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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
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