"""Data structures to hold collections of images, with optional caching.""" from __future__ import with_statement import os from glob import glob import re from copy import copy import numpy as np import six 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, six.string_types): 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