Merge branch 'batl_hldi' into 'master'

Added HLDI for the BATL database, added optional data normalization in FaceCropAlign

See merge request !59

bob/pad/face/config/batl_db.py

+#!/usr/bin/env python
+"""
+BATL Db is a database for face PAD experiments.
+"""
+
+from bob.pad.face.database import BatlPadDatabase
+
+# Directory where the data files are stored.
+# This directory is given in the .bob_bio_databases.txt file located in your home directory
+ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
+"""Value of ``~/.bob_bio_databases.txt`` for this database"""
+
+ORIGINAL_EXTENSION = ".h5"  # extension of the data files
+
+ANNOTATIONS_TEMP_DIR = ""
+
+PROTOCOL = 'nowig-color-50'
+
+database = BatlPadDatabase(
+    protocol=PROTOCOL,
+    original_directory=ORIGINAL_DIRECTORY,
+    original_extension=ORIGINAL_EXTENSION,
+    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
+    landmark_detect_method="mtcnn",
+    training_depends_on_protocol=True,
+)
+"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
+database settings.
+
+.. warning::
+
+   This class only provides a programmatic interface to load data in an orderly
+   manner, respecting usage protocols. It does **not** contain the raw
+   data files. You should procure those yourself.
+
+Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
+You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
+value to the places where you actually installed the BATL database.
+"""
+
+protocol = PROTOCOL
+"""
+You may modify this at runtime by specifying the option ``--protocol`` on the
+command-line of ``spoof.py`` or using the keyword ``protocol`` on a
+configuration file that is loaded **after** this configuration resource.
+"""
+
+groups = ["train", "dev", "eval"]
+"""The default groups to use for reproducing the baselines.
+
+You may modify this at runtime by specifying the option ``--groups`` on the
+command-line of ``spoof.py`` or using the keyword ``groups`` on a
+configuration file that is loaded **after** this configuration resource.
+"""

bob/pad/face/config/batl_db_depth.py

+#!/usr/bin/env python
+"""
+BATL Db is a database for face PAD experiments.
+"""
+
+from bob.pad.face.database import BatlPadDatabase
+
+# Directory where the data files are stored.
+# This directory is given in the .bob_bio_databases.txt file located in your home directory
+ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
+"""Value of ``~/.bob_bio_databases.txt`` for this database"""
+
+ORIGINAL_EXTENSION = ".h5"  # extension of the data files
+
+ANNOTATIONS_TEMP_DIR = ""
+
+PROTOCOL = 'nowig-depth-50'
+
+database = BatlPadDatabase(
+    protocol=PROTOCOL,
+    original_directory=ORIGINAL_DIRECTORY,
+    original_extension=ORIGINAL_EXTENSION,
+    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
+    landmark_detect_method="mtcnn",
+    training_depends_on_protocol=True,
+)
+"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
+database settings.
+
+.. warning::
+
+   This class only provides a programmatic interface to load data in an orderly
+   manner, respecting usage protocols. It does **not** contain the raw
+   data files. You should procure those yourself.
+
+Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
+You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
+value to the places where you actually installed the BATL database.
+"""
+
+protocol = PROTOCOL
+"""
+You may modify this at runtime by specifying the option ``--protocol`` on the
+command-line of ``spoof.py`` or using the keyword ``protocol`` on a
+configuration file that is loaded **after** this configuration resource.
+"""
+
+groups = ["train", "dev", "eval"]
+"""The default groups to use for reproducing the baselines.
+
+You may modify this at runtime by specifying the option ``--groups`` on the
+command-line of ``spoof.py`` or using the keyword ``groups`` on a
+configuration file that is loaded **after** this configuration resource.
+"""

