122 lines
5.1 KiB
Python
122 lines
5.1 KiB
Python
import unittest
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import numpy as np
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from skimage._shared.testing import assert_equal
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from scipy.ndimage import binary_dilation, binary_erosion
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from skimage import data, feature
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from skimage.util import img_as_float
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class TestCanny(unittest.TestCase):
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def test_00_00_zeros(self):
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'''Test that the Canny filter finds no points for a blank field'''
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result = feature.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20),
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bool))
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self.assertFalse(np.any(result))
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def test_00_01_zeros_mask(self):
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'''Test that the Canny filter finds no points in a masked image'''
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result = (feature.canny(np.random.uniform(size=(20, 20)), 4, 0, 0,
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np.zeros((20, 20), bool)))
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self.assertFalse(np.any(result))
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def test_01_01_circle(self):
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'''Test that the Canny filter finds the outlines of a circle'''
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i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
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c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
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result = feature.canny(c.astype(float), 4, 0, 0, np.ones(c.shape, bool))
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#
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# erode and dilate the circle to get rings that should contain the
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# outlines
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#
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cd = binary_dilation(c, iterations=3)
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ce = binary_erosion(c, iterations=3)
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cde = np.logical_and(cd, np.logical_not(ce))
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self.assertTrue(np.all(cde[result]))
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#
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# The circle has a radius of 100. There are two rings here, one
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# for the inside edge and one for the outside. So that's
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# 100 * 2 * 2 * 3 for those places where pi is still 3.
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# The edge contains both pixels if there's a tie, so we
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# bump the count a little.
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point_count = np.sum(result)
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self.assertTrue(point_count > 1200)
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self.assertTrue(point_count < 1600)
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def test_01_02_circle_with_noise(self):
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'''Test that the Canny filter finds the circle outlines
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in a noisy image'''
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np.random.seed(0)
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i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
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c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
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cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5
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result = feature.canny(cf, 4, .1, .2, np.ones(c.shape, bool))
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#
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# erode and dilate the circle to get rings that should contain the
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# outlines
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#
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cd = binary_dilation(c, iterations=4)
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ce = binary_erosion(c, iterations=4)
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cde = np.logical_and(cd, np.logical_not(ce))
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self.assertTrue(np.all(cde[result]))
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point_count = np.sum(result)
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self.assertTrue(point_count > 1200)
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self.assertTrue(point_count < 1600)
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def test_image_shape(self):
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self.assertRaises(ValueError, feature.canny, np.zeros((20, 20, 20)), 4,
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0, 0)
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def test_mask_none(self):
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result1 = feature.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20),
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bool))
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result2 = feature.canny(np.zeros((20, 20)), 4, 0, 0)
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self.assertTrue(np.all(result1 == result2))
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def test_use_quantiles(self):
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image = img_as_float(data.camera()[::100, ::100])
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# Correct output produced manually with quantiles
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# of 0.8 and 0.6 for high and low respectively
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correct_output = np.array(
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[[False, False, False, False, False, False],
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[False, True, True, True, False, False],
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[False, False, False, True, False, False],
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[False, False, False, True, False, False],
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[False, False, True, True, False, False],
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[False, False, False, False, False, False]])
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result = feature.canny(image, low_threshold=0.6, high_threshold=0.8,
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use_quantiles=True)
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assert_equal(result, correct_output)
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def test_invalid_use_quantiles(self):
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image = img_as_float(data.camera()[::50, ::50])
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self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
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low_threshold=0.5, high_threshold=3.6)
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self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
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low_threshold=-5, high_threshold=0.5)
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self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
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low_threshold=99, high_threshold=0.9)
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self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
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low_threshold=0.5, high_threshold=-100)
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# Example from issue #4282
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image = data.camera()
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self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
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low_threshold=50, high_threshold=150)
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def test_dtype(self):
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"""Check that the same output is produced regardless of image dtype."""
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image_uint8 = data.camera()
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image_float = img_as_float(image_uint8)
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result_uint8 = feature.canny(image_uint8)
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result_float = feature.canny(image_float)
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assert_equal(result_uint8, result_float)
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