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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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"""
Random walker segmentation algorithm
from *Random walks for image segmentation*, Leo Grady, IEEE Trans
Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83.
Installing pyamg and using the 'cg_mg' mode of random_walker improves
significantly the performance.
"""
import numpy as np
from scipy import sparse, ndimage as ndi
from .._shared.utils import warn
# executive summary for next code block: try to import umfpack from
# scipy, but make sure not to raise a fuss if it fails since it's only
# needed to speed up a few cases.
# See discussions at:
# https://groups.google.com/d/msg/scikit-image/FrM5IGP6wh4/1hp-FtVZmfcJ
# https://stackoverflow.com/questions/13977970/ignore-exceptions-printed-to-stderr-in-del/13977992?noredirect=1#comment28386412_13977992
try:
from scipy.sparse.linalg.dsolve import umfpack
old_del = umfpack.UmfpackContext.__del__
def new_del(self):
try:
old_del(self)
except AttributeError:
pass
umfpack.UmfpackContext.__del__ = new_del
UmfpackContext = umfpack.UmfpackContext()
except ImportError:
UmfpackContext = None
try:
from pyamg import ruge_stuben_solver
amg_loaded = True
except ImportError:
amg_loaded = False
from ..util import img_as_float
from scipy.sparse.linalg import cg, spsolve
import scipy
from distutils.version import LooseVersion as Version
import functools
if Version(scipy.__version__) >= Version('1.1'):
cg = functools.partial(cg, atol=0)
def _make_graph_edges_3d(n_x, n_y, n_z):
"""Returns a list of edges for a 3D image.
Parameters
----------
n_x: integer
The size of the grid in the x direction.
n_y: integer
The size of the grid in the y direction
n_z: integer
The size of the grid in the z direction
Returns
-------
edges : (2, N) ndarray
with the total number of edges::
N = n_x * n_y * (nz - 1) +
n_x * (n_y - 1) * nz +
(n_x - 1) * n_y * nz
Graph edges with each column describing a node-id pair.
"""
vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
edges_deep = np.vstack((vertices[..., :-1].ravel(),
vertices[..., 1:].ravel()))
edges_right = np.vstack((vertices[:, :-1].ravel(),
vertices[:, 1:].ravel()))
edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel()))
edges = np.hstack((edges_deep, edges_right, edges_down))
return edges
def _compute_weights_3d(data, spacing, beta, eps, multichannel):
# Weight calculation is main difference in multispectral version
# Original gradient**2 replaced with sum of gradients ** 2
gradients = np.concatenate(
[np.diff(data[..., 0], axis=ax).ravel() / spacing[ax]
for ax in [2, 1, 0] if data.shape[ax] > 1], axis=0) ** 2
for channel in range(1, data.shape[-1]):
gradients += np.concatenate(
[np.diff(data[..., channel], axis=ax).ravel() / spacing[ax]
for ax in [2, 1, 0] if data.shape[ax] > 1], axis=0) ** 2
# All channels considered together in this standard deviation
scale_factor = -beta / (10 * data.std())
if multichannel:
# New final term in beta to give == results in trivial case where
# multiple identical spectra are passed.
scale_factor /= np.sqrt(data.shape[-1])
weights = np.exp(scale_factor * gradients)
weights += eps
return -weights
def _build_laplacian(data, spacing, mask, beta, multichannel):
l_x, l_y, l_z = data.shape[:3]
edges = _make_graph_edges_3d(l_x, l_y, l_z)
weights = _compute_weights_3d(data, spacing, beta=beta, eps=1.e-10,
multichannel=multichannel)
if mask is not None:
# Remove edges of the graph connected to masked nodes, as well
# as corresponding weights of the edges.
mask0 = np.hstack([mask[..., :-1].ravel(), mask[:, :-1].ravel(),
mask[:-1].ravel()])
mask1 = np.hstack([mask[..., 1:].ravel(), mask[:, 1:].ravel(),
mask[1:].ravel()])
ind_mask = np.logical_and(mask0, mask1)
edges, weights = edges[:, ind_mask], weights[ind_mask]
# Reassign edges labels to 0, 1, ... edges_number - 1
_, inv_idx = np.unique(edges, return_inverse=True)
edges = inv_idx.reshape(edges.shape)
# Build the sparse linear system
pixel_nb = edges.shape[1]
i_indices = edges.ravel()
j_indices = edges[::-1].ravel()
data = np.hstack((weights, weights))
lap = sparse.coo_matrix((data, (i_indices, j_indices)),
shape=(pixel_nb, pixel_nb))
lap.setdiag(-np.ravel(lap.sum(axis=0)))
return lap.tocsr()
def _build_linear_system(data, spacing, labels, nlabels, mask,
beta, multichannel):
"""
Build the matrix A and rhs B of the linear system to solve.
