4808 lines
207 KiB
Python
4808 lines
207 KiB
Python
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# Copyright (C) 2003-2005 Peter J. Verveer
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#
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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
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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#
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# 3. The name of the author may not be used to endorse or promote
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# products derived from this software without specific prior
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# written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
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# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
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# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import math
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import sys
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import numpy
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from numpy import fft
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from numpy.testing import (assert_, assert_equal, assert_array_equal,
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assert_array_almost_equal, assert_almost_equal,
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suppress_warnings)
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import pytest
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from pytest import raises as assert_raises
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import scipy.ndimage as ndimage
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eps = 1e-12
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def sumsq(a, b):
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return math.sqrt(((a - b)**2).sum())
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class TestNdimage:
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def setup_method(self):
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# list of numarray data types
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self.integer_types = [
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numpy.int8, numpy.uint8, numpy.int16, numpy.uint16,
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numpy.int32, numpy.uint32, numpy.int64, numpy.uint64]
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self.float_types = [numpy.float32, numpy.float64]
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self.types = self.integer_types + self.float_types
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# list of boundary modes:
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self.modes = ['nearest', 'wrap', 'reflect', 'mirror', 'constant']
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def test_correlate01(self):
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array = numpy.array([1, 2])
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weights = numpy.array([2])
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expected = [2, 4]
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, expected)
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def test_correlate01_overlap(self):
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array = numpy.arange(256).reshape(16, 16)
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weights = numpy.array([2])
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expected = 2 * array
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ndimage.correlate1d(array, weights, output=array)
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assert_array_almost_equal(array, expected)
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def test_correlate02(self):
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array = numpy.array([1, 2, 3])
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kernel = numpy.array([1])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.correlate1d(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.convolve1d(array, kernel)
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assert_array_almost_equal(array, output)
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def test_correlate03(self):
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array = numpy.array([1])
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weights = numpy.array([1, 1])
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expected = [2]
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, expected)
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def test_correlate04(self):
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array = numpy.array([1, 2])
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tcor = [2, 3]
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tcov = [3, 4]
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weights = numpy.array([1, 1])
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, tcov)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, tcov)
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def test_correlate05(self):
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array = numpy.array([1, 2, 3])
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tcor = [2, 3, 5]
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tcov = [3, 5, 6]
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kernel = numpy.array([1, 1])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal(tcor, output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal(tcov, output)
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output = ndimage.correlate1d(array, kernel)
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assert_array_almost_equal(tcor, output)
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output = ndimage.convolve1d(array, kernel)
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assert_array_almost_equal(tcov, output)
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def test_correlate06(self):
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array = numpy.array([1, 2, 3])
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tcor = [9, 14, 17]
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tcov = [7, 10, 15]
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weights = numpy.array([1, 2, 3])
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, tcov)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, tcov)
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def test_correlate07(self):
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array = numpy.array([1, 2, 3])
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expected = [5, 8, 11]
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weights = numpy.array([1, 2, 1])
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, expected)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, expected)
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def test_correlate08(self):
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array = numpy.array([1, 2, 3])
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tcor = [1, 2, 5]
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tcov = [3, 6, 7]
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weights = numpy.array([1, 2, -1])
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output = ndimage.correlate(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve(array, weights)
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assert_array_almost_equal(output, tcov)
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output = ndimage.correlate1d(array, weights)
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assert_array_almost_equal(output, tcor)
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output = ndimage.convolve1d(array, weights)
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assert_array_almost_equal(output, tcov)
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def test_correlate09(self):
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array = []
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kernel = numpy.array([1, 1])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.correlate1d(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.convolve1d(array, kernel)
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assert_array_almost_equal(array, output)
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def test_correlate10(self):
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array = [[]]
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kernel = numpy.array([[1, 1]])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal(array, output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal(array, output)
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def test_correlate11(self):
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]])
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kernel = numpy.array([[1, 1],
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[1, 1]])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal([[4, 6, 10], [10, 12, 16]], output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal([[12, 16, 18], [18, 22, 24]], output)
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def test_correlate12(self):
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]])
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kernel = numpy.array([[1, 0],
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[0, 1]])
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output = ndimage.correlate(array, kernel)
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assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
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output = ndimage.convolve(array, kernel)
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assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
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def test_correlate13(self):
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kernel = numpy.array([[1, 0],
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[0, 1]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]], type1)
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for type2 in self.types:
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output = ndimage.correlate(array, kernel, output=type2)
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assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
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assert_equal(output.dtype.type, type2)
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output = ndimage.convolve(array, kernel,
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output=type2)
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assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
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assert_equal(output.dtype.type, type2)
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def test_correlate14(self):
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kernel = numpy.array([[1, 0],
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[0, 1]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]], type1)
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for type2 in self.types:
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output = numpy.zeros(array.shape, type2)
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ndimage.correlate(array, kernel,
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output=output)
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assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
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assert_equal(output.dtype.type, type2)
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ndimage.convolve(array, kernel, output=output)
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assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
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assert_equal(output.dtype.type, type2)
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def test_correlate15(self):
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kernel = numpy.array([[1, 0],
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[0, 1]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]], type1)
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output = ndimage.correlate(array, kernel,
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output=numpy.float32)
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assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
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assert_equal(output.dtype.type, numpy.float32)
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output = ndimage.convolve(array, kernel,
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output=numpy.float32)
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assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
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assert_equal(output.dtype.type, numpy.float32)
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def test_correlate16(self):
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kernel = numpy.array([[0.5, 0],
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[0, 0.5]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3], [4, 5, 6]], type1)
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output = ndimage.correlate(array, kernel, output=numpy.float32)
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assert_array_almost_equal([[1, 1.5, 2.5], [2.5, 3, 4]], output)
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assert_equal(output.dtype.type, numpy.float32)
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output = ndimage.convolve(array, kernel, output=numpy.float32)
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assert_array_almost_equal([[3, 4, 4.5], [4.5, 5.5, 6]], output)
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assert_equal(output.dtype.type, numpy.float32)
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def test_correlate17(self):
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array = numpy.array([1, 2, 3])
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tcor = [3, 5, 6]
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tcov = [2, 3, 5]
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kernel = numpy.array([1, 1])
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output = ndimage.correlate(array, kernel, origin=-1)
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assert_array_almost_equal(tcor, output)
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output = ndimage.convolve(array, kernel, origin=-1)
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assert_array_almost_equal(tcov, output)
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output = ndimage.correlate1d(array, kernel, origin=-1)
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assert_array_almost_equal(tcor, output)
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output = ndimage.convolve1d(array, kernel, origin=-1)
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assert_array_almost_equal(tcov, output)
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def test_correlate18(self):
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kernel = numpy.array([[1, 0],
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[0, 1]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3],
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[4, 5, 6]], type1)
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output = ndimage.correlate(array, kernel,
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output=numpy.float32,
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mode='nearest', origin=-1)
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assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
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assert_equal(output.dtype.type, numpy.float32)
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output = ndimage.convolve(array, kernel,
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output=numpy.float32,
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mode='nearest', origin=-1)
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assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
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assert_equal(output.dtype.type, numpy.float32)
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def test_correlate_mode_sequence(self):
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kernel = numpy.ones((2, 2))
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array = numpy.ones((3, 3), float)
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with assert_raises(RuntimeError):
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ndimage.correlate(array, kernel, mode=['nearest', 'reflect'])
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with assert_raises(RuntimeError):
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ndimage.convolve(array, kernel, mode=['nearest', 'reflect'])
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def test_correlate19(self):
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kernel = numpy.array([[1, 0],
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[0, 1]])
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for type1 in self.types:
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array = numpy.array([[1, 2, 3],
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|
|
[4, 5, 6]], type1)
|
||
|
|
output = ndimage.correlate(array, kernel,
|
||
|
|
output=numpy.float32,
|
||
|
|
mode='nearest', origin=[-1, 0])
|
||
|
|
assert_array_almost_equal([[5, 6, 8], [8, 9, 11]], output)
|
||
|
|
assert_equal(output.dtype.type, numpy.float32)
|
||
|
|
|
||
|
|
output = ndimage.convolve(array, kernel,
|
||
|
|
output=numpy.float32,
|
||
|
|
mode='nearest', origin=[-1, 0])
|
||
|
|
assert_array_almost_equal([[3, 5, 6], [6, 8, 9]], output)
|
||
|
|
assert_equal(output.dtype.type, numpy.float32)
|
||
|
|
|
||
|
|
def test_correlate20(self):
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
expected = [[5, 10, 15], [7, 14, 21]]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = numpy.zeros((2, 3), type2)
|
||
|
|
ndimage.correlate1d(array, weights, axis=0,
|
||
|
|
output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
ndimage.convolve1d(array, weights, axis=0,
|
||
|
|
output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
|
||
|
|
def test_correlate21(self):
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]])
|
||
|
|
expected = [[5, 10, 15], [7, 14, 21]]
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
output = ndimage.correlate1d(array, weights, axis=0)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
output = ndimage.convolve1d(array, weights, axis=0)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
|
||
|
|
def test_correlate22(self):
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
expected = [[6, 12, 18], [6, 12, 18]]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = numpy.zeros((2, 3), type2)
|
||
|
|
ndimage.correlate1d(array, weights, axis=0,
|
||
|
|
mode='wrap', output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
ndimage.convolve1d(array, weights, axis=0,
|
||
|
|
mode='wrap', output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
|
||
|
|
def test_correlate23(self):
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
expected = [[5, 10, 15], [7, 14, 21]]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = numpy.zeros((2, 3), type2)
|
||
|
|
ndimage.correlate1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
ndimage.convolve1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output)
|
||
|
|
assert_array_almost_equal(output, expected)
|
||
|
|
|
||
|
|
def test_correlate24(self):
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
tcor = [[7, 14, 21], [8, 16, 24]]
|
||
|
|
tcov = [[4, 8, 12], [5, 10, 15]]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = numpy.zeros((2, 3), type2)
|
||
|
|
ndimage.correlate1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output, origin=-1)
|
||
|
|
assert_array_almost_equal(output, tcor)
|
||
|
|
ndimage.convolve1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output, origin=-1)
|
||
|
|
assert_array_almost_equal(output, tcov)
|
||
|
|
|
||
|
|
def test_correlate25(self):
|
||
|
|
weights = numpy.array([1, 2, 1])
|
||
|
|
tcor = [[4, 8, 12], [5, 10, 15]]
|
||
|
|
tcov = [[7, 14, 21], [8, 16, 24]]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = numpy.zeros((2, 3), type2)
|
||
|
|
ndimage.correlate1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output, origin=1)
|
||
|
|
assert_array_almost_equal(output, tcor)
|
||
|
|
ndimage.convolve1d(array, weights, axis=0,
|
||
|
|
mode='nearest', output=output, origin=1)
|
||
|
|
assert_array_almost_equal(output, tcov)
|
||
|
|
|
||
|
|
def test_correlate26(self):
|
||
|
|
# test fix for gh-11661 (mirror extension of a length 1 signal)
|
||
|
|
y = ndimage.convolve1d(numpy.ones(1), numpy.ones(5), mode='mirror')
|
||
|
|
assert_array_equal(y, numpy.array(5.))
|
||
|
|
|
||
|
|
y = ndimage.correlate1d(numpy.ones(1), numpy.ones(5), mode='mirror')
|
||
|
|
assert_array_equal(y, numpy.array(5.))
