"""

#---------------------------*[Description]*---------------------------------#

# * Make pickle file for low HU value image data

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

"""

import os

import re

import tensorflow as tf

from scipy import ndimage as nd

import numpy as np

import matplotlib

matplotlib.use('tkagg')

import matplotlib.pyplot as plt

import pickle

import Make_cv_pickle_List

import lytic_HU_3cto3v_Loader_patch

flags = tf.app.flags

FLAGS = flags.FLAGS

data_Par_dir = '/data/Data_Set/BM_CV'

training_batch_size = 1

flags.DEFINE_string("data_dir", data_Par_dir, 'path of dataset')

flags.DEFINE_integer("training_batch_size", training_batch_size, "batch size for training data set")

def pause():

input('Press the <Enter> key to continue...')

def int_text_distinguisher(text):

return int(text) if text.isdigit() else text

def natural_keys(text):

return [int_text_distinguisher(c) for c in re.split('(\d+)', text)]

Sub_Name_list = []

for Par_dir, Sub_Name, File_name in os.walk(FLAGS.data_dir):

if Sub_Name:

for itr in range(len(Sub_Name)):

if 'v0.2' in Sub_Name[itr].split('_'):

Sub_Name_list.append(Sub_Name[itr])

train_File_path_list = []

for i in range(len(Sub_Name_list)):

train_path = os.path.join(FLAGS.data_dir, Sub_Name_list[i])

train_list = os.listdir(train_path)

train_File_path_list.append(train_list)

train_File_path_list = sum(train_File_path_list, [])

main_patch_pickle_list = []

for itr2 in range(len(train_File_path_list)):

if [words for words in ['crop(0, 50)'] if words in train_File_path_list[itr2].split('_')]:

main_patch_pickle_list.append(train_File_path_list[itr2])

else:

pass

main_patch_pickle_list.sort(key=natural_keys)

main_patch_pickle_list = [main_patch_pickle_list[4], main_patch_pickle_list[1], main_patch_pickle_list[2], main_patch_pickle_list[3]]

train_records = Make_cv_pickle_List.load_pickle(train_path, main_patch_pickle_list)

print('len(train_records):', len(train_records))

resize_option = {'resize': False }

low_HU_records = []

train_batch_loader = lytic_HU_3cto3v_Loader_patch.data_loader(train_records, resize_option)

MAX_ITERATION = len(train_records)

for itr in range(MAX_ITERATION):

train_batch_pre_images, train_batch_main_images, train_batch_post_images, train_batch_main_annotation, file_list = train_batch_loader.get_next_batch(FLAGS.training_batch_size)

# print(file_list)

batch_main_training_image = np.squeeze(train_batch_main_images)

batch_main_training_annotation = np.squeeze(train_batch_main_annotation)

batch_main_multiple_image = batch_main_training_image * batch_main_training_annotation

lbl, nlbl = nd.label(batch_main_multiple_image)

lbls = np.arange(1, nlbl + 1)

get_median_value = nd.labeled_comprehension(batch_main_multiple_image, lbl, lbls, np.median, float, -1)

print('Median_HU_Value:',get_median_value)

if np.all(get_median_value < 1300):

print('\r')

print('==============================')

print(file_list)

print(get_median_value)

print('==============================')

print('\r')

file_list = sum(file_list, [])

low_HU_records.append(file_list)

with open('Osteolyitc_low_value_HU_fold1.pickle', 'wb') as f:

pickle.dump(low_HU_records, f, protocol=pickle.HIGHEST_PROTOCOL)

with open('Osteolyitc_low_value_HU_fold1.pickle', 'rb') as f:

result = pickle.load(f)

print('Pickle file Ready!')

print(result)

print(len(result))