def dsift(pixels, step=1, size=3, bounds=None, window_size=2, norm=False,
fast=False, float_descriptors=False, geometry=(4, 4, 8)):
centers, output = cyvlfeat_dsift(np.rot90(pixels[0, ..., ::-1]),
step=step, size=size, bounds=bounds,
window_size=window_size, norm=norm,
fast=fast,
float_descriptors=float_descriptors,
geometry=geometry)
shape = pixels.shape[1:] - 2 * centers[:2, 0]
return (np.require(output.reshape((-1, shape[0], shape[1])),
dtype=np.double),
np.require(centers[:2, ...].T[..., ::-1].reshape(
(shape[0], shape[1], 2)), dtype=np.int))
def dsift(pixels, step=1, size=3, bounds=None, window_size=2, norm=False,
fast=False, float_descriptors=False, geometry=(4, 4, 8)):
centers, output = cyvlfeat_dsift(np.rot90(pixels[0, ..., ::-1]),
step=step, size=size, bounds=bounds,
window_size=window_size, norm=norm,
fast=fast,
float_descriptors=float_descriptors,
geometry=geometry)
shape = pixels.shape[1:] - 2 * centers[:, 0]
return (np.require(output.reshape((-1, shape[0], shape[1])),
dtype=np.double),
np.require(centers.T[..., ::-1].reshape((shape[0], shape[1], 2)),
dtype=np.int))
def dsift(
pixels,
window_step_horizontal=1,
window_step_vertical=1,
num_bins_horizontal=2,
num_bins_vertical=2,
num_or_bins=9,
cell_size_horizontal=6,
cell_size_vertical=6,
fast=True,
verbose=False,
):
r"""
Computes a 2-dimensional dense SIFT features image with ``C`` number of
channels, where
``C = num_bins_horizontal * num_bins_vertical * num_or_bins``.
Parameters
----------
pixels : :map:`Image` or subclass or ``(C, X, Y, ..., Z)`` `ndarray`
Either the image object itself or an array with the pixels. The first
dimension is interpreted as channels.
window_step_horizontal : `int`, optional
Defines the horizontal step by which the window is moved, thus it
controls the features density. The metric unit is pixels.
window_step_vertical : `int`, optional
Defines the vertical step by which the window is moved, thus it
controls the features density. The metric unit is pixels.
num_bins_horizontal : `int`, optional
Defines the number of histogram bins in the X direction.
num_bins_vertical : `int`, optional
Defines the number of histogram bins in the Y direction.
num_or_bins : `int`, optional
Defines the number of orientation histogram bins.
cell_size_horizontal : `int`, optional
Defines cell width in pixels. The cell is the region that is covered by
a spatial bin.
cell_size_vertical : `int`, optional
Defines cell height in pixels. The cell is the region that is covered by
a spatial bin.
fast : `bool`, optional
If ``True``, then the windowing function is a piecewise-flat, rather
than Gaussian. While this breaks exact SIFT equivalence, in practice it
is much faster to compute.
verbose : `bool`, optional
Flag to print SIFT related information.
Raises
------
ValueError
Only 2D arrays are supported
ValueError
Size must only contain positive integers.
ValueError
Step must only contain positive integers.
ValueError
Window size must be a positive integer.
ValueError
Geometry must only contain positive integers.
"""
# If norm is set to True, then the centers array will have a third column
# with descriptor norm, or energy, before contrast normalization.
# This information can be used to suppress low contrast descriptors.
centers, output = cyvlfeat_dsift(
pixels[0],
step=[window_step_vertical, window_step_horizontal],
size=[cell_size_vertical, cell_size_horizontal],
bounds=None,
norm=False,
fast=fast,
float_descriptors=True,
geometry=(num_bins_vertical, num_bins_horizontal, num_or_bins),
verbose=False,
)
