mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:33:19 +08:00
Juan Nunez-Iglesias
0d134987f9
Only two forms remain in use: - `from scipy import ndimage as ndi` - `from scipy.ndimage import function`
305 lines
11 KiB
Python
305 lines
11 KiB
Python
import numpy as np
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from numpy.testing import (assert_array_almost_equal as assert_close,
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assert_equal)
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from scipy import ndimage as ndi
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from skimage.feature import peak
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np.random.seed(21)
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def test_trivial_case():
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trivial = np.zeros((25, 25))
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peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True)
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assert not peak_indices # inherent boolean-ness of empty list
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peaks = peak.peak_local_max(trivial, min_distance=1, indices=False)
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assert (peaks.astype(np.bool) == trivial).all()
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def test_noisy_peaks():
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peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
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# image with noise of amplitude 0.8 and peaks of amplitude 1
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image = 0.8 * np.random.rand(20, 20)
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for r, c in peak_locations:
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image[r, c] = 1
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peaks_detected = peak.peak_local_max(image, min_distance=5)
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assert len(peaks_detected) == len(peak_locations)
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for loc in peaks_detected:
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assert tuple(loc) in peak_locations
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def test_relative_threshold():
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image = np.zeros((5, 5), dtype=np.uint8)
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image[1, 1] = 10
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image[3, 3] = 20
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peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5)
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assert len(peaks) == 1
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assert_close(peaks, [(3, 3)])
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def test_absolute_threshold():
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image = np.zeros((5, 5), dtype=np.uint8)
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image[1, 1] = 10
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image[3, 3] = 20
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peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
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assert len(peaks) == 1
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assert_close(peaks, [(3, 3)])
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def test_constant_image():
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image = 128 * np.ones((20, 20), dtype=np.uint8)
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peaks = peak.peak_local_max(image, min_distance=1)
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assert len(peaks) == 0
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def test_flat_peak():
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image = np.zeros((5, 5), dtype=np.uint8)
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image[1:3, 1:3] = 10
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peaks = peak.peak_local_max(image, min_distance=1)
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assert len(peaks) == 4
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def test_num_peaks():
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image = np.zeros((7, 7), dtype=np.uint8)
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image[1, 1] = 10
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image[1, 3] = 11
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image[1, 5] = 12
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image[3, 5] = 8
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image[5, 3] = 7
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assert len(peak.peak_local_max(image, min_distance=1)) == 5
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peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
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assert len(peaks_limited) == 2
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assert (1, 3) in peaks_limited
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assert (1, 5) in peaks_limited
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peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=4)
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assert len(peaks_limited) == 4
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assert (1, 3) in peaks_limited
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assert (1, 5) in peaks_limited
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assert (1, 1) in peaks_limited
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assert (3, 5) in peaks_limited
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def test_num_peaks3D():
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# Issue 1354: the old code only hold for 2D arrays
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# and this code would die with IndexError
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image = np.zeros((10, 10, 100))
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image[5,5,::5] = np.arange(20)
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peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
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assert len(peaks_limited) == 2
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def test_reorder_labels():
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image = np.random.uniform(size=(40, 60))
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i, j = np.mgrid[0:40, 0:60]
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labels = 1 + (i >= 20) + (j >= 30) * 2
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labels[labels == 4] = 5
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i, j = np.mgrid[-3:4, -3:4]
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footprint = (i * i + j * j <= 9)
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expected = np.zeros(image.shape, float)
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for imin, imax in ((0, 20), (20, 40)):
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for jmin, jmax in ((0, 30), (30, 60)):
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expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
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image[imin:imax, jmin:jmax], footprint=footprint)
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expected = (expected == image)
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result = peak.peak_local_max(image, labels=labels, min_distance=1,
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threshold_rel=0, footprint=footprint,
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indices=False, exclude_border=False)
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assert (result == expected).all()
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def test_indices_with_labels():
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image = np.random.uniform(size=(40, 60))
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i, j = np.mgrid[0:40, 0:60]
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labels = 1 + (i >= 20) + (j >= 30) * 2
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i, j = np.mgrid[-3:4, -3:4]
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footprint = (i * i + j * j <= 9)
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expected = np.zeros(image.shape, float)
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for imin, imax in ((0, 20), (20, 40)):
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for jmin, jmax in ((0, 30), (30, 60)):
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expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
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image[imin:imax, jmin:jmax], footprint=footprint)
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expected = (expected == image)
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result = peak.peak_local_max(image, labels=labels, min_distance=1,
