pull changes

.gitignore

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+.DS_Store

10fastfingershack/README.md

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+#Requires Selenium and Chrome Webdriver
+Install selenium and specify location in the Python Script.
+
+This is the official website.
+https://10fastfingers.com/typing-test/english
+
+The website serves a test for your typing speed. It can be easily automated because the words are loaded into the website during load and readily available in the source code.

10fastfingershack/hack.py

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+from selenium import webdriver
+driver = webdriver.Chrome("/home/suryatej/Documents/chromedriver")
+driver.get("https://10fastfingers.com/typing-test/english")
+driver.maximize_window()
+timer_element = driver.find_element_by_xpath('//*[@id="timer"]')
+timer = timer_element.get_attribute("innerHTML")
+input_element = driver.find_element_by_xpath('//*[@id="inputfield"]')
+for i in range(1,350):
+	try:
+ current_word_element = driver.find_element_by_xpath('//*[@id="row1"]/span[' + str(i) +']' )
+ current_word = current_word_element.get_attribute("innerHTML")
+ input_element.send_keys(current_word + " ")
+ #driver.implicitly_wait(10)
+	except:
+ break
+

Ascii-art-generator/Ascii-art-generator.py

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+import requests
+import numpy as np 
+
+max_no_of_fonts = 10
+text_to_show = "Enter the text here that you would like to see"
+fonts = requests.get("http://artii.herokuapp.com/fonts_list")
+font_list = np.array(fonts.content.split("\n"))
+chose = np.arange(0,400,1)
+np.random.shuffle(chose)
+selected = font_list[chose[:max_no_of_fonts]]
+for font in selected:
+	ascif = requests.get("http://artii.herokuapp.com/make?text="+text_to_show+"&font="+font)
+	print(ascif.content)

Daily-Wallpaper/README.md

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+# [Daily-Wallpaper](https://github.com/udit-001/daily-wallpaper)
+
+A python script to update desktop wallpaper on a Windows machine from a tumblr blog. For more info, [click here.](https://github.com/udit-001/daily-wallpaper)
+
+## Inspiration
+I was tired of seeing the same boring wallpaper on my desktop everyday. After learning a little bit of Python, I decided to design a script that would update my wallpaper on it's own by fetching an image from a [Tumblr Blog](http://fuckinghomepage.com/).
+
+## Installation
+Install the required dependencies/libraries by running :
+
+```bash
+$ pip install -r requirements.txt
+```
+
+## Usage
+This script can be scheduled to run daily by using the Task Scheduler utility present on Windows. You will require a BAT script to run the script from the task.
+
+```bat
+@echo off 
+cd <path to wall.pyw>
+pythonw wall.pyw
+```
+
+Now copy the above code and paste it into notepad, then modify the `<path to wall.pyw>` according to where you download the files from this repo, and save it as "dailywall.bat"
+
+If you want your script to work silently in the background without having a command prompt window opening up, then you'll also need to create a .VBS script.
+
+```vbs
+Set WshShell = CreateObject("WScript.Shell")
+WshShell.Run chr(34) & "<path to dailywall.bat>" & Chr(34), 0
+Set WshShell = Nothing
+```
+Copy the above code and paste it into notepad, then modify the `<path to dailywall.bat>` and save it as "dailywall.vbs". 
+Now add the above script in the Windows Task that you'll schedule to run daily.
+
+## Features 
+It also displays a notification on your computer when the wallpaper gets updated.
+
+![Notifications Screenshot](https://raw.githubusercontent.com/udit-001/daily-wallpaper/master/img/notification.jpg)
+
+## License
+
+> You can check out the full license [here](https://github.com/udit-001/daily-wallpaper/blob/master/LICENSE)
+
+This project is licensed under the terms of the MIT license.
+
+
+