bob/pad/face/config/batl_db_infrared.py

+#!/usr/bin/env python
+"""
+BATL Db is a database for face PAD experiments.
+"""
+
+from bob.pad.face.database import BatlPadDatabase
+
+# Directory where the data files are stored.
+# This directory is given in the .bob_bio_databases.txt file located in your home directory
+ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
+"""Value of ``~/.bob_bio_databases.txt`` for this database"""
+
+ORIGINAL_EXTENSION = ".h5"  # extension of the data files
+
+ANNOTATIONS_TEMP_DIR = ""
+
+PROTOCOL = 'nowig-infrared-50'
+
+database = BatlPadDatabase(
+    protocol=PROTOCOL,
+    original_directory=ORIGINAL_DIRECTORY,
+    original_extension=ORIGINAL_EXTENSION,
+    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
+    landmark_detect_method="mtcnn",
+    training_depends_on_protocol=True,
+)
+"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
+database settings.
+
+.. warning::
+
+   This class only provides a programmatic interface to load data in an orderly
+   manner, respecting usage protocols. It does **not** contain the raw
+   data files. You should procure those yourself.
+
+Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
+You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
+value to the places where you actually installed the BATL database.
+"""
+
+protocol = PROTOCOL
+"""
+You may modify this at runtime by specifying the option ``--protocol`` on the
+command-line of ``spoof.py`` or using the keyword ``protocol`` on a
+configuration file that is loaded **after** this configuration resource.
+"""
+
+groups = ["train", "dev", "eval"]
+"""The default groups to use for reproducing the baselines.
+
+You may modify this at runtime by specifying the option ``--groups`` on the
+command-line of ``spoof.py`` or using the keyword ``groups`` on a
+configuration file that is loaded **after** this configuration resource.
+"""

bob/pad/face/config/batl_db_thermal.py

+#!/usr/bin/env python
+"""
+BATL Db is a database for face PAD experiments.
+"""
+
+from bob.pad.face.database import BatlPadDatabase
+
+# Directory where the data files are stored.
+# This directory is given in the .bob_bio_databases.txt file located in your home directory
+ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
+"""Value of ``~/.bob_bio_databases.txt`` for this database"""
+
+ORIGINAL_EXTENSION = ".h5"  # extension of the data files
+
+ANNOTATIONS_TEMP_DIR = ""
+
+PROTOCOL = 'nowig-thermal-50'
+
+database = BatlPadDatabase(
+    protocol=PROTOCOL,
+    original_directory=ORIGINAL_DIRECTORY,
+    original_extension=ORIGINAL_EXTENSION,
+    annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
+    landmark_detect_method="mtcnn",
+    training_depends_on_protocol=True,
+)
+"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
+database settings.
+
+.. warning::
+
+   This class only provides a programmatic interface to load data in an orderly
+   manner, respecting usage protocols. It does **not** contain the raw
+   data files. You should procure those yourself.
+
+Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
+You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
+value to the places where you actually installed the BATL database.
+"""
+
+protocol = PROTOCOL
+"""
+You may modify this at runtime by specifying the option ``--protocol`` on the
+command-line of ``spoof.py`` or using the keyword ``protocol`` on a
+configuration file that is loaded **after** this configuration resource.
+"""
+
+groups = ["train", "dev", "eval"]
+"""The default groups to use for reproducing the baselines.
+
+You may modify this at runtime by specifying the option ``--groups`` on the
+command-line of ``spoof.py`` or using the keyword ``groups`` on a
+configuration file that is loaded **after** this configuration resource.
+"""

