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https://github.com/nerfstudio-project/nerfstudio.git
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Paul Wais
5c73766653
* save * save * more cleanup * save * save * update phototourism * save * works * works * cleanup * cleanup * lint * python3 default * cleanup
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
# Copyright 2022 The Nerfstudio 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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"""Helper functions for processing record3d data."""
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import json
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from pathlib import Path
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from typing import List
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import numpy as np
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from rich.console import Console
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from scipy.spatial.transform import Rotation
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from nerfstudio.process_data.process_data_utils import CAMERA_MODELS
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from nerfstudio.utils import io
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CONSOLE = Console(width=120)
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def record3d_to_json(images_paths: List[Path], metadata_path: Path, output_dir: Path, indices: np.ndarray) -> int:
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"""Converts Record3D's metadata and image paths to a JSON file.
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Args:
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images_paths: list if image paths.
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metadata_path: Path to the Record3D metadata JSON file.
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output_dir: Path to the output directory.
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indices: Indices to sample the metadata_path. Should be the same length as images_paths.
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Returns:
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The number of registered images.
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"""
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assert len(images_paths) == len(indices)
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metadata_dict = io.load_from_json(metadata_path)
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poses_data = np.array(metadata_dict["poses"]) # (N, 3, 4)
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# NB: Record3D / scipy use "scalar-last" format quaternions (x y z w)
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# https://fzheng.me/2017/11/12/quaternion_conventions_en/
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camera_to_worlds = np.concatenate(
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[Rotation.from_quat(poses_data[:, :4]).as_matrix(), poses_data[:, 4:, None]],
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axis=-1,
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).astype(np.float32)
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camera_to_worlds = camera_to_worlds[indices]
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homogeneous_coord = np.zeros_like(camera_to_worlds[..., :1, :])
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homogeneous_coord[..., :, 3] = 1
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camera_to_worlds = np.concatenate([camera_to_worlds, homogeneous_coord], -2)
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frames = []
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for i, im_path in enumerate(images_paths):
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c2w = camera_to_worlds[i]
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frame = {
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"file_path": im_path.as_posix(),
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"transform_matrix": c2w.tolist(),
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}
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frames.append(frame)
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# Camera intrinsics
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K = np.array(metadata_dict["K"]).reshape((3, 3)).T
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focal_length = K[0, 0]
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H = metadata_dict["h"]
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W = metadata_dict["w"]
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# TODO(akristoffersen): The metadata dict comes with principle points,
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# but caused errors in image coord indexing. Should update once that is fixed.
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cx, cy = W / 2, H / 2
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out = {
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"fl_x": focal_length,
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"fl_y": focal_length,
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"cx": cx,
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"cy": cy,
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"w": W,
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"h": H,
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"camera_model": CAMERA_MODELS["perspective"].name,
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}
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out["frames"] = frames
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with open(output_dir / "transforms.json", "w", encoding="utf-8") as f:
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json.dump(out, f, indent=4)
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return len(frames)
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