357 lines
12 KiB
Python
357 lines
12 KiB
Python
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# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
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# Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD 3 clause
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import numpy as np
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import pytest
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from scipy import ndimage
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from scipy.sparse.csgraph import connected_components
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from sklearn.feature_extraction.image import (
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PatchExtractor,
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_extract_patches,
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extract_patches_2d,
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grid_to_graph,
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img_to_graph,
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reconstruct_from_patches_2d,
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)
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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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assert 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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# should all be equal on grad_x and grad_y
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np.testing.assert_array_equal(
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grad_x.data[grad_x.data > 0], grad_y.data[grad_y.data > 0]
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)
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def test_img_to_graph_sparse():
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# Check that the edges are in the right position
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# when using a sparse image with a singleton component
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mask = np.zeros((2, 3), dtype=bool)
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mask[0, 0] = 1
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mask[:, 2] = 1
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x = np.zeros((2, 3))
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x[0, 0] = 1
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x[0, 2] = -1
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x[1, 2] = -2
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grad_x = img_to_graph(x, mask=mask).todense()
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desired = np.array([[1, 0, 0], [0, -1, 1], [0, 1, -2]])
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np.testing.assert_array_equal(grad_x, desired)
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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=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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assert connected_components(A)[0] == 2
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# check ordering
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mask = np.zeros((2, 3), dtype=bool)
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mask[0, 0] = 1
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mask[:, 2] = 1
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graph = grid_to_graph(2, 3, 1, mask=mask.ravel()).todense()
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desired = np.array([[1, 0, 0], [0, 1, 1], [0, 1, 1]])
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np.testing.assert_array_equal(graph, desired)
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# Checking that the function works whatever the type of mask is
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mask = np.ones((size, size), dtype=np.int16)
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A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask)
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assert connected_components(A)[0] == 1
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# Checking dtype of the graph
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mask = np.ones((size, size))
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A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=bool)
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assert A.dtype == bool
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A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=int)
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assert A.dtype == int
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A = grid_to_graph(n_x=size, n_y=size, n_z=size, mask=mask, dtype=np.float64)
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assert A.dtype == np.float64
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def test_connect_regions(raccoon_face_fxt):
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face = raccoon_face_fxt
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# subsample by 4 to reduce run time
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face = face[::4, ::4]
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for thr in (50, 150):
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mask = face > thr
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graph = img_to_graph(face, mask=mask)
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assert ndimage.label(mask)[1] == connected_components(graph)[0]
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def test_connect_regions_with_grid(raccoon_face_fxt):
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face = raccoon_face_fxt
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# subsample by 4 to reduce run time
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face = face[::4, ::4]
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mask = face > 50
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graph = grid_to_graph(*face.shape, mask=mask)
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assert ndimage.label(mask)[1] == connected_components(graph)[0]
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mask = face > 150
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graph = grid_to_graph(*face.shape, mask=mask, dtype=None)
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assert ndimage.label(mask)[1] == connected_components(graph)[0]
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@pytest.fixture
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def downsampled_face(raccoon_face_fxt):
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face = raccoon_face_fxt
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face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
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face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
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face = face.astype(np.float32)
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face /= 16.0
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return face
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@pytest.fixture
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def orange_face(downsampled_face):
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face = downsampled_face
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face_color = np.zeros(face.shape + (3,))
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face_color[:, :, 0] = 256 - face
