import os
import logging as log
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import tensorflow as tf
import cv2
import re
import tqdm
import keras
from keras.layers import Dense, Conv2D, Dropout, MaxPooling2D, Flatten
from keras.models import Sequential,load_model
from keras.callbacks import ModelCheckpoint,EarlyStopping, TensorBoard
video = cv2.VideoCapture(1)

while True:
	X_test = []
	flag,frame = video.read()
	#image = cv2.imread('/home/tpemist/Desktop/visualtest/train/info/0001_0010_0035_0054_0054.jpg')
	show = cv2.resize(frame,(640,480))
	image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	image = cv2.resize(image,(128,96))
	nparr = np.array(image) / 255.0
	X_test.append(nparr)
	X_test = np.array(X_test, dtype=np.float32)
	X_test = X_test.reshape(X_test.shape[0], 96, 128, 1)

	model = Sequential()

	model.add(Conv2D(32, (3, 3),padding = 'same', activation = 'relu', input_shape = (96,128,1)))
	model.add(MaxPooling2D((2,2)))
	model.add(Dropout(0.5))
	model.add(Conv2D(32, (3, 3),padding = 'same', activation = 'relu'))
	model.add(Conv2D(32, (3, 3),padding = 'same', activation = 'relu'))
	model.add(MaxPooling2D((2,2)))
	model.add(Dropout(0.5))
	model.add(Conv2D(32, (3, 3),padding = 'same', activation = 'relu'))
	model.add(MaxPooling2D((2,2)))
	model.add(Dropout(0.75))

	model.add(Flatten())
	model.add(Dense(200))
	model.add(Dropout(0.5))
	model.add(Dense(200))
	model.add(Dropout(0.5))
	model.add(Dense(3))

	X_test = np.array(X_test)
	model.load_weights('train_model2')
	location = model.predict(X_test)
	location = location.astype(np.float32)
	x = (location[0])[0]
	y = (location[0])[1]
	w = (location[0])[2]
	#print x,y,w
	x1 = int((x*5+(x-1)*5)/2)
	y2 = int((y*5+(y-1)*5)/2)
	w2 = int(w*2.5)
	#print x1,y2,w2
	cv2.rectangle(show, (x1,y2), (x1+w2, y2+w2), (255, 255, 255), 2)

	cv2.imshow('Video', show)
	if cv2.waitKey(1) & 0xFF == ord('q'):
		break



video.release()
cv2.destroyAllWindows()