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| """Image Utils.""" | |
| # Copyright (C) 2020 Intel Corporation | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions | |
| # and limitations under the License. | |
| import math | |
| from pathlib import Path | |
| from typing import List, Union | |
| import cv2 | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from torchvision.datasets.folder import IMG_EXTENSIONS | |
| def get_image_filenames(path: Union[str, Path]) -> List[str]: | |
| """Get image filenames. | |
| Args: | |
| path (Union[str, Path]): Path to image or image-folder. | |
| Returns: | |
| List[str]: List of image filenames | |
| """ | |
| image_filenames: List[str] | |
| if isinstance(path, str): | |
| path = Path(path) | |
| if path.is_file() and path.suffix in IMG_EXTENSIONS: | |
| image_filenames = [str(path)] | |
| if path.is_dir(): | |
| image_filenames = [str(p) for p in path.glob("**/*") if p.suffix in IMG_EXTENSIONS] | |
| if len(image_filenames) == 0: | |
| raise ValueError(f"Found 0 images in {path}") | |
| return image_filenames | |
| def read_image(path: Union[str, Path]) -> np.ndarray: | |
| """Read image from disk in RGB format. | |
| Args: | |
| path (str, Path): path to the image file | |
| Example: | |
| >>> image = read_image("test_image.jpg") | |
| Returns: | |
| image as numpy array | |
| """ | |
| path = path if isinstance(path, str) else str(path) | |
| image = cv2.imread(path) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| return image | |
| def pad_nextpow2(batch: Tensor) -> Tensor: | |
| """Compute required padding from input size and return padded images. | |
| Finds the largest dimension and computes a square image of dimensions that are of the power of 2. | |
| In case the image dimension is odd, it returns the image with an extra padding on one side. | |
| Args: | |
| batch (Tensor): Input images | |
| Returns: | |
| batch: Padded batch | |
| """ | |
| # find the largest dimension | |
| l_dim = 2 ** math.ceil(math.log(max(*batch.shape[-2:]), 2)) | |
| padding_w = [math.ceil((l_dim - batch.shape[-2]) / 2), math.floor((l_dim - batch.shape[-2]) / 2)] | |
| padding_h = [math.ceil((l_dim - batch.shape[-1]) / 2), math.floor((l_dim - batch.shape[-1]) / 2)] | |
| padded_batch = F.pad(batch, pad=[*padding_h, *padding_w]) | |
| return padded_batch | |