import numpy as np
import pandas as pd
import os, sys, glob, pickle
from pathlib import Path
from os.path import join, exists, dirname, abspath
import random
from plyfile import PlyData, PlyElement
from sklearn.neighbors import KDTree
import logging
from .utils import DataProcessing as DP
from .base_dataset import BaseDataset, BaseDatasetSplit
from ..utils import make_dir, DATASET
logging.basicConfig(
level=logging.INFO,
format='%(levelname)s - %(asctime)s - %(module)s - %(message)s',
)
log = logging.getLogger(__name__)
[docs]class Electricity3D(BaseDataset):
"""
This class is used to create a dataset based on the Electricity3D dataset,
and used in visualizer, training, or testing. The dataset includes 19 semantic classes and
covers a variety of electricity outdoor scenes.
"""
[docs] def __init__(self,
dataset_path,
name='Electricity3D',
cache_dir='./logs/cache',
use_cache=False,
num_points=65536,
class_weights=[
635262, 1881335, 3351389, 135650, 1132024, 282850, 3384,
102379, 357589, 20374, 332435, 42973, 164957, 8626, 7962,
11651, 64765, 26884, 42479
],
ignored_label_inds=[0],
val_files=['1_9_local_a', '7_29_local'],
test_result_folder='./test',
**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 (Electricity3D 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.
class_weights: The class weights to use in the dataset.
ignored_label_inds: A list of labels that should be ignored in the dataset.
val_files: The files with the data.
test_result_folder: The folder where the test results should be stored.
Returns:
class: The corresponding class.
"""
super().__init__(dataset_path=dataset_path,
name=name,
cache_dir=cache_dir,
use_cache=use_cache,
class_weights=class_weights,
num_points=num_points,
ignored_label_inds=ignored_label_inds,
val_files=val_files,
test_result_folder=test_result_folder,
**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])
self.all_files = glob.glob(str(Path(self.cfg.dataset_path) / '*.xyz'))
self.train_files = [
f for f in self.all_files if exists(
str(Path(f).parent / Path(f).name.replace('.xyz', '.labels')))
]
self.test_files = [
f for f in self.all_files if f not in self.train_files
]
self.train_files = np.sort(self.train_files)
self.test_files = np.sort(self.test_files)
self.val_files = []
for i, file_path in enumerate(self.train_files):
for val_file in cfg.val_files:
if val_file in file_path:
self.val_files.append(file_path)
break
self.train_files = np.sort(
[f for f in self.train_files if f not in self.val_files])
[docs] @staticmethod
def get_label_to_names():
"""
Returns a label to names dictonary object.
Returns:
A dict where keys are label numbers and
values are the corresponding names.
"""
label_to_names = {
0: 'unlabeled',
1: 'man-made terrain',
2: 'natural terrain',
3: 'high vegetation',
4: 'low vegetation',
5: 'buildings',
6: 'hard scape',
7: 'scanning artifacts',
8: 'cars',
9: 'utility pole',
10: 'insulator',
11: 'electrical wire',
12: 'cross bar',
13: 'stick',
14: 'fuse',
15: 'wire clip',
16: 'linker insulator',
17: 'persons',
18: 'traffic sign',
19: 'traffic light'
}
return label_to_names
[docs] def get_split(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.
"""
return Electricity3DSplit(self, split=split)
[docs] def get_split_list(self, split):
"""Returns the list of data splits available.
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
[docs] 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 resturn 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 + '.labels')
if exists(store_path):
print("{} already exists.".format(store_path))
return True
else:
return False
[docs] 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 + '.labels')
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 Electricity3DSplit(BaseDatasetSplit):
"""
This class is used to create a split for Electricity3D dataset.
Initialize the class.
Args:
dataset: The dataset to split.
split: A string identifying the dataset split that is usually one of
'training', 'test', 'validation', or 'all'.
**kwargs: The configuration of the model as keyword arguments.
Returns:
A dataset split object providing the requested subset of the data.
"""
def __init__(self, dataset, split='training'):
super().__init__(dataset, split=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 = pd.read_csv(pc_path,
header=None,
delim_whitespace=True,
dtype=np.float32).values
points = pc[:, 0:3]
feat = pc[:, 3:6]
points = np.array(points, dtype=np.float32)
feat = np.array(feat, dtype=np.float32)
if self.split != 'test':
labels = pd.read_csv(pc_path.replace(".xyz", ".labels"),
header=None,
delim_whitespace=True,
dtype=np.int32).values
labels = np.array(labels, dtype=np.int32).reshape((-1,))
else:
labels = np.zeros((points.shape[0],), dtype=np.int32)
data = {'point': points, 'feat': feat, 'label': labels}
return data
def get_attr(self, idx):
pc_path = Path(self.path_list[idx])
name = pc_path.name.replace('.xyz', '')
attr = {'name': name, 'path': str(pc_path), 'split': self.split}
return attr
DATASET._register_module(Electricity3D)