475 lines
16 KiB
Python
475 lines
16 KiB
Python
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from itertools import product
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import pytest
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import numpy as np
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from skimage.segmentation import slic
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from skimage._shared import testing
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from skimage._shared.testing import test_parallel, assert_equal
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@test_parallel()
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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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start_label=0)
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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, start_label=0)
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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, start_label=0)
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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(product(*slices))
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colors = list(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, start_label=0)
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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(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, start_label=0)
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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],
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multichannel=False, start_label=0)
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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, start_label=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, start_label=0)
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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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with testing.raises(ValueError):
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slic(img, multichannel=True, convert2lab=True, start_label=0)
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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, start_label=0)
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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, start_label=0)
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# Make sure nothing fatal occurs (e.g. buffer overflow) at low values of
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# max_size_factor
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segments_connected_low_max = slic(img, 2, compactness=0.0001,
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enforce_connectivity=True,
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convert2lab=False,
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max_size_factor=0.8,
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start_label=0)
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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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assert_equal(segments_connected_low_max, result_connected)
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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, start_label=0)
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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_more_segments_than_pixels():
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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=500, compactness=1,
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multichannel=False, convert2lab=False, start_label=0)
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assert np.all(seg.ravel() == np.arange(seg.size))
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def test_color_2d_mask():
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rnd = np.random.RandomState(0)
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msk = np.zeros((20, 21))
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msk[2:-2, 2:-2] = 1
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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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np.clip(img, 0, 1, out=img)
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seg = slic(img, n_segments=4, sigma=0, enforce_connectivity=False,
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mask=msk)
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# we expect 4 segments + masked area
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assert_equal(len(np.unique(seg)), 5)
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assert_equal(seg.shape, img.shape[:-1])
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# segments
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assert_equal(seg[2:10, 2:10], 1)
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assert_equal(seg[10:-2, 2:10], 4)
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assert_equal(seg[2:10, 10:-2], 2)
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assert_equal(seg[10:-2, 10:-2], 3)
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# non masked area
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assert_equal(seg[:2, :], 0)
