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https://github.com/wassname/scikit-image.git
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Andreas Mueller
77 lines
1.9 KiB
Python
77 lines
1.9 KiB
Python
import numpy as np
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from numpy.testing import *
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import skimage.transform as tf
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import skimage.transform.hough_transform as ht
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from skimage.transform import probabilistic_hough
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def append_desc(func, description):
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"""Append the test function ``func`` and append
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``description`` to its name.
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"""
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func.description = func.__module__ + '.' + func.__name__ + description
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return func
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from skimage.transform import *
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def test_hough():
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# Generate a test image
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img = np.zeros((100, 100), dtype=int)
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for i in range(25, 75):
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img[100 - i, i] = 1
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out, angles, d = tf.hough(img)
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y, x = np.where(out == out.max())
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dist = d[y[0]]
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theta = angles[x[0]]
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assert_equal(dist > 70, dist < 72)
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assert_equal(theta > 0.78, theta < 0.79)
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def test_hough_angles():
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img = np.zeros((10, 10))
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img[0, 0] = 1
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out, angles, d = tf.hough(img, np.linspace(0, 360, 10))
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assert_equal(len(angles), 10)
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def test_py_hough():
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ht._hough, fast_hough = ht._py_hough, ht._hough
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yield append_desc(test_hough, '_python')
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yield append_desc(test_hough_angles, '_python')
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tf._hough = fast_hough
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def test_probabilistic_hough():
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# Generate a test image
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img = np.zeros((100, 100), dtype=int)
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for i in range(25, 75):
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img[100 - i, i] = 100
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img[i, i] = 100
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# decrease default theta sampling because similar orientations may confuse
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# as mentioned in article of Galambos et al
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theta = np.linspace(0, np.pi, 45)
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lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10,
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line_gap=1)
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# sort the lines according to the x-axis
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sorted_lines = []
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for line in lines:
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line = list(line)
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line.sort(key=lambda x: x[0])
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sorted_lines.append(line)
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assert([(25, 75), (74, 26)] in sorted_lines)
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assert([(25, 25), (74, 74)] in sorted_lines)
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if __name__ == "__main__":
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run_module_suite()
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