682 lines
23 KiB
Python
682 lines
23 KiB
Python
"""
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The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
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extract features from images.
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"""
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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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# Olivier Grisel
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# Vlad Niculae
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# License: BSD 3 clause
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from itertools import product
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from numbers import Integral, Number, Real
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import numpy as np
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from numpy.lib.stride_tricks import as_strided
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from scipy import sparse
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from ..base import BaseEstimator, TransformerMixin, _fit_context
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from ..utils import check_array, check_random_state
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from ..utils._param_validation import Hidden, Interval, RealNotInt, validate_params
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__all__ = [
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"PatchExtractor",
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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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###############################################################################
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# From an image to a graph
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def _make_edges_3d(n_x, n_y, n_z=1):
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"""Returns a list of edges for a 3D image.
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Parameters
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----------
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n_x : int
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The size of the grid in the x direction.
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n_y : int
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The size of the grid in the y direction.
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n_z : integer, default=1
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The size of the grid in the z direction, defaults to 1
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"""
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vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
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edges_deep = np.vstack((vertices[:, :, :-1].ravel(), vertices[:, :, 1:].ravel()))
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edges_right = np.vstack((vertices[:, :-1].ravel(), vertices[:, 1:].ravel()))
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edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel()))
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edges = np.hstack((edges_deep, edges_right, edges_down))
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return edges
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def _compute_gradient_3d(edges, img):
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_, n_y, n_z = img.shape
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gradient = np.abs(
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img[
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edges[0] // (n_y * n_z),
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(edges[0] % (n_y * n_z)) // n_z,
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(edges[0] % (n_y * n_z)) % n_z,
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]
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- img[
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edges[1] // (n_y * n_z),
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(edges[1] % (n_y * n_z)) // n_z,
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(edges[1] % (n_y * n_z)) % n_z,
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]
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)
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return gradient
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# XXX: Why mask the image after computing the weights?
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def _mask_edges_weights(mask, edges, weights=None):
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"""Apply a mask to edges (weighted or not)"""
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inds = np.arange(mask.size)
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inds = inds[mask.ravel()]
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ind_mask = np.logical_and(np.isin(edges[0], inds), np.isin(edges[1], inds))
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edges = edges[:, ind_mask]
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if weights is not None:
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weights = weights[ind_mask]
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if len(edges.ravel()):
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maxval = edges.max()
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else:
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maxval = 0
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order = np.searchsorted(np.flatnonzero(mask), np.arange(maxval + 1))
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edges = order[edges]
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if weights is None:
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return edges
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else:
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return edges, weights
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def _to_graph(
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n_x, n_y, n_z, mask=None, img=None, return_as=sparse.coo_matrix, dtype=None
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):
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"""Auxiliary function for img_to_graph and grid_to_graph"""
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edges = _make_edges_3d(n_x, n_y, n_z)
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if dtype is None: # To not overwrite input dtype
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if img is None:
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dtype = int
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else:
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dtype = img.dtype
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if img is not None:
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img = np.atleast_3d(img)
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weights = _compute_gradient_3d(edges, img)
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if mask is not None:
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edges, weights = _mask_edges_weights(mask, edges, weights)
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diag = img.squeeze()[mask]
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else:
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diag = img.ravel()
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n_voxels = diag.size
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else:
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if mask is not None:
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mask = mask.astype(dtype=bool, copy=False)
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edges = _mask_edges_weights(mask, edges)
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n_voxels = np.sum(mask)
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else:
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n_voxels = n_x * n_y * n_z
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weights = np.ones(edges.shape[1], dtype=dtype)
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diag = np.ones(n_voxels, dtype=dtype)
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diag_idx = np.arange(n_voxels)
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i_idx = np.hstack((edges[0], edges[1]))
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j_idx = np.hstack((edges[1], edges[0]))
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graph = sparse.coo_matrix(
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(
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np.hstack((weights, weights, diag)),
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(np.hstack((i_idx, diag_idx)), np.hstack((j_idx, diag_idx))),
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),
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(n_voxels, n_voxels),
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dtype=dtype,
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)
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if return_as is np.ndarray:
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return graph.toarray()
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return return_as(graph)
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@validate_params(
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{
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"img": ["array-like"],
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"mask": [None, np.ndarray],
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"return_as": [type],
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"dtype": "no_validation", # validation delegated to numpy
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},
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prefer_skip_nested_validation=True,
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)
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def img_to_graph(img, *, mask=None, return_as=sparse.coo_matrix, dtype=None):
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"""Graph of the pixel-to-pixel gradient connections.
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Edges are weighted with the gradient values.
