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| | 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, fpath): |
| |
|
| | pointnp_px3 = pointnp_px3 @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]]) |
| | facenp_fx3 = facenp_fx3[:, [2, 1, 0]] |
| |
|
| | mesh = trimesh.Trimesh( |
| | vertices=pointnp_px3, |
| | faces=facenp_fx3, |
| | vertex_colors=colornp_px3, |
| | ) |
| | mesh.export(fpath, 'obj') |
| |
|
| |
|
| | def save_glb(pointnp_px3, facenp_fx3, colornp_px3, fpath): |
| |
|
| | pointnp_px3 = pointnp_px3 @ np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]]) |
| |
|
| | mesh = trimesh.Trimesh( |
| | vertices=pointnp_px3, |
| | faces=facenp_fx3, |
| | vertex_colors=colornp_px3, |
| | ) |
| | mesh.export(fpath, 'glb') |
| |
|
| |
|
| | 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() |
| |
|
| | |
| | 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() |
| |
|
| | |
| | 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() |
| |
|
| | |
| | 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()) |
| |
|
| | |
| | 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) |
| | |
| | uv_clip = uvs[None, ...] * 2.0 - 1.0 |
| |
|
| | |
| | uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1) |
| |
|
| | |
| | rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution)) |
| |
|
| | |
| | gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int()) |
| | mask = rast[..., 3:4] > 0 |
| | return uvs, mesh_tex_idx, gb_pos, mask |
| |
|