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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This is a little demo that simply shows ROI manipulation within
the nipy framework
Author: Bertrand Thirion, 2009
"""
print __doc__
import numpy as np
import os
import matplotlib.pylab as mp
from nipy.io.imageformats import load, save, Nifti1Image
from nipy.neurospin.spatial_models.roi import DiscreteROI, MultipleROI
import get_data_light
import tempfile
get_data_light.getIt()
# paths
swd = tempfile.mkdtemp()
data_dir = os.path.expanduser(os.path.join('~', '.nipy', 'tests', 'data'))
InputImage = os.path.join(data_dir,'spmT_0029.nii.gz')
MaskImage = os.path.join(data_dir,'mask.nii.gz')
# -----------------------------------------------------
# example 1: create the ROI froma a given position
# -----------------------------------------------------
position = [0,0,0]
nim = load(MaskImage)
affine = nim.get_affine()
shape = nim.get_shape()
roi = DiscreteROI("myroi", affine,shape)
roi.from_position(np.array(position), 5.0)
roi.make_image(os.path.join(swd,"myroi.nii"))
roi.set_feature_from_image('activ',InputImage)
roi.plot_feature('activ')
print 'Wrote an ROI mask image in %s' %os.path.join(swd,"myroi.nii")
# ----------------------------------------------------
# ---- example 2: create ROIs from a blob image ------
# ----------------------------------------------------
# --- 2.a create the blob image
import nipy.neurospin.graph.field as ff
import nipy.neurospin.spatial_models.hroi as hroi
# parameters
threshold = 3.0 # blob-forming threshold
smin = 5 # size threshold on bblobs
# prepare the data
nim = load(InputImage)
affine = nim.get_affine()
shape = nim.get_shape()
data = nim.get_data()
xyz = np.array(np.where(data)).T
F = ff.Field(xyz.shape[0])
F.from_3d_grid(xyz)
F.set_field(data[data!=0])
# compute the nested roi object
label = -np.ones(F.V)
nroi = hroi.NROI_from_field(F, affine, shape, xyz, 0, threshold, smin)
bmap = -np.zeros(F.V)
if nroi!=None:
idx = nroi.discrete_features['index']
for k in range(nroi.k):
label[idx[k]] = k
# saving the blob image,i. e. a label image
wlabel = -2*np.ones(shape)
wlabel[data!=0] = label
blobPath = os.path.join(swd,"blob.nii")
wim = Nifti1Image(wlabel, affine)
wim.get_header()['descrip'] = 'blob image extracted from %s'%InputImage
save(wim, blobPath)
# --- 2.b take blob labelled "1" as an ROI
roi = DiscreteROI( affine=affine, shape=shape)
roi.from_labelled_image(blobPath, 1)
roiPath2 = os.path.join(swd, "roi_blob_1.nii")
roi.make_image(roiPath2)
# --- 2.c take the blob closest to 'position as an ROI'
roiPath3 = os.path.join(swd, "blob_closest_to_%d_%d_%d.nii")%\
(position[0], position[1], position[2])
roi.from_position_and_image(blobPath, np.array(position))
roi.make_image(roiPath3)
# --- 2.d make a set of ROIs from all the blobs
mroi = MultipleROI( affine=affine, shape=shape)
mroi.from_labelled_image(blobPath)
roiPath4 = os.path.join(swd, "roi_all_blobs.nii")
mroi.make_image(roiPath4)
mroi.set_discrete_feature_from_image('activ', InputImage)
mroi.discrete_to_roi_features('activ')
mroi.plot_roi_feature('activ')
# ---- 2.e the same, a bit more complex
mroi = MultipleROI( affine=affine, shape=shape)
mroi.as_multiple_balls(np.array([[-10.,0.,10.]]),np.array([7.0]))
mroi.from_labelled_image(blobPath,np.arange(1,20))
mroi.from_labelled_image(blobPath,np.arange(31,50))
roiPath5 = os.path.join(swd,"roi_some_blobs.nii")
mroi.set_discrete_feature_from_image('activ',InputImage)
mroi.discrete_to_roi_features('activ')
valid = mroi.get_roi_feature('activ')>4.0
mroi.clean(valid)
mroi.make_image(roiPath5)
print "Wrote ROI mask images in %s, \n %s \n %s \n and %s" %\
(roiPath2, roiPath3, roiPath4, roiPath5)
mp.show()
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