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142 lines
5.7 KiB
Python
142 lines
5.7 KiB
Python
# Copyright (C) 2021-2022 Intel Corporation
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# Copyright (C) 2022 CVAT.ai Corporation
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#
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# SPDX-License-Identifier: MIT
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import zipfile
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from tempfile import TemporaryDirectory
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from datumaro.components.annotation import (AnnotationType, Caption, Label,
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LabelCategories)
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from datumaro.components.dataset import Dataset
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from datumaro.components.extractor import ItemTransform
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from cvat.apps.dataset_manager.bindings import (GetCVATDataExtractor,
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import_dm_annotations)
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from cvat.apps.dataset_manager.util import make_zip_archive
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from .transformations import RotatedBoxesToPolygons
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from .registry import dm_env, exporter, importer
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class AddLabelToAnns(ItemTransform):
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def __init__(self, extractor, label):
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super().__init__(extractor)
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assert isinstance(label, str)
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self._categories = {}
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label_cat = self._extractor.categories().get(AnnotationType.label)
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if not label_cat:
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label_cat = LabelCategories()
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self._label = label_cat.add(label)
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self._categories[AnnotationType.label] = label_cat
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def categories(self):
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return self._categories
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def transform_item(self, item):
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annotations = item.annotations
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for ann in annotations:
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if ann.type in [AnnotationType.polygon,
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AnnotationType.bbox, AnnotationType.mask]:
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ann.label = self._label
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return item.wrap(annotations=annotations)
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class CaptionToLabel(ItemTransform):
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def __init__(self, extractor, label):
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super().__init__(extractor)
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assert isinstance(label, str)
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self._categories = {}
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label_cat = self._extractor.categories().get(AnnotationType.label)
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if not label_cat:
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label_cat = LabelCategories()
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self._label = label_cat.add(label)
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self._categories[AnnotationType.label] = label_cat
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def categories(self):
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return self._categories
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def transform_item(self, item):
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annotations = item.annotations
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captions = [ann for ann in annotations
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if ann.type == AnnotationType.caption]
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for ann in captions:
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annotations.append(Label(self._label,
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attributes={'text': ann.caption}))
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annotations.remove(ann)
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return item.wrap(annotations=annotations)
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class LabelToCaption(ItemTransform):
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def transform_item(self, item):
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annotations = item.annotations
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anns = [p for p in annotations
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if 'text' in p.attributes]
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for ann in anns:
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annotations.append(Caption(ann.attributes['text']))
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annotations.remove(ann)
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return item.wrap(annotations=annotations)
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@exporter(name='ICDAR Recognition', ext='ZIP', version='1.0')
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def _export_recognition(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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dataset.transform(LabelToCaption)
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with TemporaryDirectory() as temp_dir:
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dataset.export(temp_dir, 'icdar_word_recognition', save_images=save_images)
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make_zip_archive(temp_dir, dst_file)
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@importer(name='ICDAR Recognition', ext='ZIP', version='1.0')
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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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zipfile.ZipFile(src_file).extractall(tmp_dir)
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dataset = Dataset.import_from(tmp_dir, 'icdar_word_recognition', env=dm_env)
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dataset.transform(CaptionToLabel, label='icdar')
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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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@exporter(name='ICDAR Localization', ext='ZIP', version='1.0')
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def _export_localization(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, 'icdar_text_localization', save_images=save_images)
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make_zip_archive(temp_dir, dst_file)
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@importer(name='ICDAR Localization', ext='ZIP', version='1.0')
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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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zipfile.ZipFile(src_file).extractall(tmp_dir)
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dataset = Dataset.import_from(tmp_dir, 'icdar_text_localization', env=dm_env)
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dataset.transform(AddLabelToAnns, label='icdar')
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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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@exporter(name='ICDAR Segmentation', ext='ZIP', version='1.0')
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def _export_segmentation(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.transform(RotatedBoxesToPolygons)
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dataset.transform('polygons_to_masks')
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dataset.transform('boxes_to_masks')
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dataset.transform('merge_instance_segments')
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dataset.export(temp_dir, 'icdar_text_segmentation', save_images=save_images)
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make_zip_archive(temp_dir, dst_file)
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@importer(name='ICDAR Segmentation', ext='ZIP', version='1.0')
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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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zipfile.ZipFile(src_file).extractall(tmp_dir)
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dataset = Dataset.import_from(tmp_dir, 'icdar_text_segmentation', env=dm_env)
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dataset.transform(AddLabelToAnns, label='icdar')
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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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