# Image Encryption Decryption # imported necessary library import tkinter from tkinter import * import tkinter as tk import tkinter.messagebox as mbox from tkinter import ttk from tkinter import filedialog from PIL import ImageTk, Image import cv2 import os import numpy as np from cv2 import * import random #created main window window = Tk() window.geometry("1000x700") window.title("Image Encryption Decryption") # defined variable global count, emig # global bright, con # global frp, tname # list of paths frp = [] tname = [] con = 1 bright = 0 panelB = None panelA = None # function defined to get the path of the image selected def getpath(path): a = path.split(r'/') # print(a) fname = a[-1] l = len(fname) location = path[:-l] return location # function defined to get the folder name from which image is selected def getfoldername(path): a = path.split(r'/') # print(a) name = a[-1] return name # function defined to get the file name of image is selected def getfilename(path): a = path.split(r'/') fname = a[-1] a = fname.split('.') a = a[0] return a # function defined to open the image file def openfilename(): filename = filedialog.askopenfilename(title='"pen') return filename # function defined to open the selected image def open_img(): global x, panelA, panelB global count, eimg, location, filename count = 0 x = openfilename() img = Image.open(x) eimg = img img = ImageTk.PhotoImage(img) temp = x location = getpath(temp) filename = getfilename(temp) # print(x) if panelA is None or panelB is None: panelA = Label(image=img) panelA.image = img panelA.pack(side="left", padx=10, pady=10) panelB = Label(image=img) panelB.image = img panelB.pack(side="right", padx=10, pady=10) else: panelA.configure(image=img) panelB.configure(image=img) panelA.image = img panelB.image = img # function defined for make the sketch of image selected def en_fun(): global x, image_encrypted, key # print(x) image_input = cv2.imread(x, 0)# 'C:/Users/aakas/Documents/flower.jpg' (x1, y) = image_input.shape image_input = image_input.astype(float) / 255.0 # print(image_input) mu, sigma = 0, 0.1 # mean and standard deviation key = np.random.normal(mu, sigma, (x1, y)) + np.finfo(float).eps # print(key) image_encrypted = image_input / key cv2.imwrite('image_encrypted.jpg', image_encrypted * 255) imge = Image.open('image_encrypted.jpg') imge = ImageTk.PhotoImage(imge) panelB.configure(image=imge) panelB.image = imge mbox.showinfo("Encrypt Status", "Image Encryted successfully.") # function defined to make the image sharp def de_fun(): global image_encrypted, key image_output = image_encrypted * key image_output *= 255.0 cv2.imwrite('image_output.jpg', image_output) imgd = Image.open('image_output.jpg') imgd = ImageTk.PhotoImage(imgd) panelB.configure(image=imgd) panelB.image = imgd mbox.showinfo("Decrypt Status", "Image decrypted successfully.") # function defined to reset the edited image to original one def reset(): # print(x) image = cv2.imread(x)[:, :, ::-1] global count, eimg count = 6 global o6 o6 = image image = Image.fromarray(o6) eimg = image image = ImageTk.PhotoImage(image) panelB.configure(image=image) panelB.image = image mbox.showinfo("Success", "Image reset to original format!") # function defined to same the edited image def save_img(): global location, filename, eimg print(filename) # eimg.save(location + filename + r"_edit.png") filename = filedialog.asksaveasfile(mode='w', defaultextension=".jpg") if not filename: return eimg.save(filename) mbox.showinfo("Success", "Encrypted Image Saved Successfully!") # top label start1 = tk.Label(text = "Image Encryption\nDecryption", font=("Arial", 40), fg="magenta") # same way bg start1.place(x = 350, y = 10) # original image label start1 = tk.Label(text = "Original\nImage", font=("Arial", 40), fg="magenta") # same way bg start1.place(x = 100, y = 270) # edited image label start1 = tk.Label(text = "Encrypted\nDecrypted\nImage", font=("Arial", 40), fg="magenta") # same way bg start1.place(x = 700, y = 230) # choose button created chooseb = Button(window, text="Choose",command=open_img,font=("Arial", 20), bg = "orange", fg = "blue", borderwidth=3, relief="raised") chooseb.place(x =30 , y =20 ) # save button created saveb = Button(window, text="Save",command=save_img,font=("Arial", 20), bg = "orange", fg = "blue", borderwidth=3, relief="raised") saveb.place(x =170 , y =20 ) # Encrypt button created enb = Button(window, text="Encrypt",command=en_fun,font=("Arial", 20), bg = "light green", fg = "blue", borderwidth=3, relief="raised") enb.place(x =150 , y =620 ) # decrypt button created deb = Button(window, text="Decrypt",command=de_fun,font=("Arial", 20), bg = "orange", fg = "blue", borderwidth=3, relief="raised") deb.place(x =450 , y =620 ) # reset button created resetb = Button(window, text="Reset",command=reset,font=("Arial", 20), bg = "yellow", fg = "blue", borderwidth=3, relief="raised") resetb.place(x =800 , y =620 ) # function created for exiting def exit_win(): if mbox.askokcancel("Exit", "Do you want to exit?"): window.destroy() # exit button created exitb = Button(window, text="EXIT",command=exit_win,font=("Arial", 20), bg = "red", fg = "blue", borderwidth=3, relief="raised") exitb.place(x =880 , y =20 ) window.protocol("WM_DELETE_WINDOW", exit_win) window.mainloop()
Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.
OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.
import sys
name = sys.stdin.readline()
print("Hello "+ name)
Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.
When ever you want to perform a set of operations based on a condition IF-ELSE is used.
if conditional-expression
#code
elif conditional-expression
#code
else:
#code
Indentation is very important in Python, make sure the indentation is followed correctly
For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.
mylist=("Iphone","Pixel","Samsung")
for i in mylist:
print(i)
While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.
while condition
#code
There are four types of collections in Python.
List is a collection which is ordered and can be changed. Lists are specified in square brackets.
mylist=["iPhone","Pixel","Samsung"]
print(mylist)
Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
Below throws an error if you assign another value to tuple again.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)
Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.
myset = {"iPhone","Pixel","Samsung"}
print(myset)
Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.
mydict = {
"brand" :"iPhone",
"model": "iPhone 11"
}
print(mydict)
Following are the libraries supported by OneCompiler's Python compiler
Name | Description |
---|---|
NumPy | NumPy python library helps users to work on arrays with ease |
SciPy | SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation |
SKLearn/Scikit-learn | Scikit-learn or Scikit-learn is the most useful library for machine learning in Python |
Pandas | Pandas is the most efficient Python library for data manipulation and analysis |
DOcplex | DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling |