app.py

# Gradio YOLOv8 Det v1.3.0
# 创建人：曾逸夫
# 创建时间：2023-12-15
# pip install gradio>=4.9.1
# python gradio_yolov8_det_v1.py


import argparse
import csv
import random
import sys
from collections import Counter
from pathlib import Path

import cv2
import gradio as gr
from gradio_imageslider import ImageSlider
import tempfile
import uuid
import numpy as np
from matplotlib import font_manager
from ultralytics import YOLO

ROOT_PATH = sys.path[0]  # 项目根目录

# --------------------- 字体库 ---------------------
SimSun_path = f"{ROOT_PATH}/fonts/SimSun.ttf"  # 宋体文件路径
TimesNesRoman_path = f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"  # 新罗马字体文件路径
# 宋体
SimSun = font_manager.FontProperties(fname=SimSun_path, size=12)
# 新罗马字体
TimesNesRoman = font_manager.FontProperties(fname=TimesNesRoman_path, size=12)

import yaml
from PIL import Image, ImageDraw, ImageFont

from util.fonts_opt import is_fonts

# Gradio YOLOv8 Det版本
GYD_VERSION = "Gradio YOLOv8 Det v1.2.1"

# 文件后缀
suffix_list = [".csv", ".yaml"]

# 字体大小
FONTSIZE = 25

# 目标尺寸
obj_style = ["小目标", "中目标", "大目标"]

GYD_TITLE = """
<p align='center'><a href='https://gitee.com/CV_Lab/gradio-yolov8-det'>
<img src='https://pycver.gitee.io/ows-pics/imgs/gradio_yolov8_det_logo.png' alt='Simple Icons' ></a>
<p align='center'>基于 Gradio 的 YOLOv8 通用计算机视觉演示系统</p><p align='center'>集成目标检测、图像分割和图像分类于一体，可自定义检测模型</p>
</p>
<p align='center'>
<a href='https://gitee.com/CV_Lab/gradio-yolov8-det'><img src='https://gitee.com/CV_Lab/gradio-yolov8-det/widgets/widget_6.svg' alt='Fork me on Gitee'></img></a>
</p>
"""

GYD_SUB_TITLE = """
作者：曾逸夫，Gitee：https://gitee.com/PyCVer ，Github：https://github.com/Zengyf-CVer
"""

EXAMPLES_DET = [
    ["./img_examples/bus.jpg", "yolov8s", "cpu", 640, 0.6, 0.5, 100, "所有尺寸"],
    ["./img_examples/giraffe.jpg", "yolov8l", "cpu", 320, 0.5, 0.45, 100, "所有尺寸"],
    ["./img_examples/zidane.jpg", "yolov8m", "cpu", 640, 0.6, 0.5, 100, "所有尺寸"],
    [
        "./img_examples/Millenial-at-work.jpg",
        "yolov8x",
        "cpu",
        1280,
        0.5,
        0.5,
        100,
        "所有尺寸",
    ],
    ["./img_examples/bus.jpg", "yolov8s-seg", "cpu", 640, 0.6, 0.5, 100, "所有尺寸"],
    [
        "./img_examples/Millenial-at-work.jpg",
        "yolov8x-seg",
        "cpu",
        1280,
        0.5,
        0.5,
        100,
        "所有尺寸",
    ],
]

EXAMPLES_CLAS = [
    ["./img_examples/img_clas/ILSVRC2012_val_00000008.JPEG", "yolov8s-cls"],
    ["./img_examples/img_clas/ILSVRC2012_val_00000018.JPEG", "yolov8l-cls"],
    ["./img_examples/img_clas/ILSVRC2012_val_00000023.JPEG", "yolov8m-cls"],
    ["./img_examples/img_clas/ILSVRC2012_val_00000067.JPEG", "yolov8m-cls"],
    ["./img_examples/img_clas/ILSVRC2012_val_00000077.JPEG", "yolov8m-cls"],
    ["./img_examples/img_clas/ILSVRC2012_val_00000247.JPEG", "yolov8m-cls"],
]

