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preprocess_synapse_data_3d.py

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+import os
+import shutil
+from time import time
+
+import numpy as np
+import SimpleITK as sitk
+import nibabel as nib
+import scipy.ndimage as ndimage
+import h5py
+
+splits = ['train', 'test']
+#train = True # Set True to process training set and set False for testset 
+
+for split in splits:
+    if(split == 'train'):
+        ct_path = './data/synapse/Abdomen/RawData/TrainSet/img' # set your path to your trainset directory
+        seg_path = './data/synapse/Abdomen/RawData/TrainSet/label' 
+        save_path = './data/synapse/train_npz_mframes/'
+    else:
+        ct_path = './data/synapse/Abdomen/RawData/TestSet/img' # set your path to your testset directory
+        seg_path = './data/synapse/Abdomen/RawData/TestSet/label'
+        save_path = './data/synapse/test_vol_h5_mframes/'
+    
+    if os.path.exists(save_path) is False:
+        os.mkdir(save_path)
+
+    upper = 275 
+    lower = -125
+
+    start_time = time()
+    min_size= 10000
+    for ct_file in os.listdir(ct_path):
+
+        ct = nib.load(os.path.join(ct_path, ct_file))
+        seg = nib.load(os.path.join(seg_path, ct_file.replace('img', 'label')))
+
+        #Convert them to numpy format, 
+        ct_array = ct.get_fdata()
+        seg_array = seg.get_fdata()
+
+        ct_array = np.clip(ct_array, lower, upper)
+    
+        #print([np.min(ct_array), np.max(ct_array)])
+    
+        #normalize each 3D image to [0, 1] 
+        ct_array = (ct_array - lower) / (upper - lower)
+    
+        #print([np.min(ct_array), np.max(ct_array)])
+    
+        ct_array = np.transpose(ct_array, (2, 0, 1))
+        seg_array = np.transpose(seg_array, (2, 0, 1))
+    
+        print('file name:', ct_file)
+        print('shape:', ct_array.shape)
+        
+        if(ct_array.shape[0] < min_size):
+            min_size = ct_array.shape[0]
+
+        ct_number = ct_file.split('.')[0]
+        if(split == 'test'):
+    	    new_ct_name = ct_number.replace('img', 'case')+'.npy.h5'
+    	    hf = h5py.File(os.path.join(save_path, new_ct_name), 'w')
+    	    hf.create_dataset('image', data=ct_array)
+    	    hf.create_dataset('label', data=seg_array)
+    	    hf.close()
+    	    continue
+    	
+        for s_idx in range(ct_array.shape[0]-2):
+            #ct_array_s = np.zeros()
+    	    ct_array_s = np.transpose(ct_array, (1, 2, 0))[:, :, s_idx:s_idx+3]
+    	    print(ct_array_s.shape)
+    	    seg_array_s = seg_array[s_idx+1, :, :]
+    	    slice_no = "{:03d}".format(s_idx)
+    	    new_ct_name = ct_number.replace('img', 'case') + '_slice' + slice_no
+    	    np.savez(os.path.join(save_path, new_ct_name), image=ct_array_s, label=seg_array_s)
+
+        
+        print('already use {:.3f} min'.format((time() - start_time) / 60))
+        print('-----------')
+    print('max_size '+str(min_size))