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import os
import json
import torch
import numpy as np
from PIL import Image
import utils3d
import imageio
import torch.nn.functional as F
import third_party.TRELLIS.trellis.modules.sparse as sp
from third_party.TRELLIS.trellis.utils import render_utils, postprocessing_utils
def get_data(model_dir, view):
image_path = os.path.join(model_dir, view['file_path'])
image = Image.open(image_path)
image = image.resize((518, 518), Image.Resampling.LANCZOS)
image = np.array(image).astype(np.float32) / 255
image = image[:, :, :3] * image[:, :, 3:]
image = torch.from_numpy(image).permute(2, 0, 1).float()
c2w = torch.tensor(view['transform_matrix'])
c2w[:3, 1:3] *= -1
extrinsics = torch.inverse(c2w)
fov = view['camera_angle_x']
intrinsics = utils3d.torch.intrinsics_from_fov_xy(torch.tensor(fov), torch.tensor(fov))
return {
'image': image,
'extrinsics': extrinsics,
'intrinsics': intrinsics
}
@torch.no_grad()
def extract_feature(output_dir, dinov2_model, transform, n_patch=518 // 14, batch_size=8, feature_name='dinov2_vitl14_reg'):
dinov2_model.eval().cuda()
with open(os.path.join(output_dir, 'app_renders', 'transforms.json'), 'r') as f:
metadata = json.load(f)
frames = metadata['frames']
data = []
for view in frames:
datum = get_data(os.path.join(output_dir, 'app_renders'), view)
datum['image'] = transform(datum['image'])
data.append(datum)
positions = utils3d.io.read_ply(os.path.join(output_dir, 'voxels', 'app_voxels.ply'))[0]
positions = torch.from_numpy(positions).float().cuda()
indices = ((positions + 0.5) * 64).long()
assert torch.all(indices >= 0) and torch.all(indices < 64), "Some vertices are out of bounds"
n_views = len(data)
N = positions.shape[0]
pack = {
'indices': indices.cpu().numpy().astype(np.uint8),
}
patchtokens_lst = []
uv_lst = []
with torch.no_grad():
for i in range(0, n_views, batch_size):
batch_data = data[i:i+batch_size]
bs = len(batch_data)
batch_images = torch.stack([d['image'] for d in batch_data]).cuda()
batch_extrinsics = torch.stack([d['extrinsics'] for d in batch_data]).cuda()
batch_intrinsics = torch.stack([d['intrinsics'] for d in batch_data]).cuda()
features = dinov2_model(batch_images, is_training=True)
uv = utils3d.torch.project_cv(positions, batch_extrinsics, batch_intrinsics)[0] * 2 - 1
patchtokens = features['x_prenorm'][:, dinov2_model.num_register_tokens + 1:].permute(0, 2, 1).reshape(bs, 1024, n_patch, n_patch)
patchtokens_lst.append(patchtokens)
uv_lst.append(uv)
patchtokens = torch.cat(patchtokens_lst, dim=0)
uv = torch.cat(uv_lst, dim=0)
pack['patchtokens'] = F.grid_sample(
patchtokens.type(torch.float16),
uv.unsqueeze(1).type(torch.float16),
mode='bilinear',
align_corners=False,
).squeeze(2).permute(0, 2, 1).cpu().numpy()
assert not torch.isnan(patchtokens.type(torch.float16)).any(), "NaNs in patchtokens"
assert not np.isnan(pack['patchtokens']).any(), "NaNs in pack patchtokens"
assert not torch.isnan(uv.unsqueeze(1).type(torch.float16)).any(), "NaNs in uv"
pack['patchtokens'] = np.mean(pack['patchtokens'], axis=0).astype(np.float16)
save_path = os.path.join(output_dir, 'features', feature_name, 'appearance.npz')
np.savez_compressed(save_path, **pack)
del patchtokens
del pack
@torch.no_grad()
def get_latent(output_dir, feature_name, latent_name, encoder):
feats = np.load(os.path.join(output_dir, 'features', feature_name, 'appearance.npz'))
feats = sp.SparseTensor(
feats = torch.from_numpy(feats['patchtokens']).type(torch.float32),
coords = torch.cat([
torch.zeros(feats['patchtokens'].shape[0], 1).int(),
torch.from_numpy(feats['indices']).int(),
], dim=1),
).cuda()
latent = encoder(feats, sample_posterior=False)
assert torch.isfinite(latent.feats).all(), "Non-finite latent"
pack = {
'feats': latent.feats.cpu().numpy().astype(np.float32),
'coords': latent.coords[:, :].cpu().numpy().astype(np.uint8),
}
save_path = os.path.join(output_dir, 'latents', latent_name, 'appearance.npz')
np.savez_compressed(save_path, **pack)
del latent
del pack
def decode_slat(generation_pipeline, feats, coords, out_meshpath, out_gspath):
# Decode Output SLAT
slat = sp.SparseTensor(
feats = feats.float(),
coords = coords.int(),
).cuda()
formats = ['mesh', 'gaussian']
with torch.no_grad():
outputs = generation_pipeline.decode_slat(slat, formats)
_, mesh_textured = postprocessing_utils.to_glb(
outputs['gaussian'][0],
outputs['mesh'][0],
# Optional parameters
simplify=0.95, # Ratio of triangles to remove in the simplification process
texture_size=1024, # Size of the texture used for the GLB
verbose=False, # Print logs
)
mesh_textured.export(out_meshpath)
# Render the outputs
video = render_utils.render_video(outputs['gaussian'][0], bg_color=[255, 255, 255])['color']
imageio.mimsave(out_gspath, video, fps=30) |