""" This file contains code for a fully convolutional
(i.e. contains zero fully connected layers) neural network
for detecting lanes. This version assumes the inputs
to be road images in the shape of 80 x 160 x 3 (RGB) with
the labels as 80 x 160 x 1 (just the G channel with a
re-drawn lane). Note that in order to view a returned image,
the predictions is later stacked with zero'ed R and B layers
and added back to the initial road image.
"""

import numpy as np
import pickle
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

# Import necessary items from Keras
from keras.models import Sequential
from keras.layers import Activation, Dropout, UpSampling2D
from keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras import regularizers


def create_model(input_shape, pool_size):
    # Create the actual neural network here
    model = Sequential()
    # Normalizes incoming inputs. First layer needs the input shape to work
    model.add(BatchNormalization(input_shape=input_shape))

    # Below layers were re-named for easier reading of model summary; this not necessary
    # Conv Layer 1
    model.add(Conv2D(8, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv1'))

    # Conv Layer 2
    model.add(Conv2D(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv2'))

    # Pooling 1
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 3
    model.add(Conv2D(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv3'))
    model.add(Dropout(0.2))

    # Conv Layer 4
    model.add(Conv2D(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv4'))
    model.add(Dropout(0.2))

    # Conv Layer 5
    model.add(Conv2D(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv5'))
    model.add(Dropout(0.2))

    # Pooling 2
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 6
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv6'))
    model.add(Dropout(0.2))

    # Conv Layer 7
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv7'))
    model.add(Dropout(0.2))

    # Pooling 3
    model.add(MaxPooling2D(pool_size=pool_size))

    # Upsample 1
    model.add(UpSampling2D(size=pool_size))

    # Deconv 1
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv1'))
    model.add(Dropout(0.2))

    # Deconv 2
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv2'))
    model.add(Dropout(0.2))

    # Upsample 2
    model.add(UpSampling2D(size=pool_size))

    # Deconv 3
    model.add(Conv2DTranspose(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv3'))
    model.add(Dropout(0.2))

    # Deconv 4
    model.add(Conv2DTranspose(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv4'))
    model.add(Dropout(0.2))

    # Deconv 5
    model.add(Conv2DTranspose(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv5'))
    model.add(Dropout(0.2))

    # Upsample 3
    model.add(UpSampling2D(size=pool_size))

    # Deconv 6
    model.add(Conv2DTranspose(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv6'))

    # Final layer - only including one channel so 1 filter
    model.add(Conv2DTranspose(1, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Final'))

    return model


def main():
    # Load training images
    train_images = pickle.load(open("full_CNN_train.p", "rb"))

    # Load image labels
    labels = pickle.load(open("full_CNN_labels.p", "rb"))

    # Make into arrays as the neural network wants these
    train_images = np.array(train_images)
    labels = np.array(labels)

    # Normalize labels - training images get normalized to start in the network
    labels = labels / 255

    # Shuffle images along with their labels, then split into training/validation sets
    train_images, labels = shuffle(train_images, labels)
    # Test size may be 10% or 20%
    X_train, X_val, y_train, y_val = train_test_split(train_images, labels, test_size=0.1)

    # Batch size, epochs and pool size below are all paramaters to fiddle with for optimization
    batch_size = 128
    epochs = 10
    pool_size = (2, 2)
    input_shape = X_train.shape[1:]

    # Create the neural network
    model = create_model(input_shape, pool_size)

    # Using a generator to help the model use less data
    # Channel shifts help with shadows slightly
    datagen = ImageDataGenerator(channel_shift_range=0.2)
    datagen.fit(X_train)

    # Compiling and training the model
    model.compile(optimizer='Adam', loss='mean_squared_error')
    model.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size),
                        steps_per_epoch=len(X_train) / batch_size,
                        epochs=epochs, verbose=1, validation_data=(X_val, y_val))

    # Freeze layers since training is done
    model.trainable = False
    model.compile(optimizer='Adam', loss='mean_squared_error')

    # Save model architecture and weights
    model.save('full_CNN_model.h5')

    # Show summary of model
    model.summary()


if __name__ == '__main__':
    main()

""" This file contains code for a fully convolutional
(i.e. contains zero fully connected layers) neural network
for detecting lanes. This version assumes the inputs
to be road images in the shape of 80 x 160 x 3 (RGB) with
the labels as 80 x 160 x 1 (just the G channel with a
re-drawn lane). Note that in order to view a returned image,
the predictions is later stacked with zero'ed R and B layers
and added back to the initial road image.
"""

import numpy as np
import pickle
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

# Import necessary items from Keras
from keras.models import Sequential
from keras.layers import Activation, Dropout, UpSampling2D
from keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras import regularizers


def create_model(input_shape, pool_size):
    # Create the actual neural network here
    model = Sequential()
    # Normalizes incoming inputs. First layer needs the input shape to work
    model.add(BatchNormalization(input_shape=input_shape))

