import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras


def unet(pretrained_weights=None, input_size=(320, 480, 1)):
	inputs = Input(input_size)
	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(1, 1, activation='sigmoid')(conv9)

	model = Model(inputs=inputs, outputs=conv10)

	model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

	# model.summary()

	if (pretrained_weights):
		model.load_weights(pretrained_weights)

	return model