538 lines
21 KiB
Python
538 lines
21 KiB
Python
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import itertools
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import numpy as np
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import pytest
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import unittest
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from skimage._shared.testing import assert_array_almost_equal
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from skimage._shared.testing import 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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class TestPeakLocalMax():
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def test_trivial_case(self):
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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 type(peak_indices) is np.ndarray
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assert peak_indices.size == 0
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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(self):
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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(self):
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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_array_almost_equal(peaks, [(3, 3)])
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def test_absolute_threshold(self):
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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_array_almost_equal(peaks, [(3, 3)])
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def test_constant_image(self):
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image = np.full((20, 20), 128, 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(self):
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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_sorted_peaks(self):
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image = np.zeros((5, 5), dtype=np.uint8)
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image[1, 1] = 20
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image[3, 3] = 10
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peaks = peak.peak_local_max(image, min_distance=1)
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assert peaks.tolist() == [[1, 1], [3, 3]]
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image = np.zeros((3, 10))
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image[1, (1, 3, 5, 7)] = (1, 2, 3, 4)
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peaks = peak.peak_local_max(image, min_distance=1)
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assert peaks.tolist() == [[1, 7], [1, 5], [1, 3], [1, 1]]
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def test_num_peaks(self):
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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, threshold_abs=0)) == 5
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peaks_limited = peak.peak_local_max(
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image, min_distance=1, threshold_abs=0, 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(
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image, min_distance=1, threshold_abs=0, 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_peaks_and_labels(self):
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image = np.zeros((7, 7), dtype=np.uint8)
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labels = np.zeros((7, 7), dtype=np.uint8) + 20
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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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peaks_limited = peak.peak_local_max(
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image, min_distance=1, threshold_abs=0, labels=labels)
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assert len(peaks_limited) == 5
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peaks_limited = peak.peak_local_max(
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image, min_distance=1, threshold_abs=0, labels=labels, num_peaks=2)
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assert len(peaks_limited) == 2
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def test_num_peaks_tot_vs_labels_4quadrants(self):
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np.random.seed(21)
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image = np.random.uniform(size=(20, 30))
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i, j = np.mgrid[0:20, 0:30]
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labels = 1 + (i >= 10) + (j >= 15) * 2
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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=True,
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num_peaks=np.inf,
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num_peaks_per_label=2)
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assert len(result) == 8
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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=True,
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num_peaks=np.inf,
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num_peaks_per_label=1)
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assert len(result) == 4
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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=True,
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num_peaks=2,
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num_peaks_per_label=2)
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assert len(result) == 2
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def test_num_peaks3D(self):
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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(self):
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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(self):
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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 = np.transpose(np.nonzero(expected == image))
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expected = expected[np.argsort(image[tuple(expected.T)])[::-1]]
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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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result = result[np.argsort(image[tuple(result.T)])[::-1]]
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assert (result == expected).all()
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def test_ndarray_indices_false(self):
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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(self):
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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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expectedNoBorder = nd_image > 0
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result = peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=2, indices=False)
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assert_equal(result, expected)
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# Check that bools work as expected
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assert_equal(
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peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=2, indices=False),
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peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=True, indices=False)
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)
