Add a video annotator

bob/bio/video/__init__.py

@@ -4,6 +4,7 @@ from . import extractor
 from . import algorithm
 from . import database
 from . import test
+from . import annotator
 
 def get_config():
   """Returns a string containing the configuration information.

bob/bio/video/annotator/Wrapper.py

+from bob.bio.face.annotator import Base
+from bob.ip.facedetect import bounding_box_from_annotation
+from bob.bio.base import load_resource
+
+from .. import utils
+
+
+def normalize_annotations(annotations, validator, max_age=-1):
+  """Normalizes the annotations of one video sequence. It fills the
+  annotations for frames from previous ones if the annotation for the current
+  frame is not valid.
+
+  Parameters
+  ----------
+  annotations : dict
+      A dict of dict where the keys to the first dict are frame indices as
+      strings (starting from 0). The inside dicts contain annotations for
+      that frame.
+  validator : callable
+      Takes a dict (annotations) and returns True if the annotations are
+      valid. This can be check based on minimal face size for example.
+  max_age : :obj:`int`, optional
+      An integer indicating for a how many frames a detected face is valid if
+      no detection occurs after such frame. A value of -1 == forever
+
+  Yields
+  ------
+  str
+      The index of frame.
+  dict
+      The corrected annotations of the frame.
+  """
+  # the annotations for the current frame
+  current = {}
+  age = 0
+
+  for k, annot in annotations.items():
+    if validator(annot):
+      current = annot
+      age = 0
+    elif max_age < 0 or age < max_age:
+      age += 1
+    else:  # no detections and age is larger than maximum allowed
+      current = {}
+
+    yield k, current
+
+
+def min_face_size_validator(annotations, min_face_size=32):
+  """Validates annotations based on face's minimal size.
+
+  Parameters
+  ----------
+  annotations : dict
+      The annotations in dictionary format.
+  min_face_size : int, optional
+      The minimal size of a face.
+
+  Returns
+  -------
+  bool
+      True, if the face is large enough.
+  """
+  bbx = bounding_box_from_annotation(**annotations)
+  if bbx.size < 32:
+    return False
+  return True
+
+
+class Wrapper(Base):
+  """Annotates video files.
+  This annotator does not support annotating only select frames of a video.
+
+  Attributes
+  ----------
+  annotator : :any:`Base`
+      The image annotator to be used.
+  max_age : int
+      see :any:`normalize_annotations`.
+  normalize : bool
+      If True, it will normalize annotations using :any:`normalize_annotations`
+  validator : object
+      see :any:`normalize_annotations` and :any:`min_face_size_validator`.
+  """
+
+  def __init__(self,
+               annotator,
+               normalize=True,
+               validator=min_face_size_validator,
+               max_age=-1,
+               **kwargs
+               ):
+    super(Wrapper, self).__init__(**kwargs)
+    self.annotator = annotator
+    self.normalize = normalize
+    self.validator = validator
+    self.max_age = max_age
+
+    # load annotator configuration
+    if isinstance(annotator, basestring):
+      self.annotator = load_resource(annotator, "annotator")
+
+  def annotate(self, frames, **kwargs):
+    if isinstance(frames, utils.FrameContainer):
+      frames = frames.as_array()
+    annotations = {}
+    for i, frame in enumerate(frames):
+      annotations[str(i)] = self.annotator(frame, **kwargs)
+    if self.normalize:
+      annotations = dict(normalize_annotations(
+          annotations, self.validator, self.max_age))
+    return annotations

bob/bio/video/annotator/__init__.py

+from .Wrapper import Wrapper, normalize_annotations, min_face_size_validator