mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:17:18 +08:00
Julius Bier Kirekgaard
375 lines
12 KiB
Python
375 lines
12 KiB
Python
import numpy as np
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from numpy.testing import assert_almost_equal, assert_equal
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import skimage.transform as tf
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from skimage.draw import line, circle_perimeter, ellipse_perimeter
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from skimage._shared._warnings import expected_warnings
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from skimage._shared.testing import test_parallel
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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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@test_parallel()
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def test_hough_line():
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# Generate a test image
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img = np.zeros((100, 150), dtype=int)
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rr, cc = line(60, 130, 80, 10)
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img[rr, cc] = 1
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out, angles, d = tf.hough_line(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_almost_equal(dist, 80.723, 1)
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assert_almost_equal(theta, 1.41, 1)
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def test_hough_line_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_line(img, np.linspace(0, 360, 10))
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assert_equal(len(angles), 10)
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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 = tf.probabilistic_hough_line(img, threshold=10, line_length=10,
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line_gap=1, theta=theta)
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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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def test_hough_line_peaks():
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img = np.zeros((100, 150), dtype=int)
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rr, cc = line(60, 130, 80, 10)
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img[rr, cc] = 1
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out, angles, d = tf.hough_line(img)
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with expected_warnings(['`background`']):
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out, theta, dist = tf.hough_line_peaks(out, angles, d)
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assert_equal(len(dist), 1)
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assert_almost_equal(dist[0], 80.723, 1)
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assert_almost_equal(theta[0], 1.41, 1)
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def test_hough_line_peaks_dist():
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img = np.zeros((100, 100), dtype=np.bool_)
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img[:, 30] = True
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img[:, 40] = True
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hspace, angles, dists = tf.hough_line(img)
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with expected_warnings(['`background`']):
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_distance=5)[0]) == 2
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_distance=15)[0]) == 1
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def test_hough_line_peaks_angle():
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with expected_warnings(['`background`']):
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check_hough_line_peaks_angle()
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def check_hough_line_peaks_angle():
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img = np.zeros((100, 100), dtype=np.bool_)
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img[:, 0] = True
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img[0, :] = True
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hspace, angles, dists = tf.hough_line(img)
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=45)[0]) == 2
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=90)[0]) == 1
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theta = np.linspace(0, np.pi, 100)
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hspace, angles, dists = tf.hough_line(img, theta)
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=45)[0]) == 2
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=90)[0]) == 1
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theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100)
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hspace, angles, dists = tf.hough_line(img, theta)
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=45)[0]) == 2
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assert len(tf.hough_line_peaks(hspace, angles, dists,
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min_angle=90)[0]) == 1
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def test_hough_line_peaks_num():
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img = np.zeros((100, 100), dtype=np.bool_)
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img[:, 30] = True
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img[:, 40] = True
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hspace, angles, dists = tf.hough_line(img)
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with expected_warnings(['`background`']):
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assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0,
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min_angle=0, num_peaks=1)[0]) == 1
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@test_parallel()
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def test_hough_circle():
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# Prepare picture
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img = np.zeros((120, 100), dtype=int)
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radius = 20
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x_0, y_0 = (99, 50)
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y, x = circle_perimeter(y_0, x_0, radius)
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img[x, y] = 1
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out1 = tf.hough_circle(img, radius)
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out2 = tf.hough_circle(img, [radius])
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assert_equal(out1, out2)
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out = tf.hough_circle(img, np.array([radius], dtype=np.intp))
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assert_equal(out, out1)
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x, y = np.where(out[0] == out[0].max())
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assert_equal(x[0], x_0)
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assert_equal(y[0], y_0)
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def test_hough_circle_extended():
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# Prepare picture
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# The circle center is outside the image
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img = np.zeros((100, 100), dtype=int)
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radius = 20
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x_0, y_0 = (-5, 50)
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y, x = circle_perimeter(y_0, x_0, radius)
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img[x[np.where(x > 0)], y[np.where(x > 0)]] = 1
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out = tf.hough_circle(img, np.array([radius], dtype=np.intp),
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full_output=True)
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x, y = np.where(out[0] == out[0].max())
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# Offset for x_0, y_0
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assert_equal(x[0], x_0 + radius)
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assert_equal(y[0], y_0 + radius)
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def test_hough_ellipse_zero_angle():
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img = np.zeros((25, 25), dtype=int)
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rx = 6
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ry = 8
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x0 = 12
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y0 = 15
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angle = 0
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rr, cc = ellipse_perimeter(y0, x0, ry, rx)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=9)
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best = result[-1]
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assert_equal(best[1], y0)
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assert_equal(best[2], x0)
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assert_almost_equal(best[3], ry, decimal=1)
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assert_almost_equal(best[4], rx, decimal=1)
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assert_equal(best[5], angle)
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_posangle1():
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# ry > rx, angle in [0:pi/2]
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img = np.zeros((30, 24), dtype=int)
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rx = 6
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ry = 12
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x0 = 10
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y0 = 15
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angle = np.pi / 1.35
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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assert_almost_equal(best[1] / 100., y0 / 100., decimal=1)
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assert_almost_equal(best[2] / 100., x0 / 100., decimal=1)
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assert_almost_equal(best[3] / 10., ry / 10., decimal=1)
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assert_almost_equal(best[4] / 100., rx / 100., decimal=1)
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assert_almost_equal(best[5], angle, decimal=1)
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_posangle2():
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# ry < rx, angle in [0:pi/2]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = np.pi / 1.35
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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assert_almost_equal(best[1] / 100., y0 / 100., decimal=1)
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assert_almost_equal(best[2] / 100., x0 / 100., decimal=1)
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assert_almost_equal(best[3] / 10., ry / 10., decimal=1)
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assert_almost_equal(best[4] / 100., rx / 100., decimal=1)
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assert_almost_equal(best[5], angle, decimal=1)
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_posangle3():
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# ry < rx, angle in [pi/2:pi]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = np.pi / 1.35 + np.pi / 2.
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_posangle4():
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# ry < rx, angle in [pi:3pi/4]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = np.pi / 1.35 + np.pi
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_negangle1():
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# ry > rx, angle in [0:-pi/2]
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img = np.zeros((30, 24), dtype=int)
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rx = 6
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ry = 12
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x0 = 10
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y0 = 15
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angle = - np.pi / 1.35
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_negangle2():
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# ry < rx, angle in [0:-pi/2]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = - np.pi / 1.35
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_negangle3():
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# ry < rx, angle in [-pi/2:-pi]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = - np.pi / 1.35 - np.pi / 2.
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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def test_hough_ellipse_non_zero_negangle4():
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# ry < rx, angle in [-pi:-3pi/4]
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img = np.zeros((30, 24), dtype=int)
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rx = 12
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ry = 6
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x0 = 10
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y0 = 15
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angle = - np.pi / 1.35 - np.pi
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rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
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img[rr, cc] = 1
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result = tf.hough_ellipse(img, threshold=15, accuracy=3)
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result.sort(order='accumulator')
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best = result[-1]
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# Check if I re-draw the ellipse, points are the same!
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# ie check API compatibility between hough_ellipse and ellipse_perimeter
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rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
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orientation=best[5])
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assert_equal(rr, rr2)
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assert_equal(cc, cc2)
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if __name__ == "__main__":
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np.testing.run_module_suite()
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