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"""This script is the differentiable renderer for Deep3DFaceRecon_pytorch
Attention, antialiasing step is missing in current version.
"""
import torch
import torch.nn.functional as F
import kornia
from kornia.geometry.camera import pixel2cam
import numpy as np
from typing import List
import nvdiffrast.torch as dr
from scipy.io import loadmat
from torch import nn
def ndc_projection(x=0.1, n=1.0, f=50.0):
return np.array([[n/x, 0, 0, 0],
[ 0, n/-x, 0, 0],
[ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
[ 0, 0, -1, 0]]).astype(np.float32)
class MeshRenderer(nn.Module):
def __init__(self,
rasterize_fov,
znear=0.1,
zfar=10,
rasterize_size=224):
super(MeshRenderer, self).__init__()
x = np.tan(np.deg2rad(rasterize_fov * 0.5)) * znear
self.ndc_proj = torch.tensor(ndc_projection(x=x, n=znear, f=zfar)).matmul(
torch.diag(torch.tensor([1., -1, -1, 1])))
self.rasterize_size = rasterize_size
self.glctx = None
def forward(self, vertex, tri, feat=None):
"""
Return:
mask -- torch.tensor, size (B, 1, H, W)
depth -- torch.tensor, size (B, 1, H, W)
features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None
Parameters:
vertex -- torch.tensor, size (B, N, 3)
tri -- torch.tensor, size (B, M, 3) or (M, 3), triangles
feat(optional) -- torch.tensor, size (B, C), features
"""
device = vertex.device
rsize = int(self.rasterize_size)
ndc_proj = self.ndc_proj.to(device)
# trans to homogeneous coordinates of 3d vertices, the direction of y is the same as v
if vertex.shape[-1] == 3:
vertex = torch.cat([vertex, torch.ones([*vertex.shape[:2], 1]).to(device)], dim=-1)
vertex[..., 1] = -vertex[..., 1]
vertex_ndc = vertex @ ndc_proj.t()
if self.glctx is None:
self.glctx = dr.RasterizeGLContext(device=device)
print("create glctx on device cuda:%d"%device.index)
ranges = None
if isinstance(tri, List) or len(tri.shape) == 3:
vum = vertex_ndc.shape[1]
fnum = torch.tensor([f.shape[0] for f in tri]).unsqueeze(1).to(device)
fstartidx = torch.cumsum(fnum, dim=0) - fnum
ranges = torch.cat([fstartidx, fnum], axis=1).type(torch.int32).cpu()
for i in range(tri.shape[0]):
tri[i] = tri[i] + i*vum
vertex_ndc = torch.cat(vertex_ndc, dim=0)
tri = torch.cat(tri, dim=0)
# for range_mode vetex: [B*N, 4], tri: [B*M, 3], for instance_mode vetex: [B, N, 4], tri: [M, 3]
tri = tri.type(torch.int32).contiguous()
rast_out, _ = dr.rasterize(self.glctx, vertex_ndc.contiguous(), tri, resolution=[rsize, rsize], ranges=ranges)
depth, _ = dr.interpolate(vertex.reshape([-1,4])[...,2].unsqueeze(1).contiguous(), rast_out, tri)
depth = depth.permute(0, 3, 1, 2)
mask = (rast_out[..., 3] > 0).float().unsqueeze(1)
depth = mask * depth
image = None
if feat is not None:
image, _ = dr.interpolate(feat, rast_out, tri)
image = image.permute(0, 3, 1, 2)
image = mask * image
return mask, depth, image
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