bob/pad/face/config/lbp_lr_batl_D_T_IR.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+This file contains configurations to run LBP and SVM based face PAD baseline.
+The settings are tuned for the Replay-attack database.
+The idea of the algorithm is introduced in the following paper: [CAM12]_.
+However some settings are different from the ones introduced in the paper.
+"""
+
+#=======================================================================================
+sub_directory = 'lbp_svm'
+"""
+Sub-directory where results will be placed.
+
+You may change this setting using the ``--sub-directory`` command-line option
+or the attribute ``sub_directory`` in a configuration file loaded **after**
+this resource.
+"""
+
+#=======================================================================================
+# define preprocessor:
+
+from ..preprocessor import FaceCropAlign
+
+from bob.bio.video.preprocessor import Wrapper
+
+from bob.bio.video.utils import FrameSelector
+
+from ..preprocessor.FaceCropAlign import auto_norm_image as _norm_func
+
+FACE_SIZE = 64 # The size of the resulting face
+RGB_OUTPUT_FLAG = False # Gray-scale output
+USE_FACE_ALIGNMENT = False # use annotations
+MAX_IMAGE_SIZE = None # no limiting here
+FACE_DETECTION_METHOD = None # use annotations
+MIN_FACE_SIZE = 50 # skip small faces
+NORMALIZATION_FUNCTION = _norm_func
+NORMALIZATION_FUNCTION_KWARGS = {}
+NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':3.0, 'norm_method':'MAD'}
+
+_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
+                                   rgb_output_flag = RGB_OUTPUT_FLAG,
+                                   use_face_alignment = USE_FACE_ALIGNMENT,
+                                   max_image_size = MAX_IMAGE_SIZE,
+                                   face_detection_method = FACE_DETECTION_METHOD,
+                                   min_face_size = MIN_FACE_SIZE,
+                                   normalization_function = NORMALIZATION_FUNCTION,
+                                   normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
+
+_frame_selector = FrameSelector(selection_style = "all")
+
+preprocessor = Wrapper(preprocessor = _image_preprocessor,
+                       frame_selector = _frame_selector)
+"""
+In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
+The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
+below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
+[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
+"""
+
+#=======================================================================================
+# define extractor:
+
+from ..extractor import LBPHistogram
+
+from bob.bio.video.extractor import Wrapper
+
+LBPTYPE = 'uniform'
+ELBPTYPE = 'regular'
+RAD = 1
+NEIGHBORS = 8
+CIRC = False
+DTYPE = None
+
+extractor = Wrapper(LBPHistogram(
+    lbptype=LBPTYPE,
+    elbptype=ELBPTYPE,
+    rad=RAD,
+    neighbors=NEIGHBORS,
+    circ=CIRC,
+    dtype=DTYPE))
+"""
+In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
+
+The parameters are similar to the ones introduced in [CAM12]_.
+"""
+
+#=======================================================================================
+# define algorithm:
+
+from bob.pad.base.algorithm import LogRegr
+
+C = 1.  # The regularization parameter for the LR classifier
+FRAME_LEVEL_SCORES_FLAG = True  # Return one score per frame
+
+algorithm = LogRegr(
+    C=C, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
+"""
+The Logistic Regression is used to classify the data into *real* and *attack* classes.
+One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
+The sub-sampling of training data is not used here, sub-sampling flags have default ``False``
+values.
+"""

bob/pad/face/database/__init__.py

@@ -4,6 +4,7 @@ from .replay_mobile import ReplayMobilePadDatabase
 from .msu_mfsd import MsuMfsdPadDatabase
 from .aggregated_db import AggregatedDbPadDatabase
 from .mifs import MIFSPadDatabase
+from .batl import BatlPadDatabase
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -29,6 +30,7 @@ __appropriate__(
     MsuMfsdPadDatabase,
     AggregatedDbPadDatabase,
     MIFSPadDatabase,
+    BatlPadDatabase,
 )
 
