mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 11:24:00 +08:00
evonneng
56113e6c75
* testing; need to merge * fixing wrong base file in models/base * pushed input parser into datasets class to abstract away fetching data in dataloader * slight name chage and all * nits * added dataparser * added licnese
119 lines
4.6 KiB
Python
119 lines
4.6 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 abc import abstractmethod
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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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def __init__(self, config: Union[cfg.DataParserConfig, cfg.InstantiateConfig], split: str):
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"""_summary_
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Args:
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image_filenames (List[str]): List of image filenames
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alpha_color (TensorType[3], optional): Sets transparent regions to specified color, otherwise black.
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"""
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super().__init__()
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self.inputs: DatasetInputs = config.setup().get_dataset_inputs(split=split)
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def __len__(self):
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return len(self.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.
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Args:
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image_idx (int): The image index in the dataset.
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Returns:
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np.uint8: an image of shape (H, W, 3 or 4)
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"""
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image_filename = self.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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image = torch.from_numpy(self.get_numpy_image(image_idx).astype("float32") / 255.0)
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if self.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.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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@abstractmethod
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def get_mask(self, image_idx: int) -> Union[TensorType["image_height", "image_width", 1], None]:
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"""Returns a mask, which indicates which pixels are valid to use with nerf."""
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if self.inputs.semantics:
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person_index = self.inputs.semantics.thing_classes.index("person")
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thing_image_filename = self.inputs.semantics.thing_filenames[image_idx]
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pil_image = Image.open(thing_image_filename)
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thing_semantics = torch.from_numpy(np.array(pil_image, dtype="int32"))[..., None]
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mask = (thing_semantics != person_index).to(torch.float32) # 1 where valid
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return mask
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return None
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@abstractmethod
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def get_semantics(self, image_idx: int) -> Union[TensorType["image_height", "image_width", "num_classes"], None]:
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"""Returns an image with semantic class values."""
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if self.inputs.semantics:
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stuff_image_filename = self.inputs.semantics.stuff_filenames[image_idx]
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pil_image = Image.open(stuff_image_filename)
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stuff_semantics = torch.from_numpy(np.array(pil_image, dtype="int32"))[..., None]
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return stuff_semantics
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return None
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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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image = self.get_image(image_idx)
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mask = self.get_mask(image_idx)
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semantics = self.get_semantics(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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if mask is not None:
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assert mask.shape[:2] == image.shape[:2]
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data["mask"] = mask
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if semantics is not None:
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assert semantics.shape[:2] == image.shape[:2]
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data["semantics"] = semantics
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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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