where(sitk.GetArrayFromImage(mymask) == 1)[0].max() + 15,
img.GetSize()[2]))
xmin = np.max((np.where(sitk.GetArrayFromImage(mymask) == 1)[0].min() - 15, 0))
ymax = np.min((np.where(sitk.GetArrayFromImage(mymask) == 1)[2].max() + 15,
img.GetSize()[0]))
ymin = np.max((np.where(sitk.GetArrayFromImage(mymask) == 1)[2].min() - 15, 0))
#outImg = img[ymin:ymax,:,xmin:xmax]
#sitk.WriteImage(outImg, outputfilename)
# transform slice by slice
registeredimagelist = [None] * img.GetSize()[1]
refImg_us = sitk.Resample(refImg, [
int(
np.round(
refImg.GetSize()[0] * refImg.GetSpacing()[0] / outputpixelsize)),
int(
np.round(
refImg.GetSize()[1] * refImg.GetSpacing()[1] / outputpixelsize)),
int(
np.round(
refImg.GetSize()[2] * refImg.GetSpacing()[2] / outputpixelsize))
], identityAffine, sitk.sitkLinear, (0, 0, 0),
(outputpixelsize, outputpixelsize, outputpixelsize),
(1, 0, 0, 0, 1, 0, 0, 0, 1), 0.0)
# crop
inflectionpoint = histFunctions.getInflectionPoint(refImg_us)
mymask = ndreg3D.imgMakeMask(refImg_us, threshold=inflectionpoint)
xmax = np.min((np.where(sitk.GetArrayFromImage(mymask) == 1)[0].max() + 15,
refImg_us.GetSize()[2]))
xmin = np.max((np.where(sitk.GetArrayFromImage(mymask) == 1)[0].min() - 15, 0))
ymax = np.min((np.where(sitk.GetArrayFromImage(mymask) == 1)[2].max() + 15,
refImg_us.GetSize()[0]))
ymin = np.max((np.where(sitk.GetArrayFromImage(mymask) == 1)[2].min() - 15, 0))
zmax = np.min((np.where(sitk.GetArrayFromImage(mymask) == 1)[1].max() + 15,
refImg_us.GetSize()[1]))
zmin = np.max((np.where(sitk.GetArrayFromImage(mymask) == 1)[1].min() - 15, 0))
croptransform = sitk.TranslationTransform(3)