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@blink1073 @jni @ahojnnes @stefanv @jjhelmus @amueller @r3vl1s @NelleV @NeilYager @cgohlke
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"""Data structures to hold collections of images, with optional caching."""
import os
from glob import glob
import re
from copy import copy
import numpy as np
from PIL import Image
from ..external.tifffile import TiffFile
__all__ = ['MultiImage', 'ImageCollection', 'concatenate_images',
'imread_collection_wrapper']
def concatenate_images(ic):
"""Concatenate all images in the image collection into an array.
Parameters
----------
ic: an iterable of images (including ImageCollection and MultiImage)
The images to be concatenated.
Returns
-------
ar : np.ndarray
An array having one more dimension than the images in `ic`.
See Also
--------
ImageCollection.concatenate, MultiImage.concatenate
Raises
------
ValueError
If images in `ic` don't have identical shapes.
"""
all_images = [img[np.newaxis, ...] for img in ic]
try:
ar = np.concatenate(all_images)
except ValueError:
raise ValueError('Image dimensions must agree.')
return ar
def alphanumeric_key(s):
"""Convert string to list of strings and ints that gives intuitive sorting.
Parameters
----------
s: string
Returns
-------
k: a list of strings and ints
Examples
--------
>>> alphanumeric_key('z23a')
['z', 23, 'a']
>>> filenames = ['f9.10.png', 'e10.png', 'f9.9.png', 'f10.10.png',
... 'f10.9.png']
>>> sorted(filenames)
['e10.png', 'f10.10.png', 'f10.9.png', 'f9.10.png', 'f9.9.png']
>>> sorted(filenames, key=alphanumeric_key)
['e10.png', 'f9.9.png', 'f9.10.png', 'f10.9.png', 'f10.10.png']
"""
k = [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]
return k
class ImageCollection(object):
"""Load and manage a collection of image files.
Note that files are always stored in alphabetical order. Also note that
slicing returns a new ImageCollection, *not* a view into the data.
Parameters
----------
load_pattern : str or list
Pattern glob or filenames to load. The path can be absolute or
relative. Multiple patterns should be separated by os.pathsep,
e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see
implementation notes below.
conserve_memory : bool, optional
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
Other parameters
----------------
load_func : callable
``imread`` by default. See notes below.
Attributes
----------
files : list of str
If a glob string is given for `load_pattern`, this attribute
stores the expanded file list. Otherwise, this is simply
equal to `load_pattern`.
Notes
-----
ImageCollection can be modified to load images from an arbitrary
source by specifying a combination of `load_pattern` and
`load_func`. For an ImageCollection ``ic``, ``ic[5]`` uses
``load_func(file_pattern[5])`` to load the image.
Imagine, for example, an ImageCollection that loads every tenth
frame from a video file::
class AVILoader:
video_file = 'myvideo.avi'
def __call__(self, frame):
return video_read(self.video_file, frame)
avi_load = AVILoader()
frames = range(0, 1000, 10) # 0, 10, 20, ...
ic = ImageCollection(frames, load_func=avi_load)
x = ic[5] # calls avi_load(frames[5]) or equivalently avi_load(50)
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)
For files with multiple images, the images will be flattened into a list
and added to the list of available images. In this case, ``load_func``
should accept the keyword argument ``img_num``.
Examples
--------
>>> import skimage.io as io
>>> from skimage import data_dir
>>> coll = io.ImageCollection(data_dir + '/chess*.png')
>>> len(coll)
2
>>> coll[0].shape
(200, 200)
>>> ic = io.ImageCollection('/tmp/work/*.png:/tmp/other/*.jpg')
"""
def __init__(self, load_pattern, conserve_memory=True, load_func=None,
**load_func_kwargs):
"""Load and manage a collection of images."""
if isinstance(load_pattern, str):
load_pattern = load_pattern.split(os.pathsep)
self._files = []
for pattern in load_pattern:
self._files.extend(glob(pattern))
self._files = sorted(self._files, key=alphanumeric_key)
self._numframes = self._find_images()
else:
self._files = load_pattern
self._numframes = len(self._files)
self._frame_index = None
if conserve_memory:
memory_slots = 1
else:
memory_slots = self._numframes
self._conserve_memory = conserve_memory
self._cached = None
if load_func is None:
from ._io import imread
self.load_func = imread
else:
self.load_func = load_func
self.load_func_kwargs = load_func_kwargs
self.data = np.empty(memory_slots, dtype=object)
@property
def files(self):
return self._files
@property
def conserve_memory(self):
return self._conserve_memory
def _find_images(self):
index = []
for fname in self._files:
if fname.lower().endswith(('.tiff', '.tif')):
with open(fname, 'rb') as f:
img = TiffFile(f)
index += [(fname, i) for i in range(len(img.pages))]
else:
try:
im = Image.open(fname)
im.seek(0)
except (IOError, OSError):
continue
i = 0
while True:
try:
im.seek(i)
except EOFError:
break
index.append((fname, i))
i += 1
if hasattr(im, 'fp') and im.fp:
im.fp.close()
self._frame_index = index
return len(index)
def __getitem__(self, n):
"""Return selected image(s) in the collection.
