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Remove deprecated is_local_maximum function

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This commit is contained in:
Johannes Schönberger
2013-10-14 18:12:02 +02:00
parent a6f59a587d
commit 5e2b04c486
3 changed files with 5 additions and 174 deletions
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skimage/morphology/__init__.py
+1 -2
View File
@@ -7,7 +7,7 @@ from .grey import (erosion, dilation, opening, closing, white_tophat,
from .selem import (square, rectangle, diamond, disk, cube, octahedron, ball,
octagon, star)
from .ccomp import label
from .watershed import watershed, is_local_maximum
from .watershed import watershed
from ._skeletonize import skeletonize, medial_axis
from .convex_hull import convex_hull_image, convex_hull_object
from .greyreconstruct import reconstruction
@@ -40,7 +40,6 @@ __all__ = ['binary_erosion',
'octagon',
'label',
'watershed',
'is_local_maximum',
'skeletonize',
'medial_axis',
'convex_hull_image',
skimage/morphology/tests/test_watershed.py
+1 -98
View File
@@ -48,8 +48,7 @@ import unittest
import numpy as np
import scipy.ndimage
from skimage.morphology.watershed import watershed, \
_slow_watershed, is_local_maximum
from skimage.morphology.watershed import watershed, _slow_watershed
eps = 1e-12
@@ -387,101 +386,5 @@ class TestWatershed(unittest.TestCase):
self.eight)
class TestIsLocalMaximum(unittest.TestCase):
def test_00_00_empty(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(~ result))
def test_01_01_one_point(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
labels[5, 5] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_02_adjacent_and_same(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5:6] = 1
labels[5, 5:6] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_03_adjacent_and_different(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5:6] = 1
expected = (image == 1)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
result = is_local_maximum(image, labels)
self.assertTrue(np.all(result == expected))
def test_01_04_not_adjacent_and_different(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 8] = .5
labels[image > 0] = 1
expected = (labels == 1)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
def test_01_05_two_objects(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 15] = .5
labels[5, 5] = 1
labels[5, 15] = 2
expected = (labels > 0)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
def test_01_06_adjacent_different_objects(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5] = 1
labels[5, 6] = 2
expected = (labels > 0)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
def test_02_01_four_quadrants(self):
np.random.seed(21)
image = np.random.uniform(size=(40, 60))
i, j = np.mgrid[0:40, 0:60]
labels = 1 + (i >= 20) + (j >= 30) * 2
i, j = np.mgrid[-3:4, -3:4]
footprint = (i * i + j * j <= 9)
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = is_local_maximum(image, labels, footprint)
self.assertTrue(np.all(result == expected))
def test_03_01_disk_1(self):
'''regression test of img-1194, footprint = [1]
Test is_local_maximum when every point is a local maximum
'''
np.random.seed(31)
image = np.random.uniform(size=(10, 20))
footprint = np.array([[1]])
result = is_local_maximum(image, np.ones((10, 20)), footprint)
self.assertTrue(np.all(result))
result = is_local_maximum(image, footprint=footprint)
self.assertTrue(np.all(result))
if __name__ == "__main__":
np.testing.run_module_suite()
skimage/morphology/watershed.py
+3 -74
View File
@@ -116,7 +116,9 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
>>> # to the background
>>> from scipy import ndimage
>>> distance = ndimage.distance_transform_edt(image)
>>> local_maxi = is_local_maximum(distance, image, np.ones((3, 3)))
>>> from skimage.feature import peak_local_max
>>> local_maxi = peak_local_max(distance, labels=image,
... footprint=np.ones((3, 3)))
>>> markers = ndimage.label(local_maxi)[0]
>>> labels = watershed(-distance, markers, mask=image)
@@ -224,79 +226,6 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
return c_output
@deprecated('feature.peak_local_max')
def is_local_maximum(image, labels=None, footprint=None):
"""
Return a boolean array of points that are local maxima
Parameters
----------
image: ndarray (2-D, 3-D, ...)
intensity image
labels: ndarray, optional
find maxima only within labels. Zero is reserved for background.
footprint: ndarray of bools, optional
binary mask indicating the neighborhood to be examined
`footprint` must be a matrix with odd dimensions, the center is taken
to be the point in question.
Returns
-------
result: ndarray of bools
mask that is True for pixels that are local maxima of `image`
See also
--------
skimage.feature.peak_local_max: Unified peak finding backend.
The more capable backend for finding local maxima.
Notes
-----
This function is now a wrapper for skimage.feature.peak_local_max() and is
retained only for convenience and backward compatibility.
Examples
--------
>>> image = np.zeros((4, 4))
>>> image[1, 2] = 2
>>> image[3, 3] = 1
>>> image
array([[ 0., 0., 0., 0.],
[ 0., 0., 2., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 1.]])
>>> is_local_maximum(image)
array([[ True, False, False, False],
[ True, False, True, False],
[ True, False, False, False],
[ True, True, False, True]], dtype=bool)
>>> image = np.arange(16).reshape((4, 4))
>>> labels = np.array([[1, 2], [3, 4]])
>>> labels = np.repeat(np.repeat(labels, 2, axis=0), 2, axis=1)
>>> labels
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])
>>> image
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> is_local_maximum(image, labels=labels)
array([[False, False, False, False],
[False, True, False, True],
[False, False, False, False],
[False, True, False, True]], dtype=bool)
"""
# call import here to prevent circular imports
from ..feature import peak_local_max
return peak_local_max(image, labels=labels, min_distance=1,
threshold_rel=0, footprint=footprint,
indices=False, exclude_border=False)
# ---------------------- deprecated ------------------------------
# Deprecate slower pure-Python code, that we keep only for
# pedagogical purposes