def _load_single_file(self, file_path, loader, dtype=np.float32):
image_data = SpatialImage2D._load_single_file(
file_path, loader, dtype)
if self.spatial_rank < 3:
return image_data
if self.original_axcodes[0] and self.output_axcodes[0]:
image_data = misc.do_reorientation(
image_data, self.original_axcodes[0], self.output_axcodes[0])
if self.original_pixdim[0] and self.output_pixdim[0]:
# verbose: warning when interpolate_order>1 for integers
image_data = misc.do_resampling(image_data,
self.original_pixdim[0],
self.output_pixdim[0],
self.interp_order[0])
return image_data
def _load_single_file(self, file_path, loader, dtype=np.float32):
image_data = SpatialImage2D._load_single_file(
file_path, loader, dtype)
if self.spatial_rank < 3:
return image_data
if self.original_axcodes[0] and self.output_axcodes[0]:
image_data = misc.do_reorientation(
image_data, self.original_axcodes[0], self.output_axcodes[0])
if self.original_pixdim[0] and self.output_pixdim[0]:
# verbose: warning when interpolate_order>1 for integers
image_data = misc.do_resampling(image_data,
self.original_pixdim[0],
self.output_pixdim[0],
self.interp_order[0])
return image_data
def _load_single_5d(self, idx=0):
if len(self._file_path) > 1:
# 3D image from multiple 2d files
raise NotImplementedError
# assuming len(self._file_path) == 1
image_obj = misc.load_image(self.file_path[idx])
image_data = image_obj.get_data()
image_data = misc.expand_to_5d(image_data)
assert image_data.shape[3] == 1, "time sequences not supported"
if self.original_axcodes[idx] and self.output_axcodes[idx]:
output_image = []
for t_pt in range(image_data.shape[3]):
mod_list = []
for mod in range(image_data.shape[4]):
spatial_slice = image_data[..., t_pt:t_pt + 1, mod:mod + 1]
spatial_slice = misc.do_reorientation(
spatial_slice,
self.original_axcodes[idx],
self.output_axcodes[idx])
mod_list.append(spatial_slice)
output_image.append(np.concatenate(mod_list, axis=4))
image_data = np.concatenate(output_image, axis=3)
if self.original_pixdim[idx] and self.output_pixdim[idx]:
assert len(self._original_pixdim[idx]) == \
len(self.output_pixdim[idx]), \
"wrong pixdim format original {} output {}".format(
self._original_pixdim[idx], self.output_pixdim[idx])
# verbose: warning when interpolate_order>1 for integers
output_image = []
for t_pt in range(image_data.shape[3]):
mod_list = []
for mod in range(image_data.shape[4]):
spatial_slice = image_data[..., t_pt:t_pt + 1, mod:mod + 1]
spatial_slice = misc.do_resampling(
spatial_slice,
self.original_pixdim[idx],
self.output_pixdim[idx],
self.interp_order[idx])
mod_list.append(spatial_slice)
output_image.append(np.concatenate(mod_list, axis=4))
image_data = np.concatenate(output_image, axis=3)
return image_data
def get_data(self):
if len(self._file_path) > 1:
# 3D image from multiple 2d files
raise NotImplementedError
# assuming len(self._file_path) == 1
image_obj = misc.load_image(self.file_path[0])
image_data = image_obj.get_data()
image_data = misc.expand_to_5d(image_data)
if self.original_axcodes[0] and self.output_axcodes[0]:
image_data = misc.do_reorientation(
image_data, self.original_axcodes[0], self.output_axcodes[0])
if self.original_pixdim[0] and self.output_pixdim[0]:
# verbose: warning when interpolate_order>1 for integers
image_data = misc.do_resampling(image_data,
self.original_pixdim[0],
self.output_pixdim[0],
self.interp_order[0])
return image_data
def _load_single_file(self, file_path, loader, dtype=np.float32):
image_data = SpatialImage2D._load_single_file(file_path, loader, dtype)
if self.spatial_rank < 3:
return image_data
pixdim = self.original_pixdim[0]
if self.original_axcodes[0] and self.output_axcodes[0]:
image_data = misc.do_reorientation(image_data,
self.original_axcodes[0],
self.output_axcodes[0])
transf, _, _ = misc.compute_orientation(self.output_axcodes[0],
self.original_axcodes[0])
if pixdim:
pixdim = tuple(pixdim[k] for k in transf[:, 0].astype(np.int))
if pixdim and self.output_pixdim[0]:
# verbose: warning when interpolate_order>1 for integers
image_data = misc.do_resampling(image_data, pixdim,
self.output_pixdim[0],
self.interp_order[0])
return image_data
def get_data(self):
if len(self._file_path) > 1:
# 3D image from multiple 2d files
mod_list = []
for mod in range(len(self.file_path)):
mod_2d = SpatialImage2D(
file_path=(self.file_path[mod], ),
name=(self.name[mod], ),
interp_order=(self.interp_order[mod], ),
output_pixdim=(self.output_pixdim[mod], ),
output_axcodes=(self.output_axcodes[mod], ),
loader=(self.loader[mod], ))
mod_data_5d = mod_2d.get_data()
mod_list.append(mod_data_5d)
try:
image_data = np.concatenate(mod_list, axis=4)
except ValueError:
tf.logging.fatal(
"multi-modal data shapes not consistent -- trying to "
"concat {}.".format([mod.shape for mod in mod_list]))
raise
return image_data
# assuming len(self._file_path) == 1
image_obj = load_image_from_file(self.file_path[0], self.loader[0])
image_data = image_obj.get_data()
image_data = misc.expand_to_5d(image_data)
if self.original_axcodes[0] and self.output_axcodes[0]:
image_data = misc.do_reorientation(image_data,
self.original_axcodes[0],
self.output_axcodes[0])
if self.original_pixdim[0] and self.output_pixdim[0]:
# verbose: warning when interpolate_order>1 for integers
image_data = misc.do_resampling(image_data,
self.original_pixdim[0],
self.output_pixdim[0],
self.interp_order[0])
return image_data