import numpy as np import os, sys, glob, pickle from pathlib import Path from os.path import join, exists, dirname, abspath from sklearn.neighbors import KDTree from tqdm import tqdm import logging import open3d as o3d from .base_dataset import BaseDataset, BaseDatasetSplit from ..utils import make_dir, DATASET log = logging.getLogger(__name__) class ParisLille3D(BaseDataset): """This class is used to create a dataset based on the ParisLille3D dataset, and used in visualizer, training, or testing. The ParisLille3D dataset is best used to train models for urban infrastructure. You can download the dataset `here `__. """ def __init__(self, dataset_path, name='ParisLille3D', cache_dir='./logs/cache', use_cache=False, num_points=65536, test_result_folder='./test', val_files=['Lille2.ply'], **kwargs): """Initialize the function by passing the dataset and other details. Args: dataset_path: The path to the dataset to use. name: The name of the dataset (ParisLille3D in this case). cache_dir: The directory where the cache is stored. use_cache: Indicates if the dataset should be cached. num_points: The maximum number of points to use when splitting the dataset. ignored_label_inds: A list of labels that should be ignored in the dataset. test_result_folder: The folder where the test results should be stored. val_files: The files that include the values. """ super().__init__(dataset_path=dataset_path, name=name, cache_dir=cache_dir, use_cache=use_cache, num_points=num_points, test_result_folder=test_result_folder, val_files=val_files, **kwargs) cfg = self.cfg self.label_to_names = self.get_label_to_names() self.num_classes = len(self.label_to_names) self.label_values = np.sort([k for k, v in self.label_to_names.items()]) self.label_to_idx = {l: i for i, l in enumerate(self.label_values)} self.ignored_labels = np.array([0]) train_path = cfg.dataset_path + "/training_10_classes/" self.train_files = glob.glob(train_path + "/*.ply") self.val_files = [ f for f in self.train_files if Path(f).name in cfg.val_files ] self.train_files = [ f for f in self.train_files if f not in self.val_files ] test_path = cfg.dataset_path + "/test_10_classes/" self.test_files = glob.glob(test_path + '*.ply') @staticmethod def get_label_to_names(): """Returns a label to names dictionary object. Returns: A dict where keys are label numbers and values are the corresponding names. """ label_to_names = { 0: 'unclassified', 1: 'ground', 2: 'building', 3: 'pole-road_sign-traffic_light', 4: 'bollard-small_pole', 5: 'trash_can', 6: 'barrier', 7: 'pedestrian', 8: 'car', 9: 'natural-vegetation' } return label_to_names def get_split(self, split): return ParisLille3DSplit(self, split=split) """Returns a dataset split. Args: split: A string identifying the dataset split that is usually one of 'training', 'test', 'validation', or 'all'. Returns: A dataset split object providing the requested subset of the data. """ def get_split_list(self, split): """Returns a dataset split. Args: split: A string identifying the dataset split that is usually one of 'training', 'test', 'validation', or 'all'. Returns: A dataset split object providing the requested subset of the data. Raises: ValueError: Indicates that the split name passed is incorrect. The split name should be one of 'training', 'test', 'validation', or 'all'. """ if split in ['test', 'testing']: files = self.test_files elif split in ['train', 'training']: files = self.train_files elif split in ['val', 'validation']: files = self.val_files elif split in ['all']: files = self.val_files + self.train_files + self.test_files else: raise ValueError("Invalid split {}".format(split)) return files def is_tested(self, attr): """Checks if a datum in the dataset has been tested. Args: dataset: The current dataset to which the datum belongs to. attr: The attribute that needs to be checked. Returns: If the dataum attribute is tested, then return the path where the attribute is stored; else, returns false. """ cfg = self.cfg name = attr['name'] path = cfg.test_result_folder store_path = join(path, self.name, name + '.txt') if exists(store_path): print("{} already exists.".format(store_path)) return True else: return False def save_test_result(self, results, attr): """Saves the output of a model. Args: results: The output of a model for the datum associated with the attribute passed. attr: The attributes that correspond to the outputs passed in results. """ cfg = self.cfg name = attr['name'].split('.')[0] path = cfg.test_result_folder make_dir(path) pred = results['predict_labels'] + 1 store_path = join(path, self.name, name + '.txt') make_dir(Path(store_path).parent) np.savetxt(store_path, pred.astype(np.int32), fmt='%d') log.info("Saved {} in {}.".format(name, store_path)) class ParisLille3DSplit(BaseDatasetSplit): def __init__(self, dataset, split='training'): super().__init__(dataset, split=split) log.info("Found {} pointclouds for {}".format(len(self.path_list), split)) def __len__(self): return len(self.path_list) def get_data(self, idx): pc_path = self.path_list[idx] log.debug("get_data called {}".format(pc_path)) pc = o3d.t.io.read_point_cloud(pc_path).point points = pc["positions"].numpy().astype(np.float32) if (self.split != 'test'): labels = pc["class"].numpy().astype(np.int32).reshape((-1,)) else: labels = np.zeros((points.shape[0],), dtype=np.int32) data = {'point': points, 'feat': None, 'label': labels} return data def get_attr(self, idx): pc_path = Path(self.path_list[idx]) name = pc_path.name.replace('.ply', '') pc_path = str(pc_path) split = self.split attr = {'idx': idx, 'name': name, 'path': pc_path, 'split': split} return attr DATASET._register_module(ParisLille3D)