def _image3_animated_gif(tag, ims):
check_module('PIL')
import PIL
from PIL.GifImagePlugin import Image as GIF
# x=numpy.random.randint(0,256,[10,10,10],numpy.uint8)
ims = [np.asarray((ims[i, :, :]).astype(np.uint8))
for i in range(ims.shape[0])]
ims = [GIF.fromarray(im) for im in ims]
s = b''
for b in PIL.GifImagePlugin.getheader(ims[0])[0]:
s += b
s += b'\x21\xFF\x0B\x4E\x45\x54\x53\x43\x41\x50' \
b'\x45\x32\x2E\x30\x03\x01\x00\x00\x00'
for i in ims:
for b in PIL.GifImagePlugin.getdata(i):
s += b
s += b'\x3B'
if IS_PYTHON2:
s = str(s)
summary_image_str = summary_pb2.Summary.Image(
height=10, width=10, colorspace=1, encoded_image_string=s)
image_summary = summary_pb2.Summary.Value(
tag=tag, image=summary_image_str)
return [summary_pb2.Summary(value=[image_summary]).SerializeToString()]
def add_network_args(parser):
parser.add_argument(
"--name",
help="Choose a net from NiftyNet/niftynet/network/ or from "
"user specified module string",
metavar='')
import niftynet.layer.activation
parser.add_argument("--activation_function",
help="Specify activation function types",
choices=list(niftynet.layer.activation.SUPPORTED_OP),
metavar='TYPE_STR',
default='relu')
parser.add_argument("--batch_size",
metavar='',
help="Set batch size of the net",
type=int,
default=2)
parser.add_argument("--decay",
help="[Training only] Set weight decay",
type=float,
default=0)
parser.add_argument("--reg_type",
metavar='TYPE_STR',
help="[Training only] Specify regulariser type_str",
type=str,
default='L2')
parser.add_argument(
"--volume_padding_size",
metavar='',
help="Set padding size of each volume (in all dimensions)",
type=spatialnumarray,
default=(0, 0, 0))
parser.add_argument("--window_sampling",
metavar='TYPE_STR',
help="How to sample patches from each loaded image:"
" 'uniform': fixed size uniformly distributed,"
" 'resize': resize image to the patch size.",
choices=['uniform', 'resize'],
default='uniform')
parser.add_argument("--queue_length",
help="Set size of preprocessing buffer queue",
metavar='',
type=int,
default=5)
import niftynet.layer.binary_masking
parser.add_argument(
"--multimod_foreground_type",
choices=list(
niftynet.layer.binary_masking.SUPPORTED_MULTIMOD_MASK_TYPES),
help="Way of combining the foreground masks from different "
"modalities. 'and' is the intersection, 'or' is the union "
"and 'multi' permits each modality to use its own mask.",
default='and')
parser.add_argument(
"--histogram_ref_file",
metavar='',
type=str,
help="A reference file of histogram for intensity normalisation",
default='')
# TODO add choices of normalisation types
import niftynet.utilities.histogram_standardisation as hist_std_module
parser.add_argument("--norm_type",
help="Type of normalisation to perform",
type=str,
default='percentile',
choices=list(hist_std_module.SUPPORTED_CUTPOINTS))
parser.add_argument("--cutoff",
help="Cutoff values for the normalisation process",
type=float_array,
default=(0.01, 0.99))
import niftynet.layer.binary_masking
parser.add_argument(
"--foreground_type",
choices=list(niftynet.layer.binary_masking.SUPPORTED_MASK_TYPES),
help="type_str of foreground masking strategy used",
default='otsu_plus')
parser.add_argument(
"--normalisation",
help="Indicates if the normalisation must be performed",
type=str2boolean,
default=False)
parser.add_argument(
"--whitening",
help="Indicates if the whitening of the data should be applied",
type=str2boolean,
default=False)
parser.add_argument(
"--normalise_foreground_only",
help="Indicates whether a foreground mask should be applied when"
" normalising volumes",
type=str2boolean,
default=False)
parser.add_argument("--weight_initializer",
help="Set the initializer for the weight parameters",
type=str,
default='he_normal')
parser.add_argument("--bias_initializer",
help="Set the initializer for the bias parameters",
type=str,
default='zeros')
try:
from niftynet.utilities.util_import import check_module
check_module('yaml')
import yaml
parser.add_argument(
"--weight_initializer_args",
help="Pass arguments to the initializer for the weight parameters",
type=yaml.load,
default={})
parser.add_argument(
"--bias_initializer_args",
help="Pass arguments to the initializer for the bias parameters",
type=yaml.load,
default={})
except ImportError:
# "PyYAML module not found")
pass
return parser
# -*- coding: utf-8 -*-
"""
Loading SimpleITK image as a Nibabel object.
