mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 10:12:05 +08:00
Tony S Yu
69 lines
1.8 KiB
Python
69 lines
1.8 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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from skimage.feature import peak
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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.random((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((3, 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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assert len(peak.peak_local_max(image, min_distance=1)) == 3
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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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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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