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Rename block_* functions to local_*

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This commit is contained in:
Johannes Schönberger
2013-07-05 14:19:10 +02:00
parent 9d806eb413
commit 4f6b39dcd3
4 changed files with 37 additions and 37 deletions
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skimage/measure/__init__.py
+6 -6
View File
@@ -3,7 +3,7 @@ from ._regionprops import regionprops, perimeter
from ._structural_similarity import structural_similarity
from ._polygon import approximate_polygon, subdivide_polygon
from .fit import LineModel, CircleModel, EllipseModel, ransac
from .blocks import block_sum, block_median, block_mean, block_min, block_max
from .local import local_sum, local_mean, local_median, local_min, local_max
__all__ = ['find_contours',
@@ -16,8 +16,8 @@ __all__ = ['find_contours',
'CircleModel',
'EllipseModel',
'ransac',
'block_sum',
'block_mean',
'block_median',
'block_min',
'block_max']
'local_sum',
'local_mean',
'local_median',
'local_min',
'local_max']
skimage/measure/blocks.py → skimage/measure/local.py
+11 -11
View File
@@ -2,7 +2,7 @@ import numpy as np
from ..util.shape import view_as_blocks, _pad_asymmetric_zeros
def _block_func(image, factors, func):
def _local_func(image, factors, func):
"""Down-sample image by applying function to local blocks.
Parameters
@@ -45,7 +45,7 @@ def _block_func(image, factors, func):
return out
def block_sum(image, block_size):
def local_sum(image, block_size):
"""Sum elements in local blocks.
The image is padded with zeros if it is not perfectly divisible by integer
@@ -75,10 +75,10 @@ def block_sum(image, block_size):
[33, 27]])
"""
return _block_func(image, block_size, np.sum)
return _local_func(image, block_size, np.sum)
def block_mean(image, block_size):
def local_mean(image, block_size):
"""Average elements in local blocks.
The image is padded with zeros if it is not perfectly divisible by integer
@@ -108,10 +108,10 @@ def block_mean(image, block_size):
[ 5.5, 4.5]])
"""
return _block_func(image, block_size, np.mean)
return _local_func(image, block_size, np.mean)
def block_median(image, block_size):
def local_median(image, block_size):
"""Median element in local blocks.
The image is padded with zeros if it is not perfectly divisible by integer
@@ -139,10 +139,10 @@ def block_median(image, block_size):
array([[ 5.]])
"""
return _block_func(image, block_size, np.median)
return _local_func(image, block_size, np.median)
def block_min(image, block_size):
def local_min(image, block_size):
"""Minimum element in local blocks.
The image is padded with zeros if it is not perfectly divisible by integer
@@ -172,10 +172,10 @@ def block_min(image, block_size):
[0, 0, 0]])
"""
return _block_func(image, block_size, np.min)
return _local_func(image, block_size, np.min)
def block_max(image, block_size):
def local_max(image, block_size):
"""Maximum element in local blocks.
The image is padded with zeros if it is not perfectly divisible by integer
@@ -205,4 +205,4 @@ def block_max(image, block_size):
[12, 14]])
"""
return _block_func(image, block_size, np.max)
return _local_func(image, block_size, np.max)
skimage/measure/tests/test_blocks.py → skimage/measure/tests/test_local.py
+18 -18
View File
@@ -1,78 +1,78 @@
import numpy as np
from numpy.testing import assert_array_equal
from skimage.measure import (block_sum, block_mean, block_median, block_min,
block_max)
from skimage.measure import (local_sum, local_mean, local_median, local_min,
local_max)
def test_block_sum():
def test_local_sum():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = block_sum(image1, (2, 3))
out1 = local_sum(image1, (2, 3))
expected1 = np.array([[ 24, 42],
[ 96, 114]])
assert_array_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_sum(image2, (3, 3))
out2 = local_sum(image2, (3, 3))
expected2 = np.array([[ 81, 108, 87],
[174, 192, 138]])
assert_array_equal(expected2, out2)
def test_block_mean():
def test_local_mean():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = block_mean(image1, (2, 3))
out1 = local_mean(image1, (2, 3))
expected1 = np.array([[ 4., 7.],
[ 16., 19.]])
assert_array_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_mean(image2, (4, 5))
out2 = local_mean(image2, (4, 5))
expected2 = np.array([[14. , 10.8],
[ 8.5, 5.7]])
assert_array_equal(expected2, out2)
def test_block_median():
def test_local_median():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = block_median(image1, (2, 3))
out1 = local_median(image1, (2, 3))
expected1 = np.array([[ 4., 7.],
[ 16., 19.]])
assert_array_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_median(image2, (4, 5))
out2 = local_median(image2, (4, 5))
expected2 = np.array([[ 14., 17.],
[ 0., 0.]])
assert_array_equal(expected2, out2)
image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
out3 = block_median(image3, (2, 4))
out3 = local_median(image3, (2, 4))
assert_array_equal(5, out3)
def test_block_min():
def test_local_min():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = block_min(image1, (2, 3))
out1 = local_min(image1, (2, 3))
expected1 = np.array([[ 0, 3],
[12, 15]])
assert_array_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_min(image2, (4, 5))
out2 = local_min(image2, (4, 5))
expected2 = np.array([[0, 0],
[0, 0]])
assert_array_equal(expected2, out2)
def test_block_max():
def test_local_max():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = block_max(image1, (2, 3))
out1 = local_max(image1, (2, 3))
expected1 = np.array([[ 8, 11],
[20, 23]])
assert_array_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_max(image2, (4, 5))
out2 = local_max(image2, (4, 5))
expected2 = np.array([[28, 31],
[36, 39]])
assert_array_equal(expected2, out2)
skimage/transform/_warps.py
+2 -2
View File
@@ -2,7 +2,7 @@ import numpy as np
from scipy import ndimage
from ._geometric import warp, SimilarityTransform, AffineTransform
from ..measure.blocks import _block_func
from ..measure.local import _local_func
def resize(image, output_shape, order=1, mode='constant', cval=0.):
@@ -260,7 +260,7 @@ def downscale_local_mean(image, factors):
[5.5, 4.5]])
"""
return _block_func(image, factors, np.mean)
return _local_func(image, factors, np.mean)
def _swirl_mapping(xy, center, rotation, strength, radius):