"""
similar to originial model.py except with flexible choice on dropout rate and # channels

REFERENCE:
1. UNet in keras: https://github.com/zhixuhao/unet

EXTENSION:
1. write unet as a custom keras model class, instead of a function
"""
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 init_unet(input_shape = (256,256,1), scale = 1, dropout = 0.5):
    """
    According to "Raw G-Band Chromosome Image Segmentation Using U-Net Based Neural Network"
    # parameters are halved compared to original UNet

    input:
        scale -- float, if scale = 0.5, then all channels are halved
        dropout -- float, dropout rate
        metrics -- list, choice in list: 'accuracy', 'dice_coef', 
    """
    inputs = Input(input_shape)
    conv1 = Conv2D(int(64 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(int(64 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size = (2, 2))(conv1)
    conv2 = Conv2D(int(128 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(int(128 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size = (2, 2))(conv2)
    conv3 = Conv2D(int(256 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(int(256 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size = (2, 2))(conv3)
    conv4 = Conv2D(int(512 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(int(512 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(dropout)(conv4)
    pool4 = MaxPooling2D(pool_size = (2, 2))(drop4)

    conv5 = Conv2D(int(1024 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(int(1024 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(dropout)(conv5)

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

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

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

    up9 = Conv2D(int(64 * scale), 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(int(64 * scale), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(int(64 * scale), 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(input = inputs, output = conv10)
    return model


if __name__ == '__main__':
    model = init_unet()
