OneClassGMM.py 12.2 KB
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 28 16:47:47 2017

@author: Olegs Nikisins
"""

# ==============================================================================
# Import what is needed here:

from bob.pad.base.algorithm import Algorithm
from bob.bio.video.utils import FrameContainer

import numpy as np

import bob.io.base

from sklearn import mixture

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from bob.pad.base.utils import convert_frame_cont_to_array, mean_std_normalize, convert_and_prepare_features
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# ==============================================================================
# Main body :


class OneClassGMM(Algorithm):
    """
    This class is designed to train a OneClassGMM based PAD system. The OneClassGMM is trained
    using data of one class (real class) only. The procedure is the following:

    1. First, the training data is mean-std normalized using mean and std of the
       real class only.

    2. Second, the OneClassGMM with ``n_components`` Gaussians is trained using samples
       of the real class.

    3. The input features are next classified using pre-trained OneClassGMM machine.

    **Parameters:**

    ``n_components`` : :py:class:`int`
        Number of Gaussians in the OneClassGMM. Default: 1 .

    ``random_state`` : :py:class:`int`
        A seed for the random number generator used in the initialization of
        the OneClassGMM. Default: 7 .

    ``frame_level_scores_flag`` : :py:class:`bool`
        Return scores for each frame individually if True. Otherwise, return a
        single score per video. Default: False.
    """

    def __init__(self,
                 n_components=1,
                 random_state=3,
                 frame_level_scores_flag=False):

        Algorithm.__init__(
            self,
            n_components=n_components,
            random_state=random_state,
            frame_level_scores_flag=frame_level_scores_flag,
            performs_projection=True,
            requires_projector_training=True)

        self.n_components = n_components

        self.random_state = random_state

        self.frame_level_scores_flag = frame_level_scores_flag

        self.machine = None  # this argument will be updated with pretrained OneClassGMM machine

        self.features_mean = None  # this argument will be updated with features mean

        self.features_std = None  # this argument will be updated with features std

        # names of the arguments of the pretrained OneClassGMM machine to be saved/loaded to/from HDF5 file:
        self.gmm_param_keys = [
            "covariance_type", "covariances_", "lower_bound_", "means_",
            "n_components", "weights_", "converged_", "precisions_",
            "precisions_cholesky_"
        ]

    # ==========================================================================
    def train_gmm(self, real, n_components, random_state):
        """
        Train OneClassGMM classifier given real class. Prior to the training the data is
        mean-std normalized.

        **Parameters:**

        ``real`` : 2D :py:class:`numpy.ndarray`
            Training features for the real class.

        ``n_components`` : :py:class:`int`
            Number of Gaussians in the OneClassGMM. Default: 1 .

        ``random_state`` : :py:class:`int`
            A seed for the random number generator used in the initialization of
            the OneClassGMM. Default: 7 .

        **Returns:**

        ``machine`` : object
            A trained OneClassGMM machine.

        ``features_mean`` : 1D :py:class:`numpy.ndarray`
            Mean of the features.

        ``features_std`` : 1D :py:class:`numpy.ndarray`
            Standart deviation of the features.
        """

        features_norm, features_mean, features_std = mean_std_normalize(
            real)
        # real is now mean-std normalized

        machine = mixture.GaussianMixture(
            n_components=n_components,
            random_state=random_state,
            covariance_type='full')

        machine.fit(features_norm)

        return machine, features_mean, features_std

    # ==========================================================================
    def save_gmm_machine_and_mean_std(self, projector_file, machine,
                                      features_mean, features_std):
        """
        Saves the OneClassGMM machine, features mean and std to the hdf5 file.
        The absolute name of the file is specified in ``projector_file`` string.

        **Parameters:**

        ``projector_file`` : :py:class:`str`
            Absolute name of the file to save the data to, as returned by
            ``bob.pad.base`` framework.

        ``machine`` : object
            The OneClassGMM machine to be saved. As returned by sklearn.linear_model
            module.

        ``features_mean`` : 1D :py:class:`numpy.ndarray`
            Mean of the features.

        ``features_std`` : 1D :py:class:`numpy.ndarray`
            Standart deviation of the features.
        """

        f = bob.io.base.HDF5File(projector_file,
                                 'w')  # open hdf5 file to save to

        for key in self.gmm_param_keys:
            data = getattr(machine, key)

            f.set(key, data)

        f.set("features_mean", features_mean)

        f.set("features_std", features_std)

        del f

    # ==========================================================================
    def train_projector(self, training_features, projector_file):
        """
        Train OneClassGMM for feature projection and save it to file.
        The ``requires_projector_training = True`` flag must be set to True
        to enable this function.

        **Parameters:**

        ``training_features`` : [[FrameContainer], [FrameContainer]]
            A list containing two elements: [0] - a list of Frame Containers with
            feature vectors for the real class; [1] - a list of Frame Containers with
            feature vectors for the attack class.

