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pointcloud_viewer.py
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__author__ = "Martin Hahner"
__contact__ = "[email protected]"
__license__ = "CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)"
# GUI adapted from
# https://memotut.com/create-a-3d-model-viewer-with-pyqt5-and-pyqtgraph-b3916/ and
# https://matplotlib.org/3.1.1/gallery/user_interfaces/embedding_in_qt_sgskip.html
import os
import copy
import gzip
import socket
import pandas
import logging
import argparse
import numpy as np
import pickle as pkl
import matplotlib as mpl
import matplotlib.cm as cm
import multiprocessing as mp
import pyqtgraph.opengl as gl
from glob import glob
from typing import List
from pathlib import Path
from plyfile import PlyData
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from pyqtgraph.Qt import QtGui
from fog_simulation import ParameterSet, RNG, simulate_fog
from SeeingThroughFog.tools.DatasetViewer.dataset_viewer import load_calib_data, read_label
from SeeingThroughFog.tools.DatasetFoggification.beta_modification import BetaRadomization
from SeeingThroughFog.tools.DatasetFoggification.lidar_foggification import haze_point_cloud
# R, G, B, alpha
COLORS = [( 0, 255, 0, 255), # cars in green
(255, 0, 0, 255), # pedestrian in red
(255, 255, 0, 255)] # cyclists in yellow
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--datasets', type=str, help='path to where you store your datasets',
default=str(Path.home() / 'datasets'))
parser.add_argument('-e', '--experiments', type=str, help='path to where you store your OpenPCDet experiments',
default=str(Path.home() / 'repositories/PCDet/output'))
args = parser.parse_args()
DATASETS_ROOT = Path(args.datasets)
EXPERIMENTS_ROOT = Path(args.experiments)
FOG = DATASETS_ROOT / 'DENSE/SeeingThroughFog/lidar_hdl64_strongest_fog_extraction'
AUDI = DATASETS_ROOT / 'A2D2/camera_lidar_semantic_bboxes'
LYFT = DATASETS_ROOT / 'LyftLevel5/Perception/train_lidar'
ARGO = DATASETS_ROOT / 'Argoverse'
PANDA = DATASETS_ROOT / 'PandaSet'
DENSE = DATASETS_ROOT / 'DENSE/SeeingThroughFog/lidar_hdl64_strongest'
KITTI = DATASETS_ROOT / 'KITTI/3D/training/velodyne'
WAYMO = DATASETS_ROOT / 'WaymoOpenDataset/WOD/train/velodyne'
HONDA = DATASETS_ROOT / 'Honda_3D/scenarios'
APOLLO = DATASETS_ROOT / 'Apollo3D'
NUSCENES = DATASETS_ROOT / 'nuScenes/sweeps/LIDAR_TOP'
if socket.gethostname() == 'beast':
DENSE = Path.home() / 'datasets_local' / 'DENSE/SeeingThroughFog/lidar_hdl64_strongest'
def get_extracted_fog_file_list(dirname: str) -> List[str]:
file_list = [y for x in os.walk(dirname) for y in glob(os.path.join(x[0], f'*.bin'))]
return sorted(file_list)
class Namespace:
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
class MyWindow(QMainWindow):
def __init__(self) -> None:
super(MyWindow, self).__init__()
self.boxes = {}
self.predictions = {}
self.result_dict = {}
self.show_predictions = True
self.prediction_threshold = 50
self.gain = True
self.noise_variant = 'v4'
self.noise = 10
self.noise_min = 0
self.noise_max = 20
self.min_fog_response = -1
self.max_fog_response = -1
self.num_fog_responses = -1
self.num_cpus = mp.cpu_count()
self.pool = mp.Pool(self.num_cpus)
self.p = ParameterSet(gamma=0.000001,
gamma_min=0.0000001,
gamma_max=0.00001,
gamma_scale=10000000)
self.p.beta_0 = self.p.gamma / np.pi
self.row_height = 20
hostname = socket.gethostname()
if hostname == 'beast':
self.monitor = QDesktopWidget().screenGeometry(1)
self.monitor.setHeight(int(0.45 * self.monitor.height()))
elif hostname == 'hox':
self.monitor = QDesktopWidget().screenGeometry(2)
self.monitor.setHeight(int(0.45 * self.monitor.height()))
else:
self.monitor = QDesktopWidget().screenGeometry(0)
self.monitor.setHeight(self.monitor.height())
self.setGeometry(self.monitor)
self.setAcceptDrops(True)
self.simulated_fog = False
self.simulated_fog_pc = None
self.simulated_fog_dense = False
self.extracted_fog = False
self.extracted_fog_pc = None
self.extracted_fog_index = -1
self.extracted_fog_mesh = None
self.extracted_fog_file_list = None
