def test_metainfo_from_h5(self):
shape = (3, 9, 10, 11)
starting_metadata = VoxelsMetadata.create_default_metadata( shape, numpy.float32, "cxyz", 1.0, "nanometers" )
f = h5py.File("dummy.h5", mode='w', driver='core', backing_store=False) # In-memory
dset = f.create_dataset( 'dset', shape=shape, dtype=numpy.float32, chunks=True )
metadata = VoxelsMetadata.create_from_h5_dataset( dset )
assert metadata.dtype.type is numpy.float32
assert metadata.shape == starting_metadata.shape, \
"Wrong shape: {} vs. {}".format( metadata.shape, starting_metadata.shape )
def test_basic_roundtrip(self):
data = numpy.random.randint(0,255, (3, 100, 200)).astype(numpy.uint8)
metadata = VoxelsMetadata.create_default_metadata(data.shape, data.dtype, 'cxy', 1.0, "nanometers")
codec = VoxelsNddataCodec( metadata )
stream = StringIO.StringIO()
codec.encode_from_ndarray(stream, data)
stream.seek(0)
roundtrip_data = codec.decode_to_ndarray(stream, data.shape)
assert roundtrip_data.flags['F_CONTIGUOUS']
self._assert_matching(roundtrip_data, data)
def test_metainfo_from_h5(self):
shape = (3, 9, 10, 11)
starting_metadata = VoxelsMetadata.create_default_metadata(
shape, numpy.float32, "cxyz", 1.0, "nanometers")
f = h5py.File("dummy.h5", mode='w', driver='core',
backing_store=False) # In-memory
dset = f.create_dataset('dset',
shape=shape,
dtype=numpy.float32,
chunks=True)
metadata = VoxelsMetadata.create_from_h5_dataset(dset)
assert metadata.dtype.type is numpy.float32
assert metadata.shape == starting_metadata.shape, \
"Wrong shape: {} vs. {}".format( metadata.shape, starting_metadata.shape )
def test_create_default_metadata(self):
metadata = VoxelsMetadata.create_default_metadata( (2,10,11), numpy.int64, "cxy", 1.5, "nanometers" )
metadata["Properties"]["Values"][0]["Label"] = "R"
metadata["Properties"]["Values"][1]["Label"] = "G"
assert len( metadata["Axes"] ) == 2
assert metadata["Axes"][0]["Label"] == "X"
assert metadata["Axes"][0]["Size"] == 10
assert metadata["Axes"][1]["Label"] == "Y"
assert metadata["Axes"][1]["Size"] == 11
assert len(metadata["Properties"]["Values"]) == 2 # 2 channels
assert metadata["Properties"]["Values"][0]["DataType"] == "int64"
assert metadata["Properties"]["Values"][1]["DataType"] == "int64"
assert metadata["Properties"]["Values"][0]["Label"] == "R"
assert metadata["Properties"]["Values"][1]["Label"] == "G"
def test_create_default_metadata(self):
metadata = VoxelsMetadata.create_default_metadata(
(2, 10, 11), numpy.int64, "cxy", 1.5, "nanometers")
metadata["Properties"]["Values"][0]["Label"] = "R"
metadata["Properties"]["Values"][1]["Label"] = "G"
assert len(metadata["Axes"]) == 2
assert metadata["Axes"][0]["Label"] == "X"
assert metadata["Axes"][0]["Size"] == 10
assert metadata["Axes"][1]["Label"] == "Y"
assert metadata["Axes"][1]["Size"] == 11
assert len(metadata["Properties"]["Values"]) == 2 # 2 channels
assert metadata["Properties"]["Values"][0]["DataType"] == "int64"
assert metadata["Properties"]["Values"][1]["DataType"] == "int64"
assert metadata["Properties"]["Values"][0]["Label"] == "R"
assert metadata["Properties"]["Values"][1]["Label"] == "G"
def _get_format_selection_error_msg(self, *args):
"""
If the currently selected format does not support the input image format,
return an error message stating why. Otherwise, return an empty string.