|
||
|
|
|
||
|
|
def test_gauss01(self):
|
||
|
|
input = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], numpy.float32)
|
||
|
|
output = ndimage.gaussian_filter(input, 0)
|
||
|
|
assert_array_almost_equal(output, input)
|
||
|
|
|
||
|
|
def test_gauss02(self):
|
||
|
|
input = numpy.array([[1, 2, 3],
|
||
|
|
[2, 4, 6]], numpy.float32)
|
||
|
|
output = ndimage.gaussian_filter(input, 1.0)
|
||
|
|
assert_equal(input.dtype, output.dtype)
|
||
|
|
assert_equal(input.shape, output.shape)
|
||
|
|
|
||
|
|
def test_gauss03(self):
|
||
|
|
# single precision data"
|
||
|
|
input = numpy.arange(100 * 100).astype(numpy.float32)
|
||
|
|
input.shape = (100, 100)
|
||
|
|
output = ndimage.gaussian_filter(input, [1.0, 1.0])
|
||
|
|
|
||
|
|
assert_equal(input.dtype, output.dtype)
|
||
|
|
assert_equal(input.shape, output.shape)
|
||
|
|
|
||
|
|
# input.sum() is 49995000.0. With single precision floats, we can't
|
||
|
|
# expect more than 8 digits of accuracy, so use decimal=0 in this test.
|
||
|
|
assert_almost_equal(output.sum(dtype='d'), input.sum(dtype='d'),
|
||
|
|
decimal=0)
|
||
|
|
assert_(sumsq(input, output) > 1.0)
|
||
|
|
|
||
|
|
def test_gauss04(self):
|
||
|
|
input = numpy.arange(100 * 100).astype(numpy.float32)
|
||
|
|
input.shape = (100, 100)
|
||
|
|
otype = numpy.float64
|
||
|
|
output = ndimage.gaussian_filter(input, [1.0, 1.0], output=otype)
|
||
|
|
assert_equal(output.dtype.type, numpy.float64)
|
||
|
|
assert_equal(input.shape, output.shape)
|
||
|
|
assert_(sumsq(input, output) > 1.0)
|
||
|
|
|
||
|
|
def test_gauss05(self):
|
||
|
|
input = numpy.arange(100 * 100).astype(numpy.float32)
|
||
|
|
input.shape = (100, 100)
|
||
|
|
otype = numpy.float64
|
||
|
|
output = ndimage.gaussian_filter(input, [1.0, 1.0],
|
||
|
|
order=1, output=otype)
|
||
|
|
assert_equal(output.dtype.type, numpy.float64)
|
||
|
|
assert_equal(input.shape, output.shape)
|
||
|
|
assert_(sumsq(input, output) > 1.0)
|
||
|
|
|
||
|
|
def test_gauss06(self):
|
||
|
|
input = numpy.arange(100 * 100).astype(numpy.float32)
|
||
|
|
input.shape = (100, 100)
|
||
|
|
otype = numpy.float64
|
||
|
|
output1 = ndimage.gaussian_filter(input, [1.0, 1.0], output=otype)
|
||
|
|
output2 = ndimage.gaussian_filter(input, 1.0, output=otype)
|
||
|
|
assert_array_almost_equal(output1, output2)
|
||
|
|
|
||
|
|
def test_gauss_memory_overlap(self):
|
||
|
|
input = numpy.arange(100 * 100).astype(numpy.float32)
|
||
|
|
input.shape = (100, 100)
|
||
|
|
output1 = ndimage.gaussian_filter(input, 1.0)
|
||
|
|
ndimage.gaussian_filter(input, 1.0, output=input)
|
||
|
|
assert_array_almost_equal(output1, input)
|
||
|
|
|
||
|
|
def test_prewitt01(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1)
|
||
|
|
output = ndimage.prewitt(array, 0)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_prewitt02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1)
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
ndimage.prewitt(array, 0, output)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_prewitt03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 0)
|
||
|
|
output = ndimage.prewitt(array, 1)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_prewitt04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.prewitt(array, -1)
|
||
|
|
output = ndimage.prewitt(array, 1)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_sobel01(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1)
|
||
|
|
output = ndimage.sobel(array, 0)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_sobel02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1)
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
ndimage.sobel(array, 0, output)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_sobel03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1)
|
||
|
|
t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 0)
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
output = ndimage.sobel(array, 1)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_sobel04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
t = ndimage.sobel(array, -1)
|
||
|
|
output = ndimage.sobel(array, 1)
|
||
|
|
assert_array_almost_equal(t, output)
|
||
|
|
|
||
|
|
def test_laplace01(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0)
|
||
|
|
tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1)
|
||
|
|
output = ndimage.laplace(array)
|
||
|
|
assert_array_almost_equal(tmp1 + tmp2, output)
|
||
|
|
|
||
|
|
def test_laplace02(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0)
|
||
|
|
tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1)
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
ndimage.laplace(array, output=output)
|
||
|
|
assert_array_almost_equal(tmp1 + tmp2, output)
|
||
|
|
|
||
|
|
def test_gaussian_laplace01(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0])
|
||
|
|
tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2])
|
||
|
|
output = ndimage.gaussian_laplace(array, 1.0)
|
||
|
|
assert_array_almost_equal(tmp1 + tmp2, output)
|
||
|
|
|
||
|
|
def test_gaussian_laplace02(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0])
|
||
|
|
tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2])
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
ndimage.gaussian_laplace(array, 1.0, output)
|
||
|
|
assert_array_almost_equal(tmp1 + tmp2, output)
|
||
|
|
|
||
|
|
def test_generic_laplace01(self):
|
||
|
|
def derivative2(input, axis, output, mode, cval, a, b):
|
||
|
|
sigma = [a, b / 2.0]
|
||
|
|
input = numpy.asarray(input)
|
||
|
|
order = [0] * input.ndim
|
||
|
|
order[axis] = 2
|
||
|
|
return ndimage.gaussian_filter(input, sigma, order,
|
||
|
|
output, mode, cval)
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
tmp = ndimage.generic_laplace(array, derivative2,
|
||
|
|
extra_arguments=(1.0,),
|
||
|
|
extra_keywords={'b': 2.0})
|
||
|
|
ndimage.gaussian_laplace(array, 1.0, output)
|
||
|
|
assert_array_almost_equal(tmp, output)
|
||
|
|
|
||
|
|
def test_gaussian_gradient_magnitude01(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0])
|
||
|
|
tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1])
|
||
|
|
output = ndimage.gaussian_gradient_magnitude(array, 1.0)
|
||
|
|
expected = tmp1 * tmp1 + tmp2 * tmp2
|
||
|
|
expected = numpy.sqrt(expected).astype(type_)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_gaussian_gradient_magnitude02(self):
|
||
|
|
for type_ in [numpy.int32, numpy.float32, numpy.float64]:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_) * 100
|
||
|
|
tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0])
|
||
|
|
tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1])
|
||
|
|
output = numpy.zeros(array.shape, type_)
|
||
|
|
ndimage.gaussian_gradient_magnitude(array, 1.0, output)
|
||
|
|
expected = tmp1 * tmp1 + tmp2 * tmp2
|
||
|
|
expected = numpy.sqrt(expected).astype(type_)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_generic_gradient_magnitude01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], numpy.float64)
|
||
|
|
|
||
|
|
def derivative(input, axis, output, mode, cval, a, b):
|
||
|
|
sigma = [a, b / 2.0]
|
||
|
|
input = numpy.asarray(input)
|
||
|
|
order = [0] * input.ndim
|
||
|
|
order[axis] = 1
|
||
|
|
return ndimage.gaussian_filter(input, sigma, order,
|
||
|
|
output, mode, cval)
|
||
|
|
tmp1 = ndimage.gaussian_gradient_magnitude(array, 1.0)
|
||
|
|
tmp2 = ndimage.generic_gradient_magnitude(
|
||
|
|
array, derivative, extra_arguments=(1.0,),
|
||
|
|
extra_keywords={'b': 2.0})
|
||
|
|
assert_array_almost_equal(tmp1, tmp2)
|
||
|
|
|
||
|
|
def test_uniform01(self):
|
||
|
|
array = numpy.array([2, 4, 6])
|
||
|
|
size = 2
|
||
|
|
output = ndimage.uniform_filter1d(array, size, origin=-1)
|
||
|
|
assert_array_almost_equal([3, 5, 6], output)
|
||
|
|
|
||
|
|
def test_uniform02(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
filter_shape = [0]
|
||
|
|
output = ndimage.uniform_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
|
||
|
|
def test_uniform03(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
filter_shape = [1]
|
||
|
|
output = ndimage.uniform_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
|
||
|
|
def test_uniform04(self):
|
||
|
|
array = numpy.array([2, 4, 6])
|
||
|
|
filter_shape = [2]
|
||
|
|
output = ndimage.uniform_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([2, 3, 5], output)
|
||
|
|
|
||
|
|
def test_uniform05(self):
|
||
|
|
array = []
|
||
|
|
filter_shape = [1]
|
||
|
|
output = ndimage.uniform_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([], output)
|
||
|
|
|
||
|
|
def test_uniform06(self):
|
||
|
|
filter_shape = [2, 2]
|
||
|
|
for type1 in self.types:
|
||
|
|
array = numpy.array([[4, 8, 12],
|
||
|
|
[16, 20, 24]], type1)
|
||
|
|
for type2 in self.types:
|
||
|
|
output = ndimage.uniform_filter(
|
||
|
|
array, filter_shape, output=type2)
|
||
|
|
assert_array_almost_equal([[4, 6, 10], [10, 12, 16]], output)
|
||
|
|
assert_equal(output.dtype.type, type2)
|
||
|
|
|
||
|
|
def test_minimum_filter01(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
filter_shape = numpy.array([2])
|
||
|
|
output = ndimage.minimum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([1, 1, 2, 3, 4], output)
|
||
|
|
|
||
|
|
def test_minimum_filter02(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
filter_shape = numpy.array([3])
|
||
|
|
output = ndimage.minimum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([1, 1, 2, 3, 4], output)
|
||
|
|
|
||
|
|
def test_minimum_filter03(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
filter_shape = numpy.array([2])
|
||
|
|
output = ndimage.minimum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([3, 2, 2, 1, 1], output)
|
||
|
|
|
||
|
|
def test_minimum_filter04(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
filter_shape = numpy.array([3])
|
||
|
|
output = ndimage.minimum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([2, 2, 1, 1, 1], output)
|
||
|
|
|
||
|
|
def test_minimum_filter05(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
filter_shape = numpy.array([2, 3])
|
||
|
|
output = ndimage.minimum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 2, 1, 1, 1],
|
||
|
|
[5, 3, 3, 1, 1]], output)
|
||
|
|
|
||
|
|
def test_minimum_filter05_overlap(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
filter_shape = numpy.array([2, 3])
|
||
|
|
ndimage.minimum_filter(array, filter_shape, output=array)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 2, 1, 1, 1],
|
||
|
|
[5, 3, 3, 1, 1]], array)
|
||
|
|
|
||
|
|
def test_minimum_filter06(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 1, 1], [1, 1, 1]]
|
||
|
|
output = ndimage.minimum_filter(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 2, 1, 1, 1],
|
||
|
|
[5, 3, 3, 1, 1]], output)
|
||
|
|
# separable footprint should allow mode sequence
|
||
|
|
output2 = ndimage.minimum_filter(array, footprint=footprint,
|
||
|
|
mode=['reflect', 'reflect'])
|
||
|
|
assert_array_almost_equal(output2, output)
|
||
|
|
|
||
|
|
def test_minimum_filter07(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.minimum_filter(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1]], output)
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
ndimage.minimum_filter(array, footprint=footprint,
|
||
|
|
mode=['reflect', 'constant'])
|
||
|
|
|
||
|
|
def test_minimum_filter08(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.minimum_filter(array, footprint=footprint, origin=-1)
|
||
|
|
assert_array_almost_equal([[3, 1, 3, 1, 1],
|
||
|
|
[5, 3, 3, 1, 1],
|
||
|
|
[3, 3, 1, 1, 1]], output)
|
||
|
|
|
||
|
|
def test_minimum_filter09(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.minimum_filter(array, footprint=footprint,
|
||
|
|
origin=[-1, 0])
|
||
|
|
assert_array_almost_equal([[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1],
|
||
|
|
[5, 3, 3, 1, 1]], output)
|
||
|
|
|
||
|
|
def test_maximum_filter01(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
filter_shape = numpy.array([2])
|
||
|
|
output = ndimage.maximum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([1, 2, 3, 4, 5], output)
|
||
|
|
|
||
|
|
def test_maximum_filter02(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
filter_shape = numpy.array([3])
|
||
|
|
output = ndimage.maximum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([2, 3, 4, 5, 5], output)
|
||
|
|
|
||
|
|
def test_maximum_filter03(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
filter_shape = numpy.array([2])
|
||
|
|
output = ndimage.maximum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([3, 3, 5, 5, 4], output)
|
||
|
|
|
||
|
|
def test_maximum_filter04(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
filter_shape = numpy.array([3])
|
||
|
|
output = ndimage.maximum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([3, 5, 5, 5, 4], output)
|
||
|
|
|
||
|
|
def test_maximum_filter05(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
filter_shape = numpy.array([2, 3])
|
||
|
|
output = ndimage.maximum_filter(array, filter_shape)
|
||
|
|
assert_array_almost_equal([[3, 5, 5, 5, 4],
|
||
|
|
[7, 9, 9, 9, 5],
|
||
|
|
[8, 9, 9, 9, 7]], output)
|
||
|
|
|
||
|
|
def test_maximum_filter06(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 1, 1], [1, 1, 1]]
