BuildingVectorOutputConfig#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model BuildingVectorOutputConfig[source]#
Config for vectorized semantic segmentation predictions.
Intended to break up clusters of buildings.
Show JSON schema
{ "title": "BuildingVectorOutputConfig", "description": "Config for vectorized semantic segmentation predictions.\n\nIntended to break up clusters of buildings.", "type": "object", "properties": { "class_id": { "description": "The prediction class that is to turned into vectors.", "title": "Class Id", "type": "integer" }, "denoise": { "default": 8, "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.", "title": "Denoise", "type": "integer" }, "threshold": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "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``.", "title": "Threshold" }, "type_hint": { "const": "building_vector_output", "default": "building_vector_output", "enum": [ "building_vector_output" ], "title": "Type Hint", "type": "string" }, "min_area": { "default": 0.0, "description": "Minimum area (in pixels^2) of anything that can be considered to be a building or a cluster of buildings. The goal is to distinguish between buildings and artifacts.", "title": "Min Area", "type": "number" }, "element_width_factor": { "default": 0.5, "description": "Width of the structural element used to break building clusters as a fraction of the width of the cluster.", "title": "Element Width Factor", "type": "number" }, "element_thickness": { "default": 0.001, "description": "Thickness of the structural element that is used to break building clusters.", "title": "Element Thickness", "type": "number" } }, "additionalProperties": false, "required": [ "class_id" ] }
- Config:
extra: str = forbid
validate_assignment: bool = True
- Fields:
- 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 element_thickness: float = 0.001#
Thickness of the structural element that is used to break building clusters.
- field element_width_factor: float = 0.5#
Width of the structural element used to break building clusters as a fraction of the width of the cluster.
- field min_area: float = 0.0#
Minimum area (in pixels^2) of anything that can be considered to be a building or a cluster of buildings. The goal is to distinguish between buildings and artifacts.
- field threshold: float | None = 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 toNone
.
- 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 aConfig
, it is returned as is.
- 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: ClassConfig | None = None) str #
- Parameters:
root (str) –
class_config (ClassConfig | None) –
- Return type:
- 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().
- to_file(uri: str, with_rv_metadata: bool = True) None #
Save a Config to a JSON file, optionally with RV metadata.
- 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:
- Raises:
ConfigError – if field is invalid