class FrameProcessor:
QUEUE_SIZE = 16
def __init__(self, args):
self.gpu_ext = args.gpu_lib
self.allow_grow = args.allow_grow and not args.no_show
log.info('OpenVINO Inference Engine')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
if args.cpu_lib and 'CPU' in {args.d_fd, args.d_lm, args.d_reid}:
core.add_extension(args.cpu_lib, 'CPU')
self.face_detector = FaceDetector(core,
args.m_fd,
args.fd_input_size,
confidence_threshold=args.t_fd,
roi_scale_factor=args.exp_r_fd)
self.landmarks_detector = LandmarksDetector(core, args.m_lm)
self.face_identifier = FaceIdentifier(core,
args.m_reid,
match_threshold=args.t_id,
match_algo=args.match_algo)
self.face_detector.deploy(args.d_fd, self.get_config(args.d_fd))
self.landmarks_detector.deploy(args.d_lm, self.get_config(args.d_lm),
self.QUEUE_SIZE)
self.face_identifier.deploy(args.d_reid, self.get_config(args.d_reid),
self.QUEUE_SIZE)
log.debug('Building faces database using images from {}'.format(
args.fg))
self.faces_database = FacesDatabase(
args.fg, self.face_identifier, self.landmarks_detector,
self.face_detector if args.run_detector else None, args.no_show)
self.face_identifier.set_faces_database(self.faces_database)
log.info('Database is built, registered {} identities'.format(
len(self.faces_database)))
def get_config(self, device):
config = {}
if device == 'GPU' and self.gpu_ext:
config['CONFIG_FILE'] = self.gpu_ext
return config
def process(self, frame):
orig_image = frame.copy()
rois = self.face_detector.infer((frame, ))
if self.QUEUE_SIZE < len(rois):
log.warning(
'Too many faces for processing. Will be processed only {} of {}'
.format(self.QUEUE_SIZE, len(rois)))
rois = rois[:self.QUEUE_SIZE]
landmarks = self.landmarks_detector.infer((frame, rois))
face_identities, unknowns = self.face_identifier.infer(
(frame, rois, landmarks))
if self.allow_grow and len(unknowns) > 0:
for i in unknowns:
# This check is preventing asking to save half-images in the boundary of images
if rois[i].position[0] == 0.0 or rois[i].position[1] == 0.0 or \
(rois[i].position[0] + rois[i].size[0] > orig_image.shape[1]) or \
(rois[i].position[1] + rois[i].size[1] > orig_image.shape[0]):
continue
crop_image = crop(orig_image, rois[i])
name = self.faces_database.ask_to_save(crop_image)
if name:
id = self.faces_database.dump_faces(
crop_image, face_identities[i].descriptor, name)
face_identities[i].id = id
return [rois, landmarks, face_identities]
class FrameProcessor:
QUEUE_SIZE = 16
def __init__(self, args):
used_devices = set([args.d_fd, args.d_lm, args.d_reid])
self.context = InferenceContext(used_devices, args.cpu_lib,
args.gpu_lib, args.perf_stats)
context = self.context
log.info("Loading models")
face_detector_net = self.load_model(args.m_fd)
assert (args.fd_input_height and args.fd_input_width) or \
(args.fd_input_height==0 and args.fd_input_width==0), \
"Both -fd_iw and -fd_ih parameters should be specified for reshape"
if args.fd_input_height and args.fd_input_width:
face_detector_net.reshape(
{"data": [1, 3, args.fd_input_height, args.fd_input_width]})
landmarks_net = self.load_model(args.m_lm)
face_reid_net = self.load_model(args.m_reid)
self.face_detector = FaceDetector(face_detector_net,
confidence_threshold=args.t_fd,
roi_scale_factor=args.exp_r_fd)
self.landmarks_detector = LandmarksDetector(landmarks_net)
self.face_identifier = FaceIdentifier(face_reid_net,
match_threshold=args.t_id,
match_algo=args.match_algo)
self.face_detector.deploy(args.d_fd, context)
self.landmarks_detector.deploy(args.d_lm,
