def __extract_local_histogram(image, mask=slice(None), bins=19, rang="image", cutoffp=(0.0, 100.0), size=None, footprint=None, output=None, mode="ignore", origin=0):
"""
Internal, single-image version of @see local_histogram
Note: Values outside of the histograms range are not considered.
Note: Mode constant is not available, instead a mode "ignore" is provided.
Note: Default dtype of returned values is float.
"""
if "constant" == mode:
raise RuntimeError('boundary mode not supported')
elif "ignore" == mode:
mode = "constant"
if 'image' == rang:
rang = tuple(numpy.percentile(image[mask], cutoffp))
elif not 2 == len(rang):
raise RuntimeError('the rang must contain exactly two elements or the string "image"')
_, bin_edges = numpy.histogram([], bins=bins, range=rang)
output, _ = _get_output(numpy.float if None == output else output, image, shape = [bins] + list(image.shape))
# threshold the image into the histogram bins represented by the output images first dimension, treat last bin separately, since upper border is inclusive
for i in range(bins - 1):
output[i] = (image >= bin_edges[i]) & (image < bin_edges[i + 1])
output[-1] = (image >= bin_edges[-2]) & (image <= bin_edges[-1])
# apply the sum filter to each dimension, then normalize by dividing through the sum of elements in the bins of each histogram
for i in range(bins):
output[i] = sum_filter(output[i], size=size, footprint=footprint, output=None, mode=mode, cval=0.0, origin=origin)
divident = numpy.sum(output, 0)
divident[0 == divident] = 1
output /= divident
# Notes on modes:
# mode=constant with a cval outside histogram range for the histogram equals a mode=constant with a cval = 0 for the sum_filter
# mode=constant with a cval inside histogram range for the histogram has no equal for the sum_filter (and does not make much sense)
# mode=X for the histogram equals mode=X for the sum_filter
# treat as multi-spectral image which intensities to extracted
return __extract_feature(__extract_intensities, [h for h in output], mask)
def test_sum_filter(self):
i = numpy.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
# test reaction to size parameter
r = sum_filter(i, size=1)
numpy.testing.assert_allclose(r, i)
e = numpy.array([[10, 14, 8], [18, 22, 12], [11, 13, 7]])
r = sum_filter(i, size=2, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
e = numpy.array([[10, 18, 14], [21, 36, 27], [18, 30, 22]])
r = sum_filter(i, size=3, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
e = numpy.array([[36, 36, 36], [36, 36, 36], [36, 36, 36]])
r = sum_filter(i, size=5, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
r = sum_filter(i, size=10, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
# test reaction to footprint parameter
fp = numpy.array([[1]])
r = sum_filter(i, footprint=fp)
numpy.testing.assert_allclose(r, i)
fp = numpy.array([[1, 1], [1, 1]])
e = numpy.array([[10, 14, 8], [18, 22, 12], [11, 13, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array([[1, 1]])
e = numpy.array([[3, 5, 3], [7, 9, 5], [11, 13, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array([[1], [1]])
e = numpy.array([[4, 6, 8], [8, 10, 12], [5, 6, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array([[1, 0], [1, 0], [0, 1]])
e = numpy.array([[5, 7, 3], [10, 13, 8], [8, 10, 12]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array([[1, 0], [0, 1], [0, 1]])
e = numpy.array([[6, 8, 0], [11, 14, 3], [9, 11, 5]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
# test border treatment modes
i = numpy.array([[1, 3, 4], [2, 2, 2]])
fp = numpy.array([[1, 0, 1]])
e = 6
r = sum_filter(i, footprint=fp, mode='mirror')
self.assertAlmostEqual(r[0, 0], e, msg='mirror mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='reflect')
self.assertAlmostEqual(r[0, 0], e, msg='reflect mode failed')
e = 7
r = sum_filter(i, footprint=fp, mode='wrap')
self.assertAlmostEqual(r[0, 0], e, msg='wrap mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='nearest')
self.assertAlmostEqual(r[0, 0], e, msg='nearest mode failed')
e = 3
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
self.assertAlmostEqual(r[0, 0], e, msg='constant mode failed')
e = 12
