app.py

import flask
import numpy as np
from cv2 import cv2
import base64
import json

#####
def nothing(x):
    pass

image_x, image_y = 64,64

from keras.models import load_model
classifier = load_model('Trained_model.h5')
from keras.preprocessing import image
# graph = tf.get_default_graph()

def predictor():
		test_image = image.load_img('1.png', target_size=(64, 64))
		test_image = image.img_to_array(test_image)
		test_image = np.expand_dims(test_image, axis = 0)
		import keras.backend.tensorflow_backend as tb
		tb._SYMBOLIC_SCOPE.value = True
		result = classifier.predict(test_image)

		if result[0][0] == 1:
				return 'A'
		elif result[0][1] == 1:
				return 'B'
		elif result[0][2] == 1:
				return 'C'
		elif result[0][3] == 1:
				return 'D'
		elif result[0][4] == 1:
				return 'E'
		elif result[0][5] == 1:
				return 'F'
		elif result[0][6] == 1:
				return 'G'
		elif result[0][7] == 1:
				return 'H'
		elif result[0][8] == 1:
				return 'I'
		elif result[0][9] == 1:
				return 'J'
		elif result[0][10] == 1:
				return 'K'
		elif result[0][11] == 1:
				return 'L'
		elif result[0][12] == 1:
				return 'M'
		elif result[0][13] == 1:
				return 'N'
		elif result[0][14] == 1:
				return 'O'
		elif result[0][15] == 1:
				return 'P'
		elif result[0][16] == 1:
				return 'Q'
		elif result[0][17] == 1:
				return 'R'
		elif result[0][18] == 1:
				return 'S'
		elif result[0][19] == 1:
				return 'T'
		elif result[0][20] == 1:
				return 'U'
		elif result[0][21] == 1:
				return 'V'
		elif result[0][22] == 1:
				return 'W'
		elif result[0][23] == 1:
				return 'X'
		elif result[0][24] == 1:
				return 'Y'
		elif result[0][25] == 1:
				return 'Z'
		else:
				return ' '
       
#####


app=flask.Flask(__name__)
@app.route('/')
def home():
	return'<h1>WELCOME to 0.0.0.0</h1>';



@app.route('/predict',methods=["POST"])
def start():
	data={'str_image':""}
	if (flask.request.method == "POST"):
		count=0
		
		d_img=flask.request.json
		#.encode is to convert string to bytes again
		imageString = base64.b64decode(d_img['image'].encode())
		

		imageString1=d_img['image'].encode()
		print(len(imageString1))
		print(len(imageString))
		#print(imageString)
		if len(imageString)==78400:
			nparr = np.fromstring(imageString, np.uint8)
			img = nparr.reshape(280,280)
			cv2.imwrite("input.jpg",img)
		#print(nparr.shape)
		#img = (decode_img['image'])
		#jpg_as_np = np.frombuffer(jpg_original, dtype=np.uint8)
		#img_decode = cv2.imdecode(jpg_as_np, cv2.IMREAD_COLOR)
		#img=cv2.imread(img_decode)
		# img_HSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
		# HSV_mask = cv2.inRange(img_HSV, (0, 15, 0), (17,170,255))
		# HSV_mask = cv2.morphologyEx(HSV_mask, cv2.MORPH_OPEN, np.ones((3,3), np.uint8))
		# img_YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
		# YCrCb_mask = cv2.inRange(img_YCrCb, (0, 135, 85), (255,180,135)) 
		# YCrCb_mask = cv2.morphologyEx(YCrCb_mask, cv2.MORPH_OPEN, np.ones((3,3), np.uint8))
		# global_mask=cv2.bitwise_and(YCrCb_mask,HSV_mask)
		# global_mask=cv2.medianBlur(global_mask,3)
		# global_mask = cv2.morphologyEx(global_mask, cv2.MORPH_OPEN, np.ones((4,4), np.uint8))
		# HSV_result = cv2.bitwise_not(global_mask)
		
		
		

		
		# cv2.waitKey(1)
		
		# jpgt = base64.b64encode(global_mask)
		# jpg_as_text=(jpgt).decode()
		
		# #print("asdsasadsads")
        
		# data['str_image']=jpg_as_text


		#######


		# img_counter = 0
		# res=''
		# img_text = ''
		# # while True:
		# # ret, frame = cam.read()
		# # frame = cv2.flip(frame,1)
		# l_h =130
		# l_s = 0
		# l_v =30
		# u_h = 179
		# u_s = 255
		# u_v = 255

		# frame = img
		# img = cv2.rectangle(frame, (425,100),(625,300), (0,255,0), thickness=2, lineType=8, shift=0)

		# lower_blue = np.array([l_h, l_s, l_v])
		# upper_blue = np.array([u_h, u_s, u_v])
		# imcrop = img#[102:298, 427:623]
		# hsv = cv2.cvtColor(imcrop, cv2.COLOR_BGR2HSV)
		# mask = cv2.inRange(hsv, lower_blue, upper_blue)
		
		# cv2.putText(frame, img_text, (30, 400), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 255, 0))
		# cv2.imshow("test", frame)
		# cv2.imshow("mask", mask)
		
		# #if cv2.waitKey(1) == ord('c'):
			
			img_name = "1.png"
			# save_img = cv2.resize(mask, (image_x, image_y))
			cv2.imwrite(img_name,img)
			# cv2.imwrite("2.png",frame)
			# print("image", len(save_img))
			img_text = predictor()
			print(img_text)
			data['predicted']=img_text
		else:
			data['predicted']=' '

	return flask.jsonify(data)		
            
if __name__ == '__main__':
	print("sagar")
	# app.run('0.0.0.0',debug=True)
	app.run("0.0.0.0", threaded=False)