content: import torch
  import numpy as np
  from plyfile import PlyData, PlyElement
  from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation
  
  
  class Gaussian:
      def __init__(
              self, 
              aabb : list,
              sh_degree : int = 0,
              mininum_kernel_size : float = 0.0,
              scaling_bias : float = 0.01,
              opacity_bias : float = 0.1,
              scaling_activation : str = "exp",
              device='cuda'
          ):
          self.init_params = {
              'aabb': aabb,
              'sh_degree': sh_degree,
              'mininum_kernel_size': mininum_kernel_size,
              'scaling_bias': scaling_bias,
              'opacity_bias': opacity_bias,
              'scaling_activation': scaling_activation,
          }
          
          self.sh_degree = sh_degree
          self.active_sh_degree = sh_degree
          self.mininum_kernel_size = mininum_kernel_size 
          self.scaling_bias = scaling_bias
          self.opacity_bias = opacity_bias
          self.scaling_activation_type = scaling_activation
          self.device = device
          self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device)
          self.setup_functions()
  
          self._xyz = None
          self._features_dc = None
          self._features_rest = None
          self._scaling = None
          self._rotation = None
          self._opacity = None
  
      def setup_functions(self):
          def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
              L = build_scaling_rotation(scaling_modifier * scaling, rotation)
              actual_covariance = L @ L.transpose(1, 2)
              symm = strip_symmetric(actual_covariance)
              return symm
          
          if self.scaling_activation_type == "exp":
              self.scaling_activation = torch.exp
              self.inverse_scaling_activation = torch.log
          elif self.scaling_activation_type == "softplus":
              self.scaling_activation = torch.nn.functional.softplus
              self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x))
  
          self.covariance_activation = build_covariance_from_scaling_rotation
  
          self.opacity_activation = torch.sigmoid
          self.inverse_opacity_activation = inverse_sigmoid
  
          self.rotation_activation = torch.nn.functional.normalize
          
          self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda()
          self.rots_bias = torch.zeros((4)).cuda()
          self.rots_bias[0] = 1
          self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda()
  
      @property
      def get_scaling(self):
          scales = self.scaling_activation(self._scaling + self.scale_bias)
          scales = torch.square(scales) + self.mininum_kernel_size ** 2
          scales = torch.sqrt(scales)
          return scales
      
      @property
      def get_rotation(self):
          return self.rotation_activation(self._rotation + self.rots_bias[None, :])
      
      @property
      def get_xyz(self):
          return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3]
      
      @property
      def get_features(self):
          return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc
      
      @property
      def get_opacity(self):
          return self.opacity_activation(self._opacity + self.opacity_bias)
      
      def get_covariance(self, scaling_modifier = 1):
          return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :])
      
      def from_scaling(self, scales):
          scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)
          self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias
          
      def from_rotation(self, rots):
          self._rotation = rots - self.rots_bias[None, :]
      
      def from_xyz(self, xyz):
          self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:]
          
      def from_features(self, features):
          self._features_dc = features
          
      def from_opacity(self, opacities):
          self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias
  
      def construct_list_of_attributes(self):
          l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
          # All channels except the 3 DC
          for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
              l.append('f_dc_{}'.format(i))
          l.append('opacity')
          for i in range(self._scaling.shape[1]):
              l.append('scale_{}'.format(i))
          for i in range(self._rotation.shape[1]):
              l.append('rot_{}'.format(i))
          return l
  
      def save_ply(self, path):
          xyz = self.get_xyz.detach().cpu().numpy()
          normals = np.zeros_like(xyz)
          f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
          opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy()
          scale = torch.log(self.get_scaling).detach().cpu().numpy()
          rotation = (self._rotation + self.rots_bias[None, :]).detach().cpu().numpy()
  
          dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
  
          elements = np.empty(xyz.shape[0], dtype=dtype_full)
          attributes = np.concatenate((xyz, normals, f_dc, opacities, scale, rotation), axis=1)
          elements[:] = list(map(tuple, attributes))
          el = PlyElement.describe(elements, 'vertex')
          PlyData([el]).write(path)
  
      def load_ply(self, 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"])
  
          if self.sh_degree > 0:
              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.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, (self.max_sh_degree + 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])
              
          # convert to actual gaussian attributes
          xyz = torch.tensor(xyz, dtype=torch.float, device=self.device)
          features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous()
          if self.sh_degree > 0:
              features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous()
          opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device))
          scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device))
          rots = torch.tensor(rots, dtype=torch.float, device=self.device)
          
          # convert to _hidden attributes
          self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:]
          self._features_dc = features_dc
          if self.sh_degree > 0:
              self._features_rest = features_extra
          else:
              self._features_rest = None
          self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias
          self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias
          self._rotation = rots - self.rots_bias[None, :]
          