notes.py

# from project set up
samples = []
with open('./data/driving_log.csv') as csvfile:
    reader = csv.reader(csvfile)
    for line in reader:
        samples.append(line)

from sklearn.model_selection import train_test_split
train_samples, validation_samples = train_test_split(samples, test_size=0.2)

images = []
measurements = []
for line in lines:
  source_path = line[0]
  filename = source_path.split('/')[-1]
  current_path = './data/IMG/' + filename
  image = cv2.imread(current_path)
  images.append(image)
  measurement = float(line[3])
  measurements.append(measurement)

X_train = np.array(images)
y_train = np.array(measurements)

def generator(samples, batch_size=64):
    num_samples = len(samples)
    while 1: # Loop forever so the generator never terminates
        shuffle(samples)
        for offset in range(0, num_samples, batch_size):
            batch_samples = samples[offset:offset+batch_size]

            images = []
            angles = []
            for batch_sample in batch_samples:
                name = './data/IMG/'+batch_sample[0].split('/')[-1]
                center_image = cv2.imread(name)
                center_angle = float(batch_sample[3])
                images.append(center_image)
                angles.append(center_angle)

            # trim image to only see section with road
            X_train = np.array(images)
            y_train = np.array(angles)
            yield sklearn.utils.shuffle(X_train, y_train)

# compile and train the model using the generator function
train_generator = generator(train_samples, batch_size=32)
validation_generator = generator(validation_samples, batch_size=32)

# extra utils
def grayscale(image):
    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

def rgb2gray(imgs):
    return np.mean(imgs, axis=3, keepdims=True)

def normalize(imgs):
    return imgs / (255.0 / 2) - 1

def preprocess(imgs):
    imgs_processed = resize(imgs)
    imgs_processed = rgb2gray(imgs_processed)
    imgs_processed = normalize(imgs_processed)
    return imgs_processed

def add_flipped(images, measurements):
    augmented_images, augmented_measurements = [], []
    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 * -1.0)
    return augmented_images, augmented_measurements