|
||
|
|
output = ndimage.maximum_filter(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[3, 5, 5, 5, 4],
|
||
|
|
[7, 9, 9, 9, 5],
|
||
|
|
[8, 9, 9, 9, 7]], output)
|
||
|
|
# separable footprint should allow mode sequence
|
||
|
|
output2 = ndimage.maximum_filter(array, footprint=footprint,
|
||
|
|
mode=['reflect', 'reflect'])
|
||
|
|
assert_array_almost_equal(output2, output)
|
||
|
|
|
||
|
|
def test_maximum_filter07(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.maximum_filter(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[3, 5, 5, 5, 4],
|
||
|
|
[7, 7, 9, 9, 5],
|
||
|
|
[7, 9, 8, 9, 7]], output)
|
||
|
|
# non-separable footprint should not allow mode sequence
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
ndimage.maximum_filter(array, footprint=footprint,
|
||
|
|
mode=['reflect', 'reflect'])
|
||
|
|
|
||
|
|
def test_maximum_filter08(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.maximum_filter(array, footprint=footprint, origin=-1)
|
||
|
|
assert_array_almost_equal([[7, 9, 9, 5, 5],
|
||
|
|
[9, 8, 9, 7, 5],
|
||
|
|
[8, 8, 7, 7, 7]], output)
|
||
|
|
|
||
|
|
def test_maximum_filter09(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.maximum_filter(array, footprint=footprint,
|
||
|
|
origin=[-1, 0])
|
||
|
|
assert_array_almost_equal([[7, 7, 9, 9, 5],
|
||
|
|
[7, 9, 8, 9, 7],
|
||
|
|
[8, 8, 8, 7, 7]], output)
|
||
|
|
|
||
|
|
def test_rank01(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
output = ndimage.rank_filter(array, 1, size=2)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
output = ndimage.percentile_filter(array, 100, size=2)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
output = ndimage.median_filter(array, 2)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
|
||
|
|
def test_rank02(self):
|
||
|
|
array = numpy.array([1, 2, 3, 4, 5])
|
||
|
|
output = ndimage.rank_filter(array, 1, size=[3])
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
output = ndimage.percentile_filter(array, 50, size=3)
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
output = ndimage.median_filter(array, (3,))
|
||
|
|
assert_array_almost_equal(array, output)
|
||
|
|
|
||
|
|
def test_rank03(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
output = ndimage.rank_filter(array, 1, size=[2])
|
||
|
|
assert_array_almost_equal([3, 3, 5, 5, 4], output)
|
||
|
|
output = ndimage.percentile_filter(array, 100, size=2)
|
||
|
|
assert_array_almost_equal([3, 3, 5, 5, 4], output)
|
||
|
|
|
||
|
|
def test_rank04(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
expected = [3, 3, 2, 4, 4]
|
||
|
|
output = ndimage.rank_filter(array, 1, size=3)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 50, size=3)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.median_filter(array, size=3)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank05(self):
|
||
|
|
array = numpy.array([3, 2, 5, 1, 4])
|
||
|
|
expected = [3, 3, 2, 4, 4]
|
||
|
|
output = ndimage.rank_filter(array, -2, size=3)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank06(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]])
|
||
|
|
expected = [[2, 2, 1, 1, 1],
|
||
|
|
[3, 3, 2, 1, 1],
|
||
|
|
[5, 5, 3, 3, 1]]
|
||
|
|
output = ndimage.rank_filter(array, 1, size=[2, 3])
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 17, size=(2, 3))
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank06_overlap(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]])
|
||
|
|
array_copy = array.copy()
|
||
|
|
expected = [[2, 2, 1, 1, 1],
|
||
|
|
[3, 3, 2, 1, 1],
|
||
|
|
[5, 5, 3, 3, 1]]
|
||
|
|
ndimage.rank_filter(array, 1, size=[2, 3], output=array)
|
||
|
|
assert_array_almost_equal(expected, array)
|
||
|
|
|
||
|
|
ndimage.percentile_filter(array_copy, 17, size=(2, 3),
|
||
|
|
output=array_copy)
|
||
|
|
assert_array_almost_equal(expected, array_copy)
|
||
|
|
|
||
|
|
def test_rank07(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]])
|
||
|
|
expected = [[3, 5, 5, 5, 4],
|
||
|
|
[5, 5, 7, 5, 4],
|
||
|
|
[6, 8, 8, 7, 5]]
|
||
|
|
output = ndimage.rank_filter(array, -2, size=[2, 3])
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank08(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]])
|
||
|
|
expected = [[3, 3, 2, 4, 4],
|
||
|
|
[5, 5, 5, 4, 4],
|
||
|
|
[5, 6, 7, 5, 5]]
|
||
|
|
output = ndimage.percentile_filter(array, 50.0, size=(2, 3))
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.rank_filter(array, 3, size=(2, 3))
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.median_filter(array, size=(2, 3))
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
# non-separable: does not allow mode sequence
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
ndimage.percentile_filter(array, 50.0, size=(2, 3),
|
||
|
|
mode=['reflect', 'constant'])
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
ndimage.rank_filter(array, 3, size=(2, 3), mode=['reflect']*2)
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
ndimage.median_filter(array, size=(2, 3), mode=['reflect']*2)
|
||
|
|
|
||
|
|
def test_rank09(self):
|
||
|
|
expected = [[3, 3, 2, 4, 4],
|
||
|
|
[3, 5, 2, 5, 1],
|
||
|
|
[5, 5, 8, 3, 5]]
|
||
|
|
footprint = [[1, 0, 1], [0, 1, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = ndimage.rank_filter(array, 1, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 35, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank10(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
expected = [[2, 2, 1, 1, 1],
|
||
|
|
[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1]]
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.rank_filter(array, 0, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 0.0, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank11(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
expected = [[3, 5, 5, 5, 4],
|
||
|
|
[7, 7, 9, 9, 5],
|
||
|
|
[7, 9, 8, 9, 7]]
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.rank_filter(array, -1, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 100.0, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank12(self):
|
||
|
|
expected = [[3, 3, 2, 4, 4],
|
||
|
|
[3, 5, 2, 5, 1],
|
||
|
|
[5, 5, 8, 3, 5]]
|
||
|
|
footprint = [[1, 0, 1], [0, 1, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = ndimage.rank_filter(array, 1, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.percentile_filter(array, 50.0,
|
||
|
|
footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
output = ndimage.median_filter(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank13(self):
|
||
|
|
expected = [[5, 2, 5, 1, 1],
|
||
|
|
[5, 8, 3, 5, 5],
|
||
|
|
[6, 6, 5, 5, 5]]
|
||
|
|
footprint = [[1, 0, 1], [0, 1, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = ndimage.rank_filter(array, 1, footprint=footprint,
|
||
|
|
origin=-1)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank14(self):
|
||
|
|
expected = [[3, 5, 2, 5, 1],
|
||
|
|
[5, 5, 8, 3, 5],
|
||
|
|
[5, 6, 6, 5, 5]]
|
||
|
|
footprint = [[1, 0, 1], [0, 1, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = ndimage.rank_filter(array, 1, footprint=footprint,
|
||
|
|
origin=[-1, 0])
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_rank15(self):
|
||
|
|
"rank filter 15"
|
||
|
|
expected = [[2, 3, 1, 4, 1],
|
||
|
|
[5, 3, 7, 1, 1],
|
||
|
|
[5, 5, 3, 3, 3]]
|
||
|
|
footprint = [[1, 0, 1], [0, 1, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[5, 8, 3, 7, 1],
|
||
|
|
[5, 6, 9, 3, 5]], type_)
|
||
|
|
output = ndimage.rank_filter(array, 0, footprint=footprint,
|
||
|
|
origin=[-1, 0])
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_generic_filter1d01(self):
|
||
|
|
weights = numpy.array([1.1, 2.2, 3.3])
|
||
|
|
|
||
|
|
def _filter_func(input, output, fltr, total):
|
||
|
|
fltr = fltr / total
|
||
|
|
for ii in range(input.shape[0] - 2):
|
||
|
|
output[ii] = input[ii] * fltr[0]
|
||
|
|
output[ii] += input[ii + 1] * fltr[1]
|
||
|
|
output[ii] += input[ii + 2] * fltr[2]
|
||
|
|
for type_ in self.types:
|
||
|
|
a = numpy.arange(12, dtype=type_)
|
||
|
|
a.shape = (3, 4)
|
||
|
|
r1 = ndimage.correlate1d(a, weights / weights.sum(), 0, origin=-1)
|
||
|
|
r2 = ndimage.generic_filter1d(
|
||
|
|
a, _filter_func, 3, axis=0, origin=-1,
|
||
|
|
extra_arguments=(weights,),
|
||
|
|
extra_keywords={'total': weights.sum()})
|
||
|
|
assert_array_almost_equal(r1, r2)
|
||
|
|
|
||
|
|
def test_generic_filter01(self):
|
||
|
|
filter_ = numpy.array([[1.0, 2.0], [3.0, 4.0]])
|
||
|
|
footprint = numpy.array([[1, 0], [0, 1]])
|
||
|
|
cf = numpy.array([1., 4.])
|
||
|
|
|
||
|
|
def _filter_func(buffer, weights, total=1.0):
|
||
|
|
weights = cf / total
|
||
|
|
return (buffer * weights).sum()
|
||
|
|
for type_ in self.types:
|
||
|
|
a = numpy.arange(12, dtype=type_)
|
||
|
|
a.shape = (3, 4)
|
||
|
|
r1 = ndimage.correlate(a, filter_ * footprint)
|
||
|
|
if type_ in self.float_types:
|
||
|
|
r1 /= 5
|
||
|
|
else:
|
||
|
|
r1 //= 5
|
||
|
|
r2 = ndimage.generic_filter(
|
||
|
|
a, _filter_func, footprint=footprint, extra_arguments=(cf,),
|
||
|
|
extra_keywords={'total': cf.sum()})
|
||
|
|
assert_array_almost_equal(r1, r2)
|
||
|
|
|
||
|
|
# generic_filter doesn't allow mode sequence
|
||
|
|
with assert_raises(RuntimeError):
|
||
|
|
r2 = ndimage.generic_filter(
|
||
|
|
a, _filter_func, mode=['reflect', 'reflect'],
|
||
|
|
footprint=footprint, extra_arguments=(cf,),
|
||
|
|
extra_keywords={'total': cf.sum()})
|
||
|
|
|
||
|
|
def test_extend01(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([1, 0])
|
||
|
|
expected_values = [[1, 1, 2],
|
||
|
|
[3, 1, 2],
|
||
|
|
[1, 1, 2],
|
||
|
|
[2, 1, 2],
|
||
|
|
[0, 1, 2]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate1d(array, weights, 0,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend02(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([1, 0, 0, 0, 0, 0, 0, 0])
|
||
|
|
expected_values = [[1, 1, 1],
|
||
|
|
[3, 1, 2],
|
||
|
|
[3, 3, 2],
|
||
|
|
[1, 2, 3],
|
||
|
|
[0, 0, 0]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate1d(array, weights, 0,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend03(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([0, 0, 1])
|
||
|
|
expected_values = [[2, 3, 3],
|
||
|
|
[2, 3, 1],
|
||
|
|
[2, 3, 3],
|
||
|
|
[2, 3, 2],
|
||
|
|
[2, 3, 0]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate1d(array, weights, 0,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend04(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1])
|
||
|
|
expected_values = [[3, 3, 3],
|
||
|
|
[2, 3, 1],
|
||
|
|
[2, 1, 1],
|
||
|
|
[1, 2, 3],
|
||
|
|
[0, 0, 0]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate1d(array, weights, 0,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend05(self):
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[4, 5, 6],
|
||
|
|
[7, 8, 9]])
|
||
|
|
weights = numpy.array([[1, 0], [0, 0]])
|
||
|
|
expected_values = [[[1, 1, 2], [1, 1, 2], [4, 4, 5]],
|
||
|
|
[[9, 7, 8], [3, 1, 2], [6, 4, 5]],
|
||
|
|
[[1, 1, 2], [1, 1, 2], [4, 4, 5]],
|
||
|
|
[[5, 4, 5], [2, 1, 2], [5, 4, 5]],
|
||
|
|
[[0, 0, 0], [0, 1, 2], [0, 4, 5]]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend06(self):
|
||
|
|
array = numpy.array([[1, 2, 3],
|
||
|
|
[4, 5, 6],
|
||
|
|
[7, 8, 9]])
|
||
|
|
weights = numpy.array([[0, 0, 0], [0, 0, 0], [0, 0, 1]])
|
||
|
|
expected_values = [[[5, 6, 6], [8, 9, 9], [8, 9, 9]],
|
||
|
|
[[5, 6, 4], [8, 9, 7], [2, 3, 1]],
|
||
|
|
[[5, 6, 6], [8, 9, 9], [8, 9, 9]],
|
||
|
|
[[5, 6, 5], [8, 9, 8], [5, 6, 5]],
|
||
|
|
[[5, 6, 0], [8, 9, 0], [0, 0, 0]]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend07(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1])
|
||
|
|
expected_values = [[3, 3, 3],
|
||
|
|
[2, 3, 1],
|
||
|
|
[2, 1, 1],
|
||
|
|
[1, 2, 3],
|
||
|
|
[0, 0, 0]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights, mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend08(self):
|
||
|
|
array = numpy.array([[1], [2], [3]])
|
||
|
|
weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]])
|
||
|
|
expected_values = [[[3], [3], [3]],
|
||
|
|
[[2], [3], [1]],
|
||
|
|
[[2], [1], [1]],
|
||
|
|
[[1], [2], [3]],
|
||
|
|
[[0], [0], [0]]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights, mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend09(self):
|
||
|
|
array = numpy.array([1, 2, 3])
|
||
|
|
weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1])
|
||
|
|
expected_values = [[3, 3, 3],
|
||
|
|
[2, 3, 1],
|
||
|
|
[2, 1, 1],
|
||
|
|
[1, 2, 3],
|
||
|
|
[0, 0, 0]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_extend10(self):
|
||
|
|
array = numpy.array([[1], [2], [3]])
|
||
|
|
weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]])
|
||
|
|
expected_values = [[[3], [3], [3]],
|
||
|
|
[[2], [3], [1]],
|
||
|
|
[[2], [1], [1]],
|
||
|
|
[[1], [2], [3]],
|
||
|
|
[[0], [0], [0]]]
|
||
|
|
for mode, expected_value in zip(self.modes, expected_values):
|
||
|
|
output = ndimage.correlate(array, weights,
|
||
|
|
mode=mode, cval=0)
|
||
|
|
assert_array_equal(output, expected_value)
|
||
|
|
|
||
|
|
def test_boundaries(self):
|
||
|
|
def shift(x):
|
||
|
|
return (x[0] + 0.5,)
|
||
|
|
|
||
|
|
data = numpy.array([1, 2, 3, 4.])