 __all__ = [_ for _ in dir() if not _.startswith('_')]

bob/pad/face/database/batl.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+
+# Used in BATLMobilePadFile class
+from bob.pad.base.database import PadDatabase, PadFile
+from bob.bio.video import FrameSelector
+from bob.extension import rc
+
+from bob.pad.face.preprocessor.FaceCropAlign import detect_face_landmarks_in_image
+
+import json
+
+import os
+
+import bob.io.base
+
+
+class BatlPadFile(PadFile):
+    """
+    A high level implementation of the File class for the BATL
+    database.
+    """
+
+    def __init__(self, f,
+                 stream_type,  # a list of streams to be loaded
+                 max_frames,
+                 reference_stream_type="color",
+                 warp_to_reference=True,
+                 convert_to_rgb=False,
+                 crop=None,
+                 video_data_only=True):
+
+        """
+        **Parameters:**
+
+        ``f`` : :py:class:`object`
+            An instance of the File class defined in the low level db interface
+            of the BATL database, in the ``bob.db.batl.models.py`` file.
+
+        ``stream_type`` : [] or :py:class:`str`
+            A types of the streams to be loaded.
+
+        ``max_frames`` : :py:class:`int`
+            A maximum number of frames to be loaded. Frames are
+            selected uniformly.
+
+        ``reference_stream_type`` : :py:class:`str`
+            Align/register all channels to this one.
+            Default: "color".
+
+        ``warp_to_reference`` : :py:class:`bool`
+            Align/register downloaded video to ``reference_stream_type``,
+            if set to ``True``.
+            Default: ``True``.
+
+        ``convert_to_rgb`` : :py:class:`bool`
+            Type cast the non-RGB data to RGB data type,
+            if set to ``True``.
+            Default: ``False``.
+
+        ``crop`` : []
+            Pre-crop the frames if given, see ``bob.db.batl`` for more
+            details.
+            Default: ``None``.
+
+        ``video_data_only`` : :py:class:`bool`
+            Load video data only if ``True``. Otherwise more meta-data
+            is loaded, for example timestamps for each frame.
+            See the ``load()`` method in the low-level database
+            interface for more details.
+            Default: ``True``.
+        """
+
+        self.f = f
+        if f.is_attack():
+            attack_type = 'attack'
+        else:
+            attack_type = None
+
+        super(BatlPadFile, self).__init__(
+            client_id=f.client_id,
+            path=f.path,
+            attack_type=attack_type,
+            file_id=f.id)
+
+        self.stream_type = stream_type
+        self.max_frames = max_frames
+        self.reference_stream_type = reference_stream_type  # "color"
+        self.warp_to_reference = warp_to_reference  # True
+        self.convert_to_rgb = convert_to_rgb  # False
+        self.crop = crop  # None
+        self.video_data_only = video_data_only  # True
+
+    def load(self, directory=None, extension='.h5',
+             frame_selector=FrameSelector(selection_style='all')):
+        """
+        Load method of the file class.
+
+        **Parameters:**
+
+        ``directory`` : :py:class:`str`
+            String containing the path to BATL database.
+            Default: ``None``.
+
+        ``extension`` : :py:class:`str`
+            Extension of the BATL database.
+            Default: ".h5".
+
+        ``frame_selector`` : :any:`bob.bio.video.FrameSelector`, optional
+            Specifying the frames to be selected.
+
+        **Returns:**
+
+        ``data`` : FrameContainer
+            Video data stored in the FrameContainer,
+            see ``bob.bio.video.utils.FrameContainer``
+            for further details.
+        """
+
+        data = self.f.load(directory=directory,
+                           extension=extension,
+                           modality=self.stream_type,
+                           reference_stream_type=self.reference_stream_type,
+                           warp_to_reference=self.warp_to_reference,
+                           convert_to_rgb=self.convert_to_rgb,
+                           crop=self.crop,
+                           max_frames=self.max_frames)
+
+        for meta_data in data.keys():
+            if meta_data != 'rppg':
+                data[meta_data] = frame_selector(data[meta_data])
+
+        if self.video_data_only:
+
+            data = data['video']
+
+        return data
+
+
+class BatlPadDatabase(PadDatabase):
+    """
+    A high level implementation of the Database class for the BATL
+    database.
+    """
+
+    def __init__(
+            self,
+            protocol='nowig',
+            original_directory=rc['bob.db.batl.directory'],
+            original_extension='.h5',
+            annotations_temp_dir="",
+            landmark_detect_method="mtcnn",
+            **kwargs):
+        """
+        **Parameters:**
+
+        ``protocol`` : str or None
+            The name of the protocol that defines the default experimental
+            setup for this database. Also a "complex" protocols can be
+            parsed.
+            For example:
+            "nowig-color-5" - nowig protocol, color data only,
+            use 5 first frames.
+            "nowig-depth-5" - nowig protocol, depth data only,
+            use 5 first frames.
+            "nowig-color" - nowig protocol, depth data only, use all frames.
+            See the ``parse_protocol`` method of this class.
+
+        ``original_directory`` : str
+            The directory where the original data of the database are stored.
+
+        ``original_extension`` : str
+            The file name extension of the original data.
+
+        ``annotations_temp_dir`` : str
+            Annotations computed in ``self.annotations(f)`` method of this
+            class will be save to this directory if path is specified /
+            non-empty string.
+            Default: ``""``.
+
+        ``landmark_detect_method`` : str
+            Method to be used to compute annotations - face bounding box and
+            landmarks. Possible options: "dlib" or "mtcnn".
+            Default: ``"mtcnn"``.
+
+        ``kwargs`` : dict
+            The arguments of the :py:class:`bob.bio.base.database.BioDatabase`
+            base class constructor.
+        """
+
+        from bob.db.batl import Database as LowLevelDatabase
+
+        self.db = LowLevelDatabase()
+
+        # Since the high level API expects different group names than what the
