Pattern.py

import numpy as np
import cv2

#Training!

def training(contoursObj):
	samples =  np.empty((0,100))
	#Empty array for the corresponding value
	valueDigit = []
	#Loop over all the images,in this case we have 9 images per digit,so in total we have 90 images(we included 0 as a digit)
	for digit in range(0,10):
		for image in range(1,10):
			#Get the image and find the contours of the number(rectangle)
			image_digit = cv2.imread("Samples\\"+str(digit)+"_"+str(image)+".jpg")
			gray = cv2.cvtColor(image_digit,cv2.COLOR_BGR2GRAY)
			blur = cv2.GaussianBlur(gray,(5,5),0)
			thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
			#Coordinates of the rectangle (x,y)->starting points, (w,h)-> width and height
			x,y,w,h = contours(image_digit)
			roi = thresh[y:y+h,x:x+w]
			roismall = cv2.resize(roi,(10,10))
			key = cv2.waitKey(0)
			valueDigit.append(int(chr(key)))
			sample = roismall.reshape((1,100))
        	        samples = np.append(samples,sample,0)
        valueDigit = np.array(valueDigit,np.float32)
	valueDigit = valueDigit.reshape((valueDigit.size,1))
	print "training complete"
	print samples.size
	print valueDigit.size
	np.savetxt('generalsamples.data',samples)
	np.savetxt('generalvaluedigits.data',valueDigit)

def contours(imageToDraw):
	imgray = cv2.cvtColor(imageToDraw,cv2.COLOR_BGR2GRAY)
	ret,thresh = cv2.threshold(imgray,127,255,0)
	contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
	x,y,w,h = cv2.boundingRect(contours[0])
	return x,y,w,h

def main():
	training(contours)

main()