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LabelStudio

Overview

This converter allows to import images with .json annotations in LabelStudio format. Supported LabelStuidio format geometry types: polygonlabels (polygon), rectanglelabels (rectangle), brushlabels (RLE masks), and choices (tags)

Result of the import

Format description

Supported image formats: .jpg, .jpeg, .mpo, .bmp, .png, .webp, .tiff, .tif, .jfif, .avif, .heic, and .heif With annotations: yes Supported annotation file extension: .json. Grouped by: Any structure (will be uploaded as a single dataset)\

Input files structure

Example data: download ⬇️

⚠️ Note: image names should correspond to the names in the annotation files (data > image field in the JSON file).

Example directory structure:

  📦input_folder
   ┣ 📂ann
   ┃  ┣ 📄annotation_1.json
   ┃  ┗ 📄annotation_4.json
   ┗ 📂img
      ┣ 🏞️IMG_0748.jpeg
      ┗ 🏞️IMG_8144.jpeg

Single-Image Annotation JSON

An annotation file should contain the following fields:

  • annotations or predictions - a list of dictionaries, each containing annotation for single image

    • result - list of dictionaries, each containing information about the objects

      • original_width - the width of the original image

      • original_height - the height of the original image

      • value - a dictionary containing information about the object

        • polygonlabels/rectanglelabels/brushlabels/choices - field with the object class name

        • points - a list of points of the object (for polygonlabels and rectanglelabels shape types)

        • rle and format - a base64 encoded mask of the object (for mask shape type)

      • type - the type of the object (one of the following: polygonlabels, rectanglelabels, brushlabels, choices, relation)

  • data - a dictionary containing information about the image

    • image - the path to the image

Example of the annotation file:

📄 annotation_1.json
[
  {
    "id": 13,
    "annotations": [
      {
        "id": 7,
        "completed_by": 1,
        "result": [
          {
            "original_width": 1280,
            "original_height": 853,
            "image_rotation": 0,
            "value": {
              "x": 14.107390372983872,
              "y": 15.524193548387096,
              "width": 61.535093245967744,
              "height": 70.36290322580646,
              "rotation": 0,
              "rectanglelabels": ["Airplane"]
            },
            "id": "eGEJdycmv3",
            "from_name": "label",
            "to_name": "image",
            "type": "rectanglelabels",
            "origin": "manual"
          }
        ],
        "was_cancelled": false,
        "ground_truth": false,
        "created_at": "2024-07-05T13:18:24.130642Z",
        "updated_at": "2024-07-05T13:18:24.130665Z",
        "draft_created_at": null,
        "lead_time": 7.935,
        "prediction": {},
        "result_count": 0,
        "unique_id": "f527f9c8-affe-469b-991a-70ec6fd79e54",
        "import_id": null,
        "last_action": null,
        "task": 13,
        "project": 8,
        "updated_by": 1,
        "parent_prediction": null,
        "parent_annotation": null,
        "last_created_by": null
      }
    ],
    "file_upload": "airplane.jpg",
    "drafts": [],
    "predictions": [],
    "data": {
      "image": "/data/upload/8/airplane.jpg"
    },
    "meta": {},
    "created_at": "2024-07-05T13:18:14.329289Z",
    "updated_at": "2024-07-05T13:18:24.152845Z",
    "inner_id": 1,
    "total_annotations": 1,
    "cancelled_annotations": 0,
    "total_predictions": 0,
    "comment_count": 0,
    "unresolved_comment_count": 0,
    "last_comment_updated_at": null,
    "project": 8,
    "updated_by": 1,
    "comment_authors": []
  }
]

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