import math
import numpy as np
from numpy import array
from skimage._shared._warnings import expected_warnings
from skimage.measure._regionprops import (regionprops, PROPS, perimeter,
_parse_docs, _props_to_dict,
regionprops_table, OBJECT_COLUMNS,
COL_DTYPES)
from skimage._shared import testing
from skimage._shared.testing import (assert_array_equal, assert_almost_equal,
assert_array_almost_equal, assert_equal)
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
SAMPLE_3D = np.zeros((6, 6, 6), dtype=np.uint8)
SAMPLE_3D[1:3, 1:3, 1:3] = 1
SAMPLE_3D[3, 2, 2] = 1
INTENSITY_SAMPLE_3D = SAMPLE_3D.copy()
def test_all_props():
region = regionprops(SAMPLE, INTENSITY_SAMPLE)[0]
for prop in PROPS:
try:
assert_almost_equal(region[prop], getattr(region, PROPS[prop]))
except TypeError: # the `slice` property causes this
pass
def test_all_props_3d():
region = regionprops(SAMPLE_3D, INTENSITY_SAMPLE_3D)[0]
for prop in PROPS:
try:
assert_almost_equal(region[prop], getattr(region, PROPS[prop]))
except (NotImplementedError, TypeError):
pass
def test_dtype():
regionprops(np.zeros((10, 10), dtype=np.int))
regionprops(np.zeros((10, 10), dtype=np.uint))
with testing.raises(TypeError):
regionprops(np.zeros((10, 10), dtype=np.float))
with testing.raises(TypeError):
regionprops(np.zeros((10, 10), dtype=np.double))
with testing.raises(TypeError):
regionprops(np.zeros((10, 10), dtype=np.bool))
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, 10), dtype=np.int))
regionprops(np.zeros((1, 1), dtype=np.int))
regionprops(np.zeros((1, 1, 1), dtype=np.int))
with testing.raises(TypeError):
regionprops(np.zeros((10, 10, 10, 2), dtype=np.int))
def test_area():
area = regionprops(SAMPLE)[0].area
assert area == np.sum(SAMPLE)
area = regionprops(SAMPLE_3D)[0].area
assert area == np.sum(SAMPLE_3D)
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))
bbox = regionprops(SAMPLE_3D)[0].bbox
assert_array_almost_equal(bbox, (1, 1, 1, 4, 3, 3))
def test_bbox_area():
padded = np.pad(SAMPLE, 5, mode='constant')
bbox_area = regionprops(padded)[0].bbox_area
assert_array_almost_equal(bbox_area, SAMPLE.size)
def test_moments_central():
mu = regionprops(SAMPLE)[0].moments_central
# determined with OpenCV
assert_almost_equal(mu[2, 0], 436.00000000000045)
# different from OpenCV results, bug in OpenCV
assert_almost_equal(mu[3, 0], -737.333333333333)
assert_almost_equal(mu[1, 1], -87.33333333333303)
assert_almost_equal(mu[2, 1], -127.5555555555593)
assert_almost_equal(mu[0, 2], 1259.7777777777774)
assert_almost_equal(mu[1, 2], 2000.296296296291)
assert_almost_equal(mu[0, 3], -760.0246913580195)
def test_centroid():
centroid = regionprops(SAMPLE)[0].centroid
# determined with MATLAB
assert_array_almost_equal(centroid, (5.66666666666666, 9.444444444444444))
def test_centroid_3d():
centroid = regionprops(SAMPLE_3D)[0].centroid
# determined by mean along axis 1 of SAMPLE_3D.nonzero()
assert_array_almost_equal(centroid, (1.66666667, 1.55555556, 1.55555556))
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)
sample = np.zeros((6, 6, 6), dtype=np.int8)
coords = np.array([[1, 1, 1], [1, 2, 1], [1, 3, 1]])
sample[coords[:, 0], coords[:, 1], coords[:, 2]] = 1
prop_coords = regionprops(sample)[0].coords
assert_array_equal(prop_coords, coords)
def test_slice():
padded = np.pad(SAMPLE, ((2, 4), (5, 2)), mode='constant')