    # Below layers were re-named for easier reading of model summary; this not necessary
    # Conv Layer 1
    model.add(Conv2D(8, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv1'))

    # Conv Layer 2
    model.add(Conv2D(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv2'))

    # Pooling 1
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 3
    model.add(Conv2D(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv3'))
    model.add(Dropout(0.2))

    # Conv Layer 4
    model.add(Conv2D(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv4'))
    model.add(Dropout(0.2))

    # Conv Layer 5
    model.add(Conv2D(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv5'))
    model.add(Dropout(0.2))

    # Pooling 2
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 6
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv6'))
    model.add(Dropout(0.2))

    # Conv Layer 7
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Conv7'))
    model.add(Dropout(0.2))

    # Pooling 3
    model.add(MaxPooling2D(pool_size=pool_size))

    # Upsample 1
    model.add(UpSampling2D(size=pool_size))

    # Deconv 1
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv1'))
    model.add(Dropout(0.2))

    # Deconv 2
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv2'))
    model.add(Dropout(0.2))

    # Upsample 2
    model.add(UpSampling2D(size=pool_size))

    # Deconv 3
    model.add(Conv2DTranspose(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv3'))
    model.add(Dropout(0.2))

    # Deconv 4
    model.add(Conv2DTranspose(32, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv4'))
    model.add(Dropout(0.2))

    # Deconv 5
    model.add(Conv2DTranspose(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv5'))
    model.add(Dropout(0.2))

    # Upsample 3
    model.add(UpSampling2D(size=pool_size))

    # Deconv 6
    model.add(Conv2DTranspose(16, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Deconv6'))

    # Final layer - only including one channel so 1 filter
    model.add(Conv2DTranspose(1, (3, 3), padding='valid', strides=(1, 1), activation='relu', name='Final'))

    return model


def main():
    # Load training images
    train_images = pickle.load(open("full_CNN_train.p", "rb"))

    # Load image labels
    labels = pickle.load(open("full_CNN_labels.p", "rb"))

    # Make into arrays as the neural network wants these
    train_images = np.array(train_images)
    labels = np.array(labels)

    # Normalize labels - training images get normalized to start in the network
    labels = labels / 255

    # Shuffle images along with their labels, then split into training/validation sets
    train_images, labels = shuffle(train_images, labels)
    # Test size may be 10% or 20%
    X_train, X_val, y_train, y_val = train_test_split(train_images, labels, test_size=0.1)

    # Batch size, epochs and pool size below are all paramaters to fiddle with for optimization
    batch_size = 128
    epochs = 10
    pool_size = (2, 2)
    input_shape = X_train.shape[1:]

    # Create the neural network
    model = create_model(input_shape, pool_size)

    # Using a generator to help the model use less data
    # Channel shifts help with shadows slightly
    datagen = ImageDataGenerator(channel_shift_range=0.2)
    datagen.fit(X_train)

    # Compiling and training the model
    model.compile(optimizer='Adam', loss='mean_squared_error')
    model.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size),
                        steps_per_epoch=len(X_train) / batch_size,
                        epochs=epochs, verbose=1, validation_data=(X_val, y_val))

    # Freeze layers since training is done
    model.trainable = False
    model.compile(optimizer='Adam', loss='mean_squared_error')

    # Save model architecture and weights
    model.save('full_CNN_model.h5')

    # Show summary of model
    model.summary()


if __name__ == '__main__':
    main()

This file contains code for a fully convolutional (i.e. contains zero fully connected layers) neural network for detecting lanes. This version assumes the inputs to be road images in the shape of 80 x 160 x 3 (RGB) with the labels as 80 x 160 x 1 (just the G channel with a re-drawn lane). Note that in order to view a returned image, the predictions is later stacked with zero'ed R and B layers and added back to the initial road image. # Import necessary items from Keras # Create the actual neural network here # Normalizes incoming inputs. First layer needs the input shape to work # Below layers were re-named for easier reading of model summary; this not necessary # Conv Layer 1 # Conv Layer 2 # Pooling 1 # Conv Layer 3 # Conv Layer 4 # Conv Layer 5 # Pooling 2 # Conv Layer 6 # Conv Layer 7 # Pooling 3 # Upsample 1 # Deconv 1 # Deconv 2 # Upsample 2 # Deconv 3 # Deconv 4 # Deconv 5 # Upsample 3 # Deconv 6 # Final layer - only including one channel so 1 filter # Load training images # Load image labels # Make into arrays as the neural network wants these # Normalize labels - training images get normalized to start in the network # Shuffle images along with their labels, then split into training/validation sets # Test size may be 10% or 20% # Batch size, epochs and pool size below are all paramaters to fiddle with for optimization # Create the neural network # Using a generator to help the model use less data # Channel shifts help with shadows slightly # Compiling and training the model # Freeze layers since training is done # Save model architecture and weights # Show summary of model