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Merge tag 'v0.8.1' into releases

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* tag 'v0.8.1':
  Update version to 0.8.1
  Remove colon as multi-line comment symbol
  Fix vectorized_ops for bento
  BUG: not all histogram memory was aligned correctly
  Add release instruction to update dev docs
  Add mailing list adresses to release instructions
  Use np.intp type for indices
  Fix `ValueError: Buffer dtype mismatch` on win-amd64
  Fix `ValueError: Buffer dtype mismatch` on win-amd64
  Fix `ValueError: Buffer dtype mismatch` on win-amd64
  Fix `ValueError: Buffer dtype mismatch` on win-amd64
  Increase version number to 0.9
This commit is contained in:
Yaroslav Halchenko
2013-06-03 10:25:48 -04:00
parent 9ae99ccadd cbcb313306
commit b433b3b715
10 changed files with 34 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files
RELEASE.txt
+8
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@@ -49,8 +49,16 @@ How to make a new release of ``skimage``
- Build using ``make gh-pages``.
- Push upstream: ``git push`` in ``gh-pages``.
- Update the development docs for the new version ``0.Xdev`` just like above
- Post release notes on mailing lists, blog, G+, etc.
- scikit-image@googlegroups.com
- scipy-user@scipy.org
- scikit-learn-general@lists.sourceforge.net
- pythonvision@googlegroups.com
Debian
------
bento.info
+1 -1
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@@ -1,5 +1,5 @@
Name: scikit-image
Version: 0.8.0
Version: 0.8.1
Summary: Image processing routines for SciPy
Url: http://scikit-image.org
DownloadUrl: http://github.com/scikit-image/scikit-image
setup.py
+1 -1
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@@ -17,7 +17,7 @@ MAINTAINER_EMAIL = 'stefan@sun.ac.za'
URL = 'http://scikit-image.org'
LICENSE = 'Modified BSD'
DOWNLOAD_URL = 'http://github.com/scikit-image/scikit-image'
VERSION = '0.8.0'
VERSION = '0.8.1'
PYTHON_VERSION = (2, 5)
DEPENDENCIES = {
'numpy': (1, 6),
skimage/draw/_draw.pyx
+7 -7
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@@ -109,14 +109,14 @@ def polygon(y, x, shape=None):
cdef Py_ssize_t r, c
#: make contigous arrays for r, c coordinates
# make contigous arrays for r, c coordinates
cdef cnp.ndarray contiguous_rdata, contiguous_cdata
contiguous_rdata = np.ascontiguousarray(y, 'double')
contiguous_cdata = np.ascontiguousarray(x, 'double')
cdef cnp.double_t* rptr = <cnp.double_t*>contiguous_rdata.data
cdef cnp.double_t* cptr = <cnp.double_t*>contiguous_cdata.data
#: output coordinate arrays
# output coordinate arrays
cdef list rr = list()
cdef list cc = list()
@@ -126,7 +126,7 @@ def polygon(y, x, shape=None):
rr.append(r)
cc.append(c)
return np.array(rr), np.array(cc)
return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp)
def ellipse(double cy, double cx, double yradius, double xradius, shape=None):
@@ -164,7 +164,7 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None):
cdef Py_ssize_t r, c
#: output coordinate arrays
# output coordinate arrays
cdef list rr = list()
cdef list cc = list()
@@ -174,7 +174,7 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None):
rr.append(r)
cc.append(c)
return np.array(rr), np.array(cc)
return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp)
def circle(double cy, double cx, double radius, shape=None):
@@ -281,7 +281,7 @@ def circle_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t radius,
y = y - 1
x = x + 1
return np.array(rr) + cy, np.array(cc) + cx
return np.array(rr, dtype=np.intp) + cy, np.array(cc, dtype=np.intp) + cx
def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius,
@@ -368,7 +368,7 @@ def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius,
err += ychange
ychange += twobsquared
return np.array(py) + cy, np.array(px) + cx
return np.array(py, dtype=np.intp) + cy, np.array(px, dtype=np.intp) + cx
def set_color(img, coords, color):
skimage/filter/_ctmf.pyx
+10 -12
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@@ -91,8 +91,9 @@ cdef struct SCoord:
Py_ssize_t y
cdef struct Histograms:
void *memory # pointer to the allocated memory
Histogram *histogram # pointer to the histogram memory
HistogramPiece accumulator # running histogram (32-byte aligned)
void *memory # pointer to the unaligned allocated memory
Histogram *histogram # pointer to the histogram memory (aligned)
PixelCount *pixel_count # pointer to the pixel count memory
cnp.uint8_t *data # pointer to the image data
