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model.py
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model.py
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import csv
import cv2
import numpy as np
from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout
from keras.layers.convolutional import Conv2D
lines = []
data_location = 'data/'
with open(data_location + 'driving_log.csv')as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
images = []
measurements = []
break_line = '\\' # for windows \\, linux use /
steering_correction = 0.1
with open(file_location + 'driving_log.csv')as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
steering_center = float(line[3])
steering_left = steering_center + steering_correction
steering_right = steering_center - steering_correction
source_path = line[0]
filename = source_path.split(break_line)[-1]
current_path = file_location + '/IMG/' + filename
image_center = cv2.imread(current_path)
source_path = line[1]
filename = source_path.split(break_line)[-1]
current_path = file_location + '/IMG/' + filename
image_left = cv2.imread(current_path)
source_path = line[2]
filename = source_path.split(break_line)[-1]
current_path = file_location + '/IMG/' + filename
image_right = cv2.imread(current_path)
images.extend(image_center, image_left, image_right)
measurements.extend((steering_center, steering_left, steering_right))
# for line in lines:
# source_path = line[0]
# filename = source_path.split(break_line)[-1]
# current_path = file_location + '/IMG/' + filename
# image = cv2.imread(current_path)
# images.append(image)
# measurement = float(line[3])
# measurements.append(measurement)
# Image processing
augmented_images, augmented_measurements = [], []
# image flipping
for image, measurement in zip(images, measurements):
augmented_images.append(image)
augmented_measurements.append(measurement)
augmented_images.append(cv2.flip(image, 1))
augmented_measurements.append(measurement * -1)
X_train = np.array(augmented_images)
y_train = np.array(augmented_measurements)
model = Sequential()
# Cropping the image
model.add(Cropping2D(cropping=((70, 20), (0, 0)), input_shape=(160, 320, 3)))
# Normalize
model.add(Lambda(lambda x: x / 255. - 0.5))
# Use of ElU: http://image-net.org/challenges/posters/JKU_EN_RGB_Schwarz_poster.pdf
model.add(Conv2D(24, 5, strides=(2, 2), activation='elu'))
model.add(Conv2D(36, 5, strides=(2, 2), activation='elu'))
model.add(Conv2D(48, 5, strides=(2, 2), activation='elu'))
model.add(Conv2D(64, 3, activation='elu'))
model.add(Conv2D(64, 3, activation='elu'))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(100, activation='elu'))
model.add(Dense(50, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(1, activation='elu'))
# model.summary()
# model.compile(loss='mse', optimizer='adam')
# history_object = model.fit(X_train, y_train, validation_split=0.2, shuffle=True, epochs=7)
# model.save('model_muti_v3.h5')
#
# from sklearn.model_selection import train_test_split
# import sklearn
# # train_samples, validation_samples = train_test_split(samples, test_size=0.2)
#
# # Generator Slows down the training process
# def generator(samples, batch_size=32):
# num_samples = len(samples)
# break_line = '\\'
# correction = 0.35
#
# while 1: # Loop forever so the generator never terminates
# samples = sklearn.utils.shuffle(samples)
# for offset in range(0, num_samples, batch_size):
# batch_samples = samples[offset:offset + batch_size]
#
# images = []
# measurements = []
#
# for line in batch_samples:
# if line[0] != '':
# steering_center = float(line[3])
# steering_left = steering_center + correction
# steering_right = steering_center - correction
#
# # center image
# source_path = line[0]
# filename = source_path.split(break_line)[-1]
# current_path = data_location + 'IMG/' + filename
# image = cv2.imread(current_path)
# images.append(image)
# measurement = steering_center
# measurements.append(measurement)
#
# # left image
# source_path = line[1]
# filename = source_path.split(break_line)[-1]
# current_path = data_location + 'IMG/' + filename
# image = cv2.imread(current_path)
# images.append(image)
# measurement = steering_left
# measurements.append(measurement)
#
# # center image
# source_path = line[2]
# filename = source_path.split(break_line)[-1]
# current_path = data_location + 'IMG/' + filename
# image = cv2.imread(current_path)
# images.append(image)
# measurement = steering_right
# measurements.append(measurement)
#
# augmented_images, augmented_measurements = [], []
# # image flipping
# for image, measurement in zip(images, measurements):
# augmented_images.append(image)
# augmented_measurements.append(measurement)
# augmented_images.append(cv2.flip(image, 1))
# augmented_measurements.append(measurement * -1)
#
# X_train = np.array(augmented_images)
# y_train = np.array(augmented_measurements)
# yield sklearn.utils.shuffle(X_train, y_train)
# # # Set our batch size
# # batch_size=50
#
# # # compile and train the model using the generator function
# # train_generator = generator(train_samples, batch_size=batch_size)
# # validation_generator = generator(validation_samples, batch_size=batch_size)
# # print(train_generator)
#
# # model.fit_generator(train_generator,
# # steps_per_epoch=math.ceil(len(train_samples)/batch_size),
# # validation_data=validation_generator,
# # validation_steps=math.ceil(len(validation_samples)/batch_size),
# # epochs=8)
# model.fit(train_generator, validation_data = validation_generator, epochs= 5)
model.summary()
model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, validation_split=0.2, shuffle=True, epochs=4)
model.save('model_sample.h5')