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import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline 

from util.util import MyDict, load_params, load_weights_of, compose_imgs, convert_to_rgb, mkdir, get_log_dir


    

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params = MyDict({
        'results_dir': 'results',  # Directory where to save the results
        'log_dir': 'log',  # Directory where the experiment was logged
        'base_dir': 'data/unet_segmentations_binary',  # Directory that contains the data
        'train_dir': 'train',  # Directory inside base_dir that contains training data
        'val_dir': 'val',  # Directory inside base_dir that contains validation data
        'test_dir': 'test',  # Directory inside base_dir that contains test data
        'load_to_memory': True,  # Whether to load the images into memory
        'expt_name': None,  # The name of the experiment to test
        'target_size': 512,  # The size of the images loaded by the iterator
        'N': 100,  # The number of samples to generate
    })


params = load_params(params)
params = MyDict(params)


    

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unet = m.g_unet(params.a_ch, params.b_ch, params.nfatob, is_binary=params.is_b_binary)
load_weights_of(unet, u.ATOB_WEIGHTS_FILE, log_dir=params.log_dir, expt_name=params.expt_name)


    

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