499 lines
22 KiB
Python
499 lines
22 KiB
Python
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"""test_watershed.py - tests the watershed function
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Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
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Website: http://www.cellprofiler.org
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Copyright (c) 2003-2009 Massachusetts Institute of Technology
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Copyright (c) 2009-2011 Broad Institute
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All rights reserved.
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Original author: Lee Kamentsky
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"""
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#Portions of this test were taken from scipy's watershed test in test_ndimage.py
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#
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# Copyright (C) 2003-2005 Peter J. Verveer
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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#
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# 3. The name of the author may not be used to endorse or promote
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# products derived from this software without specific prior
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# written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
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# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
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# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import math
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import unittest
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import pytest
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import numpy as np
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from scipy import ndimage as ndi
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from .._watershed import watershed
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from skimage.measure import label
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eps = 1e-12
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blob = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
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[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
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[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
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[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
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[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
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[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
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[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
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[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 204, 153, 103, 103, 153, 204, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
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[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
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[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
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[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
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[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
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[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
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[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
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[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
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[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]])
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def diff(a, b):
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if not isinstance(a, np.ndarray):
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a = np.asarray(a)
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if not isinstance(b, np.ndarray):
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b = np.asarray(b)
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if (0 in a.shape) and (0 in b.shape):
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return 0.0
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b[a == 0] = 0
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if (a.dtype in [np.complex64, np.complex128] or
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b.dtype in [np.complex64, np.complex128]):
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a = np.asarray(a, np.complex128)
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b = np.asarray(b, np.complex128)
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t = ((a.real - b.real)**2).sum() + ((a.imag - b.imag)**2).sum()
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else:
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a = np.asarray(a)
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a = a.astype(np.float64)
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b = np.asarray(b)
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b = b.astype(np.float64)
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t = ((a - b)**2).sum()
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return math.sqrt(t)
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class TestWatershed(unittest.TestCase):