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face_color[:, :, 1] = 256 - face / 2
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face_color[:, :, 2] = 256 - face / 4
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return face_color
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def _make_images(face):
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# make a collection of faces
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images = np.zeros((3,) + face.shape)
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images[0] = face
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images[1] = face + 1
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images[2] = face + 2
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return images
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@pytest.fixture
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def downsampled_face_collection(downsampled_face):
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return _make_images(downsampled_face)
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def test_extract_patches_all(downsampled_face):
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face = downsampled_face
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i_h, i_w = face.shape
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p_h, p_w = 16, 16
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expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
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patches = extract_patches_2d(face, (p_h, p_w))
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assert patches.shape == (expected_n_patches, p_h, p_w)
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def test_extract_patches_all_color(orange_face):
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face = orange_face
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i_h, i_w = face.shape[:2]
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p_h, p_w = 16, 16
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expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
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patches = extract_patches_2d(face, (p_h, p_w))
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assert patches.shape == (expected_n_patches, p_h, p_w, 3)
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def test_extract_patches_all_rect(downsampled_face):
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face = downsampled_face
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face = face[:, 32:97]
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i_h, i_w = face.shape
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p_h, p_w = 16, 12
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expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
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patches = extract_patches_2d(face, (p_h, p_w))
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assert patches.shape == (expected_n_patches, p_h, p_w)
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def test_extract_patches_max_patches(downsampled_face):
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face = downsampled_face
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i_h, i_w = face.shape
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p_h, p_w = 16, 16
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patches = extract_patches_2d(face, (p_h, p_w), max_patches=100)
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assert patches.shape == (100, p_h, p_w)
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expected_n_patches = int(0.5 * (i_h - p_h + 1) * (i_w - p_w + 1))
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patches = extract_patches_2d(face, (p_h, p_w), max_patches=0.5)
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assert patches.shape == (expected_n_patches, p_h, p_w)
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with pytest.raises(ValueError):
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extract_patches_2d(face, (p_h, p_w), max_patches=2.0)
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with pytest.raises(ValueError):
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extract_patches_2d(face, (p_h, p_w), max_patches=-1.0)
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def test_extract_patch_same_size_image(downsampled_face):
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face = downsampled_face
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# Request patches of the same size as image
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# Should return just the single patch a.k.a. the image
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patches = extract_patches_2d(face, face.shape, max_patches=2)
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assert patches.shape[0] == 1
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def test_extract_patches_less_than_max_patches(downsampled_face):
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face = downsampled_face
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i_h, i_w = face.shape
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p_h, p_w = 3 * i_h // 4, 3 * i_w // 4
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# this is 3185
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expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
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patches = extract_patches_2d(face, (p_h, p_w), max_patches=4000)
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assert patches.shape == (expected_n_patches, p_h, p_w)
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def test_reconstruct_patches_perfect(downsampled_face):
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face = downsampled_face
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p_h, p_w = 16, 16
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patches = extract_patches_2d(face, (p_h, p_w))
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face_reconstructed = reconstruct_from_patches_2d(patches, face.shape)
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np.testing.assert_array_almost_equal(face, face_reconstructed)
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def test_reconstruct_patches_perfect_color(orange_face):
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face = orange_face
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p_h, p_w = 16, 16
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patches = extract_patches_2d(face, (p_h, p_w))
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face_reconstructed = reconstruct_from_patches_2d(patches, face.shape)
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np.testing.assert_array_almost_equal(face, face_reconstructed)
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def test_patch_extractor_fit(downsampled_face_collection):
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faces = downsampled_face_collection
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extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
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assert extr == extr.fit(faces)
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def test_patch_extractor_max_patches(downsampled_face_collection):
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faces = downsampled_face_collection
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i_h, i_w = faces.shape[1:3]
