Source code for datumaro.plugins.data_formats.brats_numpy

# Copyright (C) 2022-2023 Intel Corporation
# SPDX-License-Identifier: MIT

import errno
import os.path as osp
from typing import List, Optional

import numpy as np

from datumaro.components.annotation import AnnotationType, Cuboid3d, LabelCategories, Mask
from datumaro.components.dataset_base import DatasetItem, SubsetBase
from datumaro.components.format_detection import FormatDetectionContext
from datumaro.components.importer import ImportContext, Importer
from import MultiframeImage
from datumaro.util.pickle_util import PickleLoader

[docs] class BratsNumpyPath: IDS_FILE = "val_ids.p" BOXES_FILE = "val_brain_bbox.p" LABELS_FILE = "labels" DATA_SUFFIX = "_data_cropped" LABEL_SUFFIX = "_label_cropped"
[docs] class BratsNumpyBase(SubsetBase): def __init__( self, path: str, *, subset: Optional[str] = None, ctx: Optional[ImportContext] = None, ): if not osp.isfile(path): raise FileNotFoundError(errno.ENOENT, "Can't find annotations file", path) super().__init__(subset=subset, media_type=MultiframeImage, ctx=ctx) self._root_dir = osp.dirname(path) self._categories = self._load_categories() self._items = list(self._load_items(path).values()) def _load_categories(self): label_cat = LabelCategories() labels_path = osp.join(self._root_dir, BratsNumpyPath.LABELS_FILE) if osp.isfile(labels_path): with open(labels_path, encoding="utf-8") as f: for line in f: label_cat.add(line.strip()) return {AnnotationType.label: label_cat} def _load_items(self, path): items = {} with open(path, "rb") as f: ids = PickleLoader.restricted_load(f) boxes = None boxes_file = osp.join(self._root_dir, BratsNumpyPath.BOXES_FILE) if osp.isfile(boxes_file): with open(boxes_file, "rb") as f: boxes = PickleLoader.restricted_load(f) for i, item_id in enumerate(ids): image_path = osp.join(self._root_dir, item_id + BratsNumpyPath.DATA_SUFFIX + ".npy") media = None if osp.isfile(image_path): data = np.load(image_path)[0].transpose() images = [0] * data.shape[2] for j in range(data.shape[2]): images[j] = data[:, :, j] media = MultiframeImage(images, path=image_path) anno = [] mask_path = osp.join(self._root_dir, item_id + BratsNumpyPath.LABEL_SUFFIX + ".npy") if osp.isfile(mask_path): mask = np.load(mask_path)[0].transpose() for j in range(mask.shape[2]): classes = np.unique(mask[:, :, j]) for class_id in classes: anno.append( Mask( image=self._lazy_extract_mask(mask[:, :, j], class_id), label=class_id, attributes={"image_id": j}, ) ) if boxes is not None: box = boxes[i] anno.append(Cuboid3d(position=list(box[0]), rotation=list(box[1]))) items[item_id] = DatasetItem(id=item_id, media=media, annotations=anno) return items @staticmethod def _lazy_extract_mask(mask, c): return lambda: mask == c
[docs] class BratsNumpyImporter(Importer):
[docs] @classmethod def detect(cls, context: FormatDetectionContext) -> None: context.require_file(BratsNumpyPath.IDS_FILE)
[docs] @classmethod def find_sources(cls, path): return cls._find_sources_recursive( path, "", "brats_numpy", filename=BratsNumpyPath.IDS_FILE )
[docs] @classmethod def get_file_extensions(cls) -> List[str]: return [osp.splitext(BratsNumpyPath.IDS_FILE)[1]]