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# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD
import numpy as np
import scipy as sp
from scipy import ndimage
from nose.tools import assert_equal, assert_true
from numpy.testing import assert_raises
from ..image import img_to_graph, grid_to_graph
from ..image import extract_patches_2d, reconstruct_from_patches_2d, \
PatchExtractor
from ...utils.graph import cs_graph_components
def test_img_to_graph():
x, y = np.mgrid[:4, :4] - 10
grad_x = img_to_graph(x)
grad_y = img_to_graph(y)
assert_equal(grad_x.nnz, grad_y.nnz)
# Negative elements are the diagonal: the elements of the original
# image. Positive elements are the values of the gradient, they
# should all be equal on grad_x and grad_y
np.testing.assert_array_equal(grad_x.data[grad_x.data > 0],
grad_y.data[grad_y.data > 0])
def test_grid_to_graph():
#Checking that the function works with graphs containing no edges
size = 2
roi_size = 1
# Generating two convex parts with one vertex
# Thus, edges will be empty in _to_graph
mask = np.zeros((size, size), dtype=np.bool)
mask[0:roi_size, 0:roi_size] = True
mask[-roi_size:, -roi_size:] = True
mask = mask.reshape(size ** 2)
A = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray)
assert_true(cs_graph_components(A)[0] == 2)
# Checking that the function works whatever the type of mask is
mask = np.ones((size, size), dtype=np.int16)
A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask)
assert_true(cs_graph_components(A)[0] == 1)
# Checking dtype of the graph
mask = np.ones((size, size))
A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=np.bool)
assert_true(A.dtype == np.bool)
A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=np.int)
assert_true(A.dtype == np.int)
A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=np.float)
assert_true(A.dtype == np.float)
def test_connect_regions():
lena = sp.misc.lena()
for thr in (50, 150):
mask = lena > thr
graph = img_to_graph(lena, mask)
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
def test_connect_regions_with_grid():
lena = sp.misc.lena()
mask = lena > 50
graph = grid_to_graph(*lena.shape, mask=mask)
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
mask = lena > 150
graph = grid_to_graph(*lena.shape, mask=mask, dtype=None)
assert_equal(ndimage.label(mask)[1], cs_graph_components(graph)[0])
def _downsampled_lena():
lena = sp.misc.lena().astype(np.float32)
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + \
lena[1::2, 1::2]
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + \
lena[1::2, 1::2]
lena = lena.astype(np.float)
lena /= 16.0
return lena
def _orange_lena(lena=None):
lena = _downsampled_lena() if lena is None else lena
lena_color = np.zeros(lena.shape + (3,))
lena_color[:, :, 0] = 256 - lena
lena_color[:, :, 1] = 256 - lena / 2
lena_color[:, :, 2] = 256 - lena / 4
return lena_color
def _make_images(lena=None):
lena = _downsampled_lena() if lena is None else lena
# make a collection of lenas
images = np.zeros((3,) + lena.shape)
images[0] = lena
images[1] = lena + 1
images[2] = lena + 2
return images
downsampled_lena = _downsampled_lena()
orange_lena = _orange_lena(downsampled_lena)
lena_collection = _make_images(downsampled_lena)
def test_extract_patches_all():
lena = downsampled_lena
i_h, i_w = lena.shape
p_h, p_w = 16, 16
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(lena, (p_h, p_w))
assert_equal(patches.shape, (expected_n_patches, p_h, p_w))
def test_extract_patches_all_color():
lena = orange_lena
i_h, i_w = lena.shape[:2]
p_h, p_w = 16, 16
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(lena, (p_h, p_w))
assert_equal(patches.shape, (expected_n_patches, p_h, p_w, 3))
def test_extract_patches_all_rect():
lena = downsampled_lena
lena = lena[:, 32:97]
i_h, i_w = lena.shape
p_h, p_w = 16, 12
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(lena, (p_h, p_w))
assert_equal(patches.shape, (expected_n_patches, p_h, p_w))
def test_extract_patches_max_patches():
lena = downsampled_lena
i_h, i_w = lena.shape
p_h, p_w = 16, 16
patches = extract_patches_2d(lena, (p_h, p_w), max_patches=100)
assert_equal(patches.shape, (100, p_h, p_w))
expected_n_patches = int(0.5 * (i_h - p_h + 1) * (i_w - p_w + 1))
patches = extract_patches_2d(lena, (p_h, p_w), max_patches=0.5)
assert_equal(patches.shape, (expected_n_patches, p_h, p_w))
assert_raises(ValueError, extract_patches_2d, lena,
(p_h, p_w),
max_patches=2.0)
assert_raises(ValueError, extract_patches_2d, lena,
(p_h, p_w),
max_patches=-1.0)
def test_reconstruct_patches_perfect():
lena = downsampled_lena
p_h, p_w = 16, 16
patches = extract_patches_2d(lena, (p_h, p_w))
lena_reconstructed = reconstruct_from_patches_2d(patches, lena.shape)
np.testing.assert_array_equal(lena, lena_reconstructed)
def test_reconstruct_patches_perfect_color():
lena = orange_lena
p_h, p_w = 16, 16
patches = extract_patches_2d(lena, (p_h, p_w))
lena_reconstructed = reconstruct_from_patches_2d(patches, lena.shape)
np.testing.assert_array_equal(lena, lena_reconstructed)
def test_patch_extractor_fit():
lenas = lena_collection
extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
assert_true(extr == extr.fit(lenas))
def test_patch_extractor_max_patches():
lenas = lena_collection
extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
patches = extr.transform(lenas)
assert_true(patches.shape == (len(lenas) * 100, 8, 8))
def test_patch_extractor_all_patches():
lenas = lena_collection
i_h, i_w = lenas.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(lenas) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(lenas)
assert_true(patches.shape == (expected_n_patches, p_h, p_w))
def test_patch_extractor_color():
lenas = _make_images(orange_lena)
i_h, i_w = lenas.shape[1:3]
p_h, p_w = 8, 8
expected_n_patches = len(lenas) * (i_h - p_h + 1) * (i_w - p_w + 1)
extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
patches = extr.transform(lenas)
assert_true(patches.shape == (expected_n_patches, p_h, p_w, 3))
if __name__ == '__main__':
import nose
nose.runmodule()
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