# Authors: Emmanuelle Gouillart # Gael Varoquaux # License: BSD 3 clause import numpy as np import scipy as sp from scipy import ndimage from scipy.sparse.csgraph import connected_components from numpy.testing import assert_raises from sklearn.feature_extraction.image import ( img_to_graph, grid_to_graph, extract_patches_2d, reconstruct_from_patches_2d, PatchExtractor, extract_patches) from sklearn.utils.testing import assert_equal, assert_true 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(connected_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(connected_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.float64) assert_true(A.dtype == np.float64) def test_connect_regions(): try: face = sp.face(gray=True) except AttributeError: # Newer versions of scipy have face in misc from scipy import misc face = misc.face(gray=True) for thr in (50, 150): mask = face > thr graph = img_to_graph(face, mask) assert_equal(ndimage.label(mask)[1], connected_components(graph)[0]) def test_connect_regions_with_grid(): try: face = sp.face(gray=True) except AttributeError: # Newer versions of scipy have face in misc from scipy import misc face = misc.face(gray=True) mask = face > 50 graph = grid_to_graph(*face.shape, mask=mask) assert_equal(ndimage.label(mask)[1], connected_components(graph)[0]) mask = face > 150 graph = grid_to_graph(*face.shape, mask=mask, dtype=None) assert_equal(ndimage.label(mask)[1], connected_components(graph)[0]) def _downsampled_face(): try: face = sp.face(gray=True) except AttributeError: # Newer versions of scipy have face in misc from scipy import misc face = misc.face(gray=True) face = face.astype(np.float32) face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]) face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]) face = face.astype(np.float32) face /= 16.0 return face def _orange_face(face=None): face = _downsampled_face() if face is None else face face_color = np.zeros(face.shape + (3,)) face_color[:, :, 0] = 256 - face face_color[:, :, 1] = 256 - face / 2 face_color[:, :, 2] = 256 - face / 4 return face_color def _make_images(face=None): face = _downsampled_face() if face is None else face # make a collection of faces images = np.zeros((3,) + face.shape) images[0] = face images[1] = face + 1 images[2] = face + 2 return images downsampled_face = _downsampled_face() orange_face = _orange_face(downsampled_face) face_collection = _make_images(downsampled_face) def test_extract_patches_all(): face = downsampled_face i_h, i_w = face.shape p_h, p_w = 16, 16 expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1) patches = extract_patches_2d(face, (p_h, p_w)) assert_equal(patches.shape, (expected_n_patches, p_h, p_w)) def test_extract_patches_all_color(): face = orange_face i_h, i_w = face.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(face, (p_h, p_w)) assert_equal(patches.shape, (expected_n_patches, p_h, p_w, 3)) def test_extract_patches_all_rect(): face = downsampled_face face = face[:, 32:97] i_h, i_w = face.shape p_h, p_w = 16, 12 expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1) patches = extract_patches_2d(face, (p_h, p_w)) assert_equal(patches.shape, (expected_n_patches, p_h, p_w)) def test_extract_patches_max_patches(): face = downsampled_face i_h, i_w = face.shape p_h, p_w = 16, 16 patches = extract_patches_2d(face, (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(face, (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, face, (p_h, p_w), max_patches=2.0) assert_raises(ValueError, extract_patches_2d, face, (p_h, p_w), max_patches=-1.0) def test_reconstruct_patches_perfect(): face = downsampled_face p_h, p_w = 16, 16 patches = extract_patches_2d(face, (p_h, p_w)) face_reconstructed = reconstruct_from_patches_2d(patches, face.shape) np.testing.assert_array_almost_equal(face, face_reconstructed) def test_reconstruct_patches_perfect_color(): face = orange_face p_h, p_w = 16, 16 patches = extract_patches_2d(face, (p_h, p_w)) face_reconstructed = reconstruct_from_patches_2d(patches, face.shape) np.testing.assert_array_almost_equal(face, face_reconstructed) def test_patch_extractor_fit(): faces = face_collection extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0) assert_true(extr == extr.fit(faces)) def test_patch_extractor_max_patches(): faces = face_collection i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 max_patches = 100 expected_n_patches = len(faces) * max_patches extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches, random_state=0) patches = extr.transform(faces) assert_true(patches.shape == (expected_n_patches, p_h, p_w)) max_patches = 0.5 expected_n_patches = len(faces) * int((i_h - p_h + 1) * (i_w - p_w + 1) * max_patches) extr = PatchExtractor(patch_size=(p_h, p_w), max_patches=max_patches, random_state=0) patches = extr.transform(faces) assert_true(patches.shape == (expected_n_patches, p_h, p_w)) def test_patch_extractor_max_patches_default(): faces = face_collection extr = PatchExtractor(max_patches=100, random_state=0) patches = extr.transform(faces) assert_equal(patches.shape, (len(faces) * 100, 19, 25)) def test_patch_extractor_all_patches(): faces = face_collection i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1) extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0) patches = extr.transform(faces) assert_true(patches.shape == (expected_n_patches, p_h, p_w)) def test_patch_extractor_color(): faces = _make_images(orange_face) i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1) extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0) patches = extr.transform(faces) assert_true(patches.shape == (expected_n_patches, p_h, p_w, 3)) def test_extract_patches_strided(): image_shapes_1D = [(10,), (10,), (11,), (10,)] patch_sizes_1D = [(1,), (2,), (3,), (8,)] patch_steps_1D = [(1,), (1,), (4,), (2,)] expected_views_1D = [(10,), (9,), (3,), (2,)] last_patch_1D = [(10,), (8,), (8,), (2,)] image_shapes_2D = [(10, 20), (10, 20), (10, 20), (11, 20)] patch_sizes_2D = [(2, 2), (10, 10), (10, 11), (6, 6)] patch_steps_2D = [(5, 5), (3, 10), (3, 4), (4, 2)] expected_views_2D = [(2, 4), (1, 2), (1, 3), (2, 8)] last_patch_2D = [(5, 15), (0, 10), (0, 8), (4, 14)] image_shapes_3D = [(5, 4, 3), (3, 3, 3), (7, 8, 9), (7, 8, 9)] patch_sizes_3D = [(2, 2, 3), (2, 2, 2), (1, 7, 3), (1, 3, 3)] patch_steps_3D = [(1, 2, 10), (1, 1, 1), (2, 1, 3), (3, 3, 4)] expected_views_3D = [(4, 2, 1), (2, 2, 2), (4, 2, 3), (3, 2, 2)] last_patch_3D = [(3, 2, 0), (1, 1, 1), (6, 1, 6), (6, 3, 4)] image_shapes = image_shapes_1D + image_shapes_2D + image_shapes_3D patch_sizes = patch_sizes_1D + patch_sizes_2D + patch_sizes_3D patch_steps = patch_steps_1D + patch_steps_2D + patch_steps_3D expected_views = expected_views_1D + expected_views_2D + expected_views_3D last_patches = last_patch_1D + last_patch_2D + last_patch_3D for (image_shape, patch_size, patch_step, expected_view, last_patch) in zip(image_shapes, patch_sizes, patch_steps, expected_views, last_patches): image = np.arange(np.prod(image_shape)).reshape(image_shape) patches = extract_patches(image, patch_shape=patch_size, extraction_step=patch_step) ndim = len(image_shape) assert_true(patches.shape[:ndim] == expected_view) last_patch_slices = [slice(i, i + j, None) for i, j in zip(last_patch, patch_size)] assert_true((patches[[slice(-1, None, None)] * ndim] == image[last_patch_slices].squeeze()).all()) def test_extract_patches_square(): # test same patch size for all dimensions face = downsampled_face i_h, i_w = face.shape p = 8 expected_n_patches = ((i_h - p + 1), (i_w - p + 1)) patches = extract_patches(face, patch_shape=p) assert_true(patches.shape == (expected_n_patches[0], expected_n_patches[1], p, p)) def test_width_patch(): # width and height of the patch should be less than the image x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) assert_raises(ValueError, extract_patches_2d, x, (4, 1)) assert_raises(ValueError, extract_patches_2d, x, (1, 4))