def __init__(self, args):
used_devices = set([args.d_fd, args.d_lm, args.d_hp, 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)
head_pose_net = self.load_model(args.m_hp)
# 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.head_pose_detector = HeadPoseDetector(head_pose_net)
self.face_detector.deploy(args.d_fd, context)
self.landmarks_detector.deploy(args.d_lm,
context,
queue_size=self.QUEUE_SIZE)
self.head_pose_detector.deploy(args.d_hp,
context,
queue_size=self.QUEUE_SIZE)
log.info("Models are loaded")
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 __init__(self, varsd):
used_devices = set([varsd["d_fd"], varsd["d_lm"], varsd["d_reid"]])
self.context = InferenceContext(used_devices, varsd["cpu_lib"],
varsd["gpu_lib"], varsd["perf_stats"])
context = self.context
log.info("Loading models")
face_detector_net = self.load_model(varsd["m_fd"])
assert (varsd["fd_input_height"] and varsd["fd_input_width"]) or \
(varsd["fd_input_height"]==0 and varsd["fd_input_width"]==0), \
"Both -fd_iw and -fd_ih parameters should be specified for reshape"
if varsd["fd_input_height"] and varsd["fd_input_width"]:
face_detector_net.reshape({
"data":
[1, 3, varsd["fd_input_height"], varsd["fd_input_width"]]
})
landmarks_net = self.load_model(varsd["m_lm"])
face_reid_net = self.load_model(varsd["m_reid"])
self.face_detector = FaceDetector(face_detector_net,
confidence_threshold=varsd["t_fd"],
roi_scale_factor=varsd["exp_r_fd"])
self.landmarks_detector = LandmarksDetector(landmarks_net)
self.face_identifier = FaceIdentifier(face_reid_net,
match_threshold=varsd["t_id"],
match_algo=varsd["match_algo"])
self.face_detector.deploy(varsd["d_fd"], context)
self.landmarks_detector.deploy(varsd["d_lm"],
context,
queue_size=self.QUEUE_SIZE)
self.face_identifier.deploy(varsd["d_reid"],
context,
queue_size=self.QUEUE_SIZE)
log.info("Models are loaded")
log.info("Building faces database using images from '%s'" %
(varsd["fg"]))
self.faces_database = FacesDatabase(
varsd["fg"], self.face_identifier, self.landmarks_detector,
self.face_detector if varsd["run_detector"] else None,
varsd["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 = varsd["allow_grow"] and not varsd["no_show"]
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)
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 __init__(self, args):
used_devices = set([args.d_fd, args.d_lm])
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")
text_detector_net = self.load_model(args.m_td)
self.text_detector = TextDetector(text_detector_net,
confidence_threshold=args.t_fd)
self.text_detector.deploy(args.d_td, context)
log.info("Models are loaded")
def __init__(self, args):
used_devices = set([args.d_pd])
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")
pedestrain_detector_net = self.load_model(args.m_pd)
self.pedestrain_detector = PedestrainDetector(pedestrain_detector_net)
self.pedestrain_detector.deploy(args.d_pd, context)
log.info("Models are loaded")
def __init__(self, args):
used_devices = set([args.d_fd, args.d_lm, args.d_reid])
print (used_devices)
self.context = InferenceContext()
context = self.context
log.info("Loading models")
# create network
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,
match_algo = args.match_algo)
#load network onto devices
self.face_detector.deploy(args.d_fd, context, enable_multi = False)
self.landmarks_detector.deploy(args.d_lm, context,
queue_size=self.QUEUE_SIZE,
enable_multi = args.multi)
self.face_identifier.deploy(args.d_reid, context,
queue_size=self.QUEUE_SIZE,
enable_multi = args.multi)
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 __init__(self, args):
used_devices = set([args.d])
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(model_xml)
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]})
self.face_detector = FaceDetector(face_detector_net,
confidence_threshold=args.t)
self.face_detector.deploy(args.d_fd, context)
log.info("Models are loaded")
self.allow_grow = args.allow_grow and not args.no_show
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")
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)))
# [print(s.label) for s in self.faces_database]
self.allow_grow = args.allow_grow and not args.no_show
