content: # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: LicenseRef-NvidiaProprietary
  #
  # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
  # property and proprietary rights in and to this material, related
  # documentation and any modifications thereto. Any use, reproduction,
  # disclosure or distribution of this material and related documentation
  # without an express license agreement from NVIDIA CORPORATION or
  # its affiliates is strictly prohibited.
  #
  # Modified by Jiale Xu
  # The modifications are subject to the same license as the original.
  
  
  """
  The ray sampler is a module that takes in camera matrices and resolution and batches of rays.
  Expects cam2world matrices that use the OpenCV camera coordinate system conventions.
  """
  
  import torch
  
  class RaySampler(torch.nn.Module):
      def __init__(self):
          super().__init__()
          self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
  
  
      def forward(self, cam2world_matrix, intrinsics, render_size):
          """
          Create batches of rays and return origins and directions.
  
          cam2world_matrix: (N, 4, 4)
          intrinsics: (N, 3, 3)
          render_size: int
  
          ray_origins: (N, M, 3)
          ray_dirs: (N, M, 2)
          """
  
          dtype = cam2world_matrix.dtype
          device = cam2world_matrix.device
          N, M = cam2world_matrix.shape[0], render_size**2
          cam_locs_world = cam2world_matrix[:, :3, 3]
          fx = intrinsics[:, 0, 0]
          fy = intrinsics[:, 1, 1]
          cx = intrinsics[:, 0, 2]
          cy = intrinsics[:, 1, 2]
          sk = intrinsics[:, 0, 1]
  
          uv = torch.stack(torch.meshgrid(
              torch.arange(render_size, dtype=dtype, device=device),
              torch.arange(render_size, dtype=dtype, device=device),
              indexing='ij',
          ))
          uv = uv.flip(0).reshape(2, -1).transpose(1, 0)
          uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
  
          x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size)
          y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size)
          z_cam = torch.ones((N, M), dtype=dtype, device=device)
  
          x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y_cam/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
          y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
  
          cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1).to(dtype)
  
          _opencv2blender = torch.tensor([
              [1, 0, 0, 0],
              [0, -1, 0, 0],
              [0, 0, -1, 0],
              [0, 0, 0, 1],
          ], dtype=dtype, device=device).unsqueeze(0).repeat(N, 1, 1)
  
          cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender)
  
          world_rel_points = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3]
  
          ray_dirs = world_rel_points - cam_locs_world[:, None, :]
          ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2).to(dtype)
  
          ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1)
  
          return ray_origins, ray_dirs
  
  
  class OrthoRaySampler(torch.nn.Module):
      def __init__(self):
          super().__init__()
          self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
  
  
      def forward(self, cam2world_matrix, ortho_scale, render_size):
          """
          Create batches of rays and return origins and directions.
  
          cam2world_matrix: (N, 4, 4)
          ortho_scale: float
          render_size: int
  
          ray_origins: (N, M, 3)
          ray_dirs: (N, M, 3)
          """
  
          N, M = cam2world_matrix.shape[0], render_size**2
  
          uv = torch.stack(torch.meshgrid(
              torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
              torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
              indexing='ij',
          ))
          uv = uv.flip(0).reshape(2, -1).transpose(1, 0)
          uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
  
          x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size)
          y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size)
          z_cam = torch.zeros((N, M), device=cam2world_matrix.device)
  
          x_lift = (x_cam - 0.5) * ortho_scale
          y_lift = (y_cam - 0.5) * ortho_scale
  
          cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
  
          _opencv2blender = torch.tensor([
              [1, 0, 0, 0],
              [0, -1, 0, 0],
              [0, 0, -1, 0],
              [0, 0, 0, 1],
          ], dtype=torch.float32, device=cam2world_matrix.device).unsqueeze(0).repeat(N, 1, 1)
  
          cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender)
  
          ray_origins = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3]
  
          ray_dirs_cam = torch.stack([
              torch.zeros((N, M), device=cam2world_matrix.device),
              torch.zeros((N, M), device=cam2world_matrix.device),
              torch.ones((N, M), device=cam2world_matrix.device),
          ], dim=-1)
          ray_dirs = torch.bmm(cam2world_matrix[:, :3, :3], ray_dirs_cam.permute(0, 2, 1)).permute(0, 2, 1)
  
          return ray_origins, ray_dirs
  