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| import os | |
| os.system('sudo apt-get install python3-dev') | |
| os.system('python setup.py install --user') | |
| import argparse | |
| import csv | |
| import numpy as np | |
| import sys | |
| sys.path.append("/home/user/.local/lib/python3.8/site-packages/diffvg-0.0.1-py3.8-linux-x86_64.egg") | |
| print(sys.path) | |
| 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("pip3 list") | |
| # import pydiffvg | |
| # | |
| # print("Sccuessfuly import diffvg ") | |
| # run_cmd("pwd") | |
| # run_cmd("ls") | |
| # run_cmd("git submodule update --init --recursive") | |
| # run_cmd("python setup.py install --user") | |
| # 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/") | |
| parser.add_argument('--initial', type=str, default="random", choices=['random', 'circle']) | |
| parser.add_argument('--signature', default="demo", 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 app_experiment_change(experiment_id): | |
| if experiment_id == "add [1] total 1 path for demonstration": | |
| return "experiment_1x1" | |
| if experiment_id == "add [1, 1, 1, 1, 1] total 5 paths one by one": | |
| return "experiment_5x1" | |
| elif experiment_id == "add [1, 1, 1, 1, 1, 1, 1, 1] total 8 paths one by one": | |
| return "experiment_8x1" | |
| elif experiment_id == "add [1,2,4,8,16,32, ...] total 128 paths": | |
| return "experiment_exp2_128" | |
| elif experiment_id == "add [1,2,4,8,16,32, ...] total 256 paths": | |
| return "experiment_exp2_256" | |
| cfg_arg = parse_args() | |
| temp_image = np.random.rand(224,224,3) | |
| temp_text = "start" | |
| temp_input = np.random.rand(224,224,3) | |
| def run_live(img, experiment_id, num_iter, cfg_arg=cfg_arg): | |
| experiment = app_experiment_change(experiment_id) | |
| cfg_arg.target = img | |
| cfg_arg.experiment = experiment | |
| img, text = main_func(img, experiment_id, num_iter, cfg_arg=cfg_arg) | |
| return img, text | |
| # 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_iteration = gr.inputs.Slider( | |
| label="Optimization Iteration", | |
| default=500, maximum=600, minimum=100, step=100) | |
| inputs_img = gr.inputs.Image(type="pil", label="Input Image", shape=[160, 160]) | |
| experiment_id = gr.inputs.Radio( | |
| choices=[ | |
| "add [1] total 1 path for demonstration", | |
| "add [1, 1, 1, 1, 1] total 5 paths one by one", | |
| "add [1, 1, 1, 1, 1, 1, 1, 1] total 8 paths one by one", | |
| "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] total 5 paths one by one", label="Path Adding Scheduler" | |
| ) | |
| # inputs | |
| inputs = [ | |
| inputs_img, # input image | |
| experiment_id, # path adding scheduler | |
| inputs_iteration, # input iteration | |
| ] | |
| # outputs | |
| outputs = gr.outputs.Image(type="numpy", label="Vectorized Image") | |
| outputs02 = gr.outputs.File(label="Generated SVG output") | |
| # title | |
| title = "LIVE: Towards Layer-wise Image Vectorization" | |
| # description | |
| description = "<div align='center'>(CVPR 2022 Oral Presentation)</div>" \ | |
| "<div align='center'>Without GPUs, LIVE will cost longer time.</div>" \ | |
| "<div align='center'>For efficiency, we rescale input to 160x160 (smaller size and fewer iterations will decrease the reconstructions).</div> " | |
| # examples | |
| examples = [ | |
| [ | |
| "./examples/1.png", | |
| "add [1] total 1 path for demonstration", | |
| 100, | |
| ], | |
| [ | |
| "./examples/2.png", | |
| "add [1, 1, 1, 1, 1] total 5 paths one by one", | |
| 300, | |
| ], | |
| [ | |
| "./examples/3.jpg", | |
| "add [1,2,4,8,16,32, ...] total 128 paths", | |
| 300, | |
| ], | |
| [ | |
| "./examples/4.png", | |
| "add [1,2,4,8,16,32, ...] total 256 paths", | |
| 300, | |
| ], | |
| [ | |
| "./examples/5.png", | |
| "add [1, 1, 1, 1, 1] total 5 paths one by one", | |
| 300, | |
| ], | |
| ] | |
| # 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="log" # 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 | |
| enable_queue=True | |
| # favicon_path="./icon/logo.ico", | |
| ) | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| main(args) | |