skimage.io#

Utilities to read and write images in various formats.

The following plug-ins are available:

Plugin

Description

matplotlib

Display or save images using Matplotlib

pil

Image reading via the Python Imaging Library

gdal

Image reading via the GDAL Library (www.gdal.org)

simpleitk

Image reading and writing via SimpleITK

tifffile

Load and save TIFF and TIFF-based images using tifffile.py

imread

Image reading and writing via imread

fits

FITS image reading via PyFITS

imageio

Image reading via the ImageIO Library

skimage.io.call_plugin

Find the appropriate plugin of 'kind' and execute it.

skimage.io.concatenate_images

Concatenate all images in the image collection into an array.

skimage.io.find_available_plugins

List available plugins.

skimage.io.imread

Load an image from file.

skimage.io.imread_collection

Load a collection of images.

skimage.io.imread_collection_wrapper

skimage.io.imsave

Save an image to file.

skimage.io.imshow

Display an image.

skimage.io.imshow_collection

Display a collection of images.

skimage.io.load_sift

Read SIFT or SURF features from externally generated file.

skimage.io.load_surf

Read SIFT or SURF features from externally generated file.

skimage.io.plugin_info

Return plugin meta-data.

skimage.io.plugin_order

Return the currently preferred plugin order.

skimage.io.pop

Pop an image from the shared image stack.

skimage.io.push

Push an image onto the shared image stack.

skimage.io.reset_plugins

skimage.io.show

Display pending images.

skimage.io.use_plugin

Set the default plugin for a specified operation.

skimage.io.ImageCollection

Load and manage a collection of image files.

skimage.io.MultiImage

A class containing all frames from multi-frame TIFF images.

skimage.io.collection

Data structures to hold collections of images, with optional caching.

skimage.io.manage_plugins

Handle image reading, writing and plotting plugins.

skimage.io.sift

skimage.io.util


skimage.io.call_plugin(kind, *args, **kwargs)[source]#

Find the appropriate plugin of ‘kind’ and execute it.

Parameters:
kind{‘imshow’, ‘imsave’, ‘imread’, ‘imread_collection’}

Function to look up.

pluginstr, optional

Plugin to load. Defaults to None, in which case the first matching plugin is used.

*args, **kwargsarguments and keyword arguments

Passed to the plugin function.


skimage.io.concatenate_images(ic)[source]#

Concatenate all images in the image collection into an array.

Parameters:
ican iterable of images

The images to be concatenated.

Returns:
array_catndarray

An array having one more dimension than the images in ic.

Raises:
ValueError

If images in ic don’t have identical shapes.

Notes

concatenate_images receives any iterable object containing images, including ImageCollection and MultiImage, and returns a NumPy array.


skimage.io.find_available_plugins(loaded=False)[source]#

List available plugins.

Parameters:
loadedbool

If True, show only those plugins currently loaded. By default, all plugins are shown.

Returns:
pdict

Dictionary with plugin names as keys and exposed functions as values.


skimage.io.imread(fname, as_gray=False, plugin=None, **plugin_args)[source]#

Load an image from file.

Parameters:
fnamestr or pathlib.Path

Image file name, e.g. test.jpg or URL.

as_graybool, optional

If True, convert color images to gray-scale (64-bit floats). Images that are already in gray-scale format are not converted.

pluginstr, optional

Name of plugin to use. By default, the different plugins are tried (starting with imageio) until a suitable candidate is found. If not given and fname is a tiff file, the tifffile plugin will be used.

Returns:
img_arrayndarray

The different color bands/channels are stored in the third dimension, such that a gray-image is MxN, an RGB-image MxNx3 and an RGBA-image MxNx4.

Other Parameters:
plugin_argskeywords

Passed to the given plugin.


skimage.io.imread_collection(load_pattern, conserve_memory=True, plugin=None, **plugin_args)[source]#

Load a collection of images.

Parameters:
load_patternstr or list

List of objects to load. These are usually filenames, but may vary depending on the currently active plugin. See the docstring for ImageCollection for the default behaviour of this parameter.

conserve_memorybool, optional

If True, never keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded.

Returns:
icImageCollection

Collection of images.

Other Parameters:
plugin_argskeywords

Passed to the given plugin.


skimage.io.imread_collection_wrapper(imread)[source]#

skimage.io.imsave(fname, arr, plugin=None, check_contrast=True, **plugin_args)[source]#

Save an image to file.

Parameters:
fnamestr or pathlib.Path

Target filename.

arrndarray of shape (M,N) or (M,N,3) or (M,N,4)

Image data.

pluginstr, optional

Name of plugin to use. By default, the different plugins are tried (starting with imageio) until a suitable candidate is found. If not given and fname is a tiff file, the tifffile plugin will be used.

check_contrastbool, optional

Check for low contrast and print warning (default: True).