+        # low level API offers, you need to convert them when necessary
+        self.low_level_group_names = (
+            'train', 'validation',
+            'test')  # group names in the low-level database interface
+        self.high_level_group_names = (
+            'train', 'dev',
+            'eval')  # names are expected to be like that in objects() function
+
+        # Always use super to call parent class methods.
+        super(BatlPadDatabase, self).__init__(
+            name='batl',
+            protocol=protocol,
+            original_directory=original_directory,
+            original_extension=original_extension,
+            **kwargs)
+
+        self.protocol = protocol
+        self.original_directory = original_directory
+        self.original_extension = original_extension
+        self.annotations_temp_dir = annotations_temp_dir
+        self.landmark_detect_method = landmark_detect_method
+
+    @property
+    def original_directory(self):
+        return self.db.original_directory
+
+    @original_directory.setter
+    def original_directory(self, value):
+        self.db.original_directory = value
+
+    def parse_protocol(self, protocol):
+        """
+        Parse the protocol name, which is give as a string.
+        An example of protocols it can parse:
+        "nowig-color-5" - nowig protocol, color data only, use 5 first frames.
+        "nowig-depth-5" - nowig protocol, depth data only, use 5 first frames.
+        "nowig-color" - nowig protocol, depth data only, use all frames.
+
+        **Parameters:**
+
+        ``protocol`` : str
+            Protocol name to be parsed. Example: "nowig-depth-5" .
+
+        **Returns:**
+
+        ``protocol`` : str
+            The name of the protocol as defined in the low level db interface.
+
+        ``stream_type`` : str
+            The name of the channel/stream_type to be loaded.
+
+        ``max_frames`` : int
+            The number of frames to be loaded.
+        """
+
+        components = protocol.split("-")
+
+        components = components + [None, None]
+
+        components = components[0:3]
+
+        protocol, stream_type, max_frames = components
+
+        if max_frames is not None:
+
+            max_frames = int(max_frames)
+
+        return protocol, stream_type, max_frames
+
+    def objects(self,
+                protocol=None,
+                groups=None,
+                purposes=None,
+                model_ids=None,
+                **kwargs):
+        """
+        This function returns lists of BatlPadFile objects, which fulfill the
+        given restrictions.
+
+        **Parameters:**
+
+        ``protocol`` : str
+            The protocol for which the clients should be retrieved.
+            The protocol is dependent on your database.
+            If you do not have protocols defined, just ignore this field.
+
+        ``purposes`` : :obj:`str` or [:obj:`str`]
+            The purposes for which File objects should be retrieved.
+            Usually it is either 'real' or 'attack'.
+
+        ``model_ids``
+            This parameter is not supported in PAD databases yet
+
+        **Returns:**
+
+        ``files`` : [BatlPadFile]
+            A list of BATLPadFile objects.
+        """
+
+        if protocol is None:
+            protocol = self.protocol
+
+        if groups is None:
+            groups = self.high_level_group_names
+
+        if purposes is None:
+            purposes = ['real', 'attack']
+
+        protocol, stream_type, max_frames = self.parse_protocol(protocol)
+
+        # Convert group names to low-level group names here.
+        groups = self.convert_names_to_lowlevel(
+            groups, self.low_level_group_names, self.high_level_group_names)
+        # Since this database was designed for PAD experiments, nothing special
+        # needs to be done here.
+
+        files = self.db.objects(protocol=protocol,
+                                groups=groups,
+                                purposes=purposes, **kwargs)
+
+        files = [BatlPadFile(f, stream_type, max_frames) for f in files]
+        return files
+
+    def annotations(self, f):
+        """
+        Computes annotations for a given file object ``f``, which
+        is an instance of the ``BatlPadFile`` class.
+
+        NOTE: you can pre-compute annotation in your first experiment
+        and then reuse them in other experiments setting
+        ``self.annotations_temp_dir`` path of this class, where
+        precomputed annotations will be saved.
+
+        **Parameters:**
+
+        ``f`` : :py:class:`object`
+            An instance of ``BatlPadFile`` defined above.
+
+        **Returns:**
+
+        ``annotations`` : :py:class:`dict`
+            A dictionary containing annotations for
+            each frame in the video.
+            Dictionary structure:
+            ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
+            Where
+            ``frameN_dict`` contains coordinates of the
+            face bounding box and landmarks in frame N.
+        """
+
+        file_path = os.path.join(self.annotations_temp_dir, f.f.path + ".json")
+
+        if not os.path.isfile(file_path):  # no file with annotations
+
+            f.stream_type = "color"
+            f.reference_stream_type = "color"
+            f.warp_to_reference = False
+            f.convert_to_rgb = False
+            f.crop = None
+            f.video_data_only = True
+
+            video = f.load(directory=self.original_directory,
+                           extension=self.original_extension)
+
+            annotations = {}
+
+            for idx, image in enumerate(video.as_array()):
+
+                frame_annotations = detect_face_landmarks_in_image(image, method=self.landmark_detect_method)
+
+                if frame_annotations:
+
+                    annotations[str(idx)] = frame_annotations
+
+            if self.annotations_temp_dir:  # if directory is not an empty string
+
+                bob.io.base.create_directories_safe(directory=os.path.split(file_path)[0], dryrun=False)
+
+                with open(file_path, 'w+') as json_file:
+
+                    json_file.write(json.dumps(annotations))
+
+        else:  # if file with annotations exists load them from file
+
+            with open(file_path, 'r') as json_file:
+
+                annotations = json.load(json_file)
+
+        if not annotations:  # if dictionary is empty
+
+            return None
+
+        return annotations

bob/pad/face/preprocessor/FaceCropAlign.py

@@ -20,6 +20,63 @@ import bob.ip.base
 import importlib
 
 
+# ==============================================================================
+def auto_norm_image(data, annotations, n_sigma=3.0, norm_method='MAD'):
+    """
+    Normalizes a single channel image to range 0-255, using the data distribution
+    Expects single channel images
+
+    **method: Gaussian , MAD, MINMAX
+    **n_sigma: The range which is normalized
+
+    """
+
+    face = data[annotations['topleft'][0]:annotations['bottomright'][0],
+                 annotations['topleft'][1]:annotations['bottomright'][1]]
+
+    face = face.astype('float')
+    data = data.astype('float')
+
+    assert(len(data.shape)==2)
+
+    face_c = np.ma.array(face).compressed()
+    # Avoiding zeros from flat field in thermal and holes in depth
+
+    face_c=face_c[face_c>1.0]
+
+    if norm_method=='STD':
+
+        mu = np.mean(face_c)
+        std = np.std(face_c)
+
+        data_n=((data-mu+n_sigma*std)/(2.0*n_sigma*std))*255.0
+
+
+    if norm_method=='MAD':
+
+        med = np.median(face_c)
+        mad = np.median(np.abs(face_c - med))
+
+        data_n = ((data-med+n_sigma*mad)/(2.0*n_sigma*mad))*255.0
+
+
+    if norm_method=='MINMAX':
+
+        t_min = np.min(face_c)
+        t_max = np.max(face_c)
+
+        data_n = ((data-t_min)/(t_max-t_min))*255.0
+
+
+    # Clamping to 0-255
+    data_n=np.maximum(data_n,0)
+    data_n=np.minimum(data_n,255)
+
+    data_n = data_n.astype('uint8')
+
+    return data_n
+
+
 # ==============================================================================
 def get_eye_pos(lm):
     """
@@ -347,6 +404,16 @@ class FaceCropAlign(Preprocessor):
     ``min_face_size`` : :py:class:`int`
         The minimal size of the face in pixels to be processed.
         Default: None.
+
+    ``normalization_function`` : function
+        Function to be applied to the input image before cropping and
+        normalization. For  example, type-casting to uint8 format and
+        data normalization, using facial region only (annotations).
+        The expected signature of the function:
+        ``normalization_function(image, annotations, **kwargs)``.
+
+    ``normalization_function_kwargs`` : :py:class:`dict`
+        Key-word arguments for the ``normalization_function``.
     """
 
