#==============================================================================#

#  Author:       Dominik Müller                                                #

#  Copyright:    2020 IT-Infrastructure for Translational Medical Research,    #

#                University of Augsburg                                        #

#                                                                              #

#  This program is free software: you can redistribute it and/or modify        #

#  it under the terms of the GNU General Public License as published by        #

#  the Free Software Foundation, either version 3 of the License, or           #

#  (at your option) any later version.                                         #

#                                                                              #

#  This program is distributed in the hope that it will be useful,             #

#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #

#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #

#  GNU General Public License for more details.                                #

#                                                                              #

#  You should have received a copy of the GNU General Public License           #

#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #

#==============================================================================#

#-----------------------------------------------------#

#                   Library imports                   #

#-----------------------------------------------------#

from miscnn.data_loading.interfaces import NIFTI_interface

from miscnn import Data_IO

from tqdm import tqdm

import os

import numpy as np

import pandas as pd

#-----------------------------------------------------#

#                    Configurations                   #

#-----------------------------------------------------#

# Data directory

path_data = "data"

#-----------------------------------------------------#

#                   Data Exploration                  #

#-----------------------------------------------------#

# Initialize Data IO Interface for NIfTI data

interface = NIFTI_interface(channels=1, classes=3)

# Create Data IO object to load and write samples in the file structure

data_io = Data_IO(interface, path_data, delete_batchDir=True)

# Access all available samples in our file structure

sample_list = data_io.get_indiceslist()

sample_list.sort()

# Print out the sample list

print("Sample list:", sample_list)

# Now let's load each sample and obtain collect diverse information from them

sample_data = {}

for index in tqdm(sample_list):

    # Sample loading

    sample = data_io.sample_loader(index, load_seg=True)

    # Create an empty list for the current asmple in our data dictionary

    sample_data[index] = []

    # Store the volume shape

    sample_data[index].append(sample.img_data.shape)

    # Identify minimum and maximum volume intensity

    sample_data[index].append(sample.img_data.min())

    sample_data[index].append(sample.img_data.max())

    # Store voxel spacing

    sample_data[index].append(sample.details["spacing"])

    # Identify and store class distribution

    unique_data, unique_counts = np.unique(sample.seg_data, return_counts=True)

    class_freq = unique_counts / np.sum(unique_counts)

    class_freq = np.around(class_freq, decimals=6)

    sample_data[index].append(tuple(class_freq))

# Transform collected data into a pandas dataframe

df = pd.DataFrame.from_dict(sample_data, orient="index",

                            columns=["vol_shape", "vol_minimum",

                                     "vol_maximum", "voxel_spacing",

                                     "class_frequency"])

# Print out the dataframe to console

with pd.option_context('display.max_rows', None, 'display.max_columns', None):

    print(df)

# Calculate mean and median shape sizes

shape_list = np.array(df["vol_shape"].tolist())

for i, a in enumerate(["X", "Y", "Z"]):

    print(a + "-Axes Mean:", np.mean(shape_list[:,i]))

    print(a + "-Axes Median:", np.median(shape_list[:,i]))

# Calculate average class frequency

df_classes = pd.DataFrame(df["class_frequency"].tolist(),

                          columns=["background", "lung_L",

                                   "lung_R", "infection"])

print(df_classes.mean(axis=0))

## The resolution of the volumes are fixed for the x,y axes to 512x512 for the

## "coronacases" data and 630x630 for the "radiopaedia" volumes.

## Strangely, a single sample "radiopaedia_14_85914_0" has only 630x401 for x,y.

## Furthermore, the sample "radiopaedia_10_85902_3" has a ~ 8x times higher

## z axis than the other radiopaedia samples.

## Overall, the aim to identify suited resampling coordinates will be tricky.

##

## When looking on the voxel spacings from the affinity matrix of the CTs,

## we see that the x,y spacings or quite similiar whereas the z axis reveal

## large differences. Resampling to a common voxel spacing is mandatory.

##

## We see that the radiopaedia ct volumes have already preprocessed

## In the description of the data set, it is documentated that they

## used a clipping to the lung window [-1250, 250] and normalized these values

## to [0,255]. Therefore we have to perform the identical preprocessing to the

## other ct volumes as well

##

## Also, the class frequency reveal a heavy bias towards the background class

## as expected in medical image segmentation

##

## In COVID-19-CT-Seg dataset, the last 10 cases from Radiopaedia have been

## adjusted to lung window [-1250,250], and then normalized to [0,255],

## we recommend to adust the first 10 cases from Coronacases with the same method.