Source code for

# Copyright 2020 - 2021 MONAI Consortium
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from typing import Dict, List, Optional, Sequence, Union, overload

from monai.utils import ensure_tuple

def _compute_path(base_dir: str, element: str, check_path: bool = False) -> str:

def _compute_path(base_dir: str, element: List[str], check_path: bool = False) -> List[str]:

def _compute_path(base_dir, element, check_path=False):
        base_dir: the base directory of the dataset.
        element: file path(s) to append to directory.
        check_path: if `True`, only compute when the result is an existing path.

        TypeError: When ``element`` contains a non ``str``.
        TypeError: When ``element`` type is not in ``Union[list, str]``.


    def _join_path(base_dir: str, item: str):
        result = os.path.normpath(os.path.join(base_dir, item))
        if check_path and not os.path.exists(result):
            # if not an existing path, don't join with base dir
            return item
        return result

    if isinstance(element, str):
        return _join_path(base_dir, element)
    if isinstance(element, list):
        for e in element:
            if not isinstance(e, str):
                return element
        return [_join_path(base_dir, e) for e in element]
    return element

def _append_paths(base_dir: str, is_segmentation: bool, items: List[Dict]) -> List[Dict]:
        base_dir: the base directory of the dataset.
        is_segmentation: whether the datalist is for segmentation task.
        items: list of data items, each of which is a dict keyed by element names.

        TypeError: When ``items`` contains a non ``dict``.

    for item in items:
        if not isinstance(item, dict):
            raise TypeError(f"Every item in items must be a dict but got {type(item).__name__}.")
        for k, v in item.items():
            if k == "image":
                item[k] = _compute_path(base_dir, v, check_path=False)
            elif is_segmentation and k == "label":
                item[k] = _compute_path(base_dir, v, check_path=False)
                # for other items, auto detect whether it's a valid path
                item[k] = _compute_path(base_dir, v, check_path=True)
    return items

[docs]def load_decathlon_datalist( data_list_file_path: str, is_segmentation: bool = True, data_list_key: str = "training", base_dir: Optional[str] = None, ) -> List[Dict]: """Load image/label paths of decathlon challenge from JSON file Json file is similar to what you get from Those dataset.json files Args: data_list_file_path: the path to the json file of datalist. is_segmentation: whether the datalist is for segmentation task, default is True. data_list_key: the key to get a list of dictionary to be used, default is "training". base_dir: the base directory of the dataset, if None, use the datalist directory. Raises: ValueError: When ``data_list_file_path`` does not point to a file. ValueError: When ``data_list_key`` is not specified in the data list file. Returns a list of data items, each of which is a dict keyed by element names, for example: .. code-block:: [ {'image': '/workspace/data/chest_19.nii.gz', 'label': 0}, {'image': '/workspace/data/chest_31.nii.gz', 'label': 1} ] """ if not os.path.isfile(data_list_file_path): raise ValueError(f"Data list file {data_list_file_path} does not exist.") with open(data_list_file_path) as json_file: json_data = json.load(json_file) if data_list_key not in json_data: raise ValueError(f'Data list {data_list_key} not specified in "{data_list_file_path}".') expected_data = json_data[data_list_key] if data_list_key == "test": expected_data = [{"image": i} for i in expected_data] if base_dir is None: base_dir = os.path.dirname(data_list_file_path) return _append_paths(base_dir, is_segmentation, expected_data)
def load_decathlon_properties( data_property_file_path: str, property_keys: Union[Sequence[str], str], ) -> Dict: """Load the properties from the JSON file contains data property with specified `property_keys`. Args: data_property_file_path: the path to the JSON file of data properties. property_keys: expected keys to load from the JSON file, for example, we have these keys in the decathlon challenge: `name`, `description`, `reference`, `licence`, `tensorImageSize`, `modality`, `labels`, `numTraining`, `numTest`, etc. """ if not os.path.isfile(data_property_file_path): raise ValueError(f"Data property file {data_property_file_path} does not exist.") with open(data_property_file_path) as json_file: json_data = json.load(json_file) properties = {} for key in ensure_tuple(property_keys): if key not in json_data: raise KeyError(f"key {key} is not in the data property file.") properties[key] = json_data[key] return properties