# -*- coding: utf-8 -*-
"""
UNET LOwered Model :
    
    This customized UNet Model has been generated lowering the filters to their 25% .
    
"""

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers 
from tensorflow.keras.layers import Input , Conv2D , MaxPooling2D , Dropout , concatenate , UpSampling2D
from tensorflow.keras import models
from tensorflow.keras import losses
from tensorflow.keras import optimizers
import numpy as np


def UNet(input_shape):
  keras.backend.clear_session()
  inputs = Input(input_shape)
  conv1 = Conv2D(16, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
  conv1 = Conv2D(16, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

  conv2 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
  conv2 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

  conv3 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
  conv3 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
  
  conv4 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
  conv4 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
  drop4 = Dropout(0.5)(conv4)
  pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

  conv5 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
  conv5 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
  drop5 = Dropout(0.5)(conv5)

  up6 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
  merge6 = concatenate([drop4,up6], axis = 3)
  conv6 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
  conv6 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

  up7 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
  merge7 = concatenate([conv3,up7], axis = 3)
  conv7 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
  conv7 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

  up8 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
  merge8 = concatenate([conv2,up8], axis = 3)
  conv8 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
  conv8 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

  up9 = Conv2D(16, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
  merge9 = concatenate([conv1,up9], axis = 3)
  conv9 = Conv2D(16, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
  conv9 = Conv2D(16, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
  conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)

  outputs = layers.Conv2D(1, 1, activation = 'sigmoid')(conv9)

  model = keras.Model(inputs = inputs , outputs = outputs,name = 'UNet')

  return model
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