from training import *
from training.label_processor import process_labels
from utilities.utilities import *

if __name__ == "__main__":

    base_path = os.path.abspath(__file__ + "/..")

    # Must include operation being done.
    # e.g. generate_data_set
    if len(sys.argv) < 2:
        raise Exception("You must include the type of preparation. Please refer to readme")

    # Generate data set by cropping out 64 length cubes from larger chunks for validation
    if sys.argv[1] == "generate_validation_set":

        # Validation directories
        data_original_path = base_path + "/data/validation/validation-original"
        data_set_path = base_path + "/data/validation/validation-set"

        # Default value is None
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])

        generate_data_set(data_original_path, data_set_path, nb_examples=nb_examples)

    # Generate data set by cropping out 64 length cubes from larger chunks for training
    elif sys.argv[1] == "generate_training_set":

        # Training directories
        data_original_path = base_path + "/data/training/training-original"
        data_set_path = base_path + "/data/training/training-set"

        # Default value is None
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])

        generate_data_set(data_original_path, data_set_path, nb_examples=nb_examples)

    # Add edge labels to your labeled data
    elif sys.argv[1] == "process_labels":

        if len(sys.argv) < 3:
            raise Exception("You must include the directory to the labels")

        labels_folder = sys.argv[2]
        output_name = "processed-labels"
        output_dir = os.path.dirname(labels_folder)
        output_folder = os.path.join(output_dir, output_name)

        if not os.path.isdir(labels_folder):
            raise Exception(labels_folder + " is not a directory. Inputs must be a folder of tiff files. Please refer to readme for more info")

        # Create output directory. Overwrite if the directory exists
        if os.path.exists(output_folder):
            print(output_folder + " already exists. Will be overwritten")
            shutil.rmtree(output_folder)

        os.makedirs(output_folder)

        for label_name in os.listdir(labels_folder):
            vol = read_tiff_stack(os.path.join(labels_folder, label_name))
            edge_vol = process_labels(vol)
            write_tiff_stack(edge_vol, os.path.join(output_folder, label_name))

    else:
        raise Exception("You must choose your type of preparation (generate_validation_set, generate_training_set, process_labels)")

808faaa0aa13bcc83284a3b960ba8b250802c11b
<mask token>

<mask token>
if __name__ == '__main__':
    base_path = os.path.abspath(__file__ + '/..')
    if len(sys.argv) < 2:
        raise Exception(
            'You must include the type of preparation. Please refer to readme')
    if sys.argv[1] == 'generate_validation_set':
        data_original_path = base_path + '/data/validation/validation-original'
        data_set_path = base_path + '/data/validation/validation-set'
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])
        generate_data_set(data_original_path, data_set_path, nb_examples=
            nb_examples)
    elif sys.argv[1] == 'generate_training_set':
        data_original_path = base_path + '/data/training/training-original'
        data_set_path = base_path + '/data/training/training-set'
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])
        generate_data_set(data_original_path, data_set_path, nb_examples=
            nb_examples)
    elif sys.argv[1] == 'process_labels':
        if len(sys.argv) < 3:
            raise Exception('You must include the directory to the labels')
        labels_folder = sys.argv[2]
        output_name = 'processed-labels'
        output_dir = os.path.dirname(labels_folder)
        output_folder = os.path.join(output_dir, output_name)
        if not os.path.isdir(labels_folder):
            raise Exception(labels_folder +
                ' is not a directory. Inputs must be a folder of tiff files. Please refer to readme for more info'
                )
        if os.path.exists(output_folder):
            print(output_folder + ' already exists. Will be overwritten')
            shutil.rmtree(output_folder)
        os.makedirs(output_folder)
        for label_name in os.listdir(labels_folder):
            vol = read_tiff_stack(os.path.join(labels_folder, label_name))
            edge_vol = process_labels(vol)
            write_tiff_stack(edge_vol, os.path.join(output_folder, label_name))
    else:
        raise Exception(
            'You must choose your type of preparation (generate_validation_set, generate_training_set, process_labels)'
            )