r = sum_filter(i, footprint=fp, mode='constant', cval=9)
self.assertAlmostEqual(r[0, 0], e, msg='constant mode failed')
fp = numpy.array([[1, 0, 0], [0, 0, 0]])
e = 3
r = sum_filter(i, footprint=fp, mode='mirror')
self.assertAlmostEqual(r[0, 0], e, msg='mirror mode failed')
e = 1
r = sum_filter(i, footprint=fp, mode='reflect')
self.assertAlmostEqual(r[0, 0], e, msg='reflect mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='wrap')
self.assertAlmostEqual(r[0, 0], e, msg='wrap mode failed')
e = 1
r = sum_filter(i, footprint=fp, mode='nearest')
self.assertAlmostEqual(r[0, 0], e, msg='nearest mode failed')
e = 0
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
self.assertAlmostEqual(r[0, 0], e, msg='constant mode failed')
e = 9
r = sum_filter(i, footprint=fp, mode='constant', cval=9)
self.assertAlmostEqual(r[0, 0], e, msg='constant mode failed')
def test_sum_filter(self):
i = numpy.array(
[[1,2,3],
[3,4,5],
[5,6,7]])
# test reaction to size parameter
r = sum_filter(i, size=1)
numpy.testing.assert_allclose(r, i)
e = numpy.array(
[[10,14, 8],
[18,22,12],
[11,13, 7]])
r = sum_filter(i, size=2, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
e = numpy.array(
[[10,18,14],
[21,36,27],
[18,30,22]])
r = sum_filter(i, size=3, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
e = numpy.array(
[[36,36,36],
[36,36,36],
[36,36,36]])
r = sum_filter(i, size=5, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
r = sum_filter(i, size=10, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
# test reaction to footprint parameter
fp = numpy.array(
[[1]])
r = sum_filter(i, footprint=fp)
numpy.testing.assert_allclose(r, i)
fp = numpy.array(
[[1,1],
[1,1]])
e = numpy.array(
[[10,14, 8],
[18,22,12],
[11,13, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array(
[[1, 1]])
e = numpy.array(
[[ 3, 5, 3],
[ 7, 9, 5],
[11,13, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array(
[[1],
[1]])
e = numpy.array(
[[ 4, 6, 8],
[ 8,10,12],
[ 5, 6, 7]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array(
[[1, 0],
[1, 0],
[0, 1]])
e = numpy.array(
[[ 5, 7, 3],
[10,13, 8],
[ 8,10,12]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
fp = numpy.array(
[[1, 0],
[0, 1],
[0, 1]])
e = numpy.array(
[[ 6, 8, 0],
[11,14, 3],
[ 9,11, 5]])
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
numpy.testing.assert_allclose(r, e)
# test border treatment modes
i = numpy.array(
[[1,3,4],
[2,2,2]])
fp = numpy.array(
[[1,0,1]])
e = 6
r = sum_filter(i, footprint=fp, mode='mirror')
self.assertAlmostEqual(r[0,0], e, msg='mirror mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='reflect')
self.assertAlmostEqual(r[0,0], e, msg='reflect mode failed')
e = 7
r = sum_filter(i, footprint=fp, mode='wrap')
self.assertAlmostEqual(r[0,0], e, msg='wrap mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='nearest')
self.assertAlmostEqual(r[0,0], e, msg='nearest mode failed')
e = 3
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
self.assertAlmostEqual(r[0,0], e, msg='constant mode failed')
e = 12
r = sum_filter(i, footprint=fp, mode='constant', cval=9)
self.assertAlmostEqual(r[0,0], e, msg='constant mode failed')
fp = numpy.array(
[[1,0,0],
[0,0,0]])
e = 3
r = sum_filter(i, footprint=fp, mode='mirror')
self.assertAlmostEqual(r[0,0], e, msg='mirror mode failed')
e = 1
r = sum_filter(i, footprint=fp, mode='reflect')
self.assertAlmostEqual(r[0,0], e, msg='reflect mode failed')
e = 4
r = sum_filter(i, footprint=fp, mode='wrap')
self.assertAlmostEqual(r[0,0], e, msg='wrap mode failed')
e = 1
r = sum_filter(i, footprint=fp, mode='nearest')
self.assertAlmostEqual(r[0,0], e, msg='nearest mode failed')
e = 0
r = sum_filter(i, footprint=fp, mode='constant', cval=0)
self.assertAlmostEqual(r[0,0], e, msg='constant mode failed')
e = 9
r = sum_filter(i, footprint=fp, mode='constant', cval=9)
self.assertAlmostEqual(r[0,0], e, msg='constant mode failed')
def count_neighbor(image, structure):
'returns image, where the current value of the voxel is the number of its neighbors'
image = image.astype(numpy.bool)
sumimage = sum_filter(image, footprint=structure, mode="constant", cval=0.0, output=numpy.uint)
sumimage[~image] = 0
return sumimage - image
