mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-13 12:40:24 +08:00
Robin Wilson
b29ad8ef6d
This allows you to specify the high and low thresholds as quantiles of the edge magnitude image, rather than as absolute edge magnitude values
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import unittest
|
|
import numpy as np
|
|
from numpy.testing import assert_equal
|
|
from scipy.ndimage import binary_dilation, binary_erosion
|
|
import skimage.feature as F
|
|
from skimage import filters, data
|
|
from skimage 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 = F.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 = (F.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 = F.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 = F.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, F.canny, np.zeros((20, 20, 20)), 4, 0, 0)
|
|
|
|
def test_mask_none(self):
|
|
result1 = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool))
|
|
result2 = F.canny(np.zeros((20, 20)), 4, 0, 0)
|
|
self.assertTrue(np.all(result1 == result2))
|
|
|
|
|
|
def test_quantile_threshold():
|
|
image = img_as_float(data.camera()[::50,::50])
|
|
|
|
# Correct output produced manually with quantiles
|
|
# of 0.8 and 0.6 for high and low respectively
|
|
correct_output = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
|
|
[0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0],
|
|
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
|
|
[0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0],
|
|
[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
|
|
[0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],
|
|
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
|
|
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
|
|
|
|
result = F.canny(image, low_threshold=0.6, high_threshold=0.8, quantile_threshold=True)
|
|
|
|
assert_equal(result, correct_output)
|
|
|
|
|