Dask pipelines fixes

Addresses some of the comments in !110 (merged)

bob/pad/face/config/qm_one_class_gmm.py

-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and one-class GMM based face PAD algorithm.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_one_class_gmm'
-"""
-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
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB 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
-
-_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)
-
-_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 of 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``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import OneClassGMM
-
-N_COMPONENTS = 50
-RANDOM_STATE = 3
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm = OneClassGMM(
-    n_components=N_COMPONENTS,
-    random_state=RANDOM_STATE,
-    frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The GMM with 50 clusters is trained using samples from the real class only. The pre-trained
-GMM is next 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``.
-"""