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assert_equal(seg[-2:, :], 0)
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assert_equal(seg[:, :2], 0)
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assert_equal(seg[:, -2:], 0)
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def test_multichannel_2d_mask():
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rnd = np.random.RandomState(0)
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msk = np.zeros((20, 20))
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msk[2:-2, 2:-2] = 1
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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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np.clip(img, 0, 1, out=img)
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seg = slic(img, n_segments=4, enforce_connectivity=False,
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mask=msk)
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# we expect 4 segments + masked area
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assert_equal(len(np.unique(seg)), 5)
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assert_equal(seg.shape, img.shape[:-1])
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# segments
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assert_equal(seg[2:10, 2:10], 2)
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assert_equal(seg[2:10, 10:-2], 1)
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assert_equal(seg[10:-2, 2:10], 4)
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assert_equal(seg[10:-2, 10:-2], 3)
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# non masked area
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assert_equal(seg[:2, :], 0)
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assert_equal(seg[-2:, :], 0)
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assert_equal(seg[:, :2], 0)
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assert_equal(seg[:, -2:], 0)
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def test_gray_2d_mask():
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rnd = np.random.RandomState(0)
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msk = np.zeros((20, 21))
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msk[2:-2, 2:-2] = 1
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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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np.clip(img, 0, 1, out=img)
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seg = slic(img, sigma=0, n_segments=4, compactness=1,
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multichannel=False, convert2lab=False, mask=msk)
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assert_equal(len(np.unique(seg)), 5)
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assert_equal(seg.shape, img.shape)
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# segments
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assert_equal(seg[2:10, 2:10], 1)
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assert_equal(seg[2:10, 10:-2], 2)
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assert_equal(seg[10:-2, 2:10], 3)
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assert_equal(seg[10:-2, 10:-2], 4)
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# non masked area
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assert_equal(seg[:2, :], 0)
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assert_equal(seg[-2:, :], 0)
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assert_equal(seg[:, :2], 0)
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assert_equal(seg[:, -2:], 0)
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def test_list_sigma_mask():
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rnd = np.random.RandomState(0)
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msk = np.zeros((2, 6))
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msk[:, 1:-1] = 1
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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, 1, 1, 2, 2, 0],
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[0, 1, 1, 2, 2, 0]], np.int)
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seg_sigma = slic(img, n_segments=2, sigma=[1, 50, 1],
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multichannel=False, mask=msk)
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assert_equal(seg_sigma, result_sigma)
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def test_spacing_mask():
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rnd = np.random.RandomState(0)
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msk = np.zeros((2, 5))
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msk[:, 1:-1] = 1
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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, 1, 1, 2, 0],
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[0, 1, 2, 2, 0]], np.int)
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result_spaced = np.array([[0, 1, 1, 1, 0],
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[0, 2, 2, 2, 0]], np.int)
|
||
|
|
img += 0.1 * rnd.normal(size=img.shape)
|
||
|
|
seg_non_spaced = slic(img, n_segments=2, sigma=0, multichannel=False,
|
||
|
|
compactness=1.0, mask=msk)
|
||
|
|
