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lfw.cpython-35.opt-1.pyc
��(XPN � @ s d Z d d l m Z m Z m Z m Z d d l m Z m Z m Z d d l m Z d d l Z d d l Z y d d l j Z Wn e k r� d d l Z Yn Xd d l m Z m Z d d l m Z d d l m Z e j e � Z d Z d Z d Z d d d g Z d d � Z! d d d d d � Z" d d � Z# d d d d d d � Z$ d d d d d e% d d � e% d d � f d d d! � Z& d d d d" d# � Z' e d$ � d d% d& � � Z( d' d d d d e% d d � e% d d � f d d( d) � Z) e d* � d d+ d, � � Z* d S)-a� Loader for the Labeled Faces in the Wild (LFW) dataset This dataset is a collection of JPEG pictures of famous people collected over the internet, all details are available on the official website: http://vis-www.cs.umass.edu/lfw/ Each picture is centered on a single face. The typical task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person. An alternative task, Face Recognition or Face Identification is: given the picture of the face of an unknown person, identify the name of the person by referring to a gallery of previously seen pictures of identified persons. Both Face Verification and Face Recognition are tasks that are typically performed on the output of a model trained to perform Face Detection. The most popular model for Face Detection is called Viola-Johns and is implemented in the OpenCV library. The LFW faces were extracted by this face detector from various online websites. � )�listdir�makedirs�remove�rename)�join�exists�isdir)� deprecatedN� )� get_data_home�Bunch� )�Memory)�bz http://vis-www.cs.umass.edu/lfw/zlfw.tgzzlfw-funneled.tgzzpairsDevTrain.txtzpairsDevTest.txtz pairs.txtc C s$ | | j � } | | j � } | S)z=Scale back to 0-1 range in case of normalization for plotting)�min�max)�faceZscaled� r �/lfw.py� scale_face9 s r Tc C s� t d | � } t | d � } | rO t | t � } t | d � } t t } n( t | t � } t | d � } t t } t | � s� t | � xl t D]d } t | | � } t | � s� | r� t | } t j d | � t j | | � q� t d | � � q� Wt | � s�t | � sd| rT| d } t j d | � t j | | � t | | � n t d | � � d d l } t j d | � | j | d � j d | � t | � | | f S)z0Helper function to download any missing LFW data� data_home�lfw_homeZ lfw_funneledZlfwz Downloading LFW metadata: %sz %s is missingz.tmpz!Downloading LFW data (~200MB): %sr Nz$Decompressing the data archive to %szr:gz�path)r r �FUNNELED_ARCHIVE_NAME�BASE_URL� ARCHIVE_NAMEr r �TARGET_FILENAMES�logger�warning�urllib� urlretrieve�IOErrorr �tarfile�info�open� extractallr ) r �funneled�download_if_missingr Z archive_path�data_folder_pathZ archive_urlZtarget_filenameZtarget_filepathZurlZarchive_path_tempr" r r r �check_fetch_lfwF sB r) c C sw yM y d d l m } Wn" t k r; d d l m } Yn Xd d l m } Wn t k rm t d � � Yn Xt d d � t d d � f } | d k r� | } n" t d d � t | | � D� � } | \ } } | j | j | j p� d } | j | j | j pd } | d k rAt | � } t | | � } t | | � } t | � } | swt j | | | f d t j �} n$ t j | | | d f d t j �} x� t | � D]� \ } } | d d k r�t j d | d | � | | � } | j d k rt d | � � t j | | d t j �} | d } | d k rG| | | � } | s_| j d d � } | | | d f <q�W| S)z Internally used to load imagesr )�imread)�imresizezIThe Python Imaging Library (PIL) is required to load data from jpeg files� Nc s s! | ] \ } } | p | Vq d S)Nr )�.0�sZdsr r r � <genexpr>� s z _load_imgs.<locals>.<genexpr>r �dtype� i� zLoading face #%05d / %05dzLFailed to read the image file %s, Please make sure that libjpeg is installedg �o@Zaxisr .)Z scipy.miscr* � ImportErrorZscipy.misc.pilutilr+ �slice�tuple�zip�stop�start�step�float�int�len�np�zerosZfloat32� enumerater r# �ndim� RuntimeErrorZasarrayZmean)� file_paths�slice_�color�resizer* r+ Z default_sliceZh_sliceZw_slice�h�w�n_faces�faces�i� file_pathZimgr r r r � _load_imgsu sL " $$ rK Fc sZ g g } } x� t t | � � D]� } t | | � � t � � sD q � f d d � t � � D� } t | � } | | k r | j d d � } | j | g | � | j | � q Wt | � } | d k r� t d | � � t j | � } t j | | � } t | | | | � } t j | � } t j j d � j | � | | | | } } | | | f S)z~Perform the actual data loading for the lfw people dataset This operation is meant to be cached by a joblib wrapper. c s g | ] } t � | � � q Sr )r )r- �f)� folder_pathr r � <listcomp>� s z%_fetch_lfw_people.<locals>.<listcomp>�_� r z*min_faces_per_person=%d is too restrictive�* )�sortedr r r r; �replace�extend� ValueErrorr< �uniqueZ searchsortedrK Zarange�randomZ RandomState�shuffle)r( rB rC rD �min_faces_per_personZ person_namesrA Z person_name�pathsZ n_picturesrG � target_names�targetrH �indicesr )rM r �_fetch_lfw_people� s, r^ g �?