artificial_artwork._demo.create_algo_runner()

Complexity

Conditions

6

Size

Total Lines	55
Code Lines	36

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

from pathlib import Path
def create_algo_runner(
    iterations=100,
    output_folder='gui-output-folder',
    content_img_file=None,
    style_img_file=None,
):
    print("[DEBUG] output type: {}".format(type(output_folder)))

    current_directory = Path.cwd()

    termination_condition = 'max-iterations'

    content_img_file = content_img_file if content_img_file else source_root_dir / 'tests' / 'data' / 'canoe_water_w300-h225.jpg'
    style_img_file = style_img_file if style_img_file else source_root_dir / 'tests' / 'data' / 'blue-red_w300-h225.jpg'

    content_image, style_image = read_images(content_img_file, style_img_file)

    load_pretrained_model_functions()  # ie import VGG ModelHandler implementation (to allow facility creating instances)
    model_design = type('ModelDesign', (), {
        'pretrained_model': ModelHandlerFacility.create('vgg'),
        'network_design': NetworkDesign.from_default_vgg()
    })
    model_design.pretrained_model.load_model_layers()

    termination_condition = TerminationConditionAdapterFactory.create(
        termination_condition,
        iterations,  # maximun number of iterations to run the algorithm
    )

    print(f' -- Termination Condition: {termination_condition.termination_condition}')

    algorithm_parameters = AlogirthmParameters(
        content_image,
        style_image,
        termination_condition,
        output_folder,
    )

    algorithm = NSTAlgorithm(algorithm_parameters)

    noisy_ratio = 0.6  # ratio

    algorithm_runner = NSTAlgorithmRunner.default(
        lambda matrix: noisy(matrix, noisy_ratio),
    )

    algorithm_runner.progress_subject.add(
        termination_condition,
    )
    algorithm_runner.persistance_subject.add(
        StylingObserver(Disk.save_image, convert_to_uint8, iterations)
    )
    return {
        'run': lambda: algorithm_runner.run(algorithm, model_design),
        'subscribe': lambda observer: algorithm_runner.progress_subject.add(observer),
    }