"""
from niftynet.utilities.util_import import check_module
check_module('SimpleITK')
import SimpleITK as sitk
import nibabel
import numpy as np
class SimpleITKAsNibabel(nibabel.Nifti1Image):
"""
Minimal interface to use a SimpleITK image as if it were
a nibabel object. Currently only supports the subset of the
interface used by NiftyNet and is read only
"""
def __init__(self, filename):
try:
self._SimpleITKImage = sitk.ReadImage(filename)
except RuntimeError as err:
if 'Unable to determine ImageIO reader' in str(err):
raise nibabel.filebasedimages.ImageFileError(str(err))
else:
raise
# self._header = SimpleITKAsNibabelHeader(self._SimpleITKImage)
affine = make_affine(self._SimpleITKImage)
# super(SimpleITKAsNibabel, self).__init__(
def test_installed(self):
check_module('tensorflow')
def add_network_args(parser):
parser.add_argument(
"--name",
help="Choose a net from NiftyNet/niftynet/network/ or from "
"user specified module string",
metavar='')
import niftynet.layer.activation
parser.add_argument(
"--activation_function",
help="Specify activation function types",
choices=list(niftynet.layer.activation.SUPPORTED_OP),
metavar='TYPE_STR',
default='relu')
parser.add_argument(
"--batch_size",
metavar='',
help="Set batch size of the net",
type=int,
default=2)
parser.add_argument(
"--decay",
help="[Training only] Set weight decay",
type=float,
default=0)
parser.add_argument(
"--reg_type",
metavar='TYPE_STR',
help="[Training only] Specify regulariser type_str",
type=str,
default='L2')
parser.add_argument(
"--volume_padding_size",
metavar='',
help="Set padding size of each volume (in all dimensions)",
type=spatialnumarray,
default=(0, 0, 0))
parser.add_argument(
"--window_sampling",
metavar='TYPE_STR',
help="How to sample patches from each loaded image:"
" 'uniform': fixed size uniformly distributed,"
" 'resize': resize image to the patch size.",
choices=['uniform', 'resize'],
default='uniform')
parser.add_argument(
"--queue_length",
help="Set size of preprocessing buffer queue",
metavar='',
type=int,
default=5)
import niftynet.layer.binary_masking
parser.add_argument(
"--multimod_foreground_type",
choices=list(
niftynet.layer.binary_masking.SUPPORTED_MULTIMOD_MASK_TYPES),
help="Way of combining the foreground masks from different "
"modalities. 'and' is the intersection, 'or' is the union "
"and 'multi' permits each modality to use its own mask.",
default='and')
parser.add_argument(
"--histogram_ref_file",
metavar='',
type=str,
help="A reference file of histogram for intensity normalisation",
default='')
# TODO add choices of normalisation types
import niftynet.utilities.histogram_standardisation as hist_std_module
parser.add_argument(
"--norm_type",
help="Type of normalisation to perform",
type=str,
default='percentile',
choices=list(hist_std_module.SUPPORTED_CUTPOINTS))
parser.add_argument(
"--cutoff",
help="Cutoff values for the normalisation process",
type=float_array,
default=(0.01, 0.99))
import niftynet.layer.binary_masking
parser.add_argument(
"--foreground_type",
choices=list(
niftynet.layer.binary_masking.SUPPORTED_MASK_TYPES),
help="type_str of foreground masking strategy used",
default='otsu_plus')
parser.add_argument(
"--normalisation",
help="Indicates if the normalisation must be performed",
type=str2boolean,
default=False)
parser.add_argument(
"--whitening",
help="Indicates if the whitening of the data should be applied",
type=str2boolean,
default=False)
parser.add_argument(
"--normalise_foreground_only",
help="Indicates whether a foreground mask should be applied when"
" normalising volumes",
type=str2boolean,
default=False)
parser.add_argument(
"--weight_initializer",
help="Set the initializer for the weight parameters",
type=str,
default='he_normal')
parser.add_argument(
"--bias_initializer",
help="Set the initializer for the bias parameters",
type=str,
default='zeros')
try:
from niftynet.utilities.util_import import check_module
check_module('yaml')
import yaml
parser.add_argument(
"--weight_initializer_args",
help="Pass arguments to the initializer for the weight parameters",
type=yaml.load,
default={})
parser.add_argument(
"--bias_initializer_args",
help="Pass arguments to the initializer for the bias parameters",
type=yaml.load,
default={})
except ImportError:
# "PyYAML module not found")
pass
return parser
def test_no_version_info(self):
check_module('importlib', 0)
def test_no_input(self):
with self.assertRaisesRegexp(ImportError, ''):
check_module([])
with self.assertRaisesRegexp(ImportError, ''):
check_module(None)
def test_self_version(self):
check_module('importlib')
def test_wrong_version(self):
with self.assertRaisesRegexp(AssertionError, ''):
check_module('tensorflow', 100)
def test_no_package(self):
with self.assertRaisesRegexp(ImportError, ''):
check_module('foobar_wrong_case')
def test_installed_min_version(self):
check_module('tensorflow', 1.0)
# -*- coding: utf-8 -*-
"""
Loading SimpleITK image as a Nibabel object.
"""
from niftynet.utilities.util_import import check_module
check_module('SimpleITK')
import SimpleITK as sitk
import nibabel
import numpy as np
class SimpleITKAsNibabel(nibabel.Nifti1Image):
"""
Minimal interface to use a SimpleITK image as if it were
a nibabel object. Currently only supports the subset of the
interface used by NiftyNet and is read only
"""
def __init__(self, filename):
try:
self._SimpleITKImage = sitk.ReadImage(filename)
except RuntimeError as err:
if 'Unable to determine ImageIO reader' in str(err):
raise nibabel.filebasedimages.ImageFileError(str(err))
else:
raise
# self._header = SimpleITKAsNibabelHeader(self._SimpleITKImage)
affine = make_affine(self._SimpleITKImage)
# super(SimpleITKAsNibabel, self).__init__(
# sitk.GetArrayFromImage(self._SimpleITKImage).transpose(), affine)