diff options
Diffstat (limited to 'silx/math/medianfilter/test/test_medianfilter.py')
| -rw-r--r-- | silx/math/medianfilter/test/test_medianfilter.py | 740 |
1 files changed, 0 insertions, 740 deletions
diff --git a/silx/math/medianfilter/test/test_medianfilter.py b/silx/math/medianfilter/test/test_medianfilter.py deleted file mode 100644 index 3a45b3d..0000000 --- a/silx/math/medianfilter/test/test_medianfilter.py +++ /dev/null @@ -1,740 +0,0 @@ -# coding: utf-8 -# ########################################################################## -# Copyright (C) 2017-2018 European Synchrotron Radiation Facility -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. -# -# ############################################################################ -"""Tests of the median filter""" - -__authors__ = ["H. Payno"] -__license__ = "MIT" -__date__ = "17/01/2018" - -import unittest -import numpy -from silx.math.medianfilter import medfilt2d, medfilt1d -from silx.math.medianfilter.medianfilter import reflect, mirror -from silx.math.medianfilter.medianfilter import MODES as silx_mf_modes -from silx.utils.testutils import ParametricTestCase -try: - import scipy - import scipy.misc -except: - scipy = None -else: - import scipy.ndimage - -import logging -_logger = logging.getLogger(__name__) - -RANDOM_FLOAT_MAT = numpy.array([ - [0.05564293, 0.62717157, 0.75002406, 0.40555336, 0.70278975], - [0.76532598, 0.02839148, 0.05272484, 0.65166994, 0.42161216], - [0.23067427, 0.74219128, 0.56049024, 0.44406320, 0.28773158], - [0.81025249, 0.20303021, 0.68382382, 0.46372299, 0.81281709], - [0.94691602, 0.07813661, 0.81651256, 0.84220106, 0.33623165]]) - -RANDOM_INT_MAT = numpy.array([ - [0, 5, 2, 6, 1], - [2, 3, 1, 7, 1], - [9, 8, 6, 7, 8], - [5, 6, 8, 2, 4]]) - - -class TestMedianFilterNearest(ParametricTestCase): - """Unit tests for the median filter in nearest mode""" - - def testFilter3_100(self): - """Test median filter on a 10x10 matrix with a 3x3 kernel.""" - dataIn = numpy.arange(100, dtype=numpy.int32) - dataIn = dataIn.reshape((10, 10)) - - dataOut = medfilt2d(image=dataIn, - kernel_size=(3, 3), - conditional=False, - mode='nearest') - self.assertTrue(dataOut[0, 0] == 1) - self.assertTrue(dataOut[9, 0] == 90) - self.assertTrue(dataOut[9, 9] == 98) - - self.assertTrue(dataOut[0, 9] == 9) - self.assertTrue(dataOut[0, 4] == 5) - self.assertTrue(dataOut[9, 4] == 93) - self.assertTrue(dataOut[4, 4] == 44) - - def testFilter3_9(self): - "Test median filter on a 3x3 matrix with a 3x3 kernel." - dataIn = numpy.array([0, -1, 1, - 12, 6, -2, - 100, 4, 12], - dtype=numpy.int16) - dataIn = dataIn.reshape((3, 3)) - dataOut = medfilt2d(image=dataIn, - kernel_size=(3, 3), - conditional=False, - mode='nearest') - self.assertTrue(dataOut.shape == dataIn.shape) - self.assertTrue(dataOut[1, 1] == 4) - self.assertTrue(dataOut[0, 0] == 0) - self.assertTrue(dataOut[0, 1] == 0) - self.assertTrue(dataOut[1, 0] == 6) - - def testFilterWidthOne(self): - """Make sure a filter of one by one give the same result as the input - """ - dataIn = numpy.arange(100, dtype=numpy.int32) - dataIn = dataIn.reshape((10, 10)) - - dataOut = medfilt2d(image=dataIn, - kernel_size=(1, 1), - conditional=False, - mode='nearest') - - self.assertTrue(numpy.array_equal(dataIn, dataOut)) - - def testFilter3_1d(self): - """Test binding and result of the 1d filter""" - self.assertTrue(numpy.array_equal( - medfilt1d(RANDOM_INT_MAT[0], kernel_size=3, conditional=False, - mode='nearest'), - [0, 2, 5, 2, 1]) - ) - - def testFilter3Conditionnal(self): - """Test that the conditional filter apply correctly in a 10x10 matrix - with a 3x3 kernel - """ - dataIn = numpy.arange(100, dtype=numpy.int32) - dataIn = dataIn.reshape((10, 10)) - - dataOut = medfilt2d(image=dataIn, - kernel_size=(3, 3), - conditional=True, - mode='nearest') - self.assertTrue(dataOut[0, 0] == 1) - self.assertTrue(dataOut[0, 1] == 1) - self.assertTrue(numpy.array_equal(dataOut[1:8, 1:8], dataIn[1:8, 1:8])) - self.assertTrue(dataOut[9, 9] == 98) - - def testFilter3_1D(self): - """Simple test of a 3x3 median filter on a 1D array""" - dataIn = numpy.arange(100, dtype=numpy.int32) - - dataOut = medfilt2d(image=dataIn, - kernel_size=(5), - conditional=False, - mode='nearest') - - self.assertTrue(dataOut[0] == 0) - self.assertTrue(dataOut[9] == 9) - self.assertTrue(dataOut[99] == 99) - - def testNaNs(self): - """Test median filter on image with NaNs in nearest mode""" - # Data with a NaN in first corner - nan_corner = numpy.arange(100.).reshape(10, 10) - nan_corner[0, 0] = numpy.nan - output = medfilt2d( - nan_corner, kernel_size=3, conditional=False, mode='nearest') - self.assertEqual(output[0, 0], 10) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 11) - self.assertEqual(output[1, 1], 12) - - # Data with some NaNs - some_nans = numpy.arange(100.).reshape(10, 10) - some_nans[0, 1] = numpy.nan - some_nans[1, 1] = numpy.nan - some_nans[1, 0] = numpy.nan - output = medfilt2d( - some_nans, kernel_size=3, conditional=False, mode='nearest') - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 20) - self.assertEqual(output[1, 1], 20) - - -class TestMedianFilterReflect(ParametricTestCase): - """Unit test for the median filter in reflect mode""" - - def testArange9(self): - """Test from a 3x3 window to RANDOM_FLOAT_MAT""" - img = numpy.arange(9, dtype=numpy.int32) - img = img.reshape(3, 3) - kernel = (3, 3) - res = medfilt2d(image=img, - kernel_size=kernel, - conditional=False, - mode='reflect') - self.assertTrue( - numpy.array_equal(res.ravel(), [1, 2, 2, 3, 4, 5, 6, 6, 7])) - - def testRandom10(self): - """Test a (5, 3) window to a RANDOM_FLOAT_MAT""" - kernel = (5, 3) - - thRes = numpy.array([ - [0.23067427, 0.56049024, 0.56049024, 0.4440632, 0.42161216], - [0.23067427, 0.62717157, 0.56049024, 0.56049024, 0.46372299], - [0.62717157, 0.62717157, 0.56049024, 0.56049024, 0.4440632], - [0.76532598, 0.68382382, 0.56049024, 0.56049024, 0.42161216], - [0.81025249, 0.68382382, 0.56049024, 0.68382382, 0.46372299]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=False, - mode='reflect') - - self.assertTrue(numpy.array_equal(thRes, res)) - - def testApplyReflect1D(self): - """Test the reflect function used for the median filter in reflect mode - """ - # test for inside values - self.assertTrue(reflect(2, 3) == 2) - # test for boundaries values - self.assertTrue(reflect(3, 3) == 2) - self.assertTrue(reflect(4, 3) == 1) - self.assertTrue(reflect(5, 3) == 0) - self.assertTrue(reflect(6, 3) == 0) - self.assertTrue(reflect(7, 3) == 1) - self.assertTrue(reflect(-1, 3) == 0) - self.assertTrue(reflect(-2, 3) == 1) - self.assertTrue(reflect(-3, 3) == 2) - self.assertTrue(reflect(-4, 3) == 2) - self.assertTrue(reflect(-5, 3) == 1) - self.assertTrue(reflect(-6, 3) == 0) - self.assertTrue(reflect(-7, 3) == 0) - - def testRandom10Conditionnal(self): - """Test the median filter in reflect mode and with the conditionnal - option""" - kernel = (3, 1) - - thRes = numpy.array([ - [0.05564293, 0.62717157, 0.75002406, 0.40555336, 0.70278975], - [0.23067427, 0.62717157, 0.56049024, 0.44406320, 0.42161216], - [0.76532598, 0.20303021, 0.56049024, 0.46372299, 0.42161216], - [0.81025249, 0.20303021, 0.68382382, 0.46372299, 0.33623165], - [0.94691602, 0.07813661, 0.81651256, 0.84220106, 0.33623165]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=True, - mode='reflect') - self.assertTrue(numpy.array_equal(thRes, res)) - - def