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Read more in the :ref:`User Guide <image_feature_extraction>`.
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Parameters
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----------
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img : array-like of shape (height, width) or (height, width, channel)
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2D or 3D image.
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mask : ndarray of shape (height, width) or \
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(height, width, channel), dtype=bool, default=None
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An optional mask of the image, to consider only part of the
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pixels.
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return_as : np.ndarray or a sparse matrix class, \
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default=sparse.coo_matrix
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The class to use to build the returned adjacency matrix.
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dtype : dtype, default=None
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The data of the returned sparse matrix. By default it is the
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dtype of img.
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Returns
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-------
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graph : ndarray or a sparse matrix class
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The computed adjacency matrix.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.feature_extraction.image import img_to_graph
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>>> img = np.array([[0, 0], [0, 1]])
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>>> img_to_graph(img, return_as=np.ndarray)
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array([[0, 0, 0, 0],
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[0, 0, 0, 1],
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[0, 0, 0, 1],
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[0, 1, 1, 1]])
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"""
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img = np.atleast_3d(img)
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n_x, n_y, n_z = img.shape
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return _to_graph(n_x, n_y, n_z, mask, img, return_as, dtype)
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@validate_params(
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{
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"n_x": [Interval(Integral, left=1, right=None, closed="left")],
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"n_y": [Interval(Integral, left=1, right=None, closed="left")],
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"n_z": [Interval(Integral, left=1, right=None, closed="left")],
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"mask": [None, np.ndarray],
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"return_as": [type],
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"dtype": "no_validation", # validation delegated to numpy
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},
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prefer_skip_nested_validation=True,
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)
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def grid_to_graph(
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n_x, n_y, n_z=1, *, mask=None, return_as=sparse.coo_matrix, dtype=int
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):
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"""Graph of the pixel-to-pixel connections.
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Edges exist if 2 voxels are connected.
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Parameters
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----------
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n_x : int
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Dimension in x axis.
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n_y : int
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Dimension in y axis.
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n_z : int, default=1
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Dimension in z axis.
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mask : ndarray of shape (n_x, n_y, n_z), dtype=bool, default=None
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An optional mask of the image, to consider only part of the
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pixels.
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return_as : np.ndarray or a sparse matrix class, \
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default=sparse.coo_matrix
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The class to use to build the returned adjacency matrix.
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dtype : dtype, default=int
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The data of the returned sparse matrix. By default it is int.
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Returns
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-------
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graph : np.ndarray or a sparse matrix class
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The computed adjacency matrix.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.feature_extraction.image import grid_to_graph
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>>> shape_img = (4, 4, 1)
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>>> mask = np.zeros(shape=shape_img, dtype=bool)
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>>> mask[[1, 2], [1, 2], :] = True
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>>> graph = grid_to_graph(*shape_img, mask=mask)
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>>> print(graph)
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(0, 0) 1
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(1, 1) 1
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"""
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return _to_graph(n_x, n_y, n_z, mask=mask, return_as=return_as, dtype=dtype)
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###############################################################################
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# From an image to a set of small image patches
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def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None):
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"""Compute the number of patches that will be extracted in an image.
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Read more in the :ref:`User Guide <image_feature_extraction>`.
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Parameters
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----------
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i_h : int
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The image height
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i_w : int
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The image with
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p_h : int
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The height of a patch
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p_w : int
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The width of a patch
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max_patches : int or float, default=None
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The maximum number of patches to extract. If `max_patches` is a float
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between 0 and 1, it is taken to be a proportion of the total number
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of patches. If `max_patches` is None, all possible patches are extracted.
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"""
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n_h = i_h - p_h + 1
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n_w = i_w - p_w + 1
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all_patches = n_h * n_w
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if max_patches:
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if isinstance(max_patches, (Integral)) and max_patches < all_patches:
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return max_patches
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elif isinstance(max_patches, (Integral)) and max_patches >= all_patches:
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return all_patches
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elif isinstance(max_patches, (Real)) and 0 < max_patches < 1:
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return int(max_patches * all_patches)
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else:
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raise ValueError("Invalid value for max_patches: %r" % max_patches)
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else:
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return all_patches
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def _extract_patches(arr, patch_shape=8, extraction_step=1):
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"""Extracts patches of any n-dimensional array in place using strides.
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Given an n-dimensional array it will return a 2n-dimensional array with
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the first n dimensions indexing patch position and the last n indexing
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the patch content. This operation is immediate (O(1)). A reshape
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performed on the first n dimensions will cause numpy to copy data, leading
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to a list of extracted patches.
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Read more in the :ref:`User Guide <image_feature_extraction>`.
