113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
# Copyright (c) 2021-2025 The University of Texas Southwestern Medical Center.
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# All rights reserved.
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted for academic and research use only (subject to the
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# limitations in the disclaimer below) provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from this
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# software without specific prior written permission.
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# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
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# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
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# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
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# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
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# BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
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# IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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import math
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import numpy as np
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def im_circ(r=1, N=128):
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X, Y = np.meshgrid(range(-N // 2, N // 2), range(-N // 2, N // 2))
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return (X * X + Y * Y) < r * r
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def test_has_tissue():
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from navigate.model.analysis.boundary_detect import has_tissue
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for _ in range(100):
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N = 2 ** np.random.randint(5, 9)
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r = np.random.randint(math.ceil(0.2 * N), int(0.4 * N))
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ds = np.random.randint(1, 6)
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print(N, r, ds)
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im = im_circ(r, N) * 1001
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mu, sig = 100 * np.random.rand() + 1, 10 * np.random.rand() + 1
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print(mu, sig)
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offsets = [None, np.ones((N, N)) * mu]
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variances = [None, np.ones((N, N)) * sig]
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for off, var in zip(offsets, variances):
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assert has_tissue(im, 0, 0, N, off, var) and not has_tissue(
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im, 0, 0, N // 2 - r, off, var
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)
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def test_find_tissue_boundary_2d():
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from skimage.transform import downscale_local_mean
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from navigate.model.analysis.boundary_detect import find_tissue_boundary_2d
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for _ in range(100):
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N = 2 ** np.random.randint(5, 9)
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r = np.random.randint(1, int(0.4 * N))
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ds = np.random.randint(1, 6)
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print(N, r, ds)
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im = im_circ(r, N)
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b = find_tissue_boundary_2d(im, ds)
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b = np.vstack([x for x in b if x is not None])
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idx_x, idx_y = np.where(downscale_local_mean(im, (ds, ds)))
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iixy = (np.unique(idx_x)[:, None] == idx_x[None, :]) * idx_y
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low, high = idx_y[np.argmax(iixy != 0, 1)], np.max(iixy, 1)
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np.testing.assert_equal(b, np.vstack([low, high]).T)
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def test_binary_detect():
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from navigate.model.analysis.boundary_detect import (
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find_tissue_boundary_2d,
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binary_detect,
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)
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for _ in range(100):
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N = 2 ** np.random.randint(5, 9)
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r = np.random.randint(1, int(0.4 * N))
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ds = np.random.randint(1, 6)
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print(N, r, ds)
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im = im_circ(r, N)
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b = find_tissue_boundary_2d(im, ds)
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assert binary_detect(im * 1001, b, ds) == b
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def test_map_boundary():
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from navigate.model.analysis.boundary_detect import map_boundary
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assert map_boundary([[1, 2]]) == [(0, 1), (0, 2)]
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assert map_boundary([None, [1, 2]]) == [(1, 1), (1, 2)]
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assert map_boundary([None, [1, 2], None]) == [(1, 1), (1, 2)]
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