self.color_dict = {0: 'x',
1: 'y',
2: 'z',
3: 'intensity',
4: 'distance',
5: 'angle',
6: 'channel'}
self.min_value = 0
self.max_value = 63
self.num_features = 5
self.color_feature = 2
self.point_size = 3
self.threshold = 50
self.dataset = None
self.success = False
self.extension = 'bin'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_name = self.color_dict[self.color_feature]
self.lastDir = None
self.current_pc = None
self.fogless_pc = None
self.current_mesh = None
self.droppedFilename = None
self.file_name = None
self.file_list = None
self.index = -1
self.centerWidget = QWidget()
self.setCentralWidget(self.centerWidget)
self.layout = QGridLayout()
self.centerWidget.setLayout(self.layout)
self.grid_dimensions = 20
self.viewer = gl.GLViewWidget()
self.viewer.setWindowTitle('drag & drop point cloud viewer')
self.viewer.setCameraPosition(distance=2 * self.grid_dimensions)
self.layout.addWidget(self.viewer, 0, 0, 1, 6)
self.grid = gl.GLGridItem()
self.grid.setSize(self.grid_dimensions, self.grid_dimensions)
self.grid.setSpacing(1, 1)
self.grid.translate(0, 0, -2)
self.viewer.addItem(self.grid)
self.reset_btn = QPushButton("reset")
self.reset_btn.clicked.connect(self.reset)
self.layout.addWidget(self.reset_btn, 1, 3, 1, 2)
self.load_kitti_btn = QPushButton("KITTI")
self.load_kitti_btn.clicked.connect(self.load_kitti)
self.layout.addWidget(self.load_kitti_btn, 2, 3)
self.load_dense_btn = QPushButton("DENSE")
self.load_dense_btn.clicked.connect(self.load_dense)
self.layout.addWidget(self.load_dense_btn, 3, 3)
self.load_honda_btn = QPushButton("H3D")
self.load_honda_btn.clicked.connect(self.load_honda)
self.layout.addWidget(self.load_honda_btn, 4, 3)
self.load_audi_btn = QPushButton("A2D2")
self.load_audi_btn.clicked.connect(self.load_audi)
self.layout.addWidget(self.load_audi_btn, 5, 3)
self.load_panda_btn = QPushButton("PandaSet")
self.load_panda_btn.clicked.connect(self.load_panda)
self.layout.addWidget(self.load_panda_btn, 6, 3)
self.load_nuscenes_btn = QPushButton("nuScenes")
self.load_nuscenes_btn.clicked.connect(self.load_nuscenes)
self.layout.addWidget(self.load_nuscenes_btn, 2, 4)
self.load_lyft_btn = QPushButton("LyftL5")
self.load_lyft_btn.clicked.connect(self.load_lyft)
self.layout.addWidget(self.load_lyft_btn, 3, 4)
self.load_kitti_btn = QPushButton("Argoverse")
self.load_kitti_btn.clicked.connect(self.load_argo)
self.layout.addWidget(self.load_kitti_btn, 4, 4)
self.load_waymo_btn = QPushButton("Waymo")
self.load_waymo_btn.clicked.connect(self.load_waymo)
self.layout.addWidget(self.load_waymo_btn, 5, 4)
self.load_apollo_btn = QPushButton("Apollo")
self.load_apollo_btn.clicked.connect(self.load_apollo)
self.layout.addWidget(self.load_apollo_btn, 6, 4)
self.load_fog_btn = QPushButton("extracted fog samples")
self.load_fog_btn.clicked.connect(self.load_extracted_fog_samples)
self.layout.addWidget(self.load_fog_btn, 7, 3, 1, 2)
if self.extracted_fog:
self.toggle_extracted_fog_btn = QPushButton("remove extracted fog")
else:
self.toggle_extracted_fog_btn = QPushButton("add extracted fog")
self.toggle_extracted_fog_btn.clicked.connect(self.toggle_extracted_fog)
self.layout.addWidget(self.toggle_extracted_fog_btn, 8, 3, 1, 2)
if self.simulated_fog:
self.toggle_simulated_fog_btn = QPushButton("remove our fog simulation")
else:
self.toggle_simulated_fog_btn = QPushButton("add our fog simulation")
self.toggle_simulated_fog_btn.clicked.connect(self.toggle_simulated_fog)
self.layout.addWidget(self.toggle_simulated_fog_btn, 9, 3, 1, 2)
self.choose_dir_btn = QPushButton("choose custom directory")
self.choose_dir_btn.clicked.connect(self.show_directory_dialog)
self.layout.addWidget(self.choose_dir_btn, 2, 1)
self.prev_btn = QPushButton("<-")
self.next_btn = QPushButton("->")
self.prev_btn.clicked.connect(self.decrement_index)
self.next_btn.clicked.connect(self.increment_index)
self.layout.addWidget(self.prev_btn, 2, 0)
self.layout.addWidget(self.next_btn, 2, 2)
self.color_title = QLabel("color code")
self.color_title.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.color_title, 3, 0)
