def prepare_input(self, index):
# read xyz, normal, color from the ply file
vertices_path = os.path.join(
self.data_root,
self.ims[index].replace("ImageSequence", "vertices")[:-4] + ".npy")
xyz = np.load(vertices_path).astype(np.float32)
nxyz = np.zeros_like(xyz).astype(np.float32)
# obtain the original bounds for point sampling
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
if cfg.big_box:
min_xyz -= 0.05
max_xyz += 0.05
else:
min_xyz[2] -= 0.05
max_xyz[2] += 0.05
can_bounds = np.stack([min_xyz, max_xyz], axis=0)
# transform smpl from the world coordinate to the smpl coordinate
params_path = os.path.join(
self.data_root,
self.ims[index].replace("ImageSequence", "smpl")[:-4] + ".npy")
params = np.load(params_path, allow_pickle=True).item()
Rh = params['Rh']
R = cv2.Rodrigues(Rh)[0].astype(np.float32)
Th = params['Th'].astype(np.float32)
xyz = np.dot(xyz - Th, R)
# transformation augmentation
xyz, center, rot, trans = if_nerf_dutils.transform_can_smpl(xyz)
# obtain the bounds for coord construction
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
if cfg.big_box:
min_xyz -= 0.05
max_xyz += 0.05
else:
min_xyz[2] -= 0.05
max_xyz[2] += 0.05
bounds = np.stack([min_xyz, max_xyz], axis=0)
cxyz = xyz.astype(np.float32)
nxyz = nxyz.astype(np.float32)
feature = np.concatenate([cxyz, nxyz], axis=1).astype(np.float32)
# construct the coordinate
dhw = xyz[:, [2, 1, 0]]
min_dhw = min_xyz[[2, 1, 0]]
max_dhw = max_xyz[[2, 1, 0]]
voxel_size = np.array(cfg.voxel_size)
coord = np.round((dhw - min_dhw) / voxel_size).astype(np.int32)
# construct the output shape
out_sh = np.ceil((max_dhw - min_dhw) / voxel_size).astype(np.int32)
x = 32
out_sh = (out_sh | (x - 1)) + 1
return feature, coord, out_sh, can_bounds, bounds, Rh, Th, center, rot, trans
def prepare_input(self, i):
# read xyz, normal, color from the npy file
vertices_path = os.path.join(self.data_root, 'vertices',
'{}.npy'.format(i))
xyz = np.load(vertices_path).astype(np.float32)
nxyz = np.zeros_like(xyz).astype(np.float32)
# obtain the original bounds for point sampling
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
min_xyz[1] -= 0.1
max_xyz[1] += 0.1
can_bounds = np.stack([min_xyz, max_xyz], axis=0)
# transform smpl from the world coordinate to the smpl coordinate
Rh = self.params['pose'][i][:3]
R = cv2.Rodrigues(Rh)[0].astype(np.float32)
Th = self.params['trans'][i].astype(np.float32)
xyz = np.dot(xyz - Th, R)
# transformation augmentation
xyz, center, rot, trans = if_nerf_dutils.transform_can_smpl(xyz)
# obtain the bounds for coord construction
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
min_xyz[1] -= 0.1
max_xyz[1] += 0.1
bounds = np.stack([min_xyz, max_xyz], axis=0)
cxyz = xyz.astype(np.float32)
nxyz = nxyz.astype(np.float32)
feature = np.concatenate([cxyz, nxyz], axis=1).astype(np.float32)
# construct the coordinate
dhw = xyz[:, [2, 1, 0]]
min_dhw = min_xyz[[2, 1, 0]]
max_dhw = max_xyz[[2, 1, 0]]
voxel_size = np.array(cfg.voxel_size)
coord = np.round((dhw - min_dhw) / voxel_size).astype(np.int32)
# construct the output shape
out_sh = np.ceil((max_dhw - min_dhw) / voxel_size).astype(np.int32)
x = 32
out_sh = (out_sh | (x - 1)) + 1
return feature, coord, out_sh, can_bounds, bounds, Rh, Th, center, rot, trans
def prepare_input(self, i):
# read xyz, normal, color from the ply file
vertices_path = os.path.join(self.data_root, cfg.vertices,
"{}.npy".format(i))
xyz = np.load(vertices_path).astype(np.float32) # (6890, 3)
nxyz = np.zeros_like(xyz).astype(np.float32) # (6890, 3) all zeros
# obtain the original bounds for point sampling
min_xyz = np.min(xyz, axis=0) # (1, 3)
max_xyz = np.max(xyz, axis=0) # (1, 3)
if cfg.big_box:
min_xyz -= 0.05
max_xyz += 0.05
else:
min_xyz[2] -= 0.05
max_xyz[2] += 0.05
can_bounds = np.stack([min_xyz, max_xyz], axis=0) # (2, 3)
# transform smpl from the world coordinate to the smpl coordinate
params_path = os.path.join(self.data_root, cfg.params,
"{}.npy".format(i))
params = np.load(params_path, allow_pickle=True).item()
Rh = params["Rh"] # (1, 3)
# Converts a rotation matrix to a rotation vector or vice versa.
# here convert rotation vector to a rotation matrix.
R = cv2.Rodrigues(Rh)[0].astype(np.float32) # (3, 3)
Th = params["Th"].astype(np.float32) # (1, 3)
xyz = np.dot(xyz - Th, R) # (6890, 3)
# transform the smpl vertices from the world coordinate to the smple coordinate
# transformation augmentation
xyz, center, rot, trans = if_nerf_dutils.transform_can_smpl(xyz)
#
# obtain the bounds for coord construction
# obtain the bounding box after the transformation augmentation
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
if cfg.big_box:
min_xyz -= 0.05
max_xyz += 0.05
else:
min_xyz[2] -= 0.05
max_xyz[2] += 0.05
bounds = np.stack([min_xyz, max_xyz], axis=0)
cxyz = xyz.astype(np.float32) # (6890,3)
nxyz = nxyz.astype(np.float32) # (6890,3) all zeros
feature = np.concatenate([cxyz, nxyz],
axis=1).astype(np.float32) # (6890, 6)
# construct the coordinate
dhw = xyz[:, [2, 1, 0]]
min_dhw = min_xyz[[2, 1, 0]]
max_dhw = max_xyz[[2, 1, 0]]
voxel_size = np.array(cfg.voxel_size)
coord = np.round((dhw - min_dhw) / voxel_size).astype(np.int32)
# voxelized smpl model coordinates
# construct the output shape
out_sh = np.ceil((max_dhw - min_dhw) / voxel_size).astype(np.int32)
x = 32
out_sh = (out_sh | (x - 1)) + 1
return feature, coord, out_sh, can_bounds, bounds, Rh, Th, center, rot, trans