Spaces:
Sleeping
Sleeping
File size: 9,443 Bytes
cfb7702 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import nvdiffrast.torch as dr
import kiui
from kiui.mesh import Mesh
import json
from pathlib import Path
import tqdm
from PIL import Image
from torchvision.transforms.functional import to_tensor
from torchvision.utils import save_image
import trimesh
from mediapy import write_image, write_video
from einops import rearrange
from kiui.op import uv_padding, safe_normalize, inverse_sigmoid
from kiui.cam import orbit_camera, get_perspective
from torchmetrics.image import LearnedPerceptualImagePatchSimilarity
from mesh import Mesh
from mediapy import read_video
import tyro
from datasets.v3d import get_uniform_poses
class Refiner(nn.Module):
def __init__(self, mesh_filename, video, num_opt=4, lpips: float = 0.0) -> None:
super().__init__()
self.output_size = 512
znear = 0.1
zfar = 10
self.mesh = Mesh.load_obj(mesh_filename)
# self.mesh.v[..., 1], self.mesh.v[..., 2] = (
# self.mesh.v[..., 2],
# self.mesh.v[..., 1],
# )
self.glctx = dr.RasterizeGLContext()
self.device = torch.device("cuda")
self.lpips_meter = LearnedPerceptualImagePatchSimilarity(
net_type="vgg", normalize=True
).to(self.device)
self.lpips = lpips
fov = 60
frames = read_video(video)
self.name = Path(video).stem
frames = frames.astype(np.float32) / 255.0
frames = np.moveaxis(frames, -1, 1)
num_frames, h, w, c = frames.shape
self.poses = get_uniform_poses(num_frames, 2.0, 0.0, opengl=True)
frames = frames.astype(np.float32) / 255.0
self.image_gt = torch.from_numpy(frames).to(self.device)
self.n_frames = len(self.poses)
self.opt_frames = np.linspace(0, self.n_frames, num_opt + 1)[:num_opt].astype(
int
)
print(self.opt_frames)
# gs renderer
self.tan_half_fov = np.tan(0.5 * np.deg2rad(fov))
self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32, device=self.device)
self.proj_matrix[0, 0] = 1 / self.tan_half_fov
self.proj_matrix[1, 1] = 1 / self.tan_half_fov
self.proj_matrix[2, 2] = (zfar + znear) / (zfar - znear)
self.proj_matrix[3, 2] = -(zfar * znear) / (zfar - znear)
self.proj_matrix[2, 3] = 1
self.glctx = dr.RasterizeGLContext()
self.proj = torch.from_numpy(get_perspective(fov)).float().to(self.device)
self.v = self.mesh.v.contiguous().float().to(self.device)
self.f = self.mesh.f.contiguous().int().to(self.device)
self.vc = self.mesh.vc.contiguous().float().to(self.device)
# self.vt = self.mesh.vt
# self.ft = self.mesh.ft
def render_normal(self, pose):
h = w = self.output_size
v = self.v
f = self.f
if not hasattr(self.mesh, "vn") or self.mesh.vn is None:
self.mesh.auto_normal()
vc = self.mesh.vn.to(self.device)
pose = torch.from_numpy(pose.astype(np.float32)).to(v.device)
vc = torch.einsum("ij, kj -> ki", pose[:3, :3].T, vc).contiguous()
# 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 @ self.proj.T
rast, rast_db = dr.rasterize(self.glctx, v_clip, f, (h, w))
alpha = torch.clamp(rast[..., -1:], 0, 1).contiguous() # [1, H, W, 1]
alpha = (
dr.antialias(alpha, rast, v_clip, f).clamp(0, 1).squeeze(-1).squeeze(0)
) # [H, W] important to enable gradients!
# color, texc_db = dr.interpolate(
# self.vc.unsqueeze(0), rast, f, rast_db=rast_db, diff_attrs="all"
# )
color, texc_db = dr.interpolate(vc.unsqueeze(0), rast, f)
color = dr.antialias(color, rast, v_clip, f)
# image = torch.sigmoid(
# dr.texture(self.albedo.unsqueeze(0), texc, uv_da=texc_db)
# ) # [1, H, W, 3]
image = color.view(1, h, w, 3)
# image = dr.antialias(image, rast, v_clip, f).clamp(0, 1)
image = image.squeeze(0).permute(2, 0, 1).contiguous() # [3, H, W]
image = (image + 1) / 2.0
image = alpha * image + (1 - alpha)
return image, alpha
def render_mesh(self, pose, use_sigmoid=True):
h = w = self.output_size
v = self.v
f = self.f
if use_sigmoid:
vc = torch.sigmoid(self.vc)
else:
vc = self.vc
pose = torch.from_numpy(pose.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 @ self.proj.T
rast, rast_db = dr.rasterize(self.glctx, v_clip, f, (h, w))
alpha = torch.clamp(rast[..., -1:], 0, 1).contiguous() # [1, H, W, 1]
alpha = (
dr.antialias(alpha, rast, v_clip, f).clamp(0, 1).squeeze(-1).squeeze(0)
) # [H, W] important to enable gradients!
