Spaces:
Build error
Build error
File size: 2,501 Bytes
c8c12e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
"""Inference Dataset."""
# 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.
from pathlib import Path
from typing import Any, Optional, Tuple, Union
import albumentations as A
from torch.utils.data.dataset import Dataset
from anomalib.data.utils import get_image_filenames, read_image
from anomalib.pre_processing import PreProcessor
class InferenceDataset(Dataset):
"""Inference Dataset to perform prediction."""
def __init__(
self,
path: Union[str, Path],
pre_process: Optional[PreProcessor] = None,
image_size: Optional[Union[int, Tuple[int, int]]] = None,
transform_config: Optional[Union[str, A.Compose]] = None,
) -> None:
"""Inference Dataset to perform prediction.
Args:
path (Union[str, Path]): Path to an image or image-folder.
pre_process (Optional[PreProcessor], optional): Pre-Processing transforms to
pre-process the input dataset. Defaults to None.
image_size (Optional[Union[int, Tuple[int, int]]], optional): Target image size
to resize the original image. Defaults to None.
transform_config (Optional[Union[str, A.Compose]], optional): Configuration file
parse the albumentation transforms. Defaults to None.
"""
super().__init__()
self.image_filenames = get_image_filenames(path)
if pre_process is None:
self.pre_process = PreProcessor(transform_config, image_size)
else:
self.pre_process = pre_process
def __len__(self) -> int:
"""Get the number of images in the given path."""
return len(self.image_filenames)
def __getitem__(self, index: int) -> Any:
"""Get the image based on the `index`."""
image_filename = self.image_filenames[index]
image = read_image(path=image_filename)
pre_processed = self.pre_process(image=image)
return pre_processed
|