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eight = np.ones((3, 3), bool)
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def test_watershed01(self):
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"watershed 1"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[ -1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 1, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0]],
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np.int8)
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out = watershed(data, markers, self.eight)
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expected = np.array([[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1]])
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error = diff(expected, out)
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assert error < eps
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def test_watershed02(self):
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"watershed 2"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.int8)
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out = watershed(data, markers)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 1, 1, 1, -1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, 1, 1, 1, 1, 1, -1],
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[-1, -1, 1, 1, 1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1]], out)
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self.assertTrue(error < eps)
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def test_watershed03(self):
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"watershed 3"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 2, 0, 3, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, -1]], np.int8)
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out = watershed(data, markers)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, 0, 2, 0, 3, 0, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 0, 2, 0, 3, 0, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1]], out)
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self.assertTrue(error < eps)
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def test_watershed04(self):
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"watershed 4"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 2, 0, 3, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, -1]], np.int8)
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out = watershed(data, markers, self.eight)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, 2, 2, 0, 3, 3, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1]], out)
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self.assertTrue(error < eps)
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def test_watershed05(self):
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"watershed 5"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 3, 0, 2, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, -1]], np.int8)
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out = watershed(data, markers, self.eight)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, 3, 3, 0, 2, 2, -1],
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[-1, 3, 3, 0, 2, 2, -1],
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[-1, 3, 3, 0, 2, 2, -1],
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[-1, 3, 3, 0, 2, 2, -1],
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[-1, 3, 3, 0, 2, 2, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1]], out)
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self.assertTrue(error < eps)
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def test_watershed06(self):
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"watershed 6"
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data = np.array([[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 0, 0, 0, 1, 0],
|
||
|
|
[0, 1, 1, 1, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
||
|
|
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[-1, 0, 0, 0, 0, 0, 0]], np.int8)
|
||
|
|
out = watershed(data, markers, self.eight)
|
||
|
|
error = diff([[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, 1, 1, 1, 1, 1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1],
|
||
|
|
[-1, -1, -1, -1, -1, -1, -1]], out)
|
||
|
|
self.assertTrue(error < eps)
|
||
|
|
|
||
|
|
def test_watershed07(self):
|
||
|
|
"A regression test of a competitive case that failed"
|
||
|
|
data = blob
|
||
|
|
mask = (data != 255)
|
||
|
|
markers = np.zeros(data.shape, int)
|
||
|
|
markers[6, 7] = 1
|
||
|
|
markers[14, 7] = 2
|
||
|
|
out = watershed(data, markers, self.eight, mask=mask)
|
||
|
|
#
|
||
|
|
# The two objects should be the same size, except possibly for the
|
||
|
|
# border region
|
||
|
|
#
|
||
|
|
size1 = np.sum(out == 1)
|
||
|
|
size2 = np.sum(out == 2)
|
||
|
|
self.assertTrue(abs(size1 - size2) <= 6)
|
||
|
|
|
||
|
|
def test_watershed08(self):
|
||
|
|
"The border pixels + an edge are all the same value"
|
||
|
|
data = blob.copy()
|
||
|
|
data[10, 7:9] = 141
|
||
|
|
mask = (data != 255)
|
||
|
|
markers = np.zeros(data.shape, int)
|
||
|
|
markers[6, 7] = 1
|
||
|
|
markers[14, 7] = 2
|
||
|
|
out = watershed(data, markers, self.eight, mask=mask)
|
||
|
|
#
|
||
|
|
# The two objects should be the same size, except possibly for the
|
||
|
|
# border region
|
||
|
|
#
|
||
|
|
size1 = np.sum(out == 1)
|
||
|
|
size2 = np.sum(out == 2)
|
||
|
|
self.assertTrue(abs(size1 - size2) <= 6)
|
||
|
|
|
||
|
|
def test_watershed09(self):
|
||
|
|
"""Test on an image of reasonable size
|
||
|
|
|
||
|
|
This is here both for timing (does it take forever?) and to
|
||
|
|
ensure that the memory constraints are reasonable
|
||
|
|
"""
|
||
|
|
image = np.zeros((1000, 1000))
|
||
|
|
coords = np.random.uniform(0, 1000, (100, 2)).astype(int)