�F �� �N � c C s� t d | d | d | � \ } } t j d | � t d | d d d d � } | j t � } | | d | d | d | d | �\ } } } t d | j t | � d � d | d | d | d d � S)a� Loader for the Labeled Faces in the Wild (LFW) people dataset This dataset is a collection of JPEG pictures of famous people collected on the internet, all details are available on the official website: http://vis-www.cs.umass.edu/lfw/ Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0. The task is called Face Recognition (or Identification): given the picture of a face, find the name of the person given a training set (gallery). The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 74. Parameters ---------- data_home : optional, 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 : boolean, optional, default: True Download and use the funneled variant of the dataset. resize : float, optional, default 0.5 Ratio used to resize the each face picture. min_faces_per_person : int, optional, default None The extracted dataset will only retain pictures of people that have at least `min_faces_per_person` different pictures. color : boolean, optional, 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_ : optional 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 : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site. Returns ------- dataset : dict-like object with the following attributes: dataset.data : numpy array of shape (13233, 2914) Each row corresponds to a ravelled face image of original size 62 x 47 pixels. Changing the ``slice_`` or resize parameters will change the shape of the output. dataset.images : numpy array of shape (13233, 62, 47) Each row is a face image corresponding to one of the 5749 people in the dataset. Changing the ``slice_`` or resize parameters will change the shape of the output. dataset.target : numpy array of shape (13233,) Labels associated to each face image. Those labels range from 0-5748 and correspond to the person IDs. dataset.DESCR : string Description of the Labeled Faces in the Wild (LFW) dataset. r r& r' z Loading LFW people faces from %s�cachedir�compress� �verboser rD rY rC rB �datar Zimagesr\ r[ �DESCRzLFW faces dataset���) r) r r# r �cacher^ r �reshaper; )r r& rD rY rC rB r' r r( �m� load_funcrH r\ r[ r r r �fetch_lfw_people� s I $ rn c C sp t | d � � } d d � | D� } Wd QRXd d � | D� } t | � } t j | d t j �} t � } x�t | � D]w\ } } t | � d k r� d | | <| d t | d � d f | d t | d � d f f } nu t | � d k r;d | | <| d t | d � d f | d t | d � d f f } n t d | d | f � � x� t | � D]� \ } \ } } y t | | � } Wn* t k r�t | t | d � � } Yn Xt t t | � � � } t | | | � } | j | � qbWqy Wt | | | | � } t | j � } | j d � } | j d d � | j d | d � | | _ | | t j d d g � f S)z}Perform the actual data loading for the LFW pairs dataset This operation is meant to be cached by a joblib wrapper. �rbc S s+ g | ]! } | j � j t d � � � q S)� )�strip�splitr )r- �lnr r r rN Q s z$_fetch_lfw_pairs.<locals>.<listcomp>Nc S s( g | ] } t | � d k r | � q S)r )r; )r- Zslr r r rN R s r0 r1 r r r � zinvalid line %d: %rzUTF-8zDifferent personsz Same person)r$ r; r< r= r: �listr> rU r � TypeError�strrR r �appendrK �shape�pop�insert�array)�index_file_pathr( rB rC rD Z index_fileZ split_linesZ pair_specsZn_pairsr\ rA rI Z componentsZpair�j�name�idxZ person_folder� filenamesrJ �pairsry rG r r r �_fetch_lfw_pairsH s> $ $ r� z�Function 'load_lfw_people' has been deprecated in 0.17 and will be removed in 0.19.Use fetch_lfw_people(download_if_missing=False) instead.c K s t d | | � S)aJ Alias for fetch_lfw_people(download_if_missing=False) .. deprecated:: 0.17 This function will be removed in 0.19. Use :func:`sklearn.datasets.fetch_lfw_people` with parameter ``download_if_missing=False`` instead. Check fetch_lfw_people.__doc__ for the documentation and parameter list. r' )rn )r' �kwargsr r r �load_lfw_people{ s r� �trainc C s" t d | d | d | � \ } } t j d | | � t d | d d d d � } | j t � } d d d d d d i } | | k r� t d | t t | j � � � f � � t | | | � } | | | d | d | d | �\ } } } t d | j t | � d � d | d | d | d d | � S) a0 Loader for the Labeled Faces in the Wild (LFW) pairs dataset This dataset is a collection of JPEG pictures of famous people collected on the internet, all details are available on the official website: http://vis-www.cs.umass.edu/lfw/ Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0. The task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person. In the official `README.txt`_ this task is described as