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threshold_rel=0, footprint=footprint,
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indices=True, exclude_border=False)
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assert (result == np.transpose(expected.nonzero())).all()
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def test_ndarray_indices_false():
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nd_image = np.zeros((5,5,5))
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nd_image[2,2,2] = 1
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peaks = peak.peak_local_max(nd_image, min_distance=1, indices=False)
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assert (peaks == nd_image.astype(np.bool)).all()
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def test_ndarray_exclude_border():
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nd_image = np.zeros((5,5,5))
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nd_image[[1,0,0],[0,1,0],[0,0,1]] = 1
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nd_image[3,0,0] = 1
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nd_image[2,2,2] = 1
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expected = np.zeros_like(nd_image, dtype=np.bool)
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expected[2,2,2] = True
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result = peak.peak_local_max(nd_image, min_distance=2, indices=False)
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assert (result == expected).all()
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def test_empty():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(~ result)
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def test_one_point():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5] = 1
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labels[5, 5] = 1
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == (labels == 1))
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def test_adjacent_and_same():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5:6] = 1
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labels[5, 5:6] = 1
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == (labels == 1))
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def test_adjacent_and_different():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5] = 1
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image[5, 6] = .5
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labels[5, 5:6] = 1
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expected = (image == 1)
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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result = peak.peak_local_max(image, labels=labels,
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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def test_not_adjacent_and_different():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5] = 1
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image[5, 8] = .5
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labels[image > 0] = 1
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expected = (labels == 1)
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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def test_two_objects():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5] = 1
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image[5, 15] = .5
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labels[5, 5] = 1
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labels[5, 15] = 2
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expected = (labels > 0)
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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def test_adjacent_different_objects():
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image = np.zeros((10, 20))
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labels = np.zeros((10, 20), int)
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image[5, 5] = 1
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image[5, 6] = .5
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labels[5, 5] = 1
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labels[5, 6] = 2
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expected = (labels > 0)
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result = peak.peak_local_max(image, labels=labels,
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footprint=np.ones((3, 3), bool),
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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def test_four_quadrants():
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image = np.random.uniform(size=(40, 60))
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i, j = np.mgrid[0:40, 0:60]
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labels = 1 + (i >= 20) + (j >= 30) * 2
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i, j = np.mgrid[-3:4, -3:4]
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footprint = (i * i + j * j <= 9)
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expected = np.zeros(image.shape, float)
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for imin, imax in ((0, 20), (20, 40)):
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for jmin, jmax in ((0, 30), (30, 60)):
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expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
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image[imin:imax, jmin:jmax], footprint=footprint)
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expected = (expected == image)
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result = peak.peak_local_max(image, labels=labels, footprint=footprint,
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result == expected)
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def test_disk():
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'''regression test of img-1194, footprint = [1]
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Test peak.peak_local_max when every point is a local maximum
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'''
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image = np.random.uniform(size=(10, 20))
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footprint = np.array([[1]])
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result = peak.peak_local_max(image, labels=np.ones((10, 20)),
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footprint=footprint,
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min_distance=1, threshold_rel=0,
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indices=False, exclude_border=False)
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assert np.all(result)
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result = peak.peak_local_max(image, footprint=footprint)
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assert np.all(result)
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def test_3D():
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image = np.zeros((30, 30, 30))
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image[15, 15, 15] = 1
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image[5, 5, 5] = 1
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assert_equal(peak.peak_local_max(image), [[15, 15, 15]])
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assert_equal(peak.peak_local_max(image, min_distance=6), [[15, 15, 15]])
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assert_equal(peak.peak_local_max(image, exclude_border=False),
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[[5, 5, 5], [15, 15, 15]])
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assert_equal(peak.peak_local_max(image, min_distance=5),
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[[5, 5, 5], [15, 15, 15]])
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def test_4D():
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image = np.zeros((30, 30, 30, 30))
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image[15, 15, 15, 15] = 1
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image[5, 5, 5, 5] = 1
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assert_equal(peak.peak_local_max(image), [[15, 15, 15, 15]])
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assert_equal(peak.peak_local_max(image, min_distance=6), [[15, 15, 15, 15]])
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assert_equal(peak.peak_local_max(image, exclude_border=False),
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[[5, 5, 5, 5], [15, 15, 15, 15]])
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assert_equal(peak.peak_local_max(image, min_distance=5),
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[[5, 5, 5, 5], [15, 15, 15, 15]])
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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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