--- a
+++ b/preprocess/measure_slices.py
@@ -0,0 +1,68 @@
+import numpy as np
+import pandas as pd
+from PIL import Image
+from tqdm import tqdm
+from preprocessingutils import pwr_transform
+import os
+from pathlib import Path
+
+from argparse import ArgumentParser
+parser = ArgumentParser()
+parser.add_argument('--dir', default='', type=str,
+                    help='location of files, should contain subdirs with splits with subdirs imgs and masks; if empty, starts from parent dir of this folder')
+
+
+def measure_slice(x, mask=None):
+    '''
+    calculate slice size and variance
+    '''
+    assert isinstance(x, np.ndarray)
+    if mask is None:
+        mask = np.ones_like(x)
+    assert isinstance(mask, np.ndarray)
+
+    size     = mask.sum()
+    variance = x[np.nonzero(mask)].std() 
+    img_max  = x[np.nonzero(mask)].max()
+    img_min  = x[np.nonzero(mask)].min()
+    img_mean = x[np.nonzero(mask)].mean()
+
+    return size, variance, img_max, img_min, img_mean
+
+args = parser.parse_args()
+if args.dir == '':
+    args.dir = Path(Path.cwd().parent / 'data' / 'nodules2d')
+dfs = {}
+
+imgs = os.listdir(os.path.join(args.dir, "imgs"))
+imgs = [x for x in imgs if x.endswith('.png')]
+df = pd.DataFrame({'img_name': imgs, 
+                    'size': np.zeros((len(imgs, ))), 
+                    'variance': np.zeros((len(imgs,))),
+                    'max': np.zeros((len(imgs,))),
+                    'min': np.zeros((len(imgs,))),
+                    'mean': np.zeros((len(imgs,)))})
+
+for i, img_name in tqdm(enumerate(imgs)):
+    img  = np.array(Image.open(os.path.join(args.dir, "imgs", img_name))) / 255
+    mask = (np.array(Image.open(os.path.join(args.dir, "masks", img_name))) / 255).astype(np.int16)
+
+    size, variance, img_max, img_min, img_mean = measure_slice(img, mask)
+    df.iloc[i, 1] = size
+    df.iloc[i, 2] = variance
+    df.iloc[i, 3] = img_max
+    df.iloc[i, 4] = img_min
+    df.iloc[i, 5] = img_mean
+    
+df = df.rename(columns={'img_name': 'name'})
+# df["name"] = df.name.apply(lambda x: os.path.join("imgs", x))
+
+# normalize values
+# scalar_vars = ["size", "variance", "img_min", "img_max", "img_mean"]
+# df[scalar_vars] = df[scalar_vars].apply(pwr_transform)
+
+# print(df.head())
+df.to_csv(os.path.join(args.dir, "measurements.csv"), index=False)
+
+
+