645 lines
22 KiB
Python
645 lines
22 KiB
Python
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"""Labeled Faces in the Wild (LFW) dataset
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This dataset is a collection of JPEG pictures of famous people collected
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over the internet, all details are available on the official website:
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http://vis-www.cs.umass.edu/lfw/
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"""
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# Copyright (c) 2011 Olivier Grisel <olivier.grisel@ensta.org>
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# License: BSD 3 clause
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import logging
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from numbers import Integral, Real
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from os import PathLike, listdir, makedirs, remove
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from os.path import exists, isdir, join
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import numpy as np
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from joblib import Memory
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from ..utils import Bunch
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from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
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from ..utils.fixes import tarfile_extractall
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from ._base import (
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RemoteFileMetadata,
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_fetch_remote,
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get_data_home,
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load_descr,
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)
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logger = logging.getLogger(__name__)
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# The original data can be found in:
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# http://vis-www.cs.umass.edu/lfw/lfw.tgz
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ARCHIVE = RemoteFileMetadata(
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filename="lfw.tgz",
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url="https://ndownloader.figshare.com/files/5976018",
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checksum="055f7d9c632d7370e6fb4afc7468d40f970c34a80d4c6f50ffec63f5a8d536c0",
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)
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# The original funneled data can be found in:
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# http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz
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FUNNELED_ARCHIVE = RemoteFileMetadata(
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filename="lfw-funneled.tgz",
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url="https://ndownloader.figshare.com/files/5976015",
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checksum="b47c8422c8cded889dc5a13418c4bc2abbda121092b3533a83306f90d900100a",
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)
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# The original target data can be found in:
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# http://vis-www.cs.umass.edu/lfw/pairsDevTrain.txt',
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# http://vis-www.cs.umass.edu/lfw/pairsDevTest.txt',
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# http://vis-www.cs.umass.edu/lfw/pairs.txt',
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TARGETS = (
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RemoteFileMetadata(
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filename="pairsDevTrain.txt",
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url="https://ndownloader.figshare.com/files/5976012",
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checksum="1d454dada7dfeca0e7eab6f65dc4e97a6312d44cf142207be28d688be92aabfa",
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),
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RemoteFileMetadata(
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filename="pairsDevTest.txt",
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url="https://ndownloader.figshare.com/files/5976009",
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checksum="7cb06600ea8b2814ac26e946201cdb304296262aad67d046a16a7ec85d0ff87c",
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),
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RemoteFileMetadata(
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filename="pairs.txt",
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url="https://ndownloader.figshare.com/files/5976006",
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checksum="ea42330c62c92989f9d7c03237ed5d591365e89b3e649747777b70e692dc1592",
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),
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)
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#
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# Common private utilities for data fetching from the original LFW website
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# local disk caching, and image decoding.
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#
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def _check_fetch_lfw(
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data_home=None, funneled=True, download_if_missing=True, n_retries=3, delay=1.0
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):
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"""Helper function to download any missing LFW data"""
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data_home = get_data_home(data_home=data_home)
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lfw_home = join(data_home, "lfw_home")
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if not exists(lfw_home):
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makedirs(lfw_home)
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for target in TARGETS:
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target_filepath = join(lfw_home, target.filename)
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if not exists(target_filepath):
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if download_if_missing:
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logger.info("Downloading LFW metadata: %s", target.url)
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_fetch_remote(
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target, dirname=lfw_home, n_retries=n_retries, delay=delay
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)
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else:
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raise OSError("%s is missing" % target_filepath)
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if funneled:
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data_folder_path = join(lfw_home, "lfw_funneled")
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archive = FUNNELED_ARCHIVE
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else:
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data_folder_path = join(lfw_home, "lfw")
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archive = ARCHIVE
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if not exists(data_folder_path):
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archive_path = join(lfw_home, archive.filename)
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if not exists(archive_path):
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if download_if_missing:
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logger.info("Downloading LFW data (~200MB): %s", archive.url)
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_fetch_remote(
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archive, dirname=lfw_home, n_retries=n_retries, delay=delay
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)
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else:
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raise OSError("%s is missing" % archive_path)
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import tarfile
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logger.debug("Decompressing the data archive to %s", data_folder_path)
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with tarfile.open(archive_path, "r:gz") as fp:
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tarfile_extractall(fp, path=lfw_home)
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remove(archive_path)
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return lfw_home, data_folder_path
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def _load_imgs(file_paths, slice_, color, resize):
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"""Internally used to load images"""
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try:
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from PIL import Image
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except ImportError:
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raise ImportError(
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"The Python Imaging Library (PIL) is required to load data "
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"from jpeg files. Please refer to "
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"https://pillow.readthedocs.io/en/stable/installation.html "
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"for installing PIL."
