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scikit-image/skimage/measure/tests/test_regionprops.py
T
Jonathan HelmusandSteven Silvester 78a0eb6b98 Implement appveyor builds 
Implement Appveyor builds

initial test

Updated appveyor.yml

New nosetest call

With conda update conda

with pillow

without pillow

TST: Change case sensitivive JPG extension

Revert "TST: Change case sensitivive JPG extension"

This reverts commit 2deed7cc63736f7c6f0387bd37df4c6643c32847.

Trying with Python 2.7

Trying with version env

Trying again with variables

Testing on all Python versions

don't allow failures

Allow failure

Do not actually use tests

Ignore failing tests

Removed Python 2.6 test

Testing only Python 2.6

Testing out more sklearn like AppVeyor CI

Added install to pip

Add artifacts

Enabled all permutations

Disable 2.6, add PIL

Python 2.6, 2.7 only with PIL

Testing with variable dependencies

Allow failure with IF ELSE

With Python 3.4

Scikit-learn like appveyor CI

Fixing paths

Undo path fix

path fix single line

path fix single line 2

Using Miniconda

More path fixes

New wheelhouse link

Added pillow to requirements.txt

Added networkx to requirements.txt

Add testing of 32/64-bit Python 2.7 and 3.4 to matrix

Debugging Cython compile

Retry with all 4 builds

Updated install.ps1 file

Updated based on latest python-appveyor-demo

Debugging pip install

Specify numpy 1.8.1 until whl is uploaded to rackspace

Use skimage-wide requirements.txt file

Minor comment change to trigger build

Install wheel and then install from WHEELHOUSE

Install six from pip

Install networkx from pip

Install pyparsing from pip

Install pytz from pip

Try using just find-links

Install the binary dependencies first, then the rest

Add pillow to the install list

Fix appveyor.yml syntax

Fix requirements.txt syntax

Fix requirements.txt syntax again

Fix appveyor call to initial install

Fix appveyor call to initial install again

Fix appveyor call to initial install yet again

Install wheel

Install wheel first

Install wheel and nose in the appveyor requirements.txt

Fix Python3 version to match python ftp site

Only use cleanup decorator if available

Add debug info to multiimage test

More debugging information

Fix handling of path separators on Windows

Add another warning guard

Fix warning handling for non-windows

Do not use TkAgg as it may be causing alloc error

Clean up echo command

Allow for unclosed file warning

Fix spacing in echo command
2015-02-28 19:57:45 -06:00

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from numpy.testing import assert_array_equal, assert_almost_equal, \
assert_array_almost_equal, assert_raises, assert_equal
import numpy as np
import math
from skimage.measure._regionprops import regionprops, PROPS, perimeter
from skimage._shared._warnings import expected_warnings
SAMPLE = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1],
[0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]
)
INTENSITY_SAMPLE = SAMPLE.copy()
INTENSITY_SAMPLE[1, 9:11] = 2
def test_all_props():
region = regionprops(SAMPLE, INTENSITY_SAMPLE)[0]
for prop in PROPS:
assert_equal(region[prop], getattr(region, PROPS[prop]))
def test_dtype():
regionprops(np.zeros((10, 10), dtype=np.int))
regionprops(np.zeros((10, 10), dtype=np.uint))
assert_raises((TypeError, RuntimeError), regionprops,
np.zeros((10, 10), dtype=np.double))
def test_ndim():
regionprops(np.zeros((10, 10), dtype=np.int))
regionprops(np.zeros((10, 10, 1), dtype=np.int))
regionprops(np.zeros((10, 10, 1, 1), dtype=np.int))
assert_raises(TypeError, regionprops, np.zeros((10, 10, 2), dtype=np.int))
def test_area():
area = regionprops(SAMPLE)[0].area
assert area == np.sum(SAMPLE)
def test_bbox():
bbox = regionprops(SAMPLE)[0].bbox
assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]))
SAMPLE_mod = SAMPLE.copy()
SAMPLE_mod[:, -1] = 0
bbox = regionprops(SAMPLE_mod)[0].bbox
assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]-1))
def test_moments_central():