|
||
|
|
expected = {'constant': [1.5, 2.5, 3.5, -1, -1, -1, -1],
|
||
|
|
'wrap': [1.5, 2.5, 3.5, 1.5, 2.5, 3.5, 1.5],
|
||
|
|
'mirror': [1.5, 2.5, 3.5, 3.5, 2.5, 1.5, 1.5],
|
||
|
|
'nearest': [1.5, 2.5, 3.5, 4, 4, 4, 4]}
|
||
|
|
|
||
|
|
for mode in expected:
|
||
|
|
assert_array_equal(
|
||
|
|
expected[mode],
|
||
|
|
ndimage.geometric_transform(data, shift, cval=-1, mode=mode,
|
||
|
|
output_shape=(7,), order=1))
|
||
|
|
|
||
|
|
def test_boundaries2(self):
|
||
|
|
def shift(x):
|
||
|
|
return (x[0] - 0.9,)
|
||
|
|
|
||
|
|
data = numpy.array([1, 2, 3, 4])
|
||
|
|
expected = {'constant': [-1, 1, 2, 3],
|
||
|
|
'wrap': [3, 1, 2, 3],
|
||
|
|
'mirror': [2, 1, 2, 3],
|
||
|
|
'nearest': [1, 1, 2, 3]}
|
||
|
|
|
||
|
|
for mode in expected:
|
||
|
|
assert_array_equal(
|
||
|
|
expected[mode],
|
||
|
|
ndimage.geometric_transform(data, shift, cval=-1, mode=mode,
|
||
|
|
output_shape=(4,)))
|
||
|
|
|
||
|
|
def test_fourier_gaussian_real01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.float32, numpy.float64], [6, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.rfft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.irfft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a), 1, decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_gaussian_complex01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.complex64, numpy.complex128], [6, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.fft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.ifft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_uniform_real01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.float32, numpy.float64], [6, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.rfft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.irfft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_uniform_complex01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.complex64, numpy.complex128], [6, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.fft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.ifft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_shift_real01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.float32, numpy.float64], [4, 11]):
|
||
|
|
expected = numpy.arange(shape[0] * shape[1], dtype=type_)
|
||
|
|
expected.shape = shape
|
||
|
|
a = fft.rfft(expected, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.irfft(a, shape[0], 0)
|
||
|
|
assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1],
|
||
|
|
decimal=dec)
|
||
|
|
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||
|
|
decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_shift_complex01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.complex64, numpy.complex128], [4, 11]):
|
||
|
|
expected = numpy.arange(shape[0] * shape[1], dtype=type_)
|
||
|
|
expected.shape = shape
|
||
|
|
a = fft.fft(expected, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_shift(a, [1, 1], -1, 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.ifft(a, shape[0], 0)
|
||
|
|
assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1],
|
||
|
|
decimal=dec)
|
||
|
|
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||
|
|
decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_ellipsoid_real01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.float32, numpy.float64], [5, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.rfft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5],
|
||
|
|
shape[0], 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.irfft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||
|
|
|
||
|
|
def test_fourier_ellipsoid_complex01(self):
|
||
|
|
for shape in [(32, 16), (31, 15)]:
|
||
|
|
for type_, dec in zip([numpy.complex64, numpy.complex128],
|
||
|
|
[5, 14]):
|
||
|
|
a = numpy.zeros(shape, type_)
|
||
|
|
a[0, 0] = 1.0
|
||
|
|
a = fft.fft(a, shape[0], 0)
|
||
|
|
a = fft.fft(a, shape[1], 1)
|
||
|
|
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
|
||
|
|
a = fft.ifft(a, shape[1], 1)
|
||
|
|
a = fft.ifft(a, shape[0], 0)
|
||
|
|
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||
|
|
|
||
|
|
def test_spline01(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([], type_)
|
||
|
|
for order in range(2, 6):
|
||
|
|
out = ndimage.spline_filter(data, order=order)
|
||
|
|
assert_array_almost_equal(out, 1)
|
||
|
|
|
||
|
|
def test_spline02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([1], type_)
|
||
|
|
for order in range(2, 6):
|
||
|
|
out = ndimage.spline_filter(data, order=order)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_spline03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([], type_)
|
||
|
|
for order in range(2, 6):
|
||
|
|
out = ndimage.spline_filter(data, order,
|
||
|
|
output=type_)
|
||
|
|
assert_array_almost_equal(out, 1)
|
||
|
|
|
||
|
|
def test_spline04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([4], type_)
|
||
|
|
for order in range(2, 6):
|
||
|
|
out = ndimage.spline_filter(data, order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_spline05(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([4, 4], type_)
|
||
|
|
for order in range(2, 6):
|
||
|
|
out = ndimage.spline_filter(data, order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]])
|
||
|
|
|
||
|
|
def test_geometric_transform01(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_geometric_transform02(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_geometric_transform03(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 1,)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_geometric_transform04(self):
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 1,)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 4, 1, 3])
|
||
|
|
|
||
|
|
def test_geometric_transform05(self):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0], x[1] - 1)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]])
|
||
|
|
|
||
|
|
def test_geometric_transform06(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0], x[1] - 1)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8],
|
||
|
|
[0, 3, 5, 3]])
|
||
|
|
|
||
|
|
def test_geometric_transform07(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 1, x[1])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5]])
|
||
|
|
|
||
|
|
def test_geometric_transform08(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 1, x[1] - 1)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_geometric_transform10(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 1, x[1] - 1)
|
||
|
|
for order in range(0, 6):
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
out = ndimage.geometric_transform(filtered, mapping, data.shape,
|
||
|
|
order=order, prefilter=False)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_geometric_transform13(self):
|
||
|
|
data = numpy.ones([2], numpy.float64)
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] // 2,)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, [4], order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_geometric_transform14(self):
|
||
|
|
data = [1, 5, 2, 6, 3, 7, 4, 4]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (2 * x[0],)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, [4], order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 2, 3, 4])
|
||
|
|
|
||
|
|
def test_geometric_transform15(self):
|
||
|
|
data = [1, 2, 3, 4]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] / 2,)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, [8], order=order)
|
||
|
|
assert_array_almost_equal(out[::2], [1, 2, 3, 4])
|
||
|
|
|
||
|
|
def test_geometric_transform16(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9.0, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0], x[1] * 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (3, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3], [5, 7], [9, 11]])
|
||
|
|
|
||
|
|
def test_geometric_transform17(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] * 2, x[1])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (1, 4),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 2, 3, 4]])
|
||
|
|
|
||
|
|
def test_geometric_transform18(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] * 2, x[1] * 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (1, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3]])
|
||
|
|
|
||
|
|
def test_geometric_transform19(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0], x[1] / 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (3, 8),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[..., ::2], data)
|
||
|
|
|
||
|
|
def test_geometric_transform20(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] / 2, x[1])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (6, 4),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ...], data)
|
||
|
|
|
||
|
|
def test_geometric_transform21(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] / 2, x[1] / 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (6, 8),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
def test_geometric_transform22(self):
|
||
|
|
data = numpy.array([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]], numpy.float64)
|
||
|
|
|
||
|
|
def mapping1(x):
|
||
|
|
return (x[0] / 2, x[1] / 2)
|
||
|
|
|
||
|
|
def mapping2(x):
|
||
|
|
return (x[0] * 2, x[1] * 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping1,
|
||
|
|
(6, 8), order=order)
|
||
|
|
out = ndimage.geometric_transform(out, mapping2,
|
||
|
|
(3, 4), order=order)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
def test_geometric_transform23(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (1, x[0] * 2)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (2,), order=order)
|
||
|
|
out = out.astype(numpy.int32)
|
||
|
|
assert_array_almost_equal(out, [5, 7])
|
||
|
|
|
||
|
|
def test_geometric_transform24(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x, a, b):
|
||
|
|
return (a, x[0] * b)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.geometric_transform(
|
||
|
|
data, mapping, (2,), order=order, extra_arguments=(1,),
|
||
|
|
extra_keywords={'b': 2})
|
||
|
|
assert_array_almost_equal(out, [5, 7])
|
||
|
|
|
||
|
|
def test_geometric_transform_endianness_with_output_parameter(self):
|
||
|
|
# geometric transform given output ndarray or dtype with
|
||
|
|
# non-native endianness. see issue #4127
|
||
|
|
data = numpy.array([1])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
for out in [data.dtype, data.dtype.newbyteorder(),
|
||
|
|
numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder())]:
|
||
|
|
returned = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [1])
|
||
|
|
|
||
|
|
def test_geometric_transform_with_string_output(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
out = ndimage.geometric_transform(data, mapping, output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_map_coordinates01(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
idx -= 1
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.map_coordinates(data, idx, order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_map_coordinates02(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
idx = numpy.indices(data.shape, numpy.float64)
|
||
|
|
idx -= 0.5
|
||
|
|
for order in range(0, 6):
|
||
|
|
out1 = ndimage.shift(data, 0.5, order=order)
|
||
|
|
out2 = ndimage.map_coordinates(data, idx, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
def test_map_coordinates03(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]], order='F')
|
||
|
|
idx = numpy.indices(data.shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data, idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data, (1, 1)))
|
||
|
|
idx = numpy.indices(data[::2].shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data[::2], idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data[::2], (1, 1)))
|
||
|
|
idx = numpy.indices(data[:, ::2].shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data[:, ::2], idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0], [0, 4], [0, 7]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data[:, ::2], (1, 1)))
|
||
|
|
|
||
|
|
def test_map_coordinates_endianness_with_output_parameter(self):
|
||
|
|
# output parameter given as array or dtype with either endianness
|
||
|
|
# see issue #4127
|
||
|
|
data = numpy.array([[1, 2], [7, 6]])
|
||
|
|
expected = numpy.array([[0, 0], [0, 1]])
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
idx -= 1
|
||
|
|
for out in [data.dtype, data.dtype.newbyteorder(), numpy.empty_like(expected),
|
||
|
|
numpy.empty_like(expected).astype(expected.dtype.newbyteorder())]:
|
||
|
|
returned = ndimage.map_coordinates(data, idx, output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, expected)
|
||
|
|
|
||
|
|
def test_map_coordinates_with_string_output(self):
|
||
|
|
data = numpy.array([[1]])
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
out = ndimage.map_coordinates(data, idx, output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [[1]])
|
||
|
|
|
||
|
|
@pytest.mark.skipif('win32' in sys.platform or numpy.intp(0).itemsize < 8,
|
||
|
|
reason="do not run on 32 bit or windows (no sparse memory)")
|
||
|
|
def test_map_coordinates_large_data(self):
|
||
|
|
# check crash on large data
|
||
|
|
try:
|
||
|
|
n = 30000
|
||
|
|
a = numpy.empty(n**2, dtype=numpy.float32).reshape(n, n)
|
||
|
|
# fill the part we might read
|
||
|
|
a[n-3:, n-3:] = 0
|
||
|
|
ndimage.map_coordinates(a, [[n - 1.5], [n - 1.5]], order=1)
|
||
|
|
except MemoryError:
|
||
|
|
raise pytest.skip("Not enough memory available")
|
||
|
|
|
||
|
|
