深海游弋的鱼 – 第 121 页 – 智障儿童欢乐多

ubuntu 14.04 LTS关闭873端口

最近在使用百度统计的网站安全功能的时候，被报告存在873端口被打开的情况,刚开始以为是被入侵了，后来发现是在测试功能的时候无意安装了rsync导致的rsyncd在873端口进行监听。

解决方法就是卸载rsync即可：

$ sudo apt-get purge --auto-remove rsync

#需要重启机器，否则端口可能出现长时间仍然开放的状态
$ sudo reboot

$ sudo apt-get purge --auto-remove rsync

#需要重启机器，否则端口可能出现长时间仍然开放的状态

$ sudo reboot

Ubuntu 14.04服务器利用Apache 2.4的.htaccess文件阻止对wp-config.php.bak的访问

最近在分析请求链接的时候发现有对wp-config.php.bak的下载请求，被吓了一跳。在WordPress的某些升级操作中，会特意备份wp-config.php方便出现问题后的回退。如果有人恶意下载这个文件，会导致数据库密码以及配置信息的泄漏，后果影响很大。

对于使用Apache 2.4的服务器来说，比较简单，只要在.htaccess中，使用如下配置即可：

#reject all .bak,.old file download because wordpress upgrade may rename 
#wp-config.php to wp-config.php.bak 
<FilesMatch (.*)\.(bak|old)>
        Order allow,deny
        deny from all
</FilesMatch>

#reject all .bak,.old file download because wordpress upgrade may rename

#wp-config.php to wp-config.php.bak

Order allow,deny

deny from all

</FilesMatch>

JavaScript修改伪元素样式(pseudo element styles)

类似如下的CSS声明：

#progress {
    background: #333;
    border-radius: 13px;
    height: 10px;
    width: 300px;
    padding: 3px;
}

#progress:after {
    content: '';
    display: block;
    background: orange;
    width: 0%;
    height: 100%;
    border-radius: 9px;
}

#progress {

background: #333;

border-radius: 13px;

height: 10px;

width: 300px;

padding: 3px;

}

#progress:after {

content: '';

display: block;

background: orange;

width: 0%;

height: 100%;

border-radius: 9px;

}

HTML中的声明如下：

<div id="progress" style="text-align: center; margin-left: auto; margin-right: auto;"></div>

1	<div id="progress" style="text-align: center; margin-left: auto; margin-right: auto;"></div>

需要动态修改CSS的width属性。
由于是伪元素样式，并不属于DOM对象,因此，我们没有办法直接通过JQuery来修改。

比较完美的解决方法如下：

定义如下函数，对样式表遍历，根据名称获取我们指定的样式对象

function getRuleWithSelector(selector) {
    var numSheets = document.styleSheets.length;
    var numRules;
    var sheetIndex;
    var ruleIndex;
    // Search through the style sheets.
    for (sheetIndex = 0; sheetIndex < numSheets; sheetIndex += 1) {
        numRules = document.styleSheets[sheetIndex].cssRules.length;
        for (ruleIndex = 0; ruleIndex < numRules; ruleIndex += 1) {
            if (document.styleSheets[sheetIndex].cssRules[ruleIndex].selectorText === selector) {
                return document.styleSheets[sheetIndex].cssRules[ruleIndex];
            }
        }
    }
    return null;
}

function isNull(arg) {
    return ((undefined == arg) || (null == arg) ||('' == arg));
}

function getRuleWithSelector(selector) {

var numSheets = document.styleSheets.length;

var numRules;

var sheetIndex;

var ruleIndex;

// Search through the style sheets.

for (sheetIndex = 0; sheetIndex < numSheets; sheetIndex += 1) {

numRules = document.styleSheets[sheetIndex].cssRules.length;

for (ruleIndex = 0; ruleIndex < numRules; ruleIndex += 1) {

if (document.styleSheets[sheetIndex].cssRules[ruleIndex].selectorText === selector) {

return document.styleSheets[sheetIndex].cssRules[ruleIndex];

}

return null;

}

function isNull(arg) {

return ((undefined == arg) || (null == arg) ||('' == arg));

