optical.converter.base.FormatSpec

class optical.converter.base.FormatSpec(root: Optional[Union[str, os.PathLike]] = None, has_split: Optional[bool] = False, df: Optional[pandas.core.frame.DataFrame] = None, format: Optional[str] = None)[source]

Bases: object

The base class to represent all annotation formats

__init__(root: Optional[Union[str, os.PathLike]] = None, has_split: Optional[bool] = False, df: Optional[pandas.core.frame.DataFrame] = None, format: Optional[str] = None)[source]

Methods

__init__([root, has_split, df, format])

bbox_scatter([split, category, limit])

plots scatter of width and height of bounding boxes

bbox_stats([split, category])

computes bbox descriptive stats e.g., mean, std etc.

convert(to[, output_dir, save_under, ...])

describe()

shows basic data distribution in different split

save(output_dir[, export_to, copy_images])

Just another api for convert.

show_distribution()

Plots distribution of labels in different splits of the dataset

split([test_size, stratified, random_state])

splits the dataset into train and validation sets

Attributes

format

splits

bbox_scatter(split: Optional[str] = None, category: Optional[str] = None, limit: int = 1000) → altair.vegalite.v4.api.Chart[source]

plots scatter of width and height of bounding boxes

Parameters

  • split (Optional[str]) – split of the dataset e.g., train, valid etc. Defaults to None.

  • category (Optional[str]) – category to filter out. Defaults to None.

  • limit (int, optional) – number of samples to plot. Defaults to 1000.

bbox_stats(split: Optional[str] = None, category: Optional[str] = None) → pandas.core.frame.DataFrame[source]

computes bbox descriptive stats e.g., mean, std etc.

Parameters

  • split (Optional[str]) – split of the dataset e.g., train, valid etc. Defaults to None.

  • category (Optional[str]) – category to filter out. Defaults to None.

Returns

stats of the bounding boxes

Return type

pd.DataFrame

describe() → pandas.core.frame.DataFrame[source]

shows basic data distribution in different split

save(output_dir: Optional[Union[str, os.PathLike]], export_to: Optional[str] = None, copy_images: bool = True)[source]

Just another api for convert. Similar to export

show_distribution() → altair.vegalite.v4.api.Chart[source]

Plots distribution of labels in different splits of the dataset

split(test_size: float = 0.2, stratified: bool = False, random_state: int = 42)[source]

splits the dataset into train and validation sets

Parameters

  • test_size (float, optional) – Fraction of total images to be kept for validation. Defaults to 0.2.

  • stratified (bool, optional) – Whether to stratify the split. Defaults to False.

  • random_state (int, optional) – random state for the split. Defaults to 42.

Returns

Returns an instance of FormatSpec class

Return type

FormatSpec