GYD_CSS = """#disp_image {
        text-align: center; /* Horizontally center the content */
    }"""


def parse_args(known=False):
    parser = argparse.ArgumentParser(description=GYD_VERSION)
    parser.add_argument(
        "--model_name", "-mn", default="yolov8s", type=str, help="model name"
    )
    parser.add_argument(
        "--model_cfg",
        "-mc",
        default="./model_config/model_name_all.yaml",
        type=str,
        help="model config",
    )
    parser.add_argument(
        "--cls_name",
        "-cls",
        default="./cls_name/cls_name_zh.yaml",
        type=str,
        help="cls name",
    )
    parser.add_argument(
        "--cls_imgnet_name",
        "-cin",
        default="./cls_name/cls_imagenet_name_zh.yaml",
        type=str,
        help="cls ImageNet name",
    )
    parser.add_argument(
        "--nms_conf",
        "-conf",
        default=0.5,
        type=float,
        help="model NMS confidence threshold",
    )
    parser.add_argument(
        "--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold"
    )
    parser.add_argument(
        "--inference_size", "-isz", default=640, type=int, help="model inference size"
    )
    parser.add_argument(
        "--max_detnum", "-mdn", default=50, type=float, help="model max det num"
    )
    parser.add_argument(
        "--slider_step", "-ss", default=0.05, type=float, help="slider step"
    )
    parser.add_argument(
        "--is_login",
        "-isl",
        action="store_true",
        default=False,
        help="is login",
    )
    parser.add_argument(
        "--usr_pwd",
        "-up",
        nargs="+",
        type=str,
        default=["admin", "admin"],
        help="user & password for login",
    )
    parser.add_argument(
        "--is_share",
        "-is",
        action="store_true",
        default=False,
        help="is login",
    )
    parser.add_argument(
        "--server_port", "-sp", default=7860, type=int, help="server port"
    )

    args = parser.parse_known_args()[0] if known else parser.parse_args()
    return args


# yaml文件解析
def yaml_parse(file_path):
    return yaml.safe_load(open(file_path, encoding="utf-8").read())


# yaml csv 文件解析
def yaml_csv(file_path, file_tag):
    file_suffix = Path(file_path).suffix
    if file_suffix == suffix_list[0]:
        # 模型名称
        file_names = [i[0] for i in list(csv.reader(open(file_path)))]  # csv版
    elif file_suffix == suffix_list[1]:
        # 模型名称
        file_names = yaml_parse(file_path).get(file_tag)  # yaml版
    else:
        print(f"{file_path}格式不正确！程序退出！")
        sys.exit()

    return file_names


# 检查网络连接
def check_online():
    # 参考：https://github.com/ultralytics/yolov5/blob/master/utils/general.py
    # Check internet connectivity
    import socket

    try:
        socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
        return True
    except OSError:
        return False


# 标签和边界框颜色设置
def color_set(cls_num):
    color_list = []
    for i in range(cls_num):
        color = tuple(np.random.choice(range(256), size=3))
        color_list.append(color)

    return color_list


# 随机生成浅色系或者深色系
def random_color(cls_num, is_light=True):
    color_list = []
    for i in range(cls_num):
        color = (
            random.randint(0, 127) + int(is_light) * 128,
            random.randint(0, 127) + int(is_light) * 128,
            random.randint(0, 127) + int(is_light) * 128,
        )
        color_list.append(color)

    return color_list


# 检测绘制
def pil_draw(img, score_l, bbox_l, cls_l, cls_index_l, textFont, color_list):
    img_pil = ImageDraw.Draw(img)
    id = 0

    for score, (xmin, ymin, xmax, ymax), label, cls_index in zip(
        score_l, bbox_l, cls_l, cls_index_l
    ):
        img_pil.rectangle(
            [xmin, ymin, xmax, ymax], fill=None, outline=color_list[cls_index], width=2
        )  # 边界框
        countdown_msg = f"{id}-{label} {score:.2f}"
        # text_w, text_h = textFont.getsize(countdown_msg)  # 标签尺寸 pillow 9.5.0
        # left, top, left + width, top + height
        # 标签尺寸 pillow 10.0.0
        text_xmin, text_ymin, text_xmax, text_ymax = textFont.getbbox(countdown_msg)
        # 标签背景
        img_pil.rectangle(
            # (xmin, ymin, xmin + text_w, ymin + text_h), # pillow 9.5.0
            (
                xmin,
                ymin,
                xmin + text_xmax - text_xmin,
                ymin + text_ymax - text_ymin,
            ),  # pillow 10.0.0
            fill=color_list[cls_index],
            outline=color_list[cls_index],
        )

