def vector_128_dsift(x, dtype=np.float32):
r"""
Computes a SIFT feature vector from a square patch (or image). Patch
**must** be square and the output vector will *always* be a ``(128,)``
vector. Please see :func:`dsift` for more information.
Parameters
----------
x : :map:`Image` or subclass or ``(C, Y, Y)`` `ndarray`
Either the image object itself or an array with the pixels. The first
dimension is interpreted as channels. Must be square i.e.
``height == width``.
dtype : ``np.dtype``, optional
The dtype of the returned vector.
Raises
------
ValueError
Only square images are supported.
"""
if not isinstance(x, np.ndarray):
x = x.pixels
if x.shape[-1] != x.shape[-2]:
raise ValueError('This feature only works with square images '
'i.e. width == height')
patch_shape = x.shape[-1]
n_bins = 4
c_size = patch_shape // n_bins
x = normalise_pixels_range(x, error_on_unknown_type=False)
return dsift(x,
window_step_horizontal=patch_shape,
window_step_vertical=patch_shape,
num_bins_horizontal=n_bins, num_bins_vertical=n_bins,
cell_size_horizontal=c_size, cell_size_vertical=c_size,
num_or_bins=8, fast=True).astype(dtype)
def _pil_to_numpy(self, normalise, convert=None):
p = self._pil_image.convert(convert) if convert else self._pil_image
p = channels_to_front(p)
if normalise:
return normalise_pixels_range(p)
else:
return p
def imageio_to_menpo(imio_reader, index):
pixels = imio_reader.get_data(index)
pixels = channels_to_front(pixels)
if self.normalise:
return Image(normalise_pixels_range(pixels), copy=False)
else:
return Image(pixels, copy=False)
def imageio_to_menpo(imio_reader, index):
pixels = imio_reader.get_data(index)
pixels = channels_to_front(pixels)
if self.normalise:
return Image(normalise_pixels_range(pixels), copy=False)
else:
return Image(pixels, copy=False)
def imageio_to_menpo(imio_reader, index):
pixels = imio_reader.get_data(index)
pixels = channels_to_front(pixels)
if pixels.shape[0] == 4:
# If normalise is False, then we return the alpha as an extra
# channel, which can be useful if the alpha channel has semantic
# meanings!
if self.normalise:
p = normalise_pixels_range(pixels[:3])
return MaskedImage(p, mask=pixels[-1].astype(np.bool),
copy=False)
else:
return Image(pixels, copy=False)
# Assumed not to have an Alpha channel
if self.normalise:
return Image(normalise_pixels_range(pixels), copy=False)
else:
return Image(pixels, copy=False)
def build(self):
import imageio
pixels = imageio.imread(self.filepath)
pixels = channels_to_front(pixels)
transparent_types = {'.png'}
filepath = Path(self.filepath)
if pixels.shape[0] == 4 and filepath.suffix in transparent_types:
# If normalise is False, then we return the alpha as an extra
# channel, which can be useful if the alpha channel has semantic
# meanings!
if self.normalise:
p = normalise_pixels_range(pixels[:3])
return MaskedImage(p, mask=pixels[-1].astype(np.bool),
copy=False)
else:
return Image(pixels, copy=False)
# Assumed not to have an Alpha channel
if self.normalise:
return Image(normalise_pixels_range(pixels), copy=False)
else:
return Image(pixels, copy=False)
def vector_128_dsift(x, dtype=np.float32):
r"""
Computes a SIFT feature vector from a square patch (or image). Patch
**must** be square and the output vector will *always* be a ``(128,)``
vector. Please see :func:`dsift` for more information.
Parameters
----------
x : :map:`Image` or subclass or ``(C, Y, Y)`` `ndarray`
Either the image object itself or an array with the pixels. The first
dimension is interpreted as channels. Must be square i.e.
``height == width``.
dtype : ``np.dtype``, optional
The dtype of the returned vector.
Raises
------
ValueError
Only square images are supported.
"""
if not isinstance(x, np.ndarray):
x = x.pixels
if x.shape[-1] != x.shape[-2]:
raise ValueError('This feature only works with square images '
'i.e. width == height')
patch_shape = x.shape[-1]
n_bins = 4
c_size = patch_shape // n_bins
x = normalise_pixels_range(x, error_on_unknown_type=False)
return dsift(x,
window_step_horizontal=patch_shape,
window_step_vertical=patch_shape,
num_bins_horizontal=n_bins,
num_bins_vertical=n_bins,
cell_size_horizontal=c_size,
cell_size_vertical=c_size,
num_or_bins=8,
fast=True).astype(dtype)