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import torch
import numpy as np
from AnimatableGaussians.utils.sh_utils import RGB2SH, SH2RGB
from AnimatableGaussians.utils.general_utils import inverse_sigmoid
from plyfile import PlyData, PlyElement
import os
def construct_list_of_attributes(_features_dc, _features_rest, _scaling, _rotation):
l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
# All channels except the 3 DC
for i in range(_features_dc.shape[1] * _features_dc.shape[2]):
l.append('f_dc_{}'.format(i))
for i in range(_features_rest.shape[1] * _features_rest.shape[2]):
l.append('f_rest_{}'.format(i))
l.append('opacity')
for i in range(_scaling.shape[1]):
l.append('scale_{}'.format(i))
for i in range(_rotation.shape[1]):
l.append('rot_{}'.format(i))
return l
def save_gaussians_as_ply(path, gaussian_vals: dict):
os.makedirs(os.path.dirname(path), exist_ok = True)
xyz = gaussian_vals['positions'].detach().cpu().numpy()
normals = np.zeros_like(xyz)
fused_color = RGB2SH(gaussian_vals['colors'].detach()[:, [2, 1, 0]])
features = torch.zeros((fused_color.shape[0], 3, (3 + 1) ** 2))
features[:, :3, 0] = fused_color
features_dc = features[:, :, 0:1].transpose(1, 2)
features_rest = features[:, :, 1:].transpose(1, 2)
f_dc = features_dc.transpose(1, 2).flatten(start_dim = 1).contiguous().cpu().numpy()
f_rest = features_rest.transpose(1, 2).flatten(start_dim = 1).contiguous().cpu().numpy()
opacities = inverse_sigmoid(gaussian_vals['opacity'].detach()).cpu().numpy()
scale = torch.log(gaussian_vals['scales'].detach()).cpu().numpy()
rotation = gaussian_vals['rotations'].detach().cpu().numpy()
dtype_full = [(attribute, 'f4') for attribute in construct_list_of_attributes(features_dc, features_rest, scale, rotation)]
elements = np.empty(xyz.shape[0], dtype = dtype_full)
attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis = 1)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, 'vertex')
PlyData([el]).write(path)
def load_gaussians_from_ply(path):
plydata = PlyData.read(path)
xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
np.asarray(plydata.elements[0]["y"]),
np.asarray(plydata.elements[0]["z"])), axis = 1)
opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
features_dc = np.zeros((xyz.shape[0], 3, 1))
features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
# assert len(extra_f_names) == 3 * (self.max_sh_degree + 1) ** 2 - 3
features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
for idx, attr_name in enumerate(extra_f_names):
features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
features_extra = features_extra.reshape((features_extra.shape[0], 3, (3 + 1) ** 2 - 1))
scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
scales = np.zeros((xyz.shape[0], len(scale_names)))
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
rots = np.zeros((xyz.shape[0], len(rot_names)))
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
# self._xyz = nn.Parameter(torch.tensor(xyz, dtype = torch.float, device = "cuda").requires_grad_(True))
# self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype = torch.float, device = "cuda").transpose(1, 2).contiguous().requires_grad_(True))
# self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype = torch.float, device = "cuda").transpose(1, 2).contiguous().requires_grad_(True))
# self._opacity = nn.Parameter(torch.tensor(opacities, dtype = torch.float, device = "cuda").requires_grad_(True))
# self._scaling = nn.Parameter(torch.tensor(scales, dtype = torch.float, device = "cuda").requires_grad_(True))
# self._rotation = nn.Parameter(torch.tensor(rots, dtype = torch.float, device = "cuda").requires_grad_(True))
#
# self.active_sh_degree = self.max_sh_degree
return {
'positions': torch.tensor(xyz, dtype = torch.float, device = "cuda"),
'colors': torch.tensor(SH2RGB(features_dc)[:, [2, 1, 0]], dtype = torch.float, device = "cuda").squeeze(-1),
'opacity': torch.sigmoid(torch.tensor(opacities, dtype = torch.float, device = "cuda")),
'scales': torch.exp(torch.tensor(scales, dtype = torch.float, device = "cuda")),
'rotations': torch.nn.functional.normalize(torch.tensor(rots, dtype = torch.float, device = "cuda")),
'features_extr': torch.tensor(features_extra, dtype = torch.float, device = "cuda")
}
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