gradio-testannimage 6.50.1

Python library for easily interacting with trained machine learning models

gradio_testannimage

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_testannimage

Usage

import gradio as gr
from gradio_testannimage import TestAnnImage


with gr.Blocks() as demo:
    with gr.Row():
        TestAnnImage(label="Blank"),  # blank component
        TestAnnImage(label="Populated"),  # populated component


if __name__ == "__main__":
    demo.launch()

TestAnnImage

Initialization

name	type	default	description
value	tuple[ numpy.ndarray | PIL.Image.Image | str, list[ tuple[ numpy.ndarray | tuple[int, int, int, int], str, ] ], ] | None	None	Tuple of base image and list of (subsection, label) pairs.
show_legend	bool	True	If True, will show a legend of the subsections.
height	int | str | None	None	The height of the image, specified in pixels if a number is passed, or in CSS units if a string is passed.
width	int | str | None	None	The width of the image, specified in pixels if a number is passed, or in CSS units if a string is passed.
color_map	dict[str, str] | None	None	A dictionary mapping labels to colors. The colors must be specified as hex codes.
label	str | None	None	The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label	bool | None	None	if True, will display label.
container	bool	True	If True, will place the component in a container - providing some extra padding around the border.
scale	int | None	None	relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
min_width	int	160	minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
visible	bool	True	If False, component will be hidden.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes	list[str] | str | None	None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render	bool	True	If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

Events

name	description
select	Event listener for when the user selects or deselects the TestAnnImage. Uses event data gradio.SelectData to carry value referring to the label of the TestAnnImage, and selected to refer to state of the TestAnnImage. See EventData documentation on how to use this event data

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As input: Should return, tuple of base image and list of subsections, with each subsection a two-part tuple where the first element is a 4 element bounding box or a 0-1 confidence mask, and the second element is the label.

def predict(
    value: AnnotatedImageData | None
) -> tuple[
       numpy.ndarray | PIL.Image.Image | str,
       list[
           tuple[
               numpy.ndarray | tuple[int, int, int, int],
               str,
           ]
       ],
   ]
   | None:
    return value

AnnotatedImageData

class AnnotatedImageData(GradioModel):
    image: FileData
    annotations: List[Annotation]

Annotation

class Annotation(GradioModel):
    image: FileData
    label: str