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")
class FrameProcessor:
QUEUE_SIZE = 16
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 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):
# print("frame.shape--", frame.shape)
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
# print("frame.shape--", frame.shape)
frame = np.expand_dims(frame, axis=0)
# print("frame.shape--", frame.shape)
self.face_detector.clear()
self.landmarks_detector.clear()
self.head_pose_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)
self.head_pose_detector.start_async(frame, rois)
landmarks = self.landmarks_detector.get_landmarks()
head_pose = self.head_pose_detector.get_head_pose()
outputs = [rois, landmarks, head_pose, 'test']
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