preprocess_image.py

import os
import sys
import pickle
import pandas

from PIL import Image
import numpy as np

import cv2

from sklearn.model_selection import train_test_split

#Cutting the image to the section, that holds the road information
def cut_images_to_arr(img_Center):
    arr_Center = np.array(img_Center)
    arr_Center = arr_Center[50:]
    return arr_Center

#Converting the RGB Image to an HLS Image
def convert_to_HLS(img):
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    return hls

#Normalizing the input Image
def normalize_image(img):
    max = 255. #np.max(img)
    return (((img) / max) - 0.5)

if __name__ == '__main__':

    #Reading the driving log to match stearing information to Images
    dataframe = pandas.read_csv('./driving_log.csv', header=None)
    driving_log = dataframe.values

    X_train = []
    y_train = []

    #Preprocess all Images with cut/convert to HLS/Normalize
    for el in driving_log:
        #path = '/Users/q367999/Documents/CarND/behaviour_cloning/' + el[0]
        img_Center = Image.open(el[0])

        img_Center = cut_images_to_arr(img_Center)
        img_Center = convert_to_HLS(img_Center)
        img_Center = normalize_image(img_Center)

        X_train.append(img_Center)
        y_train.append(el[3])

    X_train = np.array(X_train)

    #shuffle and split Training Data into Train and Validation
    X_train, X_val, y_train, y_val = train_test_split(
        X_train,
        y_train,
        test_size=0.2)

    #Pickle Data Training and Validation Data to make reuse of it.
    pickle_data = pickle.dumps(
        {
            'train_dataset': X_train,
            'train_labels': y_train,
            'val_dataset': X_val,
            'val_labels': y_val
        }, pickle.HIGHEST_PROTOCOL)

    del X_train, X_val, y_train, y_val

    pickle_size = sys.getsizeof(pickle_data)
    print(pickle_size)

    # Save the data for easy access
    pickle_file = 'train_data.pickle'
    exists = False

    max_bytes = 2 ** 31 - 1

    #Cut down Data to smaller protions, since pickle cant handle data bigger than 2**31-1 bytes.
    while not exists:
        if not os.path.isfile(pickle_file):
            print('Pickle Train_data')
            try:
                with open(pickle_file, 'wb') as p_train_data:
                    for idx in range(0, pickle_size, max_bytes):
                        p_train_data.write(pickle_data[idx:idx + max_bytes])

            except Exception as e:
                print('Unable to save data to', pickle_file, ':', e)
                raise

            print('Train_data in Pickle File.')
            exists = True
        else:
            print("Pickle Filename already in use. Choose another name: *.pickle")
            pickle_file = input("Enter: ")