testDevaModel.py

from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True,
	help="path to trained model model")
ap.add_argument("-i", "--image", required=True,
	help="path to input image")
args = vars(ap.parse_args())


''' ["ka","kha","ga","gha","kna","cha","chha","ja","jha","yna","t`a","t`ha","d`a","d`ha","adna","ta","tha","da","dha","na","pa","pha","ba","bha","ma","yaw","ra","la","waw","sha","shat","sa","ha","aksha","tra","gya","0","1","2","3","4","5","6","7","8","9"]


labels =['yna', 't`aa', 't`haa', 'd`aa', 'd`haa', 'a`dna', 'ta', 'tha', 'da', 'dha', 'ka', 'na', 'pa', 'pha', 'ba', 'bha', 'ma', 'yaw', 'ra', 'la', 'waw', 'kha', 'sha', 'shat', 'sa', 'ha', 'aksha', 'tra', 'gya', 'ga', 'gha', 'kna', 'cha', 'chha', 'ja', 'jha', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
'''
labels = [u'\u091E',u'\u091F',u'\u0920',u'\u0921',u'\u0922',u'\u0923',u'\u0924',u'\u0925',u'\u0926',u'\u0927',u'\u0915',u'\u0928',u'\u092A',u'\u092B',u'\u092c',u'\u092d',u'\u092e',u'\u092f',u'\u0930',u'\u0932',u'\u0935',u'\u0916',u'\u0936',u'\u0937',u'\u0938',u'\u0939','ksha','tra','gya',u'\u0917',u'\u0918',u'\u0919',u'\u091a',u'\u091b',u'\u091c',u'\u091d',u'\u0966',u'\u0967',u'\u0968',u'\u0969',u'\u096a',u'\u096b',u'\u096c',u'\u096d',u'\u096e',u'\u096f']
image = cv2.imread(args["image"])
orig = image.copy()
 
# pre-process the image for classification
image = cv2.resize(image, (32,32))
image = image.astype("float") / 255.0
image = img_to_array(image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#print image.shape
image = np.expand_dims(image, axis=0)
#print image.shape
image = np.expand_dims(image, axis=3)
#print image.shape
# load the trained convolutional neural network
print("[INFO] loading network...")
model = load_model(args["model"])
 
# classify the input image
lists = model.predict(image)[0]

print "The letter is ",labels[np.argmax(lists)]