# %% importing packages

import numpy as np

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

from tensorflow.keras import mixed_precision

from tensorflow.python.ops.numpy_ops import np_config

np_config.enable_numpy_behavior()

from skimage import measure

from skimage import morphology

from scipy import ndimage

import cv2 as cv

import os

import matplotlib.pyplot as plt

import tqdm

from natsort import natsorted

plt.rcParams['figure.figsize'] = [50, 150]

# %% Citations

#############################################################

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# Defining Functions

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class EncoderBlock(layers.Layer):

    '''This function returns an encoder block with two convolutional layers and 

       an option for returning both a max-pooled output with a stride and pool 

       size of (2,2) and the output of the second convolution for skip 

       connections implemented later in the network during the decoding 

       section. All padding is set to "same" for cleanliness.

       When initializing it receives the number of filters to be used in both

       of the convolutional layers as well as the kernel size and stride for 

       those same layers. It also receives the trainable variable for use with

       the batch normalization layers.'''

    def __init__(self,

                 filters,

                 kernel_size=(3,3),

                 strides=(1,1),

                 trainable=True,

                 name='encoder_block',

                 **kwargs):

        super(EncoderBlock,self).__init__(trainable, name, **kwargs)

        # When initializing this object receives a trainable parameter for

        # freezing the convolutional layers. 

        # including the image normalization within the network for easier image

        # processing during inference

        self.image_normalization = layers.Rescaling(scale=1./255)

        # below creates the first of two convolutional layers

        self.conv1 = layers.Conv2D(filters=filters,

                      kernel_size=kernel_size,

                      strides=strides,

                      padding='same',

                      name='encoder_conv1',

                      trainable=trainable)

        # second of two convolutional layers

        self.conv2 = layers.Conv2D(filters=filters,

                      kernel_size=kernel_size,

                      strides=strides,

                      padding='same',

                      name='encoder_conv2',

                      trainable=trainable)

        # creates the max-pooling layer for downsampling the image.

        self.enc_pool = layers.MaxPool2D(pool_size=(2,2),

                                    strides=(2,2),

                                    padding='same',

                                    name='enc_pool')

        # ReLU layer for activations.

        self.ReLU = layers.ReLU()

        # both batch normalization layers for use with their corresponding

        # convolutional layers.

        self.batch_norm1 = tf.keras.layers.BatchNormalization()

        self.batch_norm2 = tf.keras.layers.BatchNormalization()

    def call(self,input,normalization=False,training=True,include_pool=True):

        # first conv of the encoder block

        if normalization:

            x = self.image_normalization(input)

            x = self.conv1(x)

        else:

            x = self.conv1(input)

        x = self.batch_norm1(x,training=training)

        x = self.ReLU(x)

        # second conv of the encoder block

        x = self.conv2(x)

        x = self.batch_norm2(x,training=training)

        x = self.ReLU(x)

        # calculate and include the max pooling layer if include_pool is true.

        # This output is used for the skip connections later in the network.

        if include_pool:

            pooled_x = self.enc_pool(x)

            return(x,pooled_x)

        else:

            return(x)

#############################################################

class DecoderBlock(layers.Layer):

    '''This function returns a decoder block that when called receives both an

       input and a "skip connection". The input is passed to the 

       "up convolution" or transpose conv layer to double the dimensions before

       being concatenated with its associated skip connection from the encoder

       section of the network. All padding is set to "same" for cleanliness. 

       The decoder block also has an option for including an additional 

       "segmentation" layer, which is a (1,1) convolution with 4 filters, which

       produces the logits for the one-hot encoded ground truth. 

       When initializing it receives the number of filters to be used in the

       up convolutional layer as well as the other two forward convolutions. 

