mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 17:45:20 +08:00
Steven Silvester
4680f30466
Add a function to set up a skimage test Switch to new test helper function Import local packages that raise warnings in test setup function More fixes to doctests Fix regionprops doc test Try and fix the test_rank failure. Remove no longer needed RectangleSelector shim Skip more doctests in _regionprops Try importing another scipy subpackage
385 lines
12 KiB
Python
385 lines
12 KiB
Python
from numpy.testing import assert_array_equal, assert_almost_equal, \
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assert_array_almost_equal, assert_raises, assert_equal
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import numpy as np
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import math
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from skimage.measure._regionprops import regionprops, PROPS, perimeter
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from skimage._shared.utils import all_warnings
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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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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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with all_warnings(): # deprecation warning
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assert_equal(region[prop], getattr(region, PROPS[prop]))
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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 all_warnings(): # deprecation on dtype
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assert_raises((TypeError, RuntimeError), regionprops,
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np.zeros((10, 10), dtype=np.double))
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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, 1, 1), dtype=np.int))
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assert_raises(TypeError, regionprops, np.zeros((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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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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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[0,2], 436.00000000000045)
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# different from OpenCV results, bug in OpenCV
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assert_almost_equal(mu[0,3], -737.333333333333)
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assert_almost_equal(mu[1,1], -87.33333333333303)
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assert_almost_equal(mu[1,2], -127.5555555555593)
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assert_almost_equal(mu[2,0], 1259.7777777777774)
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assert_almost_equal(mu[2,1], 2000.296296296291)
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assert_almost_equal(mu[3,0], -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_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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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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with all_warnings(): # deprecation warning
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en = regionprops(SAMPLE)[0].euler_number
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assert en == 0
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SAMPLE_mod = SAMPLE.copy()
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SAMPLE_mod[7, -3] = 0
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with all_warnings(): # deprecation warning
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en = regionprops(SAMPLE_mod)[0].euler_number
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assert en == -1
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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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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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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], 408.0)
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assert_almost_equal(m[0,2], 2748.0)
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assert_almost_equal(m[0,3], 19776.0)
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assert_almost_equal(m[1,0], 680.0)
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assert_almost_equal(m[1,1], 3766.0)
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assert_almost_equal(m[1,2], 24836.0)
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assert_almost_equal(m[2,0], 7682.0)
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assert_almost_equal(m[2,1], 43882.0)
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assert_almost_equal(m[3,0], 95588.0)
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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], 0.08410493827160502)
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assert_almost_equal(nu[1,1], -0.016846707818929982)
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assert_almost_equal(nu[1,2], -0.002899800614433943)
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assert_almost_equal(nu[2,0], 0.24301268861454037)
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assert_almost_equal(nu[2,1], 0.045473992910668816)
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assert_almost_equal(nu[3,0], -0.017278118992041805)
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def test_orientation():
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orientation = regionprops(SAMPLE)[0].orientation
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# determined with MATLAB
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assert_almost_equal(orientation, 0.10446844651921)
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# test correct quadrant determination
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orientation2 = regionprops(SAMPLE.T)[0].orientation
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assert_almost_equal(orientation2, math.pi / 2 - orientation)
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# test diagonal regions
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diag = np.eye(10, dtype=int)
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orientation_diag = regionprops(diag)[0].orientation
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assert_almost_equal(orientation_diag, -math.pi / 4)
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orientation_diag = regionprops(np.flipud(diag))[0].orientation
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assert_almost_equal(orientation_diag, math.pi / 4)
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orientation_diag = regionprops(np.fliplr(diag))[0].orientation
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assert_almost_equal(orientation_diag, math.pi / 4)
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orientation_diag = regionprops(np.fliplr(np.flipud(diag)))[0].orientation
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assert_almost_equal(orientation_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)
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def test_solidity():
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solidity = regionprops(SAMPLE)[0].solidity
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# determined with MATLAB
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assert_almost_equal(solidity, 0.580645161290323)
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def test_weighted_moments_central():
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wmu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].weighted_moments_central
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ref = np.array(
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[[ 7.4000000000e+01, -2.1316282073e-13, 4.7837837838e+02,
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-7.5943608473e+02],
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[ 3.7303493627e-14, -8.7837837838e+01, -1.4801314828e+02,
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-1.2714707125e+03],
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[ 1.2602837838e+03, 2.1571526662e+03, 6.6989799420e+03,
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1.5304076361e+04],
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[ -7.6561796932e+02, -4.2385971907e+03, -9.9501164076e+03,
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-3.3156729271e+04]]
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)
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np.set_printoptions(precision=10)
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assert_array_almost_equal(wmu, ref)
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def test_weighted_centroid():
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centroid = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].weighted_centroid
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assert_array_almost_equal(centroid, (5.540540540540, 9.445945945945))
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def test_weighted_moments_hu():
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whu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].weighted_moments_hu
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ref = np.array([
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3.1750587329e-01,
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2.1417517159e-02,
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2.3609322038e-02,
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1.2565683360e-03,
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8.3014209421e-07,
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-3.5073773473e-05,
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6.7936409056e-06
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])
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assert_array_almost_equal(whu, ref)
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def test_weighted_moments():
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wm = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].weighted_moments
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ref = np.array(
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[[ 7.4000000000e+01, 4.1000000000e+02, 2.7500000000e+03,
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1.9778000000e+04],
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[ 6.9900000000e+02, 3.7850000000e+03, 2.4855000000e+04,
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1.7500100000e+05],
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[ 7.8630000000e+03, 4.4063000000e+04, 2.9347700000e+05,
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2.0810510000e+06],
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[ 9.7317000000e+04, 5.7256700000e+05, 3.9007170000e+06,
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2.8078871000e+07]]
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)
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assert_array_almost_equal(wm, ref)
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def test_weighted_moments_normalized():
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wnu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
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)[0].weighted_moments_normalized
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ref = np.array(
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[[ np.nan, np.nan, 0.0873590903, -0.0161217406],
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[ np.nan, -0.0160405109, -0.0031421072, -0.0031376984],
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[ 0.230146783, 0.0457932622, 0.0165315478, 0.0043903193],
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[-0.0162529732, -0.0104598869, -0.0028544152, -0.0011057191]]
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)
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assert_array_almost_equal(wnu, ref)
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def test_label_sequence():
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a = np.empty((2, 2), dtype=np.int)
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a[:, :] = 2
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ps = regionprops(a)
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assert len(ps) == 1
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assert ps[0].label == 2
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def test_pure_background():
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a = np.zeros((2, 2), dtype=np.int)
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ps = regionprops(a)
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assert len(ps) == 0
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def test_invalid():
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ps = regionprops(SAMPLE)
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def get_intensity_image():
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ps[0].intensity_image
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assert_raises(AttributeError, get_intensity_image)
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def test_equals():
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arr = np.zeros((100, 100), dtype=np.int)
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arr[0:25, 0:25] = 1
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arr[50:99, 50:99] = 2
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regions = regionprops(arr)
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r1 = regions[0]
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regions = regionprops(arr)
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r2 = regions[0]
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r3 = regions[1]
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with all_warnings(): # deprecation warning
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assert_equal(r1 == r2, True, "Same regionprops are not equal")
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assert_equal(r1 != r3, True, "Different regionprops are equal")
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if __name__ == "__main__":
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from numpy.testing import run_module_suite
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run_module_suite()
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