def create_train_data(raw_data_path,train_data_path):   
    image_rows = 420
    image_cols = 580 
    images = os.listdir(raw_data_path)
    total = int(len(images) / 2)

    imgs = np.ndarray((total,image_rows, image_cols,1), dtype=np.uint8)
    imgs_mask = np.ndarray((total,image_rows, image_cols,2), dtype=np.uint8)

    i = 0
    print('-'*30)
    print('Creating training images...')
    print('-'*30)
    for image_name in images:
        if 'mask' in image_name:
            continue
        image_mask_name = image_name.split('.')[0] + '_mask.tif'
        img = io.imread(os.path.join(raw_data_path, image_name))
        img_mask = io.imread(os.path.join(raw_data_path, image_mask_name))
        img_mask = img_mask//255
        img_mask_background = 1-img_mask
        

        img = np.array([img])
        img_mask = np.array([img_mask])
        img_mask_background = np.array([img_mask_background])

        imgs[i] = img.reshape(image_rows,image_cols,1)
        imgs_mask[i] = np.concatenate((img_mask.reshape(image_rows,image_cols,1),img_mask_background.reshape(image_rows,image_cols,1)),axis=-1)

        if i % 100 == 0:
            print('Done: {0}/{1} images'.format(i, total))
        i += 1
    print('Loading done.')
    
    np.save(os.path.join(train_data_path,"imgs_train.npy"),imgs)
    np.save(os.path.join(train_data_path,"imgs_mask_train.npy"),imgs_mask)
    
    print('Saving to .npy files done.')