""" A collection of image utilities using the Python Imaging Library (PIL). This is a local version of utility functions from scipy that are wrapping PIL functionality. These functions are deprecated in scipy 1.0.0 and will be removed in scipy 1.2.0. Therefore, the functionality used in sklearn is copied here. This file is taken from scipy/misc/pilutil.py in scipy 1.0.0. Modifications include: making this module importable if pillow is not installed, removal of DeprecationWarning, removal of functions scikit-learn does not need. Copyright (c) 2001, 2002 Enthought, Inc. All rights reserved. Copyright (c) 2003-2017 SciPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of Enthought nor the names of the SciPy Developers may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from __future__ import division, print_function, absolute_import import numpy from numpy import (amin, amax, ravel, asarray, arange, ones, newaxis, transpose, iscomplexobj, uint8, issubdtype, array) # Modification of original scipy pilutil.py to make this module importable if # pillow is not installed. If pillow is not installed, functions will raise # ImportError when called. try: try: from PIL import Image except ImportError: import Image pillow_installed = True if not hasattr(Image, 'frombytes'): Image.frombytes = Image.fromstring except ImportError: pillow_installed = False __all__ = ['bytescale', 'imread', 'imsave', 'fromimage', 'toimage', 'imresize'] PILLOW_ERROR_MESSAGE = ( "The Python Imaging Library (PIL) is required to load data " "from jpeg files. Please refer to " "https://pillow.readthedocs.io/en/stable/installation.html " "for installing PIL." ) def bytescale(data, cmin=None, cmax=None, high=255, low=0): """ Byte scales an array (image). Byte scaling means converting the input image to uint8 dtype and scaling the range to ``(low, high)`` (default 0-255). If the input image already has dtype uint8, no scaling is done. This function is only available if Python Imaging Library (PIL) is installed. Parameters ---------- data : ndarray PIL image data array. cmin : scalar, optional Bias scaling of small values. Default is ``data.min()``. cmax : scalar, optional Bias scaling of large values. Default is ``data.max()``. high : scalar, optional Scale max value to `high`. Default is 255. low : scalar, optional Scale min value to `low`. Default is 0. Returns ------- img_array : uint8 ndarray The byte-scaled array. Examples -------- >>> import numpy as np >>> from scipy.misc import bytescale >>> img = np.array([[ 91.06794177, 3.39058326, 84.4221549 ], ... [ 73.88003259, 80.91433048, 4.88878881], ... [ 51.53875334, 34.45808177, 27.5873488 ]]) >>> bytescale(img) array([[255, 0, 236], [205, 225, 4], [140, 90, 70]], dtype=uint8) >>> bytescale(img, high=200, low=100) array([[200, 100, 192], [180, 188, 102], [155, 135, 128]], dtype=uint8) >>> bytescale(img, cmin=0, cmax=255) array([[91, 3, 84], [74, 81, 5], [52, 34, 28]], dtype=uint8) """ if data.dtype == uint8: return data if high > 255: raise ValueError("`high` should be less than or equal to 255.") if low < 0: raise ValueError("`low` should be greater than or equal to 0.") if high < low: raise ValueError("`high` should be greater than or equal to `low`.") if cmin is None: cmin = data.min() if cmax is None: cmax = data.max() cscale = cmax - cmin if cscale < 0: raise ValueError("`cmax` should be larger than `cmin`.") elif cscale == 0: cscale = 1 scale = float(high - low) / cscale bytedata = (data - cmin) * scale + low return (bytedata.clip(low, high) + 0.5).astype(uint8) def imread(name, flatten=False, mode=None): """ Read an image from a file as an array. This function is only available if Python Imaging Library (PIL) is installed. Parameters ---------- name : str or file object The file name or file object to be read. flatten : bool, optional If True, flattens the color layers into a single gray-scale layer. mode : str, optional Mode to convert image to, e.g. ``'RGB'``. See the Notes for more details. Returns ------- imread : ndarray The array obtained by reading the image. Notes ----- `imread` uses the Python Imaging Library (PIL) to read an image. The following notes are from the PIL documentation. `mode` can be one of the following strings: * 'L' (8-bit pixels, black and white) * 'P' (8-bit pixels, mapped to any other mode using a color palette) * 'RGB' (3x8-bit pixels, true color) * 'RGBA' (4x8-bit pixels, true color with transparency mask) * 'CMYK' (4x8-bit pixels, color separation) * 'YCbCr' (3x8-bit