RESET_PERIOD = setup_params["BASIC"]["RESET_PERIOD"]
MODEL_SAVE_PERIOD = setup_params["BASIC"]["MODEL_SAVE_PERIOD"]
PREDICTION_PERIOD = setup_params["BASIC"]["PREDICTION_PERIOD"]
OPTIMISATION_PERIOD = setup_params["BASIC"]["OPTIMISATION_PERIOD"]
MODE = setup_params["BASIC"]["ROT_MODE"]
DISTRACTOR = setup_params["BASIC"]["DISTRACTOR"]
data_samples = setup_params["BASIC"]["NUM_SAMPLES"]
DATA_LOAD_DIR = setup_params["DATA"]["TRAIN_DATA_DIR"]
POSE_OFFSET = setup_params["DATA"]["POSE_OFFSET"]
PARAMS_TO_OFFSET = setup_params["DATA"]["PARAMS_TO_OFFSET"]
USE_GENERATOR = setup_params["DATA"]["USE_GENERATOR"]
ARCHITECTURE = setup_params["MODEL"]["ARCHITECTURE"]
BATCH_SIZE = setup_params["MODEL"]["BATCH_SIZE"]
# Format the distractor and offset dictionaries
DISTRACTOR = format_distractor_dict(DISTRACTOR, trainable_params)
if PARAMS_TO_OFFSET == "trainable_params":
PARAMS_TO_OFFSET = trainable_params
elif PARAMS_TO_OFFSET == "all_pose_and_global_rotation":
not_trainable = [0, 2]
#not_trainable = []
#trainable_params_indices = [index for index in range(85) if index not in not_trainable and index < 72]
trainable_params_indices = [
index for index in range(85)
if index not in not_trainable and index < 66
]
PARAMS_TO_OFFSET = [param_ids[index] for index in trainable_params_indices]
POSE_OFFSET = format_distractor_dict(POSE_OFFSET, PARAMS_TO_OFFSET)
# Generate the data from the SMPL parameters
smpl = SMPLModel(
def gen_data(POSE_OFFSET,
PARAMS_TO_OFFSET,
smpl,
data_samples=10000,
save_dir=None,
render_silhouette=True):
""" Generate random body poses """
POSE_OFFSET = format_distractor_dict(POSE_OFFSET, PARAMS_TO_OFFSET)
zero_params = np.zeros(shape=(85, ))
zero_pc = smpl.set_params(beta=zero_params[72:82],
pose=zero_params[0:72].reshape((24, 3)),
trans=zero_params[82:85])
#print("zero_pc: " + str(zero_pc))
# Generate and format the data
X_indices = np.array([i for i in range(data_samples)])
X_params = np.array([zero_params for i in range(data_samples)],
dtype="float32")
if not all(value == 0.0 for value in POSE_OFFSET.values()):
X_params = offset_params(X_params, PARAMS_TO_OFFSET, POSE_OFFSET)
X_pcs = np.array([
smpl.set_params(beta=params[72:82],
pose=params[0:72],
trans=params[82:85]) for params in X_params
])
else:
X_pcs = np.array([zero_pc for i in range(data_samples)],
dtype="float32")
if render_silhouette:
X_silh = []
print("Generating silhouettes...")
for pc in tqdm(X_pcs):
# Render the silhouette from the point cloud
silh = Mesh(pointcloud=pc).render_silhouette(show=False)
X_silh.append(silh)
X_silh = np.array(X_silh)
print("Finished generating data.")
if save_dir is not None:
# Save the generated data in the given location
print("Saving generated samples...")
for i in tqdm(range(data_samples)):
sample_id = "sample_{:05d}".format(i + 1)
if render_silhouette:
np.savez(save_dir + sample_id + ".npz",
smpl_params=X_params[i],
pointcloud=X_pcs[i],
silhouette=X_silh[i])
else:
np.savez(save_dir + sample_id + ".npz",
smpl_params=X_params[i],
pointcloud=X_pcs[i],
silhouette=X_silh[i])
print("Finished saving.")
if render_silhouette:
return X_params, X_pcs, X_silh
else:
return X_params, X_pcs