"""
if not self.Input.ready():
return "Input not ready"
output_format = self.OutputFormat.value
# These cases support all combinations
if output_format in ('hdf5', 'npy', 'blockwise hdf5'):
return ""
tagged_shape = self.Input.meta.getTaggedShape()
axes = OpStackWriter.get_nonsingleton_axes_for_tagged_shape( tagged_shape )
output_dtype = self.Input.meta.dtype
if output_format == 'dvid':
# dvid requires a channel axis, which must come last.
# Internally, we transpose it before sending it over the wire
if tagged_shape.keys()[-1] != 'c':
return "DVID requires the last axis to be channel."
# Make sure DVID supports this dtype/channel combo.
from pydvid.voxels import VoxelsMetadata
axiskeys = self.Input.meta.getAxisKeys()
# We reverse the axiskeys because the export operator (see below) uses transpose_axes=True
reverse_axiskeys = "".join(reversed( axiskeys ))
reverse_shape = tuple(reversed(self.Input.meta.shape))
metainfo = VoxelsMetadata.create_default_metadata( reverse_shape,
output_dtype,
reverse_axiskeys,
0.0,
'nanometers' )
try:
metainfo.determine_dvid_typename()
except Exception as ex:
return str(ex)
else:
return ""
return FormatValidity.check(self.Input.meta.getTaggedShape(),
self.Input.meta.dtype,
output_format)
def _get_format_selection_error_msg(self, *args):
"""
If the currently selected format does not support the input image format,
return an error message stating why. Otherwise, return an empty string.
"""
if not self.Input.ready():
return "Input not ready"
output_format = self.OutputFormat.value
# These cases support all combinations
if output_format in ('hdf5', 'npy', 'blockwise hdf5'):
return ""
tagged_shape = self.Input.meta.getTaggedShape()
axes = OpStackWriter.get_nonsingleton_axes_for_tagged_shape(
tagged_shape)
output_dtype = self.Input.meta.dtype
if output_format == 'dvid':
# dvid requires a channel axis, which must come last.
# Internally, we transpose it before sending it over the wire
if tagged_shape.keys()[-1] != 'c':
return "DVID requires the last axis to be channel."
# Make sure DVID supports this dtype/channel combo.
from pydvid.voxels import VoxelsMetadata
axiskeys = self.Input.meta.getAxisKeys()
# We reverse the axiskeys because the export operator (see below) uses transpose_axes=True
reverse_axiskeys = "".join(reversed(axiskeys))
reverse_shape = tuple(reversed(self.Input.meta.shape))
metainfo = VoxelsMetadata.create_default_metadata(
reverse_shape, output_dtype, reverse_axiskeys, 0.0,
'nanometers')
try:
metainfo.determine_dvid_typename()
except Exception as ex:
return str(ex)
else:
return ""
return FormatValidity.check(self.Input.meta.getTaggedShape(),
self.Input.meta.dtype, output_format)
def _get_format_selection_error_msg(self, *args):
"""
If the currently selected format does not support the input image format,
return an error message stating why. Otherwise, return an empty string.
"""
if not self.Input.ready():
return "Input not ready"
output_format = self.OutputFormat.value
# These cases support all combinations
if output_format in ('hdf5', 'npy'):
return ""
tagged_shape = self.Input.meta.getTaggedShape()
axes = OpStackWriter.get_nonsingleton_axes_for_tagged_shape( tagged_shape )
output_dtype = self.Input.meta.dtype
if output_format == 'dvid':
# dvid requires a channel axis, which must come last.
# Internally, we transpose it before sending it over the wire
if tagged_shape.keys()[-1] != 'c':
return "DVID requires the last axis to be channel."
