mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 20:56:46 +08:00
Juan Nunez-Iglesias
879a7b0bb5
It still defaults to `True` but only when the last dimension of the input array could be construed as RGB. Also, update ValueError description in docstring.
193 lines
6.1 KiB
Python
193 lines
6.1 KiB
Python
import itertools as it
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import numpy as np
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from numpy.testing import assert_equal, assert_raises
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from skimage.segmentation import slic
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def test_color_2d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 3))
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img[:10, :10, 0] = 1
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img[10:, :10, 1] = 1
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img[10:, 10:, 2] = 1
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img += 0.01 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, n_segments=4, sigma=0, enforce_connectivity=False)
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# we expect 4 segments
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape[:-1])
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assert_equal(seg[:10, :10], 0)
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assert_equal(seg[10:, :10], 2)
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assert_equal(seg[:10, 10:], 1)
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assert_equal(seg[10:, 10:], 3)
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def test_multichannel_2d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 20, 8))
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img[:10, :10, 0:2] = 1
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img[:10, 10:, 2:4] = 1
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img[10:, :10, 4:6] = 1
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img[10:, 10:, 6:8] = 1
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img += 0.01 * rnd.normal(size=img.shape)
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img = np.clip(img, 0, 1, out=img)
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seg = slic(img, n_segments=4, enforce_connectivity=False)
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# we expect 4 segments
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape[:-1])
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assert_equal(seg[:10, :10], 0)
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assert_equal(seg[10:, :10], 2)
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assert_equal(seg[:10, 10:], 1)
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assert_equal(seg[10:, 10:], 3)
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def test_gray_2d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21))
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img[:10, :10] = 0.33
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img[10:, :10] = 0.67
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img[10:, 10:] = 1.00
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img += 0.0033 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=4, compactness=1,
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multichannel=False, convert2lab=False)
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape)
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assert_equal(seg[:10, :10], 0)
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assert_equal(seg[10:, :10], 2)
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assert_equal(seg[:10, 10:], 1)
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assert_equal(seg[10:, 10:], 3)
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def test_color_3d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 22, 3))
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slices = []
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for dim_size in img.shape[:-1]:
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midpoint = dim_size // 2
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slices.append((slice(None, midpoint), slice(midpoint, None)))
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slices = list(it.product(*slices))
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colors = list(it.product(*(([0, 1],) * 3)))
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for s, c in zip(slices, colors):
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img[s] = c
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img += 0.01 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=8)
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assert_equal(len(np.unique(seg)), 8)
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for s, c in zip(slices, range(8)):
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assert_equal(seg[s], c)
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def test_gray_3d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 22))
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slices = []
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for dim_size in img.shape:
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midpoint = dim_size // 2
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slices.append((slice(None, midpoint), slice(midpoint, None)))
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slices = list(it.product(*slices))
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shades = np.arange(0, 1.000001, 1.0 / 7)
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for s, sh in zip(slices, shades):
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img[s] = sh
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img += 0.001 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=8, compactness=1,
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multichannel=False, convert2lab=False)
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assert_equal(len(np.unique(seg)), 8)
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for s, c in zip(slices, range(8)):
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assert_equal(seg[s], c)
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def test_list_sigma():
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rnd = np.random.RandomState(0)
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img = np.array([[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 1, 1, 1]], np.float)
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img += 0.1 * rnd.normal(size=img.shape)
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result_sigma = np.array([[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1]], np.int)
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seg_sigma = slic(img, n_segments=2, sigma=[1, 50, 1], multichannel=False)
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assert_equal(seg_sigma, result_sigma)
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def test_spacing():
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rnd = np.random.RandomState(0)
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img = np.array([[1, 1, 1, 0, 0],
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[1, 1, 0, 0, 0]], np.float)
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result_non_spaced = np.array([[0, 0, 0, 1, 1],
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[0, 0, 1, 1, 1]], np.int)
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result_spaced = np.array([[0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1]], np.int)
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img += 0.1 * rnd.normal(size=img.shape)
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seg_non_spaced = slic(img, n_segments=2, sigma=0, multichannel=False,
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compactness=1.0)
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seg_spaced = slic(img, n_segments=2, sigma=0, spacing=[1, 500, 1],
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compactness=1.0, multichannel=False)
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assert_equal(seg_non_spaced, result_non_spaced)
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assert_equal(seg_spaced, result_spaced)
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def test_invalid_lab_conversion():
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img = np.array([[1, 1, 1, 0, 0],
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[1, 1, 0, 0, 0]], np.float) + 1
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assert_raises(ValueError, slic, img, multichannel=True, convert2lab=True)
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def test_enforce_connectivity():
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img = np.array([[0, 0, 0, 1, 1, 1],
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[1, 0, 0, 1, 1, 0],
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[0, 0, 0, 1, 1, 0]], np.float)
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segments_connected = slic(img, 2, compactness=0.0001,
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enforce_connectivity=True,
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convert2lab=False)
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segments_disconnected = slic(img, 2, compactness=0.0001,
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enforce_connectivity=False,
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convert2lab=False)
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result_connected = np.array([[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1]], np.float)
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result_disconnected = np.array([[0, 0, 0, 1, 1, 1],
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[1, 0, 0, 1, 1, 0],
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[0, 0, 0, 1, 1, 0]], np.float)
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assert_equal(segments_connected, result_connected)
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assert_equal(segments_disconnected, result_disconnected)
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def test_slic_zero():
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# Same as test_color_2d but with slic_zero=True
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 3))
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img[:10, :10, 0] = 1
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img[10:, :10, 1] = 1
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img[10:, 10:, 2] = 1
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img += 0.01 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, n_segments=4, sigma=0, slic_zero=True)
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# we expect 4 segments
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape[:-1])
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assert_equal(seg[:10, :10], 0)
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assert_equal(seg[10:, :10], 2)
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assert_equal(seg[:10, 10:], 1)
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assert_equal(seg[10:, 10:], 3)
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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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