pixels, color video format) * 'I' (32-bit signed integer pixels) * 'F' (32-bit floating point pixels) PIL also provides limited support for a few special modes, including 'LA' ('L' with alpha), 'RGBX' (true color with padding) and 'RGBa' (true color with premultiplied alpha). When translating a color image to black and white (mode 'L', 'I' or 'F'), the library uses the ITU-R 601-2 luma transform:: L = R * 299/1000 + G * 587/1000 + B * 114/1000 When `flatten` is True, the image is converted using mode 'F'. When `mode` is not None and `flatten` is True, the image is first converted according to `mode`, and the result is then flattened using mode 'F'. """ if not pillow_installed: raise ImportError(PILLOW_ERROR_MESSAGE) im = Image.open(name) return fromimage(im, flatten=flatten, mode=mode) def imsave(name, arr, format=None): """ Save an array as an image. This function is only available if Python Imaging Library (PIL) is installed. .. warning:: This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default). Parameters ---------- name : str or file object Output file name or file object. arr : ndarray, MxN or MxNx3 or MxNx4 Array containing image values. If the shape is ``MxN``, the array represents a grey-level image. Shape ``MxNx3`` stores the red, green and blue bands along the last dimension. An alpha layer may be included, specified as the last colour band of an ``MxNx4`` array. format : str Image format. If omitted, the format to use is determined from the file name extension. If a file object was used instead of a file name, this parameter should always be used. Examples -------- Construct an array of gradient intensity values and save to file: >>> import numpy as np >>> from scipy.misc import imsave >>> x = np.zeros((255, 255)) >>> x = np.zeros((255, 255), dtype=np.uint8) >>> x[:] = np.arange(255) >>> imsave('gradient.png', x) Construct an array with three colour bands (R, G, B) and store to file: >>> rgb = np.zeros((255, 255, 3), dtype=np.uint8) >>> rgb[..., 0] = np.arange(255) >>> rgb[..., 1] = 55 >>> rgb[..., 2] = 1 - np.arange(255) >>> imsave('rgb_gradient.png', rgb) """ im = toimage(arr, channel_axis=2) if format is None: im.save(name) else: im.save(name, format) return def fromimage(im, flatten=False, mode=None): """ Return a copy of a PIL image as a numpy array. This function is only available if Python Imaging Library (PIL) is installed. Parameters ---------- im : PIL image Input image. flatten : bool If true, convert the output to grey-scale. mode : str, optional Mode to convert image to, e.g. ``'RGB'``. See the Notes of the `imread` docstring for more details. Returns ------- fromimage : ndarray The different colour bands/channels are stored in the third dimension, such that a grey-image is MxN, an RGB-image MxNx3 and an RGBA-image MxNx4. """ if not pillow_installed: raise ImportError(PILLOW_ERROR_MESSAGE) if not Image.isImageType(im): raise TypeError("Input is not a PIL image.") if mode is not None: if mode != im.mode: im = im.convert(mode) elif im.mode == 'P': # Mode 'P' means there is an indexed "palette". If we leave the mode # as 'P', then when we do `a = array(im)` below, `a` will be a 2-D # containing the indices into the palette, and not a 3-D array # containing the RGB or RGBA values. if 'transparency' in im.info: im = im.convert('RGBA') else: im = im.convert('RGB') if flatten: im = im.convert('F') elif im.mode == '1': # Workaround for crash in PIL. When im is 1-bit, the call array(im) # can cause a seg. fault, or generate garbage. See # https://github.com/scipy/scipy/issues/2138 and # https://github.com/python-pillow/Pillow/issues/350. # # This converts im from a 1-bit image to an 8-bit image. im = im.convert('L') a = array(im) return a _errstr = "Mode is unknown or incompatible with input array shape." def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None, mode=None, channel_axis=None): """Takes a numpy array and returns a PIL image. This function is only available if Python Imaging Library (PIL) is installed. The mode of the PIL image depends on the array shape and the `pal` and `mode` keywords. For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values (from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode is given as 'F' or 'I' in which case a float and/or integer array is made. .. warning:: This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default). Notes ----- For 3-D arrays, the `channel_axis` argument tells which dimension of the array holds the channel