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Parameters
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----------
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arr : ndarray
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n-dimensional array of which patches are to be extracted
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patch_shape : int or tuple of length arr.ndim.default=8
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Indicates the shape of the patches to be extracted. If an
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integer is given, the shape will be a hypercube of
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sidelength given by its value.
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extraction_step : int or tuple of length arr.ndim, default=1
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Indicates step size at which extraction shall be performed.
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If integer is given, then the step is uniform in all dimensions.
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Returns
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-------
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patches : strided ndarray
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2n-dimensional array indexing patches on first n dimensions and
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containing patches on the last n dimensions. These dimensions
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are fake, but this way no data is copied. A simple reshape invokes
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a copying operation to obtain a list of patches:
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result.reshape([-1] + list(patch_shape))
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"""
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arr_ndim = arr.ndim
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if isinstance(patch_shape, Number):
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patch_shape = tuple([patch_shape] * arr_ndim)
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if isinstance(extraction_step, Number):
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extraction_step = tuple([extraction_step] * arr_ndim)
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patch_strides = arr.strides
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slices = tuple(slice(None, None, st) for st in extraction_step)
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indexing_strides = arr[slices].strides
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patch_indices_shape = (
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(np.array(arr.shape) - np.array(patch_shape)) // np.array(extraction_step)
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) + 1
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shape = tuple(list(patch_indices_shape) + list(patch_shape))
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strides = tuple(list(indexing_strides) + list(patch_strides))
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patches = as_strided(arr, shape=shape, strides=strides)
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return patches
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@validate_params(
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{
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"image": [np.ndarray],
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"patch_size": [tuple, list],
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"max_patches": [
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Interval(RealNotInt, 0, 1, closed="neither"),
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Interval(Integral, 1, None, closed="left"),
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None,
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],
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"random_state": ["random_state"],
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},
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prefer_skip_nested_validation=True,
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)
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def extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None):
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"""Reshape a 2D image into a collection of patches.
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The resulting patches are allocated in a dedicated array.
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Read more in the :ref:`User Guide <image_feature_extraction>`.
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Parameters
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----------
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image : ndarray of shape (image_height, image_width) or \
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(image_height, image_width, n_channels)
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The original image data. For color images, the last dimension specifies
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the channel: a RGB image would have `n_channels=3`.
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patch_size : tuple of int (patch_height, patch_width)
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The dimensions of one patch.
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max_patches : int or float, default=None
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The maximum number of patches to extract. If `max_patches` is a float
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between 0 and 1, it is taken to be a proportion of the total number
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of patches. If `max_patches` is None it corresponds to the total number
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of patches that can be extracted.
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random_state : int, RandomState instance, default=None
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Determines the random number generator used for random sampling when
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`max_patches` is not None. Use an int to make the randomness
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deterministic.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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patches : array of shape (n_patches, patch_height, patch_width) or \
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(n_patches, patch_height, patch_width, n_channels)
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The collection of patches extracted from the image, where `n_patches`
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is either `max_patches` or the total number of patches that can be
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extracted.
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Examples
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--------
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>>> from sklearn.datasets import load_sample_image
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>>> from sklearn.feature_extraction import image
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>>> # Use the array data from the first image in this dataset:
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>>> one_image = load_sample_image("china.jpg")
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>>> print('Image shape: {}'.format(one_image.shape))
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Image shape: (427, 640, 3)
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>>> patches = image.extract_patches_2d(one_image, (2, 2))
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>>> print('Patches shape: {}'.format(patches.shape))
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Patches shape: (272214, 2, 2, 3)
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>>> # Here are just two of these patches:
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>>> print(patches[1])
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[[[174 201 231]
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[174 201 231]]
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[[173 200 230]
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[173 200 230]]]
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>>> print(patches[800])
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[[[187 214 243]
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[188 215 244]]
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[[187 214 243]
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[188 215 244]]]
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"""
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i_h, i_w = image.shape[:2]
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p_h, p_w = patch_size
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if p_h > i_h:
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raise ValueError(
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"Height of the patch should be less than the height of the image."
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)
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if p_w > i_w:
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raise ValueError(
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"Width of the patch should be less than the width of the image."
