576 lines
18 KiB
Python
576 lines
18 KiB
Python
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import math
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import numpy as np
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from numpy import array
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from skimage._shared._warnings import expected_warnings
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from skimage.measure._regionprops import (regionprops, PROPS, perimeter,
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_parse_docs, _props_to_dict,
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regionprops_table, OBJECT_COLUMNS,
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COL_DTYPES)
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from skimage._shared import testing
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from skimage._shared.testing import (assert_array_equal, assert_almost_equal,
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assert_array_almost_equal, assert_equal)
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SAMPLE = np.array(
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[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1],
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[0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]
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)
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INTENSITY_SAMPLE = SAMPLE.copy()
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INTENSITY_SAMPLE[1, 9:11] = 2
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SAMPLE_3D = np.zeros((6, 6, 6), dtype=np.uint8)
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SAMPLE_3D[1:3, 1:3, 1:3] = 1
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SAMPLE_3D[3, 2, 2] = 1
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INTENSITY_SAMPLE_3D = SAMPLE_3D.copy()
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def test_all_props():
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region = regionprops(SAMPLE, INTENSITY_SAMPLE)[0]
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for prop in PROPS:
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try:
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assert_almost_equal(region[prop], getattr(region, PROPS[prop]))
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except TypeError: # the `slice` property causes this
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pass
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def test_all_props_3d():
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region = regionprops(SAMPLE_3D, INTENSITY_SAMPLE_3D)[0]
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for prop in PROPS:
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try:
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assert_almost_equal(region[prop], getattr(region, PROPS[prop]))
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except (NotImplementedError, TypeError):
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pass
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def test_dtype():
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regionprops(np.zeros((10, 10), dtype=np.int))
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regionprops(np.zeros((10, 10), dtype=np.uint))
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with testing.raises(TypeError):
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regionprops(np.zeros((10, 10), dtype=np.float))
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with testing.raises(TypeError):
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regionprops(np.zeros((10, 10), dtype=np.double))
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with testing.raises(TypeError):
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regionprops(np.zeros((10, 10), dtype=np.bool))
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def test_ndim():
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regionprops(np.zeros((10, 10), dtype=np.int))
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regionprops(np.zeros((10, 10, 1), dtype=np.int))
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regionprops(np.zeros((10, 10, 10), dtype=np.int))
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regionprops(np.zeros((1, 1), dtype=np.int))
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regionprops(np.zeros((1, 1, 1), dtype=np.int))
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with testing.raises(TypeError):
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regionprops(np.zeros((10, 10, 10, 2), dtype=np.int))
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def test_area():
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area = regionprops(SAMPLE)[0].area
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assert area == np.sum(SAMPLE)
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area = regionprops(SAMPLE_3D)[0].area
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assert area == np.sum(SAMPLE_3D)
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def test_bbox():
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bbox = regionprops(SAMPLE)[0].bbox
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assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]))
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SAMPLE_mod = SAMPLE.copy()
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SAMPLE_mod[:, -1] = 0
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bbox = regionprops(SAMPLE_mod)[0].bbox
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assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]-1))
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bbox = regionprops(SAMPLE_3D)[0].bbox
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assert_array_almost_equal(bbox, (1, 1, 1, 4, 3, 3))
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def test_bbox_area():
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padded = np.pad(SAMPLE, 5, mode='constant')
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bbox_area = regionprops(padded)[0].bbox_area
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assert_array_almost_equal(bbox_area, SAMPLE.size)
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def test_moments_central():
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mu = regionprops(SAMPLE)[0].moments_central
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# determined with OpenCV
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assert_almost_equal(mu[2, 0], 436.00000000000045)
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# different from OpenCV results, bug in OpenCV
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assert_almost_equal(mu[3, 0], -737.333333333333)
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assert_almost_equal(mu[1, 1], -87.33333333333303)
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assert_almost_equal(mu[2, 1], -127.5555555555593)
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assert_almost_equal(mu[0, 2], 1259.7777777777774)
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assert_almost_equal(mu[1, 2], 2000.296296296291)
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assert_almost_equal(mu[0, 3], -760.0246913580195)
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def test_centroid():
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centroid = regionprops(SAMPLE)[0].centroid
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# determined with MATLAB
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assert_array_almost_equal(centroid, (5.66666666666666, 9.444444444444444))
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def test_centroid_3d():
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centroid = regionprops(SAMPLE_3D)[0].centroid
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# determined by mean along axis 1 of SAMPLE_3D.nonzero()
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assert_array_almost_equal(centroid, (1.66666667, 1.55555556, 1.55555556))
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def test_convex_area():
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area = regionprops(SAMPLE)[0].convex_area
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# determined with MATLAB
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assert area == 124
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def test_convex_image():
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img = regionprops(SAMPLE)[0].convex_image
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# determined with MATLAB
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ref = np.array(
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[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
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)
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assert_array_equal(img, ref)
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def test_coordinates():
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sample = np.zeros((10, 10), dtype=np.int8)
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coords = np.array([[3, 2], [3, 3], [3, 4]])
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sample[coords[:, 0], coords[:, 1]] = 1
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prop_coords = regionprops(sample)[0].coords
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assert_array_equal(prop_coords, coords)
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sample = np.zeros((6, 6, 6), dtype=np.int8)
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coords = np.array([[1, 1, 1], [1, 2, 1], [1, 3, 1]])
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sample[coords[:, 0], coords[:, 1], coords[:, 2]] = 1
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prop_coords = regionprops(sample)[0].coords
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assert_array_equal(prop_coords, coords)
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def test_slice():
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padded = np.pad(SAMPLE, ((2, 4), (5, 2)), mode='constant')
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nrow, ncol = SAMPLE.shape
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result = regionprops(padded)[0].slice
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expected = (slice(2, 2+nrow), slice(5, 5+ncol))
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assert_equal(result, expected)
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def test_eccentricity():
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eps = regionprops(SAMPLE)[0].eccentricity
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assert_almost_equal(eps, 0.814629313427)
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img = np.zeros((5, 5), dtype=np.int)
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img[2, 2] = 1
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eps = regionprops(img)[0].eccentricity
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assert_almost_equal(eps, 0)
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def test_equiv_diameter():
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diameter = regionprops(SAMPLE)[0].equivalent_diameter
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# determined with MATLAB
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assert_almost_equal(diameter, 9.57461472963)
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def test_euler_number():
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en = regionprops(SAMPLE)[0].euler_number
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assert en == 1
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SAMPLE_mod = SAMPLE.copy()
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SAMPLE_mod[7, -3] = 0
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en = regionprops(SAMPLE_mod)[0].euler_number
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assert en == 0
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def test_extent():
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extent = regionprops(SAMPLE)[0].extent
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assert_almost_equal(extent, 0.4)
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def test_moments_hu():
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hu = regionprops(SAMPLE)[0].moments_hu
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ref = np.array([
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3.27117627e-01,
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2.63869194e-02,
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2.35390060e-02,
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1.23151193e-03,
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1.38882330e-06,
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-2.72586158e-05,
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-6.48350653e-06
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])
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# bug in OpenCV caused in Central Moments calculation?
