--- a
+++ b/src/preprocessing.py
@@ -0,0 +1,71 @@
+import numpy as np
+import pandas as pd
+import os
+import click
+import glob
+import cv2
+import pydicom
+from tqdm import tqdm
+from utils import get_windowing, window_image
+from joblib import delayed, Parallel
+
+
+@click.group()
+def cli():
+    print("CLI")
+
+
+windows_range = {
+    'brain': [40, 80],
+    'bone': [600, 2800],
+    'subdual': [75, 215]
+}
+
+
+def convert_dicom_to_jpg(dicomfile, outputdir):
+    try:
+        data = pydicom.read_file(dicomfile)
+        image = data.pixel_array
+        window_center, window_width, intercept, slope = get_windowing(data)
+        id = dicomfile.split("/")[-1].split(".")[0]
+
+        images = []
+        for k, v in windows_range.items():
+            image_windowed = window_image(image, v[0], v[1], intercept, slope)
+            images.append(image_windowed)
+
+        images = np.asarray(images).transpose((1, 2, 0))
+        output_image = os.path.join(outputdir, id + ".jpg")
+        cv2.imwrite(output_image, images)
+    except:
+        print(dicomfile)
+
+
+@cli.command()
+@click.option('--inputdir', type=str)
+@click.option('--outputdir', type=str)
+def extract_images(
+    inputdir,
+    outputdir,
+):
+    os.makedirs(outputdir, exist_ok=True)
+    files = glob.glob(inputdir + "/*.dcm")
+    Parallel(n_jobs=8)(delayed(convert_dicom_to_jpg)(file, outputdir) for file in tqdm(files, total=len(files)))
+
+
+def split_by_patient(
+    train_csv,
+    train_meta_csv,
+    n_folds,
+    outdir
+):
+    os.makedirs(outdir, exist_ok=True)
+    train_df = pd.read_csv(train_csv)
+    train_meta_df = pd.read_csv(train_meta_csv)
+    train_meta_df['ID'] = train_meta_df['ID'].apply(lambda x: "_".join(x.split("_")[:2]))
+    train_meta_df = train_meta_df[['ID', 'PatientID']]
+
+
+
+if __name__ == '__main__':
+    cli()