def test_affine_transform01(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1]], order=order)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_affine_transform02(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1]], order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_affine_transform03(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_affine_transform04(self):
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 4, 1, 3])
|
||
|
|
|
||
|
|
def test_affine_transform05(self):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[0, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]])
|
||
|
|
|
||
|
|
def test_affine_transform06(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[0, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8],
|
||
|
|
[0, 3, 5, 3]])
|
||
|
|
|
||
|
|
def test_affine_transform07(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[-1, 0], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5]])
|
||
|
|
|
||
|
|
def test_affine_transform08(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[-1, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_affine_transform09(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
out = ndimage.affine_transform(filtered, [[1, 0], [0, 1]],
|
||
|
|
[-1, -1], order=order,
|
||
|
|
prefilter=False)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_affine_transform10(self):
|
||
|
|
data = numpy.ones([2], numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], output_shape=(4,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 0])
|
||
|
|
|
||
|
|
def test_affine_transform11(self):
|
||
|
|
data = [1, 5, 2, 6, 3, 7, 4, 4]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[2]], 0, (4,), order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 2, 3, 4])
|
||
|
|
|
||
|
|
def test_affine_transform12(self):
|
||
|
|
data = [1, 2, 3, 4]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], 0, (8,), order=order)
|
||
|
|
assert_array_almost_equal(out[::2], [1, 2, 3, 4])
|
||
|
|
|
||
|
|
def test_affine_transform13(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9.0, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 2]], 0, (3, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3], [5, 7], [9, 11]])
|
||
|
|
|
||
|
|
def test_affine_transform14(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[2, 0], [0, 1]], 0, (1, 4),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 2, 3, 4]])
|
||
|
|
|
||
|
|
def test_affine_transform15(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[2, 0], [0, 2]], 0, (1, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3]])
|
||
|
|
|
||
|
|
def test_affine_transform16(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0.0], [0, 0.5]], 0,
|
||
|
|
(3, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[..., ::2], data)
|
||
|
|
|
||
|
|
def test_affine_transform17(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 1]], 0,
|
||
|
|
(6, 4), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ...], data)
|
||
|
|
|
||
|
|
def test_affine_transform18(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
def test_affine_transform19(self):
|
||
|
|
data = numpy.array([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]], numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
out = ndimage.affine_transform(out, [[2.0, 0], [0, 2.0]], 0,
|
||
|
|
(3, 4), order=order)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
def test_affine_transform20(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0], [2]], 0, (2,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 3])
|
||
|
|
|
||
|
|
def test_affine_transform21(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[2], [0]], 0, (2,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 9])
|
||
|
|
|
||
|
|
def test_affine_transform22(self):
|
||
|
|
# shift and offset interaction; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[2]], [-1], (3,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 2])
|
||
|
|
|
||
|
|
def test_affine_transform23(self):
|
||
|
|
# shift and offset interaction; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], [-1], (8,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2], [0, 4, 1, 3])
|
||
|
|
|
||
|
|
def test_affine_transform24(self):
|
||
|
|
# consistency between diagonal and non-diagonal case; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
"The behavior of affine_transform with a 1-D array .* has changed")
|
||
|
|
out1 = ndimage.affine_transform(data, [2], -1, order=order)
|
||
|
|
out2 = ndimage.affine_transform(data, [[2]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
def test_affine_transform25(self):
|
||
|
|
# consistency between diagonal and non-diagonal case; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
"The behavior of affine_transform with a 1-D array .* has changed")
|
||
|
|
out1 = ndimage.affine_transform(data, [0.5], -1, order=order)
|
||
|
|
out2 = ndimage.affine_transform(data, [[0.5]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
def test_affine_transform26(self):
|
||
|
|
# test homogeneous coordinates
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
tform_original = numpy.eye(2)
|
||
|
|
offset_original = -numpy.ones((2, 1))
|
||
|
|
tform_h1 = numpy.hstack((tform_original, offset_original))
|
||
|
|
tform_h2 = numpy.vstack((tform_h1, [[0, 0, 1]]))
|
||
|
|
out1 = ndimage.affine_transform(filtered, tform_original,
|
||
|
|
offset_original.ravel(),
|
||
|
|
order=order, prefilter=False)
|
||
|
|
out2 = ndimage.affine_transform(filtered, tform_h1, order=order,
|
||
|
|
prefilter=False)
|
||
|
|
out3 = ndimage.affine_transform(filtered, tform_h2, order=order,
|
||
|
|
prefilter=False)
|
||
|
|
for out in [out1, out2, out3]:
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_affine_transform27(self):
|
||
|
|
# test valid homogeneous transformation matrix
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
tform_h1 = numpy.hstack((numpy.eye(2), -numpy.ones((2, 1))))
|
||
|
|
tform_h2 = numpy.vstack((tform_h1, [[5, 2, 1]]))
|
||
|
|
assert_raises(ValueError, ndimage.affine_transform, data, tform_h2)
|
||
|
|
|
||
|
|
def test_affine_transform_1d_endianness_with_output_parameter(self):
|
||
|
|
# 1d affine transform given output ndarray or dtype with
|
||
|
|
# either endianness. see issue #7388
|
||
|
|
data = numpy.ones((2, 2))
|
||
|
|
for out in [numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder()),
|
||
|
|
data.dtype, data.dtype.newbyteorder()]:
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
"The behavior of affine_transform with a 1-D array .* has changed")
|
||
|
|
returned = ndimage.affine_transform(data, [1, 1], output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [[1, 1], [1, 1]])
|
||
|
|
|
||
|
|
def test_affine_transform_multi_d_endianness_with_output_parameter(self):
|
||
|
|
# affine transform given output ndarray or dtype with either endianness
|
||
|
|
# see issue #4127
|
||
|
|
data = numpy.array([1])
|
||
|
|
for out in [data.dtype, data.dtype.newbyteorder(),
|
||
|
|
numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder())]:
|
||
|
|
returned = ndimage.affine_transform(data, [[1]], output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [1])
|
||
|
|
|
||
|
|
def test_affine_transform_with_string_output(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_shift01(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [1], order=order)
|
||
|
|
assert_array_almost_equal(out, [0])
|
||
|
|
|
||
|
|
def test_shift02(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [1], order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_shift03(self):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 0])
|
||
|
|
|
||
|
|
def test_shift04(self):
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, 1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 4, 1, 3])
|
||
|
|
|
||
|
|
def test_shift05(self):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [0, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]])
|
||
|
|
|
||
|
|
def test_shift06(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [0, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8],
|
||
|
|
[0, 3, 5, 3]])
|
||
|
|
|
||
|
|
def test_shift07(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [1, 0], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5]])
|
||
|
|
|
||
|
|
def test_shift08(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.shift(data, [1, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_shift09(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
for order in range(0, 6):
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
out = ndimage.shift(filtered, [1, 1], order=order, prefilter=False)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_zoom1(self):
|
||
|
|
for order in range(0, 6):
|
||
|
|
for z in [2, [2, 2]]:
|
||
|
|
arr = numpy.array(list(range(25))).reshape((5, 5)).astype(float)
|
||
|
|
arr = ndimage.zoom(arr, z, order=order)
|
||
|
|
assert_equal(arr.shape, (10, 10))
|
||
|
|
assert_(numpy.all(arr[-1, :] != 0))
|
||
|
|
assert_(numpy.all(arr[-1, :] >= (20 - eps)))
|
||
|
|
assert_(numpy.all(arr[0, :] <= (5 + eps)))
|
||
|
|
assert_(numpy.all(arr >= (0 - eps)))
|
||
|
|
assert_(numpy.all(arr <= (24 + eps)))
|
||
|
|
|
||
|
|
def test_zoom2(self):
|
||
|
|
arr = numpy.arange(12).reshape((3, 4))
|
||
|
|
out = ndimage.zoom(ndimage.zoom(arr, 2), 0.5)
|
||
|
|
assert_array_equal(out, arr)
|
||
|
|
|
||
|
|
def test_zoom3(self):
|
||
|
|
arr = numpy.array([[1, 2]])
|
||
|
|
out1 = ndimage.zoom(arr, (2, 1))
|
||
|
|
out2 = ndimage.zoom(arr, (1, 2))
|
||
|
|
|
||
|
|
assert_array_almost_equal(out1, numpy.array([[1, 2], [1, 2]]))
|
||
|
|
assert_array_almost_equal(out2, numpy.array([[1, 1, 2, 2]]))
|
||
|
|
|
||
|
|
def test_zoom_affine01(self):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
for order in range(0, 6):
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
"The behavior of affine_transform with a 1-D array .* has changed")
|
||
|
|
out = ndimage.affine_transform(data, [0.5, 0.5], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
def test_zoom_infinity(self):
|
||
|
|
# Ticket #1419 regression test
|
||
|
|
dim = 8
|
||
|
|
ndimage.zoom(numpy.zeros((dim, dim)), 1./dim, mode='nearest')
|
||
|
|
|
||
|
|
def test_zoom_zoomfactor_one(self):
|
||
|
|
# Ticket #1122 regression test
|
||
|
|
arr = numpy.zeros((1, 5, 5))
|
||
|
|
zoom = (1.0, 2.0, 2.0)
|
||
|
|
|
||
|
|
out = ndimage.zoom(arr, zoom, cval=7)
|
||
|
|
ref = numpy.zeros((1, 10, 10))
|
||
|
|
assert_array_almost_equal(out, ref)
|
||
|
|
|
||
|
|
def test_zoom_output_shape_roundoff(self):
|
||
|
|
arr = numpy.zeros((3, 11, 25))
|
||
|
|
zoom = (4.0 / 3, 15.0 / 11, 29.0 / 25)
|
||
|
|
out = ndimage.zoom(arr, zoom)
|
||
|
|
assert_array_equal(out.shape, (4, 15, 29))
|
||
|
|
|
||
|
|
def test_rotate01(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 0)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
def test_rotate02(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate03(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate04(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0]], dtype=numpy.float64)
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90, reshape=False)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate05(self):
|
||
|
|
data = numpy.empty((4, 3, 3))
|
||
|
|
for i in range(3):
|
||
|
|
data[:, :, i] = numpy.array([[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
|
||
|
|
expected = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90)
|
||
|
|
for i in range(3):
|
||
|
|
assert_array_almost_equal(out[:, :, i], expected)
|
||
|
|
|
||
|
|
def test_rotate06(self):
|
||
|
|
data = numpy.empty((3, 4, 3))
|
||
|
|
for i in range(3):
|
||
|
|
data[:, :, i] = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90)
|
||
|
|
for i in range(3):
|
||
|
|
assert_array_almost_equal(out[:, :, i], expected)
|
||
|
|
|
||
|
|
def test_rotate07(self):
|
||
|
|
data = numpy.array([[[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
data = data.transpose()
|
||
|
|
expected = numpy.array([[[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
expected = expected.transpose([2, 1, 0])
|
||
|
|
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90, axes=(0, 1))
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate08(self):
|
||
|
|
data = numpy.array([[[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
data = data.transpose()
|
||
|
|
expected = numpy.array([[[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
expected = expected.transpose()
|
||
|
|
for order in range(0, 6):
|
||
|
|
out = ndimage.rotate(data, 90, axes=(0, 1), reshape=False)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate09(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]] * 2, dtype=numpy.float64)
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
ndimage.rotate(data, 90, axes=(0, data.ndim))
|
||
|
|
|
||
|
|
def test_rotate10(self):
|
||
|
|
data = numpy.arange(45, dtype=numpy.float64).reshape((3, 5, 3))
|
||
|
|
|
||
|
|
# The output of ndimage.rotate before refactoring
|
||
|
|
expected = numpy.array([[[0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0],
|
||
|
|
[6.54914793, 7.54914793, 8.54914793],
|
||
|
|
[10.84520162, 11.84520162, 12.84520162],
|
||
|
|
[0.0, 0.0, 0.0]],
|
||
|
|
[[6.19286575, 7.19286575, 8.19286575],
|
||
|
|
[13.4730712, 14.4730712, 15.4730712],
|
||
|
|
[21.0, 22.0, 23.0],
|
||
|
|
[28.5269288, 29.5269288, 30.5269288],
|
||
|
|
[35.80713425, 36.80713425, 37.80713425]],
|
||
|
|
[[0.0, 0.0, 0.0],
|
||
|
|
[31.15479838, 32.15479838, 33.15479838],
|
||
|
|
[35.45085207, 36.45085207, 37.45085207],
|
||
|
|
[0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0]]])
|
||
|
|
|
||
|
|
out = ndimage.rotate(data, angle=12, reshape=False)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift01(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
expected = [[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift02(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers)
|
||
|
|
expected = [[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, 1, 1, 1, -1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, -1, 1, 1, 1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift03(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 2, 0, 3, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, -1]], numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers)
|
||
|
|
expected = [[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, 2, -1, 3, -1, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, -1, 2, -1, 3, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift04(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 2, 0, 3, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, -1]],
|
||
|
|
numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers,
|
||
|
|
structure=[[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
expected = [[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, 2, 2, 3, 3, 3, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift05(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 3, 0, 2, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, -1]],
|
||
|
|
numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers,
|
||
|
|
structure=[[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
expected = [[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, 3, 3, 2, 2, 2, -1],
|
||
|
|
[-1, 3, 3, 2, 2, 2, -1],
|
||
|
|
[-1, 3, 3, 2, 2, 2, -1],
|
||
|
|
[-1, 3, 3, 2, 2, 2, -1],
|
||
|
|
[-1, 3, 3, 2, 2, 2, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift06(self):
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.int8)
|
||
|
|
out = ndimage.watershed_ift(data, markers,
|
||
|
|
structure=[[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
expected = [[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_watershed_ift07(self):
|
||
|
|
shape = (7, 6)
|
||
|
|
data = numpy.zeros(shape, dtype=numpy.uint8)
|
||
|
|
data = data.transpose()
|
||
|
|
data[...] = numpy.array([[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.uint8)
|
||
|
|
markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], numpy.int8)
|
||
|
|
out = numpy.zeros(shape, dtype=numpy.int16)
|
||
|
|
out = out.transpose()
|
||
|
|
ndimage.watershed_ift(data, markers,
|
||
|
|
structure=[[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]],
|
||
|
|
output=out)
|
||
|
|
expected = [[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_bf01(self):
|
||
|
|
# brute force (bf) distance transform
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_bf(data, 'euclidean',
|
||
|
|
return_indices=True)
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 2, 4, 2, 1, 0, 0],
|
||
|
|
[0, 0, 1, 4, 8, 4, 1, 0, 0],
|
||
|
|
[0, 0, 1, 2, 4, 2, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
assert_array_almost_equal(out * out, expected)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 2, 1, 2, 2, 2, 2],
|
||
|
|
[3, 3, 3, 2, 1, 2, 3, 3, 3],
|
||
|
|
[4, 4, 4, 4, 6, 4, 4, 4, 4],
|
||
|
|
[5, 5, 6, 6, 7, 6, 6, 5, 5],
|
||
|
|
[6, 6, 6, 7, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 2, 4, 6, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 6, 7, 7, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_bf02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_bf(data, 'cityblock',
|
||
|
|
return_indices=True)
|
||
|
|
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 2, 2, 2, 1, 0, 0],
|
||
|
|
[0, 0, 1, 2, 3, 2, 1, 0, 0],
|
||
|
|
[0, 0, 1, 2, 2, 2, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 2, 1, 2, 2, 2, 2],
|
||
|
|
[3, 3, 3, 3, 1, 3, 3, 3, 3],
|
||
|
|
[4, 4, 4, 4, 7, 4, 4, 4, 4],
|
||
|
|
[5, 5, 6, 7, 7, 7, 6, 5, 5],
|
||
|
|
[6, 6, 6, 7, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 1, 4, 7, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 4, 7, 7, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(expected, ft)
|
||
|
|
|
||
|
|
def test_distance_transform_bf03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_bf(data, 'chessboard',
|
||
|
|
return_indices=True)
|
||
|
|
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 2, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 2, 2, 2, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 2, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 2, 1, 2, 2, 2, 2],
|
||
|
|
[3, 3, 4, 2, 2, 2, 4, 3, 3],
|
||
|
|
[4, 4, 5, 6, 6, 6, 5, 4, 4],
|
||
|
|
[5, 5, 6, 6, 7, 6, 6, 5, 5],
|
||
|
|
[6, 6, 6, 7, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 5, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 2, 6, 6, 7, 7, 8],
|
||
|
|
[0, 1, 1, 2, 6, 7, 7, 7, 8],
|
||
|
|
[0, 1, 2, 2, 6, 6, 7, 7, 8],
|
||
|
|
[0, 1, 2, 4, 5, 6, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_bf04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
tdt, tft = ndimage.distance_transform_bf(data, return_indices=1)
|
||
|
|
dts = []
|
||
|
|
fts = []
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ndimage.distance_transform_bf(data, distances=dt)
|
||
|
|
dts.append(dt)
|
||
|
|
ft = ndimage.distance_transform_bf(
|
||
|
|
data, return_distances=False, return_indices=1)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_bf(
|
||
|
|
data, return_distances=False, return_indices=True, indices=ft)
|
||
|
|
fts.append(ft)
|
||
|
|
dt, ft = ndimage.distance_transform_bf(
|
||
|
|
data, return_indices=1)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ft = ndimage.distance_transform_bf(
|
||
|
|
data, distances=dt, return_indices=True)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
dt = ndimage.distance_transform_bf(
|
||
|
|
data, return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_bf(
|
||
|
|
data, distances=dt, return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
for dt in dts:
|
||
|
|
assert_array_almost_equal(tdt, dt)
|
||
|
|
for ft in fts:
|
||
|
|
assert_array_almost_equal(tft, ft)
|
||
|
|
|
||
|
|
def test_distance_transform_bf05(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_bf(
|
||
|
|
data, 'euclidean', return_indices=True, sampling=[2, 2])
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 4, 4, 4, 0, 0, 0],
|
||
|
|
[0, 0, 4, 8, 16, 8, 4, 0, 0],
|
||
|
|
[0, 0, 4, 16, 32, 16, 4, 0, 0],
|
||
|
|
[0, 0, 4, 8, 16, 8, 4, 0, 0],
|
||
|
|
[0, 0, 0, 4, 4, 4, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
assert_array_almost_equal(out * out, expected)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 2, 1, 2, 2, 2, 2],
|
||
|
|
[3, 3, 3, 2, 1, 2, 3, 3, 3],
|
||
|
|
[4, 4, 4, 4, 6, 4, 4, 4, 4],
|
||
|
|
[5, 5, 6, 6, 7, 6, 6, 5, 5],
|
||
|
|
[6, 6, 6, 7, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 2, 4, 6, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 6, 7, 7, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_bf06(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_bf(
|
||
|
|
data, 'euclidean', return_indices=True, sampling=[2, 1])
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 4, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 4, 8, 4, 1, 0, 0],
|
||
|
|
[0, 0, 1, 4, 9, 4, 1, 0, 0],
|
||
|
|
[0, 0, 1, 4, 8, 4, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 4, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
assert_array_almost_equal(out * out, expected)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 2, 2, 2, 2, 2, 2],
|
||
|
|
[3, 3, 3, 3, 2, 3, 3, 3, 3],
|
||
|
|
[4, 4, 4, 4, 4, 4, 4, 4, 4],
|
||
|
|
[5, 5, 5, 5, 6, 5, 5, 5, 5],
|
||
|
|
[6, 6, 6, 6, 7, 6, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 6, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 1, 6, 7, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 7, 7, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 6, 7, 7, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 6, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_cdt01(self):
|
||
|
|
# chamfer type distance (cdt) transform
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_cdt(
|
||
|
|
data, 'cityblock', return_indices=True)
|
||
|
|
bf = ndimage.distance_transform_bf(data, 'cityblock')
|
||
|
|
assert_array_almost_equal(bf, out)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 1, 1, 1, 2, 2, 2],
|
||
|
|
[3, 3, 2, 1, 1, 1, 2, 3, 3],
|
||
|
|
[4, 4, 4, 4, 1, 4, 4, 4, 4],
|
||
|
|
[5, 5, 5, 5, 7, 7, 6, 5, 5],
|
||
|
|
[6, 6, 6, 6, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 1, 1, 4, 7, 7, 7, 8],
|
||
|
|
[0, 1, 1, 1, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_cdt02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_cdt(data, 'chessboard',
|
||
|
|
return_indices=True)
|
||
|
|
bf = ndimage.distance_transform_bf(data, 'chessboard')
|
||
|
|
assert_array_almost_equal(bf, out)
|
||
|
|
|
||
|
|
expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[2, 2, 2, 1, 1, 1, 2, 2, 2],
|
||
|
|
[3, 3, 2, 2, 1, 2, 2, 3, 3],
|
||
|
|
[4, 4, 3, 2, 2, 2, 3, 4, 4],
|
||
|
|
[5, 5, 4, 6, 7, 6, 4, 5, 5],
|
||
|
|
[6, 6, 6, 6, 7, 7, 6, 6, 6],
|
||
|
|
[7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||
|
|
[8, 8, 8, 8, 8, 8, 8, 8, 8]],
|
||
|
|
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 2, 3, 4, 6, 7, 8],
|
||
|
|
[0, 1, 1, 2, 2, 6, 6, 7, 8],
|
||
|
|
[0, 1, 1, 1, 2, 6, 7, 7, 8],
|
||
|
|
[0, 1, 1, 2, 6, 6, 7, 7, 8],
|
||
|
|
[0, 1, 2, 2, 5, 6, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8],
|
||
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8]]]
|
||
|
|
assert_array_almost_equal(ft, expected)
|
||
|
|
|
||
|
|
def test_distance_transform_cdt03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
tdt, tft = ndimage.distance_transform_cdt(data, return_indices=True)
|
||
|
|
dts = []
|
||
|
|
fts = []
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_cdt(data, distances=dt)
|
||
|
|
dts.append(dt)
|
||
|
|
ft = ndimage.distance_transform_cdt(
|
||
|
|
data, return_distances=False, return_indices=True)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_cdt(
|
||
|
|
data, return_distances=False, return_indices=True, indices=ft)
|
||
|
|
fts.append(ft)
|
||
|
|
dt, ft = ndimage.distance_transform_cdt(
|
||
|
|
data, return_indices=True)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.int32)
|
||
|
|
ft = ndimage.distance_transform_cdt(
|
||
|
|
data, distances=dt, return_indices=True)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
dt = ndimage.distance_transform_cdt(
|
||
|
|
data, return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.int32)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_cdt(data, distances=dt,
|
||
|
|
return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
for dt in dts:
|
||
|
|
assert_array_almost_equal(tdt, dt)
|
||
|
|
for ft in fts:
|
||
|
|
assert_array_almost_equal(tft, ft)
|
||
|
|
|
||
|
|
def test_distance_transform_edt01(self):
|
||
|
|
# euclidean distance transform (edt)
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out, ft = ndimage.distance_transform_edt(data, return_indices=True)
|
||
|
|
bf = ndimage.distance_transform_bf(data, 'euclidean')
|
||
|
|
assert_array_almost_equal(bf, out)
|
||
|
|
|
||
|
|
dt = ft - numpy.indices(ft.shape[1:], dtype=ft.dtype)
|
||
|
|
dt = dt.astype(numpy.float64)
|
||
|
|
numpy.multiply(dt, dt, dt)
|
||
|
|
dt = numpy.add.reduce(dt, axis=0)
|
||
|
|
numpy.sqrt(dt, dt)
|
||
|
|
|
||
|
|
assert_array_almost_equal(bf, dt)
|
||
|
|
|
||
|
|
def test_distance_transform_edt02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
tdt, tft = ndimage.distance_transform_edt(data, return_indices=True)
|
||
|
|
dts = []
|
||
|
|
fts = []
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ndimage.distance_transform_edt(data, distances=dt)
|
||
|
|
dts.append(dt)
|
||
|
|
ft = ndimage.distance_transform_edt(
|
||
|
|
data, return_distances=0, return_indices=True)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_edt(
|
||
|
|
data, return_distances=False, return_indices=True, indices=ft)
|
||
|
|
fts.append(ft)
|
||
|
|
dt, ft = ndimage.distance_transform_edt(
|
||
|
|
data, return_indices=True)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ft = ndimage.distance_transform_edt(
|
||
|
|
data, distances=dt, return_indices=True)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
dt = ndimage.distance_transform_edt(
|
||
|
|
data, return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
dt = numpy.zeros(data.shape, dtype=numpy.float64)
|
||
|
|
ft = numpy.indices(data.shape, dtype=numpy.int32)
|
||
|
|
ndimage.distance_transform_edt(
|
||
|
|
data, distances=dt, return_indices=True, indices=ft)
|
||
|
|
dts.append(dt)
|
||
|
|
fts.append(ft)
|
||
|
|
for dt in dts:
|
||
|
|
assert_array_almost_equal(tdt, dt)
|
||
|
|
for ft in fts:
|
||
|
|
assert_array_almost_equal(tft, ft)
|
||
|
|
|
||
|
|
def test_distance_transform_edt03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 2])
|
||
|
|
out = ndimage.distance_transform_edt(data, sampling=[2, 2])
|
||
|
|
assert_array_almost_equal(ref, out)
|
||
|
|
|
||
|
|
def test_distance_transform_edt4(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 1])
|
||
|
|
out = ndimage.distance_transform_edt(data, sampling=[2, 1])
|
||
|
|
assert_array_almost_equal(ref, out)
|
||
|
|
|
||
|
|
def test_distance_transform_edt5(self):
|
||
|
|
# Ticket #954 regression test
|
||
|
|
out = ndimage.distance_transform_edt(False)
|
||
|
|
assert_array_almost_equal(out, [0.])
