get_ready.py

#!/usr/bin/env python
# coding: utf-8

# In[15]:


import numpy as np
import nibabel as nib
import glob
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
from tifffile import imsave
from sklearn.preprocessing import MinMaxScaler


# In[16]:


scaler = MinMaxScaler()


# In[17]:


t2_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t2.nii'))
t1ce_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t1ce.nii'))
flair_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*flair.nii'))
mask_list = sorted(glob.glob('BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*seg.nii'))


# In[21]:


#this is for training folder:
for img in range(len(t2_list)):   #Using t1_list as all lists are of same size
    print("Now preparing image and masks number: ", img)
      
    temp_image_t2=nib.load(t2_list[img]).get_fdata()
    temp_image_t2=scaler.fit_transform(temp_image_t2.reshape(-1, temp_image_t2.shape[-1])).reshape(temp_image_t2.shape)
   
    temp_image_t1ce=nib.load(t1ce_list[img]).get_fdata()
    temp_image_t1ce=scaler.fit_transform(temp_image_t1ce.reshape(-1, temp_image_t1ce.shape[-1])).reshape(temp_image_t1ce.shape)
   
    temp_image_flair=nib.load(flair_list[img]).get_fdata()
    temp_image_flair=scaler.fit_transform(temp_image_flair.reshape(-1, temp_image_flair.shape[-1])).reshape(temp_image_flair.shape)
        
    temp_mask=nib.load(mask_list[img]).get_fdata()
    temp_mask=temp_mask.astype(np.uint8)
    temp_mask[temp_mask==4] = 3  #Reassign mask values 4 to 3
    #print(np.unique(temp_mask))
    
    
    temp_combined_images = np.stack([temp_image_flair, temp_image_t1ce, temp_image_t2], axis=3)
    temp_combined_images=temp_combined_images[56:184, 56:184, 13:141]
    temp_mask = temp_mask[56:184, 56:184, 13:141]
    
    val, counts = np.unique(temp_mask, return_counts=True)
    
    if (1 - (counts[0]/counts.sum())) > 0.01:  #At least 1% useful volume with labels that are not 0
        print("Save Me")
        temp_mask= to_categorical(temp_mask, num_classes=4)
        np.save('BraTS2020_TrainingData/input_data_3channels/images/image_'+str(img)+'.npy', temp_combined_images)
        np.save('BraTS2020_TrainingData/input_data_3channels/masks/mask_'+str(img)+'.npy', temp_mask)
        
    else:
        print("I am useless") 


# In[22]:


import splitfolders  # or import split_folders

input_folder = 'BraTS2020_TrainingData/input_data_3channels/'
output_folder = 'BraTS2020_TrainingData/input_data_128/'
# Split with a ratio.
# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.
splitfolders.ratio(input_folder, output=output_folder, seed=42, ratio=(.75, .25), group_prefix=None)