testNaNs(self): - """Test median filter on image with NaNs in reflect mode""" - # Data with a NaN in first corner - nan_corner = numpy.arange(100.).reshape(10, 10) - nan_corner[0, 0] = numpy.nan - output = medfilt2d( - nan_corner, kernel_size=3, conditional=False, mode='reflect') - self.assertEqual(output[0, 0], 10) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 11) - self.assertEqual(output[1, 1], 12) - - # Data with some NaNs - some_nans = numpy.arange(100.).reshape(10, 10) - some_nans[0, 1] = numpy.nan - some_nans[1, 1] = numpy.nan - some_nans[1, 0] = numpy.nan - output = medfilt2d( - some_nans, kernel_size=3, conditional=False, mode='reflect') - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 20) - self.assertEqual(output[1, 1], 20) - - def testFilter3_1d(self): - """Test binding and result of the 1d filter""" - self.assertTrue(numpy.array_equal( - medfilt1d(RANDOM_INT_MAT[0], kernel_size=5, conditional=False, - mode='reflect'), - [2, 2, 2, 2, 2]) - ) - - -class TestMedianFilterMirror(ParametricTestCase): - """Unit test for the median filter in mirror mode - """ - - def testApplyMirror1D(self): - """Test the reflect function used for the median filter in mirror mode - """ - # test for inside values - self.assertTrue(mirror(2, 3) == 2) - # test for boundaries values - self.assertTrue(mirror(4, 4) == 2) - self.assertTrue(mirror(5, 4) == 1) - self.assertTrue(mirror(6, 4) == 0) - self.assertTrue(mirror(7, 4) == 1) - self.assertTrue(mirror(8, 4) == 2) - self.assertTrue(mirror(-1, 4) == 1) - self.assertTrue(mirror(-2, 4) == 2) - self.assertTrue(mirror(-3, 4) == 3) - self.assertTrue(mirror(-4, 4) == 2) - self.assertTrue(mirror(-5, 4) == 1) - self.assertTrue(mirror(-6, 4) == 0) - - def testRandom10(self): - """Test a (5, 3) window to a random array""" - kernel = (3, 5) - - thRes = numpy.array([ - [0.05272484, 0.40555336, 0.42161216, 0.42161216, 0.42161216], - [0.56049024, 0.56049024, 0.4440632, 0.4440632, 0.4440632], - [0.56049024, 0.46372299, 0.46372299, 0.46372299, 0.46372299], - [0.68382382, 0.56049024, 0.56049024, 0.46372299, 0.56049024], - [0.68382382, 0.46372299, 0.68382382, 0.46372299, 0.68382382]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=False, - mode='mirror') - - self.assertTrue(numpy.array_equal(thRes, res)) - - def testRandom10Conditionnal(self): - """Test the median filter in reflect mode and with the conditionnal - option""" - kernel = (1, 3) - - thRes = numpy.array([ - [0.62717157, 0.62717157, 0.62717157, 0.70278975, 0.40555336], - [0.02839148, 0.05272484, 0.05272484, 0.42161216, 0.65166994], - [0.74219128, 0.56049024, 0.56049024, 0.44406320, 0.44406320], - [0.20303021, 0.68382382, 0.46372299, 0.68382382, 0.46372299], - [0.07813661, 0.81651256, 0.81651256, 0.81651256, 0.84220106]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=True, - mode='mirror') - - self.assertTrue(numpy.array_equal(thRes, res)) - - def testNaNs(self): - """Test median filter on image with NaNs in mirror mode""" - # Data with a NaN in first corner - nan_corner = numpy.arange(100.).reshape(10, 10) - nan_corner[0, 0] = numpy.nan - output = medfilt2d( - nan_corner, kernel_size=3, conditional=False, mode='mirror') - self.assertEqual(output[0, 0], 11) - self.assertEqual(output[0, 1], 11) - self.assertEqual(output[1, 0], 11) - self.assertEqual(output[1, 1], 12) - - # Data with some NaNs - some_nans = numpy.arange(100.).reshape(10, 10) - some_nans[0, 1] = numpy.nan - some_nans[1, 1] = numpy.nan - some_nans[1, 0] = numpy.nan - output = medfilt2d( - some_nans, kernel_size=3, conditional=False, mode='mirror') - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 12) - self.assertEqual(output[1, 