514 lines
19 KiB
Python
514 lines
19 KiB
Python
"""Loader for the 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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Each picture is centered on a single face. The typical task is called
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Face Verification: given a pair of two pictures, a binary classifier
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must predict whether the two images are from the same person.
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An alternative task, Face Recognition or Face Identification is:
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given the picture of the face of an unknown person, identify the name
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of the person by referring to a gallery of previously seen pictures of
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identified persons.
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Both Face Verification and Face Recognition are tasks that are typically
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performed on the output of a model trained to perform Face Detection. The
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most popular model for Face Detection is called Viola-Johns and is
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implemented in the OpenCV library. The LFW faces were extracted by this face
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detector from various online websites.
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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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from os import listdir, makedirs, remove
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from os.path import join, exists, isdir
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import logging
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import numpy as np
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from .base import get_data_home, _fetch_remote, RemoteFileMetadata
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from ..utils import Bunch
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from ..externals.joblib import Memory
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from ..externals.six import b
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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=('055f7d9c632d7370e6fb4afc7468d40f'
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'970c34a80d4c6f50ffec63f5a8d536c0'))
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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=('b47c8422c8cded889dc5a13418c4bc2a'
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'bbda121092b3533a83306f90d900100a'))
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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=('1d454dada7dfeca0e7eab6f65dc4e97a'
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'6312d44cf142207be28d688be92aabfa')),
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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=('7cb06600ea8b2814ac26e946201cdb30'
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'4296262aad67d046a16a7ec85d0ff87c')),
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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=('ea42330c62c92989f9d7c03237ed5d59'
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'1365e89b3e649747777b70e692dc1592')),
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)
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def scale_face(face):
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"""Scale back to 0-1 range in case of normalization for plotting"""
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scaled = face - face.min()
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scaled /= scaled.max()
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return scaled
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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(data_home=None, funneled=True, download_if_missing=True):
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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(target, dirname=lfw_home)
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else:
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raise IOError("%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",
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archive.url)
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_fetch_remote(archive, dirname=lfw_home)
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else:
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raise IOError("%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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tarfile.open(archive_path, "r:gz").extractall(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 to import imread and imresize from PIL. We do this here to prevent
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# the whole sklearn.datasets module from depending on PIL.
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try:
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try:
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from scipy.misc import imread
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except ImportError:
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from scipy.misc.pilutil import imread
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from scipy.misc import imresize
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except ImportError:
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raise ImportError("The Python Imaging Library (PIL)"
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" is required to load data from jpeg files")
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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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img = imread(file_path)
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if img.ndim is 0:
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raise RuntimeError("Failed to read the image file %s, "
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"Please make sure that libjpeg is installed"
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% file_path)
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face = np.asarray(img[slice_], dtype=np.float32)
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face /= 255.0 # scale uint8 coded colors to the [0.0, 1.0] floats
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if resize is not None:
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face = imresize(face, resize)
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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(data_folder_path, slice_=None, color=False, resize=None,
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min_faces_per_person=0):
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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("min_faces_per_person=%d is too restrictive" %
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min_faces_per_person)
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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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def fetch_lfw_people(data_home=None, funneled=True, resize=0.5,
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min_faces_per_person=0, 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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"""Loader for the Labeled Faces in the Wild (LFW) people dataset
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This dataset is a collection of JPEG pictures of famous people
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collected on the internet, all details are available on the
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official website:
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http://vis-www.cs.umass.edu/lfw/
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Each picture is centered on a single face. Each pixel of each channel
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(color in RGB) is encoded by a float in range 0.0 - 1.0.
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The task is called Face Recognition (or Identification): given the
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picture of a face, find the name of the person given a training set
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(gallery).
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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.
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Parameters
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----------
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data_home : optional, 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 : boolean, optional, default: True
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Download and use the funneled variant of the dataset.
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resize : float, optional, default 0.5
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Ratio used to resize the each face picture.
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min_faces_per_person : int, optional, 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 : boolean, optional, 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_ : optional
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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 : optional, True by default
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If False, raise a IOError 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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Returns
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-------
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dataset : dict-like object with the following attributes:
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dataset.data : numpy array of shape (13233, 2914)
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Each row corresponds to a ravelled face image of original size 62 x 47
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pixels. Changing the ``slice_`` or resize parameters will change the
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shape of the output.