}

使用时候的代码如下：

var afterRule = getRuleWithSelector("#progress::after");
if (true != isNull(afterRule)){
	afterRule.style.width = '20%';
}

var afterRule = getRuleWithSelector("#progress::after");

if (true != isNull(afterRule)){

afterRule.style.width = '20%';

}

参考链接

modify pseudo select :after in javascript

Ubuntu 16.04 LTS上使用Python3版本的PIP

Ubuntu 16.04 LTS上使用Python2与Python3是共存的，而且默认使用Python2，如果使用Python3则需要明确指定。

1.安装Python3版本的PIP

$ sudo apt-get install python3-pip

$ sudo pip3 install --upgrade pip

$ sudo apt-get install python3-pip

$ sudo pip3 install --upgrade pip

2.安装Python3版本的NumPy

$ pip3 install numpy

1	$ pip3 install numpy

3.安装Python3版本的OpenCV

$ sudo pip3 install opencv-python

1	$ sudo pip3 install opencv-python

注意，目前的Python3版本的OpenCV是不支持cv2.imshow()的，具体查看https://pypi.python.org/pypi/opencv-python，可以看到如下信息：

**Q: Why I can't open GUI windows (``cv2.imshow()``) on GNU/Linux distribution X or on macOS?**

A: Like above, OpenCV was not compiled against GTK or Carbon. Support for these might be added in the future.

**Q: Why I can't open GUI windows (``cv2.imshow()``) on GNU/Linux distribution X or on macOS?**

A: Like above, OpenCV was not compiled against GTK or Carbon. Support for these might be added in the future.

Android Studio 2.3调试小米手机安装失败

Android Studio 2.3调试小米手机MIUI 8.7.4的时候，安装APK一直失败,错误信息如下：

$ adb install-multiple -r /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_7.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_6.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_8.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_9.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_3.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_4.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/dep/dependencies.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_2.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_5.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_0.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_1.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/outputs/apk/app-debug.apk 
$ adb shell pm uninstall com.guo.duoduo.anyshareofandroid
$ adb install-multiple -r /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_7.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_6.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_8.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_9.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_3.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_4.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/dep/dependencies.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_2.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_5.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_0.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_1.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/outputs/apk/app-debug.apk 
$ adb shell pm uninstall com.guo.duoduo.anyshareofandroid
DELETE_FAILED_INTERNAL_ERROR
Error while Installing APKs

$ adb install-multiple -r /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_7.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_6.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_8.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_9.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_3.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_4.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/dep/dependencies.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_2.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_5.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_0.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/intermediates/split-apk/debug/slices/slice_1.apk /Users/longsky/Source/AnyShareOfAndroid/app/build/outputs/apk/app-debug.apk

$ adb shell pm uninstall com.guo.duoduo.anyshareofandroid

DELETE_FAILED_INTERNAL_ERROR

Error while Installing APKs

网上查询了一下，是MIUI自身的优化导致的问题。
解决方法就是在MIUI->设置->更多设置->开发者选项->启用MIUI优化，关闭这个选项就可以了。

参考链接

Android Studio 2.3 adb install-multiple Failed to create session Failure [UNS...

Python 2.7 基于twisted实现简单的web服务器

Python 2.7自带的SimpleHTTPServer默认是HTTP/1.0，导致在投放简单的视频的时候，一般是无法通过FFMPEG进行播放的,主要是HTTP/1.0不支持Content-Range导致无法快进以及视频的Seek操作。并且比较悲剧的是，如该修改成HTTP/1.1协议，默认只有一个连接在处理，导致只要第一个用户不断开，第二个连接基本上连接不上。

网上搜索了以下，找到了用twisted代码来实现比较简单，并且性能还不错的服务器。

本文实例讲述了Python基于twisted实现简单的web服务器，分享给大家供大家参考。具体方法如下：
1.首先是通过PIP安装twisted

$ sudo pip install twisted

1	$ sudo pip install twisted

2. 新建htm文件夹,在这个文件夹中放入显示的网页文件

3. 在htm文件夹的同级目录下,建立web.py,代码如下:

from twisted.web.resource import Resource 
from twisted.web import server 
from twisted.web import static 
from twisted.internet import reactor 
  
