95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import xml.etree.ElementTree as ET
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import mmcv
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import numpy as np
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from .builder import DATASETS
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from .multi_label import MultiLabelDataset
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@DATASETS.register_module()
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class VOC(MultiLabelDataset):
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"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Dataset.
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Args:
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data_prefix (str): the prefix of data path
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pipeline (list): a list of dict, where each element represents
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a operation defined in `mmcls.datasets.pipelines`
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ann_file (str | None): the annotation file. When ann_file is str,
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the subclass is expected to read from the ann_file. When ann_file
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is None, the subclass is expected to read according to data_prefix
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difficult_as_postive (Optional[bool]): Whether to map the difficult
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labels as positive. If it set to True, map difficult examples to
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positive ones(1), If it set to False, map difficult examples to
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negative ones(0). Defaults to None, the difficult labels will be
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set to '-1'.
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"""
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CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
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'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
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'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
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'tvmonitor')
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def __init__(self, difficult_as_postive=None, **kwargs):
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self.difficult_as_postive = difficult_as_postive
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super(VOC, self).__init__(**kwargs)
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if 'VOC2007' in self.data_prefix:
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self.year = 2007
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else:
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raise ValueError('Cannot infer dataset year from img_prefix.')
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def load_annotations(self):
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"""Load annotations.
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Returns:
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list[dict]: Annotation info from XML file.
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"""
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data_infos = []
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img_ids = mmcv.list_from_file(self.ann_file)
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for img_id in img_ids:
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filename = f'JPEGImages/{img_id}.jpg'
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xml_path = osp.join(self.data_prefix, 'Annotations',
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f'{img_id}.xml')
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tree = ET.parse(xml_path)
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root = tree.getroot()
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labels = []
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labels_difficult = []
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for obj in root.findall('object'):
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label_name = obj.find('name').text
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# in case customized dataset has wrong labels
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# or CLASSES has been override.
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if label_name not in self.CLASSES:
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continue
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label = self.class_to_idx[label_name]
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difficult = int(obj.find('difficult').text)
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if difficult:
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labels_difficult.append(label)
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else:
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labels.append(label)
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gt_label = np.zeros(len(self.CLASSES))
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# set difficult example first, then set postivate examples.
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# The order cannot be swapped for the case where multiple objects
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# of the same kind exist and some are difficult.
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if self.difficult_as_postive is None:
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# map difficult examples to -1,
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# it may be used in evaluation to ignore difficult targets.
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gt_label[labels_difficult] = -1
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elif self.difficult_as_postive:
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# map difficult examples to positive ones(1).
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gt_label[labels_difficult] = 1
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else:
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# map difficult examples to negative ones(0).
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gt_label[labels_difficult] = 0
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gt_label[labels] = 1
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info = dict(
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img_prefix=self.data_prefix,
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img_info=dict(filename=filename),
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gt_label=gt_label.astype(np.int8))
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data_infos.append(info)
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return data_infos
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