the "Restricted" task. As I am not sure as to implement the "Unrestricted" variant correctly, I left it as unsupported for now. .. _`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 arguments reduce them to 62 x 74. Read more in the :ref:`User Guide <labeled_faces_in_the_wild>`. Parameters ---------- subset : optional, 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 : optional, 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 : boolean, optional, default: True Download and use the funneled variant of the dataset. resize : float, optional, default 0.5 Ratio used to resize the each face picture. color : boolean, optional, 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_ : optional 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 : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site. Returns ------- The data is returned as a Bunch object with the following attributes: data : numpy array 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 : numpy array 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. DESCR : string Description of the Labeled Faces in the Wild (LFW) dataset. r r& r' z Loading %s LFW pairs from %src rd re rf r r� zpairsDevTrain.txtZtestzpairsDevTest.txtZ10_foldsz pairs.txtz+subset='%s' is invalid: should be one of %rrD rC rB rg r r� r\ r[ rh z%'%s' segment of the LFW pairs datasetri )r) r r# r rj r� rU ru rR �keysr r rk r; )Zsubsetr r& rD rC rB r' r r( rl rm Zlabel_filenamesr} r� r\ r[ r r r �fetch_lfw_pairs� s& V "$ r� z�Function 'load_lfw_pairs' has been deprecated in 0.17 and will be removed in 0.19.Use fetch_lfw_pairs(download_if_missing=False) instead.c K s t d | | � S)aG Alias for fetch_lfw_pairs(download_if_missing=False) .. deprecated:: 0.17 This function will be removed in 0.19. Use :func:`sklearn.datasets.fetch_lfw_pairs` with parameter ``download_if_missing=False`` instead. Check fetch_lfw_pairs.__doc__ for the documentation and parameter list. r' )r� )r' r� r r r �load_lfw_pairs s r� )+�__doc__�osr r r r Zos.pathr r r Z sklearn.utilsr �loggingZnumpyr< Zurllib.requestZrequestr r2 �baser r Zexternals.joblibr Z externals.sixr � getLogger�__name__r r r r r r r) rK r^ r3 rn r� r� r� r� r r r r �<module> sJ " / G ( `2 t
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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california_housing.cpython-35.opt-1.pyc
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california_housing.cpython-35.pyc
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covtype.cpython-35.opt-1.pyc
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covtype.cpython-35.pyc
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kddcup99.cpython-35.opt-1.pyc
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kddcup99.cpython-35.pyc
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lfw.cpython-35.opt-1.pyc
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lfw.cpython-35.pyc
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mlcomp.cpython-35.opt-1.pyc
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mlcomp.cpython-35.pyc
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mldata.cpython-35.opt-1.pyc
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mldata.cpython-35.pyc
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olivetti_faces.cpython-35.opt-1.pyc
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olivetti_faces.cpython-35.pyc
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rcv1.cpython-35.opt-1.pyc
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rcv1.cpython-35.pyc
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samples_generator.cpython-35.opt-1.pyc
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samples_generator.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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setup.cpython-35.pyc
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species_distributions.cpython-35.opt-1.pyc
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species_distributions.cpython-35.pyc
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svmlight_format.cpython-35.opt-1.pyc
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svmlight_format.cpython-35.pyc
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twenty_newsgroups.cpython-35.opt-1.pyc
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twenty_newsgroups.cpython-35.pyc
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