# the output shape can be calculated from looking at the range of
# centres / the window step size in each dimension. Note that cyvlfeat
# returns x, y centres.
shape = ((centers[-1, :] - centers[0, :]) / [window_step_vertical, window_step_horizontal]) + 1
# print information
if verbose:
info_str = (
"Dense SIFT features:\n"
" - Input image is {}W x {}H with {} channels.\n"
" - Sampling step of ({}W,{}H).\n"
" - {}W x {}H spatial bins and {} orientation bins.\n"
" - Cell size of {}W x {}H pixels.\n".format(
pixels.shape[2],
pixels.shape[1],
pixels.shape[0],
window_step_horizontal,
window_step_vertical,
num_bins_horizontal,
num_bins_vertical,
num_or_bins,
cell_size_horizontal,
cell_size_vertical,
)
)
if fast:
info_str += " - Fast mode is enabled.\n"
info_str += "Output image size {}W x {}H x {}.".format(int(shape[1]), int(shape[0]), output.shape[0])
print(info_str)
# return SIFT and centers in the correct form
return (
np.require(np.rollaxis(output.reshape((shape[0], shape[1], -1)), -1), dtype=np.double, requirements=["C"]),
np.require(centers.reshape((shape[0], shape[1], -1)), dtype=np.int),
)
def dsift(
pixels,
window_step_horizontal=1,
window_step_vertical=1,
num_bins_horizontal=2,
num_bins_vertical=2,
num_or_bins=9,
cell_size_horizontal=6,
cell_size_vertical=6,
fast=True,
verbose=False,
):
r"""
Computes a 2-dimensional dense SIFT features image with ``C`` number of
channels, where
``C = num_bins_horizontal * num_bins_vertical * num_or_bins``. The dense
SIFT [2]_ implementation is taken from Vlfeat [1]_.
Parameters
----------
pixels : :map:`Image` or subclass or ``(C, Y, X)`` `ndarray`
Either the image object itself or an array with the pixels. The first
dimension is interpreted as channels.
window_step_horizontal : `int`, optional
Defines the horizontal step by which the window is moved, thus it
controls the features density. The metric unit is pixels.
window_step_vertical : `int`, optional
Defines the vertical step by which the window is moved, thus it
controls the features density. The metric unit is pixels.
num_bins_horizontal : `int`, optional
Defines the number of histogram bins in the X direction.
num_bins_vertical : `int`, optional
Defines the number of histogram bins in the Y direction.
num_or_bins : `int`, optional
Defines the number of orientation histogram bins.
cell_size_horizontal : `int`, optional
Defines cell width in pixels. The cell is the region that is covered by
a spatial bin.
cell_size_vertical : `int`, optional
Defines cell height in pixels. The cell is the region that is covered by
a spatial bin.
fast : `bool`, optional
If ``True``, then the windowing function is a piecewise-flat, rather
than Gaussian. While this breaks exact SIFT equivalence, in practice it
is much faster to compute.
verbose : `bool`, optional
Flag to print SIFT related information.
Raises
------
ValueError
Only 2D arrays are supported
ValueError
Size must only contain positive integers.
ValueError
Step must only contain positive integers.
ValueError
Window size must be a positive integer.
ValueError
Geometry must only contain positive integers.
References
----------
.. [1] Vedaldi, Andrea, and Brian Fulkerson. "VLFeat: An open and portable
library of computer vision algorithms." Proceedings of the international
conference on Multimedia. ACM, 2010.
.. [2] Lowe, David G. "Distinctive image features from scale-invariant
keypoints." International journal of computer vision 60.2 (2004): 91-110.
"""
# If norm is set to True, then the centers array will have a third column
# with descriptor norm, or energy, before contrast normalization.
# This information can be used to suppress low contrast descriptors.
centers, output = cyvlfeat_dsift(
pixels[0],
step=[window_step_vertical, window_step_horizontal],
size=[cell_size_vertical, cell_size_horizontal],
bounds=None,
norm=False,
fast=fast,
float_descriptors=True,
geometry=(num_bins_vertical, num_bins_horizontal, num_or_bins),
verbose=False,
)
# the output shape can be calculated from looking at the range of
# centres / the window step size in each dimension. Note that cyvlfeat
# returns x, y centres.
shape = ((centers[-1, :] - centers[0, :]) /
[window_step_vertical, window_step_horizontal]) + 1
shape = shape.astype(np.int)
# print information
if verbose:
info_str = ("Dense SIFT features:\n"
" - Input image is {}W x {}H with {} channels.\n"
" - Sampling step of ({}W, {}H).\n"
" - {}W x {}H spatial bins and {} orientation bins.\n"
" - Cell size of {}W x {}H pixels.\n".format(
pixels.shape[2],
pixels.shape[1],
pixels.shape[0],
window_step_horizontal,
window_step_vertical,
num_bins_horizontal,
num_bins_vertical,
num_or_bins,
cell_size_horizontal,
cell_size_vertical,
))
if fast:
info_str += " - Fast mode is enabled.\n"
info_str += "Output image size {}W x {}H x {}.".format(
int(shape[1]), int(shape[0]), output.shape[0])
print(info_str)
# return SIFT and centers in the correct form
return (
np.require(
np.rollaxis(output.reshape((shape[0], shape[1], -1)), -1),
dtype=np.double,
requirements=["C"],
),
np.require(centers.reshape((shape[0], shape[1], -1)), dtype=np.int),
)