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)
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# compute the portion of the images to load to respect the slice_ parameter
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# given by the caller
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default_slice = (slice(0, 250), slice(0, 250))
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if slice_ is None:
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slice_ = default_slice
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else:
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slice_ = tuple(s or ds for s, ds in zip(slice_, default_slice))
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h_slice, w_slice = slice_
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h = (h_slice.stop - h_slice.start) // (h_slice.step or 1)
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w = (w_slice.stop - w_slice.start) // (w_slice.step or 1)
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if resize is not None:
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resize = float(resize)
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h = int(resize * h)
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w = int(resize * w)
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# allocate some contiguous memory to host the decoded image slices
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n_faces = len(file_paths)
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if not color:
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faces = np.zeros((n_faces, h, w), dtype=np.float32)
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else:
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faces = np.zeros((n_faces, h, w, 3), dtype=np.float32)
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# iterate over the collected file path to load the jpeg files as numpy
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# arrays
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for i, file_path in enumerate(file_paths):
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if i % 1000 == 0:
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logger.debug("Loading face #%05d / %05d", i + 1, n_faces)
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# Checks if jpeg reading worked. Refer to issue #3594 for more
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# details.
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pil_img = Image.open(file_path)
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pil_img = pil_img.crop(
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(w_slice.start, h_slice.start, w_slice.stop, h_slice.stop)
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)
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if resize is not None:
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pil_img = pil_img.resize((w, h))
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face = np.asarray(pil_img, dtype=np.float32)
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if face.ndim == 0:
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raise RuntimeError(
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"Failed to read the image file %s, "
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"Please make sure that libjpeg is installed" % file_path
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)
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face /= 255.0 # scale uint8 coded colors to the [0.0, 1.0] floats
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if not color:
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# average the color channels to compute a gray levels
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# representation
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face = face.mean(axis=2)
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faces[i, ...] = face
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return faces
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#
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# Task #1: Face Identification on picture with names
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#
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def _fetch_lfw_people(
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data_folder_path, slice_=None, color=False, resize=None, min_faces_per_person=0
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):
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"""Perform the actual data loading for the lfw people dataset
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This operation is meant to be cached by a joblib wrapper.
