def apply( self, img, target_header=None,
interpolation='linear', gaussian_parameter=1.0 ):
"""
Apply.
"""
if not isinstance( img, irtk.Image ):
# point transformation
pt = np.array(img, dtype='float64', copy=True)
if len(pt.shape) == 1:
tmp_pt = np.hstack((pt,[1])).astype('float64')
return np.dot( self.matrix(), tmp_pt )[:3]
else:
pt = _irtk.transform_points( self.matrix(), pt )
return pt
# tmp_pt = np.hstack((pt,[[1]]*pt.shape[0])).astype('float64')
# return np.transpose( np.dot( self.matrix(),
# np.transpose(tmp_pt) ) )[:,:3]
# if target_header is None:
# target_header = img.get_header()
if target_header is None:
(x_min, y_min, z_min, x_max, y_max, z_max ) = img.bbox(world=True)
corners = [[x_min, y_min, z_min],
[x_max, y_min, z_min],
[x_min, y_max, z_min],
[x_min, y_min, z_max],
[x_max, y_max, z_min],
[x_min, y_max, z_max],
[x_max, y_min, z_max],
[x_max, y_max, z_max]]
corners = self.apply( corners )
x_min, y_min, z_min = corners.min(axis=0)
x_max, y_max, z_max = corners.max(axis=0)
res = img.header['pixelSize'][0]
pixelSize = [res, res, res, 1]
origin = [ x_min + (x_max+1 - x_min)/2,
y_min + (y_max+1 - y_min)/2,
z_min + (z_max+1 - z_min)/2,
img.header['origin'][3] ]
dim = [ (x_max+1 - x_min)/res,
(y_max+1 - y_min)/res,
(z_max+1 - z_min)/res,
1 ]
target_header = irtk.new_header( pixelSize=pixelSize, origin=origin, dim=dim)
if isinstance( target_header, irtk.Image ):
target_header = target_header.get_header()
data = img.get_data('float32','cython')
new_data = _irtk.transform_rigid( self.tx, self.ty, self.tz,
self.rx, self.ry, self.rz,
data,
img.get_header(),
target_header,
interpolation,
gaussian_parameter )
return irtk.Image( new_data, target_header )
def apply( self, img, target_header=None,
interpolation='linear', gaussian_parameter=1.0 ):
"""
Apply.
"""
if not isinstance( img, irtk.Image ):
# point transformation
pt = np.array(img, dtype='float64', copy=True)
if len(pt.shape) == 1:
tmp_pt = np.hstack((pt,[1])).astype('float64')
return np.dot( self.matrix(), tmp_pt )[:3]
else:
pt = _irtk.transform_points( self.matrix(), pt )
return pt
# tmp_pt = np.hstack((pt,[[1]]*pt.shape[0])).astype('float64')
# return np.transpose( np.dot( self.matrix(),
# np.transpose(tmp_pt) ) )[:,:3]
# if target_header is None:
# target_header = img.get_header()
if target_header is None:
(x_min, y_min, z_min, x_max, y_max, z_max ) = img.bbox(world=True)
corners = [[x_min, y_min, z_min],
[x_max, y_min, z_min],
[x_min, y_max, z_min],
[x_min, y_min, z_max],
[x_max, y_max, z_min],
[x_min, y_max, z_max],
[x_max, y_min, z_max],
[x_max, y_max, z_max]]
corners = self.apply( corners )
x_min, y_min, z_min = corners.min(axis=0)
x_max, y_max, z_max = corners.max(axis=0)
res = img.header['pixelSize'][0]
pixelSize = [res, res, res, 1]
origin = [ x_min + (x_max+1 - x_min)/2,
y_min + (y_max+1 - y_min)/2,
z_min + (z_max+1 - z_min)/2,
img.header['origin'][3] ]
dim = [ (x_max+1 - x_min)/res,
(y_max+1 - y_min)/res,
(z_max+1 - z_min)/res,
1 ]
target_header = irtk.new_header( pixelSize=pixelSize, origin=origin, dim=dim)
if isinstance( target_header, irtk.Image ):
target_header = target_header.get_header()
data = img.get_data('float32','cython')
new_data = _irtk.transform_rigid( self.tx, self.ty, self.tz,
self.rx, self.ry, self.rz,
data,
img.get_header(),
target_header,
interpolation,
gaussian_parameter )
return irtk.Image( new_data, target_header )
def create_mask_from_all_masks(f_lists,transformations,ga,resolution=0.75):
points = []
for f, t in zip(f_lists,transformations):
m = irtk.imread(f,force_neurological=True)
points.extend( t.apply(get_corners(m)) )
points = np.array(points,dtype='float64')
x_min, y_min, z_min = points.min(axis=0)
x_max, y_max, z_max = points.max(axis=0)
pixelSize = [resolution, resolution, resolution, 1]
orientation = np.eye( 3, dtype='float64' )
origin = [ x_min + (x_max - x_min)/2,
y_min + (y_max - y_min)/2,
z_min + (z_max - z_min)/2,
0 ]
dim = [ (x_max - x_min)/resolution,
(y_max - y_min)/resolution,
(z_max - z_min)/resolution,
1 ]
header = irtk.new_header( pixelSize=pixelSize,
orientation=orientation,
origin=origin,
dim=dim )
mask = irtk.zeros( header, dtype='float32' )
for f, t in zip( f_lists, transformations ):
m = irtk.imread(f,force_neurological=True).transform(t, target=mask,interpolation="linear")
mask += m
irtk.imwrite( "debug_mask1.nii.gz", mask)
mask = irtk.Image( nd.gaussian_filter( mask, 0.5 ),
mask.get_header() )
irtk.imwrite( "debug_mask2.nii.gz", mask)
mask = (mask > 0).bbox(crop=True).astype('uint8')
scale = get_CRL(ga)/get_CRL(30.0)
template = irtk.imread(f_template,force_neurological=True)
template.header['pixelSize'][:3] /= scale
template = template.transform(target=mask,interpolation='nearest')
mask[template==0] = 0
irtk.imwrite( "debug_template.nii.gz", template)
irtk.imwrite( "debug_mask3.nii.gz", mask)
return mask
def world_align(img,pixelSize=[3,3,3,1],interpolation='linear'):
x_min, y_min, z_min, x_max, y_max, z_max = img.wbox()
x0 = ( x_min + x_max ) / 2
y0 = ( y_min + y_max ) / 2
z0 = ( z_min + z_max ) / 2
dx = int((x_max-x_min)/pixelSize[0])
dy = int((y_max-y_min)/pixelSize[1])
dz = int((z_max-z_min)/pixelSize[2])
header = irtk.new_header( pixelSize=pixelSize,
orientation=np.eye( 3, dtype='float64' ),
origin=[x0,y0,z0,0],
dim=[dx,dy,dz,1])
res = img.transform(target=header,interpolation=interpolation)
return res.bbox(crop=True)
