# modified from https://github.com/Profactor/continuous-remeshing
import nvdiffrast.torch as dr
import torch
from typing import Tuple
def _warmup(glctx, device=None):
device = 'cuda' if device is None else device
#windows workaround for https://github.com/NVlabs/nvdiffrast/issues/59
def tensor(*args, **kwargs):
return torch.tensor(*args, device=device, **kwargs)
pos = tensor([[[-0.8, -0.8, 0, 1], [0.8, -0.8, 0, 1], [-0.8, 0.8, 0, 1]]], dtype=torch.float32)
tri = tensor([[0, 1, 2]], dtype=torch.int32)
dr.rasterize(glctx, pos, tri, resolution=[256, 256])
class NormalsRenderer:
_glctx:dr.RasterizeCudaContext = None
def __init__(
self,
mv: torch.Tensor, #C,4,4
proj: torch.Tensor, #C,4,4
image_size: Tuple[int,int],
mvp = None,
device=None,
):
if mvp is None:
self._mvp = proj @ mv #C,4,4
else:
self._mvp = mvp
self._image_size = image_size
self._glctx = dr.RasterizeCudaContext(device=device)
_warmup(self._glctx, device)
def render(self,
vertices: torch.Tensor, #V,3 float
normals: torch.Tensor, #V,3 float in [-1, 1]
faces: torch.Tensor, #F,3 long
) ->torch.Tensor: #C,H,W,4
V = vertices.shape[0]
faces = faces.type(torch.int32)
vert_hom = torch.cat((vertices, torch.ones(V,1,device=vertices.device)),axis=-1) #V,3 -> V,4
vertices_clip = vert_hom @ self._mvp.transpose(-2,-1) #C,V,4
rast_out,_ = dr.rasterize(self._glctx, vertices_clip, faces, resolution=self._image_size, grad_db=False) #C,H,W,4
vert_col = (normals+1)/2 #V,3
col,_ = dr.interpolate(vert_col, rast_out, faces) #C,H,W,3
alpha = torch.clamp(rast_out[..., -1:], max=1) #C,H,W,1
col = torch.concat((col,alpha),dim=-1) #C,H,W,4
col = dr.antialias(col, rast_out, vertices_clip, faces) #C,H,W,4
return col #C,H,W,4
from pytorch3d.structures import Meshes
from pytorch3d.renderer.mesh.shader import ShaderBase
from pytorch3d.renderer import (
RasterizationSettings,
MeshRendererWithFragments,
TexturesVertex,
MeshRasterizer,
BlendParams,
FoVOrthographicCameras,
look_at_view_transform,
hard_rgb_blend,
)
class VertexColorShader(ShaderBase):
def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
blend_params = kwargs.get("blend_params", self.blend_params)
texels = meshes.sample_textures(fragments)
return hard_rgb_blend(texels, fragments, blend_params)
def render_mesh_vertex_color(mesh, cameras, H, W, blur_radius=0.0, faces_per_pixel=1, bkgd=(0., 0., 0.), dtype=torch.float32, device="cuda"):
if len(mesh) != len(cameras):
if len(cameras) % len(mesh) == 0:
mesh = mesh.extend(len(cameras))
else:
raise NotImplementedError()
# render requires everything in float16 or float32
input_dtype = dtype
blend_params = BlendParams(1e-4, 1e-4, bkgd)
# Define the settings for rasterization and shading
raster_settings = RasterizationSettings(
image_size=(H, W),
blur_radius=blur_radius,
faces_per_pixel=faces_per_pixel,
clip_barycentric_coords=True,
bin_size=None,
max_faces_per_bin=500000,
)
# Create a renderer by composing a rasterizer and a shader
# We simply render vertex colors through the custom VertexColorShader (no lighting, materials are used)
renderer = MeshRendererWithFragments(
rasterizer=MeshRasterizer(
cameras=cameras,
raster_settings=raster_settings
),
shader=VertexColorShader(
device=device,
cameras=cameras,
blend_params=blend_params
)
)
# render RGB and depth, get mask
with torch.autocast(dtype=input_dtype, device_type=torch.device(device).type):
images, _ = renderer(mesh)
return images # BHW4
class Pytorch3DNormalsRenderer:
def __init__(self, cameras, image_size, device):
self.cameras = cameras.to(device)
self._image_size = image_size
self.device = device
def render(self,
vertices: torch.Tensor, #V,3 float
normals: torch.Tensor, #V,3 float in [-1, 1]
faces: torch.Tensor, #F,3 long
) ->torch.Tensor: #C,H,W,4
mesh = Meshes(verts=[vertices], faces=[faces], textures=TexturesVertex(verts_features=[(normals + 1) / 2])).to(self.device)
return render_mesh_vertex_color(mesh, self.cameras, self._image_size[0], self._image_size[1], device=self.device)
def save_tensor_to_img(tensor, save_dir):
from PIL import Image
import numpy as np
for idx, img in enumerate(tensor):
img = img[..., :3].cpu().numpy()
img = (img * 255).astype(np.uint8)
img = Image.fromarray(img)
img.save(save_dir + f"{idx}.png")
if __name__ == "__main__":
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from mesh_reconstruction.func import make_star_cameras_orthographic, make_star_cameras_orthographic_py3d
cameras = make_star_cameras_orthographic_py3d([0, 270, 180, 90], device="cuda", focal=1., dist=4.0)
mv,proj = make_star_cameras_orthographic(4, 1)
resolution = 1024
renderer1 = NormalsRenderer(mv,proj, [resolution,resolution], device="cuda")
renderer2 = Pytorch3DNormalsRenderer(cameras, [resolution,resolution], device="cuda")
vertices = torch.tensor([[0,0,0],[0,0,1],[0,1,0],[1,0,0]], device="cuda", dtype=torch.float32)
normals = torch.tensor([[-1,-1,-1],[1,-1,-1],[-1,-1,1],[-1,1,-1]], device="cuda", dtype=torch.float32)
faces = torch.tensor([[0,1,2],[0,1,3],[0,2,3],[1,2,3]], device="cuda", dtype=torch.long)
import time
t0 = time.time()
r1 = renderer1.render(vertices, normals, faces)
print("time r1:", time.time() - t0)
t0 = time.time()
r2 = renderer2.render(vertices, normals, faces)
print("time r2:", time.time() - t0)
for i in range(4):
print((r1[i]-r2[i]).abs().mean(), (r1[i]+r2[i]).abs().mean())