PORT = 1234
  
######################################################################## 
class ReStructed(Resource): 
  """"""
  
  #---------------------------------------------------------------------- 
  def __init__(self, filename, *a): 
    """Constructor"""
    self.rst = open(filename).read() 
  def render(self, request): 
    return self.rst 
    
resource = static.File('htm/') 
resource.processors = {'.html':ReStructed} 
resource.indexNames = ['index.html'] 
  
reactor.listenTCP(PORT, server.Site(resource)) 
reactor.run()

from twisted.web.resource import Resource

from twisted.web import server

from twisted.web import static

from twisted.internet import reactor

PORT = 1234

########################################################################

class ReStructed(Resource):

""""""

#----------------------------------------------------------------------

def __init__(self, filename, *a):

"""Constructor"""

self.rst = open(filename).read()

def render(self, request):

return self.rst

resource = static.File('htm/')

resource.processors = {'.html':ReStructed}

resource.indexNames = ['index.html']

reactor.listenTCP(PORT, server.Site(resource))

reactor.run()

执行脚本

$ python web.py

1	$ python web.py

然后浏览器访问：http://127.0.0.1:1234/就可以看到内容了。

参考链接

Python基于twisted实现简单的web服务器

禁止Chrome的页面缓存

Chrome总是缓存页面，Internet Explorer,FireFox,Opera都提供了自己方式可以关掉缓存,实在不行在HTTP头中加上 "Cache-Control"为"no-cache"，可是顽固的Chrome无效，依旧自动缓存。现在新版的Chrome在Developer Tools（F12调出来）的Network标签中有了个Disable cache选项，可以解决这类问题。

如下图：

参考链接

ubuntu 16.04 LTS使用开源面部识别库Openface

Openface是一个基于深度神经网络的开源人脸识别系统。该系统基于谷歌的文章FaceNet: A Unified Embedding for Face Recognition and Clustering。Openface是卡内基梅隆大学的Brandon Amos主导的。

1.准备系统环境

如果是服务器版本的ubuntu,可能默认Python都没有安装

#如果没有安装python的话，执行最小安装命令即可，目前我们需要的是Python2
$ sudo apt-get install python-minimal
$ sudo apt-get install python-pip
$ sudo pip install --upgrade pip

#如果没有安装git的话，此处需要手工安装
$ sudo apt-get install git

#编译dlib时候需要
$ sudo apt-get install cmake
$ sudo apt-get install libboost-dev
$ sudo apt-get install libboost-python-dev

#如果没有安装python的话，执行最小安装命令即可，目前我们需要的是Python2

$ sudo apt-get install python-minimal

$ sudo apt-get install python-pip

$ sudo pip install --upgrade pip

#如果没有安装git的话，此处需要手工安装

$ sudo apt-get install git

#编译dlib时候需要

$ sudo apt-get install cmake

$ sudo apt-get install libboost-dev

$ sudo apt-get install libboost-python-dev

2.下载代码

$ git clone https://github.com/cmusatyalab/openface.git

1	$ git clone https://github.com/cmusatyalab/openface.git

3.安装`opencv`

$ sudo apt-get install libopencv-dev
$ sudo apt-get install python-opencv

1 2	$ sudo apt-get install libopencv-dev $ sudo apt-get install python-opencv

4.安装依赖的`python`库

$ cd openface
$ pip install -r requirements.txt
$ sudo pip install dlib
$ sudo pip install matplotlib

$ cd openface

$ pip install -r requirements.txt

$ sudo pip install dlib

$ sudo pip install matplotlib

5.安装`Torch7`

参考链接 ubuntu 16.04 LTS上安装Torch7

编译完成后，路径变量被加入了.bashrc，我们需要刷新一下Shell的环境变量

# 使 torch7 设置的刚刚的环境变量生效
source ~/.bashrc

1 2	# 使 torch7 设置的刚刚的环境变量生效 source ~/.bashrc

6.安装依赖的`lua`库

$ luarocks install dpnn

#下面的为选装，有些函数或方法可能会用到
$ luarocks install image
$ luarocks install nn
$ luarocks install graphicsmagick
$ luarocks install torchx
$ luarocks install csvigo