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dataset.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_`` or resize parameters will change
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the shape of the output.
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dataset.target : numpy array of shape (13233,)
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Labels associated to each face image. Those labels range from 0-5748
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and correspond to the person IDs.
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dataset.DESCR : string
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Description of the Labeled Faces in the Wild (LFW) dataset.
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"""
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lfw_home, data_folder_path = check_fetch_lfw(
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data_home=data_home, funneled=funneled,
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download_if_missing=download_if_missing)
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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(cachedir=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, resize=resize,
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min_faces_per_person=min_faces_per_person, color=color, slice_=slice_)
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# pack the results as a Bunch instance
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return Bunch(data=faces.reshape(len(faces), -1), images=faces,
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target=target, target_names=target_names,
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DESCR="LFW faces dataset")
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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(index_file_path, data_folder_path, slice_=None,
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color=False, resize=None):
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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.strip().split(b('\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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# 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=np.int)
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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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elif len(components) == 4:
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target[i] = 0
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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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else:
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raise ValueError("invalid line %d: %r" % (i + 1, components))
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for j, (name, idx) in enumerate(pair):
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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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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)
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shape.insert(0, 2)
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shape.insert(0, n_faces // 2)
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pairs.shape = shape
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return pairs, target, np.array(['Different persons', 'Same person'])
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def fetch_lfw_pairs(subset='train', data_home=None, funneled=True, resize=0.5,
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color=False, slice_=(slice(70, 195), slice(78, 172)),
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download_if_missing=True):
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"""Loader for the Labeled Faces in the Wild (LFW) pairs dataset
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This dataset is a collection of JPEG pictures of famous people
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collected on the internet, all details are available on the
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official website:
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http://vis-www.cs.umass.edu/lfw/
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Each picture is centered on a single face. Each pixel of each channel
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(color in RGB) is encoded by a float in range 0.0 - 1.0.
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The task is called Face Verification: given a pair of two pictures,
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a binary classifier must predict whether the two images are from
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the same person.
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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
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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.
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Read more in the :ref:`User Guide <labeled_faces_in_the_wild>`.
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Parameters
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----------
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subset : optional, default: 'train'
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Select the dataset to load: 'train' for the development training
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set, 'test' for the development test set, and '10_folds' for the
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official evaluation set that is meant to be used with a 10-folds
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cross validation.
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data_home : optional, default: None
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Specify another download and cache folder for the datasets. By
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default all scikit-learn data is stored in '~/scikit_learn_data'
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subfolders.
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funneled : boolean, optional, default: True
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Download and use the funneled variant of the dataset.
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resize : float, optional, default 0.5
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Ratio used to resize the each face picture.
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color : boolean, optional, 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_ : optional
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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 : optional, True by default
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If False, raise a IOError 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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Returns
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-------
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The data is returned as a Bunch object with the following attributes:
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data : numpy array of shape (2200, 5828). Shape depends on ``subset``.
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Each row corresponds to 2 ravel'd face images of original size 62 x 47
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pixels. Changing the ``slice_``, ``resize`` or ``subset`` parameters
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will change the shape of the output.
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pairs : numpy array of shape (2200, 2, 62, 47). Shape depends on
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``subset``.
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Each row has 2 face images corresponding to same or different person
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from the dataset containing 5749 people. Changing the ``slice_``,
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``resize`` or ``subset`` parameters will change the shape of the
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output.
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target : numpy array of shape (2200,). Shape depends on ``subset``.
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Labels associated to each pair of images. The two label values being
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different persons or the same person.
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DESCR : string
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Description of the Labeled Faces in the Wild (LFW) dataset.
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"""
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lfw_home, data_folder_path = check_fetch_lfw(
|
|
data_home=data_home, funneled=funneled,
|
|
download_if_missing=download_if_missing)
|
|
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(cachedir=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_)
|
|
|
|
# pack the results as a Bunch instance
|
|
return Bunch(data=pairs.reshape(len(pairs), -1), pairs=pairs,
|
|
target=target, target_names=target_names,
|
|
DESCR="'%s' segment of the LFW pairs dataset" % subset)
|