anomaly detection python

#!/usr/bin/envpython3#-*-coding:utf-8-*-# @Author : Du Wentao # @Time :2018/5/229:24# @File :scapy_test.py#encoding= Utf-8fromscapy.allimport*defworker ():ip_list=[] foripfixinrange (1,20): ip= " 192.168.0. "+str (IpFix) arppkt=ether (dst=" FF:FF:FF: Ff:ff:ff ")/arp (pdst=ip,hwdst=" Ff:ff:ff:ff:ff:ff ") res NBSP;=NBSP;SRP1 (Arppkt,timeout=1,verbose=false) ifres:print ("IP:" +res.psrc+ "MAC:" +res.hwsrc) Nbsp;ip_list.append (RES.PSRC) returnip_listif__name__== "_ _main__ ": fp=open (' Ip.txt '

First up.Which is the logo mark,is the picture to be detected.The code is as follows.#coding =utf-8import cv2import scipy as Spimg1 = Cv2.imread (' x1.jpg ', 0) # QueryImageimg2 = Cv2.imread (' x2.jpg ', 0) # Trainim age# Initiate SIFT detectorsift = Cv2. SIFT () # Find the keypoints and descriptors with SIFTkp1, des1 = Sift.detectandcompute (img1,none) kp2, des2 = Sift.detectand Compute (img2,none) # FLANN parametersflann_index_kdtree = 0index_params = dict (algorithm = flann_index_kdtree, tree

Happy Shrimphttp://blog.csdn.net/lights_joy/Welcome reprint, but please keep the author informationOnce a single area plant image has been obtained, the next step seems to be to try to classify these areas. The identification of plant species by form and vein is obviously an intuitive approach, but the application of such methods is limited by the existence of realistic conditions such as leaf overlap and illumination. Nonetheless, we still want to look at the effects of such methods. Similarly,

matrix Gxy of the image, which contains the edge information of the X-direction and y-direction of the image. Python implementation of convolution and edge detection of Prewitt operatorsFirst, we encapsulate the image convolution function in a function called Imconv.ImportNumPy as NP fromPILImportImagedefImconv (Image_array,suanzi):" "Computed convolution parameters Image_array original gray image ma

it on the official website of Python: https://pypi.python.org/pypi/pip#downloads:After the download is complete, unzip to a folder, use the CMD console to enter the Extract directory, enter:Python setup.py InstallHere is the installation of the PIP, in order to improve the system can also set the environment variables After the PIP is installed, you can enter the command in the Command Line window of the Vscode E:\

(file_path, 'rb') as f: while True: line = f.readline() if buf: print(line) else: breakwith open(file_path, 'rb') as f: for line in f: print(line)3. Description of the Memory Detection tool: For the memory footprint of Python code, it is necessary to monitor the code for memory. Here I recommend two gadgets to detect the memory footprint of

This article mainly introduces the Python Prime number detection method. The example analyzes the related skills of Python to determine the prime number. if you need it, refer to the example in this article to describe the Python Prime number detection method. Share it with

This article mainly introduces the Python script to implement cluster detection and management functions. This article describes implementation ideas, development tool selection, experience sharing, sample code, and so on, if you need a friend, you can refer to the following scenario: In a production data center, there will be a lot of test machines and production machines (that is, about 30 machines ), bec

PYTHON+OPENCV implementation of Gaussian smoothing filterPYTHON+OPENCV Realization of threshold segmentation(2016-5-10) to Opencv-python tutorials ' s documentation can be downloadedFunction:Create a slider bar to control the length threshold of the detection line, which is greater than the threshold value and is less than the threshold value ignoredNote: The fun

small, ignore it PrintCv2.contourarea (c)ifCv2.contourarea (c) "Min_area"]: Continue #compute the bounding box for the contour, draw it on the frame, #and update the text #calculates the bounding box of the outline, drawing the box in the current frame(x, Y, W, h) =Cv2.boundingrect (c) Cv2.rectangle (frame, (x, y), (x+ W, y + h), (0, 255, 0), 2) Text="occupied" #Draw the text and timestamp on the frame #write text and timestamp on the current frameCv

, and the method of integrating graph can accelerate the solution of the value of the class Haar feature.One of the most basic class Haar features is a rudimentary weak classifier, which is optimized after the weak classifier is called the optimized weak classifier.A strong classifier is formed by combining multiple optimized weak classifiers.But the application of a single strong classifier is not good in actual detection,So it was proposed to cascad

