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SimpleImageHTTPServer

Modified from SimpleHTTPServer, this file can setup a simple image server, to view folder, image, and compare images between folders.

How to use

Start

python image_server.py --path path/to/your/image/folder

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

1️⃣ Docker daemon 配置代理（推荐）

参考 Docker daemon 配置代理

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

	import json
	import os
	import shutil
	import uuid
	from datetime import datetime
	from pathlib import Path
	import platform


	def backup_file(file_path: str):

	#!/usr/bin/env python
	# -- coding: utf-8 --
	# pylint: disable=missing-docstring
	import argparse
	import os
	import sys
	import timeit
	import typing

	import onnx

	import pandas as pd
	import numpy as np
	import cv2, os
	import scipy
	from scipy import signal
	import csv

	circle_color, line_color = (255,255,0), (0,0,255)
	window_length, polyorder = 13, 2
	sd = "workout"

	import json
	import os

	from tqdm import tqdm
	from xmltodict import unparse

	# BBOX_OFFSET: Switch between 0-based and 1-based bbox.
	# The COCO dataset is in 0-based format, while the VOC dataset is 1-based.
	# To keep 0-based, set it to 0. To convert to 1-based, set it to 1.
	BBOX_OFFSET = 0


	sudo apt-get update
	sudo apt-get install python-pip
	sudo apt-get install python-setuptools m2crypto
	pip install shadowsocks
	如果是ubuntu16.04 直接 (16.04 里可以直接用apt 而不用 apt-get 这是一项改进）
	sudo apt install shadowsocks

	nohup sslocal -c /opt/trainee/shadowsocks.json &

	import numpy as np
	import tensorflow as tf

	from tensorflow.contrib import learn
	from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

	tf.logging.set_verbosity(tf.logging.INFO)


	def cnn_model_fn(features, labels, mode):