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)
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image = check_array(image, allow_nd=True)
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image = image.reshape((i_h, i_w, -1))
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n_colors = image.shape[-1]
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extracted_patches = _extract_patches(
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image, patch_shape=(p_h, p_w, n_colors), extraction_step=1
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)
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n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches)
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if max_patches:
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rng = check_random_state(random_state)
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i_s = rng.randint(i_h - p_h + 1, size=n_patches)
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j_s = rng.randint(i_w - p_w + 1, size=n_patches)
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patches = extracted_patches[i_s, j_s, 0]
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else:
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patches = extracted_patches
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patches = patches.reshape(-1, p_h, p_w, n_colors)
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# remove the color dimension if useless
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if patches.shape[-1] == 1:
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return patches.reshape((n_patches, p_h, p_w))
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else:
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return patches
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@validate_params(
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{"patches": [np.ndarray], "image_size": [tuple, Hidden(list)]},
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prefer_skip_nested_validation=True,
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)
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def reconstruct_from_patches_2d(patches, image_size):
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"""Reconstruct the image from all of its patches.
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Patches are assumed to overlap and the image is constructed by filling in
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the patches from left to right, top to bottom, averaging the overlapping
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regions.
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Read more in the :ref:`User Guide <image_feature_extraction>`.
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Parameters
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----------
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patches : ndarray of shape (n_patches, patch_height, patch_width) or \
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(n_patches, patch_height, patch_width, n_channels)
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The complete set of patches. If the patches contain colour information,
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channels are indexed along the last dimension: RGB patches would
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have `n_channels=3`.
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image_size : tuple of int (image_height, image_width) or \
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(image_height, image_width, n_channels)
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The size of the image that will be reconstructed.
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Returns
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-------
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image : ndarray of shape image_size
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The reconstructed image.
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Examples
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--------
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>>> from sklearn.datasets import load_sample_image
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>>> from sklearn.feature_extraction import image
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>>> one_image = load_sample_image("china.jpg")
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>>> print('Image shape: {}'.format(one_image.shape))
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Image shape: (427, 640, 3)
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>>> image_patches = image.extract_patches_2d(image=one_image, patch_size=(10, 10))
|
|
>>> print('Patches shape: {}'.format(image_patches.shape))
|
|
Patches shape: (263758, 10, 10, 3)
|
|
>>> image_reconstructed = image.reconstruct_from_patches_2d(
|
|
... patches=image_patches,
|
|
... image_size=one_image.shape
|
|
... )
|
|
>>> print(f"Reconstructed shape: {image_reconstructed.shape}")
|
|
Reconstructed shape: (427, 640, 3)
|
|
"""
|
|
i_h, i_w = image_size[:2]
|
|
p_h, p_w = patches.shape[1:3]
|
|
img = np.zeros(image_size)
|
|
# compute the dimensions of the patches array
|
|
n_h = i_h - p_h + 1
|
|
n_w = i_w - p_w + 1
|
|
for p, (i, j) in zip(patches, product(range(n_h), range(n_w))):
|
|
img[i : i + p_h, j : j + p_w] += p
|
|
|
|
for i in range(i_h):
|
|
for j in range(i_w):
|
|
# divide by the amount of overlap
|
|
# XXX: is this the most efficient way? memory-wise yes, cpu wise?
|
|
img[i, j] /= float(min(i + 1, p_h, i_h - i) * min(j + 1, p_w, i_w - j))
|
|
return img
|
|
|
|
|
|
class PatchExtractor(TransformerMixin, BaseEstimator):
|
|
"""Extracts patches from a collection of images.
|
|
|
|
Read more in the :ref:`User Guide <image_feature_extraction>`.
|
|
|
|
.. versionadded:: 0.9
|
|
|
|
Parameters
|
|
----------
|
|
patch_size : tuple of int (patch_height, patch_width), default=None
|
|
The dimensions of one patch. If set to None, the patch size will be
|
|
automatically set to `(img_height // 10, img_width // 10)`, where
|
|
`img_height` and `img_width` are the dimensions of the input images.
|
|
|
|
max_patches : int or float, default=None
|
|
The maximum number of patches per image to extract. If `max_patches` is
|
|
a float in (0, 1), it is taken to mean a proportion of the total number
|
|
of patches. If set to None, extract all possible patches.
|
|
|
|
random_state : int, RandomState instance, default=None
|
|
Determines the random number generator used for random sampling when
|
|
`max_patches is not None`. Use an int to make the randomness
|
|
deterministic.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
See Also
|
|
--------
|
|
reconstruct_from_patches_2d : Reconstruct image from all of its patches.
|
|
|
|
Notes
|
|
-----
|
|
This estimator is stateless and does not need to be fitted. However, we
|
|
recommend to call :meth:`fit_transform` instead of :meth:`transform`, as
|
|
parameter validation is only performed in :meth:`fit`.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.datasets import load_sample_images
|
|
>>> from sklearn.feature_extraction import image
|
|
>>> # Use the array data from the second image in this dataset:
|
|
>>> X = load_sample_images().images[1]
|
|
>>> X = X[None, ...]