Other Parameters:
plugin_argskeywords

Passed to the given plugin.

Notes

When saving a JPEG, the compression ratio may be controlled using the quality keyword argument which is an integer with values in [1, 100] where 1 is worst quality and smallest file size, and 100 is best quality and largest file size (default 75). This is only available when using the PIL and imageio plugins.


skimage.io.imshow(arr, plugin=None, **plugin_args)[source]#

Display an image.

Parameters:
arrndarray or str

Image data or name of image file.

pluginstr

Name of plugin to use. By default, the different plugins are tried (starting with imageio) until a suitable candidate is found.

Other Parameters:
plugin_argskeywords

Passed to the given plugin.

RAG Merging

RAG Merging

skimage.io.imshow_collection(ic, plugin=None, **plugin_args)[source]#

Display a collection of images.

Parameters:
icImageCollection

Collection to display.

pluginstr

Name of plugin to use. By default, the different plugins are tried until a suitable candidate is found.

Other Parameters:
plugin_argskeywords

Passed to the given plugin.


skimage.io.load_sift(f)[source]#

Read SIFT or SURF features from externally generated file.

This routine reads SIFT or SURF files generated by binary utilities from http://people.cs.ubc.ca/~lowe/keypoints/ and http://www.vision.ee.ethz.ch/~surf/.

This routine does not generate SIFT/SURF features from an image. These algorithms are patent encumbered. Please use skimage.feature.CENSURE instead.

Parameters:
filelikestring or open file

Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ .

mode{‘SIFT’, ‘SURF’}, optional

Kind of descriptor used to generate filelike.

Returns:
datarecord array with fields
  • row: int

    row position of feature

  • column: int

    column position of feature

  • scale: float

    feature scale

  • orientation: float

    feature orientation

  • data: array

    feature values


skimage.io.load_surf(f)[source]#

Read SIFT or SURF features from externally generated file.

This routine reads SIFT or SURF files generated by binary utilities from http://people.cs.ubc.ca/~lowe/keypoints/ and http://www.vision.ee.ethz.ch/~surf/.

This routine does not generate SIFT/SURF features from an image. These algorithms are patent encumbered. Please use skimage.feature.CENSURE instead.

Parameters:
filelikestring or open file

Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ .

mode{‘SIFT’, ‘SURF’}, optional

Kind of descriptor used to generate filelike.

Returns:
datarecord array with fields
  • row: int

    row position of feature

  • column: int

    column position of feature

  • scale: float

    feature scale

  • orientation: float

    feature orientation

  • data: array

    feature values


skimage.io.plugin_info(plugin)[source]#

Return plugin meta-data.

Parameters:
pluginstr

Name of plugin.

Returns:
mdict

Meta data as specified in plugin .ini.


skimage.io.plugin_order()[source]#

Return the currently preferred plugin order.

Returns:
pdict

Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value.


skimage.io.pop()[source]#

Pop an image from the shared image stack.

Returns:
imgndarray

Image popped from the stack.


skimage.io.push(img)[source]#

Push an image onto the shared image stack.

Parameters:
imgndarray

Image to push.


skimage.io.reset_plugins()[source]#

skimage.io.show()[source]#

Display pending images.

Launch the event loop of the current gui plugin, and display all pending images, queued via imshow. This is required when using imshow from non-interactive scripts.

A call to show will block execution of code until all windows have been closed.

Examples

>>> import skimage.io as io
>>> rng = np.random.default_rng()
>>> for i in range(4):
...     ax_im = io.imshow(rng.random((50, 50)))
>>> io.show() 

Region Boundary based RAGs

Region Boundary based RAGs

RAG Merging

RAG Merging

skimage.io.use_plugin(name, kind=None)[source]#

Set the default plugin for a specified operation. The plugin will be loaded if it hasn’t been already.

Parameters:
namestr

Name of plugin.

kind{‘imsave’, ‘imread’, ‘imshow’, ‘imread_collection’, ‘imshow_collection’}, optional

Set the plugin for this function. By default, the plugin is set for all functions.

See also

available_plugins

List of available plugins

Examples

To use Matplotlib as the default image reader, you would write:

>>> from skimage import io
>>> io.use_plugin('matplotlib', 'imread')

To see a list of available plugins run io.available_plugins. Note that this lists plugins that are defined, but the full list may not be usable if your system does not have the required libraries installed.

class skimage.io.ImageCollection(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs)[source]#

Bases: object

Load and manage a collection of image files.

Parameters:
load_patternstr or list of str

Pattern string or list of strings to load. The filename path can be absolute or relative.

conserve_memorybool, optional

If True, ImageCollection does not keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded.

Other Parameters:
load_funccallable

imread by default. See notes below.

**load_func_kwargsdict

Any other keyword arguments are passed to load_func.

Notes

Note that files are always returned in alphanumerical order. Also note that slicing returns a new ImageCollection, not a view into the data.

