#!/usr/bin/python

import numpy as np#Load NumPy library for easier numerical computations
from PIL import Image
import cv2
from matplotlib import pyplot as plt
from PIL import ImageFilter
import random
import pdb
import copy
import sys
# import getopt

# def main(argv):
# inputfile = ''
# outputfile = ''
# try:
#       opts, args = getopt.getopt(argv,"hi:oimg:otxt:",["ifile=","oimagefile=","otextfile="])
# except getopt.GetoptError:
#       print ('test.py -i <inputfile> -oimg <outputImagefile> -otxt <outputTextfile>')
#       sys.exit(2)
#       for opt, arg in opts:
#          if opt == '-h':
#             print ('test.py -i <inputfile> -oimg <outputImagefile> -otxt <outputTextfile>')
#             sys.exit()
#          elif opt in ("-i", "--ifile"):
#             inputfile = arg
#          elif opt in ("-oimg", "--oimagefile"):
#             outputImagefile = arg
#          elif opt in ("-otxt", "--otextfile"):
#             outputTextfile = arg
#       print ('Input file is "', inputfile)
#       print ('Output image file is "', outputImagefile)
#       print ('Output text file is "', outputTextfile)
# return inputfile, outputImagefile, outputTextfile

def fillColorOnAlphabets(img, borderPadding, cntrAreaThresh, thickness, color, greaterFlag):
thresh, binImg = cv2.threshold( img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU );

_, contours, hierarchy = cv2.findContours(binImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
res = img.copy()
# Iterate through contours and draw a slightly bigger white rectangle
# over the contours that are not big enough (the text) on the copy of the image.
for i in contours:
   cnt = cv2.contourArea(i)
   if greaterFlag == 0:
         if cnt < cntrAreaThresh:
            x,y,w,h = cv2.boundingRect(i)
            ar = float(w)/float(h)
            if ar>=0.3 and ar<=3:
               cv2.rectangle(res,(x-borderPadding,y-borderPadding),(x+w+borderPadding,y+h+borderPadding),color,thickness)

Assignment 1: Image Processing and Recognition Basics we recommend...