data. For 3-D arrays if one of the dimensions is 3, the mode is 'RGB' by default or 'YCbCr' if selected. The numpy array must be either 2 dimensional or 3 dimensional. """ if not pillow_installed: raise ImportError(PILLOW_ERROR_MESSAGE) data = asarray(arr) if iscomplexobj(data): raise ValueError("Cannot convert a complex-valued array.") shape = list(data.shape) valid = len(shape) == 2 or ((len(shape) == 3) and ((3 in shape) or (4 in shape))) if not valid: raise ValueError("'arr' does not have a suitable array shape for " "any mode.") if len(shape) == 2: shape = (shape[1], shape[0]) # columns show up first if mode == 'F': data32 = data.astype(numpy.float32) image = Image.frombytes(mode, shape, data32.tostring()) return image if mode in [None, 'L', 'P']: bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax) image = Image.frombytes('L', shape, bytedata.tostring()) if pal is not None: image.putpalette(asarray(pal, dtype=uint8).tostring()) # Becomes a mode='P' automagically. elif mode == 'P': # default gray-scale pal = (arange(0, 256, 1, dtype=uint8)[:, newaxis] * ones((3,), dtype=uint8)[newaxis, :]) image.putpalette(asarray(pal, dtype=uint8).tostring()) return image if mode == '1': # high input gives threshold for 1 bytedata = (data > high) image = Image.frombytes('1', shape, bytedata.tostring()) return image if cmin is None: cmin = amin(ravel(data)) if cmax is None: cmax = amax(ravel(data)) data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low if mode == 'I': data32 = data.astype(numpy.uint32) image = Image.frombytes(mode, shape, data32.tostring()) else: raise ValueError(_errstr) return image # if here then 3-d array with a 3 or a 4 in the shape length. # Check for 3 in datacube shape --- 'RGB' or 'YCbCr' if channel_axis is None: if (3 in shape): ca = numpy.flatnonzero(asarray(shape) == 3)[0] else: ca = numpy.flatnonzero(asarray(shape) == 4) if len(ca): ca = ca[0] else: raise ValueError("Could not find channel dimension.") else: ca = channel_axis numch = shape[ca] if numch not in [3, 4]: raise ValueError("Channel axis dimension is not valid.") bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax) if ca == 2: strdata = bytedata.tostring() shape = (shape[1], shape[0]) elif ca == 1: strdata = transpose(bytedata, (0, 2, 1)).tostring() shape = (shape[2], shape[0]) elif ca == 0: strdata = transpose(bytedata, (1, 2, 0)).tostring() shape = (shape[2], shape[1]) if mode is None: if numch == 3: mode = 'RGB' else: mode = 'RGBA' if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']: raise ValueError(_errstr) if mode in ['RGB', 'YCbCr']: if numch != 3: raise ValueError("Invalid array shape for mode.") if mode in ['RGBA', 'CMYK']: if numch != 4: raise ValueError("Invalid array shape for mode.") # Here we know data and mode is correct image = Image.frombytes(mode, shape, strdata) return image def imresize(arr, size, interp='bilinear', mode=None): """ Resize an image. This function is only available if Python Imaging Library (PIL) is installed. .. warning:: This function uses `bytescale` under the hood to rescale images to use the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. It will also cast data for 2-D images to ``uint32`` for ``mode=None`` (which is the default). Parameters ---------- arr : ndarray The array of image to be resized. size : int, float or tuple * int - Percentage of current size. * float - Fraction of current size. * tuple - Size of the output image (height, width). interp : str, optional Interpolation to use for re-sizing ('nearest', 'lanczos', 'bilinear', 'bicubic' or 'cubic'). mode : str, optional The PIL image mode ('P', 'L', etc.) to convert `arr` before resizing. If ``mode=None`` (the default), 2-D images will be treated like ``mode='L'``, i.e. casting to long integer. For 3-D and 4-D arrays, `mode` will be set to ``'RGB'`` and ``'RGBA'`` respectively. Returns ------- imresize : ndarray The resized array of image. See Also -------- toimage : Implicitly used to convert `arr` according to `mode`. scipy.ndimage.zoom : More generic implementation that does not use PIL. """ im = toimage(arr, mode=mode) ts = type(size) if issubdtype(ts, numpy.signedinteger): percent = size / 100.0 size = tuple((array(im.size)*percent).astype(int)) elif issubdtype(type(size), numpy.floating): size = tuple((array(im.size)*size).astype(int)) else: size = (size[1], size[0]) func = {'nearest': 0, 'lanczos': 1, 'bilinear': 2, 'bicubic': 3, 'cubic': 3} imnew = im.resize(size, resample=func[interp]) return fromimage(imnew)