        # 标签
        img_pil.multiline_text(
            (xmin, ymin),
            countdown_msg,
            fill=(0, 0, 0),
            font=textFont,
            align="center",
        )

        id += 1

    return img


# 绘制多边形
def polygon_drawing(img_mask, canvas, color_seg):
    # ------- RGB转BGR -------
    color_seg = list(color_seg)
    color_seg[0], color_seg[2] = color_seg[2], color_seg[0]
    color_seg = tuple(color_seg)
    # 定义多边形的顶点
    pts = np.array(img_mask, dtype=np.int32)

    # 多边形绘制
    cv2.drawContours(canvas, [pts], -1, color_seg, thickness=-1)


# 输出分割结果
def seg_output(img_path, seg_mask_list, color_list, cls_list):
    img = cv2.imread(img_path)
    img_c = img.copy()

    # w, h = img.shape[1], img.shape[0]

    # 获取分割坐标
    for seg_mask, cls_index in zip(seg_mask_list, cls_list):
        img_mask = []
        for i in range(len(seg_mask)):
            # img_mask.append([seg_mask[i][0] * w, seg_mask[i][1] * h])
            img_mask.append([seg_mask[i][0], seg_mask[i][1]])

        polygon_drawing(img_mask, img_c, color_list[int(cls_index)])  # 绘制分割图形

    img_mask_merge = cv2.addWeighted(img, 0.3, img_c, 0.7, 0)  # 合并图像

    return img_mask_merge


# 目标检测和图像分割模型加载
def model_det_loading(
    img_path, device_opt, conf, iou, infer_size, max_det, yolo_model="yolov8n.pt"
):
    model = YOLO(yolo_model)

    results = model(
        source=img_path,
        device=device_opt,
        imgsz=infer_size,
        conf=conf,
        iou=iou,
        max_det=max_det,
    )
    results = list(results)[0]
    return results


# 图像分类模型加载
def model_cls_loading(img_path, yolo_model="yolov8s-cls.pt"):
    model = YOLO(yolo_model)

    results = model(source=img_path)
    results = list(results)[0]
    return results


# YOLOv8图片检测函数
def yolo_det_img(
    img_path, model_name, device_opt, infer_size, conf, iou, max_det, obj_size
):
    global model, model_name_tmp, device_tmp

    s_obj, m_obj, l_obj = 0, 0, 0

    area_obj_all = []  # 目标面积

    score_det_stat = []  # 置信度统计
    bbox_det_stat = []  # 边界框统计
    cls_det_stat = []  # 类别数量统计
    cls_index_det_stat = []  # 1

    # 模型加载
    predict_results = model_det_loading(
        img_path,
        device_opt,
        conf,
        iou,
        infer_size,
        max_det,
        yolo_model=f"{model_name}.pt",
    )
    # 检测参数
    xyxy_list = predict_results.boxes.xyxy.cpu().numpy().tolist()
    conf_list = predict_results.boxes.conf.cpu().numpy().tolist()
    cls_list = predict_results.boxes.cls.cpu().numpy().tolist()

    # 颜色列表
    color_list = random_color(len(model_cls_name_cp), True)

    img = Image.open(img_path)
    img_cp = img.copy()

    # 图像分割
    if model_name[-3:] == "seg":
        # masks_list = predict_results.masks.xyn
        masks_list = predict_results.masks.xy
        img_mask_merge = seg_output(img_path, masks_list, color_list, cls_list)
        img = Image.fromarray(cv2.cvtColor(img_mask_merge, cv2.COLOR_BGRA2RGB))