nrow, ncol = SAMPLE.shape
result = regionprops(padded)[0].slice
expected = (slice(2, 2+nrow), slice(5, 5+ncol))
assert_equal(result, expected)
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():
en = regionprops(SAMPLE)[0].euler_number
assert en == 1
SAMPLE_mod = SAMPLE.copy()
SAMPLE_mod[7, -3] = 0
en = regionprops(SAMPLE_mod)[0].euler_number
assert en == 0
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)
img = regionprops(SAMPLE_3D)[0].image
assert_array_equal(img, SAMPLE_3D[1:4, 1:3, 1:3])
def test_label():
label = regionprops(SAMPLE)[0].label
assert_array_equal(label, 1)
label = regionprops(SAMPLE_3D)[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], 680.0)
assert_almost_equal(m[0, 2], 7682.0)
assert_almost_equal(m[0, 3], 95588.0)
assert_almost_equal(m[1, 0], 408.0)
assert_almost_equal(m[1, 1], 3766.0)
assert_almost_equal(m[1, 2], 43882.0)
assert_almost_equal(m[2, 0], 2748.0)
assert_almost_equal(m[2, 1], 24836.0)
assert_almost_equal(m[3, 0], 19776.0)
def test_moments_normalized():
nu = regionprops(SAMPLE)[0].moments_normalized
# determined with OpenCV
assert_almost_equal(nu[0, 2], 0.24301268861454037)
assert_almost_equal(nu[0, 3], -0.017278118992041805)
assert_almost_equal(nu[1, 1], -0.016846707818929982)
assert_almost_equal(nu[1, 2], 0.045473992910668816)
assert_almost_equal(nu[2, 0], 0.08410493827160502)
assert_almost_equal(nu[2, 1], -0.002899800614433943)
def test_orientation():
orient = regionprops(SAMPLE)[0].orientation
# determined with MATLAB
assert_almost_equal(orient, -1.4663278802756865)
# test diagonal regions
diag = np.eye(10, dtype=int)
orient_diag = regionprops(diag)[0].orientation
assert_almost_equal(orient_diag, -math.pi / 4)
orient_diag = regionprops(np.flipud(diag))[0].orientation
assert_almost_equal(orient_diag, math.pi / 4)
orient_diag = regionprops(np.fliplr(diag))[0].orientation
assert_almost_equal(orient_diag, math.pi / 4)
orient_diag = regionprops(np.fliplr(np.flipud(diag)))[0].orientation
assert_almost_equal(orient_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, 3.7303493627e-14, 1.2602837838e+03,
-7.6561796932e+02],
[-2.1316282073e-13, -8.7837837838e+01, 2.1571526662e+03,
-4.2385971907e+03],
[4.7837837838e+02, -1.4801314828e+02, 6.6989799420e+03,
-9.9501164076e+03],
[-7.5943608473e+02, -1.2714707125e+03, 1.5304076361e+04,
-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(
[[7.4000000e+01, 6.9900000e+02, 7.8630000e+03, 9.7317000e+04],
[4.1000000e+02, 3.7850000e+03, 4.4063000e+04, 5.7256700e+05],
[2.7500000e+03, 2.4855000e+04, 2.9347700e+05, 3.9007170e+06],
[1.9778000e+04, 1.7500100e+05, 2.0810510e+06, 2.8078871e+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, 0.2301467830, -0.0162529732],
[ np.nan, -0.0160405109, 0.0457932622, -0.0104598869],
[ 0.0873590903, -0.0031421072, 0.0165315478, -0.0028544152],
[-0.0161217406, -0.0031376984, 0.0043903193, -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
with testing.raises(AttributeError):
get_intensity_image()
def test_invalid_size():
wrong_intensity_sample = np.array([[1], [1]])
with testing.raises(ValueError):
regionprops(SAMPLE, wrong_intensity_sample)
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]
assert_equal(r1 == r2, True, "Same regionprops are not equal")
assert_equal(r1 != r3, True, "Different regionprops are equal")
