flow_segmenter.config.SegmenterConfig

class flow_segmenter.config.SegmenterConfig(*, order_lines=False, export=False, baselines=False, kraken_linemasks=False, creator='The-Flow-Project', model_names, batch_sizes=2, textline_check=True, load_existing_segmentation=False, yolo_args=None)

Configuration model for the segmentation process.

Parameters:

• order_lines (bool)

• export (bool)

• baselines (bool)

• kraken_linemasks (bool)

• creator (str)

• model_names (str | list[str])

• batch_sizes (int | list[int])

• textline_check (bool)

• load_existing_segmentation (bool)

• yolo_args (dict[str, Any] | None)

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)
validate_baselines_true()	Ensure baselines is True if kraken_linemasks is True.
validate_batch_sizes_length()	Ensure batch_sizes list matches model_names list length.
validate_batch_sizes_positive(v)	Ensure all batch sizes are positive integers.

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
model_names
batch_sizes
textline_check
load_existing_segmentation
yolo_args
order_lines
export
baselines
kraken_linemasks
creator