Daily-Wallpaper/wall.pyw

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+#!python2
+"""This script can be set to run daily on your computer using utilities like Windows Task Scheduler
+on Windows, and it would download a wallpaper
+from a blog and set it up as your wallpaper on it's own"""
+import urllib
+import os
+import ctypes
+import imghdr
+from datetime import datetime
+from bs4 import BeautifulSoup
+import requests
+from win10toast import ToastNotifier
+
+url = 'http://fuckinghomepage.com/rss'
+sourceCode = requests.get(url)
+plainText = sourceCode.text
+soup = BeautifulSoup(plainText, 'lxml')
+
+data = soup.item.description.text
+href = data.split('PICTURE OF THE DAY:')[1].split('href=')[1].split('target=')[0]
+href = href.replace('\"', ' ').strip()
+
+today = datetime.now()
+files = os.listdir(os.getcwd())
+
+dateString = str(today.day)+'-'+str(today.month)+'-'+str(today.year)
+prev = str(today.day-1)+'-'+str(today.month)+'-'+str(today.year)
+
+check = 0
+for item in files:
+ if dateString in item:
+ fileName = item
+ check = 1
+ else:
+ continue
+
+for item in files:
+ if prev in item:
+ os.remove(item)
+ else:
+ continue
+
+if check == 0:
+ urllib.urlretrieve(href, dateString)
+ ext = imghdr.what(dateString)
+ path = dateString+'.'+ext
+ os.rename(dateString, path)
+ abspath = os.path.abspath(path)
+ ctypes.windll.user32.SystemParametersInfoA(20, 0, abspath, 3)
+
+elif check == 1:
+ abspath = os.path.abspath(fileName)
+ ctypes.windll.user32.SystemParametersInfoA(20, 0, abspath, 3)
+
+print "Wallpaper set successfully."
+toaster = ToastNotifier()
+toaster.show_toast('Wallpaper Set!', 'Wallpaper was successfully downloaded.', \
+	duration=5, icon_path='wall.ico')

HistogramEqualization/HistogramEqualization.py

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+from PIL import Image
+import sys
+
+def histogramEqualization(argv):
+
+	picSource = Image.open(argv)
+	picDestination = picSource.copy()
+
+	# For the sake of simplicity, the picture gets casted to
+	# RGB if it isn't yet 
+	if picDestination.mode != 'RGB':
+ picDestination = picDestination.convert('RGB')
+
+	picDestinationArray = picDestination.load()
+	width, height = picSource.size
+
+	# Determine the maximum and minimum pixel brightness
+	histMax, histMin = 1, 256
+	for pixelX in range(width):
+ for pixelY in range(height):
+ # Even though a greyscale picture is expected the image
+ # gets loaded as RGB, thus its needed to access any of the
+ # seperate colour values, since they should have the same value.
+ # If coloured pictures are given, this will result in a greyscale
+ # representation and equalization of the red values.
+ if picDestinationArray[pixelX, pixelY][0] < histMin:
+ histMin = picDestinationArray[pixelX, pixelY][0]
+ if picDestinationArray[pixelX, pixelY][0] > histMax:
+ histMax = picDestinationArray[pixelX, pixelY][0]
+
+	# Apply histogramm equalization formula to every pixel
+	for pixelX in range(width):
+ for pixelY in range(height):
+ newVal = int(256 * ((picDestinationArray[pixelX, pixelY][0] - histMin) / (histMax - histMin)) - 1)
+ # Since the picture is loaded as RGB, we need to set the value thrice
+ picDestinationArray[pixelX, pixelY] = (newVal, newVal, newVal)
+
+	return picDestination
+
+
+
+if __name__ == "__main__":
+	if len(sys.argv) != 2:
+ print('Usage: {} <Image File>'.format(sys.argv[0]))
+	else:
+ picOut = histogramEqualization(sys.argv[1])
+ picOut.save('out.bmp')