# Make sure DVID supports this dtype/channel combo.
from pydvid.voxels import VoxelsMetadata
axiskeys = self.Input.meta.getAxisKeys()
# We reverse the axiskeys because the export operator (see below) uses transpose_axes=True
reverse_axiskeys = "".join(reversed( axiskeys ))
reverse_shape = tuple(reversed(self.Input.meta.shape))
metainfo = VoxelsMetadata.create_default_metadata( reverse_shape,
output_dtype,
reverse_axiskeys,
0.0,
'nanometers' )
try:
metainfo.determine_dvid_typename()
except Exception as ex:
return str(ex)
else:
return ""
# None of the remaining formats support more than 4 channels.
if 'c' in tagged_shape and tagged_shape['c'] > 4 and not filter(lambda fmt: fmt.name == output_format, self._3d_sequence_formats):
return "Too many channels."
# HDR format supports float32 only, and must have exactly 3 channels
if output_format == 'hdr' or output_format == 'hdr sequence':
if output_dtype == numpy.float32 and\
'c' in tagged_shape and tagged_shape['c'] == 3:
return ""
else:
return "HDR volumes must be float32, with exactly 3 channels."
# Apparently, TIFF supports everything but signed byte
if 'tif' in output_format and output_dtype == numpy.int8:
return "TIF output does not support signed byte (int8). Try unsigned (uint8)."
# Apparently, these formats support everything except uint32
# See http://github.com/ukoethe/vigra/issues/153
if output_dtype == numpy.uint32 and \
( 'pbm' in output_format or \
'pgm' in output_format or \
'pnm' in output_format or \
'ppm' in output_format ):
return "PBM/PGM/PNM/PPM do not support the uint32 pixel type."
# These formats don't support 2 channels (must be either 1 or 3)
non_dualband_formats = ['bmp', 'gif', 'jpg', 'jpeg', 'ras']
for fmt in non_dualband_formats:
if fmt in output_format and axes[0] != 'c' and 'c' in tagged_shape:
if 'c' in tagged_shape and tagged_shape['c'] != 1 and tagged_shape['c'] != 3:
return "Invalid number of channels (must be exactly 1 or 3)."
# 2D formats only support 2D images (singleton/channel axes excepted)
if filter(lambda fmt: fmt.name == output_format, self._2d_formats):
# Examples:
# OK: 'xy', 'xyc'
# NOT OK: 'xc', 'xyz'
nonchannel_axes = filter(lambda a: a != 'c', axes)
if len(nonchannel_axes) == 2:
return ""
else:
return "Input has too many dimensions for a 2D output format."
nonstep_axes = axes[1:]
nonchannel_axes = filter( lambda a: a != 'c', nonstep_axes )
# 3D sequences of 2D images require a 3D image
# (singleton/channel axes excepted, unless channel is the 'step' axis)
if filter(lambda fmt: fmt.name == output_format, self._3d_sequence_formats)\
or output_format == 'multipage tiff':
# Examples:
# OK: 'xyz', 'xyzc', 'cxy'
# NOT OK: 'cxyz'
if len(nonchannel_axes) == 2:
return ""
else:
return "Can't export 3D stack: Input is not 3D or axis are in the wrong order."
# 4D sequences of 3D images require a 4D image
# (singleton/channel axes excepted, unless channel is the 'step' axis)
if output_format == 'multipage tiff sequence':
# Examples:
# OK: 'txyz', 'txyzc', 'cxyz'
# NOT OK: 'xyzc', 'xyz', 'xyc'
if len(nonchannel_axes) == 3:
return ""
else:
return "Can't export 4D stack: Input is not 4D."
assert False, "Unknown format case: {}".format( output_format )
def _get_format_selection_error_msg(self, *args):
"""
If the currently selected format does not support the input image format,
return an error message stating why. Otherwise, return an empty string.
"""
if not self.Input.ready():
return "Input not ready"
output_format = self.OutputFormat.value
# These cases support all combinations
if output_format in ('hdf5', 'npy'):
return ""
tagged_shape = self.Input.meta.getTaggedShape()
axes = OpStackWriter.get_nonsingleton_axes_for_tagged_shape(
tagged_shape)
output_dtype = self.Input.meta.dtype
if output_format == 'dvid':
# dvid requires a channel axis, which must come last.
# Internally, we transpose it before sending it over the wire
if tagged_shape.keys()[-1] != 'c':
return "DVID requires the last axis to be channel."