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"""
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# scan the data folder content to retain people with more that
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# `min_faces_per_person` face pictures
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person_names, file_paths = [], []
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for person_name in sorted(listdir(data_folder_path)):
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folder_path = join(data_folder_path, person_name)
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if not isdir(folder_path):
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continue
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paths = [join(folder_path, f) for f in sorted(listdir(folder_path))]
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n_pictures = len(paths)
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if n_pictures >= min_faces_per_person:
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person_name = person_name.replace("_", " ")
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person_names.extend([person_name] * n_pictures)
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file_paths.extend(paths)
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n_faces = len(file_paths)
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if n_faces == 0:
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raise ValueError(
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"min_faces_per_person=%d is too restrictive" % min_faces_per_person
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)
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target_names = np.unique(person_names)
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target = np.searchsorted(target_names, person_names)
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faces = _load_imgs(file_paths, slice_, color, resize)
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# shuffle the faces with a deterministic RNG scheme to avoid having
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# all faces of the same person in a row, as it would break some
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# cross validation and learning algorithms such as SGD and online
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# k-means that make an IID assumption
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indices = np.arange(n_faces)
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np.random.RandomState(42).shuffle(indices)
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faces, target = faces[indices], target[indices]
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return faces, target, target_names
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@validate_params(
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{
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"data_home": [str, PathLike, None],
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"funneled": ["boolean"],
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"resize": [Interval(Real, 0, None, closed="neither"), None],
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"min_faces_per_person": [Interval(Integral, 0, None, closed="left"), None],
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"color": ["boolean"],
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"slice_": [tuple, Hidden(None)],
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"download_if_missing": ["boolean"],
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"return_X_y": ["boolean"],
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"n_retries": [Interval(Integral, 1, None, closed="left")],
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"delay": [Interval(Real, 0.0, None, closed="neither")],
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},
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prefer_skip_nested_validation=True,
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)
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def fetch_lfw_people(
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*,
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data_home=None,
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funneled=True,
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resize=0.5,
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min_faces_per_person=0,
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color=False,
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slice_=(slice(70, 195), slice(78, 172)),
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download_if_missing=True,
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return_X_y=False,
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n_retries=3,
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delay=1.0,
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):
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"""Load the Labeled Faces in the Wild (LFW) people dataset \
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(classification).
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Download it if necessary.
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================= =======================
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Classes 5749
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Samples total 13233
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Dimensionality 5828
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Features real, between 0 and 255
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================= =======================
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Read more in the :ref:`User Guide <labeled_faces_in_the_wild_dataset>`.
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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funneled : bool, default=True
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Download and use the funneled variant of the dataset.
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resize : float or None, default=0.5
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Ratio used to resize the each face picture. If `None`, no resizing is
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performed.
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min_faces_per_person : int, default=None
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The extracted dataset will only retain pictures of people that have at
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least `min_faces_per_person` different pictures.
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color : bool, default=False
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Keep the 3 RGB channels instead of averaging them to a single
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gray level channel. If color is True the shape of the data has
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one more dimension than the shape with color = False.
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slice_ : tuple of slice, default=(slice(70, 195), slice(78, 172))
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Provide a custom 2D slice (height, width) to extract the
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'interesting' part of the jpeg files and avoid use statistical
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correlation from the background.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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return_X_y : bool, default=False
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If True, returns ``(dataset.data, dataset.target)`` instead of a Bunch
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object. See below for more information about the `dataset.data` and
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`dataset.target` object.
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.. versionadded:: 0.20
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n_retries : int, default=3
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Number of retries when HTTP errors are encountered.
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.. versionadded:: 1.5
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delay : float, default=1.0
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Number of seconds between retries.
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.. versionadded:: 1.5
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Returns
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-------
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dataset : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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data : numpy array of shape (13233, 2914)
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Each row corresponds to a ravelled face image
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of original size 62 x 47 pixels.
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Changing the ``slice_`` or resize parameters will change the
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shape of the output.
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images : numpy array of shape (13233, 62, 47)
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Each row is a face image corresponding to one of the 5749 people in
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the dataset. Changing the ``slice_``
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or resize parameters will change the shape of the output.
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target : numpy array of shape (13233,)
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Labels associated to each face image.
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Those labels range from 0-5748 and correspond to the person IDs.
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target_names : numpy array of shape (5749,)
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Names of all persons in the dataset.
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Position in array corresponds to the person ID in the target array.
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DESCR : str
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Description of the Labeled Faces in the Wild (LFW) dataset.
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(data, target) : tuple if ``return_X_y`` is True
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A tuple of two ndarray. The first containing a 2D array of
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shape (n_samples, n_features) with each row representing one
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sample and each column representing the features. The second
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ndarray of shape (n_samples,) containing the target samples.
