ValidBboxAnnotationsDataFrame

class ethology.validators.annotations.ValidBboxAnnotationsDataFrame(*args, **kwargs)

Bases: DataFrameModel

Class for valid bounding boxes intermediate dataframes.

We use this dataframe internally as an intermediate step in the process of converting an input bounding box annotations file (VIA or COCO) to an ethology dataset. The validation checks all required columns exist and their types are correct.

image_filename

Name of the image file.

Type:

str

image_id

Unique identifier for each of the images.

Type:

int

image_width

Width of each of the images, in the same units as the input file (usually pixels).

Type:

int

image_height

Height of each of the images, in the same units as the input file (usually pixels).

Type:

int

x_min

Minimum x-coordinate of the bounding box, in the same units as the input file.

Type:

float

y_min

Minimum y-coordinate of the bounding box, in the same units as the input file.

Type:

float

width

Width of the bounding box, in the same units as the input file.

Type:

float

height

Height of the bounding box, in the same units as the input file.

Type:

float

category_id

Unique identifier for the category, as specified in the input file. A value of 0 is usually reserved for the background class.

Type:

int

category

Category of the annotation as a string.

Type:

str

supercategory

Supercategory of the annotation as a string.

Type:

str

Raises:

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

See also

pandera.api.pandas.model.DataFrameModel

Methods

build_schema_(**kwargs)
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_empty_values()	Get the default empty values for selected dataframe columns.
get_metadata()	Provide metadata for columns and schema level
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.

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.

static get_empty_values()

Get the default empty values for selected dataframe columns.

The columns are those that can be undefined in VIA and COCO files: category, supercategory, category_id, image_width and image_height.

Returns:

A dictionary with the default empty values the specified columns.

Return type:

dict

classmethod get_metadata()

Provide metadata for columns and schema level

Return type:

dict[Any, Any]

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.