Add Open Images format (#3679)
Kirill Sizov
4 years ago
committed by
GitHub
parent
1ea23e312a
commit
cc1b8190f3
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# Copyright (C) 2021 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import glob
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import os.path as osp
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from tempfile import TemporaryDirectory
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from datumaro.components.dataset import Dataset, DatasetItem
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from datumaro.plugins.open_images_format import OpenImagesPath
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from datumaro.util.image import DEFAULT_IMAGE_META_FILE_NAME
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from pyunpack import Archive
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from cvat.apps.dataset_manager.bindings import (GetCVATDataExtractor,
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find_dataset_root, import_dm_annotations, match_dm_item)
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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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def find_item_ids(path):
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image_desc_patterns = (
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OpenImagesPath.FULL_IMAGE_DESCRIPTION_FILE_NAME,
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*OpenImagesPath.SUBSET_IMAGE_DESCRIPTION_FILE_PATTERNS
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)
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image_desc_patterns = (
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osp.join(path, OpenImagesPath.ANNOTATIONS_DIR, pattern)
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for pattern in image_desc_patterns
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)
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for pattern in image_desc_patterns:
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for path in glob.glob(pattern):
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with open(path, 'r') as desc:
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next(desc)
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for row in desc:
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yield row.split(',')[0]
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@exporter(name='Open Images V6', ext='ZIP', version='1.0')
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def _export(dst_file, task_data, save_images=False):
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dataset = Dataset.from_extractors(GetCVATDataExtractor(
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task_data, include_images=save_images), env=dm_env)
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dataset.transform('polygons_to_masks')
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dataset.transform('merge_instance_segments')
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with TemporaryDirectory() as temp_dir:
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dataset.export(temp_dir, 'open_images', save_images=save_images)
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make_zip_archive(temp_dir, dst_file)
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@importer(name='Open Images V6', ext='ZIP', version='1.0')
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def _import(src_file, task_data):
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with TemporaryDirectory() as tmp_dir:
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Archive(src_file.name).extractall(tmp_dir)
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image_meta_path = osp.join(tmp_dir, OpenImagesPath.ANNOTATIONS_DIR,
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DEFAULT_IMAGE_META_FILE_NAME)
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image_meta = None
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if not osp.isfile(image_meta_path):
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image_meta = {}
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item_ids = list(find_item_ids(tmp_dir))
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root_hint = find_dataset_root(
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[DatasetItem(id=item_id) for item_id in item_ids], task_data)
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for item_id in item_ids:
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frame_info = None
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try:
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frame_id = match_dm_item(DatasetItem(id=item_id),
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task_data, root_hint)
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frame_info = task_data.frame_info[frame_id]
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except Exception: # nosec
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pass
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if frame_info is not None:
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image_meta[item_id] = (frame_info['height'], frame_info['width'])
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dataset = Dataset.import_from(tmp_dir, 'open_images',
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image_meta=image_meta, env=dm_env)
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dataset.transform('masks_to_polygons')
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import_dm_annotations(dataset, task_data)
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@ -0,0 +1,108 @@
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---
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linkTitle: 'Open Images V6'
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weight: 15
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---
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# [Open Images](https://storage.googleapis.com/openimages/web/index.html)
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- [Format specification](https://storage.googleapis.com/openimages/web/download.html)
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- Supported annotations:
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- Rectangles (detection task)
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- Tags (classification task)
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- Polygons (segmentation task)
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- Supported attributes:
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- Labels
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- `score` (should be defined for labels as `text` or `number`).
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The confidence level from 0 to 1.
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- Bounding boxes
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- `score` (should be defined for labels as `text` or `number`).
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The confidence level from 0 to 1.
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- `occluded` (both UI option and a separate attribute).
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Whether the object is occluded by another object.
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- `truncated` (should be defined for labels as `checkbox` -es).
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Whether the object extends beyond the boundary of the image.
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- `is_group_of` (should be defined for labels as `checkbox` -es).
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Whether the object represents a group of objects of the same class.
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- `is_depiction` (should be defined for labels as `checkbox` -es).
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Whether the object is a depiction (such as a drawing)
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rather than a real object.
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- `is_inside` (should be defined for labels as `checkbox` -es).
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Whether the object is seen from the inside.
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- Masks
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- `box_id` (should be defined for labels as `text`).
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An identifier for the bounding box associated with the mask.
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- `predicted_iou` (should be defined for labels as `text` or `number`).
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Predicted IoU value with respect to the ground truth.
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## Open Images export
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Downloaded file: a zip archive of the following structure:
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```
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└─ taskname.zip/
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├── annotations/
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│ ├── bbox_labels_600_hierarchy.json
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│ ├── class-descriptions.csv
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| ├── images.meta # additional file with information about image sizes
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│ ├── <subset_name>-image_ids_and_rotation.csv
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│ ├── <subset_name>-annotations-bbox.csv
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│ ├── <subset_name>-annotations-human-imagelabels.csv
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│ └── <subset_name>-annotations-object-segmentation.csv
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├── images/
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│ ├── subset1/
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│ │ ├── <image_name101.jpg>
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│ │ ├── <image_name102.jpg>
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│ │ └── ...
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│ ├── subset2/
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│ │ ├── <image_name201.jpg>
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│ │ ├── <image_name202.jpg>
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│ │ └── ...
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| ├── ...
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└── masks/
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├── subset1/
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│ ├── <mask_name101.png>
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│ ├── <mask_name102.png>
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│ └── ...
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├── subset2/
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│ ├── <mask_name201.png>
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│ ├── <mask_name202.png>
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│ └── ...
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├── ...
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```
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## Open Images import
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Uploaded file: a zip archive of the following structure:
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```
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└─ upload.zip/
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├── annotations/
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│ ├── bbox_labels_600_hierarchy.json
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│ ├── class-descriptions.csv
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| ├── images.meta # optional, file with information about image sizes
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│ ├── <subset_name>-image_ids_and_rotation.csv
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│ ├── <subset_name>-annotations-bbox.csv
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│ ├── <subset_name>-annotations-human-imagelabels.csv
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│ └── <subset_name>-annotations-object-segmentation.csv
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└── masks/
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├── subset1/
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│ ├── <mask_name101.png>
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│ ├── <mask_name102.png>
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│ └── ...
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├── subset2/
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│ ├── <mask_name201.png>
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│ ├── <mask_name202.png>
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│ └── ...
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├── ...
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```
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Image ids in the `<subset_name>-image_ids_and_rotation.csv` should match with
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image names in the task.
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