<gh_stars>1-10
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 K

def jaccard_distance(y_true, y_pred, smooth=100):
    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
    sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
    jac = (intersection + smooth) / (sum_ - intersection + smooth)
    return (1 - jac) * smooth

# TODO: Fix the dice loss function
def dice_coef(y_true, y_pred):
    smooth = 1
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection +smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) +smooth)

def dice_coef_loss(y_true, y_pred):
    print("dice loss")
    return 1-dice_coef(y_true, y_pred)

def specificity(y_true,y_pred):
    specificity=0
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    TP=K.sum(y_true_f*y_pred_f)
    TN=K.sum((1-y_true_f)*(1-y_pred_f))
    FP=K.sum((1-y_true_f)*y_pred_f)
    FN=K.sum(y_true_f*(1-y_pred_f))
    specificity=(TN)/((TN+FP))
    return specificity

def sensitivity(y_true,y_pred):
    sensitivity=0
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    TP=K.sum(y_true_f*y_pred_f)
    TN=K.sum((1-y_true_f)*(1-y_pred_f))
    FP=K.sum((1-y_true_f)*y_pred_f)
    FN=K.sum(y_true_f*(1-y_pred_f))
    sensitivity=(TP)/((TP+FN))
       
    return sensitivity


def unet(pretrained_weights = None,input_size = (256,256,1)):
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    # print(conv1)
    conv1 = BatchNormalization()(conv1)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = BatchNormalization()(conv2)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = BatchNormalization()(conv3)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(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 = BatchNormalization()(conv5)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    conv5 = BatchNormalization()(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
    up6 = BatchNormalization()(up6)
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = BatchNormalization()(conv6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
    conv6 = BatchNormalization()(conv6)
    up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv6))
    up7 = BatchNormalization()(up7)
    merge7 = concatenate([conv3, up7], axis=3)

    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = BatchNormalization()(conv7)

    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    conv7 = BatchNormalization()(conv7)
    up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv7))
    up8 = BatchNormalization()(up8)
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = BatchNormalization()(conv8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
    conv8 = BatchNormalization()(conv8)
    up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv8))
    up9 = BatchNormalization()(up9)
    merge9 = concatenate([conv1, up9], axis=3)

    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = BatchNormalization()(conv9)

    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)

    conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = BatchNormalization()(conv9)

    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
    

    model = Model(inputs, conv10)

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

    if(pretrained_weights):
      model.load_weights(pretrained_weights)

    return model
