mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:33:19 +08:00
Stefan van der Walt
89136157bb
Results from the morphology module were compared against output from the corresponding MATLAB functions, as applied to the lena test image. This image does not conform to the Debian Free Software Guidelines and had to be removed. The output is now tested against results generated with the current version of scikit-image (0.12.0), which is known to be correct due to the comparison described above.
428 lines
13 KiB
Python
428 lines
13 KiB
Python
"""Data structures to hold collections of images, with optional caching."""
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from __future__ import with_statement
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import os
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from glob import glob
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import re
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from copy import copy
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import numpy as np
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import six
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from PIL import Image
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from ..external.tifffile import TiffFile
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__all__ = ['MultiImage', 'ImageCollection', 'concatenate_images',
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'imread_collection_wrapper']
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def concatenate_images(ic):
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"""Concatenate all images in the image collection into an array.
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Parameters
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----------
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ic: an iterable of images (including ImageCollection and MultiImage)
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The images to be concatenated.
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Returns
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-------
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ar : np.ndarray
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An array having one more dimension than the images in `ic`.
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See Also
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--------
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ImageCollection.concatenate, MultiImage.concatenate
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Raises
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------
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ValueError
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If images in `ic` don't have identical shapes.
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"""
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all_images = [img[np.newaxis, ...] for img in ic]
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try:
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ar = np.concatenate(all_images)
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except ValueError:
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raise ValueError('Image dimensions must agree.')
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return ar
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def alphanumeric_key(s):
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"""Convert string to list of strings and ints that gives intuitive sorting.
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Parameters
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----------
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s: string
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Returns
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-------
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k: a list of strings and ints
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Examples
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--------
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>>> alphanumeric_key('z23a')
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['z', 23, 'a']
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>>> filenames = ['f9.10.png', 'e10.png', 'f9.9.png', 'f10.10.png',
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... 'f10.9.png']
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>>> sorted(filenames)
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['e10.png', 'f10.10.png', 'f10.9.png', 'f9.10.png', 'f9.9.png']
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>>> sorted(filenames, key=alphanumeric_key)
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['e10.png', 'f9.9.png', 'f9.10.png', 'f10.9.png', 'f10.10.png']
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"""
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k = [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]
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return k
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class ImageCollection(object):
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"""Load and manage a collection of image files.
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Note that files are always stored in alphabetical order. Also note that
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slicing returns a new ImageCollection, *not* a view into the data.
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Parameters
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----------
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load_pattern : str or list
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Pattern glob or filenames to load. The path can be absolute or
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relative. Multiple patterns should be separated by os.pathsep,
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e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see
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implementation notes below.
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conserve_memory : bool, optional
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If True, never keep more than one in memory at a specific
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time. Otherwise, images will be cached once they are loaded.
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Other parameters
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----------------
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load_func : callable
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``imread`` by default. See notes below.
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Attributes
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----------
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files : list of str
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If a glob string is given for `load_pattern`, this attribute
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stores the expanded file list. Otherwise, this is simply
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equal to `load_pattern`.
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Notes
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-----
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ImageCollection can be modified to load images from an arbitrary
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source by specifying a combination of `load_pattern` and
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`load_func`. For an ImageCollection ``ic``, ``ic[5]`` uses
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``load_func(file_pattern[5])`` to load the image.
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Imagine, for example, an ImageCollection that loads every tenth
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frame from a video file::
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class AVILoader:
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video_file = 'myvideo.avi'
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def __call__(self, frame):
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return video_read(self.video_file, frame)
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avi_load = AVILoader()
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frames = range(0, 1000, 10) # 0, 10, 20, ...
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ic = ImageCollection(frames, load_func=avi_load)
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x = ic[5] # calls avi_load(frames[5]) or equivalently avi_load(50)
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Another use of ``load_func`` would be to convert all images to ``uint8``::
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def imread_convert(f):
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return imread(f).astype(np.uint8)
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ic = ImageCollection('/tmp/*.png', load_func=imread_convert)
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For files with multiple images, the images will be flattened into a list
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and added to the list of available images. In this case, ``load_func``
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should accept the keyword argument ``img_num``.
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Examples
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--------
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>>> import skimage.io as io
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>>> from skimage import data_dir
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>>> coll = io.ImageCollection(data_dir + '/chess*.png')
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>>> len(coll)
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2
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>>> coll[0].shape
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(200, 200)
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>>> ic = io.ImageCollection('/tmp/work/*.png:/tmp/other/*.jpg')
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"""
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def __init__(self, load_pattern, conserve_memory=True, load_func=None,
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**load_func_kwargs):
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"""Load and manage a collection of images."""
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if isinstance(load_pattern, six.string_types):
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load_pattern = load_pattern.replace(os.pathsep, ':')
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load_pattern = load_pattern.split(':')
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self._files = []
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for pattern in load_pattern:
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self._files.extend(glob(pattern))
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self._files = sorted(self._files, key=alphanumeric_key)
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self._numframes = self._find_images()
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else:
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self._files = load_pattern
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self._numframes = len(self._files)
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self._frame_index = None
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if conserve_memory:
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memory_slots = 1
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else:
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memory_slots = self._numframes
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self._conserve_memory = conserve_memory
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self._cached = None
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if load_func is None:
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from ._io import imread
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self.load_func = imread
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else:
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self.load_func = load_func
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self.load_func_kwargs = load_func_kwargs
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self.data = np.empty(memory_slots, dtype=object)
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@property
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def files(self):
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return self._files
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@property
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def conserve_memory(self):
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return self._conserve_memory
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def _find_images(self):
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index = []
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for fname in self._files:
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if fname.lower().endswith(('.tiff', '.tif')):
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with open(fname, 'rb') as f:
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img = TiffFile(f)
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index += [(fname, i) for i in range(len(img.pages))]
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else:
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try:
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im = Image.open(fname)
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im.seek(0)
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except (IOError, OSError):
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index.append([fname, i])
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continue
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i = 0
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while True:
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try:
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im.seek(i)
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except EOFError:
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break
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index.append((fname, i))
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i += 1
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if hasattr(im, 'fp') and im.fp:
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im.fp.close()
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self._frame_index = index
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return len(index)
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def __getitem__(self, n):
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"""Return selected image(s) in the collection.
