text: import numpy as np
  import scipy
  import scipy.io as sio
  import scipy.misc
  from PIL import Image
  import matplotlib
  import matplotlib.pyplot as plt
  import json
  import os
  import os.path
  from tqdm import tqdm
  
  # Replace the colors with our colors 
  # This is only used for visualization purposes
  #color_mat = sio.loadmat("data_ADE/color150.mat")
  
  #StreetHazards colors
  #colors = np.array([[ 0,   0,   0],# // unlabeled     =   0,
  #        [ 70,  70,  70],# // building      =   1,
  #        [190, 153, 153],# // fence         =   2,
  #        [250, 170, 160],# // other         =   3,
  #        [220,  20,  60],# // pedestrian    =   4,
  #        [153, 153, 153],# // pole          =   5,
  #        [157, 234,  50],# // road line     =   6,
  #        [128,  64, 128],# // road          =   7,
  #        [244,  35, 232],# // sidewalk      =   8,
  #        [107, 142,  35],# // vegetation    =   9,
  #        [  0,   0, 142],# // car           =  10,
  #        [102, 102, 156],# // wall          =  11,
  #        [220, 220,   0],# // traffic sign  =  12,
  #        [ 60, 250, 240],# // anomaly       =  13,
  #
  #        ])
  
  #color_mat["colors"] = colors
  #sio.savemat("data/color150.mat", color_mat)
  
  
  #####
  #create the train and val obgt
  
  def create_odgt(root_dir, file_dir, ann_dir, out_dir, anom_files=None):
      if anom_files is  None:
          anom_files = []
      _files = []
  
      count = total = 0
      town_names = sorted(os.listdir(root_dir+file_dir))
  
      for town in town_names:
          img_files = sorted(os.listdir(os.path.join(root_dir,file_dir,town)))
          total += len(img_files)
          for img in img_files:
              ann_file = img
              ann_file_path = os.path.join(root_dir,ann_dir,town,ann_file)
              if os.path.exists(ann_file_path):
                  dict_entry = {
                      "dbName": "StreetHazards",
                      "width": 1280,
                      "height": 720,
                      "fpath_img": os.path.join(file_dir,town,img),
                      "fpath_segm": os.path.join(ann_dir,town,ann_file),
                  }
                  # If converting BDD100K uncomment out the following
                  #img = Image.open(ann_file_path)
                  #if np.any(np.logical_or( (img == 19), (img == 20) )):
                  #    anom_files.append(dict_entry)
                  #else:
                  count += 1
                  _files.append(dict_entry)
  
      print("total images in = {} and out =  {}".format(total, count))
  
      with open(out_dir, "w") as outfile:
          json.dump(_files, outfile)
  
      # If converting BDD100K uncomment out the following
      #with open(root_dir + "anom.odgt", "w") as outfile:
      #    json.dump(anom_files, outfile)
  
      return anom_files
  
  
  out_dir = "data/train.odgt"
  #modify root directory to reflect the location of where the streethazards_train was extracted to.
  root_dir = "data/train/"
  train_dir = "images/training"
  ann_dir = "annotations/training/"
  anom_files = create_odgt(root_dir, train_dir, ann_dir, out_dir)
  
  
  out_dir = "data/validation.odgt"
  train_dir = "images/validation/"
  ann_dir = "annotations/validation/"
  create_odgt(root_dir, train_dir, ann_dir, out_dir, anom_files=anom_files)
  
  
  out_dir = "data/test.odgt"
  val_dir = "images/test/"
  ann_dir = "annotations/test/"
  create_odgt(root_dir, val_dir, ann_dir, out_dir)
  
  
  
  # BDD100K label map
  #colors = np.array(
  #    [0,    # road
  #    1,     #sidewalk
  #    2,     # building
  #    3,     # wall
  #    4,     # fence
  #    5,     # pole
  #    6,     # traffic light
  #    7,     # traffic sign
  #    8,     # vegetation
  #    9,     # terrain
  #    10,    # sky
  #    11,    # person
  #    12,    # rider
  #    13,    # car
  #    14,    # truck
  #    15,    # bus
  #    16,    # train
  #    17,    # motorcycle
  #    18,    # bicycle
  #    255,]) # other
  
  ### convert BDD100K semantic segmentation images to correct labels
  
  def convert_bdd(root_dir, ann_dir):
      count = 0
      for img_loc in tqdm(os.listdir(root_dir+ann_dir)):
          img = Image.open(root_dir+ann_dir+img_loc)
          if img.ndim <= 1:
              continue
          #swap 255 with -1
          #16 -> 19
          #18 -> 16
          #19 -> 18
          # add 1 to whole array
          loc = img == 255
          img[loc] = -1
          loc = img == 16
          img[loc] = 19
          loc = img == 18
          img[loc] = 16
          loc = img == 19
          img[loc] = 18
          img += 1
          scipy.misc.toimage(img, cmin=0, cmax=255).save(root_dir+ann_dir+img_loc)
  
  
  #root_dir = "data/"
  #ann_dir = "seg/train_labels/train/"
  # convert the BDD100K semantic segmentation images.
  #convert_bdd(root_dir, ann_dir)
  
  
  
  
  
  