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assert_array_almost_equal(hu, ref)
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def test_image():
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img = regionprops(SAMPLE)[0].image
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assert_array_equal(img, SAMPLE)
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img = regionprops(SAMPLE_3D)[0].image
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assert_array_equal(img, SAMPLE_3D[1:4, 1:3, 1:3])
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def test_label():
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label = regionprops(SAMPLE)[0].label
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assert_array_equal(label, 1)
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label = regionprops(SAMPLE_3D)[0].label
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assert_array_equal(label, 1)
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def test_filled_area():
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area = regionprops(SAMPLE)[0].filled_area
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assert area == np.sum(SAMPLE)
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SAMPLE_mod = SAMPLE.copy()
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SAMPLE_mod[7, -3] = 0
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area = regionprops(SAMPLE_mod)[0].filled_area
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assert area == np.sum(SAMPLE)
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def test_filled_image():
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img = regionprops(SAMPLE)[0].filled_image
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assert_array_equal(img, SAMPLE)
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def test_major_axis_length():
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length = regionprops(SAMPLE)[0].major_axis_length
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# MATLAB has different interpretation of ellipse than found in literature,
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# here implemented as found in literature
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assert_almost_equal(length, 16.7924234999)
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def test_max_intensity():
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intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].max_intensity
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assert_almost_equal(intensity, 2)
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def test_mean_intensity():
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intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].mean_intensity
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assert_almost_equal(intensity, 1.02777777777777)
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def test_min_intensity():
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intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].min_intensity
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assert_almost_equal(intensity, 1)
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def test_minor_axis_length():
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length = regionprops(SAMPLE)[0].minor_axis_length
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# MATLAB has different interpretation of ellipse than found in literature,
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# here implemented as found in literature
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assert_almost_equal(length, 9.739302807263)
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def test_moments():
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m = regionprops(SAMPLE)[0].moments
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# determined with OpenCV
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assert_almost_equal(m[0, 0], 72.0)
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assert_almost_equal(m[0, 1], 680.0)
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assert_almost_equal(m[0, 2], 7682.0)
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assert_almost_equal(m[0, 3], 95588.0)
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assert_almost_equal(m[1, 0], 408.0)
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assert_almost_equal(m[1, 1], 3766.0)
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assert_almost_equal(m[1, 2], 43882.0)
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assert_almost_equal(m[2, 0], 2748.0)
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assert_almost_equal(m[2, 1], 24836.0)
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assert_almost_equal(m[3, 0], 19776.0)
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def test_moments_normalized():
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nu = regionprops(SAMPLE)[0].moments_normalized
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# determined with OpenCV
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assert_almost_equal(nu[0, 2], 0.24301268861454037)
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assert_almost_equal(nu[0, 3], -0.017278118992041805)
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assert_almost_equal(nu[1, 1], -0.016846707818929982)
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assert_almost_equal(nu[1, 2], 0.045473992910668816)
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assert_almost_equal(nu[2, 0], 0.08410493827160502)
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assert_almost_equal(nu[2, 1], -0.002899800614433943)
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def test_orientation():
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orient = regionprops(SAMPLE)[0].orientation
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# determined with MATLAB
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assert_almost_equal(orient, -1.4663278802756865)
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# test diagonal regions
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diag = np.eye(10, dtype=int)
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orient_diag = regionprops(diag)[0].orientation
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assert_almost_equal(orient_diag, -math.pi / 4)
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orient_diag = regionprops(np.flipud(diag))[0].orientation
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assert_almost_equal(orient_diag, math.pi / 4)
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orient_diag = regionprops(np.fliplr(diag))[0].orientation
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assert_almost_equal(orient_diag, math.pi / 4)
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orient_diag = regionprops(np.fliplr(np.flipud(diag)))[0].orientation
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assert_almost_equal(orient_diag, -math.pi / 4)
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def test_perimeter():
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per = regionprops(SAMPLE)[0].perimeter
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assert_almost_equal(per, 55.2487373415)
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per = perimeter(SAMPLE.astype('double'), neighbourhood=8)
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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')
|