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Loading is done on demand.
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Parameters
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----------
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n : int or slice
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The image number to be returned, or a slice selecting the images
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and ordering to be returned in a new ImageCollection.
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Returns
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-------
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img : ndarray or ImageCollection.
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The `n`-th image in the collection, or a new ImageCollection with
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the selected images.
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"""
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if hasattr(n, '__index__'):
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n = n.__index__()
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if type(n) not in [int, slice]:
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raise TypeError('slicing must be with an int or slice object')
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if type(n) is int:
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n = self._check_imgnum(n)
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idx = n % len(self.data)
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if ((self.conserve_memory and n != self._cached) or
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(self.data[idx] is None)):
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kwargs = self.load_func_kwargs
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if self._frame_index:
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fname, img_num = self._frame_index[n]
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if img_num is not None:
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self.data[idx] = self.load_func(fname, img_num=img_num,
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**kwargs)
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else:
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self.data[idx] = self.load_func(fname, **kwargs)
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else:
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self.data[idx] = self.load_func(self.files[n], **kwargs)
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self._cached = n
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return self.data[idx]
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else:
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# A slice object was provided, so create a new ImageCollection
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# object. Any loaded image data in the original ImageCollection
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# will be copied by reference to the new object. Image data
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# loaded after this creation is not linked.
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fidx = range(self._numframes)[n]
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new_ic = copy(self)
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if self._frame_index:
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new_ic._files = [self._frame_index[i][0] for i in fidx]
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new_ic._frame_index = [self._frame_index[i] for i in fidx]
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else:
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new_ic._files = [self._files[i] for i in fidx]
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new_ic._numframes = len(fidx)
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if self.conserve_memory:
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if self._cached in fidx:
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new_ic._cached = fidx.index(self._cached)
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new_ic.data = np.copy(self.data)
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else:
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new_ic.data = np.empty(1, dtype=object)
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else:
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new_ic.data = self.data[fidx]
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return new_ic
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def _check_imgnum(self, n):
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"""Check that the given image number is valid."""
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num = self._numframes
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if -num <= n < num:
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n = n % num
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else:
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raise IndexError("There are only %s images in the collection"
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% num)
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return n
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def __iter__(self):
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"""Iterate over the images."""
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for i in range(len(self)):
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yield self[i]
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def __len__(self):
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"""Number of images in collection."""
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return self._numframes
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def __str__(self):
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return str(self.files)
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def reload(self, n=None):
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"""Clear the image cache.
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Parameters
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----------
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n : None or int
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Clear the cache for this image only. By default, the
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entire cache is erased.
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"""
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self.data = np.empty_like(self.data)
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def concatenate(self):
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"""Concatenate all images in the collection into an array.
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Returns
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-------
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ar : np.ndarray
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An array having one more dimension than the images in `self`.
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See Also
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--------
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concatenate_images
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Raises
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------
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ValueError
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If images in the `ImageCollection` don't have identical shapes.
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"""
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return concatenate_images(self)
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def imread_collection_wrapper(imread):
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def imread_collection(load_pattern, conserve_memory=True):
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"""Return an `ImageCollection` from files matching the given pattern.
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Note that files are always stored in alphabetical order. Also note that
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slicing returns a new ImageCollection, *not* a view into the data.
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See `skimage.io.ImageCollection` for details.
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Parameters
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----------
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load_pattern : str or list
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Pattern glob or filenames to load. The path can be absolute or
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relative. Multiple patterns should be separated by a colon,
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e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see
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implementation notes below.
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conserve_memory : bool, optional
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If True, never keep more than one in memory at a specific
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time. Otherwise, images will be cached once they are loaded.
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"""
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return ImageCollection(load_pattern, conserve_memory=conserve_memory,
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load_func=imread)
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return imread_collection
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class MultiImage(ImageCollection):
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"""A class containing a single multi-frame image.
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Parameters
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----------
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filename : str
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The complete path to the image file.
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conserve_memory : bool, optional
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Whether to conserve memory by only caching a single frame. Default is
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True.
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Notes
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-----
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If ``conserve_memory=True`` the memory footprint can be reduced, however
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the performance can be affected because frames have to be read from file
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more often.
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The last accessed frame is cached, all other frames will have to be read
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from file.
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The current implementation makes use of ``tifffile`` for Tiff files and
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PIL otherwise.
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Examples
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--------
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>>> from skimage import data_dir
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>>> img = MultiImage(data_dir + '/multipage.tif') # doctest: +SKIP
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>>> len(img) # doctest: +SKIP
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2
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>>> for frame in img: # doctest: +SKIP
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... print(frame.shape) # doctest: +SKIP
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(15, 10)
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(15, 10)
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"""
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def __init__(self, filename, conserve_memory=True, dtype=None,
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**imread_kwargs):
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"""Load a multi-img."""
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from ._io import imread
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def load_func(fname, **kwargs):
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kwargs.setdefault('dtype', dtype)
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return imread(fname, **kwargs)
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self._filename = filename
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super(MultiImage, self).__init__(filename, conserve_memory,
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load_func=load_func, **imread_kwargs)
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@property
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def filename(self):
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return self._filename
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