Gradio AnnotatedImage Docs

New to Gradio? Start here: Getting Started

AnnotatedImage

gradio.AnnotatedImage(···)

Description

Displays a base image and colored subsections on top of that image. Subsections can take the from of rectangles (e.g. object detection) or masks (e.g. image segmentation).

Behavior

As input: this component does *not* accept input.

As output: expects a Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]] consisting of a base image and a list of subsections, that are either (x1, y1, x2, y2) tuples identifying object boundaries, or 0-1 confidence masks of the same shape as the image. A label is provided for each subsection.

Initialization

Parameter	Description
`value` tuple[np.ndarray \| _Image.Image \| str, list[tuple[np.ndarray \| tuple[int, int, int, int], str]]] \| None default: None	Tuple of base image and list of (subsection, label) pairs.
`show_legend` bool default: True	If True, will show a legend of the subsections.
`height` int \| None default: None	Height of the displayed image.
`width` int \| None default: None	Width of the displayed image.
`color_map` dict[str, str] \| None default: None	A dictionary mapping labels to colors. The colors must be specified as hex codes.
`label` str \| None default: None	component name in interface.
`every` float \| None default: 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 default: None	if True, will display label.
`container` bool default: True	If True, will place the component in a container - providing some extra padding around the border.
`scale` int \| None default: 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 default: 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 default: True	If False, component will be hidden.
`elem_id` str \| None default: 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 default: 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.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.AnnotatedImage`	"annotatedimage"	Uses default values

Demos

import gradio as gr
import numpy as np
import random

with gr.Blocks() as demo:
    section_labels = [
        "apple",
        "banana",
        "carrot",
        "donut",
        "eggplant",
        "fish",
        "grapes",
        "hamburger",
        "ice cream",
        "juice",
    ]

    with gr.Row():
        num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
        num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")

    with gr.Row():
        img_input = gr.Image()
        img_output = gr.AnnotatedImage().style(
            color_map={"banana": "#a89a00", "carrot": "#ffae00"}
        )

    section_btn = gr.Button("Identify Sections")
    selected_section = gr.Textbox(label="Selected Section")

    def section(img, num_boxes, num_segments):
        sections = []
        for a in range(num_boxes):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            w = random.randint(0, img.shape[1] - x)
            h = random.randint(0, img.shape[0] - y)
            sections.append(((x, y, x + w, y + h), section_labels[a]))
        for b in range(num_segments):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
            mask = np.zeros(img.shape[:2])
            for i in range(img.shape[0]):
                for j in range(img.shape[1]):
                    dist_square = (i - y) ** 2 + (j - x) ** 2
                    if dist_square < r**2:
                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
            sections.append((mask, section_labels[b + num_boxes]))
        return (img, sections)

    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)

    def select_section(evt: gr.SelectData):
        return section_labels[evt.index]

    img_output.select(select_section, None, selected_section)

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

Methods

select

gradio.AnnotatedImage.select(fn, ···)

Description

Event listener for when the user selects Image subsection. Uses event data gradio.SelectData to carry `value` referring to selected subsection label, and `index` to refer to subsection index. See EventData documentation on how to use this event data.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| Sequence[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| Sequence[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None \| Literal[False] default: None	Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when This listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

← Components

Audio →

AnnotatedImage

Description
Behavior
Initialization
Shortcuts
Demos
Methods
select