--- a
+++ b/src/preprocessing_3w.py
@@ -0,0 +1,186 @@
+import numpy as np
+import pandas as pd
+import os
+import click
+import glob
+import cv2
+import pydicom
+from tqdm import tqdm
+from joblib import delayed, Parallel
+import random
+import pydicom
+from scipy import ndimage
+import pydicom
+from skimage import exposure
+
+
+def window_image(img, window_center, window_width, intercept, slope):
+    img = (img * slope + intercept)
+    img_min = window_center - window_width // 2
+    img_max = window_center + window_width // 2
+    img[img < img_min] = img_min
+    img[img > img_max] = img_max
+    return img
+
+
+def get_first_of_dicom_field_as_int(x):
+    # get x[0] as in int is x is a 'pydicom.multival.MultiValue', otherwise get int(x)
+    if type(x) == pydicom.multival.MultiValue:
+        return int(x[0])
+    else:
+        return int(x)
+
+
+def get_windowing(data):
+    dicom_fields = [data[('0028', '1050')].value,  # window center
+                    data[('0028', '1051')].value,  # window width
+                    data[('0028', '1052')].value,  # intercept
+                    data[('0028', '1053')].value]  # slope
+    return [get_first_of_dicom_field_as_int(x) for x in dicom_fields]
+
+
+@click.group()
+def cli():
+    print("CLI")
+
+
+windows_range = {
+    'brain': [40, 80],
+    'bone': [600, 2800],
+    'subdual': [75, 215]
+}
+
+
+def refine_label(label_mask):
+    label_mask = label_mask.astype(np.bool)
+    # Fill hole
+    label_mask = ndimage.binary_fill_holes(label_mask)
+    # Get largest connected component
+    label_im, nb_labels = ndimage.label(label_mask)
+    sizes = ndimage.sum(label_mask, label_im, range(nb_labels + 1))
+    mask_size = sizes < max(sizes)
+    remove_pixel = mask_size[label_im]
+    label_im[remove_pixel] = 0
+    labels = np.unique(label_im)
+    label_mask = np.searchsorted(labels, label_im)
+    return label_mask
+
+
+def cut_edge(image, keep_margin):
+    '''
+    function that cuts zero edge
+    '''
+    H, W = image.shape
+    H_s, H_e = 0, H - 1
+    W_s, W_e = 0, W - 1
+
+    while H_s < H:
+        if image[H_s, :].sum() != 0:
+            break
+        H_s += 1
+    while H_e > H_s:
+        if image[H_e, :].sum() != 0:
+            break
+        H_e -= 1
+    while W_s < W:
+        if image[:, W_s].sum() != 0:
+            break
+        W_s += 1
+    while W_e > W_s:
+        if image[:, W_e].sum() != 0:
+            break
+        W_e -= 1
+    if keep_margin != 0:
+        H_s = max(0, H_s - keep_margin)
+        H_e = min(H - 1, H_e + keep_margin)
+        W_s = max(0, W_s - keep_margin)
+        W_e = min(W - 1, W_e + keep_margin)
+    return int(H_s), int(H_e) + 1, int(W_s), int(W_e) + 1
+
+
+def pre_preocessing(image, pad_size=(512, 512)):
+    # Convert to [0, 255]
+    # image = (image-image.min()) / (image.max() - image.min())
+    # image= image*255
+    image[image < 0] = 0
+    # Remove unwanted region
+    mask = image > 0
+    mask = refine_label(mask)
+    image = image * mask
+    # Center crop and pad to size
+    # mask = image>0
+    # min_H_s, max_H_e, min_W_s, max_W_e = cut_edge(mask, 32)
+    # image = image[min_H_s: max_H_e, min_W_s:max_W_e]
+    # Pad to size
+    H, W = image.shape
+    pad_H, pad_W = pad_size[0], pad_size[1]
+    pad_H0 = max((pad_H - H) // 2, 0)
+    pad_H1 = max(pad_H - H - pad_H0, 0)
+    pad_W0 = max((pad_W - W) // 2, 0)
+    pad_W1 = max(pad_W - W - pad_W0, 0)
+    image = np.pad(image, [(pad_H0, pad_H1), (pad_W0, pad_W1)], mode='constant', constant_values=0)
+    return image
+
+
+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 = []
+        # count =0
+
+        for k, v in windows_range.items():
+            image_windowed = window_image(image, v[0], v[1], intercept, slope)
+            image_windowed = pre_preocessing(image_windowed, pad_size=(512, 512))
+            images.append(image_windowed)
+
+            # image_windowed = exposure.equalize_adapthist(image_windowed, clip_limit=0.01)
+            # min_value= image_windowed.min()
+            # max_value = image_windowed.max()
+            # print (image_windowed.min(),image_windowed.max())
+            # if count ==0:
+            #     image_windowed=np.uint8(image_windowed)
+            #     clahe = cv2.createCLAHE(clipLimit = 1.0, tileGridSize = (8,8))
+            #     image_windowed = clahe.apply(image_windowed)
+            #     images.append(image_windowed)
+            # print (image_windowed.min(),image_windowed.max())
+            # count +=1
+        images = np.asarray(images).transpose((1, 2, 0))
+        # print (images.shape)
+
+        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()
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