ImageCollection image loading can be customized through load_func. For an ImageCollection ic, ic[5] calls load_func(load_pattern[5]) to load that image.

For example, here is an ImageCollection that, for each video provided, loads every second frame:

import imageio.v3 as iio3
import itertools

def vidread_step(f, step):
    vid = iio3.imiter(f)
    return list(itertools.islice(vid, None, None, step)

video_file = 'no_time_for_that_tiny.gif'
ic = ImageCollection(video_file, load_func=vidread_step, step=2)

ic  # is an ImageCollection object of length 1 because 1 video is provided

x = ic[0]
x[5]  # the 10th frame of the first video

Alternatively, if load_func is provided and load_pattern is a sequence, an ImageCollection of corresponding length will be created, and the individual images will be loaded by calling load_func with the matching element of the load_pattern as its first argument. In this case, the elements of the sequence do not need to be names of existing files (or strings at all). For example, to create an ImageCollection containing 500 images from a video:

class FrameReader:
    def __init__ (self, f):
        self.f = f
    def __call__ (self, index):
        return iio3.imread(self.f, index=index)

ic = ImageCollection(range(500), load_func=FrameReader('movie.mp4'))

ic  # is an ImageCollection object of length 500

Another use of load_func would be to convert all images to uint8:

def imread_convert(f):
    return imread(f).astype(np.uint8)

ic = ImageCollection('/tmp/*.png', load_func=imread_convert)

Examples

>>> import imageio.v3 as iio3
>>> import skimage.io as io

# Where your images are located >>> data_dir = os.path.join(os.path.dirname(__file__), ‘../data’)

>>> coll = io.ImageCollection(data_dir + '/chess*.png')
>>> len(coll)
2
>>> coll[0].shape
(200, 200)
>>> image_col = io.ImageCollection([f'{data_dir}/*.png', '{data_dir}/*.jpg'])
>>> class MultiReader:
...     def __init__ (self, f):
...         self.f = f
...     def __call__ (self, index):
...         return iio3.imread(self.f, index=index)
...
>>> filename = data_dir + '/no_time_for_that_tiny.gif'
>>> ic = io.ImageCollection(range(24), load_func=MultiReader(filename))
>>> len(image_col)
23
>>> isinstance(ic[0], np.ndarray)
True
Attributes:
fileslist of str

If a pattern string is given for load_pattern, this attribute stores the expanded file list. Otherwise, this is equal to load_pattern.

__init__(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs)[source]#

Load and manage a collection of images.

concatenate()[source]#

Concatenate all images in the collection into an array.

Returns:
arnp.ndarray

An array having one more dimension than the images in self.

Raises:
ValueError

If images in the ImageCollection don’t have identical shapes.

property conserve_memory#
property files#
reload(n=None)[source]#

Clear the image cache.

Parameters:
nNone or int

Clear the cache for this image only. By default, the entire cache is erased.

class skimage.io.MultiImage(filename, conserve_memory=True, dtype=None, **imread_kwargs)[source]#

Bases: ImageCollection

A class containing all frames from multi-frame TIFF images.

Parameters:
load_patternstr or list of str

Pattern glob or filenames to load. The path can be absolute or relative.

conserve_memorybool, optional

Whether to conserve memory by only caching the frames of a single image. Default is True.

Notes

MultiImage returns a list of image-data arrays. In this regard, it is very similar to ImageCollection, but the two differ in their treatment of multi-frame images.

For a TIFF image containing N frames of size WxH, MultiImage stores all frames of that image as a single element of shape (N, W, H) in the list. ImageCollection instead creates N elements of shape (W, H).

For an animated GIF image, MultiImage reads only the first frame, while ImageCollection reads all frames by default.

Examples

# Where your images are located >>> data_dir = os.path.join(os.path.dirname(__file__), ‘../data’)

>>> multipage_tiff = data_dir + '/multipage.tif'
>>> multi_img = MultiImage(multipage_tiff)
>>> len(multi_img)  # multi_img contains one element
1
>>> multi_img[0].shape  # this element is a two-frame image of shape:
(2, 15, 10)
>>> image_col = ImageCollection(multipage_tiff)
>>> len(image_col)  # image_col contains two elements
2
>>> for frame in image_col:
...     print(frame.shape)  # each element is a frame of shape (15, 10)
...
(15, 10)
(15, 10)
__init__(filename, conserve_memory=True, dtype=None, **imread_kwargs)[source]#

Load a multi-img.

concatenate()[source]#

Concatenate all images in the collection into an array.

Returns:
arnp.ndarray

An array having one more dimension than the images in self.

Raises:
ValueError

If images in the ImageCollection don’t have identical shapes.

property conserve_memory#
property filename#
property files#
reload(n=None)[source]#

Clear the image cache.

Parameters:
nNone or int

Clear the cache for this image only. By default, the entire cache is erased.