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* add licensing headers * add licensing to run actions * rerun headers file * remove trailing white spaces
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# Copyright 2022 The Plenoptix Team. 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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Code to handle loading blender datasets.
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"""
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import os
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from typing import Optional
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import imageio
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import numpy as np
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import torch
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from torchtyping import TensorType
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from pyrad.data.structs import DatasetInputs, SceneBounds
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from pyrad.utils.io import load_from_json
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def load_blender_data(
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basedir: str,
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downscale_factor: int = 1,
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alpha_color: Optional[TensorType[3]] = None,
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split: str = "train",
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scale_factor: float = 1.0,
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) -> DatasetInputs:
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"""Processes the a blender dataset directory.
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Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37.
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Args:
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data_directory (str): Location of data
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downscale_factor (int, optional): How much to downscale images. Defaults to 1.0.
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alpha_color (TensorType[3], optional): Sets transparent regions to specified color, otherwise black.
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split (str, optional): Which dataset split to generate.
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scale_factor (float): How much to scale the camera origins by.
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Returns:
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DatasetInputs
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"""
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# pylint: disable=unused-argument
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# pylint: disable-msg=too-many-locals
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meta = load_from_json(os.path.join(basedir, f"transforms_{split}.json"))
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image_filenames = []
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poses = []
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for frame in meta["frames"]:
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fname = os.path.join(basedir, frame["file_path"].replace("./", "") + ".png")
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image_filenames.append(fname)
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poses.append(np.array(frame["transform_matrix"]))
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poses = np.array(poses).astype(np.float32)
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img_0 = imageio.imread(image_filenames[0])
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image_height, image_width = img_0.shape[:2]
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camera_angle_x = float(meta["camera_angle_x"])
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focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x)
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cx = image_width / 2.0
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cy = image_height / 2.0
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camera_to_world = torch.from_numpy(poses[:, :3]) # camera to world transform
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num_cameras = len(image_filenames)
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num_intrinsics_params = 3
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intrinsics = torch.ones((num_cameras, num_intrinsics_params), dtype=torch.float32)
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intrinsics *= torch.tensor([cx, cy, focal_length])
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# in x,y,z order
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camera_to_world[..., 3] *= scale_factor
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scene_bounds = SceneBounds(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32))
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dataset_inputs = DatasetInputs(
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image_filenames=image_filenames,
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downscale_factor=downscale_factor,
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alpha_color=alpha_color,
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intrinsics=intrinsics * 1.0 / downscale_factor, # downscaling the intrinsics here
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camera_to_world=camera_to_world,
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scene_bounds=scene_bounds,
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)
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return dataset_inputs
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