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evonneng
d50c71ac43
* fixing docstrings and small nits; up to fileds * finished adding and fixing docstrings
96 lines
3.5 KiB
Python
96 lines
3.5 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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Some dataset code.
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"""
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from __future__ import annotations
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from typing import Dict, Union
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import numpy as np
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import numpy.typing as npt
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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from torchtyping import TensorType
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from nerfactory.configs import base as cfg
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from nerfactory.datamanagers.structs import DatasetInputs
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from nerfactory.utils.misc import is_not_none
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class InputDataset(Dataset):
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"""Dataset that returns images.
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Args:
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config: the DataParserConfig used to instantiate class, which in turn is used to load dataset inputs
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"""
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def __init__(self, config: Union[cfg.DataParserConfig, cfg.InstantiateConfig], split: str):
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super().__init__()
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self.dataset_inputs: DatasetInputs = config.setup().get_dataset_inputs(split=split)
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def __len__(self):
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return len(self.dataset_inputs.image_filenames)
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def get_numpy_image(self, image_idx: int) -> npt.NDArray[np.uint8]:
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"""Returns the image of shape (H, W, 3 or 4).
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Args:
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image_idx: The image index in the dataset.
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"""
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image_filename = self.dataset_inputs.image_filenames[image_idx]
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pil_image = Image.open(image_filename)
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image = np.array(pil_image, dtype="uint8") # shape is (h, w, 3 or 4)
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assert len(image.shape) == 3
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assert image.dtype == np.uint8
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assert image.shape[2] in [3, 4], f"Image shape of {image.shape} is in correct."
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return image
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def get_image(self, image_idx: int) -> TensorType["image_height", "image_width", "num_channels"]:
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"""Returns a 3 channel image.
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Args:
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image_idx: The image index in the dataset.
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"""
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image = torch.from_numpy(self.get_numpy_image(image_idx).astype("float32") / 255.0)
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if self.dataset_inputs.alpha_color is not None and image.shape[-1] == 4:
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assert image.shape[-1] == 4
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image = image[:, :, :3] * image[:, :, -1:] + self.dataset_inputs.alpha_color * (1.0 - image[:, :, -1:])
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else:
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image = image[:, :, :3]
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return image
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def get_data(self, image_idx) -> Dict:
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"""Returns the ImageDataset data as a dictionary.
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Args:
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image_idx: The image index in the dataset.
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"""
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image = self.get_image(image_idx)
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data = {"image_idx": image_idx}
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assert is_not_none(image)
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data["image"] = image
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for _, data_func_dict in self.dataset_inputs.additional_inputs.items():
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assert "func" in data_func_dict, "Missing function to process data: specify `func` in `additional_inputs`"
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func = data_func_dict["func"]
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assert "kwargs" in data_func_dict, "No data to process: specify `kwargs` in `additional_inputs`"
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data.update(func(image_idx, **data_func_dict["kwargs"]))
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return data
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def __getitem__(self, image_idx):
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data = self.get_data(image_idx)
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return data
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