import numpy as np

import keras

import argparse

import os

import tf_models

import tensorflow as tf

from keras.models import Sequential, Model

from keras.layers import Dense, Conv3D, Dropout, Flatten, Input, concatenate, Reshape, Lambda, Permute

from keras.layers.core import Dense, Dropout, Activation, Reshape

from keras.layers.convolutional import Conv3D, Conv3DTranspose, UpSampling3D

from keras.layers.pooling import AveragePooling3D

from keras.layers import Input

from keras.layers.merge import concatenate

from keras.layers.normalization import BatchNormalization

from tensorflow.contrib.keras.python.keras.backend import learning_phase

from nibabel import load as load_nii

from sklearn.preprocessing import scale

import matplotlib.pyplot as plt

# SAVE_PATH = 'unet3d_baseline.hdf5'

# OFFSET_W = 16

# OFFSET_H = 16

# OFFSET_C = 4

# HSIZE = 64

# WSIZE = 64

# CSIZE = 16

# batches_h, batches_w, batches_c = (224-HSIZE)/OFFSET_H+1, (224-WSIZE)/OFFSET_W+1, (152 - CSIZE)/OFFSET_C+1

def parse_inputs():

    parser = argparse.ArgumentParser(description='Test different nets with 3D data.')

    parser.add_argument('-r', '--root-path', dest='root_path', default='/mnt/disk1/dat/lchen63/brain/data/data2')

    parser.add_argument('-m', '--model-path', dest='model_path',

                        default='NoneDense-0')

    parser.add_argument('-ow', '--offset-width', dest='offset_w', type=int, default=12)

    parser.add_argument('-oh', '--offset-height', dest='offset_h', type=int, default=12)

    parser.add_argument('-oc', '--offset-channel', dest='offset_c', nargs='+', type=int, default=12)

    parser.add_argument('-ws', '--width-size', dest='wsize', type=int, default=38)

    parser.add_argument('-hs', '--height-size', dest='hsize', type=int, default=38)

    parser.add_argument('-cs', '--channel-size', dest='csize', type=int, default=38)

    parser.add_argument('-ps', '--pred-size', dest='psize', type=int, default=12)

    parser.add_argument('-gpu', '--gpu', dest='gpu', type=str, default='0')

    parser.add_argument('-mn', '--model_name', dest='model_name', type=str, default='dense24')

    parser.add_argument('-nc', '--correction', dest='correction', type=bool, default=True)

    return vars(parser.parse_args())

options = parse_inputs()

os.environ["CUDA_VISIBLE_DEVICES"] = options['gpu']

def segmentation_loss(y_true, y_pred, n_classes):

    y_true = tf.reshape(y_true, (-1, n_classes))

    y_pred = tf.reshape(y_pred, (-1, n_classes))

    return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,

                                                                  logits=y_pred))

def vox_preprocess(vox):

    vox_shape = vox.shape

    vox = np.reshape(vox, (-1, vox_shape[-1]))

    vox = scale(vox, axis=0)

    return np.reshape(vox, vox_shape)

def one_hot(y, num_classees):

    y_ = np.zeros([len(y), num_classees])

    y_[np.arange(len(y)), y] = 1

    return y_

def dice_coef_np(y_true, y_pred, num_classes):

    """

    :param y_true: sparse labels

    :param y_pred: sparse labels

    :param num_classes: number of classes

    :return:

    """

    y_true = y_true.astype(int)

    y_pred = y_pred.astype(int)

    y_true = y_true.flatten()

    y_true = one_hot(y_true, num_classes)

    y_pred = y_pred.flatten()

    y_pred = one_hot(y_pred, num_classes)

    intersection = np.sum(y_true * y_pred, axis=0)

    return (2. * intersection) / (np.sum(y_true, axis=0) + np.sum(y_pred, axis=0))

def DenseNetUnit3D(x, growth_rate, ksize, n, bn_decay=0.99):

    for i in range(n):

        concat = x

        x = BatchNormalization(center=True, scale=True, momentum=bn_decay)(x)

        x = Activation('relu')(x)

        x = Conv3D(filters=growth_rate, kernel_size=ksize, padding='same', kernel_initializer='he_uniform',

                   use_bias=False)(x)

        x = concatenate([concat, x])

    return x

def DenseNetTransit(x, rate=1, name=None):

    if rate != 1:

        out_features = x.get_shape().as_list()[-1] * rate

        x = BatchNormalization(center=True, scale=True, name=name + '_bn')(x)

        x = Activation('relu', name=name + '_relu')(x)

