first attempt to add the vulnarability analysis to the code

bob/paper/makeup_aim/config/aim_vulun.py

+#!/usr/bin/env python
+
+"""
+AIM dataset for vulnurabitlity analysis agains makeup. Default configuration for grandtest protocol
+
+"""
+
+from bob.paper.makeup_aim.database import AIMVulunDataset
+
+
+ORIGINAL_DIRECTORY = "[AIM_DIRECTORY]"
+ORIGINAL_EXTENSION = ".h5" 
+ANNOTATION_DIRECTORY = "[AIM_ANNOTATION_DIRECTORY]"
+PROTOCOL = "grandtest"
+
+database = AIMVulunDataset(
+    protocol=PROTOCOL,
+    original_directory=ORIGINAL_DIRECTORY,
+    original_extension=ORIGINAL_EXTENSION,
+    annotation_directory=ANNOTATION_DIRECTORY,
+    training_depends_on_protocol=True,
+)
+
+groups = ["dev"]
+
+#----------------------------------------------------------

bob/paper/makeup_aim/config/exp_aim_vulun.py

+#!/usr/bin/env python
+
+"""
+This file contains configuration to run vulunaribility analysis experiments for AIM dataset.
+"""
+#--------------------------------------------------------------------
+
+# sub_directory where the results will be placed
+sub_directory = 'aim_vulun'
+
+#--------------------------------------------------------------------
+
+# define preprocessor:
+
+from bob.pad.face.preprocessor import FaceCropAlign
+from bob.bio.video.preprocessor import Wrapper
+from bob.bio.video.utils import FrameSelector
+
+# parameters and constants
+FACE_SIZE = 128
+RGB_OUTPUT_FLAG = False
+USE_FACE_ALIGNMENT = True
+ALIGNMENT_TYPE = "lightcnn"
+MAX_IMAGE_SIZE = None
+FACE_DETECTION_METHOD = None
+MIN_FACE_SIZE = 50
+
+_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
+                                    rgb_output_flag=RGB_OUTPUT_FLAG,
+                                    use_face_alignment=USE_FACE_ALIGNMENT,
+                                    alignment_type=ALIGNMENT_TYPE,
+                                    max_image_size=MAX_IMAGE_SIZE,
+                                    face_detection_method=FACE_DETECTION_METHOD,
+                                    min_face_size=MIN_FACE_SIZE,
+                                   )
+
+_frame_selector = FrameSelector(selection_style = "spread")
+
+preprocessor = Wrapper(preprocessor = _image_preprocessor, frame_selector = _frame_selector)
+
+#--------------------------------------------------------------------
+
+# define extractor:
+
+from bob.paper.makeup_aim.extractor import FICNN
+from bob.bio.video.extractor import Wrapper
+from bob.extension import rc
+import os
+
+_model_dir = rc.get("LIGHTCNN9_MODEL_DIRECTORY")
+_model_name = "LightCNN_9Layers_checkpoint.pth.tar"
+_model_file = os.path.join(_model_dir, _model_name)
+if not os.path.exists(_model_file):
+    print("Error: Could not find the LightCNN-9 model at [{}].\nPlease follow the download instructions from README".format(_model_dir))
+    exit(0)
+
+extractor = Wrapper(FICNN(model_file=_model_file))
+
+#--------------------------------------------------------------------
+
+# define algorithm:
+
+from bob.bio.base.config.algorithm.distance_cosine import algorithm
+from bob.bio.video.algorithm import Wrapper
+
+algorithm = Wrapper(algorithm)
+
+#--------------------------------------------------------------------
\ No newline at end of file

bob/paper/makeup_aim/database/aim.py

@@ -161,7 +161,7 @@ class AIMDatabase(FileListPadDatabase):
 
         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
+        ``self.annotations_directory`` path of this class, where
         precomputed annotations will be saved.
 
