import cv2
import numpy as np
from datetime import datetime
import os
from PIL import Image
import hashlib, os, math, time
import Image
import ImageEnhance
from pytesser import *
from urllib import urlretrieve
import math
import random
iconset = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
samples=np.empty((0,100))
responses =[]
for letter in iconset:
for img in os.listdir('iconset/%s/'%(letter)):
img = cv2.imread(img)
im3 = img.copy()
height, width, depth = im3.shape
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
roi = thresh[0:height, 0:width]
roismall = cv2.resize(roi,(10,10))
responses.append(j)
sample = roismall.reshape((1,100))
samples = np.append(samples,sample,0)
print "training complete"