    # 判断检测对象是否为空
    if xyxy_list != []:
        # ---------------- 加载字体 ----------------
        yaml_index = cls_name.index(".yaml")
        cls_name_lang = cls_name[yaml_index - 2 : yaml_index]

        if cls_name_lang == "zh":
            # 中文
            textFont = ImageFont.truetype(
                str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE
            )
        elif cls_name_lang in ["en", "ru", "es", "ar"]:
            # 英文、俄语、西班牙语、阿拉伯语
            textFont = ImageFont.truetype(
                str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE
            )
        elif cls_name_lang == "ko":
            # 韩语
            textFont = ImageFont.truetype(
                str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE
            )

        for i in range(len(xyxy_list)):
            # ------------ 边框坐标 ------------
            x0 = int(xyxy_list[i][0])
            y0 = int(xyxy_list[i][1])
            x1 = int(xyxy_list[i][2])
            y1 = int(xyxy_list[i][3])

            # ---------- 加入目标尺寸 ----------
            w_obj = x1 - x0
            h_obj = y1 - y0
            area_obj = w_obj * h_obj  # 目标尺寸

            if obj_size == obj_style[0] and area_obj > 0 and area_obj <= 32**2:
                obj_cls_index = int(cls_list[i])  # 类别索引
                cls_index_det_stat.append(obj_cls_index)

                obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                cls_det_stat.append(obj_cls)

                bbox_det_stat.append((x0, y0, x1, y1))

                conf = float(conf_list[i])  # 置信度
                score_det_stat.append(conf)

                area_obj_all.append(area_obj)
            elif (
                obj_size == obj_style[1] and area_obj > 32**2 and area_obj <= 96**2
            ):
                obj_cls_index = int(cls_list[i])  # 类别索引
                cls_index_det_stat.append(obj_cls_index)

                obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                cls_det_stat.append(obj_cls)

                bbox_det_stat.append((x0, y0, x1, y1))

                conf = float(conf_list[i])  # 置信度
                score_det_stat.append(conf)

                area_obj_all.append(area_obj)
            elif obj_size == obj_style[2] and area_obj > 96**2:
                obj_cls_index = int(cls_list[i])  # 类别索引
                cls_index_det_stat.append(obj_cls_index)

                obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                cls_det_stat.append(obj_cls)

                bbox_det_stat.append((x0, y0, x1, y1))

                conf = float(conf_list[i])  # 置信度
                score_det_stat.append(conf)

                area_obj_all.append(area_obj)
            elif obj_size == "所有尺寸":
                obj_cls_index = int(cls_list[i])  # 类别索引
                cls_index_det_stat.append(obj_cls_index)

                obj_cls = model_cls_name_cp[obj_cls_index]  # 类别
                cls_det_stat.append(obj_cls)

                bbox_det_stat.append((x0, y0, x1, y1))

                conf = float(conf_list[i])  # 置信度
                score_det_stat.append(conf)

                area_obj_all.append(area_obj)

        det_img = pil_draw(
            img,
            score_det_stat,
            bbox_det_stat,
            cls_det_stat,
            cls_index_det_stat,
            textFont,
            color_list,
        )

        # -------------- 目标尺寸计算 --------------
        for i in range(len(area_obj_all)):
            if 0 < area_obj_all[i] <= 32**2:
                s_obj = s_obj + 1
            elif 32**2 < area_obj_all[i] <= 96**2:
                m_obj = m_obj + 1
            elif area_obj_all[i] > 96**2:
                l_obj = l_obj + 1

        sml_obj_total = s_obj + m_obj + l_obj
        objSize_dict = {}
        objSize_dict = {
            obj_style[i]: [s_obj, m_obj, l_obj][i] / sml_obj_total for i in range(3)
        }

        # ------------ 类别统计 ------------
        clsRatio_dict = {}
        clsDet_dict = Counter(cls_det_stat)
        clsDet_dict_sum = sum(clsDet_dict.values())
        for k, v in clsDet_dict.items():
            clsRatio_dict[k] = v / clsDet_dict_sum

        images = (det_img, img_cp)
        images_names = ("det", "raw")
        images_path = tempfile.mkdtemp()
        images_paths = []
        uuid_name = uuid.uuid4()
        for image, image_name in zip(images, images_names):
            image.save(images_path + f"/img_{uuid_name}_{image_name}.jpg")
            images_paths.append(images_path + f"/img_{uuid_name}_{image_name}.jpg")

        gr.Info("图片检测成功！")
        return (det_img, img_cp), images_paths, objSize_dict, clsRatio_dict
    else:
        raise gr.Error("图片检测失败！")


