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train.py
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train.py
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'''
-> This is the training file to train the model.
-> This file starts training the model from scratch.
'''
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow logging
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import keras
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Conv2D, Dropout, Flatten, Dense
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from imgaug import augmenters as iaa
import cv2
import pandas as pd
import ntpath
import random
def main():
# Define directory and load dataset
directory = 'Dataset_Self_Driving_Car_'
columns = ['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed']
data = pd.read_csv(os.path.join(directory, 'driving_log.csv'), names=columns)
pd.set_option('display.max_colwidth', 1)
# Preprocess data
data['center'] = data['center'].apply(path_leaf)
data['left'] = data['left'].apply(path_leaf)
data['right'] = data['right'].apply(path_leaf)
data = preprocess_data(data)
# Generate image paths and steering angles
image_paths, steerings = load_img_steering(os.path.join(directory, 'IMG'), data)
# Split data into training and validation sets
X_train, X_valid, y_train, y_valid = train_test_split(image_paths, steerings, test_size=0.2, random_state=6)
# Create model
model = nvidia_model()
# Train model
train_model(model, X_train, y_train, X_valid, y_valid)
# Save model
model.save('models/nvidia_model.h5')
def path_leaf(path):
head, tail = ntpath.split(path)
return tail
def preprocess_data(data):
# Preprocess data
num_bins = 25
samples_per_bin = 600
hist, bins = np.histogram(data['steering'], num_bins)
center = (bins[:-1] + bins[1:]) * 0.5
remove_list = []
for j in range(num_bins):
list_ = []
for i in range(len(data['steering'])):
if data['steering'][i] >= bins[j] and data['steering'][i] <= bins[j + 1]:
list_.append(i)
list_ = shuffle(list_)
list_ = list_[samples_per_bin:]
remove_list.extend(list_)
data.drop(data.index[remove_list], inplace=True)
return data
def load_img_steering(datadir, df):
image_path = []
steering = []
for i in range(len(df)):
indexed_data = df.iloc[i]
center, left, right = indexed_data[0], indexed_data[1], indexed_data[2]
image_path.append(os.path.join(datadir, center.strip()))
steering.append(float(indexed_data[3]))
# left image append
image_path.append(os.path.join(datadir, left.strip()))
steering.append(float(indexed_data[3]) + 0.15)
# right image append
image_path.append(os.path.join(datadir, right.strip()))
steering.append(float(indexed_data[3]) - 0.15)
image_paths = np.asarray(image_path)
steerings = np.asarray(steering)
return image_paths, steerings
def nvidia_model():
kernel1 = 5
filters1 = 24
kernel2 = 5
filters2 = 36
kernel3 = 5
filters3 = 48
kernel4 = 3
filters4 = 64
kernel5 = 3
filters5 = 64
model = Sequential()
# Define the model architecture
model.add(Conv2D(filters1, (kernel1, kernel1), strides=(2, 2), input_shape=(66, 200, 3), activation='sigmoid')) # Input layer (convolution layer)
model.add(Conv2D(filters2, (kernel2, kernel2), strides=(2, 2), activation='elu')) # Convolution layer
model.add(Conv2D(filters3, (kernel3, kernel3), strides=(2, 2), activation='elu')) # Convolution layer
model.add(Conv2D(filters4, (kernel4, kernel4), activation='elu')) # Convolution layer
model.add(Conv2D(filters5, (kernel5, kernel5), activation='elu')) # Convolution layer
model.add(Dropout(0.5)) # Dropout layer to prevent overfitting
model.add(Flatten()) # Flatten layer to convert 2D feature maps to 1D feature vectors
model.add(Dense(100, activation='elu')) # Hidden layer (fully connected)
model.add(Dropout(0.5)) # Dropout layer to prevent overfitting
model.add(Dense(50, activation='elu')) # Hidden layer (fully connected)
model.add(Dropout(0.5)) # Dropout layer to prevent overfitting
model.add(Dense(10, activation='elu')) # Hidden layer (fully connected)
model.add(Dropout(0.5)) # Dropout layer to prevent overfitting
model.add(Dense(1)) # Output layer (prediction of steering angle)
optimizer = Adam(learning_rate=1e-3)
model.compile(loss='mse', optimizer=optimizer) # Loss function: mean squared error
return model
def train_model(model, X_train, y_train, X_valid, y_valid):
history = model.fit(batch_generator(X_train, y_train, 100, 1),
steps_per_epoch=300,
epochs=200,
validation_data=batch_generator(X_valid, y_valid, 100, 0),
validation_steps=200,
verbose=1,
shuffle=1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['training', 'validation'])
plt.title('Loss')
plt.xlabel('Epoch')
plt.show()
def batch_generator(image_paths, steering_ang, batch_size, istraining):
while True:
batch_img = []
batch_steering = []
for i in range(batch_size):
random_index = random.randint(0, len(image_paths) - 1)
if istraining:
im, steering = random_augment(image_paths[random_index], steering_ang[random_index])
else:
im = mpimg.imread(image_paths[random_index])
steering = steering_ang[random_index]
im = img_preprocess(im)
batch_img.append(im)
batch_steering.append(steering)
yield (np.asarray(batch_img), np.asarray(batch_steering))
def img_preprocess(img):
img = img[60:135, :, :]
img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
img = cv2.GaussianBlur(img, (3, 3), 0)
img = cv2.resize(img, (200, 66))
img = img / 255
return img
def random_augment(image, steering_angle):
image = mpimg.imread(image)
if np.random.rand() < 0.5:
image = pan(image)
if np.random.rand() < 0.5:
image = zoom(image)
if np.random.rand() < 0.5:
image = img_random_brightness(image)
if np.random.rand() < 0.5:
image, steering_angle = img_random_flip(image, steering_angle)
return image, steering_angle
def img_random_brightness(image):
brightness = iaa.Multiply((0.2, 1.2))
image = brightness.augment_image(image)
return image
def img_random_flip(image, steering_angle):
image = cv2.flip(image, 1)
steering_angle = -steering_angle
return image, steering_angle
def zoom(image):
zoom = iaa.Affine(scale=(1, 1.3))
image = zoom.augment_image(image)
return image
def pan(image):
pan = iaa.Affine(translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)})
image = pan.augment_image(image)
return image
if __name__ == "__main__":
main()