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import torch
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import xatlas
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import trimesh
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import cv2
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import numpy as np
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import nvdiffrast.torch as dr
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from PIL import Image
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def save_obj(pointnp_px3, facenp_fx3, colornp_px3, fpath):
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pointnp_px3 = pointnp_px3 @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]])
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mesh = trimesh.Trimesh(
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vertices=pointnp_px3,
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faces=facenp_fx3,
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vertex_colors=colornp_px3,
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)
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mesh.export(fpath, 'obj')
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def save_glb(pointnp_px3, facenp_fx3, colornp_px3, fpath):
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pointnp_px3 = pointnp_px3 @ np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]])
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mesh = trimesh.Trimesh(
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vertices=pointnp_px3,
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faces=facenp_fx3,
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vertex_colors=colornp_px3,
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)
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mesh.export(fpath, 'glb')
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def save_obj_with_mtl(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, texmap_hxwx3, fname):
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import os
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fol, na = os.path.split(fname)
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na, _ = os.path.splitext(na)
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matname = '%s/%s.mtl' % (fol, na)
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fid = open(matname, 'w')
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fid.write('newmtl material_0\n')
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fid.write('Kd 1 1 1\n')
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fid.write('Ka 0 0 0\n')
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fid.write('Ks 0.4 0.4 0.4\n')
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fid.write('Ns 10\n')
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fid.write('illum 2\n')
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fid.write('map_Kd %s.png\n' % na)
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fid.close()
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fid = open(fname, 'w')
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fid.write('mtllib %s.mtl\n' % na)
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for pidx, p in enumerate(pointnp_px3):
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pp = p
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fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2]))
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for pidx, p in enumerate(tcoords_px2):
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pp = p
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fid.write('vt %f %f\n' % (pp[0], pp[1]))
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fid.write('usemtl material_0\n')
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for i, f in enumerate(facenp_fx3):
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f1 = f + 1
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f2 = facetex_fx3[i] + 1
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fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
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fid.close()
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lo, hi = 0, 1
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img = np.asarray(texmap_hxwx3, dtype=np.float32)
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img = (img - lo) * (255 / (hi - lo))
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img = img.clip(0, 255)
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mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True)
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mask = (mask <= 3.0).astype(np.float32)
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kernel = np.ones((3, 3), 'uint8')
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dilate_img = cv2.dilate(img, kernel, iterations=1)
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img = img * (1 - mask) + dilate_img * mask
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img = img.clip(0, 255).astype(np.uint8)
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Image.fromarray(np.ascontiguousarray(img[::-1, :, :]), 'RGB').save(f'{fol}/{na}.png')
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def loadobj(meshfile):
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v = []
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f = []
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meshfp = open(meshfile, 'r')
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for line in meshfp.readlines():
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data = line.strip().split(' ')
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data = [da for da in data if len(da) > 0]
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if len(data) != 4:
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continue
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if data[0] == 'v':
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v.append([float(d) for d in data[1:]])
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if data[0] == 'f':
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data = [da.split('/')[0] for da in data]
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f.append([int(d) for d in data[1:]])
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meshfp.close()
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facenp_fx3 = np.array(f, dtype=np.int64) - 1
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pointnp_px3 = np.array(v, dtype=np.float32)
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return pointnp_px3, facenp_fx3
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def loadobjtex(meshfile):
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v = []
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vt = []
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f = []
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ft = []
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meshfp = open(meshfile, 'r')
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for line in meshfp.readlines():
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data = line.strip().split(' ')
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data = [da for da in data if len(da) > 0]
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if not ((len(data) == 3) or (len(data) == 4) or (len(data) == 5)):
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continue
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if data[0] == 'v':
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assert len(data) == 4
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v.append([float(d) for d in data[1:]])
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if data[0] == 'vt':
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if len(data) == 3 or len(data) == 4:
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vt.append([float(d) for d in data[1:3]])
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if data[0] == 'f':
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data = [da.split('/') for da in data]
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if len(data) == 4:
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f.append([int(d[0]) for d in data[1:]])
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ft.append([int(d[1]) for d in data[1:]])
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elif len(data) == 5:
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idx1 = [1, 2, 3]
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data1 = [data[i] for i in idx1]
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f.append([int(d[0]) for d in data1])
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ft.append([int(d[1]) for d in data1])
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idx2 = [1, 3, 4]
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data2 = [data[i] for i in idx2]
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f.append([int(d[0]) for d in data2])
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ft.append([int(d[1]) for d in data2])
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meshfp.close()
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facenp_fx3 = np.array(f, dtype=np.int64) - 1
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ftnp_fx3 = np.array(ft, dtype=np.int64) - 1
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pointnp_px3 = np.array(v, dtype=np.float32)
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uvs = np.array(vt, dtype=np.float32)
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return pointnp_px3, facenp_fx3, uvs, ftnp_fx3
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def interpolate(attr, rast, attr_idx, rast_db=None):
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return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
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def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
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vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
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indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
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uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
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mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
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uv_clip = uvs[None, ...] * 2.0 - 1.0
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uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
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rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
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gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
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mask = rast[..., 3:4] > 0
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return uvs, mesh_tex_idx, gb_pos, mask
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