Source code for super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation

import scipy.io
from PIL import Image

from super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation import PascalVOC2012SegmentationDataSet

PASCAL_AUG_CLASSES = [
    'background', 'airplane', 'bicycle', 'bird', 'boat', 'bottle',
    'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
    'motorcycle', 'person', 'potted-plant', 'sheep', 'sofa', 'train',
    'tv'
]


[docs]class PascalAUG2012SegmentationDataSet(PascalVOC2012SegmentationDataSet): """ PascalAUG2012SegmentationDataSet - Segmentation Data Set Class for Pascal AUG 2012 Data Set """ def __init__(self, *args, **kwargs): self.sample_suffix = '.jpg' self.target_suffix = '.mat' super().__init__(sample_suffix=self.sample_suffix, target_suffix=self.target_suffix, *args, **kwargs) # THERE ARE 21 CLASSES, INCLUDING BACKGROUND self.classes = PASCAL_AUG_CLASSES
[docs] @staticmethod def target_loader(target_path: str) -> Image: """ target_loader :param target_path: The path to the target data :return: The loaded target """ mat = scipy.io.loadmat(target_path, mat_dtype=True, squeeze_me=True, struct_as_record=False) mask = mat['GTcls'].Segmentation return Image.fromarray(mask)