from cryptography.fernet import Fernet def encrypt(message: bytes, key: bytes): return Fernet(key).encrypt(message) def decrypt(token: bytes, key: bytes): return Fernet(key).decrypt(token) key = Fernet.generate_key() # store in a secure location #ex) key is 'Fn1dPza4Gchl7KpPE4kz2oJEMFXYG39ykpSLcsT1icU=' message = 'This is scret string' #encryption enstr = encrypt(message.encode(), key) #decryption destr = decrypt(enstr, key).decode() print('input:', message) print('encryption:', enstr) print('decryption:', destr)
8/24/2019
python string encryption, decryption - example code
8/21/2019
get similarity between two graphs
Basically, this example use networkX python library.
I made very simple two graphs which are G1, G2
Let see here:
and nx.graph_edit_distance this function calculate how much edit graph can be became isomorphic, that is return value of the function.
Check the example code.
..
..
I made very simple two graphs which are G1, G2
Let see here:
and nx.graph_edit_distance this function calculate how much edit graph can be became isomorphic, that is return value of the function.
Check the example code.
..
#https://stackoverflow.com/questions/11804730/networkx-add-node-with-specific-position
#https://stackoverflow.com/questions/23975773/how-to-compare-directed-graphs-in-networkx
import matplotlib.pyplot as plt
import networkx as nx
G1=nx.Graph()
G1.add_node(1,pos=(1,1))
G1.add_node(2,pos=(2,2))
G1.add_node(3,pos=(3,1))
G1.add_edge(1,2)
G1.add_edge(1,3)
pos=nx.get_node_attributes(G1,'pos')
plt.figure('graph1')
nx.draw(G1,pos, with_labels=True)
G2=nx.Graph()
G2.add_node(1,pos=(10,10))
G2.add_node(2,pos=(20,20))
G2.add_node(3,pos=(30,10))
G2.add_node(4,pos=(40,30))
G2.add_edge(1,2)
G2.add_edge(1,3)
G2.add_edge(1,4)
pos2=nx.get_node_attributes(G2,'pos')
plt.figure('b')
nx.draw(G2,pos2, with_labels=True)
dist = nx.graph_edit_distance(G1, G2)
print(dist)
plt.show()
8/20/2019
compare text using fuzzy wuzzy in python
just refer to this example..it's simple and very useful.
#pip install fuzzywuzzy
from fuzzywuzzy import process
candidate = ["Atlanta Falcons", "New York Jetss", "New York Giants", "Dallas Cowboys"]search = "new york jets"
r1 = process.extract(search, candidate)
#r1 = process.extract(search, candidate, limit=3)
search = "cowboys"r2 = process.extractOne(search, candidate)
search = "new york jets"r3 = process.extractBests(search, candidate, score_cutoff=70)
print(r1)#[('New York Jetss', 96), ('New York Giants', 79), ('Atlanta Falcons', 29), ('Dallas Cowboys', 22)]
print(r2)#('Dallas Cowboys', 90)
print(r3)#[('Dallas Cowboys', 90)]
Subscribe to:
Posts (Atom)
-
make well divided linear coordinate And make pair coordinate Please see code for detail explanation. import numpy as np import cv2 ...
-
As you can see in the following video, I created a class that stitching n cameras in real time. https://www.youtube.com/user/feelmare/sear...
-
In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv <-> rgb converting example code. refer to this page -> ht...
-
Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul...
-
* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm...
-
Proceed with the project to update the 2012 version, or that you must reinstall Visual Studio 2010. If you are using Visual Studio 2...
-
1. Map : Tasks read from and write to specific data elements. 2. Gather : each calculation gathers input data elements together from di...
-
fig 1. Left: set 4 points (Left Top, Right Top, Right Bottom, Left Bottom), right:warped image to (0,0) (300,0), (300,300), (0,300) Fi...
-
opencv lecture 4-1 example code < gist start > < gist end >
-
This is dithering example, it make image like a stippling effect. I referenced to blew website. wiki page: https://en.wikipedia.org/wik...