seg_spaced = slic(img, n_segments=2, sigma=0, spacing=[1, 50, 1],
|
||
|
|
compactness=1.0, multichannel=False, mask=msk)
|
||
|
|
assert_equal(seg_non_spaced, result_non_spaced)
|
||
|
|
assert_equal(seg_spaced, result_spaced)
|
||
|
|
|
||
|
|
|
||
|
|
def test_enforce_connectivity_mask():
|
||
|
|
msk = np.zeros((3, 6))
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||
|
|
msk[:, 1:-1] = 1
|
||
|
|
img = np.array([[0, 0, 0, 1, 1, 1],
|
||
|
|
[1, 0, 0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 1, 1, 0]], np.float)
|
||
|
|
|
||
|
|
segments_connected = slic(img, 2, compactness=0.0001,
|
||
|
|
enforce_connectivity=True,
|
||
|
|
convert2lab=False, mask=msk)
|
||
|
|
segments_disconnected = slic(img, 2, compactness=0.0001,
|
||
|
|
enforce_connectivity=False,
|
||
|
|
convert2lab=False, mask=msk)
|
||
|
|
|
||
|
|
# Make sure nothing fatal occurs (e.g. buffer overflow) at low values of
|
||
|
|
# max_size_factor
|
||
|
|
segments_connected_low_max = slic(img, 2, compactness=0.0001,
|
||
|
|
enforce_connectivity=True,
|
||
|
|
convert2lab=False,
|
||
|
|
max_size_factor=0.8, mask=msk)
|
||
|
|
|
||
|
|
result_connected = np.array([[0, 1, 1, 2, 2, 0],
|
||
|
|
[0, 1, 1, 2, 2, 0],
|
||
|
|
[0, 1, 1, 2, 2, 0]], np.float)
|
||
|
|
|
||
|
|
result_disconnected = np.array([[0, 1, 1, 2, 2, 0],
|
||
|
|
[0, 1, 1, 2, 2, 0],
|
||
|
|
[0, 1, 1, 2, 2, 0]], np.float)
|
||
|
|
|
||
|
|
assert_equal(segments_connected, result_connected)
|
||
|
|
assert_equal(segments_disconnected, result_disconnected)
|
||
|
|
assert_equal(segments_connected_low_max, result_connected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_slic_zero_mask():
|
||
|
|
|
||
|
|
rnd = np.random.RandomState(0)
|
||
|
|
msk = np.zeros((20, 21))
|
||
|
|
msk[2:-2, 2:-2] = 1
|
||
|
|
img = np.zeros((20, 21, 3))
|
||
|
|
img[:10, :10, 0] = 1
|
||
|
|
img[10:, :10, 1] = 1
|
||
|
|
img[10:, 10:, 2] = 1
|
||
|
|
img += 0.01 * rnd.normal(size=img.shape)
|
||
|
|
np.clip(img, 0, 1, out=img)
|
||
|
|
seg = slic(img, n_segments=4, sigma=0, slic_zero=True,
|
||
|
|
mask=msk)
|
||
|
|
|
||
|
|
# we expect 4 segments + masked area
|
||
|
|
assert_equal(len(np.unique(seg)), 5)
|
||
|
|
assert_equal(seg.shape, img.shape[:-1])
|
||
|
|
# segments
|
||
|
|
assert_equal(seg[2:10, 2:10], 1)
|
||
|
|
assert_equal(seg[2:10, 10:-2], 2)
|
||
|
|
assert_equal(seg[10:-2, 2:10], 3)
|
||
|
|
assert_equal(seg[10:-2, 10:-2], 4)
|
||
|
|
# non masked area
|
||
|
|
assert_equal(seg[:2, :], 0)
|
||
|
|
assert_equal(seg[-2:, :], 0)
|
||
|
|
assert_equal(seg[:, :2], 0)
|
||
|
|
assert_equal(seg[:, -2:], 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_more_segments_than_pixels_mask():
|
||
|
|
rnd = np.random.RandomState(0)
|
||
|
|
msk = np.zeros((20, 21))
|
||
|
|
msk[2:-2, 2:-2] = 1
|
||
|
|
img = np.zeros((20, 21))
|
||
|
|
img[:10, :10] = 0.33
|
||
|
|
img[10:, :10] = 0.67
|
||
|
|
img[10:, 10:] = 1.00
|
||
|
|
img += 0.0033 * rnd.normal(size=img.shape)
|
||
|
|
np.clip(img, 0, 1, out=img)
|
||
|
|
seg = slic(img, sigma=0, n_segments=500, compactness=1,
|
||
|
|
multichannel=False, convert2lab=False, mask=msk)
|
||
|
|
|
||
|
|
expected = np.arange(seg[2:-2, 2:-2].size) + 1
|
||
|
|
assert np.all(seg[2:-2, 2:-2].ravel() == expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_color_3d_mask():
|
||
|
|
|
||
|
|
msk = np.zeros((20, 21, 22))
|
||
|
|
msk[2:-2, 2:-2, 2:-2] = 1
|
||
|
|
|
||
|
|
rnd = np.random.RandomState(0)
|
||
|
|
img = np.zeros((20, 21, 22, 3))
|
||
|
|
slices = []
|
||
|
|
for dim_size in msk.shape:
|
||
|
|
midpoint = dim_size // 2
|
||
|
|
slices.append((slice(None, midpoint), slice(midpoint, None)))
|
||
|
|
slices = list(product(*slices))
|
||
|
|
colors = list(product(*(([0, 1],) * 3)))
|
||
|
|
for s, c in zip(slices, colors):
|
||
|
|
img[s] = c
|
||
|
|
img += 0.01 * rnd.normal(size=img.shape)
|
||
|
|
np.clip(img, 0, 1, out=img)
|
||
|
|
|
||
|
|
seg = slic(img, sigma=0, n_segments=8, mask=msk)
|
||
|
|
|
||
|
|
# we expect 8 segments + masked area
|
||
|
|
assert_equal(len(np.unique(seg)), 9)
|
||
|
|
for s, c in zip(slices, range(1, 9)):
|
||
|
|
assert_equal(seg[s][2:-2, 2:-2, 2:-2], c)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gray_3d_mask():
|
||
|
|
|
||
|
|
msk = np.zeros((20, 21, 22))
|
||
|
|
msk[2:-2, 2:-2, 2:-2] = 1
|
||
|
|
|
||
|
|
rnd = np.random.RandomState(0)
|
||
|
|
img = np.zeros((20, 21, 22))
|
||
|
|
slices = []
|
||
|
|
for dim_size in img.shape:
|
||
|
|
midpoint = dim_size // 2
|
||
|
|
slices.append((slice(None, midpoint), slice(midpoint, None)))
|
||
|
|
slices = list(product(*slices))
|
||
|
|
shades = np.linspace(0, 1, 8)
|
||
|
|
for s, sh in zip(slices, shades):
|
||
|
|
img[s] = sh
|
||
|
|
img += 0.001 * rnd.normal(size=img.shape)
|
||
|
|
np.clip(img, 0, 1, out=img)
|
||
|
|
seg = slic(img, sigma=0, n_segments=8, multichannel=False,
|
||
|
|
convert2lab=False, mask=msk)
|
||
|
|
|
||
|
|
# we expect 8 segments + masked area
|
||
|
|
assert_equal(len(np.unique(seg)), 9)
|
||
|
|
for s, c in zip(slices, range(1, 9)):
|
||
|
|
assert_equal(seg[s][2:-2, 2:-2, 2:-2], c)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", ['float32', 'float64', 'uint8', 'int'])
|
||
|
|
def test_dtype_support(dtype):
|
||
|
|
img = np.random.rand(28, 28).astype(dtype)
|
||
|
|
|
||
|
|
# Simply run the function to assert that it runs without error
|
||
|
|
slic(img, start_label=1)
|