#https://youtu.be/csFGTLT6_WQ
# u-net model

import os

import segmentation_models as sm
from keras.layers import (
    BatchNormalization,
    Conv2D,
    Conv2DTranspose,
    Dropout,
    Input,
    MaxPooling2D,
    concatenate,
)
from keras.models import Model

os.environ["SM_FRAMEWORK"] = "tf.keras"
os.environ["_TF_KERAS_FRAMEWORK_NAME"] = "tf.keras"

sm.set_framework('tf.keras')

################################################################
def SpecSeg(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS):
#Build the model
    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
    s = inputs

    #Contraction path
    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(s)
    c1 = Dropout(0.1)(c1)
    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c1)
    c1 = BatchNormalization(axis=-1)(c1)
    p1 = MaxPooling2D((2, 2))(c1)

    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p1)
    c2 = Dropout(0.1)(c2)
    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c2)
    c2 = BatchNormalization(axis=-1)(c2)
    p2 = MaxPooling2D((2, 2))(c2)

    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p2)
    c3 = Dropout(0.2)(c3)
    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c3)
    c3 = BatchNormalization(axis=-1)(c3)
    p3 = MaxPooling2D((2, 2))(c3)

    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p3)
    c4 = Dropout(0.2)(c4)
    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c4)
    c4 = BatchNormalization(axis=-1)(c4)
    p4 = MaxPooling2D((2, 2))(c4)

    c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p4)
    c5 = Dropout(0.3)(c5)
    c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c5)
    c5 = BatchNormalization(axis=-1)(c5)

    #Expansive path
    u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
    u6 = concatenate([u6, c4])
    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u6)
    c6 = Dropout(0.2)(c6)
    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c6)

    u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
    u7 = concatenate([u7, c3])
    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u7)
    c7 = Dropout(0.2)(c7)
    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c7)

    u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
    u8 = concatenate([u8, c2])
    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u8)
    c8 = Dropout(0.1)(c8)
    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c8)

    u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
    u9 = concatenate([u9, c1], axis=3)
    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u9)
    c9 = Dropout(0.1)(c9)
    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c9)

    outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)

    model = Model(inputs=[inputs], outputs=[outputs], name = 'SpecSeg')

    # model.summary()
    return model