import collections.abc as collections_abc
import os
import random
import warnings
from collections import OrderedDict
from functools import partial
from pathlib import Path
from typing import Any
from menpo.base import (
LazyList,
MenpoDeprecationWarning,
menpo_src_dir_path,
partial_doc,
)
from menpo.visualize import print_progress
from .extensions import (
ffmpeg_video_types,
image_landmark_types,
image_types,
pickle_types,
)
from ..utils import _norm_path, _normalize_extension, _possible_extensions_from_filepath
# TODO: Remove once deprecated
def _parse_deprecated_normalise(normalise, normalize):
if normalise is not None and normalize is not None:
raise ValueError(
"normalise is now deprecated, do not set both " "normalize and normalise."
)
elif normalise is not None:
warnings.warn(
"normalise is no longer supported and will be removed in "
"a future version of Menpo. Use normalize instead.",
MenpoDeprecationWarning,
)
normalize = normalise
elif normalize is None:
normalize = True
return normalize
def _data_dir_path(base_path):
r"""A path to the built in ./data folder on this machine.
Returns
-------
path : ``pathlib.Path``
The path to the local ./data folder
"""
return base_path() / "data"
def _data_path_to(data_dir_path, builtin_assets, asset_filename):
r"""
The path to a builtin asset in the ./data folder on this machine.
Parameters
----------
asset_filename : `str`
The filename (with extension) of a file builtin to Menpo. The full
set of allowed names is given by :func:`ls_builtin_assets()`
Returns
-------
data_path : `pathlib.Path`
The path to a given asset in the ./data folder
Raises
------
ValueError
If the asset_filename doesn't exist in the `data` folder.
"""
asset_path = data_dir_path() / asset_filename
if not asset_path.is_file():
raise ValueError(
"{} is not a builtin asset: {}".format(asset_filename, builtin_assets())
)
return asset_path
def _import_builtin_asset(
data_path_to, object_types, landmark_types, asset_name, **kwargs
):
r"""Single builtin asset (landmark or image) importer.
Imports the relevant builtin asset from the ``./data`` directory that
ships with the project.
Parameters
----------
asset_name : `str`
The filename of a builtin asset (see :map:`ls_builtin_assets`
for allowed values)
Returns
-------
asset :
An instantiated :map:`Image`, :map:`PointCloud` or
:map:`PointCloud` asset.
"""
if kwargs != {}:
normalize = _parse_deprecated_normalise(
kwargs.get("normalise"), kwargs.get("normalize")
)
kwargs["normalize"] = normalize
if "normalise" in kwargs:
del kwargs["normalise"]
asset_path = data_path_to(asset_name)
# Import could be either an image or a set of landmarks, so we try
# importing them both separately.
try:
return _import(
asset_path,
object_types,
landmark_ext_map=landmark_types,
landmark_attach_func=_import_object_attach_landmarks,
importer_kwargs=kwargs,
)
except ValueError:
return _import(asset_path, landmark_types, importer_kwargs=kwargs)
def _ls_builtin_assets(data_dir_path):
r"""List all the builtin asset examples provided.
Returns
-------
file_paths : list of `str`
Filenames of all assets in the data directory shipped with the
project.
"""
return [p.name for p in data_dir_path().glob("*") if not p.is_dir()]
def _register_importer(ext_map, extension, callable):
r"""
Register a new importer for the given extension.
Parameters
----------
ext_map : `{'str' -> 'callable'}` dict
Extensions map to callable.
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, str):
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
)
_menpo_import_builtin_asset = partial_doc(
_import_builtin_asset, menpo_data_path_to, image_types, image_landmark_types
)
[docs]def image_paths(pattern):
r"""
Return image filepaths that Menpo can import that match the glob pattern.
"""
return glob_with_suffix(pattern, image_types)
[docs]def video_paths(pattern):
r"""
Return video filepaths that Menpo can import that match the glob pattern.
"""
return glob_with_suffix(pattern, ffmpeg_video_types)
[docs]def landmark_file_paths(pattern):
r"""
Return landmark file filepaths that Menpo can import that match the glob
pattern.
"""
return glob_with_suffix(pattern, image_landmark_types)
[docs]def pickle_paths(pattern):
r"""
Return pickle filepaths that Menpo can import that match the glob
pattern.
"""
return glob_with_suffix(pattern, pickle_types)
def merge_all_dicts(dicts):
"""
Use dict.update to build a single dictionary from a list of dictionaries.
