# Authors: Emmanuelle Gouillart # Gael Varoquaux # 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()