| import os |
| import json |
| from typing import Union |
| import numpy as np |
| import pandas as pd |
| import torch |
| from torch.utils.data import Dataset |
| import utils3d |
| from .components import StandardDatasetBase |
| from ..representations.octree import DfsOctree as Octree |
| from ..renderers import OctreeRenderer |
|
|
|
|
| class SparseStructure(StandardDatasetBase): |
| """ |
| Sparse structure dataset |
| |
| Args: |
| roots (str): path to the dataset |
| resolution (int): resolution of the voxel grid |
| min_aesthetic_score (float): minimum aesthetic score of the instances to be included in the dataset |
| """ |
|
|
| def __init__(self, |
| roots, |
| resolution: int = 64, |
| min_aesthetic_score: float = 5.0, |
| ): |
| self.resolution = resolution |
| self.min_aesthetic_score = min_aesthetic_score |
| self.value_range = (0, 1) |
|
|
| super().__init__(roots) |
| |
| def filter_metadata(self, metadata): |
| stats = {} |
| metadata = metadata[metadata[f'voxelized']] |
| stats['Voxelized'] = len(metadata) |
| metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score] |
| stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata) |
| return metadata, stats |
|
|
| def get_instance(self, root, instance): |
| position = utils3d.io.read_ply(os.path.join(root, 'voxels', f'{instance}.ply'))[0] |
| coords = ((torch.tensor(position) + 0.5) * self.resolution).int().contiguous() |
| ss = torch.zeros(1, self.resolution, self.resolution, self.resolution, dtype=torch.long) |
| ss[:, coords[:, 0], coords[:, 1], coords[:, 2]] = 1 |
| return {'ss': ss} |
|
|
| @torch.no_grad() |
| def visualize_sample(self, ss: Union[torch.Tensor, dict]): |
| ss = ss if isinstance(ss, torch.Tensor) else ss['ss'] |
| |
| renderer = OctreeRenderer() |
| renderer.rendering_options.resolution = 512 |
| renderer.rendering_options.near = 0.8 |
| renderer.rendering_options.far = 1.6 |
| renderer.rendering_options.bg_color = (0, 0, 0) |
| renderer.rendering_options.ssaa = 4 |
| renderer.pipe.primitive = 'voxel' |
| |
| |
| yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2] |
| yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4) |
| yaws = [y + yaws_offset for y in yaws] |
| pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)] |
|
|
| exts = [] |
| ints = [] |
| for yaw, pitch in zip(yaws, pitch): |
| orig = torch.tensor([ |
| np.sin(yaw) * np.cos(pitch), |
| np.cos(yaw) * np.cos(pitch), |
| np.sin(pitch), |
| ]).float().cuda() * 2 |
| fov = torch.deg2rad(torch.tensor(30)).cuda() |
| extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda()) |
| intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov) |
| exts.append(extrinsics) |
| ints.append(intrinsics) |
|
|
| images = [] |
| |
| |
| ss = ss.cuda() |
| for i in range(ss.shape[0]): |
| representation = Octree( |
| depth=10, |
| aabb=[-0.5, -0.5, -0.5, 1, 1, 1], |
| device='cuda', |
| primitive='voxel', |
| sh_degree=0, |
| primitive_config={'solid': True}, |
| ) |
| coords = torch.nonzero(ss[i, 0], as_tuple=False) |
| representation.position = coords.float() / self.resolution |
| representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.resolution)), dtype=torch.uint8, device='cuda') |
|
|
| image = torch.zeros(3, 1024, 1024).cuda() |
| tile = [2, 2] |
| for j, (ext, intr) in enumerate(zip(exts, ints)): |
| res = renderer.render(representation, ext, intr, colors_overwrite=representation.position) |
| image[:, 512 * (j // tile[1]):512 * (j // tile[1] + 1), 512 * (j % tile[1]):512 * (j % tile[1] + 1)] = res['color'] |
| images.append(image) |
| |
| return torch.stack(images) |
| |