HistogramEqualization/README.md

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+# Histogram Equalization with Phyton
+
+This script takes grayscale pictures and spreads out their colour range to the fullest. Sometimes grayscale pictures look dull or foggy because they don't use the full range of possible gray colours. This script uses the formula described in [Wikipedia](https://de.wikipedia.org/wiki/Punktoperator_(Bildverarbeitung)#Histogrammspreizung_und_-stauchung) and applies it pixel by pixel on the given picture.
+
+This example picture is taken from Wikimedia and belongs to [Phillip Capper](https://commons.wikimedia.org/wiki/File:Hawkes_Bay_NZ.jpg).
+![Example of unequalized Picture](https://upload.wikimedia.org/wikipedia/commons/0/08/Unequalized_Hawkes_Bay_NZ.jpg)
+
+And this is the result of the script.
+![The example picture after processing](Equalized_example_picture.bmp)
+
+## Installation
+The script tries to use a minimum amount of modules, but still needs one; Pillow. Install it with
+
+```bash
+$ pip install pillow
+```
+
+## Usage
+This script takes a picture and puts out the processed picture as 'out.bmp' in the destination of the script.
+
+```bat
+Usage: HistogrammEqualization.py <Image File>
+```

Kth Nearest Neighbour (ML Algorithm)/KNN.py

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+from numpy import *
+import operator
+
+
+def classify(x,dataset,labels,k):
+ datasetsize=dataset.shape[0]
+ diffmat=tile(x,(datasetsize,1))-dataset
+ sqdiffmat=diffmat**2
+ sqdistances=sqdiffmat.sum(axis=1)
+ distances=sqdistances**0.5
+ sorteddistindices=distances.argsort()
+ classcount={}
+ for i in range(k):
+
+ voteIlabel=labels[sorteddistindices[i]]
+ classcount[voteIlabel]=classcount.get(voteIlabel,0) +1
+
+ sortedclasscount=sorted(classcount.items(),key=operator.itemgetter(1),reverse=True)
+ return sortedclasscount[0][0]
+
+def autonorm(dataset):
+ minval=dataset.min(0)
+ maxval=dataset.max(0)
+ ranges=maxval-minval
+ normdataset=zeros(shape(dataset))
+ m=dataset.shape[0]
+ normdataset=dataset-tile(minval,(m,1))
+ normdataset=normdataset/tile(ranges,(m,1))
+ return normdataset,ranges,minval
+
+
+def file2matrix(filename):
+ fr=open(filename)
+ numberoflines=len(fr.readlines())
+ returnmat=zeros((numberoflines,7))
+ classLabelvec=[]
+ fr=open(filename)
+ index=0
+ for line in fr.readlines():
+ line=line.strip()
+ listfromline=line.split(",")
+ returnmat[index,:]=listfromline[1:8]
+ classLabelvec.append((listfromline[0]))
+ index+=1
+ return returnmat,classLabelvec
+
+def classperson():
+ print("Abalone is a common name for any of a group of small to very large sea snails, marine gastropod molluscs in the family Haliotidae.")
+ print("\n")
+ print("From certain input about Abalone this algorithm will predict whether Abalone is \nM-> Male,\nF->Female or\nI->Infant ")
+ print("\n")
+ print("Accuracy of this algorithm is 65 to 70%")
+ input1 =float(input("enter length of Abalone"))
+ input2 = float(input("enter diameter"))
+ input3 = float(input("enter height"))
+ input4 = float(input("enter whole weight"))
+ input5 = float(input("enter shucked weight"))
+ input6 = float(input("enter viscera weight"))
+ input7 = float(input("enter shell weight"))
+
+
+ datamat, lab = file2matrix("KNN_support/abalone.data")
+ normmat, ran, minval = autonorm(datamat)
+
+ inarr=array([input1,input2,input3,input4,input5,input6,input7])
+ classresult=classify((inarr-minval)/ran,normmat,lab,3)
+ print("\n")
+ print(classresult)
+
+
+classperson()