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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
import torch
import xatlas
import trimesh
import cv2
import numpy as np
import nvdiffrast.torch as dr
from PIL import Image
def save_obj(pointnp_px3, facenp_fx3, colornp_px3, fname):
mesh = trimesh.Trimesh(
vertices=pointnp_px3,
faces=facenp_fx3,
vertex_colors=colornp_px3,
)
mesh.export(fname, 'obj')
def save_obj_with_mtl(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, texmap_hxwx3, fname):
import os
fol, na = os.path.split(fname)
na, _ = os.path.splitext(na)
matname = '%s/%s.mtl' % (fol, na)
fid = open(matname, 'w')
fid.write('newmtl material_0\n')
fid.write('Kd 1 1 1\n')
fid.write('Ka 0 0 0\n')
fid.write('Ks 0.4 0.4 0.4\n')
fid.write('Ns 10\n')
fid.write('illum 2\n')
fid.write('map_Kd %s.png\n' % na)
fid.close()
####
fid = open(fname, 'w')
fid.write('mtllib %s.mtl\n' % na)
for pidx, p in enumerate(pointnp_px3):
pp = p
fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2]))
for pidx, p in enumerate(tcoords_px2):
pp = p
fid.write('vt %f %f\n' % (pp[0], pp[1]))
fid.write('usemtl material_0\n')
for i, f in enumerate(facenp_fx3):
f1 = f + 1
f2 = facetex_fx3[i] + 1
fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
fid.close()
# save texture map
lo, hi = 0, 1
img = np.asarray(texmap_hxwx3, dtype=np.float32)
img = (img - lo) * (255 / (hi - lo))
img = img.clip(0, 255)
mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True)
mask = (mask <= 3.0).astype(np.float32)
kernel = np.ones((3, 3), 'uint8')
dilate_img = cv2.dilate(img, kernel, iterations=1)
img = img * (1 - mask) + dilate_img * mask
img = img.clip(0, 255).astype(np.uint8)
Image.fromarray(np.ascontiguousarray(img[::-1, :, :]), 'RGB').save(f'{fol}/{na}.png')
def loadobj(meshfile):
v = []
f = []
meshfp = open(meshfile, 'r')
for line in meshfp.readlines():
data = line.strip().split(' ')
data = [da for da in data if len(da) > 0]
if len(data) != 4:
continue
if data[0] == 'v':
v.append([float(d) for d in data[1:]])
if data[0] == 'f':
data = [da.split('/')[0] for da in data]
f.append([int(d) for d in data[1:]])
meshfp.close()
# torch need int64
facenp_fx3 = np.array(f, dtype=np.int64) - 1
pointnp_px3 = np.array(v, dtype=np.float32)
return pointnp_px3, facenp_fx3
def loadobjtex(meshfile):
v = []
vt = []
f = []
ft = []
meshfp = open(meshfile, 'r')
for line in meshfp.readlines():
data = line.strip().split(' ')
data = [da for da in data if len(da) > 0]
if not ((len(data) == 3) or (len(data) == 4) or (len(data) == 5)):
continue
if data[0] == 'v':
assert len(data) == 4
v.append([float(d) for d in data[1:]])
if data[0] == 'vt':
if len(data) == 3 or len(data) == 4:
vt.append([float(d) for d in data[1:3]])
if data[0] == 'f':
data = [da.split('/') for da in data]
if len(data) == 4:
f.append([int(d[0]) for d in data[1:]])
ft.append([int(d[1]) for d in data[1:]])
elif len(data) == 5:
idx1 = [1, 2, 3]
data1 = [data[i] for i in idx1]
f.append([int(d[0]) for d in data1])
ft.append([int(d[1]) for d in data1])
idx2 = [1, 3, 4]
data2 = [data[i] for i in idx2]
f.append([int(d[0]) for d in data2])
ft.append([int(d[1]) for d in data2])
meshfp.close()
# torch need int64
facenp_fx3 = np.array(f, dtype=np.int64) - 1
ftnp_fx3 = np.array(ft, dtype=np.int64) - 1
pointnp_px3 = np.array(v, dtype=np.float32)
uvs = np.array(vt, dtype=np.float32)
return pointnp_px3, facenp_fx3, uvs, ftnp_fx3
# ==============================================================================================
def interpolate(attr, rast, attr_idx, rast_db=None):
return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
# Convert to tensors
indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
# mesh_v_tex. ture
uv_clip = uvs[None, ...] * 2.0 - 1.0
# pad to four component coordinate
uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
# rasterize
rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
# Interpolate world space position
gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
mask = rast[..., 3:4] > 0
return uvs, mesh_tex_idx, gb_pos, mask
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