        x = Conv3D(filters=out_features, kernel_size=1, strides=1, padding='same', kernel_initializer='he_normal',

                   use_bias=False, name=name + '_conv')(x)

    x = AveragePooling3D(pool_size=2, strides=2, padding='same')(x)

    return x

def dense_net(input):

    x = Conv3D(filters=24, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding='same', use_bias=False)(

        input)

    x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)

    x = DenseNetTransit(x)

    x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)

    x = DenseNetTransit(x)

    x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)

    x = BatchNormalization()(x)

    x = Activation('relu')(x)

    return x

def dense_model(patch_size, num_classes):

    merged_inputs = Input(shape=patch_size + (4,), name='merged_inputs')

    flair = Reshape(patch_size + (1,))(

        Lambda(

            lambda l: l[:, :, :, :, 0],

            output_shape=patch_size + (1,))(merged_inputs),

    )

    t2 = Reshape(patch_size + (1,))(

        Lambda(lambda l: l[:, :, :, :, 1], output_shape=patch_size + (1,))(merged_inputs)

    )

    t1 = Lambda(lambda l: l[:, :, :, :, 2:], output_shape=patch_size + (2,))(merged_inputs)

    flair = dense_net(flair)

    t2 = dense_net(t2)

    t1 = dense_net(t1)

    t2 = concatenate([flair, t2])

    t1 = concatenate([t2, t1])

    tumor = Conv3D(2, kernel_size=1, strides=1, name='tumor')(flair)

    core = Conv3D(3, kernel_size=1, strides=1, name='core')(t2)

    enhancing = Conv3D(num_classes, kernel_size=1, strides=1, name='enhancing')(t1)

    net = Model(inputs=merged_inputs, outputs=[tumor, core, enhancing])

    return net

def norm(image):

    image = np.squeeze(image)

    image_nonzero = image[np.nonzero(image)]

    return (image - image_nonzero.mean()) / image_nonzero.std()

def vox_generator_test(all_files):

    path = options['root_path']

    while 1:

        for file in all_files:

            p = file

            if options['correction']:

                flair = load_nii(os.path.join(path, file, file + '_flair_corrected.nii.gz')).get_data()

                t2 = load_nii(os.path.join(path, file, file + '_t2_corrected.nii.gz')).get_data()

                t1 = load_nii(os.path.join(path, file, file + '_t1_corrected.nii.gz')).get_data()

                t1ce = load_nii(os.path.join(path, file, file + '_t1ce_corrected.nii.gz')).get_data()

            else:

                flair = load_nii(os.path.join(path, p, p + '_flair.nii.gz')).get_data()

                t2 = load_nii(os.path.join(path, p, p + '_t2.nii.gz')).get_data()

                t1 = load_nii(os.path.join(path, p, p + '_t1.nii.gz')).get_data()

                t1ce = load_nii(os.path.join(path, p, p + '_t1ce.nii.gz')).get_data()

            data = np.array([flair, t2, t1, t1ce])

            data = np.transpose(data, axes=[1, 2, 3, 0])

            data_norm = np.array([norm(flair), norm(t2), norm(t1), norm(t1ce)])

            data_norm = np.transpose(data_norm, axes=[1, 2, 3, 0])

            labels = load_nii(os.path.join(path, p, p + '_seg.nii.gz')).get_data()

            yield data, data_norm, labels

def main():

    test_files = []

    with open('test.txt') as f:

        for line in f:

            test_files.append(line[:-1])

    num_labels = 5

    OFFSET_H = options['offset_h']

    OFFSET_W = options['offset_w']

    OFFSET_C = options['offset_c']

    HSIZE = options['hsize']

    WSIZE = options['wsize']

    CSIZE = options['csize']

    PSIZE = options['psize']

    SAVE_PATH = options['model_path']

    model_name = options['model_name']

    OFFSET_PH = (HSIZE - PSIZE) / 2

    OFFSET_PW = (WSIZE - PSIZE) / 2

    OFFSET_PC = (CSIZE - PSIZE) / 2

    batches_w = int(np.ceil((240 - WSIZE) / float(OFFSET_W))) + 1

    batches_h = int(np.ceil((240 - HSIZE) / float(OFFSET_H))) + 1

    batches_c = int(np.ceil((155 - CSIZE) / float(OFFSET_C))) + 1

    flair_t2_node = tf.placeholder(dtype=tf.float32, shape=(None, HSIZE, WSIZE, CSIZE, 2))

    t1_t1ce_node = tf.placeholder(dtype=tf.float32, shape=(None, HSIZE, WSIZE, CSIZE, 2))

