training.py

import sys

import numpy as np
import cv2

im = cv2.imread('training2.png')
im3 = im.copy()

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
# im = cv2.imshow("image window", thresh)
# k = cv2.waitKey(0)

#################      Now finding Contours         ###################

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

print(len(contours))

samples =  np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]

for cnt in contours:
    if cv2.contourArea(cnt)>50:
        [x,y,w,h] = cv2.boundingRect(cnt)
        print(h)

        if  h>16:
            cv2.rectangle(im,(x,y),(x+w,y+h),(255,0,0),2)
            roi = thresh[y:y+h,x:x+w]
            roismall = cv2.resize(roi,(10,10))
            cv2.imshow('norm',im)
            key = cv2.waitKey(0)

            if key == 27:  # (escape to quit)
                sys.exit()
            elif key in keys:
                responses.append(int(chr(key)))
                sample = roismall.reshape((1,100))
                samples = np.append(samples,sample,0)
                cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)

responses = np.array(responses,np.float32)
responses = responses.reshape((responses.size,1))
print("training complete")

np.savetxt('generalsamples.data',samples)
np.savetxt('generalresponses.data',responses)