pose_dim = 3
input_shape = ntu_dataconf.input_shape
"""Build the pose estimation model."""
model_pe = reception.build(input_shape,
num_joints,
dim=pose_dim,
num_blocks=num_blocks,
depth_maps=depth_maps,
ksize=(5, 5),
concat_pose_confidence=False)
"""Build the full model using the previous pose estimation one."""
model = action.build_merge_model(model_pe,
num_actions,
input_shape,
num_frames,
num_joints,
num_blocks,
pose_dim=pose_dim,
num_context_per_joint=0,
pose_net_version='v2')
"""Load pre-trained model."""
weights_path = get_file(weights_file,
TF_WEIGHTS_PATH,
md5_hash=md5_hash,
cache_subdir='models')
model.load_weights(weights_path)
"""Load kinect"""
final_path = "E:\\Bachelorarbeit-SS20\\datasets\\Benset256\\frames\\S00462C00000A00003\\00048.jpg"
#load Image
input_shape = pennaction_dataconf.input_shape
num_joints = 16
num_actions = 15
"""Build pose and action models."""
model_pe = reception.build(input_shape,
num_joints,
dim=2,
num_blocks=num_blocks,
num_context_per_joint=2,
ksize=(5, 5))
model = action.build_merge_model(model_pe,
num_actions,
input_shape,
num_frames,
num_joints,
num_blocks,
pose_dim=2,
pose_net_version='v1',
full_trainable=False)
"""Load pre-trained model."""
weights_path = get_file(weights_file,
TF_WEIGHTS_PATH,
md5_hash=md5_hash,
cache_subdir='models')
model.load_weights(weights_path)
"""Load PennAction dataset."""
penn_seq = PennAction('datasets/PennAction',
pennaction_dataconf,
poselayout=pa16j2d,
topology='sequences',
