def get_standard_tiff_data(new_tiff_filename,
axis_order="tzyxc",
pages_to_channel=1,
memmap=False):
"""
Reads a tiff file and returns a standard 5D array and the metadata.
Args:
new_tiff_filename(str): the TIFF file to read in
axis_order(int): how to order the axes (by
default returns "tzyxc").
pages_to_channel(int): if channels are not normally
stored in the channel variable,
but are stored as pages (or as
a mixture), then this will
split neighboring pages into
separate channels. (by default
is 1 so changes nothing)
memmap(bool): allows one to load the array
using a memory mapped file as
opposed to reading it directly.
(by default is False)
Returns:
(ndarray/memmap, ndarray): an array with the axis order
specified and description
metadata.
"""
assert (pages_to_channel > 0)
new_tiff_description = []
with tifffile.TiffFile(new_tiff_filename) as new_tiff_file:
if memmap:
try:
# tifffile >= 0.13.0
new_tiff_array = new_tiff_file.asarray(out="memmap")
except TypeError:
# tifffile < 0.13.0
new_tiff_array = new_tiff_file.asarray(memmap=True)
else:
new_tiff_array = new_tiff_file.asarray()
for i in iters.irange(0, len(new_tiff_file.pages), pages_to_channel):
new_tiff_description.append([])
for j in iters.irange(pages_to_channel):
each_page = new_tiff_file.pages[i + j]
each_metadata = each_page.tags
each_desc = u""
try:
each_desc = unicode(
each_metadata["image_description"].value)
except KeyError:
pass
new_tiff_description[-1].append(each_desc)
new_tiff_description = numpy.array(new_tiff_description)
# Add a singleton channel if none is present.
if new_tiff_array.ndim == 3:
new_tiff_array = new_tiff_array[None]
# Fit the old VIGRA style array. (may try to remove in the future)
new_tiff_array = new_tiff_array.transpose(
tuple(iters.irange(new_tiff_array.ndim - 1, 1, -1)) + (1, 0))
# Check to make sure the dimensions are ok
if (new_tiff_array.ndim == 5):
pass
elif (new_tiff_array.ndim == 4):
# Has no z. So, add this.
new_tiff_array = xnumpy.add_singleton_axis_beginning(new_tiff_array)
else:
raise Exception(
"Invalid dimensionality for TIFF. Found shape to be \"" +
repr(new_tiff_array.shape) + "\".")
# Some people use pages to hold time and channel data. So, we need to
# restructure it. However, if they are properly structuring their TIFF
# file, then they shouldn't incur a penalty.
if pages_to_channel > 1:
new_tiff_array = new_tiff_array.reshape(new_tiff_array.shape[:-2] + (
new_tiff_array.shape[-2] // pages_to_channel,
pages_to_channel * new_tiff_array.shape[-1],
))
new_tiff_array = xnumpy.tagging_reorder_array(new_tiff_array,
from_axis_order="zyxtc",
to_axis_order=axis_order,
to_copy=True)
# Currently is `tc` order so convert it to the expected order.
if axis_order.index("t") > axis_order.index("c"):
new_tiff_description = new_tiff_description.T.copy()
return (new_tiff_array, new_tiff_description)
def get_standard_tiff_data(new_tiff_filename,
axis_order="tzyxc",
pages_to_channel=1,
memmap=False):
"""
Reads a tiff file and returns a standard 5D array and the metadata.
Args:
new_tiff_filename(str): the TIFF file to read in
axis_order(int): how to order the axes (by
default returns "tzyxc").
pages_to_channel(int): if channels are not normally
stored in the channel variable,
but are stored as pages (or as
a mixture), then this will
split neighboring pages into
separate channels. (by default
is 1 so changes nothing)
memmap(bool): allows one to load the array
using a memory mapped file as
opposed to reading it directly.
(by default is False)
Returns:
(ndarray/memmap, ndarray): an array with the axis order
specified and description
metadata.
"""
assert (pages_to_channel > 0)
new_tiff_description = []
with tifffile.TiffFile(new_tiff_filename) as new_tiff_file:
new_tiff_array = new_tiff_file.asarray(memmap=memmap)
for i in iters.irange(
0,
len(new_tiff_file),
pages_to_channel
):
new_tiff_description.append([])
for j in iters.irange(pages_to_channel):
each_page = new_tiff_file[i + j]
each_metadata = each_page.tags
each_desc = u""
try:
each_desc = unicode(
each_metadata["image_description"].value
)
except KeyError:
pass
new_tiff_description[-1].append(
each_desc
)
new_tiff_description = numpy.array(new_tiff_description)
# Add a singleton channel if none is present.
if new_tiff_array.ndim == 3:
new_tiff_array = new_tiff_array[None]
# Fit the old VIGRA style array. (may try to remove in the future)
new_tiff_array = new_tiff_array.transpose(
tuple(iters.irange(new_tiff_array.ndim - 1, 1, -1)) + (1, 0)
)
# Check to make sure the dimensions are ok
if (new_tiff_array.ndim == 5):
pass
elif (new_tiff_array.ndim == 4):
# Has no z. So, add this.
new_tiff_array = xnumpy.add_singleton_axis_beginning(new_tiff_array)
else:
raise Exception(
"Invalid dimensionality for TIFF. Found shape to be \"" +
repr(new_tiff_array.shape) + "\"."