If any keys will be overwritten as the dictionary is built then a warning
is emitted.
Parameters
----------
dicts : `list` of `dict`
The list of dictionaries to merge
Returns
-------
new_dict : `dict`
New single dictionary formed from merging the list of dictionaries
"""
new_dict = OrderedDict()
for d in dicts:
intersection = set(d.keys()) & set(new_dict.keys())
# Are there any overlapping keys?
if intersection:
warnings.warn(
"Found a keys that will be " "overwritten - {}".format(intersection)
)
new_dict.update(d)
return new_dict
def same_name(path, paths_callable=landmark_file_paths):
r"""
Default image landmark resolver. Returns all landmarks found to have
the same stem as the asset.
"""
# pattern finding all landmarks with the same stem
pattern = path.with_suffix(".*")
# find all the assets we can with this name
lmarks = [import_landmark_file(p) for p in paths_callable(pattern)]
# now we have to merge all the dictionaries into a single dictionary
return merge_all_dicts(lmarks)
def same_name_video(path, frame_number, paths_callable=landmark_file_paths):
r"""
Default video landmark resolver. Returns all landmarks found to have
the same stem as the asset.
"""
# pattern finding all landmarks with the same stem
pattern = path.with_name("{}_{}.*".format(path.stem, frame_number))
# find all the assets we can with this name
lmarks = [import_landmark_file(p) for p in paths_callable(pattern)]
# now we have to merge all the dictionaries into a single dictionary
return merge_all_dicts(lmarks)
def resolve_from_paths(names_to_path):
r"""Landmark Resolver
Helper function for landmark resolvers which functions similarly to the
landmark resolving in menpo before 0.9. Given a dictionary of keys
(landmark group names) to paths - import each landmark at the given path.
Since landmark importing may return more than one group the recovered
groups are merged into a single dictionary and any clashing keys are
reported.
Since landmark importing now returns a dictionary, to maintain the previous
functionality single key dictionaries have their key replaced by the key
provided by the user. In the case of a multi-key result, the key given
by the user is *prepended* to the key returned by the importer.
Parameters
----------
names_to_path : `dict` {`str`: `Path`}
Old-style landmark resolver output - a dictionary mapping landmark
group names to paths to import from.
Returns
-------
landmarks_dict : `dict` {`str`: :map:`PointCloud`}
Dictionary mapping landmark group names to :map:`PointCloud` instances
or their subclasses
Examples
--------
::
def landmark_resolver(path):
expected = {'new_key': path.with_name(path.stem + '_new.ljson')}
return menpo.io.input.resolve_from_paths(expected)
image = menpo.io.import_image('/some/image.png',
landmark_resolver=landmark_resolver)
print(image.landmarks.keys()) # Expect one key -> "new_key"
"""
dicts_to_merge = []
for k, path in names_to_path.items():
new_dict = import_landmark_file(path)
if len(new_dict) == 1:
new_dict = OrderedDict([(k, list(new_dict.values())[0])])
else:
new_dict = OrderedDict(
("{}_{}".format(k, new_k), v) for new_k, v in new_dict.items()
)
dicts_to_merge.append(new_dict)
return merge_all_dicts(dicts_to_merge)
[docs]def import_image(filepath, landmark_resolver=same_name, normalize=None, normalise=None):
r"""Single image (and associated landmarks) importer.
If an image file is found at `filepath`, returns an :map:`Image` or
subclass representing it. By default, landmark files sharing the same
filename stem will be imported and attached with a group name based on the
extension of the landmark file, although this behavior can be customised
(see `landmark_resolver`). If the image defines a mask, this mask will be
imported.
Parameters
----------
filepath : `pathlib.Path` or `str`
A relative or absolute filepath to an image file.
landmark_resolver : `function` or `None`, optional
This function will be used to find landmarks for the
image. The function should take one argument (the path to the image) and
return a dictionary of the form ``{'group_name': 'landmark_filepath'}``
Default finds landmarks with the same name as the image file.
If ``None``, landmark importing will be skipped.
normalize : `bool`, optional
If ``True``, normalize the image pixels between 0 and 1 and convert
to floating point. If false, the native datatype of the image will be
maintained (commonly `uint8`). Note that in general Menpo assumes
:map:`Image` instances contain floating point data - if you disable
this flag you will have to manually convert the images you import to
floating point before doing most Menpo operations. This however can be
useful to save on memory usage if you only wish to view or crop images.
normalise: `bool`, optional
Deprecated version of normalize. Please use the normalize arg.