A and B are two block of the laplacian of the image graph.
"""
if mask is None:
labels = labels.ravel()
else:
labels = labels[mask]
indices = np.arange(labels.size)
seeds_mask = labels > 0
unlabeled_indices = indices[~seeds_mask]
seeds_indices = indices[seeds_mask]
lap_sparse = _build_laplacian(data, spacing, mask=mask,
beta=beta, multichannel=multichannel)
rows = lap_sparse[unlabeled_indices, :]
lap_sparse = rows[:, unlabeled_indices]
B = -rows[:, seeds_indices]
seeds = labels[seeds_mask]
seeds_mask = sparse.csc_matrix(np.hstack(
[np.atleast_2d(seeds == lab).T for lab in range(1, nlabels + 1)]))
rhs = B.dot(seeds_mask)
return lap_sparse, rhs
def _solve_linear_system(lap_sparse, B, tol, mode):
if mode is None:
mode = 'cg_j'
if mode == 'cg_mg' and not amg_loaded:
warn('"cg_mg" not available, it requires pyamg to be installed. '
'The "cg_j" mode will be used instead.',
stacklevel=2)
mode = 'cg_j'
if mode == 'bf':
X = spsolve(lap_sparse, B.toarray()).T
else:
maxiter = None
if mode == 'cg':
if UmfpackContext is None:
warn('"cg" mode may be slow because UMFPACK is not available. '
'Consider building Scipy with UMFPACK or use a '
'preconditioned version of CG ("cg_j" or "cg_mg" modes).',
stacklevel=2)
M = None
elif mode == 'cg_j':
M = sparse.diags(1.0 / lap_sparse.diagonal())
else:
# mode == 'cg_mg'
lap_sparse = lap_sparse.tocsr()
ml = ruge_stuben_solver(lap_sparse)
M = ml.aspreconditioner(cycle='V')
maxiter = 30
cg_out = [
cg(lap_sparse, B[:, i].toarray(), tol=tol, M=M, maxiter=maxiter)
for i in range(B.shape[1])]
if np.any([info > 0 for _, info in cg_out]):
warn("Conjugate gradient convergence to tolerance not achieved. "
"Consider decreasing beta to improve system conditionning.",
stacklevel=2)
X = np.asarray([x for x, _ in cg_out])
return X
def _preprocess(labels):
label_values, inv_idx = np.unique(labels, return_inverse=True)
if not (label_values == 0).any():
warn('Random walker only segments unlabeled areas, where '
'labels == 0. No zero valued areas in labels were '
'found. Returning provided labels.',
stacklevel=2)
return labels, None, None, None, None
# If some labeled pixels are isolated inside pruned zones, prune them
# as well and keep the labels for the final output
null_mask = labels == 0
pos_mask = labels > 0
mask = labels >= 0
fill = ndi.binary_propagation(null_mask, mask=mask)
isolated = np.logical_and(pos_mask, np.logical_not(fill))
pos_mask[isolated] = False
# If the array has pruned zones, be sure that no isolated pixels
# exist between pruned zones (they could not be determined)
if label_values[0] < 0 or np.any(isolated):
isolated = np.logical_and(
np.logical_not(ndi.binary_propagation(pos_mask, mask=mask)),
null_mask)
labels[isolated] = -1
if np.all(isolated[null_mask]):
warn('All unlabeled pixels are isolated, they could not be '
'determined by the random walker algorithm.',
stacklevel=2)
return labels, None, None, None, None
mask[isolated] = False
mask = np.atleast_3d(mask)
else:
mask = None
# Reorder label values to have consecutive integers (no gaps)
zero_idx = np.searchsorted(label_values, 0)
labels = np.atleast_3d(inv_idx.reshape(labels.shape) - zero_idx)
nlabels = label_values[zero_idx + 1:].shape[0]
inds_isolated_seeds = np.nonzero(isolated)
isolated_values = labels[inds_isolated_seeds]
return labels, nlabels, mask, inds_isolated_seeds, isolated_values
def random_walker(data, labels, beta=130, mode='cg_j', tol=1.e-3, copy=True,
multichannel=False, return_full_prob=False, spacing=None):
"""Random walker algorithm for segmentation from markers.