context,
queue_size=self.QUEUE_SIZE)
self.face_identifier.deploy(args.d_reid,
context,
queue_size=self.QUEUE_SIZE)
log.info("Models are loaded")
log.info("Building faces database using images from '%s'" % (args.fg))
self.faces_database = FacesDatabase(
args.fg, self.face_identifier, self.landmarks_detector,
self.face_detector if args.run_detector else None, args.no_show)
self.face_identifier.set_faces_database(self.faces_database)
log.info("Database is built, registered %s identities" % \
(len(self.faces_database)))
self.allow_grow = args.allow_grow and not args.no_show
def load_model(self, model_path):
model_path = osp.abspath(model_path)
model_weights_path = osp.splitext(model_path)[0] + ".bin"
log.info("Loading the model from '%s'" % (model_path))
assert osp.isfile(model_path), \
"Model description is not found at '%s'" % (model_path)
assert osp.isfile(model_weights_path), \
"Model weights are not found at '%s'" % (model_weights_path)
model = self.context.ie_core.read_network(model_path,
model_weights_path)
log.info("Model is loaded")
return model
def process(self, frame):
assert len(frame.shape) == 3, \
"Expected input frame in (H, W, C) format"
assert frame.shape[2] in [3, 4], \
"Expected BGR or BGRA input"
orig_image = frame.copy()
frame = frame.transpose((2, 0, 1)) # HWC to CHW
frame = np.expand_dims(frame, axis=0)
self.face_detector.clear()
self.landmarks_detector.clear()
self.face_identifier.clear()
self.face_detector.start_async(frame)
rois = self.face_detector.get_roi_proposals(frame)
if self.QUEUE_SIZE < len(rois):
log.warning("Too many faces for processing." \
" Will be processed only %s of %s." % \
(self.QUEUE_SIZE, len(rois)))
rois = rois[:self.QUEUE_SIZE]
self.landmarks_detector.start_async(frame, rois)
landmarks = self.landmarks_detector.get_landmarks()
self.face_identifier.start_async(frame, rois, landmarks)
face_identities, unknowns = self.face_identifier.get_matches()
if self.allow_grow and len(unknowns) > 0:
for i in unknowns:
# This check is preventing asking to save half-images in the boundary of images
if rois[i].position[0] == 0.0 or rois[i].position[1] == 0.0 or \
(rois[i].position[0] + rois[i].size[0] > orig_image.shape[1]) or \
(rois[i].position[1] + rois[i].size[1] > orig_image.shape[0]):
continue
crop = orig_image[
int(rois[i].position[1]):int(rois[i].position[1] +
rois[i].size[1]),
int(rois[i].position[0]):int(rois[i].position[0] +
rois[i].size[0])]
name = self.faces_database.ask_to_save(crop)
if name:
id = self.faces_database.dump_faces(
crop, face_identities[i].descriptor, name)
face_identities[i].id = id
outputs = [rois, landmarks, face_identities]
return outputs
def get_performance_stats(self):
stats = {
'face_detector': self.face_detector.get_performance_stats(),
'landmarks': self.landmarks_detector.get_performance_stats(),
'face_identifier': self.face_identifier.get_performance_stats(),
}
return stats
class FrameProcessor:
QUEUE_SIZE = 16
def __init__(self, args):
used_devices = set([args.d_fd, args.d_lm, args.d_reid])
self.context = InferenceContext()
context = self.context
context.load_plugins(used_devices, args.cpu_lib, args.gpu_lib)
for d in used_devices:
context.get_plugin(d).set_config(
{"PERF_COUNT": "YES" if args.perf_stats else "NO"})
log.info("Loading models")
face_detector_net = self.load_model(args.m_fd)
landmarks_net = self.load_model(args.m_lm)
face_reid_net = self.load_model(args.m_reid)
self.face_detector = FaceDetector(
face_detector_net,
confidence_threshold=args.t_fd,
