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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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facenp_fx3 = facenp_fx3[:, [2, 1, 0]] |
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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_ply(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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facenp_fx3 = facenp_fx3[:, [2, 1, 0]] |
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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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) |
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mesh.export(fpath, 'ply') |
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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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