# -*- coding: utf-8 -*-
"""
Created on Sun May 10 21:42:46 2020

@author: serdarhelli
"""
#### MODEL ###
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization,concatenate,Conv2DTranspose,Dropout
from tensorflow.keras.models import Model


def UNET (input_shape=(512,512,1),last_activation='sigmoid'):
    inputs=Input(shape=input_shape)
    
    conv1 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    d1=Dropout(0.1)(conv1)
    conv2 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d1)
    b=BatchNormalization()(conv2)
    
    pool1 = MaxPooling2D(pool_size=(2, 2))(b)
    conv3 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    d2=Dropout(0.2)(conv3)
    conv4 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d2)
    b1=BatchNormalization()(conv4)
    
    pool2 = MaxPooling2D(pool_size=(2, 2))(b1)
    conv5 = Conv2D(128,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    d3=Dropout(0.3)(conv5)
    conv6 = Conv2D(128,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d3)
    b2=BatchNormalization()(conv6)
    
    pool3 = MaxPooling2D(pool_size=(2, 2))(b2)
    conv7 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    d4=Dropout(0.4)(conv7)
    conv8 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d4)
    b3=BatchNormalization()(conv8)
    
    pool4 = MaxPooling2D(pool_size=(2, 2))(b3)
    conv9 = Conv2D(512,(3,3),activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    d5=Dropout(0.5)(conv9)
    conv10 = Conv2D(512,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d5)
    b4=BatchNormalization()(conv10)
    
    
    conv11 = Conv2DTranspose(512,(4,4), activation = 'relu', padding = 'same', strides=(2,2),kernel_initializer = 'he_normal')(b4)
    x= concatenate([conv11,conv8])
    conv12 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)
    d6=Dropout(0.4)(conv12)
    conv13 = Conv2D(256,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d6)
    b5=BatchNormalization()(conv13)
    
    
    conv14 = Conv2DTranspose(256,(4,4), activation = 'relu', padding = 'same', strides=(2,2),kernel_initializer = 'he_normal')(b5)
    x1=concatenate([conv14,conv6])
    conv15 = Conv2D(128,3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x1)
    d7=Dropout(0.3)(conv15)
    conv16 = Conv2D(128,3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d7)
    b6=BatchNormalization()(conv16)
    
    conv17 = Conv2DTranspose(128,(4,4), activation = 'relu', padding = 'same',strides=(2,2), kernel_initializer = 'he_normal')(b6)
    x2=concatenate([conv17,conv4])
    conv18 = Conv2D(64,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x2)
    d8=Dropout(0.2)(conv18)
    conv19 = Conv2D(64,(3,3) ,activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d8)
    b7=BatchNormalization()(conv19)
    
    conv20 = Conv2DTranspose(64,(4,4), activation = 'relu', padding = 'same',strides=(2,2), kernel_initializer = 'he_normal')(b7)
    x3=concatenate([conv20,conv2])
    conv21 = Conv2D(32,(3,3) ,activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x3)
    d9=Dropout(0.1)(conv21)
    conv22 = Conv2D(32,(3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(d9)
    
    outputs = Conv2D(1,(1,1), activation = last_activation, padding = 'same', kernel_initializer = 'he_normal')(conv22)
    model2 = Model( inputs = inputs, outputs = outputs)
    
    return model2