def segment_normal_distribution_shift_flip_brightness_shadow_reg():
    data_set = DriveDataSet.from_csv(
        "datasets/udacity-sample-track-1/driving_log.csv",
        crop_images=True,
        all_cameras_images=True,
        filter_method=drive_record_filter_include_all)
    # fine tune every part of training data so that make it meat std distrubtion
    allocator = AngleSegmentRecordAllocator(
        data_set,
        AngleSegment((-1.5, -0.5), 10),  # big sharp left
        AngleSegment((-0.5, -0.25), 14),  # sharp left
        AngleSegment((-0.25, -0.249),
                     3),  # sharp turn left (zero right camera)
        AngleSegment((-0.249, -0.1), 10),  # big turn left
        AngleSegment((-0.1, 0), 11),  # straight left
        AngleSegment((0, 0.001), 4),  # straight zero center camera
        AngleSegment((0.001, 0.1), 11),  # straight right
        AngleSegment((0.1, 0.25), 10),  # big turn right
        AngleSegment((0.25, 0.251), 3),  # sharp turn right (zero left camera)
        AngleSegment((0.251, 0.5), 14),  # sharp right
        AngleSegment((0.5, 1.5), 10)  # big sharp right
    )
    # a pipe line with shift -> flip -> brightness -> shadow augment processes
    augment = pipe_line_generators(
        shift_image_generator(angle_offset_pre_pixel=0.002), flip_generator,
        brightness_image_generator(0.35), shadow_generator)
    data_generator = DataGenerator(allocator.allocate, augment)
    model = nvidia_with_regularizer(input_shape=data_set.output_shape(),
                                    dropout=0.2)
    Trainer(model,
            learning_rate=0.0001,
            epoch=45,
            multi_process=use_multi_process,
            custom_name=inspect.stack()[0][3]).fit_generator(
                data_generator.generate(batch_size=256))
def raw_data_centre_left_right_image_crop():
    # crop_images=True was the only difference
    data_set = DriveDataSet.from_csv(
        "datasets/udacity-sample-track-1/driving_log.csv",
        crop_images=True,
        all_cameras_images=True,
        filter_method=drive_record_filter_include_all)
    allocator = RecordRandomAllocator(data_set)
    generator = image_itself
    data_generator = DataGenerator(allocator.allocate, generator)
    model = nvidia(input_shape=data_set.output_shape(), dropout=0.5)
    Trainer(model,
            learning_rate=0.0001,
            epoch=10,
            custom_name=inspect.stack()[0][3]).fit_generator(
                data_generator.generate(batch_size=128))
def raw_data_centre_left_right_crop_shift_flip():
    data_set = DriveDataSet.from_csv(
        "datasets/udacity-sample-track-1/driving_log.csv",
        crop_images=True,
        all_cameras_images=True,
        filter_method=drive_record_filter_include_all)
    allocator = RecordRandomAllocator(data_set)
    # shift_image_generator was the only difference
    generator = pipe_line_generators(
        shift_image_generator(angle_offset_pre_pixel=0.002), flip_generator)
    data_generator = DataGenerator(allocator.allocate, generator)
    model = nvidia(input_shape=data_set.output_shape(), dropout=0.5)
    Trainer(model,
            learning_rate=0.0001,
            epoch=20,
            multi_process=use_multi_process,
            custom_name=inspect.stack()[0][3]).fit_generator(
                data_generator.generate(batch_size=128))
def raw_data_centre_image_no_dropout():
    # Create DriveDataSet from csv file, you can specify crop image, using all cameras and which data will included in
    data_set = DriveDataSet.from_csv(
        "datasets/udacity-sample-track-1/driving_log.csv",
        crop_images=False,
        all_cameras_images=False,
        filter_method=drive_record_filter_include_all)
    # What the data distribution will be, below example just randomly return data from data set, so that the
    # distribution will be same with what original data set have
    allocator = RecordRandomAllocator(data_set)
    # what's the data augment pipe line have, this have no pipe line, just the image itself
    augment = image_itself
    # connect allocator and augment together
    data_generator = DataGenerator(allocator.allocate, augment)
    # create the model
    model = nvidia(input_shape=data_set.output_shape(), dropout=0.0)
    # put everthing together, start a real Keras training process with fit_generator
    Trainer(model,
            learning_rate=0.0001,
            epoch=10,
            custom_name=inspect.stack()[0][3]).fit_generator(
                data_generator.generate(batch_size=128))
def segment_left_centre_right():
    data_set = DriveDataSet.from_csv(
        "datasets/udacity-sample-track-1/driving_log.csv",
        crop_images=True,
        all_cameras_images=True,
        filter_method=drive_record_filter_include_all)

    allocator = AngleTypeWithZeroRecordAllocator(
        data_set,
        left_percentage=20,
        right_percentage=20,
        zero_percentage=8,
        zero_left_percentage=6,
        zero_right_percentage=6,
        left_right_image_offset_angle=0.25)
    generator = pipe_line_generators(
        shift_image_generator(angle_offset_pre_pixel=0.002), flip_generator,
        brightness_image_generator(0.25), shadow_generator)
    data_generator = DataGenerator(allocator.allocate, generator)
    model = nvidia(input_shape=data_set.output_shape(), dropout=0.5)
    Trainer(model, learning_rate=0.0001,
            epoch=10).fit_generator(data_generator.generate(batch_size=128))
 def test_data_generator_should_able_to_extend_easily(self):
     dataset = DriveDataSet.from_csv("resources/driving_log_mini.csv")
     self.assertEqual(len(dataset), 6)