# color, texc_db = dr.interpolate(
# self.vc.unsqueeze(0), rast, f, rast_db=rast_db, diff_attrs="all"
# )
color, texc_db = dr.interpolate(vc.unsqueeze(0), rast, f)
color = dr.antialias(color, rast, v_clip, f)
# image = torch.sigmoid(
# dr.texture(self.albedo.unsqueeze(0), texc, uv_da=texc_db)
# ) # [1, H, W, 3]
image = color.view(1, h, w, 3)
# image = dr.antialias(image, rast, v_clip, f).clamp(0, 1)
image = image.squeeze(0).permute(2, 0, 1).contiguous() # [3, H, W]
image = alpha * image + (1 - alpha)
return image, alpha
def refine_texture(self, texture_resolution: int = 512, iters: int = 5000):
h = w = texture_resolution
albedo = torch.ones(h * w, 3, device=self.device, dtype=torch.float32) * 0.5
albedo = albedo.view(h, w, -1)
vc_original = self.vc.clone()
self.vc = nn.Parameter(inverse_sigmoid(vc_original)).to(self.device)
optimizer = torch.optim.Adam(
[
{"params": self.vc, "lr": 1e-3},
]
)
pbar = tqdm.trange(iters)
for i in pbar:
index = np.random.choice(self.opt_frames)
pose = self.poses[index]
image_gt = self.image_gt[index]
image_pred, _ = self.render_mesh(pose)
# if i % 1000 == 0:
# save_image(image_pred, f"tmp/image_pred_{i}.png")
# save_image(image_gt, f"tmp/image_gt_{i}.png")
loss = F.mse_loss(image_pred, image_gt)
if self.lpips > 0.0:
loss += (
self.lpips_meter(
image_gt.clamp(0, 1)[None], image_pred.clamp(0, 1)[None]
)
* self.lpips
)
# * 10.0
loss.backward()
optimizer.step()
optimizer.zero_grad()
pbar.set_description(f"MSE = {loss.item():.6f}")
@torch.no_grad()
def render_spiral(self):
images = []
for i, pose in enumerate(self.poses):
image, _ = self.render_mesh(pose, use_sigmoid=False)
images.append(image)
images = torch.stack(images)
images = images.cpu().numpy()
images = rearrange(images, "b c h w -> b h w c")
if not Path("renders").exists():
Path("renders").mkdir(parents=True, exist_ok=True)
write_video(f"renders/{self.name}.mp4", images, fps=3)
@torch.no_grad()
def render_normal_spiral(self):
images = []
for i, pose in enumerate(self.poses):
image, _ = self.render_normal(pose)
images.append(image)
images = torch.stack(images)
images = images.cpu().numpy()
images = rearrange(images, "b c h w -> b h w c")
Path("renders").mkdir(exist_ok=True, parents=True)
write_video(f"renders/{self.name}_normal.mp4", images, fps=3)
def export(self, filename):
mesh = trimesh.Trimesh(
vertices=self.mesh.v.cpu().numpy(),
faces=self.mesh.f.cpu().numpy(),
vertex_colors=torch.sigmoid(self.vc.detach()).cpu().numpy(),
)
self.vc.data = torch.sigmoid(self.vc.detach())
trimesh.repair.fix_inversion(mesh)
mesh.export(filename)
def do_refine(
mesh: str,
scene: str,
num_opt: int = 4,
iters: int = 2000,
skip_refine: bool = False,
render_normal: bool = True,
lpips: float = 1.0,
):
refiner = Refiner(
# "tmp/corgi_size_1.obj",
mesh,
scene,
num_opt=num_opt,
lpips=lpips,
)
if not skip_refine:
refiner.refine_texture(512, iters)
save_path = Path("refined") / f"{Path(scene).stem}.obj"
if not save_path.parent.exists():
save_path.parent.mkdir(exist_ok=True, parents=True)
refiner.export(str(save_path))
refiner.render_spiral()
if render_normal:
refiner.render_normal_spiral()
if __name__ == "__main__":
tyro.cli(do_refine)
|