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assert_equal(
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peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=0, indices=False),
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peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=False, indices=False)
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)
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# Check both versions with no border
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assert_equal(
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peak.peak_local_max(nd_image, min_distance=2,
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exclude_border=0, indices=False),
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expectedNoBorder,
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)
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assert_equal(
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peak.peak_local_max(nd_image,
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exclude_border=False, indices=False),
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expectedNoBorder,
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)
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def test_empty(self):
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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_empty_non2d_indices(self):
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image = np.zeros((10, 10, 10))
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result = peak.peak_local_max(image,
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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=True, exclude_border=False)
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assert result.shape == (0, image.ndim)
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def test_one_point(self):
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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(self):
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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(self):
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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(self):
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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(self):
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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(self):
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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(self):
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image = np.random.uniform(size=(20, 30))
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i, j = np.mgrid[0:20, 0:30]
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labels = 1 + (i >= 10) + (j >= 15) * 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, 10), (10, 20)):
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||
|
|
for jmin, jmax in ((0, 15), (15, 30)):
|
||
|
|
expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
|
||
|
|
image[imin:imax, jmin:jmax], footprint=footprint)
|
||
|
|
expected = (expected == image)
|
||
|
|
result = peak.peak_local_max(image, labels=labels, footprint=footprint,
|
||
|
|
min_distance=1, threshold_rel=0,
|
||
|
|
indices=False, exclude_border=False)
|
||
|
|
assert np.all(result == expected)
|
||
|
|
|
||
|
|
def test_disk(self):
|
||
|
|
'''regression test of img-1194, footprint = [1]
|
||
|
|
Test peak.peak_local_max when every point is a local maximum
|
||
|
|
'''
|
||
|
|
image = np.random.uniform(size=(10, 20))
|
||
|
|
footprint = np.array([[1]])
|
||
|
|
result = peak.peak_local_max(image, labels=np.ones((10, 20)),
|
||
|
|
footprint=footprint,
|
||
|
|
min_distance=1, threshold_rel=0,
|
||
|
|
threshold_abs=-1, indices=False,
|
||
|
|
exclude_border=False)
|
||
|
|
assert np.all(result)
|
||
|
|
result = peak.peak_local_max(image, footprint=footprint, threshold_abs=-1,
|
||
|
|
indices=False, exclude_border=False)
|
||
|
|
assert np.all(result)
|
||
|
|
|
||
|
|
def test_3D(self):
|
||
|
|
image = np.zeros((30, 30, 30))
|
||
|
|
image[15, 15, 15] = 1
|
||
|
|
image[5, 5, 5] = 1
|
||
|
|
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
|
||
|
|
[[15, 15, 15]])
|
||
|
|
assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
|
||
|
|
[[15, 15, 15]])
|
||
|
|
assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
|
||
|
|
exclude_border=False).tolist()) == \
|
||
|
|
[[5, 5, 5], [15, 15, 15]]
|
||
|
|
assert sorted(peak.peak_local_max(image, min_distance=5,
|
||
|
|
threshold_rel=0).tolist()) == \
|
||
|
|
[[5, 5, 5], [15, 15, 15]]
|
||
|
|
|
||
|
|
def test_4D(self):
|
||
|
|
image = np.zeros((30, 30, 30, 30))
|
||
|
|
image[15, 15, 15, 15] = 1
|
||
|
|
image[5, 5, 5, 5] = 1
|
||
|
|
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
|
||
|
|
[[15, 15, 15, 15]])
|
||
|
|
assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
|
||
|
|
[[15, 15, 15, 15]])
|
||
|
|
assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
|
||
|
|
exclude_border=False).tolist()) == \
|
||
|
|
[[5, 5, 5, 5], [15, 15, 15, 15]]
|
||
|
|
assert sorted(peak.peak_local_max(image, min_distance=5,
|
||
|
|
threshold_rel=0).tolist()) == \
|
||
|
|
[[5, 5, 5, 5], [15, 15, 15, 15]]
|
||
|
|
|
||
|
|
def test_threshold_rel_default(self):
|
||
|
|
image = np.ones((5, 5))
|
||
|
|
|
||
|
|
image[2, 2] = 1
|
||
|
|
assert len(peak.peak_local_max(image)) == 0
|
||
|
|
|
||
|
|
image[2, 2] = 2
|
||
|
|
assert_equal(peak.peak_local_max(image), [[2, 2]])
|
||
|
|
|
||
|
|
image[2, 2] = 0
|
||
|
|
assert len(peak.peak_local_max(image, min_distance=0)) == image.size - 1
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
["indices"],
|
||
|
|
[[indices] for indices in itertools.product(range(5), range(5))],
|
||
|
|
)
|
||
|
|
def test_exclude_border(indices):
|
||
|
|
image = np.zeros((5, 5))
|
||
|
|
image[indices] = 1
|
||
|
|
|
||
|
|
# exclude_border = False, means it will always be found.
|
||
|
|
assert len(peak.peak_local_max(image, exclude_border=False)) == 1
|
||
|
|
|
||
|
|
# exclude_border = 0, means it will always be found.
|
||
|
|
assert len(peak.peak_local_max(image, exclude_border=0)) == 1
|
||
|
|
|
||
|
|
# exclude_border = True, min_distance=1 means it will be found unless it's
|
||
|
|
# on the edge.
|
||
|
|
if indices[0] in (0, 4) or indices[1] in (0, 4):
|
||
|
|
expected_peaks = 0
|
||
|
|
else:
|
||
|
|
expected_peaks = 1
|
||
|
|
assert len(peak.peak_local_max(
|
||
|
|
image, min_distance=1, exclude_border=True)) == expected_peaks
|
||
|
|
|
||
|
|
# exclude_border = (1, 0) means it will be found unless it's on the edge of
|
||
|
|
# the first dimension.
|
||
|
|
if indices[0] in (0, 4):
|
||
|
|
expected_peaks = 0
|
||
|
|
else:
|
||
|
|
expected_peaks = 1
|
||
|
|
assert len(peak.peak_local_max(
|
||
|
|
image, exclude_border=(1, 0))) == expected_peaks
|
||
|
|
|
||
|
|
# exclude_border = (0, 1) means it will be found unless it's on the edge of
|
||
|
|
# the second dimension.
|
||
|
|
if indices[1] in (0, 4):
|
||
|
|
expected_peaks = 0
|
||
|
|
else:
|
||
|
|
expected_peaks = 1
|
||
|
|
assert len(peak.peak_local_max(
|
||
|
|
image, exclude_border=(0, 1))) == expected_peaks
|
||
|
|
|
||
|
|
|
||
|
|
def test_exclude_border_errors():
|
||
|
|
image = np.zeros((5, 5))
|
||
|
|
|
||
|
|
# exclude_border doesn't have the right cardinality.
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
assert peak.peak_local_max(image, exclude_border=(1,))
|
||
|
|
|
||
|
|
# exclude_border doesn't have the right type
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
assert peak.peak_local_max(image, exclude_border=1.0)