Random walker algorithm is implemented for gray-level or multichannel
images.
Parameters
----------
data : array_like
Image to be segmented in phases. Gray-level `data` can be two- or
three-dimensional; multichannel data can be three- or four-
dimensional (multichannel=True) with the highest dimension denoting
channels. Data spacing is assumed isotropic unless the `spacing`
keyword argument is used.
labels : array of ints, of same shape as `data` without channels dimension
Array of seed markers labeled with different positive integers
for different phases. Zero-labeled pixels are unlabeled pixels.
Negative labels correspond to inactive pixels that are not taken
into account (they are removed from the graph). If labels are not
consecutive integers, the labels array will be transformed so that
labels are consecutive. In the multichannel case, `labels` should have
the same shape as a single channel of `data`, i.e. without the final
dimension denoting channels.
beta : float, optional
Penalization coefficient for the random walker motion
(the greater `beta`, the more difficult the diffusion).
mode : string, available options {'cg', 'cg_j', 'cg_mg', 'bf'}
Mode for solving the linear system in the random walker algorithm.
- 'bf' (brute force): an LU factorization of the Laplacian is
computed. This is fast for small images (<1024x1024), but very slow
and memory-intensive for large images (e.g., 3-D volumes).
- 'cg' (conjugate gradient): the linear system is solved iteratively
using the Conjugate Gradient method from scipy.sparse.linalg. This is
less memory-consuming than the brute force method for large images,
but it is quite slow.
- 'cg_j' (conjugate gradient with Jacobi preconditionner): the
Jacobi preconditionner is applyed during the Conjugate
gradient method iterations. This may accelerate the
convergence of the 'cg' method.
- 'cg_mg' (conjugate gradient with multigrid preconditioner): a
preconditioner is computed using a multigrid solver, then the
solution is computed with the Conjugate Gradient method. This mode
requires that the pyamg module is installed.
tol : float, optional
tolerance to achieve when solving the linear system using
the conjugate gradient based modes ('cg', 'cg_j' and 'cg_mg').
copy : bool, optional
If copy is False, the `labels` array will be overwritten with
the result of the segmentation. Use copy=False if you want to
save on memory.
multichannel : bool, optional
If True, input data is parsed as multichannel data (see 'data' above
for proper input format in this case).
return_full_prob : bool, optional
If True, the probability that a pixel belongs to each of the
labels will be returned, instead of only the most likely
label.
spacing : iterable of floats, optional
Spacing between voxels in each spatial dimension. If `None`, then
the spacing between pixels/voxels in each dimension is assumed 1.
Returns
-------
output : ndarray
* If `return_full_prob` is False, array of ints of same shape
and data type as `labels`, in which each pixel has been
labeled according to the marker that reached the pixel first
by anisotropic diffusion.
* If `return_full_prob` is True, array of floats of shape
`(nlabels, labels.shape)`. `output[label_nb, i, j]` is the
probability that label `label_nb` reaches the pixel `(i, j)`
first.
See also
--------
skimage.morphology.watershed: watershed segmentation
A segmentation algorithm based on mathematical morphology
and "flooding" of regions from markers.
Notes
-----
Multichannel inputs are scaled with all channel data combined. Ensure all
channels are separately normalized prior to running this algorithm.
The `spacing` argument is specifically for anisotropic datasets, where
data points are spaced differently in one or more spatial dimensions.
Anisotropic data is commonly encountered in medical imaging.
The algorithm was first proposed in [1]_.
The algorithm solves the diffusion equation at infinite times for
sources placed on markers of each phase in turn. A pixel is labeled with
the phase that has the greatest probability to diffuse first to the pixel.
The diffusion equation is solved by minimizing x.T L x for each phase,
where L is the Laplacian of the weighted graph of the image, and x is
the probability that a marker of the given phase arrives first at a pixel
by diffusion (x=1 on markers of the phase, x=0 on the other markers, and
the other coefficients are looked for). Each pixel is attributed the label
for which it has a maximal value of x. The Laplacian L of the image
is defined as:
- L_ii = d_i, the number of neighbors of pixel i (the degree of i)
- L_ij = -w_ij if i and j are adjacent pixels
The weight w_ij is a decreasing function of the norm of the local gradient.