|
|
>>> print(f"Image shape: {X.shape}")
|
|
Image shape: (1, 427, 640, 3)
|
|
>>> pe = image.PatchExtractor(patch_size=(10, 10))
|
|
>>> pe_trans = pe.transform(X)
|
|
>>> print(f"Patches shape: {pe_trans.shape}")
|
|
Patches shape: (263758, 10, 10, 3)
|
|
>>> X_reconstructed = image.reconstruct_from_patches_2d(pe_trans, X.shape[1:])
|
|
>>> print(f"Reconstructed shape: {X_reconstructed.shape}")
|
|
Reconstructed shape: (427, 640, 3)
|
|
"""
|
|
|
|
_parameter_constraints: dict = {
|
|
"patch_size": [tuple, None],
|
|
"max_patches": [
|
|
None,
|
|
Interval(RealNotInt, 0, 1, closed="neither"),
|
|
Interval(Integral, 1, None, closed="left"),
|
|
],
|
|
"random_state": ["random_state"],
|
|
}
|
|
|
|
def __init__(self, *, patch_size=None, max_patches=None, random_state=None):
|
|
self.patch_size = patch_size
|
|
self.max_patches = max_patches
|
|
self.random_state = random_state
|
|
|
|
@_fit_context(prefer_skip_nested_validation=True)
|
|
def fit(self, X, y=None):
|
|
"""Only validate the parameters of the estimator.
|
|
|
|
This method allows to: (i) validate the parameters of the estimator and
|
|
(ii) be consistent with the scikit-learn transformer API.
|
|
|
|
Parameters
|
|
----------
|
|
X : ndarray of shape (n_samples, image_height, image_width) or \
|
|
(n_samples, image_height, image_width, n_channels)
|
|
Array of images from which to extract patches. For color images,
|
|
the last dimension specifies the channel: a RGB image would have
|
|
`n_channels=3`.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Returns the instance itself.
|
|
"""
|
|
return self
|
|
|
|
def transform(self, X):
|
|
"""Transform the image samples in `X` into a matrix of patch data.
|
|
|
|
Parameters
|
|
----------
|
|
X : ndarray of shape (n_samples, image_height, image_width) or \
|
|
(n_samples, image_height, image_width, n_channels)
|
|
Array of images from which to extract patches. For color images,
|
|
the last dimension specifies the channel: a RGB image would have
|
|
`n_channels=3`.
|
|
|
|
Returns
|
|
-------
|
|
patches : array of shape (n_patches, patch_height, patch_width) or \
|
|
(n_patches, patch_height, patch_width, n_channels)
|
|
The collection of patches extracted from the images, where
|
|
`n_patches` is either `n_samples * max_patches` or the total
|
|
number of patches that can be extracted.
|
|
"""
|
|
X = self._validate_data(
|
|
X=X,
|
|
ensure_2d=False,
|
|
allow_nd=True,
|
|
ensure_min_samples=1,
|
|
ensure_min_features=1,
|
|
reset=False,
|
|
)
|
|
random_state = check_random_state(self.random_state)
|
|
n_imgs, img_height, img_width = X.shape[:3]
|
|
if self.patch_size is None:
|
|
patch_size = img_height // 10, img_width // 10
|
|
else:
|
|
if len(self.patch_size) != 2:
|
|
raise ValueError(
|
|
"patch_size must be a tuple of two integers. Got"
|
|
f" {self.patch_size} instead."
|
|
)
|
|
patch_size = self.patch_size
|
|
|
|
n_imgs, img_height, img_width = X.shape[:3]
|
|
X = np.reshape(X, (n_imgs, img_height, img_width, -1))
|
|
n_channels = X.shape[-1]
|
|
|
|
# compute the dimensions of the patches array
|
|
patch_height, patch_width = patch_size
|
|
n_patches = _compute_n_patches(
|
|
img_height, img_width, patch_height, patch_width, self.max_patches
|
|
)
|
|
patches_shape = (n_imgs * n_patches,) + patch_size
|
|
if n_channels > 1:
|
|
patches_shape += (n_channels,)
|
|
|
|
# extract the patches
|
|
patches = np.empty(patches_shape)
|
|
for ii, image in enumerate(X):
|
|
patches[ii * n_patches : (ii + 1) * n_patches] = extract_patches_2d(
|
|
image,
|
|
patch_size,
|
|
max_patches=self.max_patches,
|
|
random_state=random_state,
|
|
)
|
|
return patches
|
|
|
|
def _more_tags(self):
|
|
return {"X_types": ["3darray"], "stateless": True}
|