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Merge pull request #1299 from ahojnnes/block

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Avoid using `block_reduce` for upsampling
This commit is contained in:
Stefan van der Walt
2014-12-15 11:48:32 +02:00
parent 7fe31b280d 125d53ba52
commit ea95835017
2 changed files with 25 additions and 14 deletions
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skimage/measure/block.py
+6 -2
View File
@@ -13,8 +13,8 @@ def block_reduce(image, block_size, func=np.sum, cval=0):
Array containing down-sampling integer factor along each axis.
func : callable
Function object which is used to calculate the return value for each
local block. This function must implement an ``axis`` parameter such as
``numpy.sum`` or ``numpy.min``.
local block. This function must implement an ``axis`` parameter such
as ``numpy.sum`` or ``numpy.min``.
cval : float
Constant padding value if image is not perfectly divisible by the
block size.
@@ -58,6 +58,10 @@ def block_reduce(image, block_size, func=np.sum, cval=0):
pad_width = []
for i in range(len(block_size)):
if block_size[i] < 1:
raise ValueError("Down-sampling factors must be >= 1. Use "
"`skimage.transform.resize` to up-sample an "
"image.")
if image.shape[i] % block_size[i] != 0:
after_width = block_size[i] - (image.shape[i] % block_size[i])
else:
skimage/measure/tests/test_block.py
+19 -12
View File
@@ -1,5 +1,5 @@
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal, assert_raises
from skimage.measure import block_reduce
@@ -8,13 +8,13 @@ def test_block_reduce_sum():
out1 = block_reduce(image1, (2, 3))
expected1 = np.array([[ 24, 42],
[ 96, 114]])
assert_array_equal(expected1, out1)
assert_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (3, 3))
expected2 = np.array([[ 81, 108, 87],
[174, 192, 138]])
assert_array_equal(expected2, out2)
assert_equal(expected2, out2)
def test_block_reduce_mean():
@@ -22,13 +22,13 @@ def test_block_reduce_mean():
out1 = block_reduce(image1, (2, 3), func=np.mean)
expected1 = np.array([[ 4., 7.],
[ 16., 19.]])
assert_array_equal(expected1, out1)
assert_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=np.mean)
expected2 = np.array([[14. , 10.8],
[ 8.5, 5.7]])
assert_array_equal(expected2, out2)
assert_equal(expected2, out2)
def test_block_reduce_median():
@@ -36,17 +36,17 @@ def test_block_reduce_median():
out1 = block_reduce(image1, (2, 3), func=np.median)
expected1 = np.array([[ 4., 7.],
[ 16., 19.]])
assert_array_equal(expected1, out1)
assert_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=np.median)
expected2 = np.array([[ 14., 17.],
[ 0., 0.]])
assert_array_equal(expected2, out2)
assert_equal(expected2, out2)
image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
out3 = block_reduce(image3, (2, 4), func=np.median)
assert_array_equal(5, out3)
assert_equal(5, out3)
def test_block_reduce_min():
@@ -54,13 +54,13 @@ def test_block_reduce_min():
out1 = block_reduce(image1, (2, 3), func=np.min)
expected1 = np.array([[ 0, 3],
[12, 15]])
assert_array_equal(expected1, out1)
assert_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=np.min)
expected2 = np.array([[0, 0],
[0, 0]])
assert_array_equal(expected2, out2)
assert_equal(expected2, out2)
def test_block_reduce_max():
@@ -68,13 +68,20 @@ def test_block_reduce_max():
out1 = block_reduce(image1, (2, 3), func=np.max)
expected1 = np.array([[ 8, 11],
[20, 23]])
assert_array_equal(expected1, out1)
assert_equal(expected1, out1)
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = block_reduce(image2, (4, 5), func=np.max)
expected2 = np.array([[28, 31],
[36, 39]])
assert_array_equal(expected2, out2)
assert_equal(expected2, out2)
def test_invalid_block_size():
image = np.arange(4 * 6).reshape(4, 6)
assert_raises(ValueError, block_reduce, image, [1, 2, 3])
assert_raises(ValueError, block_reduce, image, [1, 0.5])
if __name__ == "__main__":