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 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 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 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 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 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 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 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 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 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 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):
"""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 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 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 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 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 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 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 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_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 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 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 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 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 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 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 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 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 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 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 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))
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 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 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))
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 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 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 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 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 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 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 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 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 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 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 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 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 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 _applyFilter(self):
# medfilt2D requires the data to be C-contiguous with silx <= 0.9
self.addImage(medfilt2d(numpy.ascontiguousarray(self._data),
kernel_size=self.medfilt_width),
colormap=self._colormap,
legend=self._legend)
def _computeFilteredImage(self, kernelWidth, conditional):
assert(self.plot is not None)
return medfilt2d(self._originalImage,
(kernelWidth, kernelWidth),
conditional)
def _applyFilter(self):
# medfilt2D requires the data to be C-contiguous with silx <= 0.9
self.addImage(medfilt2d(numpy.ascontiguousarray(self._data),
kernel_size=self.medfilt_width),
colormap=self._colormap,
legend=self._legend)
