mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 17:02:55 +08:00
Steven Silvester
0debedd82c
Reset plugins prior to running collections test Handle warnings in morphology pkg Add __init__ for morpohology tests Handle warnings for novice pkg Handle warnings for restoration pkg Handle warnings for segmentation pkg Handle warnings for _shared pkg Handle warnings for transform pkg Handle warnings for util pkg Handle warnings in viewer module
369 lines
12 KiB
Python
369 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.utils import all_warnings
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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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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 all_warnings(): # _ccomp deprecation
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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 all_warnings(): # _ccomp deprecation
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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 all_warnings(): # _ccomp deprecation
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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 all_warnings(): # _ccomp deprecation
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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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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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out = tf.hough_circle(img, np.array([radius], dtype=np.intp))
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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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