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