mu = regionprops(SAMPLE)[0].moments_central
# 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)
assert_almost_equal(mu[1,1], -87.33333333333303)
assert_almost_equal(mu[1,2], -127.5555555555593)
assert_almost_equal(mu[2,0], 1259.7777777777774)
assert_almost_equal(mu[2,1], 2000.296296296291)
assert_almost_equal(mu[3,0], -760.0246913580195)
def test_centroid():
centroid = regionprops(SAMPLE)[0].centroid
# determined with MATLAB
assert_array_almost_equal(centroid, (5.66666666666666, 9.444444444444444))
def test_convex_area():
area = regionprops(SAMPLE)[0].convex_area
# determined with MATLAB
assert area == 124
def test_convex_image():
img = regionprops(SAMPLE)[0].convex_image
# determined with MATLAB
ref = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
)
assert_array_equal(img, ref)
def test_coordinates():
sample = np.zeros((10, 10), dtype=np.int8)
coords = np.array([[3, 2], [3, 3], [3, 4]])
sample[coords[:, 0], coords[:, 1]] = 1
prop_coords = regionprops(sample)[0].coords
assert_array_equal(prop_coords, coords)
def test_eccentricity():
eps = regionprops(SAMPLE)[0].eccentricity
assert_almost_equal(eps, 0.814629313427)
img = np.zeros((5, 5), dtype=np.int)
img[2, 2] = 1
eps = regionprops(img)[0].eccentricity
assert_almost_equal(eps, 0)
def test_equiv_diameter():
diameter = regionprops(SAMPLE)[0].equivalent_diameter
# determined with MATLAB
assert_almost_equal(diameter, 9.57461472963)
def test_euler_number():
with expected_warnings(['`background`|CObject type']):
en = regionprops(SAMPLE)[0].euler_number
assert en == 0
SAMPLE_mod = SAMPLE.copy()
SAMPLE_mod[7, -3] = 0
with expected_warnings(['`background`|CObject type']):
en = regionprops(SAMPLE_mod)[0].euler_number
assert en == -1
def test_extent():
extent = regionprops(SAMPLE)[0].extent
assert_almost_equal(extent, 0.4)
def test_moments_hu():
hu = regionprops(SAMPLE)[0].moments_hu
ref = np.array([
3.27117627e-01,
2.63869194e-02,
2.35390060e-02,
1.23151193e-03,
1.38882330e-06,
-2.72586158e-05,
6.48350653e-06
])
# bug in OpenCV caused in Central Moments calculation?
assert_array_almost_equal(hu, ref)
def test_image():
img = regionprops(SAMPLE)[0].image
assert_array_equal(img, SAMPLE)
def test_label():
label = regionprops(SAMPLE)[0].label
assert_array_equal(label, 1)
def test_filled_area():
area = regionprops(SAMPLE)[0].filled_area
assert area == np.sum(SAMPLE)
SAMPLE_mod = SAMPLE.copy()
SAMPLE_mod[7, -3] = 0
area = regionprops(SAMPLE_mod)[0].filled_area
assert area == np.sum(SAMPLE)
def test_filled_image():
img = regionprops(SAMPLE)[0].filled_image
assert_array_equal(img, SAMPLE)
def test_major_axis_length():
length = regionprops(SAMPLE)[0].major_axis_length
# MATLAB has different interpretation of ellipse than found in literature,
# here implemented as found in literature
assert_almost_equal(length, 16.7924234999)
def test_max_intensity():
intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].max_intensity
assert_almost_equal(intensity, 2)
def test_mean_intensity():
intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].mean_intensity
assert_almost_equal(intensity, 1.02777777777777)
def test_min_intensity():
intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].min_intensity
assert_almost_equal(intensity, 1)
def test_minor_axis_length():
length = regionprops(SAMPLE)[0].minor_axis_length
# MATLAB has different interpretation of ellipse than found in literature,
# here implemented as found in literature
assert_almost_equal(length, 9.739302807263)
def test_moments():
m = regionprops(SAMPLE)[0].moments
# 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)
assert_almost_equal(m[0,3], 19776.0)
assert_almost_equal(m[1,0], 680.0)
assert_almost_equal(m[1,1], 3766.0)
assert_almost_equal(m[1,2], 24836.0)
assert_almost_equal(m[2,0], 7682.0)
assert_almost_equal(m[2,1], 43882.0)
assert_almost_equal(m[3,0], 95588.0)
def test_moments_normalized():
nu = regionprops(SAMPLE)[0].moments_normalized
# 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)
assert_almost_equal(nu[2,0], 0.24301268861454037)