Returns
-------
images : :map:`Image` or list of
An instantiated :map:`Image` or subclass thereof or a list of images.
"""
normalize = _parse_deprecated_normalise(normalise, normalize)
kwargs = {"normalize": normalize}
return _import(
filepath,
image_types,
landmark_ext_map=image_landmark_types,
landmark_resolver=landmark_resolver,
landmark_attach_func=_import_object_attach_landmarks,
importer_kwargs=kwargs,
)
[docs]def import_video(
filepath,
landmark_resolver=same_name_video,
normalize=None,
normalise=None,
importer_method="ffmpeg",
exact_frame_count=True,
):
r"""Single video (and associated landmarks) importer.
If a video file is found at `filepath`, returns an :map:`LazyList` wrapping
all the frames of the video. By default, landmark files sharing the same
filename stem will be imported and attached with a group name based on the
extension of the landmark file appended with the frame number, although this
behavior can be customised (see `landmark_resolver`).
.. warning::
This method currently uses ffmpeg to perform the importing. In order
to recover accurate frame counts from videos it is necessary to use
ffprobe to count the frames. This involves reading the entire
video in to memory which may cause a delay in loading despite the lazy
nature of the video loading within Menpo.
If ffprobe cannot be found, and `exact_frame_count` is ``False``,
Menpo falls back to ffmpeg itself which is not accurate and the user
should proceed at their own risk.
Parameters
----------
filepath : `pathlib.Path` or `str`
A relative or absolute filepath to a video file.
landmark_resolver : `function` or `None`, optional
This function will be used to find landmarks for the
video. The function should take two arguments (the path to the video and
the frame number) and return a dictionary of the form ``{'group_name':
'landmark_filepath'}`` Default finds landmarks with the same name as the
video file, appended with '_{frame_number}'.
If ``None``, landmark importing will be skipped.
normalize : `bool`, optional
If ``True``, normalize the frame pixels between 0 and 1 and convert
to floating point. If ``False``, the native datatype of the image will
be maintained (commonly `uint8`). Note that in general Menpo assumes
:map:`Image` instances contain floating point data - if you disable this
flag you will have to manually convert the farmes you import to floating
point before doing most Menpo operations. This however can be useful to
save on memory usage if you only wish to view or crop the frames.
normalise : `bool`, optional
Deprecated version of normalize. Please use the normalize arg.
importer_method : {'ffmpeg'}, optional
A string representing the type of importer to use, by default ffmpeg
is used.
exact_frame_count: `bool`, optional
If ``True``, the import fails if ffprobe is not available
(reading from ffmpeg's output returns inexact frame count)
Returns
-------
frames : :map:`LazyList`
An lazy list of :map:`Image` or subclass thereof which wraps the frames
of the video. This list can be treated as a normal list, but the frame
is only read when the video is indexed or iterated.
Examples
--------
>>> video = menpo.io.import_video('video.avi')
>>> # Lazily load the 100th frame without reading the entire video
>>> frame100 = video[100]
"""
normalize = _parse_deprecated_normalise(normalise, normalize)
kwargs = {"normalize": normalize, "exact_frame_count": exact_frame_count}
video_importer_methods = {"ffmpeg": ffmpeg_video_types}
if importer_method not in video_importer_methods:
raise ValueError(
"Unsupported importer method requested. Valid values "
"are: {}".format(video_importer_methods.keys())
)
return _import(
filepath,
video_importer_methods[importer_method],
landmark_ext_map=image_landmark_types,
landmark_resolver=landmark_resolver,
landmark_attach_func=_import_lazylist_attach_landmarks,
importer_kwargs=kwargs,
)
[docs]def import_landmark_file(filepath, group=None, asset=None):
r"""Single landmark file importer.
If a landmark file is found at ``filepath``, returns a dictionary
of landmarks where keys are the group names and the values are
:map:`PointCloud` or subclasses. If the optional ``group`` argument is
supplied then a single group with the given name is returned rather than
a dictionary
Parameters
----------
filepath : `pathlib.Path` or `str`
A relative or absolute filepath to an landmark file.
group : `str`, optional
The name of the landmark group to return from the landmark dictionary.
