import argparse
import csv
import sys
from pathlib import Path

import gradio as gr
import torch
import yaml
from PIL import Image

ROOT_PATH = sys.path[0]  # 根目录

# 模型路径
model_path = "ultralytics/yolov5"
# 模型名称临时变量
model_name_tmp = ""
# 设备临时变量
device_tmp = ""
# 文件后缀
suffix_list = [".csv", ".yaml"]
def parse_args(known=False):
    parser = argparse.ArgumentParser(description="Gradio LIVE")
    parser.add_argument(
        "--model_name", "-mn", default="yolov5s", type=str, help="model name"
    )
    parser.add_argument(
        "--model_cfg",
        "-mc",
        default="./model_config/model_name_p5_all.yaml",
        type=str,
        help="model config",
    )
    parser.add_argument(
        "--cls_name",
        "-cls",
        default="./cls_name/cls_name.yaml",
        type=str,
        help="cls 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(
        "--label_dnt_show",
        "-lds",
        action="store_false",
        default=True,
        help="label show",
    )
    parser.add_argument(
        "--device",
        "-dev",
        default="cpu",
        type=str,
        help="cuda or cpu, hugging face only cpu",
    )
    parser.add_argument(
        "--inference_size", "-isz", default=640, type=int, help="model inference size"
    )

    args = parser.parse_known_args()[0] if known else parser.parse_args()
    return args
#  模型加载
def model_loading(model_name, device):

    # 加载本地模型
    model = torch.hub.load(model_path, model_name, force_reload=True, device=device)

    return model
# 检测信息
def export_json(results, model, img_size):

    return [
        [
            {
                "id": int(i),
                "class": int(result[i][5]),
                "class_name": model.model.names[int(result[i][5])],
                "normalized_box": {
                    "x0": round(result[i][:4].tolist()[0], 6),
                    "y0": round(result[i][:4].tolist()[1], 6),
                    "x1": round(result[i][:4].tolist()[2], 6),
                    "y1": round(result[i][:4].tolist()[3], 6),
                },
                "confidence": round(float(result[i][4]), 2),
                "fps": round(1000 / float(results.t[1]), 2),
                "width": img_size[0],
                "height": img_size[1],
            }
            for i in range(len(result))
        ]
        for result in results.xyxyn
    ]
def yolo_det(img, experiment_id, device=None, model_name=None, inference_size=None, conf=None, iou=None, label_opt=None, model_cls=None):

    global model, model_name_tmp, device_tmp

    if model_name_tmp != model_name:
        # 模型判断，避免反复加载
        model_name_tmp = model_name
        model = model_loading(model_name_tmp, device)
    elif device_tmp != device:
        device_tmp = device
        model = model_loading(model_name_tmp, device)

    # -----------模型调参-----------
    model.conf = conf  # NMS 置信度阈值
    model.iou = iou  # NMS IOU阈值
    model.max_det = 1000  # 最大检测框数
    model.classes = model_cls  # 模型类别

    results = model(img, size=inference_size)  # 检测
    results.render(labels=label_opt)  # 渲染

    det_img = Image.fromarray(results.imgs[0])  # 检测图片

    det_json = export_json(results, model, img.size)[0]  # 检测信息

    return det_img, det_json









# yaml文件解析
def yaml_parse(file_path):
    return yaml.safe_load(open(file_path, "r", 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


def main(args):
    gr.close_all()

    global model

    slider_step = 0.05  # 滑动步长

    nms_conf = args.nms_conf
    nms_iou = args.nms_iou
    label_opt = args.label_dnt_show
    model_name = args.model_name
    model_cfg = args.model_cfg
    cls_name = args.cls_name
    device = args.device
    inference_size = args.inference_size

    # 模型加载
    model = model_loading(model_name, device)

    model_names = yaml_csv(model_cfg, "model_names")
    model_cls_name = yaml_csv(cls_name, "model_cls_name")


    # -------------------Inputs-------------------
    inputs_img = gr.inputs.Image(type="pil", label="Input Image")
    experiment_id = gr.inputs.Radio(
        choices=[
            "add [1, 1, 1, 1, 1] total 5 paths",
            "add [1, 1, 1, 1, 1, 1, 1, 1] total 8 paths",
            "add [1,2,4,8,16,32, ...] total 128 paths",
            "add [1,2,4,8,16,32, ...] total 256 paths"], type="value", default="add [1,1,1,1,1] paths", label="Path Adding Scheduler"
    )





    device = gr.inputs.Dropdown(
        choices=["cpu"], default=device, type="value", label="设备"
    )
    inputs_model = gr.inputs.Dropdown(
        choices=model_names, default=model_name, type="value", label="模型"
    )
    inputs_size = gr.inputs.Radio(
        choices=[320, 640], default=inference_size, label="推理尺寸"
    )
    input_conf = gr.inputs.Slider(
        0, 1, step=slider_step, default=nms_conf, label="置信度阈值"
    )
    inputs_iou = gr.inputs.Slider(
        0, 1, step=slider_step, default=nms_iou, label="IoU 阈值"
    )
    inputs_label = gr.inputs.Checkbox(default=label_opt, label="标签显示")
    inputs_clsName = gr.inputs.CheckboxGroup(
        choices=model_cls_name, default=model_cls_name, type="index", label="类别"
    )

    # 输入参数
    inputs = [
        inputs_img,  # 输入图片
        experiment_id, # path adding scheduler
        # device,  # 设备
        # inputs_model,  # 模型
        # inputs_size,  # 推理尺寸
        # input_conf,  # 置信度阈值
        # inputs_iou,  # IoU阈值
        # inputs_label,  # 标签显示
        # inputs_clsName,  # 类别
    ]
    # 输出参数
    outputs = gr.outputs.Image(type="pil", label="检测图片")
    outputs02 = gr.outputs.JSON(label="检测信息")

    # 标题
    title = "LIVE: Towards Layer-wise Image Vectorization"
    # 描述
    description = "<div align='center'>(CVPR 2022 Oral Presentation)</div>"

    # 示例图片
    examples = [
        [
            "./examples/1.png",
            "add [1, 1, 1, 1, 1] total 5 paths",
        ],
        [
            "./examples/2.png",
            "add [1, 1, 1, 1, 1] total 5 paths",
        ],
        [
            "./examples/3.jpg",
            "add [1,2,4,8,16,32, ...] total 128 paths",
        ],
        [
            "./examples/4.png",
            "add [1,2,4,8,16,32, ...] total 256 paths",
        ],
        [
            "./examples/5.png",
            "add [1, 1, 1, 1, 1] total 5 paths",
        ],
    ]

    # 接口
    gr.Interface(
        fn=yolo_det,
        inputs=inputs,
        outputs=[outputs, outputs02],
        title=title,
        description=description,
        examples=examples,
        theme="seafoam",
        # live=True, # 实时变更输出
        flagging_dir="run"  # 输出目录
        # ).launch(inbrowser=True, auth=['admin', 'admin'])
    ).launch(
        inbrowser=True,  # 自动打开默认浏览器
        show_tips=True,  # 自动显示gradio最新功能
        # favicon_path="./icon/logo.ico",
    )


if __name__ == "__main__":
    args = parse_args()
    main(args)