print("X_indices shape: " + str(X_indices.shape))
print("X_params shape: " + str(X_params.shape))
print("X_pcs shape: " + str(X_pcs.shape))
else:
# Generate/load 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 DATA_LOAD_DIR is not None:
X_params, X_pcs = load_data(DATA_LOAD_DIR,
num_samples=data_samples,
load_silhouettes=False)
else:
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 MODE == "EULER":
# Convert from Rodrigues to Euler angles
X_params = rodrigues_to_euler(X_params, smpl)
X_data = [np.array(X_indices), np.array(X_params), np.array(X_pcs)]
if ARCHITECTURE == "OptLearnerMeshNormalStaticModArchitecture":
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