# Path: piecewisecrf/datasets/kitti/kitti.py
# class KittiDataset(Dataset):
#     '''
# 
#     Kitti dataset abstraction
# 
#     '''
#     def __init__(self, train_dir=None, val_dir=None, test_dir=None):
#         super(KittiDataset, self).__init__(train_dir, val_dir, test_dir)
# 
#         self.labels = [
#             # name id trainId category catId hasInstances ignoreInEval color
#             Label('sky', 0, 0, 'void', 0, False, False, (128, 128, 128)),
#             Label('building', 1, 1, 'void', 0, False, False, (128, 0, 0)),
#             Label('road', 2, 2, 'void', 0, False, False, (128, 64, 128)),
#             Label('sidewalk', 3, 3, 'void', 0, False, False, (0, 0, 192)),
#             Label('fence', 4, 4, 'void', 0, False, False, (64, 64, 128)),
#             Label('vegetation', 5, 5, 'void', 0, False, False, (128, 128, 0)),
#             Label('pole', 6, 6, 'void', 0, False, False, (192, 192, 128)),
#             Label('car', 7, 7, 'void', 0, False, False, (64, 0, 128)),
#             Label('sign', 8, 8, 'void', 0, False, False, (192, 128, 128)),
#             Label('pedestrian', 9, 9, 'void', 0, False, False, (64, 64, 0)),
#             Label('cyclist', 10, 10, 'void', 0, False, False, (0, 128, 192)),
#             Label('ignore', 11, 11, 'void', 0, False, True, (0, 0, 0)),
#         ]
# 
#         self.classes = list(range(11))
# 
#         self.create_collections()


import argparse
import os
import random
import shutil
import tqdm
import numpy as np
import skimage
import skimage.data
from piecewisecrf.datasets.kitti.kitti import KittiDataset
        Number of images to pick

    Returns
    -------
    array: numpy array
        Picked image names


    '''
    images = os.listdir(labels_dir)
    return random.sample(images, size)


def _label_statistics(image_paths):
    '''

    Calculates label statistics (number of picked pixels for each class)

    Parameters
    ----------
    image_paths : list
        List of absolute paths for picked images

    Returns
    -------
    array: numpy array
        Number of selected pixels per class


    '''

# Path: piecewisecrf/datasets/kitti/kitti.py
# class KittiDataset(Dataset):
#     '''
# 
#     Kitti dataset abstraction
# 
#     '''
#     def __init__(self, train_dir=None, val_dir=None, test_dir=None):
#         super(KittiDataset, self).__init__(train_dir, val_dir, test_dir)
# 
#         self.labels = [
#             # name id trainId category catId hasInstances ignoreInEval color
#             Label('sky', 0, 0, 'void', 0, False, False, (128, 128, 128)),
#             Label('building', 1, 1, 'void', 0, False, False, (128, 0, 0)),
#             Label('road', 2, 2, 'void', 0, False, False, (128, 64, 128)),
#             Label('sidewalk', 3, 3, 'void', 0, False, False, (0, 0, 192)),
#             Label('fence', 4, 4, 'void', 0, False, False, (64, 64, 128)),
#             Label('vegetation', 5, 5, 'void', 0, False, False, (128, 128, 0)),
#             Label('pole', 6, 6, 'void', 0, False, False, (192, 192, 128)),
#             Label('car', 7, 7, 'void', 0, False, False, (64, 0, 128)),
#             Label('sign', 8, 8, 'void', 0, False, False, (192, 128, 128)),
#             Label('pedestrian', 9, 9, 'void', 0, False, False, (64, 64, 0)),
#             Label('cyclist', 10, 10, 'void', 0, False, False, (0, 128, 192)),
#             Label('ignore', 11, 11, 'void', 0, False, True, (0, 0, 0)),
#         ]
# 
#         self.classes = list(range(11))
# 
#         self.create_collections()


# Path: piecewisecrf/datasets/kitti/train_validation_split.py
import argparse
import os
import random
import shutil
import tqdm
import numpy as np
import skimage
import skimage.data
from piecewisecrf.datasets.kitti.kitti import KittiDataset

        Number of images to pick

    Returns
    -------
    array: numpy array
        Picked image names


    '''
    images = os.listdir(labels_dir)
    return random.sample(images, size)


def _label_statistics(image_paths):
    '''

    Calculates label statistics (number of picked pixels for each class)

    Parameters
    ----------
    image_paths : list
        List of absolute paths for picked images

    Returns
    -------
    array: numpy array
        Number of selected pixels per class


    '''

    ds = KittiDataset()