0], 21) - self.assertEqual(output[1, 1], 20) - - def testFilter3_1d(self): - """Test binding and result of the 1d filter""" - self.assertTrue(numpy.array_equal( - medfilt1d(RANDOM_INT_MAT[0], kernel_size=5, conditional=False, - mode='mirror'), - [2, 5, 2, 5, 2]) - ) - -class TestMedianFilterShrink(ParametricTestCase): - """Unit test for the median filter in mirror mode - """ - - def testRandom_3x3(self): - """Test the median filter in shrink mode and with the conditionnal - option""" - kernel = (3, 3) - - thRes = numpy.array([ - [0.62717157, 0.62717157, 0.62717157, 0.65166994, 0.65166994], - [0.62717157, 0.56049024, 0.56049024, 0.44406320, 0.44406320], - [0.74219128, 0.56049024, 0.46372299, 0.46372299, 0.46372299], - [0.74219128, 0.68382382, 0.56049024, 0.56049024, 0.46372299], - [0.81025249, 0.81025249, 0.68382382, 0.81281709, 0.81281709]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=False, - mode='shrink') - - self.assertTrue(numpy.array_equal(thRes, res)) - - def testBounds(self): - """Test the median filter in shrink mode with 3 different kernels - which should return the same result due to the large values of kernels - used. - """ - kernel1 = (1, 9) - kernel2 = (1, 11) - kernel3 = (1, 21) - - thRes = numpy.array([[2, 2, 2, 2, 2], - [2, 2, 2, 2, 2], - [8, 8, 8, 8, 8], - [5, 5, 5, 5, 5]]) - - resK1 = medfilt2d(image=RANDOM_INT_MAT, - kernel_size=kernel1, - conditional=False, - mode='shrink') - - resK2 = medfilt2d(image=RANDOM_INT_MAT, - kernel_size=kernel2, - conditional=False, - mode='shrink') - - resK3 = medfilt2d(image=RANDOM_INT_MAT, - kernel_size=kernel3, - conditional=False, - mode='shrink') - - self.assertTrue(numpy.array_equal(resK1, thRes)) - self.assertTrue(numpy.array_equal(resK2, resK1)) - self.assertTrue(numpy.array_equal(resK3, resK1)) - - def testRandom_3x3Conditionnal(self): - """Test the median filter in reflect mode and with the conditionnal - option""" - kernel = (3, 3) - - thRes = numpy.array([ - [0.05564293, 0.62717157, 0.62717157, 0.40555336, 0.65166994], - [0.62717157, 0.56049024, 0.05272484, 0.65166994, 0.42161216], - [0.23067427, 0.74219128, 0.56049024, 0.44406320, 0.46372299], - [0.81025249, 0.20303021, 0.68382382, 0.46372299, 0.81281709], - [0.81025249, 0.81025249, 0.81651256, 0.81281709, 0.81281709]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=True, - mode='shrink') - - self.assertTrue(numpy.array_equal(res, thRes)) - - def testRandomInt(self): - """Test 3x3 kernel on RANDOM_INT_MAT - """ - kernel = (3, 3) - - thRes = numpy.array([[3, 2, 5, 2, 6], - [5, 3, 6, 6, 7], - [6, 6, 6, 6, 7], - [8, 8, 7, 7, 7]]) - - resK1 = medfilt2d(image=RANDOM_INT_MAT, - kernel_size=kernel, - conditional=False, - mode='shrink') - - self.assertTrue(numpy.array_equal(resK1, thRes)) - - def testNaNs(self): - """Test median filter on image with NaNs in shrink mode""" - # Data with a NaN in first corner - nan_corner = numpy.arange(100.).reshape(10, 10) - nan_corner[0, 0] = numpy.nan - output = medfilt2d( - nan_corner, kernel_size=3, conditional=False, mode='shrink') - self.assertEqual(output[0, 0], 10) - self.assertEqual(output[0, 1], 10) - self.assertEqual(output[1, 0], 11) - self.assertEqual(output[1, 1], 12) - - # Data with some NaNs - some_nans = numpy.arange(100.).reshape(10, 10) - some_nans[0, 1] = numpy.nan - some_nans[1, 1] = numpy.nan - some_nans[1, 0] = numpy.nan - output = medfilt2d( - some_nans, kernel_size=3, conditional=False, mode='shrink') - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 20) - self.assertEqual(output[1, 1], 20) - - def testFilter3_1d(self): - """Test binding and result of the 1d filter""" - self.assertTrue(numpy.array_equal( - medfilt1d(RANDOM_INT_MAT[0], kernel_size=3, conditional=False, - mode='shrink'), - [5, 