dev(narugo): add gradio demo for yolo

import numpy as np

from PIL import Image

from hbutils.color import rnd_colors

from hfutils.utils import hf_fs_path, hf_normpath

from huggingface_hub import HfFileSystem, hf_hub_download

from natsort import natsorted

from ..data import load_image, rgb_encode, ImageTyping

from ..utils import open_onnx_model

try:

    import gradio as gr

except (ImportError, ModuleNotFoundError):

    gr = None

__all__ = [

    'YOLOModel',

    'yolo_predict',

]

def _check_gradio_env():

    if gr is None:

        raise EnvironmentError(f'Gradio required for launching webui-based demo.\n'

                               f'Please install it with `pip install dghs-imgutils[demo]`.')

def _v_fix(v):

    return int(round(v))

def _bbox_fix(bbox):

    return tuple(map(_v_fix, bbox))

def _yolo_xywh2xyxy(x: np.ndarray) -> np.ndarray:

    """

    Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format.

        """

        self._models.clear()

    def make_ui(self, default_model_name: Optional[str] = None,

                default_conf_threshold: float = 0.25, default_iou_threshold: float = 0.7):

        _check_gradio_env()

        model_list = self.model_names

        default_model_name = default_model_name or natsorted(self.model_names)[-1]

        def _gr_detect(image: ImageTyping, model_name: str,

                       iou_threshold: float = 0.7, score_threshold: float = 0.25) \

                -> gr.AnnotatedImage:

            _, _, labels = self._open_model(model_name=model_name)

            _colors = list(map(str, rnd_colors(len(labels))))

            _color_map = dict(zip(labels, _colors))

            return gr.AnnotatedImage(

                value=(image, [

                    (_bbox_fix(bbox), label)

                    for bbox, label, _ in self.predict(

                        image=image,

                        model_name=model_name,

                        iou_threshold=iou_threshold,

                        conf_threshold=score_threshold,

                    )

                ]),

                color_map=_color_map,

                label='Labeled',

            )

        with gr.Row():

            with gr.Column():

                gr_input_image = gr.Image(type='pil', label='Original Image')

                gr_model = gr.Dropdown(model_list, value=default_model_name, label='Model')

                with gr.Row():

                    gr_iou_threshold = gr.Slider(0.0, 1.0, default_iou_threshold, label='IOU Threshold')

                    gr_score_threshold = gr.Slider(0.0, 1.0, default_conf_threshold, label='Score Threshold')

                gr_submit = gr.Button(value='Submit', variant='primary')

            with gr.Column():

                gr_output_image = gr.AnnotatedImage(label="Labeled")

            gr_submit.click(

                _gr_detect,

                inputs=[

                    gr_input_image,

                    gr_model,

                    gr_iou_threshold,

                    gr_score_threshold,

                ],

                outputs=[gr_output_image],

            )

    def launch_demo(self, default_model_name: Optional[str] = None,

                    default_conf_threshold: float = 0.25, default_iou_threshold: float = 0.7,

                    server_port: int = 7860, **kwargs):

        _check_gradio_env()

        with gr.Blocks() as demo:

            self.make_ui(

                default_model_name=default_model_name,

                default_conf_threshold=default_conf_threshold,

                default_iou_threshold=default_iou_threshold,

            )

        demo.launch(

            server_port=server_port,

            **kwargs,

        )

@lru_cache()

def _open_models_for_repo_id(repo_id: str, hf_token: Optional[str] = None) -> YOLOModel:

gradio>=4.44.0

 No newline at end of file