This ensures that diffusion is easier between pixels of similar values.
When the Laplacian is decomposed into blocks of marked and unmarked
pixels::
L = M B.T
B A
with first indices corresponding to marked pixels, and then to unmarked
pixels, minimizing x.T L x for one phase amount to solving::
A x = - B x_m
where x_m = 1 on markers of the given phase, and 0 on other markers.
This linear system is solved in the algorithm using a direct method for
small images, and an iterative method for larger images.
References
----------
.. [1] Leo Grady, Random walks for image segmentation, IEEE Trans Pattern
Anal Mach Intell. 2006 Nov;28(11):1768-83.
:DOI:`10.1109/TPAMI.2006.233`.
Examples
--------
>>> np.random.seed(0)
>>> a = np.zeros((10, 10)) + 0.2 * np.random.rand(10, 10)
>>> a[5:8, 5:8] += 1
>>> b = np.zeros_like(a, dtype=np.int32)
>>> b[3, 3] = 1 # Marker for first phase
>>> b[6, 6] = 2 # Marker for second phase
>>> random_walker(a, b)
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
"""
# Parse input data
if mode not in ('cg_mg', 'cg', 'bf', 'cg_j', None):
raise ValueError(
"{mode} is not a valid mode. Valid modes are 'cg_mg',"
" 'cg', 'cg_j', 'bf' and None".format(mode=mode))
# Spacing kwarg checks
if spacing is None:
spacing = np.ones(3)
elif len(spacing) == labels.ndim:
if len(spacing) == 2:
# Need a dummy spacing for singleton 3rd dim
spacing = np.r_[spacing, 1.]
spacing = np.asarray(spacing)
else:
raise ValueError('Input argument `spacing` incorrect, should be an '
'iterable with one number per spatial dimension.')
# This algorithm expects 4-D arrays of floats, where the first three
# dimensions are spatial and the final denotes channels. 2-D images have
# a singleton placeholder dimension added for the third spatial dimension,
# and single channel images likewise have a singleton added for channels.
# The following block ensures valid input and coerces it to the correct
# form.
if not multichannel:
if data.ndim not in (2, 3):
raise ValueError('For non-multichannel input, data must be of '
'dimension 2 or 3.')
if data.shape != labels.shape:
raise ValueError('Incompatible data and labels shapes.')
data = np.atleast_3d(img_as_float(data))[..., np.newaxis]
else:
if data.ndim not in (3, 4):
raise ValueError('For multichannel input, data must have 3 or 4 '
'dimensions.')
if data.shape[:-1] != labels.shape:
raise ValueError('Incompatible data and labels shapes.')
data = img_as_float(data)
if data.ndim == 3: # 2D multispectral, needs singleton in 3rd axis
data = data[:, :, np.newaxis, :]
labels_shape = labels.shape
labels_dtype = labels.dtype
if copy:
labels = np.copy(labels)
(labels, nlabels, mask,
inds_isolated_seeds, isolated_values) = _preprocess(labels)
if isolated_values is None:
# No non isolated zero valued areas in labels were
# found. Returning provided labels.
if return_full_prob:
# Return the concatenation of the masks of each unique label
return np.concatenate([np.atleast_3d(labels == lab)
for lab in np.unique(labels) if lab > 0],
axis=-1)
return labels
# Build the linear system (lap_sparse, B)
lap_sparse, B = _build_linear_system(data, spacing, labels, nlabels, mask,
beta, multichannel)
# Solve the linear system lap_sparse X = B
# where X[i, j] is the probability that a marker of label i arrives
# first at pixel j by anisotropic diffusion.
X = _solve_linear_system(lap_sparse, B, tol, mode)
# Build the output according to return_full_prob value
# Put back labels of isolated seeds
labels[inds_isolated_seeds] = isolated_values
labels = labels.reshape(labels_shape)
if return_full_prob:
mask = labels == 0
out = np.zeros((nlabels,) + labels_shape)
for lab, (label_prob, prob) in enumerate(zip(out, X), start=1):
label_prob[mask] = prob
label_prob[labels == lab] = 1
else:
X = np.argmax(X, axis=0) + 1
out = labels.astype(labels_dtype)
out[labels == 0] = X
return out