import numpy as np

import os

from sklearn.model_selection import KFold

import tensorflow as tf

file_list = np.asarray(os.listdir("Submission_segmentation/data/WCEBleedGen/WCEBleedGen/bleeding/Images"))

image_path = "Submission_segmentation/data/WCEBleedGen/WCEBleedGen/bleeding/Images/" 

mask_path = "Submission_segmentation/data/WCEBleedGen/WCEBleedGen/bleeding/Annotations/"

batchsize = 30

data_size = len(file_list)

num_epoch = 5

splits = 10

kf = KFold(n_splits=splits)

valsize = data_size // splits

trainsize = data_size - valsize

my_model = "efficientnetb1"

data_num = np.arange(data_size)

img_size = (256, 256)

num_classes = 3

reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(

    monitor='val_loss', factor=0.1, patience=3, verbose=0, mode='min',

    min_delta=0.0001, cooldown=4, min_lr=0

)

print(file_list)