app.py

import argparse
import csv
import sys
sys.path.append("./LIVE")
from pathlib import Path

import gradio as gr
import torch
import yaml
from PIL import Image
from subprocess import call
import torch
import cv2
import matplotlib.pyplot as plt
import random
import argparse
import math
import errno
from tqdm import tqdm
import yaml
from easydict import EasyDict as edict

def run_cmd(command):
    try:
        print(command)
        call(command, shell=True)
    except KeyboardInterrupt:
        print("Process interrupted")
        sys.exit(1)

run_cmd("gcc --version")
run_cmd("pwd")
run_cmd("ls")
run_cmd("git submodule update --init --recursive")
run_cmd("python setup.py install --user")
run_cmd("ls")
run_cmd("python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/")


from main import main_func

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--debug', action='store_true', default=False)
    parser.add_argument("--config", default="config/base.yaml", type=str)
    parser.add_argument("--experiment", type=str)
    parser.add_argument("--seed", type=int)
    parser.add_argument("--target", type=str, help="target image path")
    parser.add_argument('--log_dir', metavar='DIR', default="log/debug")
    parser.add_argument('--initial', type=str, default="random", choices=['random', 'circle'])
    parser.add_argument('--signature', nargs='+', type=str)
    parser.add_argument('--seginit', nargs='+', type=str)
    parser.add_argument("--num_segments", type=int, default=4)
    # parser.add_argument("--num_paths", type=str, default="1,1,1")
    # parser.add_argument("--num_iter", type=int, default=500)
    # parser.add_argument('--free', action='store_true')
    # Please ensure that image resolution is divisible by pool_size; otherwise the performance would drop a lot.
    # parser.add_argument('--pool_size', type=int, default=40, help="the pooled image size for next path initialization")
    # parser.add_argument('--save_loss', action='store_true')
    # parser.add_argument('--save_init', action='store_true')
    # parser.add_argument('--save_image', action='store_true')
    # parser.add_argument('--save_video', action='store_true')
    # parser.add_argument('--print_weight', action='store_true')
    # parser.add_argument('--circle_init_radius',  type=float)
    cfg = edict()
    args = parser.parse_args()
    cfg.debug = args.debug
    cfg.config = args.config
    cfg.experiment = args.experiment
    cfg.seed = args.seed
    cfg.target = args.target
    cfg.log_dir = args.log_dir
    cfg.initial = args.initial
    cfg.signature = args.signature
    # set cfg num_segments in command
    cfg.num_segments = args.num_segments
    if args.seginit is not None:
        cfg.seginit = edict()
        cfg.seginit.type = args.seginit[0]
        if cfg.seginit.type == 'circle':
            cfg.seginit.radius = float(args.seginit[1])
    return cfg





def run_live(img, experiment_id):
    main_func(img, experiment_id)
    return 0, 1









# 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


def run_cmd(command):
    try:
        print(command)
        call(command, shell=True)
    except KeyboardInterrupt:
        print("Process interrupted")
        sys.exit(1)

run_cmd("gcc --version")
run_cmd("pwd")
run_cmd("ls")
run_cmd("git submodule update --init --recursive")
run_cmd("python setup.py install --user")
run_cmd("ls")
run_cmd("python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/")






# # 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()
    # -------------------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"
    )

    # inputs
    inputs = [
        inputs_img,  # input image
        experiment_id, # path adding scheduler
    ]
    # outputs
    outputs = gr.outputs.Image(type="pil", label="检测图片")
    outputs02 = gr.outputs.JSON(label="检测信息")

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

    # examples
    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",
        ],
    ]

    # Interface
    gr.Interface(
        fn=run_live,
        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)