# Make sure DVID supports this dtype/channel combo.
from pydvid.voxels import VoxelsMetadata
axiskeys = self.Input.meta.getAxisKeys()
# We reverse the axiskeys because the export operator (see below) uses transpose_axes=True
reverse_axiskeys = "".join(reversed(axiskeys))
reverse_shape = tuple(reversed(self.Input.meta.shape))
metainfo = VoxelsMetadata.create_default_metadata(
reverse_shape, output_dtype, reverse_axiskeys, 0.0,
'nanometers')
try:
metainfo.determine_dvid_typename()
except Exception as ex:
return str(ex)
else:
return ""
# None of the remaining formats support more than 4 channels.
if 'c' in tagged_shape and tagged_shape['c'] > 4 and not filter(
lambda fmt: fmt.name == output_format,
self._3d_sequence_formats):
return "Too many channels."
# HDR format supports float32 only, and must have exactly 3 channels
if output_format == 'hdr' or output_format == 'hdr sequence':
if output_dtype == numpy.float32 and\
'c' in tagged_shape and tagged_shape['c'] == 3:
return ""
else:
return "HDR volumes must be float32, with exactly 3 channels."
# Apparently, TIFF supports everything but signed byte
if 'tif' in output_format and output_dtype == numpy.int8:
return "TIF output does not support signed byte (int8). Try unsigned (uint8)."
# Apparently, these formats support everything except uint32
# See http://github.com/ukoethe/vigra/issues/153
if output_dtype == numpy.uint32 and \
( 'pbm' in output_format or \
'pgm' in output_format or \
'pnm' in output_format or \
'ppm' in output_format ):
return "PBM/PGM/PNM/PPM do not support the uint32 pixel type."
# These formats don't support 2 channels (must be either 1 or 3)
non_dualband_formats = ['bmp', 'gif', 'jpg', 'jpeg', 'ras']
for fmt in non_dualband_formats:
if fmt in output_format and axes[0] != 'c' and 'c' in tagged_shape:
if 'c' in tagged_shape and tagged_shape[
'c'] != 1 and tagged_shape['c'] != 3:
return "Invalid number of channels (must be exactly 1 or 3)."
# 2D formats only support 2D images (singleton/channel axes excepted)
if filter(lambda fmt: fmt.name == output_format, self._2d_formats):
# Examples:
# OK: 'xy', 'xyc'
# NOT OK: 'xc', 'xyz'
nonchannel_axes = filter(lambda a: a != 'c', axes)
if len(nonchannel_axes) == 2:
return ""
else:
return "Input has too many dimensions for a 2D output format."
nonstep_axes = axes[1:]
nonchannel_axes = filter(lambda a: a != 'c', nonstep_axes)
# 3D sequences of 2D images require a 3D image
# (singleton/channel axes excepted, unless channel is the 'step' axis)
if filter(lambda fmt: fmt.name == output_format, self._3d_sequence_formats)\
or output_format == 'multipage tiff':
# Examples:
# OK: 'xyz', 'xyzc', 'cxy'
# NOT OK: 'cxyz'
if len(nonchannel_axes) == 2:
return ""
else:
return "Can't export 3D stack: Input is not 3D or axis are in the wrong order."
# 4D sequences of 3D images require a 4D image
# (singleton/channel axes excepted, unless channel is the 'step' axis)
if output_format == 'multipage tiff sequence':
# Examples:
# OK: 'txyz', 'txyzc', 'cxyz'
# NOT OK: 'xyzc', 'xyz', 'xyc'
if len(nonchannel_axes) == 3:
return ""
else:
return "Can't export 4D stack: Input is not 4D."
assert False, "Unknown format case: {}".format(output_format)
