import os
import numpy as np
import pandas as pd
import cv2

import tensorflow as tf
from tensorflow.python.keras import Sequential
from tensorflow.keras import layers, optimizers
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.utils import plot_model
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler
import tensorflow.keras.backend as K


# lets create model now
def resblock(X, f):
    '''
    function for creating res block
    '''
    X_copy = X  #copy of input

    # main path
    X = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X)
    X = BatchNormalization()(X)
    X = Activation('relu')(X)

    X = Conv2D(f, kernel_size=(3,3), padding='same', kernel_initializer='he_normal')(X)
    X = BatchNormalization()(X)

    # shortcut path
    X_copy = Conv2D(f, kernel_size=(1,1), kernel_initializer='he_normal')(X_copy)
    X_copy = BatchNormalization()(X_copy)

    # Adding the output from main path and short path together
    X = Add()([X, X_copy])
    X = Activation('relu')(X)

    return X

def upsample_concat(x, skip):
    '''
    funtion for upsampling image
    '''
    X = UpSampling2D((2,2))(x)
    merge = Concatenate()([X, skip])

    return merge


def load_model():

    input_shape = (256,256,3)
    X_input = Input(input_shape) #iniating tensor of input shape

    # Stage 1
    conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input)
    conv_1 = BatchNormalization()(conv_1)
    conv_1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv_1)
    conv_1 = BatchNormalization()(conv_1)
    pool_1 = MaxPool2D((2,2))(conv_1)

    # stage 2
    conv_2 = resblock(pool_1, 32)
    pool_2 = MaxPool2D((2,2))(conv_2)

    # Stage 3
    conv_3 = resblock(pool_2, 64)
    pool_3 = MaxPool2D((2,2))(conv_3)

    # Stage 4
    conv_4 = resblock(pool_3, 128)
    pool_4 = MaxPool2D((2,2))(conv_4)

    # Stage 5 (bottle neck)
    conv_5 = resblock(pool_4, 256)

    # Upsample Stage 1
    up_1 = upsample_concat(conv_5, conv_4)
    up_1 = resblock(up_1, 128)

    # Upsample Stage 2
    up_2 = upsample_concat(up_1, conv_3)
    up_2 = resblock(up_2, 64)

    # Upsample Stage 3
    up_3 = upsample_concat(up_2, conv_2)
    up_3 = resblock(up_3, 32)

    # Upsample Stage 4
    up_4 = upsample_concat(up_3, conv_1)
    up_4 = resblock(up_4, 16)

    # final output
    out = Conv2D(1, (1,1), kernel_initializer='he_normal', padding='same', activation='sigmoid')(up_4)

    seg_model = Model(X_input, out)

    return seg_model