60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
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# Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD
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import numpy as np
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import scipy as sp
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from scipy import ndimage
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import nose
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from ..image import img_to_graph, grid_to_graph
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from ...utils.graph import cs_graph_components
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def test_img_to_graph():
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x, y = np.mgrid[:4, :4] - 10
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grad_x = img_to_graph(x)
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grad_y = img_to_graph(y)
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nose.tools.assert_equal(grad_x.nnz, grad_y.nnz)
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# Negative elements are the diagonal: the elements of the original
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# image. Positive elements are the values of the gradient, they
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# shoudl all be equal on grad_x and grad_y
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np.testing.assert_array_equal(grad_x.data[grad_x.data > 0],
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grad_y.data[grad_y.data > 0])
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def test_grid_to_graph():
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#Checking that the function works with graphs containing no edges
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size = 2
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roi_size = 1
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# Generating two convex parts with one vertex
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# Thus, edges will be empty in _to_graph
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mask = np.zeros((size, size), dtype=np.bool)
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mask[0:roi_size, 0:roi_size] = True
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mask[-roi_size:, -roi_size:] = True
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mask = mask.reshape(size**2)
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A = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray)
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def test_connect_regions():
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lena = sp.lena()
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for thr in (50, 150):
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mask = lena > thr
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graph = img_to_graph(lena, mask)
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nose.tools.assert_equal(ndimage.label(mask)[1],
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cs_graph_components(graph)[0])
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def test_connect_regions_with_grid():
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lena = sp.lena()
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mask = lena > 50
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graph = grid_to_graph(*lena.shape, **{'mask' : mask})
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nose.tools.assert_equal(ndimage.label(mask)[1],
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cs_graph_components(graph)[0])
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mask = lena > 150
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graph = grid_to_graph(*lena.shape, **{'mask' : mask, 'dtype' : None})
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nose.tools.assert_equal(ndimage.label(mask)[1],
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cs_graph_components(graph)[0])
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