run.py

import pandas
import numpy as np

from keras.preprocessing import image
from keras.models import Model
from keras.models import Sequential
from keras.layers import Conv2D, Dense, Flatten, Dropout, MaxPooling2D
from keras.layers import *
from keras.optimizers import Adam
from keras import backend as K
from keras import regularizers
import scipy.misc

import cv2
from subprocess import call
import os


model = Sequential()

model.add(Conv2D(24,(5,5),activation='relu',input_shape=(200,200,3),kernel_regularizer=regularizers.l2(0.01)))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(36,(5,5),activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(48,(3,3),activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(64,(3,3),activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Conv2D(128,(3,3),activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(MaxPool2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(128,activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(Dropout(0.3))

model.add(Dense(64,activation='relu',kernel_regularizer=regularizers.l2(0.01)))
model.add(Dropout(0.2))

model.add(Dense(1,activation='tanh',kernel_regularizer=regularizers.l2(0.01)))

model.summary()

adam = Adam(0.0001)
model.compile(optimizer = adam,loss = 'mse',metrics=['mae'])

model.load_weights('model_iter-2.h5')

img_str = cv2.imread('steering_wheel.jpg',0)
img_str = cv2.resize(img_str, (200,200), interpolation = cv2.INTER_AREA)
rows,cols = img_str.shape

smoothed_angle = 0


i = 36000
while(cv2.waitKey(10) != ord('q') and i<45567):
	img1 = image.load_img("driving_dataset/" + str(i) + ".jpg",color_mode='rgb')
	img1 = image.img_to_array(img1)/255.0
	img2 = image.load_img("driving_dataset/" + str(i) + ".jpg",color_mode='rgb')
	img2 = image.img_to_array(img2)/255.0
	img2 = img2[76:,:,:]
	img2 = cv2.resize(img2,(200,200))
	img_resh = np.reshape(img2,[1,200,200,3])
	degrees = model.predict(img_resh) * 180.0 / scipy.pi
	print("Predicted steering angle: " + str(degrees) + " degrees")
	cv2.imshow("frame", cv2.cvtColor(img1, cv2.COLOR_RGB2BGR))
	smoothed_angle += 0.2 * pow(abs((degrees - smoothed_angle)), 2.0 / 3.0) * (degrees - smoothed_angle) / abs(degrees - smoothed_angle)
	M = cv2.getRotationMatrix2D((cols/2,rows/2),-int(smoothed_angle),1)
	dst = cv2.warpAffine(img_str,M,(cols,rows))
	cv2.imshow("steering wheel", dst)
	i += 1

cv2.destroyAllWindows()