Messages - geometry_2d#

This package contains messages that are used in the 2D geometry space.

Point#

This message represents a point in the 2D space.

pydantic model duckietown_messages.geometry_2d.point.Point#

Show JSON schema
{
   "title": "Point",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "x": {
         "title": "X",
         "description": "X coordinate of the point",
         "type": "number"
      },
      "y": {
         "title": "Y",
         "description": "Y coordinate of the point",
         "type": "number"
      }
   },
   "required": [
      "x",
      "y"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "example": "0.1.3",
               "type": "string"
            },
            "frame": {
               "title": "Frame",
               "description": "Reference frame this data is captured in",
               "type": "string"
            },
            "txt": {
               "title": "Txt",
               "description": "Auxiliary data attached to the message",
               "type": "object"
            }
         }
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field x: float [Required]#

X coordinate of the point

field y: float [Required]#

Y coordinate of the point

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#
classmethod from_rawdata(rd: dtps_http.structures.RawData) → duckietown_messages.base.BaseMessage#
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod parse_obj(obj: Any) → Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#
to_rawdata() → dtps_http.structures.RawData#
classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

ROI (Region of Interest)#

This message represents a region (of interest) in the 2D space.

pydantic model duckietown_messages.geometry_2d.roi.ROI#

Show JSON schema
{
   "title": "ROI",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "height": {
         "title": "Height",
         "description": "Height of the region",
         "minimum": 0,
         "type": "integer"
      },
      "width": {
         "title": "Width",
         "description": "Width of the region",
         "minimum": 0,
         "type": "integer"
      },
      "x": {
         "title": "X",
         "description": "Leftmost pixel of the region",
         "default": 0,
         "minimum": 0,
         "type": "integer"
      },
      "y": {
         "title": "Y",
         "description": "Topmost pixel of the region",
         "default": 0,
         "minimum": 0,
         "type": "integer"
      }
   },
   "required": [
      "height",
      "width"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "example": "0.1.3",
               "type": "string"
            },
            "frame": {
               "title": "Frame",
               "description": "Reference frame this data is captured in",
               "type": "string"
            },
            "txt": {
               "title": "Txt",
               "description": "Auxiliary data attached to the message",
               "type": "object"
            }
         }
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field height: int [Required]#

Height of the region

Constraints

  • minimum = 0

field width: int [Required]#

Width of the region

Constraints

  • minimum = 0

field x: int = 0#

Leftmost pixel of the region

Constraints

  • minimum = 0

field y: int = 0#

Topmost pixel of the region

Constraints

  • minimum = 0

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#
classmethod from_rawdata(rd: dtps_http.structures.RawData) → duckietown_messages.base.BaseMessage#
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod parse_obj(obj: Any) → Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#
to_rawdata() → dtps_http.structures.RawData#
classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

Homography#

This message represents a homography transformation matrix.

pydantic model duckietown_messages.geometry_2d.homography.Homography#

Show JSON schema
{
   "title": "Homography",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "data": {
         "title": "Data",
         "description": "Homography matrix (flattened)",
         "type": "array",
         "items": {
            "type": "number"
         }
      }
   },
   "required": [
      "data"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "example": "0.1.3",
               "type": "string"
            },
            "frame": {
               "title": "Frame",
               "description": "Reference frame this data is captured in",
               "type": "string"
            },
            "txt": {
               "title": "Txt",
               "description": "Auxiliary data attached to the message",
               "type": "object"
            }
         }
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field data: List[float] [Required]#

Homography matrix (flattened)

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#
classmethod from_rawdata(rd: dtps_http.structures.RawData) → duckietown_messages.base.BaseMessage#
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod parse_obj(obj: Any) → Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#
to_rawdata() → dtps_http.structures.RawData#
classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#