from training import *
from training.label_processor import process_labels
from utilities.utilities import *
if __name__ == '__main__':
    base_path = os.path.abspath(__file__ + '/..')
    if len(sys.argv) < 2:
        raise Exception(
            'You must include the type of preparation. Please refer to readme')
    if sys.argv[1] == 'generate_validation_set':
        data_original_path = base_path + '/data/validation/validation-original'
        data_set_path = base_path + '/data/validation/validation-set'
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])
        generate_data_set(data_original_path, data_set_path, nb_examples=
            nb_examples)
    elif sys.argv[1] == 'generate_training_set':
        data_original_path = base_path + '/data/training/training-original'
        data_set_path = base_path + '/data/training/training-set'
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])
        generate_data_set(data_original_path, data_set_path, nb_examples=
            nb_examples)
    elif sys.argv[1] == 'process_labels':
        if len(sys.argv) < 3:
            raise Exception('You must include the directory to the labels')
        labels_folder = sys.argv[2]
        output_name = 'processed-labels'
        output_dir = os.path.dirname(labels_folder)
        output_folder = os.path.join(output_dir, output_name)
        if not os.path.isdir(labels_folder):
            raise Exception(labels_folder +
                ' is not a directory. Inputs must be a folder of tiff files. Please refer to readme for more info'
                )
        if os.path.exists(output_folder):
            print(output_folder + ' already exists. Will be overwritten')
            shutil.rmtree(output_folder)
        os.makedirs(output_folder)
        for label_name in os.listdir(labels_folder):
            vol = read_tiff_stack(os.path.join(labels_folder, label_name))
            edge_vol = process_labels(vol)
            write_tiff_stack(edge_vol, os.path.join(output_folder, label_name))
    else:
        raise Exception(
            'You must choose your type of preparation (generate_validation_set, generate_training_set, process_labels)'
            )

from training import *
from training.label_processor import process_labels
from utilities.utilities import *

if __name__ == "__main__":

    base_path = os.path.abspath(__file__ + "/..")

    # Must include operation being done.
    # e.g. generate_data_set
    if len(sys.argv) < 2:
        raise Exception("You must include the type of preparation. Please refer to readme")

    # Generate data set by cropping out 64 length cubes from larger chunks for validation
    if sys.argv[1] == "generate_validation_set":

        # Validation directories
        data_original_path = base_path + "/data/validation/validation-original"
        data_set_path = base_path + "/data/validation/validation-set"

        # Default value is None
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])

        generate_data_set(data_original_path, data_set_path, nb_examples=nb_examples)

    # Generate data set by cropping out 64 length cubes from larger chunks for training
    elif sys.argv[1] == "generate_training_set":

        # Training directories
        data_original_path = base_path + "/data/training/training-original"
        data_set_path = base_path + "/data/training/training-set"

        # Default value is None
        nb_examples = None
        if len(sys.argv) > 2:
            nb_examples = int(sys.argv[2])

        generate_data_set(data_original_path, data_set_path, nb_examples=nb_examples)

    # Add edge labels to your labeled data
    elif sys.argv[1] == "process_labels":

        if len(sys.argv) < 3:
            raise Exception("You must include the directory to the labels")

        labels_folder = sys.argv[2]
        output_name = "processed-labels"
        output_dir = os.path.dirname(labels_folder)
        output_folder = os.path.join(output_dir, output_name)

        if not os.path.isdir(labels_folder):
            raise Exception(labels_folder + " is not a directory. Inputs must be a folder of tiff files. Please refer to readme for more info")

        # Create output directory. Overwrite if the directory exists
        if os.path.exists(output_folder):
            print(output_folder + " already exists. Will be overwritten")
            shutil.rmtree(output_folder)

        os.makedirs(output_folder)

        for label_name in os.listdir(labels_folder):
            vol = read_tiff_stack(os.path.join(labels_folder, label_name))
            edge_vol = process_labels(vol)
            write_tiff_stack(edge_vol, os.path.join(output_folder, label_name))

    else:
        raise Exception("You must choose your type of preparation (generate_validation_set, generate_training_set, process_labels)")
