RLE
RLE, Run-Length Encoding, is a type of label with a list of numbers to indicate whether the pixels are in the target region. It’s often used for CV tasks such as semantic segmentation.
Each data can be assigned with multiple RLE labels.
The structure of one RLE label is like:
{
"rle": [
int,
...
]
"category": <str>
"attributes": {
<key>: <value>
...
...
}
"instance": <str>
}
To create a LabeledRLE
label:
>>> from tensorbay.label import LabeledRLE
>>> rle_label = LabeledRLE(
... [8, 4, 1, 3, 12, 7, 16, 2, 9, 2],
... category="category",
... attributes={"attribute_name": "attribute_value"},
... instance="instance_ID"
... )
>>> rle_label
LabeledRLE [
8,
4,
1,
...
](
(category): 'category',
(attributes): {...},
(instance): 'instance_ID'
)
RLE.rle
LabeledRLE
extends RLE
.
To construct a LabeledRLE
instance with only the rle format mask.
>>> LabeledRLE([8, 4, 1, 3, 12, 7, 16, 2, 9, 2])
LabeledRLE [
8,
4,
1,
...
]()
RLE.category
The category of the object inside the region represented by rle format mask. See category for details.
RLE.attributes
Attributes are the additional information about this object, which are stored in key-value pairs. See attributes for details.
RLE.instance
Instance is the unique id for the object inside the region represented by rle format mask, which is mostly used for tracking tasks. See instance for details.
RLESubcatalog
Before adding the RLE labels to data,
RLESubcatalog
should be defined.
RLESubcatalog
has categories, attributes and tracking information,
see common category information,
attributes information and
tracking information for details.
The catalog with only RLE subcatalog is typically stored in a json file as follows:
{
"RLE": { <object>*
"description": <string>! -- Subcatalog description, (default: "").
"isTracking": <boolean>! -- Whether this type of label in the dataset contains tracking
information, (default: false).
"categoryDelimiter": <string> -- The delimiter in category names indicating subcategories.
Recommended delimiter is ".". There is no "categoryDelimiter"
field by default which means the category is of one level.
"categories": [ <array> -- Category list, which contains all category information.
{
"name": <string>* -- Category name.
"description": <string>! -- Category description, (default: "").
},
...
...
],
"attributes": [ <array> -- Attribute list, which contains all attribute information.
{
"name": <string>* -- Attribute name.
"enum": [...], <array> -- All possible options for the attribute.
"type": <string or array> -- Type of the attribute including "boolean", "integer",
"number", "string", "array" and "null". And it is not
required when "enum" is provided.
"minimum": <number> -- Minimum value of the attribute when type is "number".
"maximum": <number> -- Maximum value of the attribute when type is "number".
"items": { <object> -- Used only if the attribute type is "array".
"enum": [...], <array> -- All possible options for elements in the attribute array.
"type": <string or array> -- Type of elements in the attribute array.
"minimum": <number> -- Minimum value of elements in the attribute array when type is
"number".
"maximum": <number> -- Maximum value of elements in the attribute array when type is
"number".
},
"parentCategories": [...], <array> -- Indicates the category to which the attribute belongs. Do not
add this field if it is a global attribute.
"description": <string>! -- Attribute description, (default: "").
},
...
...
]
}
}
Note
*
indicates that the field is required. !
indicates that the field has a default value.
To add a LabeledRLE
label to one data:
>>> from tensorbay.dataset import Data
>>> data = Data("local_path")
>>> data.label.rle = []
>>> data.label.rle.append(rle_label)
Note
One data may contain multiple RLE labels,
so the Data.label.rle
must be a list.