        ``projector_file`` : :py:class:`str`
            The file to save the trained projector to, as returned by the
            ``bob.pad.base`` framework.
        """

        # training_features[0] - training features for the REAL class.
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        real = convert_and_prepare_features(
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            training_features[0])  # output is array

        # training_features[1] - training features for the ATTACK class.
        #        attack = self.convert_and_prepare_features(training_features[1]) # output is array

        # Train the OneClassGMM machine and get normalizers:
        machine, features_mean, features_std = self.train_gmm(
            real=real,
            n_components=self.n_components,
            random_state=self.random_state)

        # Save the GNN machine and normalizers:
        self.save_gmm_machine_and_mean_std(projector_file, machine,
                                           features_mean, features_std)

    # ==========================================================================
    def load_gmm_machine_and_mean_std(self, projector_file):
        """
        Loads the machine, features mean and std from the hdf5 file.
        The absolute name of the file is specified in ``projector_file`` string.

        **Parameters:**

        ``projector_file`` : :py:class:`str`
            Absolute name of the file to load the trained projector from, as
            returned by ``bob.pad.base`` framework.

        **Returns:**

        ``machine`` : object
            The loaded OneClassGMM machine. As returned by sklearn.mixture module.

        ``features_mean`` : 1D :py:class:`numpy.ndarray`
            Mean of the features.

        ``features_std`` : 1D :py:class:`numpy.ndarray`
            Standart deviation of the features.
        """

        f = bob.io.base.HDF5File(projector_file,
                                 'r')  # file to read the machine from

        # initialize the machine:
        machine = mixture.GaussianMixture()

        # set the params of the machine:
        for key in self.gmm_param_keys:
            data = f.read(key)

            setattr(machine, key, data)

        features_mean = f.read("features_mean")

        features_std = f.read("features_std")

        del f

        return machine, features_mean, features_std

    # ==========================================================================
    def load_projector(self, projector_file):
        """
        Loads the machine, features mean and std from the hdf5 file.
        The absolute name of the file is specified in ``projector_file`` string.

        This function sets the arguments ``self.machine``, ``self.features_mean``
        and ``self.features_std`` of this class with loaded machines.

        The function must be capable of reading the data saved with the
        :py:meth:`train_projector` method of this class.

        Please register `performs_projection = True` in the constructor to
        enable this function.

        **Parameters:**

        ``projector_file`` : :py:class:`str`
            The file to read the projector from, as returned by the
            ``bob.pad.base`` framework. In this class the names of the files to
            read the projectors from are modified, see ``load_machine`` and
            ``load_cascade_of_machines`` methods of this class for more details.
        """

        machine, features_mean, features_std = self.load_gmm_machine_and_mean_std(
            projector_file)

        self.machine = machine

        self.features_mean = features_mean

        self.features_std = features_std

    # ==========================================================================
    def project(self, feature):
        """
        This function computes a vector of scores for each sample in the input
        array of features. The following steps are applied:

        1. First, the input data is mean-std normalized using mean and std of the
           real class only.

        2. The input features are next classified using pre-trained OneClassGMM machine.

        Set ``performs_projection = True`` in the constructor to enable this function.
        It is assured that the :py:meth:`load_projector` was **called before** the
        ``project`` function is executed.

        **Parameters:**

        ``feature`` : FrameContainer or 2D :py:class:`numpy.ndarray`
            Two types of inputs are accepted.
            A Frame Container conteining the features of an individual,
            see ``bob.bio.video.utils.FrameContainer``.
            Or a 2D feature array of the size (N_samples x N_features).

        **Returns:**

        ``scores`` : 1D :py:class:`numpy.ndarray`
            Vector of scores. Scores for the real class are expected to be
            higher, than the scores of the negative / attack class.
            In this case scores are the weighted log probabilities.
        """

        # 1. Convert input array to numpy array if necessary.
        if isinstance(
                feature,
                FrameContainer):  # if FrameContainer convert to 2D numpy array

            features_array = convert_frame_cont_to_array(feature)

        else:

            features_array = feature

        features_array_norm, _, _ = mean_std_normalize(
            features_array, self.features_mean, self.features_std)

        scores = self.machine.score_samples(features_array_norm)

        return scores

    # ==========================================================================
    def score(self, toscore):
        """
        Returns a probability of a sample being a real class.

        **Parameters:**

        ``toscore`` : 1D :py:class:`numpy.ndarray`
            Vector with scores for each frame/sample defining the probability
            of the frame being a sample of the real class.

        **Returns:**

        ``score`` : [:py:class:`float`]
            If ``frame_level_scores_flag = False`` a single score is returned.
            One score per video. This score is placed into a list, because
            the ``score`` must be an iterable.
            Score is a probability of a sample being a real class.
            If ``frame_level_scores_flag = True`` a list of scores is returned.
            One score per frame/sample.
        """

        if self.frame_level_scores_flag:

            score = list(toscore)

        else:

            score = [np.mean(toscore)]  # compute a single score per video

        return score