         **Parameters:**

bob/paper/makeup_aim/database/aim_vulun.py

+"""
+Implementation of dataset interface of AIM for Face Recognition using RGB images. 
+@author: Zohreh Mostaani
+
+"""
+
+# Imports
+from bob.bio.base.database import FileListBioDatabase
+from bob.bio.video.database import VideoBioFile
+from bob.bio.video import FrameSelector
+from bob.io.base import HDF5File, create_directories_safe
+from bob.extension import rc
+from bob.pad.face.preprocessor.FaceCropAlign import detect_face_landmarks_in_image
+
+import numpy as np
+import json
+import os
+import pkg_resources
+
+import logging
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+#--------------------------------------------------------------------
+
+class File(VideoBioFile):
+    """
+    The file interface for rgb data.
+    """
+
+    def load(self, directory=None, extension=None,
+             frame_selector=FrameSelector()):
+        filepath = self.make_path(directory, extension)
+
+        with HDF5File(filepath) as f:
+            data = f['/intelsr300_bgr']
+            # bgr to rgb
+            data = data[..., ::-1]
+            # to bob format
+            data = np.moveaxis(data, -1, 1)
+        return frame_selector(data)
+
+
+class AIMVulunDataset(FileListBioDatabase):
+    """
+    A high level implementation of a Database class for AIM dataset used for vulnurabitliy analysis.
+    """
+
+
+    def __init__(
+            self,
+            name="AIM_vulun",
+            original_directory=None,
+            original_extension='.h5',
+            protocol="grandtest",
+            annotation_directory=None,
+            annotation_extension='.json',
+            annotation_type='json',
+            models_depend_on_protocol=True,
+            bio_file_class=File,
+            use_dense_probe_file_list=False,
+            groups="dev",
+            **kwargs):
+
+        """
+        **Parameters:**
+
+        ``original_directory`` : str or None
+            original directory refers to the location of AIM_vulun parent directory 
+
+        ``original_extension`` : str or None
+            extension of original data
+
+        ``groups`` : str or [str]
+            The groups for which the clients should be returned.
+            Usually, groups are one or more elements of ['train', 'dev', 'eval'].
+            Default: 'dev'.
+
+        ``protocol`` : str
+            The protocol for which the clients should be retrieved.
+            Default: 'grandtest'.
+
+        """
+
+        filelists_directory = pkg_resources.resource_filename( __name__, "/lists/aim_vulun/")
+        self.filelists_directory = filelists_directory
+
+        # init the parent class using super.
+        super(AIMVulunDataset, self).__init__(
+            filelists_directory=filelists_directory,
+            name=name,
+            protocol=protocol,
+            original_directory=original_directory,
+            original_extension=original_extension,
+            annotation_directory=annotation_directory,
+            annotation_extension=annotation_extension,
+            annotation_type=annotation_type,
+            bio_file_class=bio_file_class,
+            models_depend_on_protocol=models_depend_on_protocol,
+            **kwargs)
+
+        self.bio_file_class=bio_file_class
+        self.annotation_directory = annotation_directory
+        self.landmark_detect_method = "mtcnn"
+        self.protocol = protocol
+
+        logger.info("Dataset: {}".format(self.name))
+        logger.info("Original directory: {}; Annotation directory: {}".format(self.original_directory, self.annotation_directory))
+
+#--------------------------------------------------------------------
+
+    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.annotation_directory`` path of this class, where
+        precomputed annotations will be saved.
+
+        **Parameters:**
+
+        ``f`` : :py:class:`object`
+            An instance of ``File`` 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.annotation_directory, f.path + ".json")
+
+        # If annotations do not exist, then generate.
+        if not os.path.isfile(file_path):
+
+            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 directory is not an empty string
+            if self.annotation_directory:
+                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))
+
+        # if file with annotations exists load them from file
+        else:
+            with open(file_path, 'r') as json_file:
+                annotations = json.load(json_file)
+
+        # if dictionary is empty
+        if not annotations:
+            logger.warning("Empty annotations for %s", f.path)
+            return None
+
+        return annotations
+
+#--------------------------------------------------------------------
\ No newline at end of file

bob/paper/makeup_aim/database/lists/aim_vulun/grandtest/dev/for_models.lst

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