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101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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SDFStudio dataset.
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"""
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from pathlib import Path
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from typing import Dict
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import numpy as np
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import torch
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from torch import Tensor
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from nerfstudio.data.dataparsers.base_dataparser import DataparserOutputs
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from nerfstudio.data.datasets.base_dataset import InputDataset
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class SDFDataset(InputDataset):
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"""Dataset that returns images and depths.
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Args:
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dataparser_outputs: description of where and how to read input images.
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scale_factor: The scaling factor for the dataparser outputs.
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"""
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exclude_batch_keys_from_device = InputDataset.exclude_batch_keys_from_device + ["depth", "normal"]
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def __init__(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0):
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super().__init__(dataparser_outputs, scale_factor)
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# can be none if monoprior not included
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self.depth_filenames = self.metadata["depth_filenames"]
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self.normal_filenames = self.metadata["normal_filenames"]
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self.camera_to_worlds = self.metadata["camera_to_worlds"]
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# can be none if auto orient not enabled in dataparser
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self.transform = self.metadata["transform"]
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self.include_mono_prior = self.metadata["include_mono_prior"]
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def get_metadata(self, data: Dict) -> Dict:
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# TODO supports foreground_masks
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metadata = {}
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if self.include_mono_prior:
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depth_filepath = self.depth_filenames[data["image_idx"]]
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normal_filepath = self.normal_filenames[data["image_idx"]]
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camtoworld = self.camera_to_worlds[data["image_idx"]]
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# Scale depth images to meter units and also by scaling applied to cameras
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depth_image, normal_image = self.get_depths_and_normals(
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depth_filepath=depth_filepath, normal_filename=normal_filepath, camtoworld=camtoworld
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)
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metadata["depth"] = depth_image
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metadata["normal"] = normal_image
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return metadata
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def get_depths_and_normals(self, depth_filepath: Path, normal_filename: Path, camtoworld: Tensor):
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"""function to process additional depths and normal information
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Args:
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depth_filepath: path to depth file
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normal_filename: path to normal file
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camtoworld: camera to world transformation matrix
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"""
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# load mono depth
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depth = np.load(depth_filepath)
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depth = torch.from_numpy(depth).float()
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# load mono normal
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normal = np.load(normal_filename)
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# transform normal to world coordinate system
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normal = normal * 2.0 - 1.0 # omnidata output is normalized so we convert it back to normal here
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normal = torch.from_numpy(normal).float()
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rot = camtoworld[:3, :3]
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normal_map = normal.reshape(3, -1)
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normal_map = torch.nn.functional.normalize(normal_map, p=2, dim=0)
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normal_map = rot @ normal_map
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normal = normal_map.permute(1, 0).reshape(*normal.shape[1:], 3)
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if self.transform is not None:
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h, w, _ = normal.shape
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normal = self.transform[:3, :3] @ normal.reshape(-1, 3).permute(1, 0)
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normal = normal.permute(1, 0).reshape(h, w, 3)
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return depth, normal
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