def test_iterate_all_props():
region = regionprops(SAMPLE)[0]
p0 = {p: region[p] for p in region}
region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE)[0]
p1 = {p: region[p] for p in region}
assert len(p0) < len(p1)
def test_cache():
SAMPLE_mod = SAMPLE.copy()
region = regionprops(SAMPLE_mod)[0]
f0 = region.filled_image
region._label_image[:10] = 1
f1 = region.filled_image
# Changed underlying image, but cache keeps result the same
assert_array_equal(f0, f1)
# Now invalidate cache
region._cache_active = False
f1 = region.filled_image
assert np.any(f0 != f1)
def test_docstrings_and_props():
def foo():
"""foo"""
has_docstrings = bool(foo.__doc__)
region = regionprops(SAMPLE)[0]
docs = _parse_docs()
props = [m for m in dir(region) if not m.startswith('_')]
nr_docs_parsed = len(docs)
nr_props = len(props)
if has_docstrings:
assert_equal(nr_docs_parsed, nr_props)
ds = docs['weighted_moments_normalized']
assert 'iteration' not in ds
assert len(ds.split('\n')) > 3
else:
assert_equal(nr_docs_parsed, 0)
def test_props_to_dict():
regions = regionprops(SAMPLE)
out = _props_to_dict(regions)
assert out == {'label': array([1]),
'bbox-0': array([0]), 'bbox-1': array([0]),
'bbox-2': array([10]), 'bbox-3': array([18])}
regions = regionprops(SAMPLE)
out = _props_to_dict(regions, properties=('label', 'area', 'bbox'),
separator='+')
assert out == {'label': array([1]), 'area': array([72]),
'bbox+0': array([0]), 'bbox+1': array([0]),
'bbox+2': array([10]), 'bbox+3': array([18])}
def test_regionprops_table():
out = regionprops_table(SAMPLE)
assert out == {'label': array([1]),
'bbox-0': array([0]), 'bbox-1': array([0]),
'bbox-2': array([10]), 'bbox-3': array([18])}
out = regionprops_table(SAMPLE, properties=('label', 'area', 'bbox'),
separator='+')
assert out == {'label': array([1]), 'area': array([72]),
'bbox+0': array([0]), 'bbox+1': array([0]),
'bbox+2': array([10]), 'bbox+3': array([18])}
out = regionprops_table(np.zeros((2, 2), dtype=int),
properties=('label', 'area', 'bbox'),
separator='+')
assert len(out) == 6
assert len(out['label']) == 0
assert len(out['area']) == 0
assert len(out['bbox+0']) == 0
assert len(out['bbox+1']) == 0
assert len(out['bbox+2']) == 0
assert len(out['bbox+3']) == 0
def test_props_dict_complete():
region = regionprops(SAMPLE)[0]
properties = [s for s in dir(region) if not s.startswith('_')]
assert set(properties) == set(PROPS.values())
def test_column_dtypes_complete():
assert set(COL_DTYPES.keys()).union(OBJECT_COLUMNS) == set(PROPS.values())
def test_column_dtypes_correct():
msg = 'mismatch with expected type,'
region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE)[0]
for col in COL_DTYPES:
r = region[col]
if col in OBJECT_COLUMNS:
assert COL_DTYPES[col] == object
continue
t = type(np.ravel(r)[0])
if np.issubdtype(t, np.floating):
assert COL_DTYPES[col] == float, (
f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
)
elif np.issubdtype(t, np.integer):
assert COL_DTYPES[col] == int, (
f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
)
else:
assert False, (
f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
)
def test_deprecated_coords_argument():
with expected_warnings(['coordinates keyword argument']):
region = regionprops(SAMPLE, coordinates='rc')
with testing.raises(ValueError):
region = regionprops(SAMPLE, coordinates='xy')