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p_h, p_w = 8, 8
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max_patches = 100
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expected_n_patches = len(faces) * max_patches
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extr = PatchExtractor(
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patch_size=(p_h, p_w), max_patches=max_patches, random_state=0
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)
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patches = extr.transform(faces)
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assert patches.shape == (expected_n_patches, p_h, p_w)
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max_patches = 0.5
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expected_n_patches = len(faces) * int(
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(i_h - p_h + 1) * (i_w - p_w + 1) * max_patches
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)
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extr = PatchExtractor(
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patch_size=(p_h, p_w), max_patches=max_patches, random_state=0
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)
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patches = extr.transform(faces)
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assert patches.shape == (expected_n_patches, p_h, p_w)
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def test_patch_extractor_max_patches_default(downsampled_face_collection):
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faces = downsampled_face_collection
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extr = PatchExtractor(max_patches=100, random_state=0)
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patches = extr.transform(faces)
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assert patches.shape == (len(faces) * 100, 19, 25)
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def test_patch_extractor_all_patches(downsampled_face_collection):
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faces = downsampled_face_collection
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i_h, i_w = faces.shape[1:3]
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p_h, p_w = 8, 8
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expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
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extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
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patches = extr.transform(faces)
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assert patches.shape == (expected_n_patches, p_h, p_w)
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def test_patch_extractor_color(orange_face):
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faces = _make_images(orange_face)
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i_h, i_w = faces.shape[1:3]
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p_h, p_w = 8, 8
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expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
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extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0)
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patches = extr.transform(faces)
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assert patches.shape == (expected_n_patches, p_h, p_w, 3)
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def test_extract_patches_strided():
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image_shapes_1D = [(10,), (10,), (11,), (10,)]
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patch_sizes_1D = [(1,), (2,), (3,), (8,)]
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patch_steps_1D = [(1,), (1,), (4,), (2,)]
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expected_views_1D = [(10,), (9,), (3,), (2,)]
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last_patch_1D = [(10,), (8,), (8,), (2,)]
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image_shapes_2D = [(10, 20), (10, 20), (10, 20), (11, 20)]
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patch_sizes_2D = [(2, 2), (10, 10), (10, 11), (6, 6)]
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patch_steps_2D = [(5, 5), (3, 10), (3, 4), (4, 2)]
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expected_views_2D = [(2, 4), (1, 2), (1, 3), (2, 8)]
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last_patch_2D = [(5, 15), (0, 10), (0, 8), (4, 14)]
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image_shapes_3D = [(5, 4, 3), (3, 3, 3), (7, 8, 9), (7, 8, 9)]
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patch_sizes_3D = [(2, 2, 3), (2, 2, 2), (1, 7, 3), (1, 3, 3)]
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patch_steps_3D = [(1, 2, 10), (1, 1, 1), (2, 1, 3), (3, 3, 4)]
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expected_views_3D = [(4, 2, 1), (2, 2, 2), (4, 2, 3), (3, 2, 2)]
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last_patch_3D = [(3, 2, 0), (1, 1, 1), (6, 1, 6), (6, 3, 4)]
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image_shapes = image_shapes_1D + image_shapes_2D + image_shapes_3D
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patch_sizes = patch_sizes_1D + patch_sizes_2D + patch_sizes_3D
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patch_steps = patch_steps_1D + patch_steps_2D + patch_steps_3D
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expected_views = expected_views_1D + expected_views_2D + expected_views_3D
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last_patches = last_patch_1D + last_patch_2D + last_patch_3D
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for image_shape, patch_size, patch_step, expected_view, last_patch in zip(
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image_shapes, patch_sizes, patch_steps, expected_views, last_patches
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):
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image = np.arange(np.prod(image_shape)).reshape(image_shape)
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patches = _extract_patches(
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image, patch_shape=patch_size, extraction_step=patch_step
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|
)
|
||
|
|
|
||
|
|
ndim = len(image_shape)
|
||
|
|
|
||
|
|
assert patches.shape[:ndim] == expected_view
|
||
|
|
last_patch_slices = tuple(
|
||
|
|
slice(i, i + j, None) for i, j in zip(last_patch, patch_size)
|
||
|
|
)
|
||
|
|
assert (
|
||
|
|
patches[(-1, None, None) * ndim] == image[last_patch_slices].squeeze()
|
||
|
|
).all()
|
||
|
|
|
||
|
|
|
||
|
|
def test_extract_patches_square(downsampled_face):
|
||
|
|
# 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 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]])
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
extract_patches_2d(x, (4, 1))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
extract_patches_2d(x, (1, 4))
|
||
|
|
|
||
|
|
|
||
|
|
def test_patch_extractor_wrong_input(orange_face):
|
||
|
|
"""Check that an informative error is raised if the patch_size is not valid."""
|
||
|
|
faces = _make_images(orange_face)
|
||
|
|
err_msg = "patch_size must be a tuple of two integers"
|
||
|
|
extractor = PatchExtractor(patch_size=(8, 8, 8))
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
extractor.transform(faces)
|