|
||
|
|
|
||
|
|
def test_generate_structure01(self):
|
||
|
|
struct = ndimage.generate_binary_structure(0, 1)
|
||
|
|
assert_array_almost_equal(struct, 1)
|
||
|
|
|
||
|
|
def test_generate_structure02(self):
|
||
|
|
struct = ndimage.generate_binary_structure(1, 1)
|
||
|
|
assert_array_almost_equal(struct, [1, 1, 1])
|
||
|
|
|
||
|
|
def test_generate_structure03(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 1)
|
||
|
|
assert_array_almost_equal(struct, [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]])
|
||
|
|
|
||
|
|
def test_generate_structure04(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
assert_array_almost_equal(struct, [[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_iterate_structure01(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
out = ndimage.iterate_structure(struct, 2)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0]])
|
||
|
|
|
||
|
|
def test_iterate_structure02(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1],
|
||
|
|
[0, 1]]
|
||
|
|
out = ndimage.iterate_structure(struct, 2)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 1],
|
||
|
|
[0, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 1],
|
||
|
|
[0, 0, 1]])
|
||
|
|
|
||
|
|
def test_iterate_structure03(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
out = ndimage.iterate_structure(struct, 2, 1)
|
||
|
|
expected = [[0, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0]]
|
||
|
|
assert_array_almost_equal(out[0], expected)
|
||
|
|
assert_equal(out[1], [2, 2])
|
||
|
|
|
||
|
|
def test_binary_erosion01(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, 1)
|
||
|
|
|
||
|
|
def test_binary_erosion02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, 1)
|
||
|
|
|
||
|
|
def test_binary_erosion03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [0])
|
||
|
|
|
||
|
|
def test_binary_erosion04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_binary_erosion05(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_erosion06(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion07(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_erosion08(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion09(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [0, 0, 0, 0, 0])
|
||
|
|
|
||
|
|
def test_binary_erosion10(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 0, 0, 0, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion11(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 0, 1, 0, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion12(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
origin=-1)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 0, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion13(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
origin=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 0, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_erosion14(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
struct = [1, 1]
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 0, 0, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion15(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([5], type_)
|
||
|
|
data[2] = 0
|
||
|
|
struct = [1, 1]
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
origin=-1)
|
||
|
|
assert_array_almost_equal(out, [1, 0, 0, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_erosion16(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 1], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [[1]])
|
||
|
|
|
||
|
|
def test_binary_erosion17(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 1], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [[0]])
|
||
|
|
|
||
|
|
def test_binary_erosion18(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 3], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0]])
|
||
|
|
|
||
|
|
def test_binary_erosion19(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 3], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_binary_erosion20(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3, 3], type_)
|
||
|
|
out = ndimage.binary_erosion(data)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]])
|
||
|
|
|
||
|
|
def test_binary_erosion21(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3, 3], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_binary_erosion22(self):
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_erosion(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion23(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion24(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion25(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 0, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion26(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 0, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 1],
|
||
|
|
[0, 0, 0, 0, 1, 0, 0, 1],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 1]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
origin=(-1, -1))
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion27(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=2)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion28(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=2, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion29(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_erosion(data, struct,
|
||
|
|
border_value=1, iterations=3)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion30(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=3, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
# test with output memory overlap
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=3, output=data)
|
||
|
|
assert_array_almost_equal(data, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion31(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 0, 1],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 1]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=1, output=out, origin=(-1, -1))
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion32(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_erosion(data, struct,
|
||
|
|
border_value=1, iterations=2)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion33(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 1, 1],
|
||
|
|
[0, 0, 0, 0, 0, 0, 1],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
mask = [[1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 1, 1],
|
||
|
|
[0, 0, 0, 1, 0, 0, 1],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_erosion(data, struct,
|
||
|
|
border_value=1, mask=mask, iterations=-1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion34(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
mask = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_erosion(data, struct,
|
||
|
|
border_value=1, mask=mask)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion35(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
mask = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
tmp = [[0, 0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 0, 1],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 1]]
|
||
|
|
expected = numpy.logical_and(tmp, mask)
|
||
|
|
tmp = numpy.logical_and(data, numpy.logical_not(mask))
|
||
|
|
expected = numpy.logical_or(expected, tmp)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=1, output=out,
|
||
|
|
origin=(-1, -1), mask=mask)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion36(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 0, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
mask = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
tmp = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 1],
|
||
|
|
[0, 0, 0, 0, 1, 0, 0, 1],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 1]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]])
|
||
|
|
expected = numpy.logical_and(tmp, mask)
|
||
|
|
tmp = numpy.logical_and(data, numpy.logical_not(mask))
|
||
|
|
expected = numpy.logical_or(expected, tmp)
|
||
|
|
out = ndimage.binary_erosion(data, struct, mask=mask,
|
||
|
|
border_value=1, origin=(-1, -1))
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion37(self):
|
||
|
|
a = numpy.array([[1, 0, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[1, 0, 1]], dtype=bool)
|
||
|
|
b = numpy.zeros_like(a)
|
||
|
|
out = ndimage.binary_erosion(a, structure=a, output=b, iterations=0,
|
||
|
|
border_value=True, brute_force=True)
|
||
|
|
assert_(out is b)
|
||
|
|
assert_array_equal(
|
||
|
|
ndimage.binary_erosion(a, structure=a, iterations=0,
|
||
|
|
border_value=True),
|
||
|
|
b)
|
||
|
|
|
||
|
|
def test_binary_erosion38(self):
|
||
|
|
data = numpy.array([[1, 0, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[1, 0, 1]], dtype=bool)
|
||
|
|
iterations = 2.0
|
||
|
|
with assert_raises(TypeError):
|
||
|
|
_ = ndimage.binary_erosion(data, iterations=iterations)
|
||
|
|
|
||
|
|
def test_binary_erosion39(self):
|
||
|
|
iterations = numpy.int32(3)
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=iterations, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_erosion40(self):
|
||
|
|
iterations = numpy.int64(3)
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
data = numpy.array([[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_erosion(data, struct, border_value=1,
|
||
|
|
iterations=iterations, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation01(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, 1)
|
||
|
|
|
||
|
|
def test_binary_dilation02(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, 0)
|
||
|
|
|
||
|
|
def test_binary_dilation03(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
def test_binary_dilation04(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([1], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [0])
|
||
|
|
|
||
|
|
def test_binary_dilation05(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_dilation06(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([3], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [0, 0, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation07(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([3], type_)
|
||
|
|
data[1] = 1
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_dilation08(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
data[3] = 1
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1, 1])
|
||
|
|
|
||
|
|
def test_binary_dilation09(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 0, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation10(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
out = ndimage.binary_dilation(data, origin=-1)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation11(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
out = ndimage.binary_dilation(data, origin=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 0, 0, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation12(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_dilation(data, struct)
|
||
|
|
assert_array_almost_equal(out, [1, 0, 1, 0, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation13(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_dilation(data, struct, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 0, 1, 0, 1])
|
||
|
|
|
||
|
|
def test_binary_dilation14(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_dilation(data, struct, origin=-1)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 0, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation15(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([5], type_)
|
||
|
|
data[1] = 1
|
||
|
|
struct = [1, 0, 1]
|
||
|
|
out = ndimage.binary_dilation(data, struct,
|
||
|
|
origin=-1, border_value=1)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 0, 1, 0])
|
||
|
|
|
||
|
|
def test_binary_dilation16(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 1], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [[1]])
|
||
|
|
|
||
|
|
def test_binary_dilation17(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([1, 1], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [[0]])
|
||
|
|
|
||
|
|
def test_binary_dilation18(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([1, 3], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_binary_dilation19(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.ones([3, 3], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_binary_dilation20(self):
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([3, 3], type_)
|
||
|
|
data[1, 1] = 1
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]])
|
||
|
|
|
||
|
|
def test_binary_dilation21(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.zeros([3, 3], type_)
|
||
|
|
data[1, 1] = 1
|
||
|
|
out = ndimage.binary_dilation(data, struct)
|
||
|
|
assert_array_almost_equal(out, [[1, 1, 1],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 1, 1]])
|
||
|
|
|
||
|
|
def test_binary_dilation22(self):
|
||
|
|
expected = [[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation23(self):
|
||
|
|
expected = [[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 1],
|
||
|
|
[1, 1, 0, 0, 0, 1, 0, 1],
|
||
|
|
[1, 0, 0, 1, 1, 1, 1, 1],
|
||
|
|
[1, 0, 1, 1, 1, 1, 0, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 0, 1, 0, 0, 1, 0, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation24(self):
|
||
|
|
expected = [[1, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, origin=(1, 1))
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation25(self):
|
||
|
|
expected = [[1, 1, 0, 0, 0, 0, 1, 1],
|
||
|
|
[1, 0, 0, 0, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 0, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 0, 0, 1, 0, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, origin=(1, 1), border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation26(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
expected = [[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, struct)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation27(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, struct)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation28(self):
|
||
|
|
expected = [[1, 1, 1, 1],
|
||
|
|
[1, 0, 0, 1],
|
||
|
|
[1, 0, 0, 1],
|
||
|
|
[1, 1, 1, 1]]
|
||
|
|
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation29(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_dilation(data, struct, iterations=2)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation30(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_dilation(data, struct, iterations=2, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation31(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_dilation(data, struct, iterations=3)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation32(self):
