Input/Output ¶

Image I/O ¶

Image IO (input/output) module.

timagetk.io.image.apply_mask(img, mask_filename, masking_value=0, crop_mask=False)[source]¶

Load and apply a z-projection mask (2D) to a SpatialImage (2D/3D).

In case of an intensity image, allows removing unwanted signal intensities. If the SpatialImage is 3D, it is applied in z-direction. The mask should contain a distinct “masking value”.

Parameters:

img (timagetk.components.spatial_image.SpatialImage) – Image to modify with the mask.
mask_filename (str) – String giving the location of the mask file
masking_value (int, optional) – Value (default 0) defining the masked region
crop_mask (bool, optional) – If True (default False), the returned SpatialImage is cropped around the non-masked region

Returns:

timagetk.components.spatial_image.SpatialImage – The masked SpatialImage.

timagetk.io.image.default_image_attributes(array, log_undef=<bound method Logger.debug of <Logger timagetk.io.image (INFO)>>, **kwargs)[source]¶

Set the default image attribute values given the array dimensionality.

Parameters:

array (numpy.ndarray) – The array to use as image.
log_undef (fun) – The logging level to use for undefined image attributes.
dtype (str or numpy.dtype) – A valid dtype to use with this array, checked against AVAIL_TYPES. Defaults to array.dtype.
axes_order (str) – The physical axes ordering to use with this array. Defaults to default_axes_order(array).
origin (list of int) – The coordinate of the origin for this array. Defaults to default_origin(array).
unit (float) – The units associated to the voxel-sizes to use with this array. Defaults to DEFAULT_SIZE_UNIT.
voxelsize (list of float) – The voxel-sizes to use with this array. Defaults to default_voxelsize(array).
metadata (dict) – The dictionary of metadata to use with this array. Defaults to {}.

Returns:

dict – The image attributes dictionary.

See also

timagetk.io.util.POSS_EXT

Example

>>> from timagetk.array_util import random_spatial_image
>>> import tempfile
>>> import numpy as np
>>> from timagetk.io.image import imsave, imread
>>> from timagetk import SpatialImage
>>> sp_image = random_spatial_image((3, 4, 5), voxelsize=[0.3, 0.4 ,0.5])
>>> # Save the image in a temporary file:
>>> tmp_path = tempfile.NamedTemporaryFile()
>>> imsave(tmp_path.name+'.tif', sp_image)
>>> # Load this file:
>>> sp_image_cp = imread(tmp_path.name+'.tif')
>>> # Compare arrays and voxelizes:
>>> np.testing.assert_array_equal(sp_image, sp_image_cp)
>>> np.testing.assert_array_equal(sp_image.voxelsize, sp_image_cp.voxelsize)

>>> sp_image = random_spatial_image((3, 4), voxelsize=[0.3, 0.4])
>>> # Save the image in a temporary file:
>>> tmp_path = tempfile.NamedTemporaryFile()
>>> imsave(tmp_path.name+'.tif', sp_image)
>>> # Load this file:
>>> sp_image_cp = imread(tmp_path.name+'.tif')
>>> # Compare arrays and voxelizes:
>>> np.testing.assert_array_equal(sp_image, sp_image_cp)
>>> np.testing.assert_array_equal(sp_image.voxelsize, sp_image_cp.voxelsize)

>>> from timagetk import MultiChannelImage
>>> from timagetk.io import imsave
>>> from timagetk.synthetic_data.nuclei_image import example_nuclei_signal_images
>>> n_img, s_img, _, _= example_nuclei_signal_images(n_points=10, extent=20, voxelsize=(1,1,1))
>>> img = MultiChannelImage({'nuclei':n_img, 'signal':s_img})
>>> imsave('~/test.tif', img)

>>> tmp_path = tempfile.NamedTemporaryFile()
>>> imsave(tmp_path.name+'.tif', img)

timagetk.io.image.read_czi_image(czi_file, channel_names=None, **kwargs)[source]¶

Read Carl Zeiss Image (CZI) file.

Parameters:

czi_file (str) – File path to the image to load.
channel_names (list of str, optional) – Defines channel names to use. By default, try to load channel names from the image metadata.
pattern (str, optional) – Physical dimension order to use to load the image, “.C.ZXY.” by default. By default, try to load the pattern from the image metadata.
return_order (str) – Physical dimension order to use for the returned array. Defaults to “ZYXC”.

Returns:

timagetk.SpatialImage or timagetk.MultiChannelImage – If a single channel and time-point is found, returns a SpatialImage instance. If multiple channels are found but with a single time-point, returns a MultiChannelImage instance.

Notes

If more than one channel is found, return a MultiChannelImage.

