def sub_sample_vol(image_file, dest_file, dsf, interpolation="continuous", verb_lvl=0):
from nipy.labs.datasets.volumes.volume_img import VolumeImg # ,CompositionError
pyhrf.verbose(verb_lvl, "Subsampling at dsf=%d, %s -> %s" % (dsf, image_file, dest_file))
interp = interpolation
data_src, src_meta = read_volume(image_file)
affine = src_meta[0]
daffine = dsf * affine
original_dtype = data_src.dtype
if np.issubdtype(np.int, data_src.dtype):
# to avoid error "array type 5 not supported" on int arrays ...
# data_src = np.asarray(np.asfarray(data_src), data_src.dtype)
data_src = np.asfarray(data_src)
if data_src.ndim == 4:
ref_vol = data_src[:, :, :, 0]
else:
ref_vol = data_src
# print 'ref_vol:', ref_vol.shape
img_src = VolumeImg(ref_vol, affine, "mine")
img_dest = img_src.as_volume_img(daffine, interpolation=interpolation)
# setup dest geometry:
dest_header = src_meta[1].copy()
# dest_header['sform'] = img_src.affine
dpixdim = np.array(src_meta[1]["pixdim"])
dpixdim[1:4] *= dsf
# print 'pixdim:', ','.join(map(str,src_meta[1]['pixdim']))
# print 'dpixdim:', ','.join(map(str,dpixdim))
dest_header["pixdim"] = list(dpixdim)
sh = ref_vol[::dsf, ::dsf, ::dsf, ...].shape
# print 'sh:', sh
dest_meta = (daffine, dest_header)
# do the resampling:
if data_src.ndim == 3:
vol = img_dest.get_data()[: sh[0], : sh[1], : sh[2]]
if ref_vol.dtype == np.int32 or np.allclose(np.round(ref_vol), ref_vol): # if input is int
vol = np.round(vol).astype(np.int32)
write_volume(vol, dest_file, dest_meta)
elif data_src.ndim == 4:
imgs = [VolumeImg(i, affine, "mine") for i in np.rollaxis(data_src, 3, 0)]
dvols = [i.as_volume_img(daffine, interpolation=interp).get_data() for i in imgs]
# print 'dvols[0]:', dvols[0].shape
dvols = np.array(dvols)
# print 'dvols:', dvols.shape
sub_vols = np.rollaxis(dvols[:, : sh[0], : sh[1], : sh[2]], 0, 4)
# print 'sub_vols:', sub_vols.shape
write_volume(sub_vols, dest_file, dest_meta)
else:
raise Exception("Nb of dims (%d) not handled. Only 3D or 4D" % data_src.ndim)
# wm = wm_img.get_data()
# csf_img = as_volume_img(CSF_PATH)
# csf = csf_img.get_data()
# probTotal = gm + wm + csf
# dim = np.shape(probTotal)
# ind = probTotal > 0
# gm[ind] = gm[ind] / probTotal[ind]
# wm[ind] = wm[ind] / probTotal[ind]
# csf[ind] = csf[ind] / probTotal[ind]
# tissue = np.array([gm.ravel(), wm.ravel(), csf.ravel()])
# tissue_mask = tissue.argmax(axis=0) + 1
# tissue_mask[probTotal.ravel() == 0] = 0
# brain = np.reshape(tissue_mask == 1, (dim[0], dim[1], dim[2]))
brain = gm > 0.15
brain = binary_dilation(brain, structure=generate_binary_structure(3, 2))
brain_img = VolumeImg(brain, affine=gm_img.affine, world_space=None, interpolation="nearest")
brain_img = brain_img.resampled_to_img(ref_img, interpolation="nearest")
brain = brain_img.get_data()
brain = binary_closing(brain, structure=generate_binary_structure(3, 2))
labels, n_labels = label(brain) # Remove isolated voxels
for i in np.unique(labels)[2:]:
brain[labels == i] = 0
# Spatial smoothing to encourage smooth parcels
dim = np.shape(brain)
tc = tc.reshape((dim[0], dim[1], dim[2], -1))
n_tpts = tc.shape[-1]
for t in np.arange(n_tpts):
tc[:, :, :, t] = gaussian_filter(tc[:, :, :, t], sigma=1)
tc = tc.reshape((-1, n_tpts))
tc = tc[brain.ravel() == 1, :]
def sub_sample_vol(image_file, dest_file, dsf, interpolation='continuous',
verb_lvl=0):
# ,CompositionError
from nipy.labs.datasets.volumes.volume_img import VolumeImg
logger.log(verb_lvl, 'Subsampling at dsf=%d, %s -> %s', dsf, image_file,
dest_file)
interp = interpolation
data_src, src_meta = read_volume(image_file)
affine = src_meta[0]
daffine = dsf * affine
original_dtype = data_src.dtype
if np.issubdtype(np.int, data_src.dtype):
