68 lines
2.6 KiB
Markdown
68 lines
2.6 KiB
Markdown
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---
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title: 'COCO Keypoints'
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linkTitle: 'COCO Keypoints'
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weight: 5
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description: 'How to export and import data in COCO Keypoints format'
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---
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The COCO Keypoints format is designed specifically for human pose estimation tasks, where the objective
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is to identify and localize body joints (keypoints) on a human figure within an image.
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This specialized format is used with a variety of state-of-the-art models focused on pose estimation.
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For more information, see:
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- [COCO Keypoint site](https://cocodataset.org/#keypoints-2020)
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- [Format specification](https://open-edge-platform.github.io/datumaro/latest/docs/data-formats/formats/coco.html)
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- [Example of the archive](https://open-edge-platform.github.io/datumaro/latest/docs/data-formats/formats/coco.html#import-coco-dataset)
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## COCO Keypoints export
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For export of images:
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- Supported annotations: Skeletons
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- Attributes:
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- `is_crowd` This can either be a checkbox or an integer
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(with values of 0 or 1). It indicates that the instance
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(or group of objects) should include an RLE-encoded mask in the `segmentation` field.
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All shapes within the group coalesce into a single, overarching mask,
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with the largest shape setting the properties for the entire object group.
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- `score`: This numerical field represents the annotation `score`.
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- Arbitrary attributes: These will be stored within the `attributes`
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section of the annotation.
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- Tracks: Not supported.
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Downloaded file is a .zip archive with the following structure:
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```
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archive.zip/
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├── images/
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│
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│ ├── <image_name1.ext>
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│ ├── <image_name2.ext>
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│ └── ...
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├──<annotations>.xml
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```
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## COCO import
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Uploaded file: a single unpacked `*.json` or a zip archive with the structure described
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[here](https://open-edge-platform.github.io/datumaro/latest/docs/data-formats/formats/coco.html#import-coco-dataset)
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(without images).
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- supported annotations: Skeletons
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`person_keypoints`,
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Support for COCO tasks via Datumaro is described [here](https://open-edge-platform.github.io/datumaro/latest/docs/data-formats/formats/coco.html#export-to-other-formats)
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For example, [support for COCO keypoints over Datumaro](https://github.com/openvinotoolkit/cvat/issues/2910#issuecomment-726077582):
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1. Install [Datumaro](https://github.com/openvinotoolkit/datumaro)
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`pip install datumaro`
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2. Export the task in the `Datumaro` format, unzip
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3. Export the Datumaro project in `coco` / `coco_person_keypoints` formats
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`datum export -f coco -p path/to/project [-- --save-images]`
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This way, one can export CVAT points as single keypoints or
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keypoint lists (without the `visibility` COCO flag).
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