mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-10 16:13:47 +08:00
Anders Boesen Lindbo Larsen
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
import numpy as np
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from numpy.testing import assert_raises, assert_almost_equal
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from numpy import sqrt, ceil
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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 daisy
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def test_daisy_color_image_unsupported_error():
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img = np.zeros((20, 20, 3))
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assert_raises(ValueError, daisy, img)
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def test_daisy_desc_dims():
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img = img_as_float(data.lena()[:128, :128].mean(axis=2))
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rings = 2
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histograms = 4
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orientations = 3
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descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
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assert(descs.shape[2] == (rings*histograms + 1)*orientations)
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rings = 4
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histograms = 5
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orientations = 13
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descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
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assert(descs.shape[2] == (rings*histograms + 1)*orientations)
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def test_descs_shape():
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img = img_as_float(data.lena()[:256, :256].mean(axis=2))
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radius = 20
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step = 8
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descs = daisy(img, radius=radius, step=step)
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assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step)))
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assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step)))
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img = img[:-1,:-2]
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radius = 5
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step = 3
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descs = daisy(img, radius=radius, step=step)
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assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step)))
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assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step)))
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def test_daisy_incompatible_sigmas_and_radii():
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img = img_as_float(data.lena()[:128, :128].mean(axis=2))
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sigmas = [1, 2]
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radii = [1, 2]
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assert_raises(ValueError, daisy, img, sigmas=sigmas, ring_radii=radii)
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def test_daisy_normalization():
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img = img_as_float(data.lena()[:64, :64].mean(axis=2))
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descs = daisy(img, normalization='l1')
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for i in range(descs.shape[0]):
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for j in range(descs.shape[1]):
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assert_almost_equal(np.sum(descs[i,j,:]), 1)
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descs_ = daisy(img)
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assert_almost_equal(descs, descs_)
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descs = daisy(img, normalization='l2')
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for i in range(descs.shape[0]):
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for j in range(descs.shape[1]):
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assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1)
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orientations = 8
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descs = daisy(img, orientations=orientations, normalization='daisy')
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desc_dims = descs.shape[2]
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for i in range(descs.shape[0]):
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for j in range(descs.shape[1]):
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for k in range(0, desc_dims, orientations):
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assert_almost_equal(sqrt(np.sum(
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descs[i,j,k:k+orientations]**2)), 1)
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img = np.zeros((50, 50))
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descs = daisy(img, normalization='off')
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for i in range(descs.shape[0]):
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for j in range(descs.shape[1]):
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assert_almost_equal(np.sum(descs[i,j,:]), 0)
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assert_raises(ValueError, daisy, img, normalization='does_not_exist')
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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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