8000 Added pilutils tests taken from scipy · scikit-learn/scikit-learn@bbb3d24 · GitHub
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Jonathan Tammo Siebert
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Added pilutils tests taken from scipy
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‎sklearn/utils/tests/data/blocks2bit.png

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‎sklearn/utils/tests/data/box1.png

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‎sklearn/utils/tests/data/foo3x5x4indexed.png

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‎sklearn/utils/tests/data/icon.png

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‎sklearn/utils/tests/data/icon_mono.png

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‎sklearn/utils/tests/data/icon_mono_flat.png

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‎sklearn/utils/tests/data/pattern4bit.png

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‎sklearn/utils/tests/test_pilutils.py

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a. Redistributions of source code must retain the above copyright notice,
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"""
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Tests for the utility functions wrapping PIL functions
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This is a local version of the tests for utility functions from scipy that
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are wrapping PIL functionality. These functions are deprecated in scipy 1.0.0
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and will be removed in scipy 1.2.0.
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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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17
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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 os.path
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import numpy as np
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import pytest
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from numpy.testing import (assert_equal, assert_array_equal, assert_)
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from numpy.ma.testutils import assert_mask_equal
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from sklearn.utils._pilutil import (_bytescale, _imread, _imresize,
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_fromimage)
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_have_image = True
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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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except ImportError:
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_have_image = False
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# Function / method decorator for skipping PIL tests on import failure
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_pilskip = pytest.mark.skipif(
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not _have_image, reason='Need to import PIL for this test')
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datapath = os.path.dirname(__file__)
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class TestPILUtil(object):
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def test_imresize(self):
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im = np.random.random((10, 20))
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for T in np.sctypes['float'] + [float]:
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# 1.1 rounds to below 1.1 for float16, 1.101 works
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im1 = _imresize(im, T(1.101))
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assert_equal(im1.shape, (11, 22))
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@_pilskip
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def test_bytescale(self):
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x = np.array([0, 1, 2], np.uint8)
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y = np.array([0, 1, 2])
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assert_equal(_bytescale(x), x)
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assert_equal(_bytescale(y), [0, 128, 255])
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@_pilskip
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def test_bytescale_mask(self):
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a = np.ma.MaskedArray(data=[1, 2, 3], mask=[False, False, True])
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actual = _bytescale(a)
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expected = [0, 255, 3]
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assert_equal(expected, actual)
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assert_mask_equal(a.mask, actual.mask)
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assert_(isinstance(actual, np.ma.MaskedArray))
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def check_fromimage(filename, irange, shape):
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fp = open(filename, "rb")
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img = _fromimage(Image.open(fp))
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fp.close()
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imin, imax = irange
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assert_equal(img.min(), imin)
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assert_equal(img.max(), imax)
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assert_equal(img.shape, shape)
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@_pilskip
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def test_fromimage():
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# Test generator for parametric tests
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# Tuples in the list are (filename, (datamin, datamax), shape).
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files = [('icon.png', (0, 255), (48, 48, 4)),
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('icon_mono.png', (0, 255), (48, 48, 4)),
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('icon_mono_flat.png', (0, 255), (48, 48, 3))]
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for fn, irange, shape in files:
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check_fromimage(os.path.join(datapath, 'data', fn), irange, shape)
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@_pilskip
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def test_imread_indexed_png():
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# The file `foo3x5x4indexed.png` was created with this array
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# (3x5 is (height)x(width)):
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data = np.array([[[127, 0, 255, 255],
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[127, 0, 255, 255],
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[127, 0, 255, 255],
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[127, 0, 255, 255],
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[127, 0, 255, 255]],
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[[192, 192, 255, 0],
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[192, 192, 255, 0],
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[0, 0, 255, 0],
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[0, 0, 255, 0],
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[0, 0, 255, 0]],
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[[0, 31, 255, 255],
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[0, 31, 255, 255],
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[0, 31, 255, 255],
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[0, 31, 255, 255],
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[0, 31, 255, 255]]], dtype=np.uint8)
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filename = os.path.join(datapath, 'data', 'foo3x5x4indexed.png')
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with open(filename, 'rb') as f:
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im = _imread(f)
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assert_array_equal(im, data)
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@_pilskip
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def test_imread_1bit():
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# box1.png is a 48x48 grayscale image with bit depth 1.
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# The border pixels are 1 and the rest are 0 F987 .
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filename = os.path.join(datapath, 'data', 'box1.png')
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with open(filename, 'rb') as f:
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im = _imread(f)
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assert_equal(im.dtype, np.uint8)
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expected = np.zeros((48, 48), dtype=np.uint8)
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# When scaled up from 1 bit to 8 bits, 1 becomes 255.
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expected[:, 0] = 255
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expected[:, -1] = 255
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expected[0, :] = 255
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expected[-1, :] = 255
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assert_equal(im, expected)
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@_pilskip
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def test_imread_2bit():
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# blocks2bit.png is a 12x12 grayscale image with bit depth 2.
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# The pattern is 4 square subblocks of size 6x6. Upper left
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# is all 0, upper right is all 1, lower left is all 2, lower
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# right is all 3.
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# When scaled up to 8 bits, the values become [0, 85, 170, 255].
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filename = os.path.join(datapath, 'data', 'blocks2bit.png')
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with open(filename, 'rb') as f:
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im = _imread(f)
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assert_equal(im.dtype, np.uint8)
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expected = np.zeros((12, 12), dtype=np.uint8)
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expected[:6, 6:] = 85
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expected[6:, :6] = 170
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expected[6:, 6:] = 255
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assert_equal(im, expected)
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@_pilskip
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def test_imread_4bit():
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# pattern4bit.png is a 12(h) x 31(w) grayscale image with bit depth 4.
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# The value in row j and column i is maximum(j, i) % 16.
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# When scaled up to 8 bits, the values become [0, 17, 34, ..., 255].
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filename = os.path.join(datapath, 'data', 'pattern4bit.png')
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with open(filename, 'rb') as f:
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im = _imread(f)
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assert_equal(im.dtype, np.uint8)
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j, i = np.meshgrid(np.arange(12), np.arange(31), indexing='ij')
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expected = 17 * (np.maximum(j, i) % 16).astype(np.uint8)
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assert_equal(im, expected)

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