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import numpy as np |
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import torch |
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from skimage import measure |
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from lib.libmise import mise |
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import trimesh |
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def generate_mesh(func, verts, level_set=0, res_init=32, res_up=3, point_batch=5000): |
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scale = 1.1 |
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verts = verts.data.cpu().numpy() |
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gt_bbox = np.stack([verts.min(axis=0), verts.max(axis=0)], axis=0) |
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gt_center = (gt_bbox[0] + gt_bbox[1]) * 0.5 |
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gt_scale = (gt_bbox[1] - gt_bbox[0]).max() |
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mesh_extractor = mise.MISE(res_init, res_up, level_set) |
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points = mesh_extractor.query() |
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while points.shape[0] != 0: |
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orig_points = points |
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points = points.astype(np.float32) |
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points = (points / mesh_extractor.resolution - 0.5) * scale |
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points = points * gt_scale + gt_center |
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points = torch.tensor(points).float().cuda() |
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values = [] |
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for _, pnts in enumerate((torch.split(points,point_batch,dim=0))): |
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out = func(pnts) |
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values.append(out['sdf'].data.cpu().numpy()) |
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values = np.concatenate(values, axis=0).astype(np.float64)[:,0] |
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mesh_extractor.update(orig_points, values) |
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points = mesh_extractor.query() |
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value_grid = mesh_extractor.to_dense() |
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verts, faces, normals, values = measure.marching_cubes_lewiner( |
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volume=value_grid, |
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gradient_direction='ascent', |
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level=level_set) |
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verts = (verts / mesh_extractor.resolution - 0.5) * scale |
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verts = verts * gt_scale + gt_center |
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faces = faces[:, [0,2,1]] |
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meshexport = trimesh.Trimesh(verts, faces, normals, vertex_colors=values) |
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connected_comp = meshexport.split(only_watertight=False) |
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max_area = 0 |
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max_comp = None |
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for comp in connected_comp: |
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if comp.area > max_area: |
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max_area = comp.area |
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max_comp = comp |
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meshexport = max_comp |
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return meshexport |
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