     # ==========================================================================
@@ -355,14 +422,18 @@ class FaceCropAlign(Preprocessor):
                  use_face_alignment,
                  max_image_size=None,
                  face_detection_method=None,
-                 min_face_size=None):
+                 min_face_size=None,
+                 normalization_function=None,
+                 normalization_function_kwargs = None):
 
         Preprocessor.__init__(self, face_size=face_size,
                               rgb_output_flag=rgb_output_flag,
                               use_face_alignment=use_face_alignment,
                               max_image_size=max_image_size,
                               face_detection_method=face_detection_method,
-                              min_face_size=min_face_size)
+                              min_face_size=min_face_size,
+                              normalization_function=normalization_function,
+                              normalization_function_kwargs = normalization_function_kwargs)
 
         self.face_size = face_size
         self.rgb_output_flag = rgb_output_flag
@@ -371,6 +442,8 @@ class FaceCropAlign(Preprocessor):
         self.max_image_size = max_image_size
         self.face_detection_method = face_detection_method
         self.min_face_size = min_face_size
+        self.normalization_function = normalization_function
+        self.normalization_function_kwargs = normalization_function_kwargs
 
         self.supported_face_detection_method = ["dlib", "mtcnn"]
 
@@ -465,6 +538,10 @@ class FaceCropAlign(Preprocessor):
 
                 return None
 
+        if self.normalization_function is not None:
+
+            image = self.normalization_function(image, annotations, **self.normalization_function_kwargs)
+
         norm_face_image = normalize_image_size(image=image,
                                                annotations=annotations,
                                                face_size=self.face_size,

setup.py

@@ -63,6 +63,10 @@ setup(
             'msu-mfsd = bob.pad.face.config.msu_mfsd:database',
             'aggregated-db = bob.pad.face.config.aggregated_db:database',
             'mifs = bob.pad.face.config.mifs:database',
+            'batl-db = bob.pad.face.config.batl_db:database',
+            'batl-db-infrared = bob.pad.face.config.batl_db_infrared:database',
+            'batl-db-depth = bob.pad.face.config.batl_db_depth:database',
+            'batl-db-thermal = bob.pad.face.config.batl_db_thermal:database',
         ],
 
         # registered configurations:
@@ -73,6 +77,10 @@ setup(
             'msu-mfsd = bob.pad.face.config.msu_mfsd',
             'aggregated-db = bob.pad.face.config.aggregated_db',
             'mifs = bob.pad.face.config.mifs',
+            'batl-db = bob.pad.face.config.batl_db',
+            'batl-db-infrared = bob.pad.face.config.batl_db_infrared',
+            'batl-db-depth = bob.pad.face.config.batl_db_depth',
+            'batl-db-thermal = bob.pad.face.config.batl_db_thermal',
 
             # baselines using SVM:
             'lbp-svm = bob.pad.face.config.lbp_svm',
@@ -88,6 +96,7 @@ setup(
 
             # baselines using LR:
             'qm-lr = bob.pad.face.config.qm_lr',  # this pipe-line can be used both for individual and Aggregated databases.
+            'lbp-lr-batl-D-T-IR = bob.pad.face.config.lbp_lr_batl_D_T_IR',  # this pipe-line can be used both for BATL databases, Depth, Thermal and Infrared channels.
 
             # baselines using GMM:
             'qm-one-class-gmm = bob.pad.face.config.qm_one_class_gmm',  # this pipe-line can be used both for individual and Aggregated databases.