expansion/dataloader/kitti15list_val_lidar.py

import torch.utils.data as data

from PIL import Image
import os
import os.path
import numpy as np

IMG_EXTENSIONS = [
    '.jpg', '.JPG', '.jpeg', '.JPEG',
    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]


def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)

def dataloader(filepath):

  left_fold  = 'image_2/'
  flow_noc   = 'flow_occ/'

  train = [img for img in os.listdir(filepath+left_fold) if img.find('_10') > -1]

#  train = [i for i in train if int(i.split('_')[0])%5!=0]
  with open('/data/gengshay/kitti_scene/devkit/mapping/train_mapping.txt','r') as f:
    flags = [True if len(i)>1 else False for i in f.readlines()]
  train = [fn for (it,fn) in enumerate(sorted(train)) if flags[it] ][100:]

  l0_train  = [filepath+left_fold+img for img in train]
  l1_train = [filepath+left_fold+img.replace('_10','_11') for img in train]
  flow_train = [filepath+flow_noc+img for img in train]


  return sorted(l0_train), sorted(l1_train), sorted(flow_train)