# YOLOv8图片分类函数
def yolo_cls_img(img_path, model_name):
    # 模型加载
    predict_results = model_cls_loading(img_path, yolo_model=f"{model_name}.pt")

    det_img = Image.open(img_path)
    clas_ratio_list = predict_results.probs.top5conf.tolist()
    clas_index_list = predict_results.probs.top5

    clas_name_list = []
    for i in clas_index_list:
        # clas_name_list.append(predict_results.names[i])
        clas_name_list.append(model_cls_imagenet_name_cp[i])

    clsRatio_dict = {}
    index_cls = 0
    clsDet_dict = Counter(clas_name_list)
    for k, v in clsDet_dict.items():
        clsRatio_dict[k] = clas_ratio_list[index_cls]
        index_cls += 1

    return det_img, clsRatio_dict


def main(args):
    gr.close_all()

    global model_cls_name_cp, model_cls_imagenet_name_cp, cls_name

    nms_conf = args.nms_conf
    nms_iou = args.nms_iou
    model_name = args.model_name
    model_cfg = args.model_cfg
    cls_name = args.cls_name
    cls_imagenet_name = args.cls_imgnet_name  # ImageNet类别
    inference_size = args.inference_size
    max_detnum = args.max_detnum
    slider_step = args.slider_step

    is_fonts(f"{ROOT_PATH}/fonts")  # 检查字体文件

    model_names = yaml_csv(model_cfg, "model_names")  # 模型名称
    model_cls_name = yaml_csv(cls_name, "model_cls_name")  # 类别名称
    model_cls_imagenet_name = yaml_csv(cls_imagenet_name, "model_cls_name")  # 类别名称

    model_cls_name_cp = model_cls_name.copy()  # 类别名称
    model_cls_imagenet_name_cp = model_cls_imagenet_name.copy()  # 类别名称

    custom_theme = gr.themes.Soft(primary_hue="blue").set(
        button_secondary_background_fill="*neutral_100",
        button_secondary_background_fill_hover="*neutral_200",
    )

    custom_css = GYD_CSS

    # ------------ Gradio Blocks ------------
    with gr.Blocks(theme=custom_theme, css=custom_css) as gyd:
        with gr.Row():
            gr.Markdown(GYD_TITLE)
        with gr.Row():
            gr.Markdown(GYD_SUB_TITLE)
        with gr.Row():
                with gr.Tabs():
                    with gr.TabItem("目标检测与图像分割"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                with gr.Row():
                                    inputs_img = gr.Image(image_mode="RGB", type="filepath", label="原始图片")
                                with gr.Row():
                                    device_opt = gr.Radio(choices=["cpu", "0", "1", "2", "3"], value="cpu", label="设备")
                                with gr.Row():
                                    inputs_model = gr.Dropdown(choices=model_names, value=model_name, type="value", label="模型")
                                with gr.Accordion("高级设置", open=True):
                                    with gr.Row():
                                        inputs_size = gr.Slider(320, 1600, step=1, value=inference_size, label="推理尺寸")
                                        max_det = gr.Slider(1, 1000, step=1, value=max_detnum, label="最大检测数")
                                    with gr.Row():
                                        input_conf = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="置信度阈值")
                                        inputs_iou = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU 阈值")
                                    with gr.Row():
                                        obj_size = gr.Radio(choices=["所有尺寸", "小目标", "中目标", "大目标"], value="所有尺寸", label="目标尺寸")
                                with gr.Row():
                                    gr.ClearButton(inputs_img, value="清除")
                                    det_btn_img = gr.Button(value='检测', variant="primary")
                            with gr.Column(scale=1):
                                # with gr.Row():
                                #     outputs_img = gr.Image(type="pil", label="检测图片")
                                with gr.Row():
                                    outputs_img_slider = ImageSlider(position=0.5, label="检测图片")
                                with gr.Row():
                                    outputs_imgfiles = gr.Files(label="图片下载")
                                with gr.Row():
                                    outputs_objSize = gr.Label(label="目标尺寸占比统计")
                                with gr.Row():
                                    outputs_clsSize = gr.Label(label="类别检测占比统计")
                        with gr.Row():
                            gr.Examples(
                                examples=EXAMPLES_DET,
                                fn=yolo_det_img,
                                inputs=[
                                    inputs_img, inputs_model, device_opt, inputs_size, input_conf, inputs_iou, max_det,
                                    obj_size],
                                # outputs=[outputs_img, outputs_objSize, outputs_clsSize],
                                cache_examples=False)