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.. versionadded:: 0.20
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Examples
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|
--------
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>>> from sklearn.datasets import fetch_lfw_people
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>>> lfw_people = fetch_lfw_people()
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|
>>> lfw_people.data.shape
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(13233, 2914)
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>>> lfw_people.target.shape
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(13233,)
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>>> for name in lfw_people.target_names[:5]:
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... print(name)
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AJ Cook
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AJ Lamas
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Aaron Eckhart
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Aaron Guiel
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Aaron Patterson
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"""
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lfw_home, data_folder_path = _check_fetch_lfw(
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data_home=data_home,
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funneled=funneled,
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download_if_missing=download_if_missing,
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n_retries=n_retries,
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delay=delay,
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)
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logger.debug("Loading LFW people faces from %s", lfw_home)
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# wrap the loader in a memoizing function that will return memmaped data
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# arrays for optimal memory usage
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m = Memory(location=lfw_home, compress=6, verbose=0)
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load_func = m.cache(_fetch_lfw_people)
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# load and memoize the pairs as np arrays
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faces, target, target_names = load_func(
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data_folder_path,
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resize=resize,
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min_faces_per_person=min_faces_per_person,
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color=color,
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slice_=slice_,
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)
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X = faces.reshape(len(faces), -1)
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fdescr = load_descr("lfw.rst")
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if return_X_y:
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return X, target
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# pack the results as a Bunch instance
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return Bunch(
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data=X, images=faces, target=target, target_names=target_names, DESCR=fdescr
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)
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#
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# Task #2: Face Verification on pairs of face pictures
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#
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def _fetch_lfw_pairs(
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index_file_path, data_folder_path, slice_=None, color=False, resize=None
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):
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"""Perform the actual data loading for the LFW pairs dataset
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This operation is meant to be cached by a joblib wrapper.
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"""
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# parse the index file to find the number of pairs to be able to allocate
|
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# the right amount of memory before starting to decode the jpeg files
|
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with open(index_file_path, "rb") as index_file:
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split_lines = [ln.decode().strip().split("\t") for ln in index_file]
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pair_specs = [sl for sl in split_lines if len(sl) > 2]
|
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n_pairs = len(pair_specs)
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|
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|
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# iterating over the metadata lines for each pair to find the filename to
|
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# decode and load in memory
|
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target = np.zeros(n_pairs, dtype=int)
|
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|
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file_paths = list()
|
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for i, components in enumerate(pair_specs):
|
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|
|
if len(components) == 3:
|
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target[i] = 1
|
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pair = (
|
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(components[0], int(components[1]) - 1),
|
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(components[0], int(components[2]) - 1),
|
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|
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)
|
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elif len(components) == 4:
|
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target[i] = 0
|
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|
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pair = (
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(components[0], int(components[1]) - 1),
|
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(components[2], int(components[3]) - 1),
|
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|
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)
|
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else:
|
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raise ValueError("invalid line %d: %r" % (i + 1, components))
|
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|
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for j, (name, idx) in enumerate(pair):
|
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|
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try:
|
||
|
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person_folder = join(data_folder_path, name)
|
||
|
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except TypeError:
|
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|
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person_folder = join(data_folder_path, str(name, "UTF-8"))
|
||
|
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filenames = list(sorted(listdir(person_folder)))
|
||
|
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file_path = join(person_folder, filenames[idx])
|
||
|
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file_paths.append(file_path)
|
||
|
|
|
||
|
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pairs = _load_imgs(file_paths, slice_, color, resize)
|
||
|
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shape = list(pairs.shape)
|
||
|
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n_faces = shape.pop(0)
|
||
|
|
shape.insert(0, 2)
|
||
|
|
shape.insert(0, n_faces // 2)
|
||
|
|
pairs.shape = shape
|
||
|
|
|
||
|
|
return pairs, target, np.array(["Different persons", "Same person"])
|
||
|
|
|
||
|
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|
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|
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@validate_params(
|
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|
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{
|
||
|
|
"subset": [StrOptions({"train", "test", "10_folds"})],
|
||
|
|
"data_home": [str, PathLike, None],
|
||
|
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"funneled": ["boolean"],
|
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|
|
"resize": [Interval(Real, 0, None, closed="neither"), None],
|
||
|
|
"color": ["boolean"],
|
||
|
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"slice_": [tuple, Hidden(None)],
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||
|
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"download_if_missing": ["boolean"],
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||
|
|
"n_retries": [Interval(Integral, 1, None, closed="left")],
|
||
|
|
"delay": [Interval(Real, 0.0, None, closed="neither")],
|
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|
|
},
|
||
|
|
prefer_skip_nested_validation=True,
|
||
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)
|
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|
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def fetch_lfw_pairs(
|
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|
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*,
|
||
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subset="train",
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data_home=None,
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||
|
|
funneled=True,
|
||
|
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resize=0.5,
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||
|
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color=False,
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slice_=(slice(70, 195), slice(78, 172)),
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|
|
download_if_missing=True,
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|
n_retries=3,
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|
|
delay=1.0,
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|
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):
|
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|
|
"""Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
|
||
|
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|
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|
|
Download it if necessary.
