pytorch yolov3 代码详解_超详细的Pytorch版yolov3代码中文注释详解（四）

from __future__ import division

import time

import torch

import torch.nn as nn

from torch.autograd import Variable

import numpy as np

import cv2

from util import *

import argparse

import os

import os.path as osp

from darknet import Darknet

import pickle as pkl

import pandas as pd

import random

def arg_parse():

"""视频检测模块的参数转换"""

#创建一个ArgumentParser对象，格式: 参数名, 目标参数(dest是字典的key),帮助信息,默认值,类型

parser = argparse.ArgumentParser(description='YOLO v3 检测模型')

parser.add_argument("--bs", dest = "bs", help = "Batch size，默认为 1", default = 1)

parser.add_argument("--confidence", dest = "confidence", help = "目标检测结果置信度阈值", default = 0.5)

parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS非极大值抑制阈值", default = 0.4)

parser.add_argument("--cfg", dest = 'cfgfile', help =

"配置文件",

default = "cfg/yolov3.cfg", type = str)

parser.add_argument("--weights", dest = 'weightsfile', help =

"模型权重",

default = "yolov3.weights", type = str)

parser.add_argument("--reso", dest = 'reso', help =

"网络输入分辨率. 分辨率越高,则准确率越高; 反之亦然",

default = "416", type = str)

parser.add_argument("--video", dest = "videofile", help = "待检测视频目录", default = "video.avi", type = str)

return parser.p