Delete gaussian_renderer/.ipynb_checkpoints

gaussian_renderer/.ipynb_checkpoints/__init__-checkpoint.py

@@ -1,104 +0,0 @@
-#
-# Copyright (C) 2023, Inria
-# GRAPHDECO research group, https://team.inria.fr/graphdeco
-# All rights reserved.
-#
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
-#
-# For inquiries contact  [email protected]
-#
-
-import torch
-import math
-from depth_diff_gaussian_rasterization_min import GaussianRasterizationSettings, GaussianRasterizer
-from scene.gaussian_model import GaussianModel
-from utils.sh import eval_sh
-
-def render(viewpoint_camera, pc: GaussianModel, opt, bg_color: torch.Tensor, scaling_modifier=1.0, override_color=None, render_only=False):
-    """
-    Render the scene. 
-    
-    Background tensor (bg_color) must be on GPU!
-    """
- 
-    # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
-    screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device="cuda") + 0
-    try:
-        screenspace_points.retain_grad()
-    except:
-        pass
-
-    # Set up rasterization configuration
-    tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
-    tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
-
-    raster_settings = GaussianRasterizationSettings(
-        image_height=int(viewpoint_camera.image_height),
-        image_width=int(viewpoint_camera.image_width),
-        tanfovx=tanfovx,
-        tanfovy=tanfovy,
-        bg=bg_color,
-        scale_modifier=scaling_modifier,
-        viewmatrix=viewpoint_camera.world_view_transform,
-        projmatrix=viewpoint_camera.full_proj_transform,
-        sh_degree=pc.active_sh_degree,
-        campos=viewpoint_camera.camera_center,
-        prefiltered=False,
-        debug=opt.debug
-    )
-
-    rasterizer = GaussianRasterizer(raster_settings=raster_settings)
-
-    means3D = pc.get_xyz
-    means2D = screenspace_points
-    opacity = pc.get_opacity
-
-    # If precomputed 3d covariance is provided, use it. If not, then it will be computed from
-    # scaling / rotation by the rasterizer.
-    scales = None
-    rotations = None
-    cov3D_precomp = None
-    if opt.compute_cov3D_python:
-        cov3D_precomp = pc.get_covariance(scaling_modifier)
-    else:
-        scales = pc.get_scaling
-        rotations = pc.get_rotation
-
-    # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
-    # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
-    shs = None
-    colors_precomp = None
-    if override_color is None:
-        if opt.convert_SHs_python:
-            shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2)
-            dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1))
-            dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True)
-            sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized)
-            colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
-        else:
-            shs = pc.get_features
-    else:
-        colors_precomp = override_color
-
-    # Rasterize visible Gaussians to image, obtain their radii (on screen). 
-    rendered_image, radii, depth = rasterizer(
-        means3D = means3D,
-        means2D = means2D,
-        shs = shs,
-        colors_precomp = colors_precomp,
-        opacities = opacity,
-        scales = scales,
-        rotations = rotations,
-        cov3D_precomp = cov3D_precomp)
-
-    # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
-    # They will be excluded from value updates used in the splitting criteria.
-    if render_only:
-        return {"render": rendered_image, "depth": depth}
-    else:
-        return {"render": rendered_image,
-                "viewspace_points": screenspace_points,
-                "visibility_filter" : radii > 0,
-                "radii": radii,
-                "depth": depth}