                    with gr.TabItem("图像分类"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                with gr.Row():
                                    inputs_img_cls = gr.Image(image_mode="RGB", type="filepath", label="原始图片")
                                with gr.Row():
                                    inputs_model_cls = gr.Dropdown(choices=[
                                        "yolov8n-cls", "yolov8s-cls", "yolov8l-cls", "yolov8m-cls", "yolov8x-cls"],
                                                                value="yolov8s-cls",
                                                                type="value",
                                                                label="模型")
                                with gr.Row():
                                    gr.ClearButton(inputs_img, value="清除")
                                    det_btn_img_cls = gr.Button(value='检测', variant="primary")
                            with gr.Column(scale=1):
                                with gr.Row():
                                    outputs_img_cls = gr.Image(type="pil", label="检测图片")
                                with gr.Row():
                                    outputs_ratio_cls = gr.Label(label="图像分类结果")
                        with gr.Row():
                            gr.Examples(
                                examples=EXAMPLES_CLAS,
                                fn=yolo_cls_img,
                                inputs=[inputs_img_cls, inputs_model_cls],
                                # outputs=[outputs_img_cls, outputs_ratio_cls],
                                cache_examples=False)
                            

        with gr.Accordion("Gradio YOLOv8 Det 安装与使用教程"):
            gr.Markdown(
                """## Gradio YOLOv8 Det 安装与使用教程
            ```shell
            conda create -n yolo python==3.8
            conda activate yolo # 进入环境
            git clone https://gitee.com/CV_Lab/gradio-yolov8-det.git
            cd gradio-yolov8-det
            pip install -r ./requirements.txt -U
            ```
            ```shell
            # 共享模式
            python gradio_yolov8_det_v1.py -is # 在浏览器中以共享模式打开，https://**.gradio.app/
            # 自定义模型配置
            python gradio_yolov8_det_v1.py -mc ./model_config/model_name_all.yaml
            # 自定义下拉框默认模型名称
            python gradio_yolov8_det_v1.py -mn yolov8m
            # 自定义类别名称
            python gradio_yolov8_det_v1.py -cls ./cls_name/cls_name_zh.yaml  （目标检测与图像分割）
            python gradio_yolov8_det_v1.py -cin ./cls_name/cls_imgnet_name_zh.yaml  （图像分类）
            # 自定义NMS置信度阈值
            python gradio_yolov8_det_v1.py -conf 0.8
            # 自定义NMS IoU阈值
            python gradio_yolov8_det_v1.py -iou 0.5
            # 设置推理尺寸，默认为640
            python gradio_yolov8_det_v1.py -isz 320
            # 设置最大检测数，默认为50
            python gradio_yolov8_det_v1.py -mdn 100
            # 设置滑块步长，默认为0.05
            python gradio_yolov8_det_v1.py -ss 0.01
            ```
            """
            )

        det_btn_img.click(
            fn=yolo_det_img,
            inputs=[
                inputs_img,
                inputs_model,
                device_opt,
                inputs_size,
                input_conf,
                inputs_iou,
                max_det,
                obj_size,
            ],
            outputs=[
                outputs_img_slider,
                outputs_imgfiles,
                outputs_objSize,
                outputs_clsSize,
            ],
        )

        det_btn_img_cls.click(
            fn=yolo_cls_img,
            inputs=[inputs_img_cls, inputs_model_cls],
            outputs=[outputs_img_cls, outputs_ratio_cls],
        )

    return gyd


if __name__ == "__main__":
    args = parse_args()
    gyd = main(args)
    is_share = args.is_share

    gyd.queue().launch(
        inbrowser=True,  # 自动打开默认浏览器
        share=is_share,  # 项目共享，其他设备可以访问
        favicon_path="./icon/logo.ico",  # 网页图标
        show_error=True,  # 在浏览器控制台中显示错误信息
        quiet=True,  # 禁止大多数打印语句
    )