ValidBboxAnnotationsCOCO#

class ethology.validators.annotations.ValidBboxAnnotationsCOCO(*args, **kwargs)[source]#

Bases: DataFrameModel

Class for COCO-exportable bounding box annotations dataframes.

The validation checks the required columns exist and their types are correct. It additionally checks that the index and the annotation_id column are equal.

idx#

Index of the dataframe. Should be greater than or equal to 0 and equal to the annotation_id column.

Type:: pandera.typing.Index[int]

annotation_id#

Unique identifier for the annotation. Should be equal to the index.

Type:: int

image_id#

Unique identifier for each of the images.

Type:: int

image_filename#

Filename of the image.

Type:: str

image_width#

Width of each of the images.

Type:: int

image_height#

Height of each of the images.

Type:: int

bbox#

Bounding box coordinates as xmin, ymin, width, height.

Type:: list[float]

area#

Bounding box area.

Type:: float

segmentation#

Bounding box segmentation masks as list of lists of coordinates.

Type:: list[list[float]]

category#

Category of the annotation.

Type:: str

supercategory#

Supercategory of the annotation.

Type:: str

iscrowd#

Whether the annotation is a crowd. Should be 0 or 1.

Type:: int

Raises:: pandera.errors.SchemaError – If the dataframe does not match the schema.

Notes

See COCO format documentation for more details.

Methods

`build_schema_`(**kwargs)
`check_idx_and_annotation_id`(df)	Check that the index and the `annotation_id` column are equal.
`empty`(*_args)	Create an empty DataFrame with the schema of this model.
`example`(**kwargs)	Generate an example of this data model specification.
`from_json`(source)	Load a schema from JSON.
`from_yaml`(yaml_schema)	Load a schema from YAML.
`get_metadata`()	Provide metadata for columns and schema level
`map_df_columns_to_COCO_fields`()	Map COCO-exportable dataframe columns to COCO fields.
`pydantic_validate`(schema_model)	Verify that the input is a compatible dataframe model.
`strategy`(**kwargs)	Create a data synthesis strategy.
`to_json`([target])	Convert this model's schema to JSON.
`to_json_schema`()	Serialize schema metadata into json-schema format.
`to_schema`()	Create `DataFrameSchema` from the `DataFrameModel`.
`to_yaml`([stream])	Convert this model's schema to YAML.
`validate`(check_obj[, head, tail, sample, ...])	Validate a DataFrame based on the schema specification.

class Config#

Bases: BaseConfig

add_missing_columns: bool = False#: add columns to dataframe if they are missing

coerce: bool = False#: coerce types of all schema components

description: str | None = None#: arbitrary textual description

drop_invalid_rows: bool = False#: drop invalid rows on validation

dtype: Union[str, type, DataType, ExtensionDtype, dtype, None] = None#: datatype of the dataframe. This overrides the data types specified in any of the fields.

from_format: Union[Literal[Formats.csv], Literal[Formats.dict], Literal[Formats.json], Literal[Formats.feather], Literal[Formats.parquet], Literal[Formats.pickle], Literal[Formats.json_normalize], Callable, None] = None#: data format before validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, assumes a data structure compatible with the pandas.DataFrame constructor.

from_format_kwargs: dict[str, Any] | None = None#: a dictionary keyword arguments to pass into the reader function that converts the object of type from_format to a pandera-validate-able data structure. The reader function is implemented in the pandera.typing generic types via the from_format and to_format methods.

metadata: dict | None = None#: a dictionary object to store key-value data at schema level

multiindex_coerce: bool = False#: coerce types of all MultiIndex components

multiindex_name: str | None = None#: name of multiindex

multiindex_ordered: bool = True#: validate MultiIndex in order

multiindex_strict: Union[bool, Literal['filter']] = False#: make sure all specified columns are in validated MultiIndex - if "filter", removes indexes not specified in the schema

multiindex_unique = None#: make sure the MultiIndex is unique along the list of columns

name: str | None = 'ValidBboxAnnotationsCOCO'#: name of schema

on_missing_columns: str | None = None#: action to take when an optional column is missing from the dataframe. None (default) passes silently, "warn" emits a SchemaWarning. Overridable per field via Field(on_missing=...).

ordered: bool = False#: validate columns order

strict: Union[bool, Literal['filter']] = False#: make sure all specified columns are in the validated dataframe - if "filter", removes columns not specified in the schema

title: str | None = None#: human-readable label for schema

to_format: Union[Literal[Formats.csv], Literal[Formats.dict], Literal[Formats.json], Literal[Formats.feather], Literal[Formats.parquet], Literal[Formats.pickle], Literal[Formats.json_normalize], Callable, None] = None#: data format to serialize into after validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, returns a dataframe.

to_format_buffer: Union[str, Callable, None] = None#: Buffer to be provided when to_format is a custom callable. See docs for example of how to implement an example of a to format function.

to_format_kwargs: dict[str, Any] | None = None#: a dictionary keyword arguments to pass into the writer function that converts the pandera-validate-able object to type to_format. The writer function is implemented in the pandera.typing generic types via the from_format and to_format methods.

unique: Union[str, list[str], None] = None#: make sure certain column combinations are unique

unique_column_names: bool = False#: make sure dataframe column names are unique

classmethod check_idx_and_annotation_id(df)[source]#

Check that the index and the annotation_id column are equal.

Parameters:: df (DataFrame) – The dataframe to check.
Returns:: A boolean indicating whether the index and the annotation_id column are equal for all rows.
Return type:: bool

classmethod empty(*_args)#

Create an empty DataFrame with the schema of this model.

Return type:: DataFrame[Self]

classmethod example(**kwargs)#

Generate an example of this data model specification.

Return type:: DataFrameBase[Self]

classmethod from_json(source)#

Load a schema from JSON.

Parameters:: source – str, Path, or file stream with JSON content.
Returns:: the backend-specific schema object.

classmethod from_yaml(yaml_schema)#

Load a schema from YAML.

Parameters:: yaml_schema – str, Path, or file stream with YAML content.
Returns:: the backend-specific schema object.

classmethod get_metadata()#

Provide metadata for columns and schema level

Return type:: dict[Any, Any]

static map_df_columns_to_COCO_fields()[source]#

Map COCO-exportable dataframe columns to COCO fields.

Returns:: A dictionary mapping each column in the COCO-exportable dataframe to the corresponding fields in the equivalent COCO file.
Return type:: dict

classmethod pydantic_validate(schema_model)#

Verify that the input is a compatible dataframe model.

Return type:: DataFrameModel

classmethod strategy(**kwargs)#: Create a data synthesis strategy.

classmethod to_json(target=None, **kwargs)#: Convert this model’s schema to JSON.

classmethod to_json_schema()#

Serialize schema metadata into json-schema format.

Parameters:: dataframe_schema – schema to write to json-schema format.

Note

This function is currently does not fully specify a pandera schema, and is primarily used internally to render OpenAPI docs via the FastAPI integration.

classmethod to_schema()#

Create DataFrameSchema from the DataFrameModel.

Return type:: TSchema

classmethod to_yaml(stream=None)#: Convert this model’s schema to YAML.

classmethod validate(check_obj, head=None, tail=None, sample=None, random_state=None, lazy=False, inplace=False)#

Validate a DataFrame based on the schema specification.

Parameters:

check_obj (DataFrame) – the dataframe to be validated.
head (int | None) – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.
tail (int | None) – validate the last n rows. Rows overlapping with head or sample are de-duplicated.
sample (int | None) – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.
random_state (int | None) – random seed for the sample argument.
lazy (bool) – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.
inplace (bool) – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Return type:

DataFrame[Self]

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.