If None, then a dictionary is returned where keys are the group names
and the values are :map:`PointCloud` or subclasses.
asset : `object`, optional
The object the landmark belongs to (useful for things like rescaling)
Returns
-------
landmarks : `dict` {`str`: :map:`PointCloud`} or :map:`PointCloud`
Dictionary mapping landmark groups to :map:`PointCloud` or subclasses
OR
:map:`PointCloud` or subclass if ``group == None``
"""
lmark_dict = _import(filepath, image_landmark_types, asset=asset)
if group:
return lmark_dict[group]
else:
return lmark_dict
[docs]def import_pickle(filepath, **kwargs):
r"""Import a pickle file of arbitrary Python objects.
Menpo unambiguously uses ``.pkl`` as it's choice of extension for Pickle
files. Menpo also supports automatic importing and exporting of gzip
compressed pickle files - just choose a ``filepath`` ending ``pkl.gz`` and
gzip compression will automatically be applied. Compression can massively
reduce the filesize of a pickle file at the cost of longer import and
export times.
Parameters
----------
filepath : `pathlib.Path` or `str`
A relative or absolute filepath to a ``.pkl`` or ``.pkl.gz`` file.
Returns
-------
object : `object`
Whatever Python objects are present in the Pickle file
"""
return _import(filepath, pickle_types, importer_kwargs=kwargs)
[docs]def import_pickles(
pattern,
max_pickles=None,
shuffle=False,
as_generator=False,
verbose=False,
**kwargs,
):
r"""Multiple pickle importer.
Menpo unambiguously uses ``.pkl`` as it's choice of extension for Pickle
files. Menpo also supports automatic importing and exporting of gzip
compressed pickle files - just choose a ``filepath`` ending ``pkl.gz`` and
gzip compression will automatically be applied. Compression can massively
reduce the filesize of a pickle file at the cost of longer import and
export times.
Note that this is a function returns a :map:`LazyList`. Therefore, the
function will return immediately and indexing into the returned list
will load a pickle at run time. If all pickles should be loaded, then simply
wrap the returned :map:`LazyList` in a Python `list`.
Parameters
----------
pattern : `str`
A glob path pattern to search for pickles. Every pickle found to match
the glob will be imported one by one. See :map:`pickle_paths` for more
details of what pickles will be found.
max_pickles : positive `int`, optional
If not ``None``, only import the first ``max_pickles`` found. Else,
import all.
shuffle : `bool`, optional
If ``True``, the order of the returned pickles will be randomised. If
``False``, the order of the returned pickles will be alphanumerically
ordered.
as_generator : `bool`, optional
If ``True``, the function returns a generator and assets will be yielded
one after another when the generator is iterated over.
verbose : `bool`, optional
If ``True`` progress of the importing will be dynamically reported with
a progress bar.
Returns
-------
lazy_list : :map:`LazyList` or generator of Python objects
A :map:`LazyList` or generator yielding whatever Python objects are
present in the Pickle file instances that match the glob pattern
provided.
Raises
------
ValueError
If no pickles are found at the provided glob.
"""
return _import_glob_lazy_list(
pattern,
pickle_types,
max_assets=max_pickles,
shuffle=shuffle,
as_generator=as_generator,
verbose=verbose,
importer_kwargs=kwargs,
)
[docs]def import_images(
pattern,
max_images=None,
shuffle=False,
landmark_resolver=same_name,
normalize=None,
normalise=None,
as_generator=False,
verbose=False,
):
r"""Multiple image (and associated landmarks) importer.
For each image found creates an importer than returns a :map:`Image` or
subclass representing it. By default, landmark files sharing the same
filename stem will be imported and attached with a group name based on the
extension of the landmark file, although this behavior can be customised
(see `landmark_resolver`). If the image defines a mask, this mask will be
imported.
Note that this is a function returns a :map:`LazyList`. Therefore, the
function will return immediately and indexing into the returned list
will load an image at run time. If all images should be loaded, then simply
wrap the returned :map:`LazyList` in a Python `list`.
Parameters
----------
pattern : `str`
A glob path pattern to search for images. Every image found to match
the glob will be imported one by one. See :map:`image_paths` for more
details of what images will be found.
max_images : positive `int`, optional
If not ``None``, only import the first ``max_images`` found. Else,
import all.
shuffle : `bool`, optional
If ``True``, the order of the returned images will be randomised. If
``False``, the order of the returned images will be alphanumerically
ordered.
landmark_resolver : `function` or `None`, optional
This function will be used to find landmarks for the
image. The function should take one argument (the image itself) and
return a dictionary of the form ``{'group_name': 'landmark_filepath'}``
Default finds landmarks with the same name as the image file.