|
||
|
|
struct = [[0, 1],
|
||
|
|
[1, 1]]
|
||
|
|
expected = [[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]], bool)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_dilation(data, struct, iterations=3, output=out)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation33(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
|
||
|
|
out = ndimage.binary_dilation(data, struct, iterations=-1,
|
||
|
|
mask=mask, border_value=0)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation34(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.zeros(mask.shape, bool)
|
||
|
|
out = ndimage.binary_dilation(data, struct, iterations=-1,
|
||
|
|
mask=mask, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_dilation35(self):
|
||
|
|
tmp = [[1, 1, 0, 0, 0, 0, 1, 1],
|
||
|
|
[1, 0, 0, 0, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1, 0, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[0, 1, 0, 0, 1, 0, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 1]]
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]])
|
||
|
|
mask = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
expected = numpy.logical_and(tmp, mask)
|
||
|
|
tmp = numpy.logical_and(data, numpy.logical_not(mask))
|
||
|
|
expected = numpy.logical_or(expected, tmp)
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_dilation(data, mask=mask,
|
||
|
|
origin=(1, 1), border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_propagation01(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
|
||
|
|
out = ndimage.binary_propagation(data, struct,
|
||
|
|
mask=mask, border_value=0)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_propagation02(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.zeros(mask.shape, bool)
|
||
|
|
out = ndimage.binary_propagation(data, struct,
|
||
|
|
mask=mask, border_value=1)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_opening01(self):
|
||
|
|
expected = [[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_opening(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_opening02(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
expected = [[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_opening(data, struct)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_closing01(self):
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0, 1, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_closing(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_closing02(self):
|
||
|
|
struct = ndimage.generate_binary_structure(2, 2)
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[1, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_closing(data, struct)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_fill_holes01(self):
|
||
|
|
expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_fill_holes(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_fill_holes02(self):
|
||
|
|
expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_fill_holes(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_binary_fill_holes03(self):
|
||
|
|
expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 1],
|
||
|
|
[0, 0, 1, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 1, 0, 1, 1, 1],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0, 1],
|
||
|
|
[0, 1, 0, 1, 0, 1, 0, 1],
|
||
|
|
[0, 0, 1, 0, 0, 1, 1, 1],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], bool)
|
||
|
|
out = ndimage.binary_fill_holes(data)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_grey_erosion01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
output = ndimage.grey_erosion(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1]], output)
|
||
|
|
|
||
|
|
def test_grey_erosion01_overlap(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
ndimage.grey_erosion(array, footprint=footprint, output=array)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1]], array)
|
||
|
|
|
||
|
|
def test_grey_erosion02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
output = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal([[2, 2, 1, 1, 1],
|
||
|
|
[2, 3, 1, 3, 1],
|
||
|
|
[5, 5, 3, 3, 1]], output)
|
||
|
|
|
||
|
|
def test_grey_erosion03(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[1, 1, 1], [1, 1, 1]]
|
||
|
|
output = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal([[1, 1, 0, 0, 0],
|
||
|
|
[1, 2, 0, 2, 0],
|
||
|
|
[4, 4, 2, 2, 0]], output)
|
||
|
|
|
||
|
|
def test_grey_dilation01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[0, 1, 1], [1, 0, 1]]
|
||
|
|
output = ndimage.grey_dilation(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal([[7, 7, 9, 9, 5],
|
||
|
|
[7, 9, 8, 9, 7],
|
||
|
|
[8, 8, 8, 7, 7]], output)
|
||
|
|
|
||
|
|
def test_grey_dilation02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[0, 1, 1], [1, 0, 1]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
output = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal([[7, 7, 9, 9, 5],
|
||
|
|
[7, 9, 8, 9, 7],
|
||
|
|
[8, 8, 8, 7, 7]], output)
|
||
|
|
|
||
|
|
def test_grey_dilation03(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[0, 1, 1], [1, 0, 1]]
|
||
|
|
structure = [[1, 1, 1], [1, 1, 1]]
|
||
|
|
output = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal([[8, 8, 10, 10, 6],
|
||
|
|
[8, 10, 9, 10, 8],
|
||
|
|
[9, 9, 9, 8, 8]], output)
|
||
|
|
|
||
|
|
def test_grey_opening01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
tmp = ndimage.grey_erosion(array, footprint=footprint)
|
||
|
|
expected = ndimage.grey_dilation(tmp, footprint=footprint)
|
||
|
|
output = ndimage.grey_opening(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_grey_opening02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = ndimage.grey_dilation(tmp, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
output = ndimage.grey_opening(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_grey_closing01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
tmp = ndimage.grey_dilation(array, footprint=footprint)
|
||
|
|
expected = ndimage.grey_erosion(tmp, footprint=footprint)
|
||
|
|
output = ndimage.grey_closing(array, footprint=footprint)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_grey_closing02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = ndimage.grey_erosion(tmp, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
output = ndimage.grey_closing(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_morphological_gradient01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp1 = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
tmp2 = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp1 - tmp2
|
||
|
|
output = numpy.zeros(array.shape, array.dtype)
|
||
|
|
ndimage.morphological_gradient(array, footprint=footprint,
|
||
|
|
structure=structure, output=output)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_morphological_gradient02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp1 = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
tmp2 = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp1 - tmp2
|
||
|
|
output = ndimage.morphological_gradient(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_morphological_laplace01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp1 = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
tmp2 = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp1 + tmp2 - 2 * array
|
||
|
|
output = numpy.zeros(array.shape, array.dtype)
|
||
|
|
ndimage.morphological_laplace(array, footprint=footprint,
|
||
|
|
structure=structure, output=output)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_morphological_laplace02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp1 = ndimage.grey_dilation(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
tmp2 = ndimage.grey_erosion(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp1 + tmp2 - 2 * array
|
||
|
|
output = ndimage.morphological_laplace(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_white_tophat01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_opening(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = array - tmp
|
||
|
|
output = numpy.zeros(array.shape, array.dtype)
|
||
|
|
ndimage.white_tophat(array, footprint=footprint,
|
||
|
|
structure=structure, output=output)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_white_tophat02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_opening(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = array - tmp
|
||
|
|
output = ndimage.white_tophat(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_white_tophat03(self):
|
||
|
|
array = numpy.array([[1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 1]], dtype=numpy.bool_)
|
||
|
|
structure = numpy.ones((3, 3), dtype=numpy.bool_)
|
||
|
|
expected = numpy.array([[0, 1, 1, 0, 0, 0, 0],
|
||
|
|
[1, 0, 0, 1, 1, 1, 0],
|
||
|
|
[1, 0, 0, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 0, 0, 0, 1],
|
||
|
|
[0, 1, 1, 0, 1, 0, 1],
|
||
|
|
[0, 1, 1, 0, 0, 0, 1],
|
||
|
|
[0, 0, 0, 1, 1, 1, 1]], dtype=numpy.bool_)
|
||
|
|
|
||
|
|
output = ndimage.white_tophat(array, structure=structure)
|
||
|
|
assert_array_equal(expected, output)
|
||
|
|
|
||
|
|
def test_white_tophat04(self):
|
||
|
|
array = numpy.eye(5, dtype=numpy.bool_)
|
||
|
|
structure = numpy.ones((3, 3), dtype=numpy.bool_)
|
||
|
|
|
||
|
|
# Check that type mismatch is properly handled
|
||
|
|
output = numpy.empty_like(array, dtype=numpy.float64)
|
||
|
|
ndimage.white_tophat(array, structure=structure, output=output)
|
||
|
|
|
||
|
|
def test_black_tophat01(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_closing(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp - array
|
||
|
|
output = numpy.zeros(array.shape, array.dtype)
|
||
|
|
ndimage.black_tophat(array, footprint=footprint,
|
||
|
|
structure=structure, output=output)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_black_tophat02(self):
|
||
|
|
array = numpy.array([[3, 2, 5, 1, 4],
|
||
|
|
[7, 6, 9, 3, 5],
|
||
|
|
[5, 8, 3, 7, 1]])
|
||
|
|
footprint = [[1, 0, 1], [1, 1, 0]]
|
||
|
|
structure = [[0, 0, 0], [0, 0, 0]]
|
||
|
|
tmp = ndimage.grey_closing(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
expected = tmp - array
|
||
|
|
output = ndimage.black_tophat(array, footprint=footprint,
|
||
|
|
structure=structure)
|
||
|
|
assert_array_almost_equal(expected, output)
|
||
|
|
|
||
|
|
def test_black_tophat03(self):
|
||
|
|
array = numpy.array([[1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 1]], dtype=numpy.bool_)
|
||
|
|
structure = numpy.ones((3, 3), dtype=numpy.bool_)
|
||
|
|
expected = numpy.array([[0, 1, 1, 1, 1, 1, 1],
|
||
|
|
[1, 0, 0, 0, 0, 0, 1],
|
||
|
|
[1, 0, 0, 0, 0, 0, 1],
|
||
|
|
[1, 0, 0, 0, 0, 0, 1],
|
||
|
|
[1, 0, 0, 0, 1, 0, 1],
|
||
|
|
[1, 0, 0, 0, 0, 0, 1],
|
||
|
|
[1, 1, 1, 1, 1, 1, 0]], dtype=numpy.bool_)
|
||
|
|
|
||
|
|
output = ndimage.black_tophat(array, structure=structure)
|
||
|
|
assert_array_equal(expected, output)
|
||
|
|
|
||
|
|
def test_black_tophat04(self):
|
||
|
|
array = numpy.eye(5, dtype=numpy.bool_)
|
||
|
|
structure = numpy.ones((3, 3), dtype=numpy.bool_)
|
||
|
|
|
||
|
|
# Check that type mismatch is properly handled
|
||
|
|
output = numpy.empty_like(array, dtype=numpy.float64)
|
||
|
|
ndimage.black_tophat(array, structure=structure, output=output)
|
||
|
|
|
||
|
|
def test_hit_or_miss01(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 0],
|
||
|
|
[1, 1, 1, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1],
|
||
|
|
[0, 0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1, 1],
|
||
|
|
[0, 0, 0, 0, 0]], type_)
|
||
|
|
out = numpy.zeros(data.shape, bool)
|
||
|
|
ndimage.binary_hit_or_miss(data, struct, output=out)
|
||
|
|
assert_array_almost_equal(expected, out)
|
||
|
|
|
||
|
|
def test_hit_or_miss02(self):
|
||
|
|
struct = [[0, 1, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 1, 0]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 0, 0, 1, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_hit_or_miss(data, struct)
|
||
|
|
assert_array_almost_equal(expected, out)
|
||
|
|
|
||
|
|
def test_hit_or_miss03(self):
|
||
|
|
struct1 = [[0, 0, 0],
|
||
|
|
[1, 1, 1],
|
||
|
|
[0, 0, 0]]
|
||
|
|
struct2 = [[1, 1, 1],
|
||
|
|
[0, 0, 0],
|
||
|
|
[1, 1, 1]]
|
||
|
|
expected = [[0, 0, 0, 0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]]
|
||
|
|
for type_ in self.types:
|
||
|
|
data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 1, 1, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], type_)
|
||
|
|
out = ndimage.binary_hit_or_miss(data, struct1, struct2)
|
||
|
|
assert_array_almost_equal(expected, out)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDilateFix:
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
# dilation related setup
|
||
|
|
self.array = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0],
|
||
|
|
[0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0]], dtype=numpy.uint8)
|
||
|
|
|
||
|
|
self.sq3x3 = numpy.ones((3, 3))
|
||
|
|
dilated3x3 = ndimage.binary_dilation(self.array, structure=self.sq3x3)
|
||
|
|
self.dilated3x3 = dilated3x3.view(numpy.uint8)
|
||
|
|
|
||
|
|
def test_dilation_square_structure(self):
|
||
|
|
result = ndimage.grey_dilation(self.array, structure=self.sq3x3)
|
||
|
|
# +1 accounts for difference between grey and binary dilation
|
||
|
|
assert_array_almost_equal(result, self.dilated3x3 + 1)
|
||
|
|
|
||
|
|
def test_dilation_scalar_size(self):
|
||
|
|
result = ndimage.grey_dilation(self.array, size=3)
|
||
|
|
assert_array_almost_equal(result, self.dilated3x3)
|
||
|
|
|
||
|
|
class TestBinaryOpeningClosing:
|
||
|
|
|
||
|
|
def setup_method(self):
|
||
|
|
a = numpy.zeros((5,5), dtype=bool)
|
||
|
|
a[1:4, 1:4] = True
|
||
|
|
a[4,4] = True
|
||
|
|
self.array = a
|
||
|
|
self.sq3x3 = numpy.ones((3,3))
|
||
|
|
self.opened_old = ndimage.binary_opening(self.array, self.sq3x3,
|
||
|
|
1, None, 0)
|
||
|
|
self.closed_old = ndimage.binary_closing(self.array, self.sq3x3,
|
||
|
|
1, None, 0)
|
||
|
|
|
||
|
|
def test_opening_new_arguments(self):
|
||
|
|
opened_new = ndimage.binary_opening(self.array, self.sq3x3, 1, None,
|
||
|
|
0, None, 0, False)
|
||
|
|
assert_array_equal(opened_new, self.opened_old)
|
||
|
|
|
||
|
|
def test_closing_new_arguments(self):
|
||
|
|
closed_new = ndimage.binary_closing(self.array, self.sq3x3, 1, None,
|
||
|
|
0, None, 0, False)
|
||
|
|
assert_array_equal(closed_new, self.closed_old)
|