"""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.
"""
# Copyright (c) 2011 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause

from os import listdir, makedirs, remove, rename
from os.path import join, exists, isdir

import logging
import numpy as np

try:
    import urllib.request as urllib  # for backwards compatibility
except ImportError:
    import urllib

from .base import get_data_home, Bunch
from ..externals.joblib import Memory

from ..externals.six import b

logger = logging.getLogger(__name__)


BASE_URL = "http://vis-www.cs.umass.edu/lfw/"
ARCHIVE_NAME = "lfw.tgz"
FUNNELED_ARCHIVE_NAME = "lfw-funneled.tgz"
TARGET_FILENAMES = [
    'pairsDevTrain.txt',
    'pairsDevTest.txt',
    'pairs.txt',
]


def scale_face(face):
    """Scale back to 0-1 range in case of normalization for plotting"""
    scaled = face - face.min()
    scaled /= scaled.max()
    return scaled


#
# Common private utilities for data fetching from the original LFW website
# local disk caching, and image decoding.
#


def check_fetch_lfw(data_home=None, funneled=True, download_if_missing=True):
    """Helper function to download any missing LFW data"""
    data_home = get_data_home(data_home=data_home)
    lfw_home = join(data_home, "lfw_home")

    if funneled:
        archive_path = join(lfw_home, FUNNELED_ARCHIVE_NAME)
        data_folder_path = join(lfw_home, "lfw_funneled")
        archive_url = BASE_URL + FUNNELED_ARCHIVE_NAME
    else:
        archive_path = join(lfw_home, ARCHIVE_NAME)
        data_folder_path = join(lfw_home, "lfw")
        archive_url = BASE_URL + ARCHIVE_NAME

    if not exists(lfw_home):
        makedirs(lfw_home)

    for target_filename in TARGET_FILENAMES:
        target_filepath = join(lfw_home, target_filename)
        if not exists(target_filepath):
            if download_if_missing:
                url = BASE_URL + target_filename
                logger.warning("Downloading LFW metadata: %s", url)
                urllib.urlretrieve(url, target_filepath)
            else:
                raise IOError("%s is missing" % target_filepath)

    if not exists(data_folder_path):

        if not exists(archive_path):
            if download_if_missing:
                archive_path_temp = archive_path + ".tmp"
                logger.warning("Downloading LFW data (~200MB): %s",
                               archive_url)
                urllib.urlretrieve(archive_url, archive_path_temp)
                rename(archive_path_temp, archive_path)
            else:
                raise IOError("%s is missing" % target_filepath)

        import tarfile
        logger.info("Decompressing the data archive to %s", data_folder_path)
        tarfile.open(archive_path, "r:gz").extractall(path=lfw_home)
        remove(archive_path)

    return lfw_home, data_folder_path


def _load_imgs(file_paths, slice_, color, resize):
    """Internally used to load images"""

    # Try to import imread and imresize from PIL. We do this here to prevent
    # the whole sklearn.datasets module from depending on PIL.
    try:
        try:
            from scipy.misc import imread
        except ImportError:
            from scipy.misc.pilutil import imread
        from scipy.misc import imresize
    except ImportError:
        raise ImportError("The Python Imaging Library (PIL)"
                          " is required to load data from jpeg files")

    # compute the portion of the images to load to respect the slice_ parameter
    # given by the caller
    default_slice = (slice(0, 250), slice(0, 250))
    if slice_ is None:
        slice_ = default_slice
    else:
        slice_ = tuple(s or ds for s, ds in zip(slice_, default_slice))

    h_slice, w_slice = slice_
    h = (h_slice.stop - h_slice.start) // (h_slice.step or 1)
    w = (w_slice.stop - w_slice.start) // (w_slice.step or 1)

    if resize is not None: