# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Utility functions to allow easy re-use of common operations across dataloaders"""
from pathlib import Path
from typing import List, Tuple, Union
import cv2
import numpy as np
import torch
from PIL import Image
[docs]def get_image_mask_tensor_from_path(filepath: Path, scale_factor: float = 1.0) -> torch.Tensor:
"""
Utility function to read a mask image from the given path and return a boolean tensor
"""
pil_mask = Image.open(filepath)
if scale_factor != 1.0:
width, height = pil_mask.size
newsize = (int(width * scale_factor), int(height * scale_factor))
pil_mask = pil_mask.resize(newsize, resample=Image.Resampling.NEAREST)
mask_tensor = torch.from_numpy(np.array(pil_mask)).unsqueeze(-1).bool()
if len(mask_tensor.shape) != 3:
raise ValueError("The mask image should have 1 channel")
return mask_tensor
[docs]def get_semantics_and_mask_tensors_from_path(
filepath: Path, mask_indices: Union[List, torch.Tensor], scale_factor: float = 1.0
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Utility function to read segmentation from the given filepath
If no mask is required - use mask_indices = []
"""
if isinstance(mask_indices, List):
mask_indices = torch.tensor(mask_indices, dtype=torch.int64).view(1, 1, -1)
pil_image = Image.open(filepath)
if scale_factor != 1.0:
width, height = pil_image.size
newsize = (int(width * scale_factor), int(height * scale_factor))
pil_image = pil_image.resize(newsize, resample=Image.Resampling.NEAREST)
semantics = torch.from_numpy(np.array(pil_image, dtype="int64"))[..., None]
mask = torch.sum(semantics == mask_indices, dim=-1, keepdim=True) == 0
return semantics, mask
[docs]def get_depth_image_from_path(
filepath: Path,
height: int,
width: int,
scale_factor: float,
interpolation: int = cv2.INTER_NEAREST,
) -> torch.Tensor:
"""Loads, rescales and resizes depth images.
Filepath points to a 16-bit or 32-bit depth image, or a numpy array `*.npy`.
Args:
filepath: Path to depth image.
height: Target depth image height.
width: Target depth image width.
scale_factor: Factor by which to scale depth image.
interpolation: Depth value interpolation for resizing.
Returns:
Depth image torch tensor with shape [height, width, 1].
"""
if filepath.suffix == ".npy":
image = np.load(filepath) * scale_factor
image = cv2.resize(image, (width, height), interpolation=interpolation)
else:
image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)
image = image.astype(np.float64) * scale_factor
image = cv2.resize(image, (width, height), interpolation=interpolation)
return torch.from_numpy(image[:, :, np.newaxis])