|
||
|
|
markers = np.zeros((1000, 1000), int)
|
||
|
|
idx = 1
|
||
|
|
for x, y in coords:
|
||
|
|
image[x, y] = 1
|
||
|
|
markers[x, y] = idx
|
||
|
|
idx += 1
|
||
|
|
|
||
|
|
image = ndi.gaussian_filter(image, 4)
|
||
|
|
watershed(image, markers, self.eight)
|
||
|
|
ndi.watershed_ift(image.astype(np.uint16), markers, self.eight)
|
||
|
|
|
||
|
|
def test_watershed10(self):
|
||
|
|
"watershed 10"
|
||
|
|
data = np.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]], np.uint8)
|
||
|
|
markers = np.array([[1, 0, 0, 2],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0],
|
||
|
|
[3, 0, 0, 4]], np.int8)
|
||
|
|
out = watershed(data, markers, self.eight)
|
||
|
|
error = diff([[1, 1, 2, 2],
|
||
|
|
[1, 1, 2, 2],
|
||
|
|
[3, 3, 4, 4],
|
||
|
|
[3, 3, 4, 4]], out)
|
||
|
|
self.assertTrue(error < eps)
|
||
|
|
|
||
|
|
def test_watershed11(self):
|
||
|
|
'''Make sure that all points on this plateau are assigned to closest seed'''
|
||
|
|
# https://github.com/scikit-image/scikit-image/issues/803
|
||
|
|
#
|
||
|
|
# Make sure that no point in a level image is farther away
|
||
|
|
# from its seed than any other
|
||
|
|
#
|
||
|
|
image = np.zeros((21, 21))
|
||
|
|
markers = np.zeros((21, 21), int)
|
||
|
|
markers[5, 5] = 1
|
||
|
|
markers[5, 10] = 2
|
||
|
|
markers[10, 5] = 3
|
||
|
|
markers[10, 10] = 4
|
||
|
|
|
||
|
|
structure = np.array([[False, True, False],
|
||
|
|
[True, True, True],
|
||
|
|
[False, True, False]])
|
||
|
|
out = watershed(image, markers, structure)
|
||
|
|
i, j = np.mgrid[0:21, 0:21]
|
||
|
|
d = np.dstack(
|
||
|
|
[np.sqrt((i.astype(float)-i0)**2, (j.astype(float)-j0)**2)
|
||
|
|
for i0, j0 in ((5, 5), (5, 10), (10, 5), (10, 10))])
|
||
|
|
dmin = np.min(d, 2)
|
||
|
|
self.assertTrue(np.all(d[i, j, out[i, j]-1] == dmin))
|
||
|
|
|
||
|
|
|
||
|
|
def test_watershed12(self):
|
||
|
|
"The watershed line"
|
||
|
|
data = np.array([[203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153],
|
||
|
|
[203, 255, 203, 153, 153, 153, 102, 102, 102, 102, 102, 102, 153, 153, 153, 153],
|
||
|
|
[203, 255, 203, 203, 153, 153, 102, 102, 77, 0, 102, 102, 153, 153, 203, 203],
|
||
|
|
[203, 255, 255, 203, 153, 153, 153, 102, 102, 102, 102, 153, 153, 203, 203, 255],
|
||
|
|
[203, 203, 255, 203, 203, 203, 153, 153, 153, 153, 153, 153, 203, 203, 255, 255],
|
||
|
|
[153, 203, 255, 255, 255, 203, 203, 203, 203, 203, 203, 203, 203, 255, 255, 203],
|
||
|
|
[153, 203, 203, 203, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 203, 203],
|
||
|
|
[153, 153, 153, 203, 203, 203, 203, 203, 255, 203, 203, 203, 203, 203, 203, 153],
|
||
|
|
[102, 102, 153, 153, 153, 153, 203, 203, 255, 203, 203, 255, 203, 153, 153, 153],
|
||
|
|
[102, 102, 102, 102, 102, 153, 203, 255, 255, 203, 203, 203, 203, 153, 102, 153],
|
||
|
|
[102, 51, 51, 102, 102, 153, 203, 255, 203, 203, 153, 153, 153, 153, 102, 153],
|
||
|
|
[ 77, 51, 51, 102, 153, 153, 203, 255, 203, 203, 203, 153, 102, 102, 102, 153],
|
||
|
|
[ 77, 0, 51, 102, 153, 203, 203, 255, 203, 255, 203, 153, 102, 51, 102, 153],
|
||
|
|
[ 77, 0, 51, 102, 153, 203, 255, 255, 203, 203, 203, 153, 102, 0, 102, 153],
|
||
|
|
[102, 0, 51, 102, 153, 203, 255, 203, 203, 153, 153, 153, 102, 102, 102, 153],
|
||
|
|
[102, 102, 102, 102, 153, 203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153]])
|
||
|
|
markerbin = (data==0)
|
||
|
|
marker = label(markerbin)
|
||
|
|
ws = watershed(data, marker, connectivity=2, watershed_line=True)
|
||
|
|
for lab, area in zip(range(4), [34,74,74,74]):
|
||
|
|
self.assertTrue(np.sum(ws == lab) == area)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def test_compact_watershed():
|
||
|
|
image = np.zeros((5, 6))
|
||
|
|
image[:, 3:] = 1
|
||
|
|
seeds = np.zeros((5, 6), dtype=int)
|
||
|
|
seeds[2, 0] = 1
|
||
|
|
seeds[2, 3] = 2
|
||
|
|
compact = watershed(image, seeds, compactness=0.01)
|
||
|
|
expected = np.array([[1, 1, 1, 2, 2, 2],
|
||
|
|
[1, 1, 1, 2, 2, 2],
|
||
|
|
[1, 1, 1, 2, 2, 2],
|
||
|
|
[1, 1, 1, 2, 2, 2],
|
||
|
|
[1, 1, 1, 2, 2, 2]], dtype=int)
|
||
|
|
np.testing.assert_equal(compact, expected)
|
||
|
|
normal = watershed(image, seeds)
|
||
|
|
expected = np.ones(image.shape, dtype=int)
|
||
|
|
expected[2, 3:] = 2
|
||
|
|
np.testing.assert_equal(normal, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_numeric_seed_watershed():
|
||
|
|
"""Test that passing just the number of seeds to watershed works."""
|
||
|
|
image = np.zeros((5, 6))
|
||
|
|
image[:, 3:] = 1
|
||
|
|
compact = watershed(image, 2, compactness=0.01)
|
||
|
|
expected = np.array([[1, 1, 1, 1, 2, 2],
|
||
|
|
[1, 1, 1, 1, 2, 2],
|
||
|
|
[1, 1, 1, 1, 2, 2],
|
||
|
|
[1, 1, 1, 1, 2, 2],
|
||
|
|
[1, 1, 1, 1, 2, 2]], dtype=np.int32)
|
||
|
|
np.testing.assert_equal(compact, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_incorrect_markers_shape():
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
image = np.ones((5, 6))
|
||
|
|
markers = np.ones((5, 7))
|
||
|
|
output = watershed(image, markers)
|
||
|
|
|
||
|
|
|
||
|
|
def test_incorrect_mask_shape():
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
image = np.ones((5, 6))
|
||
|
|
mask = np.ones((5, 7))
|
||
|
|
output = watershed(image, markers=4, mask=mask)
|
||
|
|
|
||
|
|
|
||
|
|
def test_markers_in_mask():
|
||
|
|
data = blob
|
||
|
|
mask = (data != 255)
|
||
|
|
out = watershed(data, 25, connectivity=2, mask=mask)
|
||
|
|
# There should be no markers where the mask is false
|
||
|
|
assert np.all(out[~mask] == 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_no_markers():
|
||
|
|
data = blob
|
||
|
|
mask = (data != 255)
|
||
|
|
out = watershed(data, mask=mask)
|
||
|
|
assert np.max(out) == 2
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
np.testing.run_module_suite()
|