For the pattern variable, use:

C as channel;

T as time;

X as x-axis (rows);

Y as y-axis (planes);

Z as z-axis (columns);

Examples

>>> from timagetk.io.image import read_czi_image
>>> from timagetk.io.image import _image_from_url
>>> im_url = "https://zenodo.org/record/3737795/files/qDII-CLV3-PIN1-PI-E35-LD-SAM4.czi"
>>> im_fname = _image_from_url(im_url)  # download the CZI image
>>> ch_names = ["DII-VENUS-N7", "pPIN1:PIN1-GFP", "Propidium Iodide", "pRPS5a:TagBFP-SV40", "pCLV3:mCherry-N7"]
>>> image = read_czi_image(im_fname, ch_names)
>>> print(image.get_channel_names())
['DII-VENUS-N7', 'pPIN1:PIN1-GFP', 'Propidium Iodide', 'pRPS5a:TagBFP-SV40', 'pCLV3:mCherry-N7']
>>> print(image)

>>> im_url = "https://zenodo.org/record/3737795/files/qDII-CLV3-PIN1-PI-E35-LD-SAM4.czi"
>>> im_fname = _image_from_url(im_url)  # download the CZI image
>>> from czifile import CziFile
>>> czi_img = CziFile(im_fname)
>>> import xmltodict
>>> md = xmltodict.parse(czi_img.metadata())['ImageDocument']['Metadata']

timagetk.io.image.read_lsm_image(lsm_file, channel_names=None, **kwargs)[source]¶

Read LSM file.

Parameters:

lsm_file (str) – File path to the image to load.
channel_names (list of str, optional) – Defines channel names to use. By default, try to load channel names from the image metadata.
pattern (str, optional) – Physical dimension order to use to load the image, “TZCYX” by default. By default, try to load the pattern from the image metadata.
return_order (str) – Physical dimension order to use for the returned array. Defaults to “ZYXC”.

Returns:

Notes

If more than one channel is found, return a MultiChannelImage.

For the pattern variable, use:

C as channel;

T as time;

X as x-axis (rows);

Y as y-axis (planes);

Z as z-axis (columns);

Examples

>>> from timagetk.io.image import read_lsm_image
>>> from timagetk.io.util import shared_data
>>> im = read_lsm_image(shared_data("090223-p58-flo-top.lsm", "p58/raw"))
>>> im.get_voxelsize()
[1.0, 0.20031953747828882, 0.20031953747828882]
>>> im.get_shape()
[50, 460, 460]
>>> im.unit
1e-06
>>> im.axes_order
'ZYX'

timagetk.io.image.read_tiff_image(tiff_file, channel_names=None, **kwargs)[source]¶

Read (ImageJ) TIFF image file.

Parameters:

tiff_file (str) – File path to the image to load.
channel_names (list of str, optional) – Defines channel names to use. By default, try to load channel names from the image metadata.
pattern (str, optional) – Physical dimension order to use to load the image, “ZCYX” by default. By default, try to load the pattern from the image metadata.
return_order (str) – Physical dimension order to use for the returned array. Defaults to “ZYXC”.

Returns:

Notes

If more than one channel is found, return a MultiChannelImage.

For the pattern variable, use:

C as channel;

X as x-axis (rows);

Y as y-axis (planes);

Z as z-axis (columns);

Examples

>>> from timagetk.io.image import read_tiff_image
>>> from timagetk.io.util import shared_data
>>> im = read_tiff_image(shared_data("p58_t0_fused.tif", "p58/fusion"))
>>> im.get_voxelsize()
[0.2003195434808731, 0.20031954352751363, 0.20031954352751363]
>>> im.get_shape()
[59, 460, 460]
>>> im.axes_order
'ZYX'

timagetk.io.image.save_tiff_image(tiff_file, img, **kwargs)[source]¶

Save an image as a TIFF file.

Parameters:

tiff_file (str or pathlib.Path) – File path where to save the image.
img (timagetk.SpatialImage or timagetk.LabelledImage or timagetk.TissueImage3D or timagetk.MultiChannelImage) – Image instance to save on drive.
pattern (str, optional) – Physical dimension order to use to load the image, “ZCYX” by default. By default, try to load the pattern from the image metadata.

Notes

For the pattern variable, use:

C as channel;

X as x-axis (rows);

Y as y-axis (planes);

Z as z-axis (columns);

Example

>>> import tempfile
>>> from timagetk import MultiChannelImage
>>> from timagetk.io.image import save_tiff_image
>>> # EXAMPLE#1 - Save a SpatialImage as a TIFF file:
>>> from timagetk.array_util import random_spatial_image
>>> sp_image = random_spatial_image((3, 4, 5), voxelsize=[0.3, 0.4 ,0.5])
>>> # Save the image in a temporary file:
>>> tmp_path = tempfile.NamedTemporaryFile()
>>> save_tiff_image(tmp_path.name+'.tif', sp_image)

>>> # EXAMPLE#2 - Save a MultiChannel as a TIFF file:
>>> from timagetk.synthetic_data.nuclei_image import example_nuclei_signal_images
>>> n_img, s_img, _, _= example_nuclei_signal_images(n_points=10, extent=20, voxelsize=(1,1,1))
>>> img = MultiChannelImage({'nuclei':n_img, 'signal':s_img})
>>> tmp_path = tempfile.NamedTemporaryFile()
>>> save_tiff_image(tmp_path.name+'.tif', img)

Accepted image formats are: |POSS_EXT|.

Transformation I/O ¶

Input/Output for Trsf objects.

timagetk.io.trsf.read_trsf(filename)[source]¶

Read a transformation file.

Parameters:: filename (str) – Name of the file containing a Trsf object.
Returns:: Trsf – The loaded transformation object.

timagetk.io.trsf.save_trsf(trsf, filename)[source]¶

Write a Trsf under given filename.

Parameters:

trsf (Trsf) – Transformation object to save.
filename (str) – Name of the file to write.

Table of Contents

Input/Output¶

Image I/O¶

Transformation I/O¶

Input/Output ¶

Image I/O ¶

Transformation I/O ¶