PolygonVectorOutputConfig

Note

All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model PolygonVectorOutputConfig

Config for vectorized semantic segmentation predictions.

Show JSON schema

{
   "title": "PolygonVectorOutputConfig",
   "description": "Config for vectorized semantic segmentation predictions.",
   "type": "object",
   "properties": {
      "class_id": {
         "title": "Class Id",
         "description": "The prediction class that is to turned into vectors.",
         "type": "integer"
      },
      "denoise": {
         "title": "Denoise",
         "description": "Diameter of the circular structural element used to remove high-frequency signals from the image. Smaller values will reduce less noise and make vectorization slower and more memory intensive (especially for large images). Larger values will remove more noise and make vectorization faster but might also remove legitimate detections.",
         "default": 8,
         "type": "integer"
      },
      "threshold": {
         "title": "Threshold",
         "description": "Probability threshold for creating the binary mask for the pixels of this class. Pixels will be considered to belong to this class if their probability for this class is >= ``threshold``. Note that Raster Vision treats classes as mutually exclusive so the threshold should vary with the number of total classes. ``None`` is equivalent to setting this to (1 / num_classes). Defaults to ``None``.",
         "type": "number"
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "polygon_vector_output",
         "enum": [
            "polygon_vector_output"
         ],
         "type": "string"
      }
   },
   "required": [
      "class_id"
   ],
   "additionalProperties": false
}

Config

• extra: str = forbid

• validate_assignment: bool = True

Fields

• class_id (int)

• denoise (int)

• threshold (Optional[float])

• type_hint (Literal['polygon_vector_output'])

field class_id: int [Required]

The prediction class that is to turned into vectors.

field denoise: int = 8

Diameter of the circular structural element used to remove high-frequency signals from the image. Smaller values will reduce less noise and make vectorization slower and more memory intensive (especially for large images). Larger values will remove more noise and make vectorization faster but might also remove legitimate detections.

field threshold: Optional[float] = None

Probability threshold for creating the binary mask for the pixels of this class. Pixels will be considered to belong to this class if their probability for this class is >= threshold. Note that Raster Vision treats classes as mutually exclusive so the threshold should vary with the number of total classes. None is equivalent to setting this to (1 / num_classes). Defaults to None.

field type_hint: Literal['polygon_vector_output'] = 'polygon_vector_output'

build()

Build an instance of the corresponding type of object using this config.

For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.

classmethod deserialize(inp: str | dict | Config) → Self

Deserialize Config from a JSON file or dict, upgrading if possible.

If inp is already a Config, it is returned as is.

Parameters

inp (str | dict | Config) – a URI to a JSON file or a dict.

Return type

Self

classmethod from_dict(cfg_dict: dict) → Self

Deserialize Config from a dict.

Parameters

cfg_dict (dict) – Dict to deserialize.

Return type

Self

classmethod from_file(uri: str) → Self

Deserialize Config from a JSON file, upgrading if possible.

Parameters

uri (str) – URI to load from.

Return type

Self

get_uri(root: str, class_config: Optional[ClassConfig] = None) → str

Parameters

• root (str) –

• class_config (Optional[ClassConfig]) –

Return type

str

recursive_validate_config()

Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.

revalidate()

Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432

to_file(uri: str, with_rv_metadata: bool = True) → None

Save a Config to a JSON file, optionally with RV metadata.

Parameters

• uri (str) – URI to save to.

• with_rv_metadata (bool) – If True, inject Raster Vision metadata such as plugin_versions, so that the config can be upgraded when loaded.

Return type

None

update(*args, **kwargs)

Update any fields before validation.

Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.

validate_config()

Validate fields that should be checked after update is called.

This is to complement the builtin validation that Pydantic performs at the time of object construction.

validate_list(field: str, valid_options: List[str])

Validate a list field.

Parameters

• field (str) – name of field to validate

• valid_options (List[str]) – values that field is allowed to take

Raises

ConfigError – if field is invalid

vectorize(mask: np.ndarray) → Iterator[BaseGeometry]

Vectorize binary mask representing the target class into polygons.

Parameters

mask (np.ndarray) –

Return type

Iterator[BaseGeometry]