Supported import types

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:

  
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.

      json
      
    
      {
  "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):

  
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.

      json
      
    
      [
  {
    "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]]
      }
    ]
  }
]
    

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:

  
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.

      json
      
    
      {
  "$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:

  
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.

      json
      
    
      [
  {
    "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

  
Hello, thank you for using the code provided by CloudFactory. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add it to your system to complement it.

      python
      
    
      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.