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import torch
import torch.nn as nn
import numpy as np
import os
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
look_at_view_transform,
PerspectiveCameras,
FoVPerspectiveCameras,
PointLights,
DirectionalLights,
Materials,
RasterizationSettings,
MeshRenderer,
MeshRasterizer,
SoftPhongShader,
TexturesUV,
TexturesVertex,
blending,
)
from pytorch3d.ops import interpolate_face_attributes
from pytorch3d.renderer.blending import (
BlendParams,
hard_rgb_blend,
sigmoid_alpha_blend,
softmax_rgb_blend,
)
class SoftSimpleShader(nn.Module):
"""
Per pixel lighting - the lighting model is applied using the interpolated
coordinates and normals for each pixel. The blending function returns the
soft aggregated color using all the faces per pixel.
To use the default values, simply initialize the shader with the desired
device e.g.
"""
def __init__(
self, device="cpu", cameras=None, lights=None, materials=None, blend_params=None
):
super().__init__()
self.lights = lights if lights is not None else PointLights(device=device)
self.materials = (
materials if materials is not None else Materials(device=device)
)
self.cameras = cameras
self.blend_params = blend_params if blend_params is not None else BlendParams()
def to(self, device):
# Manually move to device modules which are not subclasses of nn.Module
self.cameras = self.cameras.to(device)
self.materials = self.materials.to(device)
self.lights = self.lights.to(device)
return self
def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
texels = meshes.sample_textures(fragments)
blend_params = kwargs.get("blend_params", self.blend_params)
cameras = kwargs.get("cameras", self.cameras)
if cameras is None:
msg = "Cameras must be specified either at initialization \
or in the forward pass of SoftPhongShader"
raise ValueError(msg)
znear = kwargs.get("znear", getattr(cameras, "znear", 1.0))
zfar = kwargs.get("zfar", getattr(cameras, "zfar", 100.0))
images = softmax_rgb_blend(
texels, fragments, blend_params, znear=znear, zfar=zfar
)
return images
class Render_3DMM(nn.Module):
def __init__(
self,
focal=1015,
img_h=500,
img_w=500,
batch_size=1,
device=torch.device("cuda:0"),
):
super(Render_3DMM, self).__init__()
self.focal = focal
self.img_h = img_h
self.img_w = img_w
self.device = device
self.renderer = self.get_render(batch_size)
dir_path = os.path.dirname(os.path.realpath(__file__))
topo_info = np.load(
os.path.join(dir_path, "3DMM", "topology_info.npy"), allow_pickle=True
).item()
self.tris = torch.as_tensor(topo_info["tris"]).to(self.device)
self.vert_tris = torch.as_tensor(topo_info["vert_tris"]).to(self.device)
def compute_normal(self, geometry):
vert_1 = torch.index_select(geometry, 1, self.tris[:, 0])
vert_2 = torch.index_select(geometry, 1, self.tris[:, 1])
vert_3 = torch.index_select(geometry, 1, self.tris[:, 2])
nnorm = torch.cross(vert_2 - vert_1, vert_3 - vert_1, 2)
tri_normal = nn.functional.normalize(nnorm, dim=2)
v_norm = tri_normal[:, self.vert_tris, :].sum(2)
vert_normal = v_norm / v_norm.norm(dim=2).unsqueeze(2)
return vert_normal
def get_render(self, batch_size=1):
half_s = self.img_w * 0.5
R, T = look_at_view_transform(10, 0, 0)
R = R.repeat(batch_size, 1, 1)
T = torch.zeros((batch_size, 3), dtype=torch.float32).to(self.device)
cameras = FoVPerspectiveCameras(
device=self.device,
R=R,
T=T,
znear=0.01,
zfar=20,
fov=2 * np.arctan(self.img_w // 2 / self.focal) * 180.0 / np.pi,
)
lights = PointLights(
device=self.device,
location=[[0.0, 0.0, 1e5]],
ambient_color=[[1, 1, 1]],
specular_color=[[0.0, 0.0, 0.0]],
diffuse_color=[[0.0, 0.0, 0.0]],
)
sigma = 1e-4
raster_settings = RasterizationSettings(
image_size=(self.img_h, self.img_w),
blur_radius=np.log(1.0 / 1e-4 - 1.0) * sigma / 18.0,
faces_per_pixel=2,
perspective_correct=False,
)
blend_params = blending.BlendParams(background_color=[0, 0, 0])
renderer = MeshRenderer(
rasterizer=MeshRasterizer(raster_settings=raster_settings, cameras=cameras),
shader=SoftSimpleShader(
lights=lights, blend_params=blend_params, cameras=cameras
),
)
return renderer.to(self.device)
@staticmethod
def Illumination_layer(face_texture, norm, gamma):
n_b, num_vertex, _ = face_texture.size()
n_v_full = n_b * num_vertex
gamma = gamma.view(-1, 3, 9).clone()
gamma[:, :, 0] += 0.8
gamma = gamma.permute(0, 2, 1)
a0 = np.pi
a1 = 2 * np.pi / np.sqrt(3.0)
a2 = 2 * np.pi / np.sqrt(8.0)
c0 = 1 / np.sqrt(4 * np.pi)
c1 = np.sqrt(3.0) / np.sqrt(4 * np.pi)
c2 = 3 * np.sqrt(5.0) / np.sqrt(12 * np.pi)
d0 = 0.5 / np.sqrt(3.0)
Y0 = torch.ones(n_v_full).to(gamma.device).float() * a0 * c0
norm = norm.view(-1, 3)
nx, ny, nz = norm[:, 0], norm[:, 1], norm[:, 2]
arrH = []
arrH.append(Y0)
arrH.append(-a1 * c1 * ny)
arrH.append(a1 * c1 * nz)
arrH.append(-a1 * c1 * nx)
arrH.append(a2 * c2 * nx * ny)
arrH.append(-a2 * c2 * ny * nz)
arrH.append(a2 * c2 * d0 * (3 * nz.pow(2) - 1))
arrH.append(-a2 * c2 * nx * nz)
arrH.append(a2 * c2 * 0.5 * (nx.pow(2) - ny.pow(2)))
H = torch.stack(arrH, 1)
Y = H.view(n_b, num_vertex, 9)
lighting = Y.bmm(gamma)
face_color = face_texture * lighting
return face_color
def forward(self, rott_geometry, texture, diffuse_sh):
face_normal = self.compute_normal(rott_geometry)
face_color = self.Illumination_layer(texture, face_normal, diffuse_sh)
face_color = TexturesVertex(face_color)
mesh = Meshes(
rott_geometry,
self.tris.float().repeat(rott_geometry.shape[0], 1, 1),
face_color,
)
rendered_img = self.renderer(mesh)
rendered_img = torch.clamp(rendered_img, 0, 255)
return rendered_img
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