Supported import types

Contents

Hasty support an import of existing annotations of the following types:

• bbox - Bounding boxes (as a JSON file)
• polygon - Polygons (as a JSON file)
• semantic - Semantic segmentation (as a PNG image)
• label_class - Label classes (as a JSON file)
• hasty_export - JSON File with a structure of the Hasty JSON V1.1

bbox or polygon

The JSON should be of the following format:

 {
  "project_name": "Project Name",
  "create_date": "2019-10-22 19:04:16Z",
  "export_format_version": "1.1",
  "export_date": "2019-11-19 16:21:18Z",
  "attributes": [
    {
      "name": "ewerwer",
      "type": "TEXT",
      "values": [
        "black",
        "white"
      ]
    }
  ],
  "label_classes": [
    {
      "class_name": "cat",
      "color": "#1f78b44d",
      "class_type": "object",
      "attributes": [
        "black",
        "white"
      ]
    },
    {
      "class_name": "dog",
      "color": "#e31a1c4d",
      "class_type": "object"
    }
  ],
  "images": [
    {
      "image_name": "IMG_000002.jpg",
      "dataset_name": "train dataset",
      "width": 500,
      "height": 430,
      "image_status": "TO REVIEW",
      "labels": [
        {
          "class_name": "cat",
          "bbox": [
            102,
            45,
            420,
            404
          ],
          "polygon": null,
          "mask": null,
          "z_index": 1,
          "attributes": {
            "attribute_name": "xyz"
          }
        }
      ],
      "tags": [
        "xyz",
        "pqr"
      ]
    }
  ]
}

As an example, here are two annotations, one bounding box (cat) and one polygon (dining table):

[
  {
    "image_name": "2010_000001.jpg",
    "labels": [
      {
        "class_name": "cat",
        "bbox": [128, 13, 340, 308]
      }
    ]
  },
  {
    "image_name": "2010_000001.jpg",
    "labels": [
      {
        "class_name": "diningtable",
        "polygon": [[122, 222], [73, 236], [72, 250], [2, 256], [2, 332], [498, 332],
          [498, 295], [429, 260], [339, 231], [282, 223], [297, 246], [306, 285], [280, 302],
          [255, 271], [224, 260], [222, 304], [199, 310], [171, 290], [171, 265], 
          [131, 244], [128, 222]]
      }
    ]
  }
]

semantic

Each file with a semantic segmentation should be an image in *.png format. The filename should be the same (without extension) as the corresponding image's name. The image file can contain one or three channels.

label_class

Label classes can be imported through the use of a JSON-file with in the following schema:

{
  "$id": "http://example.com/root.json",
  "type": "array",
  "title": "List of Label Classes",
  "items": {
    "type": "object",
    "png_index": "number",
    "required": [
      "name",
      "type"
    ],
    "properties": {
      "name": {
        "type": "string",
        "title": "Class name",
        "examples": [
          "floor"
        ]
      },
      {
      "png_index": {
        "type": "number",
        "title": "PNG index",
        "examples": [
          18
        ]
      },
      "color": {
        "type": "string",
        "title": "Class color (can be assigned automatically)",
        "examples": [
          "#F453A3"
        ]
      },
      "type": {
        "type": "string",
        "title": "object or background",
        "default": "object",
        "examples": [
          "background"
        ]
      }
    }
  }
}

As an example:

[
  {
    "color": "#8000ff4d",
    "png_index": 1,
    "name": "wall",
    "type": "background"
  },
  {
    "color": "#396bf94d",
    "png_index": 17,
    "name": "plate",
    "type": "object"
  }
]

As you can see, there are two types of classes: object (1) and background (2). These correspond to the foreground (1) and semantic (2) classes you see in the tool.

RLE Encoding Example

def rle_encoding(x: np.array):
    """
    Encode binary mask to RLE
    Args:
        x (np.array): numpy array of shape (height, width), 1 - mask, 0 - background
    Returns run length as list
    """
    dots = np.where(x.flatten() == 1)[0]  # Order right-then-down
    run_lengths = []
    prev = -2
    for b in dots:
        if b > prev + 1:
            run_lengths.extend((b + 1, 0))
        run_lengths[-1] += 1
        prev = b
    return run_lengths

Easiest way to import

If you are looking to import masks or do other, more complex imports with attributes and image tags as well as annotations, you need to format your data into our own supported Hasty JSON v1.1.