$ luarocks install dpnn

#下面的为选装，有些函数或方法可能会用到

$ luarocks install image

$ luarocks install nn

$ luarocks install graphicsmagick

$ luarocks install torchx

$ luarocks install csvigo

7.编译代码

$ python setup.py build
$ sudo python setup.py install

1 2	$ python setup.py build $ sudo python setup.py install

8.下载预训练后的数据

$ sh models/get-models.sh
#参考 https://cmusatyalab.github.io/openface/models-and-accuracies/ 下载对应的模型
$ wget https://storage.cmusatyalab.org/openface-models/nn4.v1.t7 -O models/openface/nn4.v1.t7

$ sh models/get-models.sh

#参考 https://cmusatyalab.github.io/openface/models-and-accuracies/ 下载对应的模型

$ wget https://storage.cmusatyalab.org/openface-models/nn4.v1.t7 -O models/openface/nn4.v1.t7

9.执行测试Demo

执行的脚本face_detect.py代码如下：

#!/usr/bin/env python2

import argparse
import cv2
import os
import dlib

import numpy as np
np.set_printoptions(precision=2)
import openface

from matplotlib import cm

fileDir = os.path.dirname(os.path.realpath(__file__))
modelDir = os.path.join(fileDir, '..', 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--dlibFacePredictor',
        type=str,
        help="Path to dlib's face predictor.",
        default=os.path.join(
            dlibModelDir,
            "shape_predictor_68_face_landmarks.dat"))
    parser.add_argument(
        '--networkModel',
        type=str,
        help="Path to Torch network model.",
        default='models/openface/nn4.v1.t7')
    # Download model from:
    # https://storage.cmusatyalab.org/openface-models/nn4.v1.t7
    parser.add_argument('--imgDim', type=int,
                        help="Default image dimension.", default=96)
    # parser.add_argument('--width', type=int, default=640)
    # parser.add_argument('--height', type=int, default=480)
    parser.add_argument('--width', type=int, default=1280)
    parser.add_argument('--height', type=int, default=800)
    parser.add_argument('--scale', type=int, default=1.0)
    parser.add_argument('--cuda', action='store_true')
    parser.add_argument('--image', type=str,help='Path of image to recognition')

    args = parser.parse_args()
    if (None == args.image) or (not os.path.exists(args.image)):
	print '--image not set or image file not exists'
	exit()

    align = openface.AlignDlib(args.dlibFacePredictor)
    net = openface.TorchNeuralNet(
        args.networkModel,
        imgDim=args.imgDim,
        cuda=args.cuda)

    cv2.namedWindow('video', cv2.WINDOW_NORMAL)

    frame = cv2.imread(args.image)  
    bbs = align.getAllFaceBoundingBoxes(frame)
    for i, bb in enumerate(bbs):
	# landmarkIndices set  "https://cmusatyalab.github.io/openface/models-and-accuracies/"
        alignedFace = align.align(96, frame, bb,
                                      landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
        rep = net.forward(alignedFace)

        center = bb.center()
        centerI = 0.7 * center.x * center.y / \
                (args.scale * args.scale * args.width * args.height)
        color_np = cm.Set1(centerI)
        color_cv = list(np.multiply(color_np[:3], 255))

        bl = (int(bb.left() / args.scale), int(bb.bottom() / args.scale))
        tr = (int(bb.right() / args.scale), int(bb.top() / args.scale))
        cv2.rectangle(frame, bl, tr, color=color_cv, thickness=3)

    cv2.imshow('video', frame)

    cv2.waitKey (0)  

    cv2.destroyAllWindows()

