def chapter_augmenters_multiplyelementwise():
    aug = iaa.MultiplyElementwise((0.5, 1.5))
    run_and_save_augseq(
        "multiplyelementwise.jpg", aug,
        [ia.quokka(size=(512, 512)) for _ in range(1)], cols=1, rows=1,
        quality=90
    )

    aug = iaa.MultiplyElementwise((0.5, 1.5), per_channel=True)
    run_and_save_augseq(
        "multiplyelementwise_per_channel.jpg", aug,
        [ia.quokka(size=(512, 512)) for _ in range(1)], cols=1, rows=1,
        quality=90
    ) 

def __init__(self):
        self.aug = A.MultiplicativeNoise((0, 1), per_channel=True, elementwise=True, p=1)
        self.imgaug_transform = iaa.MultiplyElementwise(mul=(0, 1), per_channel=True) 

def main():
    args = parse_args()
    package_versions = get_package_versions()
    if args.print_package_versions:
        print(package_versions)
    images_per_second = defaultdict(dict)
    libraries = args.libraries
    data_dir = args.data_dir
    paths = list(sorted(os.listdir(data_dir)))
    paths = paths[: args.images]
    imgs_cv2 = [read_img_cv2(os.path.join(data_dir, path)) for path in paths]
    imgs_pillow = [read_img_pillow(os.path.join(data_dir, path)) for path in paths]

    benchmarks = [
        HorizontalFlip(),
        VerticalFlip(),
        Rotate(),
        ShiftScaleRotate(),
        Brightness(),
        Contrast(),
        BrightnessContrast(),
        ShiftRGB(),
        ShiftHSV(),
        Gamma(),
        Grayscale(),
        RandomCrop64(),
        PadToSize512(),
        Resize512(),
        RandomSizedCrop_64_512(),
        Posterize(),
        Solarize(),
        Equalize(),
        Multiply(),
        MultiplyElementwise(),
    ]
    for library in libraries:
        imgs = imgs_pillow if library in ("torchvision", "augmentor", "pillow") else imgs_cv2
        pbar = tqdm(total=len(benchmarks))
        for benchmark in benchmarks:
            pbar.set_description("Current benchmark: {} | {}".format(library, benchmark))
            benchmark_images_per_second = None
            if benchmark.is_supported_by(library):
                timer = Timer(lambda: benchmark.run(library, imgs))
                run_times = timer.repeat(number=1, repeat=args.runs)
                benchmark_images_per_second = [1 / (run_time / args.images) for run_time in run_times]
            images_per_second[library][str(benchmark)] = benchmark_images_per_second
            pbar.update(1)
        pbar.close()
    pd.set_option("display.width", 1000)
    df = pd.DataFrame.from_dict(images_per_second)
    df = df.applymap(lambda r: format_results(r, args.show_std))
    df = df[libraries]
    augmentations = [str(i) for i in benchmarks]
    df = df.reindex(augmentations)
    if args.markdown:
        makedown_generator = MarkdownGenerator(df, package_versions)
        makedown_generator.print()
    else:
        print(df.head(len(augmentations)))