       The received kernel_size and stride is used for the forward convolutions,

       with the up convolution kernel and stride set to be (2,2).'''

    def __init__(self,

                 filters,

                 trainable=True,

                 kernel_size=(3,3),

                 strides=(1,1),

                 name='DecoderBlock',

                 **kwargs):

        super(DecoderBlock,self).__init__(trainable, name, **kwargs)

        # creating the up convolution layer

        self.up_conv = layers.Conv2DTranspose(filters=filters,

                                              kernel_size=(2,2),

                                              strides=(2,2),

                                              padding='same',

                                              name='decoder_upconv',

                                              trainable=trainable)

        # the first of two forward convolutional layers

        self.conv1 = layers.Conv2D(filters=filters,

                                   kernel_size=kernel_size,

                                   strides=strides,

                                   padding='same',

                                   name ='decoder_conv1',

                                   trainable=trainable)

        # second convolutional layer

        self.conv2 = layers.Conv2D(filters=filters,

                                   kernel_size=kernel_size,

                                   strides=strides,

                                   padding='same',

                                   name ='decoder_conv2',

                                   trainable=trainable)

        # this creates the output prediction logits layer.

        self.seg_out = layers.Conv2D(filters=6,

                        kernel_size=(1,1),

                        name='conv_feature_map')

        # ReLU for activation of all above layers

        self.ReLU = layers.ReLU()

        # the individual batch normalization layers for their respective 

        # convolutional layers.

        self.batch_norm1 = tf.keras.layers.BatchNormalization()

        self.batch_norm2 = tf.keras.layers.BatchNormalization()

    def call(self,input,skip_conn,training=True,segmentation=False,prob_dist=True):

        up = self.up_conv(input) # perform image up convolution

        # concatenate the input and the skip_conn along the features axis

        concatenated = layers.concatenate([up,skip_conn],axis=-1)

        # first convolution 

        x = self.conv1(concatenated)

        x = self.batch_norm1(x,training=training)

        x = self.ReLU(x)

        # second convolution

        x = self.conv2(x)

        x = self.batch_norm2(x,training=training)

        x = self.ReLU(x)

        # if segmentation is True, then run the segmentation (1,1) convolution

        # and use the Softmax to produce a probability distribution.

        if segmentation:

            seg = self.seg_out(x)

            # deliberately set as "float32" to ensure proper calculation if 

            # switching to mixed precision for efficiency

            if prob_dist:

                seg = layers.Softmax(dtype='float32')(seg)

            return(seg)

        else:

            return(x)

#############################################################

class uNet(keras.Model):

    '''This is a sub-classed model that uses the encoder and decoder blocks

       defined above to create a custom unet. The differences from the original 

       paper include a variable filter scalar (filter_multiplier), batch 

       normalization between each convolutional layer and the associated ReLU 

       activation, as well as feature normalization implemented in the first 

       layer of the network.'''

    def __init__(self,filter_multiplier=2,**kwargs):

        super(uNet,self).__init__()

        # Defining encoder blocks

        self.encoder_block1 = EncoderBlock(filters=2*filter_multiplier,

                                           name='Enc1')

        self.encoder_block2 = EncoderBlock(filters=4*filter_multiplier,

                                           name='Enc2')

        self.encoder_block3 = EncoderBlock(filters=8*filter_multiplier,

                                           name='Enc3')

        self.encoder_block4 = EncoderBlock(filters=16*filter_multiplier,

                                           name='Enc4')

        self.encoder_block5 = EncoderBlock(filters=32*filter_multiplier,

                                           name='Enc5')

        # Defining decoder blocks. The names are in reverse order to make it 

        # (hopefully) easier to understand which skip connections are associated

        # with which decoder layers.

        self.decoder_block4 = DecoderBlock(filters=16*filter_multiplier,

                                           name='Dec4')

        self.decoder_block3 = DecoderBlock(filters=8*filter_multiplier,

                                           name='Dec3')

        self.decoder_block2 = DecoderBlock(filters=4*filter_multiplier,

                                           name='Dec2')

        self.decoder_block1 = DecoderBlock(filters=2*filter_multiplier,

                                           name='Dec1')

    def call(self,inputs,training,predict=False,threshold=3):

        # encoder    

        enc1,enc1_pool = self.encoder_block1(input=inputs,normalization=True,training=training)

        enc2,enc2_pool = self.encoder_block2(input=enc1_pool,training=training)

        enc3,enc3_pool = self.encoder_block3(input=enc2_pool,training=training)

        enc4,enc4_pool = self.encoder_block4(input=enc3_pool,training=training)

        enc5 = self.encoder_block5(input=enc4_pool,

                                   include_pool=False,

                                   training=training)

