/
mesh_renderer_4d.py
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mesh_renderer_4d.py
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import os
import math
import cv2
import trimesh
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import nvdiffrast.torch as dr
from mesh import Mesh, safe_normalize
def scale_img_nhwc(x, size, mag='bilinear', min='bilinear'):
assert (x.shape[1] >= size[0] and x.shape[2] >= size[1]) or (x.shape[1] < size[0] and x.shape[2] < size[1]), "Trying to magnify image in one dimension and minify in the other"
y = x.permute(0, 3, 1, 2) # NHWC -> NCHW
if x.shape[1] > size[0] and x.shape[2] > size[1]: # Minification, previous size was bigger
y = torch.nn.functional.interpolate(y, size, mode=min)
else: # Magnification
if mag == 'bilinear' or mag == 'bicubic':
y = torch.nn.functional.interpolate(y, size, mode=mag, align_corners=True)
else:
y = torch.nn.functional.interpolate(y, size, mode=mag)
return y.permute(0, 2, 3, 1).contiguous() # NCHW -> NHWC
def scale_img_hwc(x, size, mag='bilinear', min='bilinear'):
return scale_img_nhwc(x[None, ...], size, mag, min)[0]
def scale_img_nhw(x, size, mag='bilinear', min='bilinear'):
return scale_img_nhwc(x[..., None], size, mag, min)[..., 0]
def scale_img_hw(x, size, mag='bilinear', min='bilinear'):
return scale_img_nhwc(x[None, ..., None], size, mag, min)[0, ..., 0]
def trunc_rev_sigmoid(x, eps=1e-6):
x = x.clamp(eps, 1 - eps)
return torch.log(x / (1 - x))
def make_divisible(x, m=8):
return int(math.ceil(x / m) * m)
class Renderer(nn.Module):
def __init__(self, opt):
super().__init__()
self.opt = opt
self.mesh_list =[]
for i in range(14):
mesh = Mesh.load(self.opt.mesh.replace('.obj', f'_{i:03d}.obj'), resize=False)
self.mesh_list.append(mesh)
if not self.opt.force_cuda_rast and (not self.opt.gui or os.name == 'nt'):
self.glctx = dr.RasterizeGLContext()
else:
self.glctx = dr.RasterizeCudaContext()
# extract trainable parameters
# self.v_offsets = nn.Parameter(torch.zeros_like(self.mesh.v))
# self.raw_albedo = nn.Parameter(trunc_rev_sigmoid(self.mesh.albedo))
self.v_offsets_list = nn.ParameterList([])
self.raw_albedo_list = nn.ParameterList([])
for mesh in self.mesh_list:
v_offsets = nn.Parameter(torch.zeros_like(mesh.v))
raw_albedo = nn.Parameter(trunc_rev_sigmoid(mesh.albedo))
self.v_offsets_list.append(v_offsets)
self.raw_albedo_list.append(raw_albedo)
def get_params(self):
params = [
{'params': self.raw_albedo_list, 'lr': self.opt.texture_lr},
]
if self.opt.train_geo:
params.append({'params': self.v_offsets_list, 'lr': self.opt.geom_lr})
return params
@torch.no_grad()
def export_mesh(self, save_path):
for i, (mesh, v_offsets, raw_albedo) in enumerate(zip(self.mesh_list, self.v_offsets_list, self.raw_albedo_list)):
mesh.v = (mesh.v + v_offsets).detach()
mesh.albedo = torch.sigmoid(raw_albedo.detach())
mesh.write(save_path.replace('.obj', f'_{i:03d}.obj'))
mesh.write(save_path.replace('.glb', f'_{i:03d}.glb'))
def render(self, t, pose, proj, h0, w0, ssaa=1, bg_color=1, texture_filter='linear-mipmap-linear'):
mesh = self.mesh_list[t]
raw_albedo = self.raw_albedo_list[t]
v_offsets = self.v_offsets_list[t]
# do super-sampling
if ssaa != 1:
h = make_divisible(h0 * ssaa, 8)
w = make_divisible(w0 * ssaa, 8)
else:
h, w = h0, w0
results = {}
# get v
if self.opt.train_geo:
v = mesh.v + v_offsets # [N, 3]
else:
v = mesh.v
pose = torch.from_numpy(pose.astype(np.float32)).to(v.device)
proj = torch.from_numpy(proj.astype(np.float32)).to(v.device)
# get v_clip and render rgb
v_cam = torch.matmul(F.pad(v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
v_clip = v_cam @ proj.T
rast, rast_db = dr.rasterize(self.glctx, v_clip, mesh.f, (h, w))
alpha = (rast[0, ..., 3:] > 0).float()
depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1]
depth = depth.squeeze(0) # [H, W, 1]
texc, texc_db = dr.interpolate(mesh.vt.unsqueeze(0).contiguous(), rast, mesh.ft, rast_db=rast_db, diff_attrs='all')
albedo = dr.texture(raw_albedo.unsqueeze(0), texc, uv_da=texc_db, filter_mode=texture_filter) # [1, H, W, 3]
albedo = torch.sigmoid(albedo)
# get vn and render normal
if self.opt.train_geo:
i0, i1, i2 = mesh.f[:, 0].long(), mesh.f[:, 1].long(), mesh.f[:, 2].long()
v0, v1, v2 = v[i0, :], v[i1, :], v[i2, :]
face_normals = torch.cross(v1 - v0, v2 - v0)
face_normals = safe_normalize(face_normals)
vn = torch.zeros_like(v)
vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals)
vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals)
vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals)
vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device))
else:
vn = mesh.vn
normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, mesh.fn)
normal = safe_normalize(normal[0])
# rotated normal (where [0, 0, 1] always faces camera)
rot_normal = normal @ pose[:3, :3]
viewcos = rot_normal[..., [2]]
# antialias
albedo = dr.antialias(albedo, rast, v_clip, mesh.f).squeeze(0) # [H, W, 3]
albedo = alpha * albedo + (1 - alpha) * bg_color
# ssaa
if ssaa != 1:
albedo = scale_img_hwc(albedo, (h0, w0))
alpha = scale_img_hwc(alpha, (h0, w0))
depth = scale_img_hwc(depth, (h0, w0))
normal = scale_img_hwc(normal, (h0, w0))
viewcos = scale_img_hwc(viewcos, (h0, w0))
results['image'] = albedo.clamp(0, 1)
results['alpha'] = alpha
results['depth'] = depth
results['normal'] = (normal + 1) / 2
results['viewcos'] = viewcos
return results