Source code for

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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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 = 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 = 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])