meerkat.interactive.app.src.lib.component.core.ImageAnnotator

class ImageAnnotator(data, *, categories=None, segmentations=None, points=None, boxes=None, opacity: float = 0.85, selected_category: str = '', on_add_category: meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddCategoryInterface] = None, on_add_box: meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddBoxInterface] = None, on_add_point: meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddPointInterface] = None)

__init__(data, *, categories=None, segmentations=None, points=None, boxes=None, opacity: float = 0.85, selected_category: str = '', on_add_category: Optional[meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddCategoryInterface]] = None, on_add_box: Optional[meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddBoxInterface]] = None, on_add_point: Optional[meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.image_annotator.AddPointInterface]] = None)

Parameters

• data – The base image. Strings must be base64 encoded or a filepath to the image.

• categories – The categories in the image. These categories will be used for all annotations. Can either be a list of category names, a dictionary mapping category names to colors, or a DataFrame with two columns (“name” and “color”).

• segmentations – A list of (mask, category) tuples.

• opacity – The initial opacity of the segmentation masks.

• on_select – An endpoint to call when the user clicks on the image.

Methods

__init__(data, *[, categories, ...])	param data The base image.
append(other)
clear_annotations([annotation_type])
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_id(id)
from_orm(obj)
get_components()
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
prepare_categories(categories)
prepare_data(data)
prepend_meerkat_id_prefix(id)
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes

alias
component_name
event_names
events
frontend	Returns a Pydantic model that can be should be sent to the frontend.
frontend_alias
id
identifiable_group
library
namespace
path
prop_bindings
prop_names
props
slots
slottable
virtual_props	Props, and all events (as_*) as props.
wrapper_import_style
data
categories
segmentations
points
boxes
opacity
selected_category
on_add_category
on_add_box
on_add_point