|
||
|
|
|
||
|
|
# exclude_border is a tuple of the right cardinality but contains
|
||
|
|
# non-integer values.
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
assert peak.peak_local_max(image, exclude_border=(1, 'a'))
|
||
|
|
|
||
|
|
# exclude_border is a tuple of the right cardinality but contains a
|
||
|
|
# negative value.
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
assert peak.peak_local_max(image, exclude_border=(1, -1))
|
||
|
|
|
||
|
|
# exclude_border is a negative value.
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
assert peak.peak_local_max(image, exclude_border=-1)
|
||
|
|
|
||
|
|
|
||
|
|
class TestProminentPeaks(unittest.TestCase):
|
||
|
|
def test_isolated_peaks(self):
|
||
|
|
image = np.zeros((15, 15))
|
||
|
|
x0, y0, i0 = (12, 8, 1)
|
||
|
|
x1, y1, i1 = (2, 2, 1)
|
||
|
|
x2, y2, i2 = (5, 13, 1)
|
||
|
|
image[y0, x0] = i0
|
||
|
|
image[y1, x1] = i1
|
||
|
|
image[y2, x2] = i2
|
||
|
|
out = peak._prominent_peaks(image)
|
||
|
|
assert len(out[0]) == 3
|
||
|
|
for i, x, y in zip (out[0], out[1], out[2]):
|
||
|
|
self.assertTrue(i in (i0, i1, i2))
|
||
|
|
self.assertTrue(x in (x0, x1, x2))
|
||
|
|
self.assertTrue(y in (y0, y1, y2))
|
||
|
|
|
||
|
|
def test_threshold(self):
|
||
|
|
image = np.zeros((15, 15))
|
||
|
|
x0, y0, i0 = (12, 8, 10)
|
||
|
|
x1, y1, i1 = (2, 2, 8)
|
||
|
|
x2, y2, i2 = (5, 13, 10)
|
||
|
|
image[y0, x0] = i0
|
||
|
|
image[y1, x1] = i1
|
||
|
|
image[y2, x2] = i2
|
||
|
|
out = peak._prominent_peaks(image, threshold=None)
|
||
|
|
assert len(out[0]) == 3
|
||
|
|
for i, x, y in zip (out[0], out[1], out[2]):
|
||
|
|
self.assertTrue(i in (i0, i1, i2))
|
||
|
|
self.assertTrue(x in (x0, x1, x2))
|
||
|
|
out = peak._prominent_peaks(image, threshold=9)
|
||
|
|
assert len(out[0]) == 2
|
||
|
|
for i, x, y in zip (out[0], out[1], out[2]):
|
||
|
|
self.assertTrue(i in (i0, i2))
|
||
|
|
self.assertTrue(x in (x0, x2))
|
||
|
|
self.assertTrue(y in (y0, y2))
|
||
|
|
|
||
|
|
def test_peaks_in_contact(self):
|
||
|
|
image = np.zeros((15, 15))
|
||
|
|
x0, y0, i0 = (8, 8, 1)
|
||
|
|
x1, y1, i1 = (7, 7, 1) # prominent peak
|
||
|
|
x2, y2, i2 = (6, 6, 1)
|
||
|
|
image[y0, x0] = i0
|
||
|
|
image[y1, x1] = i1
|
||
|
|
image[y2, x2] = i2
|
||
|
|
out = peak._prominent_peaks(image, min_xdistance=3,
|
||
|
|
min_ydistance=3,)
|
||
|
|
assert_equal(out[0], np.array((i1,)))
|
||
|
|
assert_equal(out[1], np.array((x1,)))
|
||
|
|
assert_equal(out[2], np.array((y1,)))
|
||
|
|
|
||
|
|
def test_input_labels_unmodified(self):
|
||
|
|
image = np.zeros((10, 20))
|
||
|
|
labels = np.zeros((10, 20), int)
|
||
|
|
image[5, 5] = 1
|
||
|
|
labels[5, 5] = 1
|
||
|
|
labelsin = labels.copy()
|
||
|
|
result = peak.peak_local_max(image, labels=labels,
|
||
|
|
footprint=np.ones((3, 3), bool),
|
||
|
|
min_distance=1, threshold_rel=0,
|
||
|
|
indices=False, exclude_border=False)
|
||
|
|
assert np.all(labels == labelsin)
|
||
|
|
|
||
|
|
def test_many_objects(self):
|
||
|
|
mask = np.zeros([500, 500], dtype=bool)
|
||
|
|
x, y = np.indices((500, 500))
|
||
|
|
x_c = x // 20 * 20 + 10
|
||
|
|
y_c = y // 20 * 20 + 10
|
||
|
|
mask[(x - x_c) ** 2 + (y - y_c) ** 2 < 8 ** 2] = True
|
||
|
|
labels, num_objs = ndi.label(mask)
|
||
|
|
dist = ndi.distance_transform_edt(mask)
|
||
|
|
local_max = peak.peak_local_max(dist, min_distance=20, indices=True,
|
||
|
|
exclude_border=False, labels=labels)
|
||
|
|
assert len(local_max) == 625
|