def load_face_filter(self, filter_lists, ref_threshold):
""" Load faces to filter out of images """
if not any(val for val in filter_lists.values()):
return None
filter_files = [self.set_face_filter(f_type, filter_lists[f_type])
for f_type in ("filter", "nfilter")]
if any(filters for filters in filter_files):
facefilter = FilterFunc(filter_files[0],
filter_files[1],
ref_threshold)
logger.debug("Face filter: %s", facefilter)
return facefilter
def load_face_filter(self, filter_lists, ref_threshold, aligner, detector,
multiprocess):
""" Load faces to filter out of images """
if not any(val for val in filter_lists.values()):
return None
facefilter = None
filter_files = [
self.set_face_filter(f_type, filter_lists[f_type])
for f_type in ("filter", "nfilter")
]
if any(filters for filters in filter_files):
facefilter = FilterFunc(filter_files[0], filter_files[1], detector,
aligner, multiprocess, ref_threshold)
logger.debug("Face filter: %s", facefilter)
else:
self.valid = False
return facefilter
def _load_face_filter(self, filter_lists, ref_threshold, aligner, detector, multiprocess):
""" Set up and load the :class:`~lib.face_filter.FaceFilter`.
Parameters
----------
filter_lists: dict
The filter and nfilter image paths
ref_threshold: float
The reference threshold for a positive match
aligner: str
The aligner to use
detector: str
The detector to use
multiprocess: bool
Whether to run the extraction pipeline in single process mode or not
Returns
-------
:class:`~lib.face_filter.FaceFilter`
The face filter
"""
if not any(val for val in filter_lists.values()):
return None
facefilter = None
filter_files = [self._set_face_filter(f_type, filter_lists[f_type])
for f_type in ("filter", "nfilter")]
if any(filters for filters in filter_files):
facefilter = FilterFunc(filter_files[0],
filter_files[1],
detector,
aligner,
multiprocess,
ref_threshold)
logger.debug("Face filter: %s", facefilter)
else:
self.valid = False
return facefilter