        # enc4 = self.encoder_block4(input=enc3_pool,

        #                            include_pool=False,

        #                            training=training)

        # decoder

        dec4 = self.decoder_block4(input=enc5,skip_conn=enc4,training=training)

        dec3 = self.decoder_block3(input=dec4,skip_conn=enc3,training=training)

        dec2 = self.decoder_block2(input=dec3,skip_conn=enc2,training=training)

        prob_dist_out = self.decoder_block1(input=dec2,

                                            skip_conn=enc1,

                                            segmentation=True,

                                            training=training)

        if predict:

            seg_logits_out = self.decoder_block1(input=dec2,

                                                 skip_conn=enc1,

                                                 segmentation=True,

                                                 training=training,

                                                 prob_dist=False)

        # This prediction is included to allow one to seta threshold for the 

        # uncertainty, deemed an arbitrary value that corresponds to the 

        # maximum value of the logits predicted at a specific point in the 

        # image. It only includes predictions for the vascular and neural 

        # tissues if they are above the confidence threshold, if they are below

        # the threshold the predictions are defaulted to muscle, connective,

        # or background.

        if predict:

            # rename the value for consistency and write protection.

            y_pred = seg_logits_out

            pred_shape = (1,1024,1024,6)

            # Getting an image-sized preliminary segmentation prediction

            squeezed_prediction = tf.squeeze(tf.argmax(y_pred,axis=-1))

            # initializing the variable used for storing the maximum logits at 

            # each pixel location.

            max_value_predictions = tf.zeros((1024,1024))

            # cycle through all the classes 

            for idx in range(6):

                # current class logits

                current_slice = tf.squeeze(y_pred[:,:,:,idx])

                # find the locations where this class is predicted

                current_indices = squeezed_prediction == idx

                # define the shape so that this function can run in graph mode

                # and not need eager execution.

                current_indices.set_shape((1024,1024))

                # Get the indices of where the idx class is predicted

                indices = tf.where(squeezed_prediction == idx)

                # get the output of boolean_mask to enable scatter update of the

                # tensor. This is required because tensors do not support 

                # mask indexing.

                values_updates = tf.boolean_mask(current_slice,current_indices).astype(tf.double)

                # Place the maximum logit values at each point in an 

                # image-size matrix, indicating the confidence in the prediction

                # at each pixel. 

                max_value_predictions = tf.tensor_scatter_nd_update(max_value_predictions,indices,values_updates.astype(tf.float32))

            for idx in [3,4]:

                mask_list = []

                for idx2 in range(6):

                    if idx2 == idx:

                        if idx2 == 4:

                            threshold = threshold - 2

                        mid_mask = max_value_predictions<threshold

                        mask_list.append(mid_mask.astype(tf.float32))

                    else:

                        mask_list.append(tf.zeros((1024,1024)))

                mask = tf.expand_dims(tf.stack(mask_list,axis=-1),axis=0)

                indexes = tf.where(mask)

                values_updates = tf.boolean_mask(tf.zeros(pred_shape),mask).astype(tf.double)

                seg_logits_out = tf.tensor_scatter_nd_update(seg_logits_out,indexes,values_updates.astype(tf.float32))

                prob_dist_out = layers.Softmax(dtype='float32')(seg_logits_out)

            # print("updated logits!")

        return(prob_dist_out)

#############################################################

def get_image_blocks(image,tile_distance=512,tile_size=1024):

    '''Receives an image as well as a minimum distance between tiles. 

       Returns the name of the image processed, the image dimensions, and a list

       of tile centers evenly distributed across the tissue surface.'''

    image_dimensions = image.shape

    safe_mask = np.zeros([image_dimensions[0],image_dimensions[1]])

    safe_mask[int(tile_size/2):image_dimensions[0]-int(tile_size/2),

              int(tile_size/2):image_dimensions[1]-int(tile_size/2)] = 1

    grid_0 = np.arange(0,image_dimensions[0],tile_distance)

    grid_1 = np.arange(0,image_dimensions[1],tile_distance)

    center_indexes = []

    for grid0 in grid_0:

        for grid1 in grid_1:

            if safe_mask[grid0,grid1]:

                center_indexes.append([grid0,grid1])

    return([image_dimensions,center_indexes])

#############################################################

def get_reduced_tile_indexes(tile_center,returned_size=1024):

    start_0 = int(tile_center[0] - returned_size/2)

    end_0 = int(tile_center[0] + returned_size/2)

    start_1 = int(tile_center[1] - returned_size/2)

    end_1 = int(tile_center[1] + returned_size/2)

    return([start_0,end_0],[start_1,end_1])