This example summarizes the common detection string correlation functions in Python. Share to everyone for your reference. The specific analysis is as follows: The following Python code can be used to detect strings, including whether they are all numbers, whether they contain numbers, whether they contain a title word, whether they contain uppercase letters, wh

of disabled pep8 warnings and errors: let g:PyFlakeDisabledMessages = ‘E501‘ "Default height of quickfix window: let g:PyFlakeCWindow = 6 "Whether to place signs or not: let g:PyFlakeSigns = 1 "Maximum line length for PyFlakeAuto command let g:PyFlakeMaxLineLength = 100 "Visual-mode key command for PyFlakeAuto let g:PyFlakeRangeCommand = ‘Q‘ UseFlakes will be prompted directly on the code editing page, with a malformed monogram redThe PEP8 is a hint in the Output window of vimAnd Flake8 can pro

test, encountered as long as there is no error, or does not exist, the direct error stop program. It was later discovered that response.status_code! = 200 was wrong when it took the status code. Because some websites cannot be opened, the status code is not returned. So the program will not know! ==200 how to deal with it. Workaround: Catching exceptions using try except else The specific code is: Try:response = Requests.get (Url[i].strip (), Verify=false, Allow_redirects=true, timeout=5) if re

This article mainly introduces the GoogleIP availability detection script implemented by Python. this script requires the Python3.4 + environment. For more information, see Python 3.4 +, A parameter is used to test the search service or GAE service. To test the GAE service, you must first modify the two variables at the beginning. Read the list of IP addresses or

========> "no" presence injected!') Else: Print(U'site ========> presence injected!') #field (A[i1])new_str=a[i1]+'\ n'o=open ('Ture_sql.txt','a') O.write (NEW_STR) Err='true' Print(U'1. Batch injection, exist 1.txt of this catalogue 2. Single point injection, and determine the number of fields') ID=int (Input ("ID?:"))if(id==1): T1=threading. Thread (target=Piliang) T1.start () T1.join ()elif(id==2): URL=input ('URL:') judge () T2=threading. Thr

So find a station, with For loop, batch grabbed 100 files, about 10 minutes to complete, look up the account and password about 10, testing basic can be logged in, if it is a large number of visits to the site, may be caught more time! I used the existing exp to write a convenient batch detection of the vulnerability, mainly practicing python, feel not practice progress too slow The code is as f

#!/usr/bin/envpython3#author:zhouxia#date:2016-08-05importsocketdefcheckip (Ipaddr,port): try:sock=socket.socket ( Socket.af_inet,socket. SOCK_STREAM) sock.settimeout (3) sock.connect ((ipaddr,port)) returntrue exceptsocket.errorase: returnFalsefinally: Sock.close () if__name__== ' __main__ ': file=open ("Ip_ List.txt ") checkinfo=open (" Check_info.txt ", ' w+ ') Line=file.readline () whileline: ifline== "":continue iplist=line.split (' ') ipaddr=iplist[0]port =int (Iplist[1]) status=checki

1, according to the file header.#是否为带BOM头的UTF8文件def Isutf8bomfile (pathfile): if B ' \xef\xbb\xbf ' = = Open (pathfile, mode= ' RB '). Read (3)): Return Truereturn False2, with Cchardet Library.>>> Import cchardet>>> cchardet.detect (open (Pathfile, ' RB '). Read ()) {' Encoding ': ' UTF-8 ', ' Confidence ': 0.9900000095367432}Related reading:1, u ' \ufeff ' in Python string (header information with several text inside)2. Wxmedit Cross-platform text/1

cropping is complete, all connections to Source_node are judged as foreground, sink_node on the background. - continue this process until the classification is convergent. About $ - - """ -Mask2 = Np.where ((mask = = 2) | (Mask = = 0), 0, 1). Astype ("uint8") AIMG = img*mask2[:,:, Np.newaxis] + thePlt.subplot (121), Plt.imshow (IMG) -Plt.title ("Grabcut"), Plt.xticks ([]), Plt.yticks ([]) $Plt.subplot (122), Plt.imshow (Cv2.cvtcolor (Cv2.imread (".. /data/mm2.jpeg"), Cv2. COLOR_BGR2RG