|
||
|
|
|
||
|
|
================= =======================
|
||
|
|
Classes 2
|
||
|
|
Samples total 13233
|
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|
Dimensionality 5828
|
||
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|
Features real, between 0 and 255
|
||
|
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================= =======================
|
||
|
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|
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|
|
In the official `README.txt`_ this task is described as the
|
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|
|
"Restricted" task. As I am not sure as to implement the
|
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|
|
"Unrestricted" variant correctly, I left it as unsupported for now.
|
||
|
|
|
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|
|
.. _`README.txt`: http://vis-www.cs.umass.edu/lfw/README.txt
|
||
|
|
|
||
|
|
The original images are 250 x 250 pixels, but the default slice and resize
|
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|
|
arguments reduce them to 62 x 47.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <labeled_faces_in_the_wild_dataset>`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
subset : {'train', 'test', '10_folds'}, default='train'
|
||
|
|
Select the dataset to load: 'train' for the development training
|
||
|
|
set, 'test' for the development test set, and '10_folds' for the
|
||
|
|
official evaluation set that is meant to be used with a 10-folds
|
||
|
|
cross validation.
|
||
|
|
|
||
|
|
data_home : str or path-like, default=None
|
||
|
|
Specify another download and cache folder for the datasets. By
|
||
|
|
default all scikit-learn data is stored in '~/scikit_learn_data'
|
||
|
|
subfolders.
|
||
|
|
|
||
|
|
funneled : bool, default=True
|
||
|
|
Download and use the funneled variant of the dataset.
|
||
|
|
|
||
|
|
resize : float, default=0.5
|
||
|
|
Ratio used to resize the each face picture.
|
||
|
|
|
||
|
|
color : bool, default=False
|
||
|
|
Keep the 3 RGB channels instead of averaging them to a single
|
||
|
|
gray level channel. If color is True the shape of the data has
|
||
|
|
one more dimension than the shape with color = False.
|
||
|
|
|
||
|
|
slice_ : tuple of slice, default=(slice(70, 195), slice(78, 172))
|
||
|
|
Provide a custom 2D slice (height, width) to extract the
|
||
|
|
'interesting' part of the jpeg files and avoid use statistical
|
||
|
|
correlation from the background.
|
||
|
|
|
||
|
|
download_if_missing : bool, default=True
|
||
|
|
If False, raise an OSError if the data is not locally available
|
||
|
|
instead of trying to download the data from the source site.
|
||
|
|
|
||
|
|
n_retries : int, default=3
|
||
|
|
Number of retries when HTTP errors are encountered.
|
||
|
|
|
||
|
|
.. versionadded:: 1.5
|
||
|
|
|
||
|
|
delay : float, default=1.0
|
||
|
|
Number of seconds between retries.
|
||
|
|
|
||
|
|
.. versionadded:: 1.5
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
data : :class:`~sklearn.utils.Bunch`
|
||
|
|
Dictionary-like object, with the following attributes.
|
||
|
|
|
||
|
|
data : ndarray of shape (2200, 5828). Shape depends on ``subset``.
