venv/Lib/python3.10/site-packages/gradio/components/annotated_image.py

"""gr.AnnotatedImage() component."""

from __future__ import annotations

from typing import Literal

import numpy as np
from gradio_client.documentation import document, set_documentation_group
from gradio_client.serializing import JSONSerializable
from PIL import Image as _Image  # using _ to minimize namespace pollution

from gradio import utils
from gradio.components.base import IOComponent, _Keywords
from gradio.deprecation import warn_style_method_deprecation
from gradio.events import (
    EventListenerMethod,
    Selectable,
)

set_documentation_group("component")

_Image.init()  # fixes https://github.com/gradio-app/gradio/issues/2843


@document()
class AnnotatedImage(Selectable, IOComponent, JSONSerializable):
    """
    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).
    Preprocessing: this component does *not* accept input.
    Postprocessing: 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.

    Demos: image_segmentation
    """

    def __init__(
        self,
        value: tuple[
            np.ndarray | _Image.Image | str,
            list[tuple[np.ndarray | tuple[int, int, int, int], str]],
        ]
        | None = None,
        *,
        show_legend: bool = True,
        height: int | None = None,
        width: int | None = None,
        color_map: dict[str, str] | None = None,
        label: str | None = None,
        every: float | None = None,
        show_label: bool | None = None,
        container: bool = True,
        scale: int | None = None,
        min_width: int = 160,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        **kwargs,
    ):
        """
        Parameters:
            value: Tuple of base image and list of (subsection, label) pairs.
            show_legend: If True, will show a legend of the subsections.
            height: Height of the displayed image.
            width: Width of the displayed image.
            color_map: A dictionary mapping labels to colors. The colors must be specified as hex codes.
            label: component name in interface.
            every: 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: if True, will display label.
            container: If True, will place the component in a container - providing some extra padding around the border.
            scale: 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: 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: If False, component will be hidden.
            elem_id: 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: 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.
        """
        self.show_legend = show_legend
        self.height = height
        self.width = width
        self.color_map = color_map
        self.select: EventListenerMethod
        """
        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.
        """
        IOComponent.__init__(
            self,
            label=label,
            every=every,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            value=value,
            **kwargs,
        )

    def get_config(self):
        return {
            "show_legend": self.show_legend,
            "value": self.value,
            "height": self.height,
            "width": self.width,
            "color_map": self.color_map,
            "selectable": self.selectable,
            **IOComponent.get_config(self),
        }

    @staticmethod
    def update(
        value: tuple[
            np.ndarray | _Image.Image | str,
            list[tuple[np.ndarray | tuple[int, int, int, int], str]],
        ]
        | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
        show_legend: bool | None = None,
        height: int | None = None,
        width: int | None = None,
        color_map: dict[str, str] | None = None,
        label: str | None = None,
        show_label: bool | None = None,
        container: bool | None = None,
        scale: int | None = None,
        min_width: int | None = None,
        visible: bool | None = None,
    ):
        updated_config = {
            "show_legend": show_legend,
            "height": height,
            "width": width,
            "color_map": color_map,
            "label": label,
            "show_label": show_label,
            "container": container,
            "scale": scale,
            "min_width": min_width,
            "visible": visible,
            "value": value,
            "__type__": "update",
        }
        return updated_config

    def postprocess(
        self,
        y: tuple[
            np.ndarray | _Image.Image | str,
            list[tuple[np.ndarray | tuple[int, int, int, int], str]],
        ],
    ) -> tuple[dict, list[tuple[dict, str]]] | None:
        """
        Parameters:
            y: 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.
        Returns:
            Tuple of base image file and list of subsections, with each subsection a two-part tuple where the first element image path of the mask, and the second element is the label.
        """
        if y is None:
            return None
        base_img = y[0]
        if isinstance(base_img, str):
            base_img_path = base_img
            base_img = np.array(_Image.open(base_img))
        elif isinstance(base_img, np.ndarray):
            base_file = self.img_array_to_temp_file(base_img, dir=self.DEFAULT_TEMP_DIR)
            base_img_path = str(utils.abspath(base_file))
        elif isinstance(base_img, _Image.Image):
            base_file = self.pil_to_temp_file(base_img, dir=self.DEFAULT_TEMP_DIR)
            base_img_path = str(utils.abspath(base_file))
            base_img = np.array(base_img)
        else:
            raise ValueError(
                "AnnotatedImage only accepts filepaths, PIL images or numpy arrays for the base image."
            )
        self.temp_files.add(base_img_path)

        sections = []
        color_map = self.color_map or {}

        def hex_to_rgb(value):
            value = value.lstrip("#")
            lv = len(value)
            return [int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3)]

        for mask, label in y[1]:
            mask_array = np.zeros((base_img.shape[0], base_img.shape[1]))
            if isinstance(mask, np.ndarray):
                mask_array = mask
            else:
                x1, y1, x2, y2 = mask
                border_width = 3
                mask_array[y1:y2, x1:x2] = 0.5
                mask_array[y1:y2, x1 : x1 + border_width] = 1
                mask_array[y1:y2, x2 - border_width : x2] = 1
                mask_array[y1 : y1 + border_width, x1:x2] = 1
                mask_array[y2 - border_width : y2, x1:x2] = 1

            if label in color_map:
                rgb_color = hex_to_rgb(color_map[label])
            else:
                rgb_color = [255, 0, 0]
            colored_mask = np.zeros((base_img.shape[0], base_img.shape[1], 4))
            solid_mask = np.copy(mask_array)
            solid_mask[solid_mask > 0] = 1

            colored_mask[:, :, 0] = rgb_color[0] * solid_mask
            colored_mask[:, :, 1] = rgb_color[1] * solid_mask
            colored_mask[:, :, 2] = rgb_color[2] * solid_mask
            colored_mask[:, :, 3] = mask_array * 255

            colored_mask_img = _Image.fromarray((colored_mask).astype(np.uint8))

            mask_file = self.pil_to_temp_file(
                colored_mask_img, dir=self.DEFAULT_TEMP_DIR
            )
            mask_file_path = str(utils.abspath(mask_file))
            self.temp_files.add(mask_file_path)

            sections.append(
                ({"name": mask_file_path, "data": None, "is_file": True}, label)
            )

        return {"name": base_img_path, "data": None, "is_file": True}, sections

    def style(
        self,
        *,
        height: int | None = None,
        width: int | None = None,
        color_map: dict[str, str] | None = None,
        **kwargs,
    ):
        """
        This method is deprecated. Please set these arguments in the constructor instead.
        """
        warn_style_method_deprecation()
        if height is not None:
            self.height = height
        if width is not None:
            self.width = width
        if color_map is not None:
            self.color_map = color_map
        return self