import unittest
import numpy as np
import pytest
from skimage._shared.testing import assert_equal
from scipy.ndimage import binary_dilation, binary_erosion
from skimage import data, feature
from skimage.util import img_as_float
class TestCanny(unittest.TestCase):
def test_00_00_zeros(self):
'''Test that the Canny filter finds no points for a blank field'''
result = feature.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20),
bool))
self.assertFalse(np.any(result))
def test_00_01_zeros_mask(self):
'''Test that the Canny filter finds no points in a masked image'''
result = (feature.canny(np.random.uniform(size=(20, 20)), 4, 0, 0,
np.zeros((20, 20), bool)))
self.assertFalse(np.any(result))
def test_01_01_circle(self):
'''Test that the Canny filter finds the outlines of a circle'''
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
result = feature.canny(c.astype(float), 4, 0, 0, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=3)
ce = binary_erosion(c, iterations=3)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
#
# The circle has a radius of 100. There are two rings here, one
# for the inside edge and one for the outside. So that's
# 100 * 2 * 2 * 3 for those places where pi is still 3.
# The edge contains both pixels if there's a tie, so we
# bump the count a little.
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_01_02_circle_with_noise(self):
'''Test that the Canny filter finds the circle outlines
in a noisy image'''
np.random.seed(0)
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5
result = feature.canny(cf, 4, .1, .2, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=4)
ce = binary_erosion(c, iterations=4)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_image_shape(self):
self.assertRaises(ValueError, feature.canny, np.zeros((20, 20, 20)), 4,
0, 0)
def test_mask_none(self):
result1 = feature.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20),
bool))
result2 = feature.canny(np.zeros((20, 20)), 4, 0, 0)
self.assertTrue(np.all(result1 == result2))
def test_use_quantiles(self):
image = img_as_float(data.camera()[::100, ::100])
# Correct output produced manually with quantiles
# of 0.8 and 0.6 for high and low respectively
correct_output = np.array(
[[False, False, False, False, False, False],
[False, True, True, True, False, False],
[False, False, False, True, False, False],
[False, False, False, True, False, False],
[False, False, True, True, False, False],
[False, False, False, False, False, False]])
result = feature.canny(image, low_threshold=0.6, high_threshold=0.8,
use_quantiles=True)
assert_equal(result, correct_output)
def test_img_all_ones(self):
image = np.ones((10, 10))
assert np.all(feature.canny(image) == 0)
def test_invalid_use_quantiles(self):
image = img_as_float(data.camera()[::50, ::50])
self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
low_threshold=0.5, high_threshold=3.6)
self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
low_threshold=-5, high_threshold=0.5)
self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
low_threshold=99, high_threshold=0.9)
self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
low_threshold=0.5, high_threshold=-100)
# Example from issue #4282
image = data.camera()
self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
low_threshold=50, high_threshold=150)
def test_dtype(self):
"""Check that the same output is produced regardless of image dtype."""
image_uint8 = data.camera()
image_float = img_as_float(image_uint8)
result_uint8 = feature.canny(image_uint8)
result_float = feature.canny(image_float)
assert_equal(result_uint8, result_float)
low = 0.1
high = 0.2
assert_equal(feature.canny(image_float, 1.0, low, high),
feature.canny(image_uint8, 1.0, 255 * low, 255 * high))
def test_full_mask_matches_no_mask(self):
"""The masked and unmasked algorithms should return the same result.
"""
image = data.camera()
for mode in ('constant', 'nearest', 'reflect'):
assert_equal(
feature.canny(image, mode=mode),
feature.canny(image, mode=mode, mask=np.ones_like(image, dtype=bool))
)
def test_unsupported_int64(self):
for dtype in (np.int64, np.uint64):
image = np.zeros((10, 10), dtype=dtype)
image[3, 3] = np.iinfo(dtype).max
with pytest.raises(
ValueError, match="64-bit integer images are not supported"
):
feature.canny(image)