# to avoid error "array type 5 not supported" on int arrays ...
#data_src = np.asarray(np.asfarray(data_src), data_src.dtype)
data_src = np.asfarray(data_src)
if data_src.ndim == 4:
ref_vol = data_src[:, :, :, 0]
else:
ref_vol = data_src
img_src = VolumeImg(ref_vol, affine, 'mine')
img_dest = img_src.as_volume_img(daffine, interpolation=interpolation)
# setup dest geometry:
dest_header = src_meta[1].copy()
dpixdim = np.array(src_meta[1]['pixdim'])
dpixdim[1:4] *= dsf
dest_header['pixdim'] = list(dpixdim)
sh = ref_vol[::dsf, ::dsf, ::dsf, ...].shape
dest_meta = (daffine, dest_header)
# do the resampling:
if data_src.ndim == 3:
vol = img_dest.get_data()[:sh[0], :sh[1], :sh[2]]
if ref_vol.dtype == np.int32 or \
np.allclose(np.round(ref_vol), ref_vol): # if input is int
vol = np.round(vol).astype(np.int32)
write_volume(vol, dest_file, dest_meta)
elif data_src.ndim == 4:
imgs = [VolumeImg(i, affine, 'mine')
for i in np.rollaxis(data_src, 3, 0)]
dvols = [i.as_volume_img(daffine, interpolation=interp).get_data()
for i in imgs]
# print 'dvols[0]:', dvols[0].shape
dvols = np.array(dvols)
# print 'dvols:', dvols.shape
sub_vols = np.rollaxis(dvols[:, :sh[0], :sh[1], :sh[2]], 0, 4)
# print 'sub_vols:', sub_vols.shape
write_volume(sub_vols, dest_file, dest_meta)
else:
raise Exception('Nb of dims (%d) not handled. Only 3D or 4D'
% data_src.ndim)
#wm = wm_img.get_data()
#csf_img = as_volume_img(CSF_PATH)
#csf = csf_img.get_data()
#probTotal = gm + wm + csf
#dim = np.shape(probTotal)
#ind = probTotal > 0
#gm[ind] = gm[ind] / probTotal[ind]
#wm[ind] = wm[ind] / probTotal[ind]
#csf[ind] = csf[ind] / probTotal[ind]
#tissue = np.array([gm.ravel(), wm.ravel(), csf.ravel()])
#tissue_mask = tissue.argmax(axis=0) + 1
#tissue_mask[probTotal.ravel() == 0] = 0
#brain = np.reshape(tissue_mask == 1, (dim[0], dim[1], dim[2]))
brain = gm > 0.15
brain = binary_dilation(brain, structure=generate_binary_structure(3, 2))
brain_img = VolumeImg(brain, affine=gm_img.affine, world_space=None, interpolation='nearest')
brain_img = brain_img.resampled_to_img(ref_img, interpolation='nearest')
brain = brain_img.get_data()
brain = binary_closing(brain, structure=generate_binary_structure(3, 2))
labels, n_labels = label(brain) # Remove isolated voxels
for i in np.unique(labels)[2:]:
brain[labels == i] = 0
# Spatial smoothing to encourage smooth parcels
dim = np.shape(brain)
tc = tc.reshape((dim[0], dim[1], dim[2], -1))
n_tpts = tc.shape[-1]
for t in np.arange(n_tpts):
tc[:, :, :, t] = gaussian_filter(tc[:, :, :, t], sigma=1)
tc = tc.reshape((-1, n_tpts))
tc = tc[brain.ravel() == 1, :]