If ``None``, landmark importing will be skipped.
normalize : `bool`, optional
If ``True``, normalize the image pixels between 0 and 1 and convert
to floating point. If false, the native datatype of the image will be
maintained (commonly `uint8`). Note that in general Menpo assumes
:map:`Image` instances contain floating point data - if you disable
this flag you will have to manually convert the images you import to
floating point before doing most Menpo operations. This however can be
useful to save on memory usage if you only wish to view or crop images.
normalise : `bool`, optional
Deprecated version of normalize. Please use the normalize arg.
as_generator : `bool`, optional
If ``True``, the function returns a generator and assets will be yielded
one after another when the generator is iterated over.
verbose : `bool`, optional
If ``True`` progress of the importing will be dynamically reported with
a progress bar.
Returns
-------
lazy_list : :map:`LazyList` or generator of :map:`Image`
A :map:`LazyList` or generator yielding :map:`Image` instances found
to match the glob pattern provided.
Raises
------
ValueError
If no images are found at the provided glob.
Examples
--------
Import images at 20% scale from a huge collection:
>>> rescale_20p = lambda x: x.rescale(0.2)
>>> images = menpo.io.import_images('./massive_image_db/*') # Returns immediately
>>> images = images.map(rescale_20p) # Returns immediately
>>> images[0] # Get the first image, resize, lazily loaded
"""
normalize = _parse_deprecated_normalise(normalise, normalize)
kwargs = {"normalize": normalize}
return _import_glob_lazy_list(
pattern,
image_types,
max_assets=max_images,
shuffle=shuffle,
landmark_resolver=landmark_resolver,
landmark_ext_map=image_landmark_types,
landmark_attach_func=_import_object_attach_landmarks,
as_generator=as_generator,
verbose=verbose,
importer_kwargs=kwargs,
)
[docs]def import_videos(
pattern,
max_videos=None,
shuffle=False,
landmark_resolver=same_name_video,
normalize=None,
normalise=None,
importer_method="ffmpeg",
exact_frame_count=True,
as_generator=False,
verbose=False,
):
r"""Multiple video (and associated landmarks) importer.
For each video found yields a :map:`LazyList`. By default, landmark files
sharing the same filename stem will be imported and attached with a group
name based on the extension of the landmark file appended with the frame
number, although this behavior can be customised (see `landmark_resolver`).
Note that this is a function returns a :map:`LazyList`. Therefore, the
function will return immediately and indexing into the returned list
will load an image at run time. If all images should be loaded, then simply
wrap the returned :map:`LazyList` in a Python `list`.
.. warning::
This method currently uses ffmpeg to perform the importing. In order
to recover accurate frame counts from videos it is necessary to use
ffprobe to count the frames. This involves reading the entire
video in to memory which may cause a delay in loading despite the lazy
nature of the video loading within Menpo.
If ffprobe cannot be found, and `exact_frame_count` is ``False``,
Menpo falls back to ffmpeg itself which is not accurate and the user
should proceed at their own risk.
Parameters
----------
pattern : `str`
A glob path pattern to search for videos. Every video found to match
the glob will be imported one by one. See :map:`video_paths` for more
details of what videos will be found.
max_videos : positive `int`, optional
If not ``None``, only import the first ``max_videos`` found. Else,
import all.
shuffle : `bool`, optional
If ``True``, the order of the returned videos will be randomised. If
``False``, the order of the returned videos will be alphanumerically
ordered.
landmark_resolver : `function` or `None`, optional
This function will be used to find landmarks for the
video. The function should take two arguments (the path to the video and
the frame number) and return a dictionary of the form ``{'group_name':
'landmark_filepath'}`` Default finds landmarks with the same name as the
video file, appended with '_{frame_number}'.