Loading is done on demand.
Parameters
----------
n : int or slice
The image number to be returned, or a slice selecting the images
and ordering to be returned in a new ImageCollection.
Returns
-------
img : ndarray or ImageCollection.
The `n`-th image in the collection, or a new ImageCollection with
the selected images.
"""
if hasattr(n, '__index__'):
n = n.__index__()
if type(n) not in [int, slice]:
raise TypeError('slicing must be with an int or slice object')
if type(n) is int:
n = self._check_imgnum(n)
idx = n % len(self.data)
if ((self.conserve_memory and n != self._cached) or
(self.data[idx] is None)):
kwargs = self.load_func_kwargs
if self._frame_index:
fname, img_num = self._frame_index[n]
if img_num is not None:
kwargs['img_num'] = img_num
try:
self.data[idx] = self.load_func(fname, **kwargs)
# Account for functions that do not accept an img_num kwarg
except TypeError as e:
if "unexpected keyword argument 'img_num'" in str(e):
del kwargs['img_num']
self.data[idx] = self.load_func(fname, **kwargs)
else:
raise
else:
self.data[idx] = self.load_func(self.files[n], **kwargs)
self._cached = n
return self.data[idx]
else:
# A slice object was provided, so create a new ImageCollection
# object. Any loaded image data in the original ImageCollection
# will be copied by reference to the new object. Image data
# loaded after this creation is not linked.
fidx = range(self._numframes)[n]
new_ic = copy(self)
if self._frame_index:
new_ic._files = [self._frame_index[i][0] for i in fidx]
new_ic._frame_index = [self._frame_index[i] for i in fidx]
else:
new_ic._files = [self._files[i] for i in fidx]
new_ic._numframes = len(fidx)
if self.conserve_memory:
if self._cached in fidx:
new_ic._cached = fidx.index(self._cached)
new_ic.data = np.copy(self.data)
else:
new_ic.data = np.empty(1, dtype=object)
else:
new_ic.data = self.data[fidx]
return new_ic
def _check_imgnum(self, n):
"""Check that the given image number is valid."""
num = self._numframes
if -num <= n < num:
n = n % num
else:
raise IndexError("There are only %s images in the collection"
% num)
return n
def __iter__(self):
"""Iterate over the images."""
for i in range(len(self)):
yield self[i]
def __len__(self):
"""Number of images in collection."""
return self._numframes
def __str__(self):
return str(self.files)
def reload(self, n=None):
"""Clear the image cache.
Parameters
----------
n : None or int
Clear the cache for this image only. By default, the
entire cache is erased.
"""
self.data = np.empty_like(self.data)
def concatenate(self):
"""Concatenate all images in the collection into an array.
Returns
-------
ar : np.ndarray
An array having one more dimension than the images in `self`.
See Also
--------
concatenate_images
Raises
------
ValueError
If images in the `ImageCollection` don't have identical shapes.
"""
return concatenate_images(self)
def imread_collection_wrapper(imread):
def imread_collection(load_pattern, conserve_memory=True):
"""Return an `ImageCollection` from files matching the given pattern.
Note that files are always stored in alphabetical order. Also note that
slicing returns a new ImageCollection, *not* a view into the data.
See `skimage.io.ImageCollection` for details.
Parameters
----------
load_pattern : str or list
Pattern glob or filenames to load. The path can be absolute or
relative. Multiple patterns should be separated by a colon,
e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see
implementation notes below.
conserve_memory : bool, optional
If True, never keep more than one in memory at a specific
time. Otherwise, images will be cached once they are loaded.
"""
return ImageCollection(load_pattern, conserve_memory=conserve_memory,
load_func=imread)
return imread_collection
class MultiImage(ImageCollection):
"""A class containing a single multi-frame image.
Parameters
----------
filename : str
The complete path to the image file.
conserve_memory : bool, optional
Whether to conserve memory by only caching a single frame. Default is
True.
Notes
-----
If ``conserve_memory=True`` the memory footprint can be reduced, however
the performance can be affected because frames have to be read from file
more often.
The last accessed frame is cached, all other frames will have to be read
from file.
The current implementation makes use of ``tifffile`` for Tiff files and
PIL otherwise.
Examples
--------
>>> from skimage import data_dir
>>> img = MultiImage(data_dir + '/multipage.tif') # doctest: +SKIP
>>> len(img) # doctest: +SKIP
2
>>> for frame in img: # doctest: +SKIP
... print(frame.shape) # doctest: +SKIP
(15, 10)
(15, 10)
"""
def __init__(self, filename, conserve_memory=True, dtype=None,
**imread_kwargs):
"""Load a multi-img."""
from ._io import imread
def load_func(fname, **kwargs):
kwargs.setdefault('dtype', dtype)
return imread(fname, **kwargs)
self._filename = filename
super(MultiImage, self).__init__(filename, conserve_memory,
load_func=load_func, **imread_kwargs)
@property
def filename(self):
return self._filename
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