|
||
|
|
Each row corresponds to 2 ravel'd face images
|
||
|
|
of original size 62 x 47 pixels.
|
||
|
|
Changing the ``slice_``, ``resize`` or ``subset`` parameters
|
||
|
|
will change the shape of the output.
|
||
|
|
pairs : ndarray of shape (2200, 2, 62, 47). Shape depends on ``subset``
|
||
|
|
Each row has 2 face images corresponding
|
||
|
|
to same or different person from the dataset
|
||
|
|
containing 5749 people. Changing the ``slice_``,
|
||
|
|
``resize`` or ``subset`` parameters will change the shape of the
|
||
|
|
output.
|
||
|
|
target : numpy array of shape (2200,). Shape depends on ``subset``.
|
||
|
|
Labels associated to each pair of images.
|
||
|
|
The two label values being different persons or the same person.
|
||
|
|
target_names : numpy array of shape (2,)
|
||
|
|
Explains the target values of the target array.
|
||
|
|
0 corresponds to "Different person", 1 corresponds to "same person".
|
||
|
|
DESCR : str
|
||
|
|
Description of the Labeled Faces in the Wild (LFW) dataset.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.datasets import fetch_lfw_pairs
|
||
|
|
>>> lfw_pairs_train = fetch_lfw_pairs(subset='train')
|
||
|
|
>>> list(lfw_pairs_train.target_names)
|
||
|
|
['Different persons', 'Same person']
|
||
|
|
>>> lfw_pairs_train.pairs.shape
|
||
|
|
(2200, 2, 62, 47)
|
||
|
|
>>> lfw_pairs_train.data.shape
|
||
|
|
(2200, 5828)
|
||
|
|
>>> lfw_pairs_train.target.shape
|
||
|
|
(2200,)
|
||
|
|
"""
|
||
|
|
lfw_home, data_folder_path = _check_fetch_lfw(
|
||
|
|
data_home=data_home,
|
||
|
|
funneled=funneled,
|
||
|
|
download_if_missing=download_if_missing,
|
||
|
|
n_retries=n_retries,
|
||
|
|
delay=delay,
|
||
|
|
)
|
||
|
|
logger.debug("Loading %s LFW pairs from %s", subset, lfw_home)
|
||
|
|
|
||
|
|
# wrap the loader in a memoizing function that will return memmaped data
|
||
|
|
# arrays for optimal memory usage
|
||
|
|
m = Memory(location=lfw_home, compress=6, verbose=0)
|
||
|
|
load_func = m.cache(_fetch_lfw_pairs)
|
||
|
|
|
||
|
|
# select the right metadata file according to the requested subset
|
||
|
|
label_filenames = {
|
||
|
|
"train": "pairsDevTrain.txt",
|
||
|
|
"test": "pairsDevTest.txt",
|
||
|
|
"10_folds": "pairs.txt",
|
||
|
|
}
|
||
|
|
if subset not in label_filenames:
|
||
|
|
raise ValueError(
|
||
|
|
"subset='%s' is invalid: should be one of %r"
|
||
|
|
% (subset, list(sorted(label_filenames.keys())))
|
||
|
|
)
|
||
|
|
index_file_path = join(lfw_home, label_filenames[subset])
|
||
|
|
|
||
|
|
# load and memoize the pairs as np arrays
|
||
|
|
pairs, target, target_names = load_func(
|
||
|
|
index_file_path, data_folder_path, resize=resize, color=color, slice_=slice_
|
||
|
|
)
|
||
|
|
|
||
|
|
fdescr = load_descr("lfw.rst")
|
||
|
|
|
||
|
|
# pack the results as a Bunch instance
|
||
|
|
return Bunch(
|
||
|
|
data=pairs.reshape(len(pairs), -1),
|
||
|
|
pairs=pairs,
|
||
|
|
target=target,
|
||
|
|
target_names=target_names,
|
||
|
|
DESCR=fdescr,
|
||
|
|
)
|