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64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
# Copyright (C) 2020 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import os
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import os.path as osp
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import shutil
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from glob import glob
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from tempfile import TemporaryDirectory
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from datumaro.components.dataset import Dataset
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from pyunpack import Archive
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from cvat.apps.dataset_manager.bindings import (GetCVATDataExtractor,
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ProjectData, import_dm_annotations)
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from cvat.apps.dataset_manager.util import make_zip_archive
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from .registry import dm_env, exporter, importer
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@exporter(name='PASCAL VOC', ext='ZIP', version='1.1')
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def _export(dst_file, instance_data, save_images=False):
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dataset = Dataset.from_extractors(GetCVATDataExtractor(
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instance_data, include_images=save_images), env=dm_env)
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with TemporaryDirectory() as temp_dir:
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dataset.export(temp_dir, 'voc', save_images=save_images,
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label_map='source')
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make_zip_archive(temp_dir, dst_file)
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@importer(name='PASCAL VOC', ext='ZIP', version='1.1')
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def _import(src_file, instance_data, load_data_callback=None):
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with TemporaryDirectory() as tmp_dir:
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Archive(src_file.name).extractall(tmp_dir)
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# put label map from the task if not present
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labelmap_file = osp.join(tmp_dir, 'labelmap.txt')
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if not osp.isfile(labelmap_file):
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labels_meta = instance_data.meta['project']['labels'] \
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if isinstance(instance_data, ProjectData) else instance_data.meta['task']['labels']
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labels = (label['name'] + ':::' for _, label in labels_meta)
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with open(labelmap_file, 'w') as f:
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f.write('\n'.join(labels))
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# support flat archive layout
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anno_dir = osp.join(tmp_dir, 'Annotations')
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if not osp.isdir(anno_dir):
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anno_files = glob(osp.join(tmp_dir, '**', '*.xml'), recursive=True)
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subsets_dir = osp.join(tmp_dir, 'ImageSets', 'Main')
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os.makedirs(subsets_dir, exist_ok=True)
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with open(osp.join(subsets_dir, 'train.txt'), 'w') as subset_file:
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for f in anno_files:
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subset_file.write(osp.splitext(osp.basename(f))[0] + '\n')
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os.makedirs(anno_dir, exist_ok=True)
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for f in anno_files:
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shutil.move(f, anno_dir)
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dataset = Dataset.import_from(tmp_dir, 'voc', env=dm_env)
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dataset.transform('masks_to_polygons')
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if load_data_callback is not None:
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load_data_callback(dataset, instance_data)
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import_dm_annotations(dataset, instance_data)
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