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Commit 389b0a2

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Merge pull request avinashkranjan#941 from codebuzzer01/master
Cryptography
2 parents e91c8cd + fb45382 commit 389b0a2

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‎Cryptography/README.md

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# CRYPTOGRAPHY
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## DESCRIPTION
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The objective of this project is to encode and decode messages using a common key. In this project, users have to enter the message to encode or decode. Users have to select the mode to choose the encoding and decoding process. The same key must be used to process the encoding and decoding for the same message.
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## PROJECT REQUISITES
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To build this project we will use the basic concept of python, Tkinter, and base64 library.
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- Tkinter is a standard GUI python library.
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- base64 module provides a function to encode the binary data to ASCII characters and decode that ASCII characters back to binary data.
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- Tkinter is a standard GUI python library base64 module that provides a function to encode the binary data to ASCII characters and decode that ASCII characters back to binary data.
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## PROJECT STRUCTURE
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These are the step to build message encode – decode python project-
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- Import module
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- Create display window
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- Define function
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- Define labels and buttons
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## AUTHOR NAME
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[ANUSHKA CHITRANSHI](https://github.com/codebuzzer01)

‎Cryptography/crypto.py

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from tkinter import Tk
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from tkinter import Label
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from tkinter import BOTTOM
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from tkinter import StringVar
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from tkinter import Entry
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from tkinter import Button
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import base64
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# initialize window
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root = Tk()
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root.geometry('500x300')
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root.resizable(0, 0)
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# title of the window
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root.title("Cryptography World")
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# label
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Label(root, text='ENCODE DECODE', font='arial 20 bold').pack()
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Label(root, text='By Anushka Chitranshi', font='arial 20 bold').pack(side=BOTTOM)
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# define variables
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Text = StringVar()
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private_key = StringVar()
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mode = StringVar()
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Result = StringVar()
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# function to encode
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def Encode(key, message):
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"""Encode the message."""
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enc = []
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for i in enumerate(message):
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key_c = key[i % len(key)]
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enc.append(chr((ord(message[i]) + ord(key_c)) % 256))
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return base64.urlsafe_b64encode("".join(enc).encode()).decode()
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# function to decode
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def Decode(key, message):
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"""Decode the message."""
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dec = []
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message = base64.urlsafe_b64decode(message).decode()
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for i in enumerate(message):
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key_c = key[i % len(key)]
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dec.append(chr((256 + ord(message[i]) - ord(key_c)) % 256))
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return "".join(dec)
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# function to set mode
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def Mode():
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"""Take mode of cryptography."""
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if mode.get() == 'e':
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Result.set(Encode(private_key.get(), Text.get()))
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elif mode.get() == 'd':
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Result.set(Decode(private_key.get(), Text.get()))
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else:
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Result.set('Invalid Mode')
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# Function to exit window
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def Exit():
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"""Exit the window."""
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root.destroy()
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# Function to reset
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def Reset():
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"""Reset the screen."""
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Text.set("")
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private_key.set("")
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mode.set("")
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Result.set("")
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Label(
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root, font='arial 12 bold', text='MESSAGE'
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).place(x=60, y=60)
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Entry(
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root, font='arial 10', textvariable=Text, bg='ghost white'
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).place(x=290, y=60)
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# key
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Label(root, font='arial 12 bold', text='KEY').place(x=60, y=90)
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Entry(
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root, font='arial 10', textvariable=private_key , bg='ghost white'
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).place(x=290, y=90)
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# mode
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Label(
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root, font='arial 12 bold', text='MODE(e-encode, d-decode)'
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).place(x=60, y=120)
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Entry(
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root, font='arial 10', textvariable=mode , bg='ghost white'
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).place(x=290, y=120)
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# result
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Entry(
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root, font='arial 10 bold', textvariable=Result, bg='ghost white'
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).place(x=290, y=150)
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# result button
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Button(
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root, font='arial 10 bold', text='RESULT', padx=2, bg='LightGray', command=Mode
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).place(x=60, y=150)
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# reset button
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Button(
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root, font='anson', text='RESET', width=6, command=Reset, bg='Green', padx=2
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).place(x=80, y=190)
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# exit button
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Button(
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root, font='anson', text='EXIT', width=6, command=Exit, bg='Red', padx=2, pady=2
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).place(x=180, y=190)
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root.mainloop()

‎Gender-Age Detection/README.md

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# GENDER-AGE DETECTION
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## DESCRIPTION
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The objective of this project is to identify the gender and approximate age of a person given in an image automatically.
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## PROJECT REQUISITES
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To build this project we will use
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- Deep Learning to accurately identify the gender and age of a person from a single image of a face.
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- We will use this [dataset](https://www.kaggle.com/ttungl/adience-benchmark-gender-and-age-classification)
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- The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following 8 ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100).
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## PROJECT STRUCTURE
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These are the step to build Gender-Age Detection python project-
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- Detect faces
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- Classify into Male/Female
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- Classify into one of the 8 age ranges
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- Put the results on the image and display it
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## AUTHOR NAME
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[ANUSHKA CHITRANSHI](https://github.com/codebuzzer01)
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name: "CaffeNet"
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input: "data"
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input_dim: 1
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input_dim: 3
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input_dim: 227
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input_dim: 227
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layers {
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name: "conv1"
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type: CONVOLUTION
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bottom: "data"
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top: "conv1"
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convolution_param {
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num_output: 96
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kernel_size: 7
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stride: 4
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}
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}
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layers {
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name: "relu1"
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type: RELU
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bottom: "conv1"
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top: "conv1"
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}
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layers {
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name: "pool1"
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type: POOLING
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bottom: "conv1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm1"
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type: LRN
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bottom: "pool1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv2"
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type: CONVOLUTION
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bottom: "norm1"
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top: "conv2"
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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}
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}
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layers {
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name: "relu2"
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type: RELU
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bottom: "conv2"
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top: "conv2"
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}
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layers {
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name: "pool2"
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type: POOLING
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bottom: "conv2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm2"
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type: LRN
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bottom: "pool2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv3"
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type: CONVOLUTION
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bottom: "norm2"
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top: "conv3"
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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}
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}
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layers{
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name: "relu3"
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type: RELU
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bottom: "conv3"
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top: "conv3"
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}
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layers {
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name: "pool5"
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type: POOLING
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bottom: "conv3"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "fc6"
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type: INNER_PRODUCT
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bottom: "pool5"
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top: "fc6"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu6"
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type: RELU
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bottom: "fc6"
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top: "fc6"
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}
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layers {
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name: "drop6"
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type: DROPOUT
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc7"
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type: INNER_PRODUCT
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bottom: "fc6"
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top: "fc7"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu7"
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type: RELU
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bottom: "fc7"
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top: "fc7"
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}
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layers {
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name: "drop7"
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type: DROPOUT
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc8"
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type: INNER_PRODUCT
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bottom: "fc7"
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top: "fc8"
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inner_product_param {
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num_output: 8
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}
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}
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layers {
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name: "prob"
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type: SOFTMAX
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bottom: "fc8"
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top: "prob"
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}

‎Gender-Age Detection/age_net.caffemodel

43.5 MB
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