If ``None``, landmark importing will be skipped.
normalize : `bool`, optional
If ``True``, normalize the frame pixels between 0 and 1 and convert
to floating point. If ``False``, the native datatype of the image will
be maintained (commonly `uint8`). Note that in general Menpo assumes
:map:`Image` instances contain floating point data - if you disable this
flag you will have to manually convert the frames you import to floating
point before doing most Menpo operations. This however can be useful to
save on memory usage if you only wish to view or crop the frames.
normalise : `bool`, optional
Deprecated version of normalize. Please use the normalize arg.
importer_method : {'ffmpeg'}, optional
A string representing the type of importer to use, by default ffmpeg
is used.
as_generator : `bool`, optional
If ``True``, the function returns a generator and assets will be yielded
one after another when the generator is iterated over.
exact_frame_count: `bool`, optional
If True, the import fails if ffmprobe is not available
(reading from ffmpeg's output returns inexact frame count)
verbose : `bool`, optional
If ``True`` progress of the importing will be dynamically reported with
a progress bar.
Returns
-------
lazy_list : :map:`LazyList` or generator of :map:`LazyList`
A :map:`LazyList` or generator yielding :map:`LazyList` instances that
wrap the video object.
Raises
------
ValueError
If no videos are found at the provided glob.
Examples
--------
Import videos at and rescale every frame of each video:
>>> videos = []
>>> for video in menpo.io.import_videos('./set_of_videos/*'):
>>> frames = []
>>> for frame in video:
>>> # rescale to a sensible size as we go
>>> frames.append(frame.rescale(0.2))
>>> videos.append(frames)
"""
normalize = _parse_deprecated_normalise(normalise, normalize)
kwargs = {"normalize": normalize, "exact_frame_count": exact_frame_count}
video_importer_methods = {"ffmpeg": ffmpeg_video_types}
if importer_method not in video_importer_methods:
raise ValueError(
"Unsupported importer method requested. Valid values "
"are: {}".format(video_importer_methods.keys())
)
return _import_glob_lazy_list(
pattern,
video_importer_methods[importer_method],
max_assets=max_videos,
shuffle=shuffle,
landmark_resolver=landmark_resolver,
landmark_ext_map=image_landmark_types,
landmark_attach_func=_import_lazylist_attach_landmarks,
as_generator=as_generator,
verbose=verbose,
importer_kwargs=kwargs,
)
[docs]def import_landmark_files(
pattern, max_landmarks=None, shuffle=False, as_generator=False, verbose=False
):
r"""Import Multiple landmark files.
For each landmark file found returns an importer then
returns a :map:`LabelledPointUndirectedGraph` or a :map:`PointCloud`.
Note that this is a function returns a :map:`LazyList`. Therefore, the
function will return immediately and indexing into the returned list
will load the landmarks at run time. If all landmarks should be loaded, then
simply wrap the returned :map:`LazyList` in a Python `list`.
Parameters
----------
pattern : `str`
A glob path pattern to search for landmark files. Every
landmark file found to match the glob will be imported one by one.
See :map:`landmark_file_paths` for more details of what landmark files
will be found.
max_landmarks : positive `int`, optional
If not ``None``, only import the first ``max_landmark_files`` found.
Else, import all.
shuffle : `bool`, optional
If ``True``, the order of the returned landmark files will be
randomised. If ``False``, the order of the returned landmark files will
be alphanumerically ordered.
as_generator : `bool`, optional
If ``True``, the function returns a generator and assets will be yielded
one after another when the generator is iterated over.
verbose : `bool`, optional
If ``True`` progress of the importing will be dynamically reported.
Returns
-------
lazy_list : :map:`LazyList` or generator
A :map:`LazyList` or generator yielding :map:`PointCloud` or
:map:`LabelledPointUndirectedGraph` instances found to match the glob
pattern provided.
Raises
------
ValueError
If no landmarks are found at the provided glob.