cnp.uint8_t *mask # pointer to the image mask
@@ -142,9 +143,6 @@ cdef struct Histograms:
Py_ssize_t row_stride # stride between one row and the next
Py_ssize_t col_stride # stride between one column and the next
# The accumulator holds the running histogram
#
HistogramPiece accumulator
#
# The running count of pixels in the accumulator
#
@@ -188,10 +186,12 @@ cdef Histograms *allocate_histograms(Py_ssize_t rows,
memory_size = (adjusted_stripe_length *
(sizeof(Histogram) + sizeof(PixelCount)) +
sizeof(Histograms) + 32)
sizeof(Histograms) + 64)
ptr = malloc(memory_size)
memset(ptr, 0, memory_size)
ph = <Histograms *> ptr
# align ph.accumulator to 32-byte boundary
roundoff = (<Py_ssize_t> ptr + 31) % 32
ph = <Histograms *> (ptr + 31 - roundoff)
if not ptr:
return ph
ph.memory = ptr
@@ -201,10 +201,8 @@ cdef Histograms *allocate_histograms(Py_ssize_t rows,
#
# Align histogram memory to a 32-byte boundary
#
roundoff = <Py_ssize_t>ptr
roundoff += 31
roundoff -= roundoff % 32
ptr = <void *> roundoff
roundoff = (<Py_ssize_t> ptr + 31) % 32
ptr += 31 - roundoff
ph.histogram = <Histogram *> ptr
#
# Fill in the statistical things we keep around
@@ -696,7 +694,7 @@ cdef int c_median_filter(Py_ssize_t rows,
#
# Initialize the accumulator (octagon histogram) to zero
#
memset(&ph.accumulator, 0, sizeof(ph.accumulator))
memset(&(ph.accumulator), 0, sizeof(ph.accumulator))
ph.accumulator_count = 0
for i in range(16):
ph.last_update_column[i] = -radius-1
skimage/filter/setup.py
+1 -1
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@@ -26,7 +26,7 @@ def configuration(parent_package='', top_path=None):
cython(['rank/bilateral_rank.pyx'], working_path=base_path)
config.add_extension('_ctmf', sources=['_ctmf.c'],
include_dirs=[get_numpy_include_dirs()])
include_dirs=[get_numpy_include_dirs()])
config.add_extension('_denoise_cy', sources=['_denoise_cy.c'],
include_dirs=[get_numpy_include_dirs(), '../_shared'])
config.add_extension('rank._core8', sources=['rank/_core8.c'],
skimage/measure/tests/test_regionprops.py
+3 -3
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@@ -49,7 +49,7 @@ def test_bbox():
def test_central_moments():
mu = regionprops(SAMPLE, ['CentralMoments'])[0]['CentralMoments']
#: determined with OpenCV
# determined with OpenCV
assert_almost_equal(mu[0,2], 436.00000000000045)
# different from OpenCV results, bug in OpenCV
assert_almost_equal(mu[0,3], -737.333333333333)
@@ -198,7 +198,7 @@ def test_minor_axis_length():
def test_moments():
m = regionprops(SAMPLE, ['Moments'])[0]['Moments']
#: determined with OpenCV
# determined with OpenCV
assert_almost_equal(m[0,0], 72.0)
assert_almost_equal(m[0,1], 408.0)
assert_almost_equal(m[0,2], 2748.0)
@@ -213,7 +213,7 @@ def test_moments():
def test_normalized_moments():
nu = regionprops(SAMPLE, ['NormalizedMoments'])[0]['NormalizedMoments']
#: determined with OpenCV
# determined with OpenCV
assert_almost_equal(nu[0,2], 0.08410493827160502)
assert_almost_equal(nu[1,1], -0.016846707818929982)
assert_almost_equal(nu[1,2], -0.002899800614433943)
skimage/morphology/_convex_hull.pyx
+1 -1
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@@ -31,7 +31,7 @@ def possible_hull(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, mode="c"] img):
# rows storage slots for right boundary pixels
# cols storage slots for bottom boundary pixels
cdef cnp.ndarray[dtype=cnp.intp_t, ndim=2] nonzero = \
np.ones((2 * (rows + cols), 2), dtype=np.int)
np.ones((2 * (rows + cols), 2), dtype=np.intp)
nonzero *= -1
for r in range(rows):
skimage/segmentation/_felzenszwalb_cy.pyx
+1 -1
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@@ -73,7 +73,7 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20)
cdef cnp.intp_t *edges_p = <cnp.intp_t*>edges.data
cdef cnp.float_t *costs_p = <cnp.float_t*>costs.data
cdef cnp.ndarray[cnp.intp_t, ndim=1] segment_size \
= np.ones(width * height, dtype=np.int)
= np.ones(width * height, dtype=np.intp)
# inner cost of segments
cdef cnp.ndarray[cnp.float_t, ndim=1] cint = np.zeros(width * height)
cdef int seg0, seg1, seg_new, e
skimage/transform/hough_transform.py
+1 -1
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@@ -164,7 +164,7 @@ def hough_circle(img, radius, normalize=True):
Hough transform accumulator for each radius
"""
return _hough_circle(img, radius, normalize)
return _hough_circle(img, radius.astype(np.intp), normalize)
def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
threshold=None, num_peaks=np.inf):