skimage.io#

Reading and saving of images and videos.

concatenate_images

Concatenate all images in the image collection into an array.

imread

Load an image from file.

imread_collection

Load a collection of images.

imread_collection_wrapper

imsave

Save an image to file.

load_sift

Read SIFT or SURF features from externally generated file.

load_surf

Read SIFT or SURF features from externally generated file.

pop

Pop an image from the shared image stack.

push

Push an image onto the shared image stack.

ImageCollection

Load and manage a collection of image files.

MultiImage

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

See also

ImageCollection.concatenate
MultiImage.concatenate

Notes

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

skimage.io.imread(fname, as_gray=False, plugin=<DEPRECATED>, **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.

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_argsDEPRECATED

The plugin infrastructure is deprecated.

pluginDEPRECATED

plugin is deprecated.

Deprecated since version 0.25.

skimage.io.imread_collection(load_pattern, conserve_memory=True, plugin=<DEPRECATED>, **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 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_argsDEPRECATED

The plugin infrastructure is deprecated.

pluginDEPRECATED

plugin is deprecated.

Deprecated since version 0.25.

skimage.io.imread_collection_wrapper(imread)[source]#
skimage.io.imsave(fname, arr, plugin=<DEPRECATED>, *, 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.

check_contrastbool, optional

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

Other Parameters:
plugin_argsDEPRECATED

The plugin infrastructure is deprecated.

pluginDEPRECATED

plugin is deprecated.

Deprecated since version 0.25.

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.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.

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

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.

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 skimage.io.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 skimage.io.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 skimage.io.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
__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 skimage.io.ImageCollection do not have identical shapes.

See also

skimage.io.concatenate_images
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 skimage.io.ImageCollection do not have identical shapes.

See also

skimage.io.concatenate_images
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.