update DL-save_nifti.py

scripts/DL_save_nifti.py

@@ -10,6 +10,8 @@ import numpy as np
 import pandas as pd
 import SimpleITK as sitk
 
+from tqdm import tqdm
+
 
 dir_in = '../Images_png'
 dir_out = '../Images_nifti'
@@ -22,19 +24,17 @@ def slices2nifti(ims, fn_out, spacing):
     image_itk.SetSpacing(spacing)
     image_itk.SetDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0))
     sitk.WriteImage(image_itk, os.path.join(dir_out, fn_out))
-    print(fn_out, 'saved')
+    # print(f'{fn_out} saved')
 
 
 def load_slices(dir, slice_idxs):
     """load slices from 16-bit png files"""
-    slice_idxs = np.array(slice_idxs)
-    assert np.all(slice_idxs[1:] - slice_idxs[:-1] == 1)
     ims = []
     for slice_idx in slice_idxs:
         path = os.path.join(dir_in, dir, f'{slice_idx:03d}.png')
         im = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # Read as 16-bit image
-        assert im is not None, f'error reading {path}'
-        print(f'read {path}')
+        assert im is not None, f'Error reading: {path}'
+        # print(f'read {path}')
 
         # the 16-bit png file has an intensity bias of 32768
         ims.append((im.astype(np.int32) - 32768).astype(np.int16))
@@ -45,34 +45,14 @@ if __name__ == '__main__':
 
     # Read spacings and image indices in DeepLesion
     dl_info = pd.read_csv(info_fn)
-    idxs = dl_info[['Patient_index', 'Study_index', 'Series_ID']].values
-    spacings = dl_info['Spacing_mm_px_'].apply(lambda x: np.array(x.split(", "), dtype=float)).values
-    spacings = np.stack(spacings)
+    spacings = dl_info['Spacing_mm_px_'].str.split(',', expand=True).astype(float).values
+    ranges = dl_info['Slice_range'].str.split(',', expand=True).astype(int).values
+    img_dirs = dl_info['File_name'].str.rsplit('_', n=1).str[0].values
 
     if not os.path.exists(dir_out):
         os.mkdir(dir_out)
-    img_dirs = sorted(os.listdir(dir_in))
-    for dir1 in img_dirs:
-        # find the image info according to the folder's name
-        idxs1 = np.array([int(d) for d in dir1.split('_')])
-        i1 = np.where(np.all(idxs == idxs1, axis=1))[0]
-        spacings1 = spacings[i1[0]]
-
-        fns = os.listdir(os.path.join(dir_in, dir1))
-        slices = sorted([int(d[:-4]) for d in fns if d.endswith('.png')])
-
-        # Each folder contains png slices from one series (volume)
-        # There may be several sub-volumes in each volume depending on the key slices
-        # We group the slices into sub-volumes according to continuity of the slice indices
-        groups = []
-        for slice_idx in slices:
-            if len(groups) != 0 and slice_idx == groups[-1][-1]+1:
-                groups[-1].append(slice_idx)
-            else:
-                groups.append([slice_idx])
 
-        for group in groups:
-            # group contains slices indices of a sub-volume
-            ims = load_slices(dir1, group)
-            fn_out = f'{dir1}_{group[0]:03d}-{group[-1]:03d}.nii.gz'
-            slices2nifti(ims, fn_out, spacings1)
+    for idx, (img_dir, range, spacing) in tqdm(enumerate(zip(img_dirs, ranges, spacings)), total=len(img_dirs)):
+        ims = load_slices(img_dir, np.arange(*range))
+        fn_out = f'{img_dir}_{range[0]:03d}-{range[-1]:03d}.nii.gz'
+        slices2nifti(ims, fn_out, spacing)