"""
return _import_glob_lazy_list(
pattern,
image_landmark_types,
max_assets=max_landmarks,
shuffle=shuffle,
as_generator=as_generator,
verbose=verbose,
)
def _import_glob_lazy_list(
pattern,
extension_map,
max_assets=None,
landmark_resolver=same_name,
shuffle=False,
as_generator=False,
landmark_ext_map=None,
landmark_attach_func=None,
importer_kwargs=None,
verbose=False,
):
filepaths = list(glob_with_suffix(pattern, extension_map, sort=(not shuffle)))
if shuffle:
random.shuffle(filepaths)
if (max_assets is not None) and max_assets <= 0:
raise ValueError(
"Max elements should be positive" " ({} provided)".format(max_assets)
)
elif max_assets:
filepaths = filepaths[:max_assets]
n_files = len(filepaths)
if n_files == 0:
raise ValueError("The glob {} yields no assets".format(pattern))
lazy_list = LazyList(
[
partial(
_import,
f,
extension_map,
landmark_resolver=landmark_resolver,
landmark_ext_map=landmark_ext_map,
landmark_attach_func=landmark_attach_func,
importer_kwargs=importer_kwargs,
)
for f in filepaths
]
)
if verbose and as_generator:
# wrap the generator with the progress reporter
lazy_list = print_progress(
lazy_list, prefix="Importing assets", n_items=n_files
)
elif verbose:
print("Found {} assets, index the returned LazyList to import.".format(n_files))
if as_generator:
return (a for a in lazy_list)
else:
return lazy_list
def _import_object_attach_landmarks(
built_objects, landmark_resolver, landmark_ext_map=None
):
# handle landmarks
if landmark_ext_map is not None and landmark_resolver is not None:
for x in built_objects:
lm_dict = landmark_resolver(x.path)
if lm_dict is None:
continue
for group_name, lm_obj in lm_dict.items():
if x.n_dims == lm_obj.n_dims:
x.landmarks[group_name] = lm_obj
def _import_lazylist_attach_landmarks(
built_objects, landmark_resolver, landmark_ext_map=None
):
# handle landmarks
if landmark_ext_map is not None and landmark_resolver is not None:
for k, x in enumerate(built_objects):
# Use the users function to find landmarks - builds a list
# of functions that we will map against the frames in order to
# attach a landmark per frame.
lm_resolvers = [
partial(landmark_resolver, x.path, i) for i in range(len(x))
]
def wrap_landmarks(lm_resolver, obj):
lm_dict = lm_resolver()
if lm_dict is not None:
for group_name, lm_obj in lm_dict.items():
if obj.n_dims == lm_obj.n_dims:
obj.landmarks[group_name] = lm_obj
return obj
# Provide the lm_resolver for each wrap_landmarks function and then
# lazily map against the underlying importers.
new_ll = x.map([partial(wrap_landmarks, lmr) for lmr in lm_resolvers])
built_objects[k] = new_ll
def _import(
filepath,
extensions_map,
landmark_resolver=same_name,
landmark_ext_map=None,
landmark_attach_func=None,
asset=None,
importer_kwargs=None,
):
r"""
Finds an importer for the filepath passed in and then calls it with the
filepath and optionally an asset, returning either a list of assets or a
single asset, depending on the file type.
The type of assets returned are specified by the `extensions_map`.
Parameters
----------
filepath : `Path` or `str`
The filepath to import.
extensions_map : `dict` (String, :class:`menpo.io.base.Importer`)
A map from extensions to importers. The importers are expected to be
non-instantiated classes. The extensions are expected to
contain the leading period eg. `.obj`.
landmark_ext_map : `dict` (str, :map:`Importer`), optional
If not None an attempt will be made to import annotations with
extensions defined in this mapping. If None, no attempt will be
made to import annotations.
landmark_resolver : `function` or `None`, optional
If not ``None``, this function will be used to find landmarks for each
asset. The function should take one argument (the asset itself) and
return a dictionary of the form {'group_name': 'landmark_filepath'}.
If ``None``, landmark importing will be skipped.
asset : `object`, optional
Passed through to the importer callable.
importer_kwargs : `dict`, optional
kwargs that will be supplied to the importer if not None
Returns
-------
assets : asset or list of assets
The loaded asset or list of assets.
"""
path = _norm_path(filepath)
if not path.is_file():
raise ValueError("{} is not a file".format(path))
# below could raise ValueError as well...
importer_callable = importer_for_filepath(path, extensions_map)
if importer_kwargs is None:
importer_kwargs = {}
built_objects = importer_callable(path, asset=asset, **importer_kwargs)
# landmarks are iterable so check for list precisely
if not isinstance(built_objects, list):
built_objects = [built_objects]
# attach path if there is no x.path already.
def attach_path(obj):
if not hasattr(obj, "path"):
try:
obj.path = path
except AttributeError:
pass # that's fine! Probably a dict/list from PickleImporter.
for x in built_objects:
# Handle lazy lists differently
if isinstance(x, collections_abc.Sequence) and not isinstance(x, LazyList):
for subx in x:
attach_path(subx)
elif isinstance(x, collections_abc.Mapping):
for subx in x.values():
attach_path(subx)
else:
attach_path(x)
if landmark_attach_func is not None and landmark_resolver is not None:
landmark_attach_func(
built_objects, landmark_resolver, landmark_ext_map=landmark_ext_map
)
if len(built_objects) == 1:
built_objects = built_objects[0]
return built_objects
def _pathlib_glob_for_pattern(pattern, sort=True):
r"""Generator for glob matching a string path pattern
Splits the provided ``pattern`` into a root path for pathlib and a
subsequent glob pattern to be applied.