2, 5, 2, 6]) - ) - -class TestMedianFilterConstant(ParametricTestCase): - """Unit test for the median filter in constant mode - """ - - def testRandom10(self): - """Test a (5, 3) window to a random array""" - kernel = (3, 5) - - thRes = numpy.array([ - [0., 0.02839148, 0.05564293, 0.02839148, 0.], - [0.05272484, 0.40555336, 0.4440632, 0.42161216, 0.28773158], - [0.05272484, 0.44406320, 0.46372299, 0.42161216, 0.28773158], - [0.20303021, 0.46372299, 0.56049024, 0.44406320, 0.33623165], - [0., 0.07813661, 0.33623165, 0.07813661, 0.]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=False, - mode='constant') - - self.assertTrue(numpy.array_equal(thRes, res)) - - RANDOM_FLOAT_MAT = numpy.array([ - [0.05564293, 0.62717157, 0.75002406, 0.40555336, 0.70278975], - [0.76532598, 0.02839148, 0.05272484, 0.65166994, 0.42161216], - [0.23067427, 0.74219128, 0.56049024, 0.44406320, 0.28773158], - [0.81025249, 0.20303021, 0.68382382, 0.46372299, 0.81281709], - [0.94691602, 0.07813661, 0.81651256, 0.84220106, 0.33623165]]) - - def testRandom10Conditionnal(self): - """Test the median filter in reflect mode and with the conditionnal - option""" - kernel = (1, 3) - - print(RANDOM_FLOAT_MAT) - - thRes = numpy.array([ - [0.05564293, 0.62717157, 0.62717157, 0.70278975, 0.40555336], - [0.02839148, 0.05272484, 0.05272484, 0.42161216, 0.42161216], - [0.23067427, 0.56049024, 0.56049024, 0.44406320, 0.28773158], - [0.20303021, 0.68382382, 0.46372299, 0.68382382, 0.46372299], - [0.07813661, 0.81651256, 0.81651256, 0.81651256, 0.33623165]]) - - res = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=True, - mode='constant') - - self.assertTrue(numpy.array_equal(thRes, res)) - - def testNaNs(self): - """Test median filter on image with NaNs in constant mode""" - # Data with a NaN in first corner - nan_corner = numpy.arange(100.).reshape(10, 10) - nan_corner[0, 0] = numpy.nan - output = medfilt2d(nan_corner, - kernel_size=3, - conditional=False, - mode='constant', - cval=0) - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 2) - self.assertEqual(output[1, 0], 10) - self.assertEqual(output[1, 1], 12) - - # Data with some NaNs - some_nans = numpy.arange(100.).reshape(10, 10) - some_nans[0, 1] = numpy.nan - some_nans[1, 1] = numpy.nan - some_nans[1, 0] = numpy.nan - output = medfilt2d(some_nans, - kernel_size=3, - conditional=False, - mode='constant', - cval=0) - self.assertEqual(output[0, 0], 0) - self.assertEqual(output[0, 1], 0) - self.assertEqual(output[1, 0], 0) - self.assertEqual(output[1, 1], 20) - - def testFilter3_1d(self): - """Test binding and result of the 1d filter""" - self.assertTrue(numpy.array_equal( - medfilt1d(RANDOM_INT_MAT[0], kernel_size=5, conditional=False, - mode='constant'), - [0, 2, 2, 2, 1]) - ) - -class TestGeneralExecution(ParametricTestCase): - """Some general test on median filter application""" - - def testTypes(self): - """Test that all needed types have their implementation of the median - filter - """ - for mode in silx_mf_modes: - for testType in [numpy.float32, numpy.float64, numpy.int16, - numpy.uint16, numpy.int32, numpy.int64, - numpy.uint64]: - with self.subTest(mode=mode, type=testType): - data = (numpy.random.rand(10, 10) * 65000).astype(testType) - out = medfilt2d(image=data, - kernel_size=(3, 3), - conditional=False, - mode=mode) - self.assertTrue(out.dtype.type is testType) - - def testInputDataIsNotModify(self): - """Make sure input data is not modify by the median filter""" - dataIn = numpy.arange(100, dtype=numpy.int32) - dataIn = dataIn.reshape((10, 10)) - dataInCopy = dataIn.copy() - - for mode in silx_mf_modes: - with