from menpo.base import partial_doc from .landmark import asf_importer, pts_importer asf_image_importer = partial_doc(asf_importer, image_origin=True) pts_image_importer = partial_doc(pts_importer, image_origin=True)
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),
)
# A predefined method for a 'faster' dsift method
fast_dsift = partial_doc(
dsift,
fast=True,
cell_size_vertical=5,
cell_size_horizontal=5,
num_bins_horizontal=1,
num_bins_vertical=1,
num_or_bins=8,
)
# Predefined dsift that returns a 128d vector
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
----------
extension : `str`
File extension to support. May be multi-part e.g. '.tar.gz'
callable : `callable`
The callable to invoke if a file with the provided extension is
discovered during importing. Should take a single argument (the
filepath) and any number of kwargs.
"""
if not isinstance(extension, basestring):
raise ValueError("Only string type keys are supported.")
if extension in ext_map:
warnings.warn("Replacing an existing importer for the '{}' "
"extension.".format(extension))
ext_map[_normalize_extension(extension)] = callable
register_image_importer = partial_doc(_register_importer, image_types)
register_video_importer = partial_doc(_register_importer, ffmpeg_video_types)
register_landmark_importer = partial_doc(_register_importer,
image_landmark_types)
register_pickle_importer = partial_doc(_register_importer, pickle_types)
menpo_data_dir_path = partial_doc(_data_dir_path, menpo_src_dir_path)
menpo_ls_builtin_assets = partial_doc(_ls_builtin_assets, menpo_data_dir_path)
menpo_data_path_to = partial_doc(_data_path_to, menpo_data_dir_path,
menpo_ls_builtin_assets)
Default image texture resolver. Returns **the lexicographically
sorted first** texture found to have the same stem as the asset. A warning
is raised if more than one texture is found.
"""
# pattern finding all landmarks with the same stem
pattern = path.with_suffix('.*')
texture_paths = sorted(paths_callable(pattern))
if len(texture_paths) > 1:
warnings.warn('More than one texture found for file, returning '
'only the first.')
if not texture_paths:
return None
return texture_paths[0]
same_name_landmark = partial_doc(same_name, paths_callable=landmark_file_paths)
menpo3d_data_dir_path = partial_doc(_data_dir_path, menpo3d_src_dir_path)
menpo3d_ls_builtin_assets = partial_doc(_ls_builtin_assets,
menpo3d_data_dir_path)
menpo3d_data_path_to = partial_doc(_data_path_to, menpo3d_data_dir_path,
menpo3d_ls_builtin_assets)
_menpo3d_import_builtin_asset = partial_doc(_import_builtin_asset,
menpo3d_data_path_to,
mesh_types,
mesh_landmark_types,
texture_resolver=same_name_texture)
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))
# A predefined method for a 'faster' dsift method
fast_dsift = partial_doc(dsift, fast=True, cell_size_vertical=5,
cell_size_horizontal=5, num_bins_horizontal=1,
num_bins_vertical=1, num_or_bins=8)
# Predefined dsift that returns a 128d vector
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.
extension : `str`
File extension to support. May be multi-part e.g. '.tar.gz'
callable : `callable`
The callable to invoke if a file with the provided extension is
discovered during importing. Should take a single argument (the
filepath) and any number of kwargs.
"""
if not isinstance(extension, basestring):
raise ValueError('Only string type keys are supported.')
if extension in ext_map:
warnings.warn("Replacing an existing importer for the '{}' "
"extension.".format(extension))
ext_map[_normalize_extension(extension)] = callable
register_image_importer = partial_doc(_register_importer, image_types)
register_video_importer = partial_doc(_register_importer, ffmpeg_video_types)
register_landmark_importer = partial_doc(_register_importer,
image_landmark_types)
register_pickle_importer = partial_doc(_register_importer, pickle_types)
menpo_data_dir_path = partial_doc(_data_dir_path, menpo_src_dir_path)
menpo_ls_builtin_assets = partial_doc(_ls_builtin_assets, menpo_data_dir_path)
menpo_data_path_to = partial_doc(_data_path_to, menpo_data_dir_path,
menpo_ls_builtin_assets)
from menpo.base import partial_doc from .features import igo double_igo = partial_doc(igo, double_angles=True)
Default image texture resolver. Returns **the lexicographically
sorted first** texture found to have the same stem as the asset. A warning
is raised if more than one texture is found.
"""
# pattern finding all landmarks with the same stem
pattern = path.with_suffix('.*')
texture_paths = sorted(paths_callable(pattern))
if len(texture_paths) > 1:
warnings.warn('More than one texture found for file, returning '
'only the first.')
if not texture_paths:
return None
return texture_paths[0]
same_name_landmark = partial_doc(same_name, paths_callable=landmark_file_paths)
menpo3d_data_dir_path = partial_doc(_data_dir_path, menpo3d_src_dir_path)
menpo3d_ls_builtin_assets = partial_doc(_ls_builtin_assets,
menpo3d_data_dir_path)
menpo3d_data_path_to = partial_doc(_data_path_to, menpo3d_data_dir_path,
menpo3d_ls_builtin_assets)
_menpo3d_import_builtin_asset = partial_doc(_import_builtin_asset,
menpo3d_data_path_to,
mesh_types, mesh_landmark_types,
texture_resolver=same_name_texture)
from menpo.base import partial_doc from .landmark import asf_importer, pts_importer asf_image_importer = partial_doc(asf_importer, image_origin=True) pts_image_importer = partial_doc(pts_importer, image_origin=True)
from menpo.base import partial_doc from .features import igo, hog double_igo = partial_doc(igo, double_angles=True) sparse_hog = partial_doc(hog, mode='sparse')