Parameters
----------
pattern : `str`
Path including glob patterns. If no glob patterns are present and the
pattern is a dir, a '**/*' pattern will be automatically added.
sort : `bool`, optional
If True, the returned paths will be sorted. If False, no guarantees are
made about the ordering of the results.
Yields
------
Path : A path to a file matching the provided pattern.
Raises
------
ValueError
If the pattern doesn't contain a '*' wildcard and is not a directory
"""
pattern = _norm_path(pattern)
pattern_str = str(pattern)
gsplit = pattern_str.split("*", 1)
if len(gsplit) == 1:
# no glob provided. Is the provided pattern a dir?
if Path(pattern).is_dir():
preglob = pattern_str
pattern = "*"
else:
raise ValueError("{} is an invalid glob and " "not a dir".format(pattern))
else:
preglob = gsplit[0]
pattern = "*" + gsplit[1]
if not os.path.isdir(preglob):
# the glob pattern is in the middle of a path segment. pair back
# to the nearest dir and add the reminder to the pattern
preglob, pattern_prefix = os.path.split(preglob)
pattern = pattern_prefix + pattern
p = Path(preglob)
paths = p.glob(str(pattern))
if sort:
paths = sorted(paths)
return paths
def glob_with_suffix(pattern, extensions_map, sort=True):
r"""
Filters the results from the glob pattern passed in to only those files
that have an importer given in `extensions_map`.
Parameters
----------
pattern : string
A UNIX style glob pattern to match against.
extensions_map : dictionary (String, :class:`menpo.io.base.Importer`)
A map from extensions to importers. The importers are expected to be
non-instantiated classes. The extensions are expected to
contain the leading period eg. `.obj`.
sort : `bool`, optional
If True, the returned paths will be sorted. If False, no guarantees are
made about the ordering of the results.
Yields
------
filepaths : list of string
The list of filepaths that have valid extensions.
"""
for path in _pathlib_glob_for_pattern(pattern, sort=sort):
possible_exts = _possible_extensions_from_filepath(path)
if any([ext in extensions_map for ext in possible_exts]):
yield path
def importer_for_filepath(filepath, extensions_map):
r"""
Given a filepath, return the appropriate importer as mapped by the
extension map.
Parameters
----------
filepath : `pathlib.Path`
The filepath to get importers for.
extensions_map : dictionary (String, :class:`menpo.io.base.Importer`)
A map from extensions to importers. The importers are expected to be
a subclass of :class:`Importer`. The extensions are expected to
contain the leading period eg. `.obj`.
Returns
--------
importer: :class:`menpo.io.base.Importer` instance
Importer as found in the `extensions_map` instantiated for the
filepath provided.
"""
possible_exts = _possible_extensions_from_filepath(filepath)
# we couldn't find an importer for all the suffixes (e.g .foo.bar)
# maybe the file stem has '.' in it? -> try again but this time just use the
# final suffix (.bar). (Note we first try '.foo.bar' as we want to catch
# cases like '.pkl.gz')
importer_callable = None
while importer_callable is None and possible_exts:
importer_callable = extensions_map.get(possible_exts.pop(0))
if importer_callable is None:
raise ValueError(
"{} does not have a " "suitable importer.".format(filepath.name)
)
return importer_callable
# Create special callable that can both be called with a builtin asset name
# and has dynamic methods attached that list the available builtin assets
class BuiltinAssets(object):
def __init__(self, import_builtin_callable):
self.import_builtin_asset = import_builtin_callable
self._builtin_files = {
asset.replace(".", "_"): partial(_menpo_import_builtin_asset, asset)
for asset in menpo_ls_builtin_assets()
}
def __call__(self, asset_name, **kwargs):
return self.import_builtin_asset(asset_name, **kwargs)
def __getattr__(self, file_name) -> Any:
return self._builtin_files[file_name]
import_builtin_asset = BuiltinAssets(_menpo_import_builtin_asset)