gradio-image-prompter-visible-box 0.1.0

A gradio component to upload images and process point/box prompts with more visible boxes.

gradio_image_prompter_visible_box

A gradio component to upload images and process point/box prompts with more visible boxes.

Installation

pip install gradio_image_prompter_visible_box

Usage

import gradio as gr
from gradio_image_prompter import ImagePrompter

demo = gr.Interface(
    lambda prompts: (prompts["image"], prompts["points"]),
    ImagePrompter(show_label=False),
    [gr.Image(show_label=False), gr.Dataframe(label="Points")],
)
if __name__ == '__main__':
    demo.launch()

ImagePrompter

Initialization

name	type	default	description
value	str | _Image.Image | np.ndarray | None	None	A PIL Image, numpy array, path or URL for the default value. If callable, it will be called set the initial value.
height	int | None	None	Height of the displayed image in pixels.
width	int | None	None	Width of the displayed image in pixels.
image_mode	Literal[ "1", "L", "P", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F", ]	"RGB"	"RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html.
sources	list[Literal["upload", "clipboard"]] | None	None	List of sources for the image.
type	Literal["numpy", "pil", "filepath"]	"numpy"	The format the image is converted before being passed into the prediction function.
label	str | None	None	The label for this component.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open.
show_label	bool | None	None	if True, will display label.
show_download_button	bool	True	If True, will display button to download image.
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. Should be an integer.
min_width	int	160	minimum pixel width, will wrap if not sufficient screen space to satisfy this value.
interactive	bool | None	None	if True, will allow users to upload and edit an image; if False, can only be used to display images.
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.
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.
render	bool	True	If False, component will not render be rendered in the Blocks context.
show_share_button	bool | None	None	If True, show a share icon that allows user to share outputs to Hugging Face Spaces Discussions.

Events

name	description
clear	This listener is triggered when the user clears the ImagePrompter using the X button for the component.
change	Triggered when the value of the ImagePrompter changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
stream	This listener is triggered when the user streams the ImagePrompter.
select	Event listener for when the user selects or deselects the ImagePrompter. Uses event data gradio.SelectData to carry value referring to the label of the ImagePrompter, and selected to refer to state of the ImagePrompter. See EventData documentation on how to use this event data
upload	This listener is triggered when the user uploads a file into the ImagePrompter.

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 output: Is passed, passes the uploaded image as a numpy.array, PIL.Image or str filepath depending on type. For SVGs, the type parameter is ignored and the filepath of the SVG is returned.

def predict(
    value: PromptValue | None
) -> PromptValue:
    return value