import os

# The path points to the original images and is

# used if a preprocessing step needs to be executed

ALL_IMGS = 'D:/stage1'

LABELS_INPUT_DIR = './input'

PATIENT_LABELS_CSV = LABELS_INPUT_DIR + '/stage1_labels.csv'

TEST_PATIENTS_IDS = LABELS_INPUT_DIR + '/stage1_sample_submission.csv'

VALIDATION_PATINETS_IDS = LABELS_INPUT_DIR + '/validation_data.csv'

TRAINING_PATIENTS_IDS = LABELS_INPUT_DIR + '/training_data.csv'

MODELS_STORE_DIR = './models'

SOLUTION_FILE_PATH = './solution_last.csv'

REAL_SOLUTION_CSV = './input/stage1_solution.csv'

RESTORE_MODEL_CKPT = 'model_best_sensitivity.ckpt'

SUMMARIES_DIR = './summaries/model_summary'

RESTORE = False

START_STEP = 1

COLUMN_NAME = 'cancer'

ID_COLUMN_NAME = 'id'

CANCER_CLS = 1

NO_CANCER_CLS = 0

# Image properties

OUT_SCAN = -2000

MIN_BOUND = -1000.0

MAX_BOUND = 400.0

BACKGROUND = 0

BATCH_SIZE = 1

NUM_CHANNELS = 1

N_CLASSES = 2

# Preprocessing options used for the defined models

BASELINE_PREPROCESS = 0

MORPHOLOGICAL_OPERATIONS = 1

WATERSHED = 2

FETCHED_DATA_DIR = './fetch_data'

# Configuration for the buckets with preprocessed images

# to download the data from

bucket_names = {

    BASELINE_PREPROCESS: 'baseline-preprocess',

    MORPHOLOGICAL_OPERATIONS: 'segmented-lungs',

    WATERSHED: 'segmented-lungs-watershed'  

}

# construct the path using the object name in the buckets, since

# by default images are stored in directories with the object name

preprocessed_imgs = {

    BASELINE_PREPROCESS: os.path.join(FETCHED_DATA_DIR, 'baseline_preprocessing'),

    MORPHOLOGICAL_OPERATIONS: os.path.join(FETCHED_DATA_DIR, 'segmented_morph_op'),

    WATERSHED: os.path.join(FETCHED_DATA_DIR, 'segmented_watershed')  

}

# Defined models

BASELINE = 'baseline'

BASELINE_WITH_SEGMENTATION = 'baseline_with_segmentation'

NO_REGULARIZATION = 'no_regularization'

NO_REGULARIZATION_WATERSHED = 'no_regularization_watershed'

DROPOUT_L2NORM_REGULARIZARION = 'with_regularization'

REGULARIZATION_MORE_SLICES = 'regularization_more_slices'

WITH_DATA_AUGMENTATION = 'more_slices_augmentation'

model_to_img_shape = { 

    BASELINE: (100, 128, 128),

    BASELINE_WITH_SEGMENTATION: (100, 128, 128),

    NO_REGULARIZATION: (140, 256, 256),

    NO_REGULARIZATION_WATERSHED: (140, 256, 256),

    DROPOUT_L2NORM_REGULARIZARION: (140, 256, 256),

    REGULARIZATION_MORE_SLICES: (180, 256, 256),

    WITH_DATA_AUGMENTATION: (180, 256, 256)

}

model_to_preprocessing = {

    BASELINE: BASELINE_PREPROCESS,

    BASELINE_WITH_SEGMENTATION: MORPHOLOGICAL_OPERATIONS,

    NO_REGULARIZATION: MORPHOLOGICAL_OPERATIONS,

    NO_REGULARIZATION_WATERSHED: WATERSHED,

    DROPOUT_L2NORM_REGULARIZARION: WATERSHED,

    REGULARIZATION_MORE_SLICES: WATERSHED,

    WITH_DATA_AUGMENTATION: WATERSHED

}

# This configuration must be changed in order to select other

# predefined model for training

SELECTED_MODEL = WITH_DATA_AUGMENTATION

SEGMENTATION_ALGO = model_to_preprocessing.get(SELECTED_MODEL, 

    model_to_preprocessing[WITH_DATA_AUGMENTATION]) 

IMG_SHAPE = model_to_img_shape.get(SELECTED_MODEL, 

                                   model_to_img_shape[WITH_DATA_AUGMENTATION])

SLICES, IMAGE_PXL_SIZE_X, IMAGE_PXL_SIZE_Y = IMG_SHAPE

SEGMENTED_LUNGS_DIR = preprocessed_imgs[SEGMENTATION_ALGO]

BUCKET_IN_USE = bucket_names[SEGMENTATION_ALGO]

REQUIRED_IMGS_COUNT = 1595

# Google cloud API client related

# Use for downloading preprocessed images from the

# cloud buckets

PROJECT_NAME = 'lung-cancer-tests'

CREDENTIALS_DIR = './fetch_data'

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] =  os.path.join(CREDENTIALS_DIR,

    'lung-cancer-tests-168b7b36ab99.json')