roi_scale_factor=args.exp_r_fd,
)
self.landmarks_detector = LandmarksDetector(landmarks_net)
self.face_identifier = FaceIdentifier(face_reid_net,
match_threshold=args.t_id)
self.face_detector.deploy(args.d_fd, context)
self.landmarks_detector.deploy(args.d_lm,
context,
queue_size=self.QUEUE_SIZE)
self.face_identifier.deploy(args.d_reid,
context,
queue_size=self.QUEUE_SIZE)
log.info("Models are loaded")
if args.fc in "LOAD":
self.faces_database = pickle.loads(open(args.fpl, "rb").read())
log.info("Face database loaded from {}.".format(args.fpl))
else:
log.info("Building faces database using images from '%s'" %
(args.fg))
self.faces_database = FacesDatabase(
args.fg,
self.face_identifier,
self.landmarks_detector,
self.face_detector if args.run_detector else None,
args.no_show,
)
if args.fc in "SAVE":
with open(args.fps, "wb") as f:
f.write(pickle.dumps(self.faces_database))
f.close()
log.info("Face database {} saved".format(args.fps))
self.face_identifier.set_faces_database(self.faces_database)
log.info("Database is built, registered %s identities" %
(len(self.faces_database)))
self.allow_grow = args.allow_grow and not args.no_show
def load_model(self, model_path):
model_path = osp.abspath(model_path)
model_description_path = model_path
model_weights_path = osp.splitext(model_path)[0] + ".bin"
log.info("Loading the model from '%s'" % (model_description_path))
assert osp.isfile(
model_description_path
), "Model description is not found at '%s'" % (model_description_path)
assert osp.isfile(
model_weights_path), "Model weights are not found at '%s'" % (
model_weights_path)
model = IENetwork(model_description_path, model_weights_path)
log.info("Model is loaded")
return model
def process(self, frame):
assert len(
frame.shape) == 3, "Expected input frame in (H, W, C) format"
assert frame.shape[2] in [3, 4], "Expected BGR or BGRA input"
orig_image = frame.copy()
frame = frame.transpose((2, 0, 1)) # HWC to CHW
frame = np.expand_dims(frame, axis=0)
self.face_detector.clear()
self.landmarks_detector.clear()
self.face_identifier.clear()
self.face_detector.start_async(frame)
rois = self.face_detector.get_roi_proposals(frame)
if self.QUEUE_SIZE < len(rois):
log.warning("Too many faces for processing."
" Will be processed only %s of %s." %
(self.QUEUE_SIZE, len(rois)))
rois = rois[:self.QUEUE_SIZE]
self.landmarks_detector.start_async(frame, rois)
landmarks = self.landmarks_detector.get_landmarks()
self.face_identifier.start_async(frame, rois, landmarks)
face_identities, unknowns = self.face_identifier.get_matches()
if self.allow_grow and len(unknowns) > 0:
for i in unknowns:
# This check is preventing asking to save half-images in the boundary of images
if (rois[i].position[0] == 0.0 or rois[i].position[1] == 0.0
or (rois[i].position[0] + rois[i].size[0] >
orig_image.shape[1])
or (rois[i].position[1] + rois[i].size[1] >
orig_image.shape[0])):
continue
crop = orig_image[
int(rois[i].position[1]):int(rois[i].position[1] +
rois[i].size[1]),
int(rois[i].position[0]):int(rois[i].position[0] +
rois[i].size[0]), ]
name = self.faces_database.ask_to_save(crop)
if name:
id = self.faces_database.dump_faces(
crop, face_identities[i].descriptor, name)
face_identities[i].id = id
outputs = [rois, landmarks, face_identities]
return outputs
def get_performance_stats(self):
stats = {
"face_detector": self.face_detector.get_performance_stats(),
"landmarks": self.landmarks_detector.get_performance_stats(),
"face_identifier": self.face_identifier.get_performance_stats(),
}
return stats