)
# Some people use pages to hold time and channel data. So, we need to
# restructure it. However, if they are properly structuring their TIFF
# file, then they shouldn't incur a penalty.
if pages_to_channel > 1:
new_tiff_array = new_tiff_array.reshape(
new_tiff_array.shape[:-2] +
(new_tiff_array.shape[-2] / pages_to_channel,
pages_to_channel * new_tiff_array.shape[-1],)
)
new_tiff_array = xnumpy.tagging_reorder_array(
new_tiff_array,
from_axis_order="zyxtc",
to_axis_order=axis_order,
to_copy=True
)
# Currently is `tc` order so convert it to the expected order.
if axis_order.index("t") > axis_order.index("c"):
new_tiff_description = new_tiff_description.T.copy()
return(new_tiff_array, new_tiff_description)
def get_standard_tiff_array(new_tiff_filename, axis_order = "tzyxc", pages_to_channel = 1):
"""
Reads a tiff file and returns a standard 5D array.
Args:
new_tiff_filename(str): the TIFF file to read in
axis_order(int): how to order the axes (by default returns "tzyxc").
pages_to_channel(int): if channels are not normally stored in the channel variable, but are
stored as pages (or as a mixture), then this will split neighboring
pages into separate channels. (by default is 1 so changes nothing)
Returns:
(numpy.ndarray): an array with the axis order specified.
"""
assert (pages_to_channel > 0)
# Get the shape and dtype information
shape, dtype = get_multipage_tiff_shape_dtype(new_tiff_filename).values()
# Turn dtype into something that VIGRA's readImage or readVolume will take
dtype_str = ""
if dtype is None:
dtype_str = ""
elif (dtype is numpy.float64) or (dtype is float):
dtype_str = "DOUBLE"
elif (dtype is numpy.float32):
dtype_str = "FLOAT"
elif (dtype is numpy.uint32):
dtype_str = "UINT32"
elif (dtype is numpy.int32):
dtype_str = "INT32"
elif (dtype is numpy.uint16):
dtype_str = "UINT16"
elif (dtype is numpy.int16):
dtype_str = "INT16"
elif (dtype is numpy.uint8):
dtype_str = "UINT8"
else:
raise Exception("Unacceptable dtype of " + repr(dtype))
if shape[-2] > 1:
# Our algorithm expect double precision
new_tiff_array = vigra.impex.readVolume(new_tiff_filename, dtype = dtype_str)
# Convert to normal array
new_tiff_array = new_tiff_array.view(numpy.ndarray)
else:
# Our algorithm expect double precision
new_tiff_array = vigra.impex.readImage(new_tiff_filename, dtype = dtype_str)
# Convert to normal array
new_tiff_array = new_tiff_array.view(numpy.ndarray)
# Need to add singleton time dimension before channel
new_tiff_array = xnumpy.add_singleton_axis_pos(new_tiff_array, -2)
# Check to make sure the dimensions are ok
if (new_tiff_array.ndim == 5):
pass
elif (new_tiff_array.ndim == 4):
# Has no z. So, add this.
new_tiff_array = xnumpy.add_singleton_axis_beginning(new_tiff_array)
else:
raise Exception("Invalid dimensionality for TIFF. Found shape to be \"" + repr(new_tiff_array.shape) + "\".")
# Some people use pages to hold time and channel data. So, we need to restructure it.
# However, if they are properly structuring their TIFF file, then they shouldn't incur a penalty.
if pages_to_channel > 1:
new_tiff_array = new_tiff_array.reshape(new_tiff_array.shape[:-2] + (new_tiff_array.shape[-2] / pages_to_channel, pages_to_channel * new_tiff_array.shape[-1],))
new_tiff_array = xnumpy.tagging_reorder_array(new_tiff_array,
from_axis_order = "zyxtc",
to_axis_order = axis_order,
to_copy = True)
return(new_tiff_array)
def get_standard_tiff_array(new_tiff_filename,
axis_order="tzyxc",
pages_to_channel=1,
memmap=False):
"""
Reads a tiff file and returns a standard 5D array.
Args:
new_tiff_filename(str): the TIFF file to read in
axis_order(int): how to order the axes (by
default returns "tzyxc").
pages_to_channel(int): if channels are not normally
stored in the channel variable,
but are stored as pages (or as
a mixture), then this will
split neighboring pages into
separate channels. (by default
is 1 so changes nothing)
memmap(bool): allows one to load the array
using a memory mapped file as
opposed to reading it directly.
(by default is False)
Returns:
(numpy.ndarray or numpy.memmap): an array with the axis order
specified.
"""
assert (pages_to_channel > 0)
with tifffile.TiffFile(new_tiff_filename) as new_tiff_file:
new_tiff_array = new_tiff_file.asarray(memmap=memmap)
# Add a singleton channel if none is present.
if new_tiff_array.ndim == 3:
new_tiff_array = new_tiff_array[None]
# Fit the old VIGRA style array. (may try to remove in the future)
new_tiff_array = new_tiff_array.transpose(
tuple(iters.irange(new_tiff_array.ndim - 1, 1, -1)) + (1, 0)
)
# Check to make sure the dimensions are ok
if (new_tiff_array.ndim == 5):
pass
elif (new_tiff_array.ndim == 4):
# Has no z. So, add this.
new_tiff_array = xnumpy.add_singleton_axis_beginning(new_tiff_array)
else:
raise Exception(
"Invalid dimensionality for TIFF. Found shape to be \"" +
repr(new_tiff_array.shape) + "\"."
)
# Some people use pages to hold time and channel data. So, we need to
# restructure it. However, if they are properly structuring their TIFF
# file, then they shouldn't incur a penalty.
if pages_to_channel > 1:
new_tiff_array = new_tiff_array.reshape(
new_tiff_array.shape[:-2] +
(new_tiff_array.shape[-2] / pages_to_channel,
pages_to_channel * new_tiff_array.shape[-1],)
)
new_tiff_array = xnumpy.tagging_reorder_array(
new_tiff_array,
from_axis_order="zyxtc",
to_axis_order=axis_order,
to_copy=True
)
return(new_tiff_array)