self.subTest(mode=mode): - medfilt2d(image=dataIn, - kernel_size=(3, 3), - conditional=False, - mode=mode) - self.assertTrue(numpy.array_equal(dataIn, dataInCopy)) - - def testAllNaNs(self): - """Test median filter on image all NaNs""" - all_nans = numpy.empty((10, 10), dtype=numpy.float32) - all_nans[:] = numpy.nan - - for mode in silx_mf_modes: - for conditional in (True, False): - with self.subTest(mode=mode, conditional=conditional): - output = medfilt2d( - all_nans, - kernel_size=3, - conditional=conditional, - mode=mode, - cval=numpy.nan) - self.assertTrue(numpy.all(numpy.isnan(output))) - - def testConditionalWithNaNs(self): - """Test that NaNs are propagated through conditional median filter""" - for mode in silx_mf_modes: - with self.subTest(mode=mode): - image = numpy.ones((10, 10), dtype=numpy.float32) - nan_mask = numpy.zeros_like(image, dtype=bool) - nan_mask[0, 0] = True - nan_mask[4, :] = True - nan_mask[6, 4] = True - image[nan_mask] = numpy.nan - output = medfilt2d( - image, - kernel_size=3, - conditional=True, - mode=mode) - out_isnan = numpy.isnan(output) - self.assertTrue(numpy.all(out_isnan[nan_mask])) - self.assertFalse( - numpy.any(out_isnan[numpy.logical_not(nan_mask)])) - - -def _getScipyAndSilxCommonModes(): - """return the mode which are comparable between silx and scipy""" - modes = silx_mf_modes.copy() - del modes['shrink'] - return modes - - -@unittest.skipUnless(scipy is not None, "scipy not available") -class TestVsScipy(ParametricTestCase): - """Compare scipy.ndimage.median_filter vs silx.math.medianfilter - on comparable - """ - def testWithArange(self): - """Test vs scipy with different kernels on arange matrix""" - data = numpy.arange(10000, dtype=numpy.int32) - data = data.reshape(100, 100) - - kernels = [(3, 7), (7, 5), (1, 1), (3, 3)] - modesToTest = _getScipyAndSilxCommonModes() - for kernel in kernels: - for mode in modesToTest: - with self.subTest(kernel=kernel, mode=mode): - resScipy = scipy.ndimage.median_filter(input=data, - size=kernel, - mode=mode) - resSilx = medfilt2d(image=data, - kernel_size=kernel, - conditional=False, - mode=mode) - - self.assertTrue(numpy.array_equal(resScipy, resSilx)) - - def testRandomMatrice(self): - """Test vs scipy with different kernels on RANDOM_FLOAT_MAT""" - kernels = [(3, 7), (7, 5), (1, 1), (3, 3)] - modesToTest = _getScipyAndSilxCommonModes() - for kernel in kernels: - for mode in modesToTest: - with self.subTest(kernel=kernel, mode=mode): - resScipy = scipy.ndimage.median_filter(input=RANDOM_FLOAT_MAT, - size=kernel, - mode=mode) - - resSilx = medfilt2d(image=RANDOM_FLOAT_MAT, - kernel_size=kernel, - conditional=False, - mode=mode) - - self.assertTrue(numpy.array_equal(resScipy, resSilx)) - - def testAscentOrLena(self): - """Test vs scipy with """ - if hasattr(scipy.misc, 'ascent'): - img = scipy.misc.ascent() - else: - img = scipy.misc.lena() - - kernels = [(3, 1), (3, 5), (5, 9), (9, 3)] - modesToTest = _getScipyAndSilxCommonModes() - - for kernel in kernels: - for mode in modesToTest: - with self.subTest(kernel=kernel, mode=mode): - resScipy = scipy.ndimage.median_filter(input=img, - size=kernel, - mode=mode) - - resSilx = medfilt2d(image=img, - kernel_size=kernel, - conditional=False, - mode=mode) - - self.assertTrue(numpy.array_equal(resScipy, resSilx)) - - -def suite(): - test_suite = unittest.TestSuite() - for test in [TestGeneralExecution, - TestVsScipy, - TestMedianFilterNearest, - TestMedianFilterReflect, - TestMedianFilterMirror, - TestMedianFilterShrink, - TestMedianFilterConstant]: - test_suite.addTest( - unittest.defaultTestLoader.loadTestsFromTestCase(test)) - return test_suite - - -if __name__ == '__main__': - unittest.main(defaultTest='suite') |