assert_almost_equal(nu[2,1], 0.045473992910668816)
assert_almost_equal(nu[3,0], -0.017278118992041805)
def test_orientation():
orientation = regionprops(SAMPLE)[0].orientation
# determined with MATLAB
assert_almost_equal(orientation, 0.10446844651921)
# test correct quadrant determination
orientation2 = regionprops(SAMPLE.T)[0].orientation
assert_almost_equal(orientation2, math.pi / 2 - orientation)
# test diagonal regions
diag = np.eye(10, dtype=int)
orientation_diag = regionprops(diag)[0].orientation
assert_almost_equal(orientation_diag, -math.pi / 4)
orientation_diag = regionprops(np.flipud(diag))[0].orientation
assert_almost_equal(orientation_diag, math.pi / 4)
orientation_diag = regionprops(np.fliplr(diag))[0].orientation
assert_almost_equal(orientation_diag, math.pi / 4)
orientation_diag = regionprops(np.fliplr(np.flipud(diag)))[0].orientation
assert_almost_equal(orientation_diag, -math.pi / 4)
def test_perimeter():
per = regionprops(SAMPLE)[0].perimeter
assert_almost_equal(per, 55.2487373415)
per = perimeter(SAMPLE.astype('double'), neighbourhood=8)
assert_almost_equal(per, 46.8284271247)
def test_solidity():
solidity = regionprops(SAMPLE)[0].solidity
# determined with MATLAB
assert_almost_equal(solidity, 0.580645161290323)
def test_weighted_moments_central():
wmu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].weighted_moments_central
ref = np.array(
[[ 7.4000000000e+01, -2.1316282073e-13, 4.7837837838e+02,
-7.5943608473e+02],
[ 3.7303493627e-14, -8.7837837838e+01, -1.4801314828e+02,
-1.2714707125e+03],
[ 1.2602837838e+03, 2.1571526662e+03, 6.6989799420e+03,
1.5304076361e+04],
[ -7.6561796932e+02, -4.2385971907e+03, -9.9501164076e+03,
-3.3156729271e+04]]
)
np.set_printoptions(precision=10)
assert_array_almost_equal(wmu, ref)
def test_weighted_centroid():
centroid = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].weighted_centroid
assert_array_almost_equal(centroid, (5.540540540540, 9.445945945945))
def test_weighted_moments_hu():
whu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].weighted_moments_hu
ref = np.array([
3.1750587329e-01,
2.1417517159e-02,
2.3609322038e-02,
1.2565683360e-03,
8.3014209421e-07,
-3.5073773473e-05,
6.7936409056e-06
])
assert_array_almost_equal(whu, ref)
def test_weighted_moments():
wm = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].weighted_moments
ref = np.array(
[[ 7.4000000000e+01, 4.1000000000e+02, 2.7500000000e+03,
1.9778000000e+04],
[ 6.9900000000e+02, 3.7850000000e+03, 2.4855000000e+04,
1.7500100000e+05],
[ 7.8630000000e+03, 4.4063000000e+04, 2.9347700000e+05,
2.0810510000e+06],
[ 9.7317000000e+04, 5.7256700000e+05, 3.9007170000e+06,
2.8078871000e+07]]
)
assert_array_almost_equal(wm, ref)
def test_weighted_moments_normalized():
wnu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
)[0].weighted_moments_normalized
ref = np.array(
[[ np.nan, np.nan, 0.0873590903, -0.0161217406],
[ np.nan, -0.0160405109, -0.0031421072, -0.0031376984],
[ 0.230146783, 0.0457932622, 0.0165315478, 0.0043903193],
[-0.0162529732, -0.0104598869, -0.0028544152, -0.0011057191]]
)
assert_array_almost_equal(wnu, ref)
def test_label_sequence():
a = np.empty((2, 2), dtype=np.int)
a[:, :] = 2
ps = regionprops(a)
assert len(ps) == 1
assert ps[0].label == 2
def test_pure_background():
a = np.zeros((2, 2), dtype=np.int)
ps = regionprops(a)
assert len(ps) == 0
def test_invalid():
ps = regionprops(SAMPLE)
def get_intensity_image():
ps[0].intensity_image
assert_raises(AttributeError, get_intensity_image)
def test_equals():
arr = np.zeros((100, 100), dtype=np.int)
arr[0:25, 0:25] = 1
arr[50:99, 50:99] = 2
regions = regionprops(arr)
r1 = regions[0]
regions = regionprops(arr)
r2 = regions[0]
r3 = regions[1]
with expected_warnings(['`background`|CObject type']):
assert_equal(r1 == r2, True, "Same regionprops are not equal")
assert_equal(r1 != r3, True, "Different regionprops are equal")
if __name__ == "__main__":
from numpy.testing import run_module_suite
run_module_suite()
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