#! /usr/bin/env python
# coding=utf-8
#================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#
#   Editor      : VIM
#   File name   : Unet.py
#   Author      : YunYang1994
#   Created date: 2019-09-20 16:49:08
#   Description :
#
#================================================================

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Input, MaxPooling2D, Dropout, concatenate, UpSampling2D


# def Unet(num_class, image_size):

    # inputs = Input(shape=[image_size, image_size, 3])
    # conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    # conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    # pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    # conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    # conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    # pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    # conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    # conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    # pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    # conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    # conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    # drop4 = Dropout(0.5)(conv4)
    # pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    # conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    # conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    # drop5 = Dropout(0.5)(conv5)

    # up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    # merge6 = concatenate([drop4,up6], axis = 3)
    # conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
    # conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    # up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    # merge7 = concatenate([conv3,up7], axis = 3)
    # conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
    # conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    # up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    # merge8 = concatenate([conv2,up8], axis = 3)
    # conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
    # conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    # up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    # merge9 = concatenate([conv1,up9], axis = 3)
    # conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    # conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    # conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    # conv10 = Conv2D(num_class, 1)(conv9)

    # return Model(inputs = inputs, outputs = conv10)

def Unet(num_class, image_size):

    inputs = Input(shape=[image_size, image_size, 3])
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same')(merge6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same')(conv6)

    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv6))
    merge7 = concatenate([conv3,up7], axis = 3)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same')(merge7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same')(conv7)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv7))
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same')(conv8)

    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same')(merge9)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same')(conv9)
    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same')(conv9)
    conv10 = Conv2D(num_class, 1)(conv9)

    return Model(inputs = inputs, outputs = conv10)