#############################################################

def segment_tiles(unet,center_indexes,image,threshold=3,scaling_factor=1,tile_size=1024):

    m,n,z = image.shape

    segmentation = np.zeros((m,n))

    for idx in tqdm.tqdm(range(len(center_indexes))):

        center = center_indexes[idx]

        dim0, dim1 = get_reduced_tile_indexes(center,tile_size)

        sub_sectioned_tile = image[dim0[0]:dim0[1],dim1[0]:dim1[1]] 

        full_tile_dim0,full_tile_dim1,z = sub_sectioned_tile.shape

        color_tile = sub_sectioned_tile[:,:,0:3]

        if scaling_factor > 1:

            height = color_tile.shape[0]

            width = color_tile.shape[1]

            height2 = int(height/scaling_factor)

            width2 = int(width/scaling_factor)

            color_tile = cv.resize(color_tile,[height2,width2],cv.INTER_AREA)

        color_tile = color_tile[None,:,:,:]

        prediction = unet(color_tile,predict=True,threshold=threshold)

        prediction_tile = np.squeeze(np.asarray(tf.argmax(prediction,axis=-1)).astype(np.float32).copy())

        if scaling_factor > 1:

            prediction_tile = cv.resize(prediction_tile,[full_tile_dim0,full_tile_dim1],cv.INTER_NEAREST)

        dim0, dim1 = get_reduced_tile_indexes(center,returned_size=512)

        # fix this hard coding of the tile indexes for the prediction

        segmentation[dim0[0]:dim0[1],dim1[0]:dim1[1]] = prediction_tile[256:768,256:768]

    return(segmentation)

#############################################################

def double_check_produced_dataset(new_directory,image_idx=0):

    '''this function samples a random image from a given directory, crops off 

       the ground truth from the 4th layer, and displays the color image to 

       verify they work.'''

    os.chdir(new_directory)

    file_names = tf.io.gfile.glob('./*.png')

    file_names = natsorted(file_names)

    # pick a random image index number

    if image_idx == 0:

        image_idx = int(np.random.random()*len(file_names))

    else:

        pass

    print(image_idx)

    # reading specific file from the random index

    segmentation = cv.imread(file_names[image_idx],cv.IMREAD_UNCHANGED)

    # changing the color for the tile from BGR to RGB

    print(file_names[image_idx])

    # plotting the images next to each other

    plt.imshow(segmentation,vmin=0, vmax=6)

    print(np.unique(segmentation))

    plt.show()

#############################################################

#############################################################

# %%

full_image_directory = '/var/confocaldata/HumanNodal/HeartData/16/02/JPG/'

file_names = tf.io.gfile.glob(full_image_directory + '*.jpg')

file_names = natsorted(file_names)

# %%

tile_size = 1024

unet_directory =  '/home/briancottle/Research/Semantic_Segmentation/dataset_shards_6'

os.chdir(unet_directory)

sample_data = np.zeros((1,1024,1024,3)).astype(np.int8)

unet = uNet(filter_multiplier=12)

out = unet(sample_data,training=False,predict=True,threshold=3)

unet.summary()

unet.load_weights('/var/confocaldata/HumanNodal/HeartData/Best Networks/unet_seg_weights.63-0.9172-0.0065.h5')

# %%

image = cv.imread(file_names[250],cv.IMREAD_UNCHANGED)

image = cv.copyMakeBorder(image,2000,2000,2000,2000,cv.BORDER_REPLICATE)

# %%

dimensions,center_indexes = get_image_blocks(image,

                                             tile_distance=512,

                                             tile_size=tile_size

                                             )

segmentation = segment_tiles(unet,

                             center_indexes,

                             image,

                             threshold=3,

                             scaling_factor=1,

                             tile_size=tile_size)

# %%

corrected_image = cv.cvtColor(image,cv.COLOR_BGR2RGB)

plt.imshow(corrected_image[:,:,0:3])

plt.show()

# %%

# plt.imshow(image[:,:,3])

# plt.show()

plt.imshow(segmentation)

plt.show()

# %%

double_check_produced_dataset('/var/confocaldata/HumanNodal/HeartData/10/02/uNet_Segmentations',

                              image_idx=0)

# %%