    if model_name == 'dense48':

        flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat_large(input=flair_t2_node, name='flair')

        t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat_large(input=t1_t1ce_node, name='t1')

    elif model_name == 'no_dense':

        flair_t2_15, flair_t2_27 = tf_models.PlainCounterpart(input=flair_t2_node, name='flair')

        t1_t1ce_15, t1_t1ce_27 = tf_models.PlainCounterpart(input=t1_t1ce_node, name='t1')

    elif model_name == 'dense24':

        flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat(input=flair_t2_node, name='flair')

        t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat(input=t1_t1ce_node, name='t1')

    elif model_name == 'dense24_nocorrection':

        flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat(input=flair_t2_node, name='flair')

        t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat(input=t1_t1ce_node, name='t1')

    else:

        print' No such model name '

    t1_t1ce_15 = concatenate([t1_t1ce_15, flair_t2_15])

    t1_t1ce_27 = concatenate([t1_t1ce_27, flair_t2_27])

    t1_t1ce_15 = Conv3D(num_labels, kernel_size=1, strides=1, padding='same', name='t1_t1ce_15_cls')(t1_t1ce_15)

    t1_t1ce_27 = Conv3D(num_labels, kernel_size=1, strides=1, padding='same', name='t1_t1ce_27_cls')(t1_t1ce_27)

    t1_t1ce_score = t1_t1ce_15[:, 13:25, 13:25, 13:25, :] + \

                    t1_t1ce_27[:, 13:25, 13:25, 13:25, :]

    saver = tf.train.Saver()

    data_gen_test = vox_generator_test(test_files)

    dice_whole, dice_core, dice_et = [], [], []

    with tf.Session() as sess:

        saver.restore(sess, SAVE_PATH)

        for i in range(len(test_files)):

            print 'predicting %s' % test_files[i]

            x, x_n, y = data_gen_test.next()

            pred = np.zeros([240, 240, 155, 5])

            for hi in range(batches_h):

                offset_h = min(OFFSET_H * hi, 240 - HSIZE)

                offset_ph = offset_h + OFFSET_PH

                for wi in range(batches_w):

                    offset_w = min(OFFSET_W * wi, 240 - WSIZE)

                    offset_pw = offset_w + OFFSET_PW

                    for ci in range(batches_c):

                        offset_c = min(OFFSET_C * ci, 155 - CSIZE)

                        offset_pc = offset_c + OFFSET_PC

                        data = x[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]

                        data_norm = x_n[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]

                        data_norm = np.expand_dims(data_norm, 0)

                        if not np.max(data) == 0 and np.min(data) == 0:

                            score = sess.run(fetches=t1_t1ce_score,

                                             feed_dict={flair_t2_node: data_norm[:, :, :, :, :2],

                                                        t1_t1ce_node: data_norm[:, :, :, :, 2:],

                                                        learning_phase(): 0}

                                             )

                            pred[offset_ph:offset_ph + PSIZE, offset_pw:offset_pw + PSIZE, offset_pc:offset_pc + PSIZE,

                            :] += np.squeeze(score)

            pred = np.argmax(pred, axis=-1)

            pred = pred.astype(int)

            print 'calculating dice...'

            whole_pred = (pred > 0).astype(int)

            whole_gt = (y > 0).astype(int)

            core_pred = (pred == 1).astype(int) + (pred == 4).astype(int)

            core_gt = (y == 1).astype(int) + (y == 4).astype(int)

            et_pred = (pred == 4).astype(int)

            et_gt = (y == 4).astype(int)

            dice_whole_batch = dice_coef_np(whole_gt, whole_pred, 2)

            dice_core_batch = dice_coef_np(core_gt, core_pred, 2)

            dice_et_batch = dice_coef_np(et_gt, et_pred, 2)

            dice_whole.append(dice_whole_batch)

            dice_core.append(dice_core_batch)

            dice_et.append(dice_et_batch)

            print dice_whole_batch

            print dice_core_batch

            print dice_et_batch

        dice_whole = np.array(dice_whole)

        dice_core = np.array(dice_core)

        dice_et = np.array(dice_et)

        print 'mean dice whole:'

        print np.mean(dice_whole, axis=0)

        print 'mean dice core:'

        print np.mean(dice_core, axis=0)

        print 'mean dice enhance:'

        print np.mean(dice_et, axis=0)

        np.save(model_name + '_dice_whole', dice_whole)

        np.save(model_name + '_dice_core', dice_core)

        np.save(model_name + '_dice_enhance', dice_et)

        print 'pred saved'

if __name__ == '__main__':

    main()