#!/usr/bin/env python2

import argparse

import cv2

import os

import dlib

import numpy as np

np.set_printoptions(precision=2)

import openface

from matplotlib import cm

fileDir = os.path.dirname(os.path.realpath(__file__))

modelDir = os.path.join(fileDir, '..', 'models')

dlibModelDir = os.path.join(modelDir, 'dlib')

if __name__ == '__main__':

parser = argparse.ArgumentParser()

parser.add_argument(

'--dlibFacePredictor',

type=str,

help="Path to dlib's face predictor.",

default=os.path.join(

dlibModelDir,

"shape_predictor_68_face_landmarks.dat"))

parser.add_argument(

'--networkModel',

type=str,

help="Path to Torch network model.",

default='models/openface/nn4.v1.t7')

# Download model from:

# https://storage.cmusatyalab.org/openface-models/nn4.v1.t7

parser.add_argument('--imgDim', type=int,

help="Default image dimension.", default=96)

# parser.add_argument('--width', type=int, default=640)

# parser.add_argument('--height', type=int, default=480)

parser.add_argument('--width', type=int, default=1280)

parser.add_argument('--height', type=int, default=800)

parser.add_argument('--scale', type=int, default=1.0)

parser.add_argument('--cuda', action='store_true')

parser.add_argument('--image', type=str,help='Path of image to recognition')

args = parser.parse_args()

if (None == args.image) or (not os.path.exists(args.image)):

print '--image not set or image file not exists'

exit()

align = openface.AlignDlib(args.dlibFacePredictor)

net = openface.TorchNeuralNet(

args.networkModel,

imgDim=args.imgDim,

cuda=args.cuda)

cv2.namedWindow('video', cv2.WINDOW_NORMAL)

frame = cv2.imread(args.image)

bbs = align.getAllFaceBoundingBoxes(frame)

for i, bb in enumerate(bbs):

# landmarkIndices set "https://cmusatyalab.github.io/openface/models-and-accuracies/"

alignedFace = align.align(96, frame, bb,

landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)

rep = net.forward(alignedFace)

center = bb.center()

centerI = 0.7 * center.x * center.y / \

(args.scale * args.scale * args.width * args.height)

color_np = cm.Set1(centerI)

color_cv = list(np.multiply(color_np[:3], 255))

bl = (int(bb.left() / args.scale), int(bb.bottom() / args.scale))

tr = (int(bb.right() / args.scale), int(bb.top() / args.scale))

cv2.rectangle(frame, bl, tr, color=color_cv, thickness=3)

cv2.imshow('video', frame)

cv2.waitKey (0)

cv2.destroyAllWindows()

在Shell中如下执行代码：

$ python demos/face_detect.py --image=xx.jpeg

1	$ python demos/face_detect.py --image=xx.jpeg

识别完成后会弹出识别到的面部图片。

macOS Sierra (10.12.4)下使用Android Studio打开Android Virtual Device Manager报告“/dev/kvm is not found”错误

Android Virtual Device Manager突然出现了/dev/kvm is not found这个错误，我猜测大概Hardware_Accelerated_Execution_Manager丢失了某些文件，或者没安装好HAXM。

继续阅读macOS Sierra (10.12.4)下使用Android Studio打开Android Virtual Device Manager报告“/dev/kvm is not found”错误

ubuntu 16.04 LTS上16进制文件编辑&比较工具wxHexEditor

ubuntu 16.04 LTS上比较好用的16进制编辑&比较工具，当属wxHexEditor,官网地址在http://www.wxhexeditor.org/。

在Ubuntu系统上，可以使用如下命令安装：

$ sudo apt-get install wxhexeditor

$ wxHexEditor

$ sudo apt-get install wxhexeditor

$ wxHexEditor

一	二	三	四	五	六	日
			1	2	3	4
5	6	7	8	9	10	11
12	13	14	15	16	17	18
19	20	21	22	23	24	25
26	27	28	29	30	31

参考链接

参考链接

参考链接

参考链接

1.准备系统环境

2.下载代码

3.安装opencv

4.安装依赖的python库

5.安装Torch7

6.安装依赖的lua库

7.编译代码

8.下载预训练后的数据

9.执行测试Demo

参考链接

3.安装`opencv`

4.安装依赖的`python`库

5.安装`Torch7`

6.安装依赖的`lua`库