mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 17:45:20 +08:00
Steven Silvester
0e61374a89
Add a helper function to check for low contrast Add a check for low contrast when using imsave Use the low contrast helper in imshow and make sure warnings are always shown Clean up parameter names and add doctests Remove unnecessary warning context Remove unnecessary warning context Add dtype ranges for 64bit types Update tests with new warnings Fix doctest logic Fix doctest logic Add a low contrast test with multiple dtypes Fix check for color images Fix color check again Add support for int32 types Relax assertion for 32bit builds Add a low contrast test with multiple dtypes Add a low contrast test with multiple dtypes Fix check for color images Fix color check again Add support for int32 types
383 lines
12 KiB
Python
383 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._warnings import expected_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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assert_almost_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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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 expected_warnings(['`background`|CObject type']):
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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 expected_warnings(['`background`|CObject type']):
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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 expected_warnings(['`background`|CObject type']):
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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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