mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 12:58:49 +08:00
Malcolm Reynolds
130 lines
3.6 KiB
Python
130 lines
3.6 KiB
Python
import numpy as np
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from numpy.testing import assert_array_equal
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from skimage import data
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from skimage import img_as_float
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from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi,
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corner_subpix, peak_local_max, corner_peaks)
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def test_square_image():
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im = np.zeros((50, 50)).astype(float)
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im[:25, :25] = 1.
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# Moravec
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results = peak_local_max(corner_moravec(im))
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# interest points along edge
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assert len(results) == 57
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# Harris
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results = peak_local_max(corner_harris(im))
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# interest at corner
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assert len(results) == 1
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# Shi-Tomasi
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results = peak_local_max(corner_shi_tomasi(im))
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# interest at corner
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assert len(results) == 1
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def test_noisy_square_image():
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im = np.zeros((50, 50)).astype(float)
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im[:25, :25] = 1.
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np.random.seed(seed=1234)
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im = im + np.random.uniform(size=im.shape) * .2
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# Moravec
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results = peak_local_max(corner_moravec(im))
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# undefined number of interest points
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assert results.any()
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# Harris
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results = peak_local_max(corner_harris(im, sigma=1.5))
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assert len(results) == 1
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# Shi-Tomasi
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results = peak_local_max(corner_shi_tomasi(im, sigma=1.5))
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assert len(results) == 1
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def test_squared_dot():
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im = np.zeros((50, 50))
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im[4:8, 4:8] = 1
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im = img_as_float(im)
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# Moravec fails
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# Harris
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results = peak_local_max(corner_harris(im))
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assert (results == np.array([[6, 6]])).all()
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# Shi-Tomasi
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results = peak_local_max(corner_shi_tomasi(im))
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assert (results == np.array([[6, 6]])).all()
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def test_rotated_lena():
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"""
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The harris filter should yield the same results with an image and it's
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rotation.
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"""
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im = img_as_float(data.lena().mean(axis=2))
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im_rotated = im.T
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# Moravec
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results = peak_local_max(corner_moravec(im))
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results_rotated = peak_local_max(corner_moravec(im_rotated))
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assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
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assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
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# Harris
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results = peak_local_max(corner_harris(im))
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results_rotated = peak_local_max(corner_harris(im_rotated))
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assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
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assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
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# Shi-Tomasi
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results = peak_local_max(corner_shi_tomasi(im))
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results_rotated = peak_local_max(corner_shi_tomasi(im_rotated))
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assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
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assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
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def test_subpix():
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img = np.zeros((50, 50))
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img[:25,:25] = 255
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img[25:,25:] = 255
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corner = peak_local_max(corner_harris(img), num_peaks=1)
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subpix = corner_subpix(img, corner)
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assert_array_equal(subpix[0], (24.5, 24.5))
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def test_num_peaks():
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"""For a bunch of different values of num_peaks, check that
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peak_local_max returns exactly the right amount of peaks. Test
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is run on Lena in order to produce a sufficient number of corners"""
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lena_corners = corner_harris(data.lena())
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for i in range(20):
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n = np.random.random_integers(20)
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results = peak_local_max(lena_corners, num_peaks=n)
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assert (results.shape[0] == n)
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def test_corner_peaks():
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response = np.zeros((5, 5))
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response[2:4, 2:4] = 1
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corners = corner_peaks(response, exclude_border=False)
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assert len(corners) == 1
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corners = corner_peaks(response, exclude_border=False, min_distance=0)
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assert len(corners) == 4
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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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