self.color_label = QLabel(self.color_name)
self.color_label.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.color_label, 3, 2)
self.color_slider = QSlider(Qt.Horizontal)
self.color_slider.setMinimum(0)
self.color_slider.setMaximum(6)
self.color_slider.setValue(self.color_feature)
self.color_slider.setTickPosition(QSlider.TicksBelow)
self.color_slider.setTickInterval(1)
self.layout.addWidget(self.color_slider, 3, 1)
self.color_slider.valueChanged.connect(self.color_slider_change)
self.threshold_title = QLabel("fog threshold")
self.threshold_title.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.threshold_title, 4, 0)
self.threshold_label = QLabel(str(self.threshold))
self.threshold_label.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.threshold_label, 4, 2)
self.threshold_slider = QSlider(Qt.Horizontal)
self.threshold_slider.setMinimum(0)
self.threshold_slider.setMaximum(255)
self.threshold_slider.setValue(self.threshold)
self.threshold_slider.setTickPosition(QSlider.TicksBelow)
self.threshold_slider.setTickInterval(1)
self.layout.addWidget(self.threshold_slider, 4, 1)
self.threshold_slider.valueChanged.connect(self.threshold_slider_change)
self.file_name_label = QLabel()
self.file_name_label.setAlignment(Qt.AlignCenter)
self.file_name_label.setMaximumSize(self.monitor.width(), 20)
self.layout.addWidget(self.file_name_label, 1, 1, 1, 1)
self.reset_btn.setEnabled(False)
self.next_btn.setEnabled(False)
self.prev_btn.setEnabled(False)
self.toggle_extracted_fog_btn.setEnabled(False)
self.toggle_simulated_fog_btn.setEnabled(False)
###########################
# fog simulation controls #
###########################
self.current_row = 5
self.mor_label = QLabel(f'meteorological optical range (MOR) = {round(self.p.mor, 2)}m')
self.mor_label.setAlignment(Qt.AlignCenter)
self.mor_label.setMaximumSize(self.monitor.width(), self.row_height)
self.layout.addWidget(self.mor_label, self.current_row, 1)
self.current_row += 1
self.alpha_title = QLabel('attenuation coefficient')
self.alpha_title.setAlignment(Qt.AlignRight)
self.layout.addWidget(self.alpha_title, self.current_row, 0)
self.alpha_slider = QSlider(Qt.Horizontal)
self.alpha_slider.setMinimum(int(self.p.alpha_min * self.p.alpha_scale))
self.alpha_slider.setMaximum(int(self.p.alpha_max * self.p.alpha_scale))
self.alpha_slider.setValue(int(self.p.alpha * self.p.alpha_scale))
self.layout.addWidget(self.alpha_slider, self.current_row, 1)
self.alpha_slider.valueChanged.connect(self.update_labels)
self.alpha_label = QLabel(f"\u03B1 = {self.p.alpha}")
self.alpha_label.setAlignment(Qt.AlignLeft)
self.layout.addWidget(self.alpha_label, self.current_row, 2)
self.current_row += 1
self.beta_title = QLabel('backscattering coefficient')
self.beta_title.setAlignment(Qt.AlignRight)
self.layout.addWidget(self.beta_title, self.current_row, 0)
self.beta_slider = QSlider(Qt.Horizontal)
self.beta_slider.setMinimum(int(self.p.beta_min * self.p.beta_scale))
self.beta_slider.setMaximum(int(self.p.beta_max * self.p.beta_scale))
self.beta_slider.setValue(int(self.p.beta * self.p.beta_scale))
self.layout.addWidget(self.beta_slider, self.current_row, 1)
self.beta_slider.valueChanged.connect(self.update_labels)
self.beta_label = QLabel(f"\u03B2 = {round(self.p.beta * self.p.mor, 3)} / MOR")
self.beta_label.setAlignment(Qt.AlignLeft)
self.layout.addWidget(self.beta_label, self.current_row, 2)
self.current_row += 1
self.gamma_title = QLabel("reflextivity of the hard target")
self.gamma_title.setAlignment(Qt.AlignRight)
self.layout.addWidget(self.gamma_title, self.current_row, 0)
self.gamma_slider = QSlider(Qt.Horizontal)
self.gamma_slider.setMinimum(int(self.p.gamma_min * self.p.gamma_scale))
self.gamma_slider.setMaximum(int(self.p.gamma_max * self.p.gamma_scale))
self.gamma_slider.setValue(int(self.p.gamma * self.p.gamma_scale))
self.layout.addWidget(self.gamma_slider, self.current_row, 1)
self.gamma_slider.valueChanged.connect(self.update_labels)
self.gamma_label = QLabel(f"\u0393 = {self.p.gamma}")
self.gamma_label.setAlignment(Qt.AlignLeft)
self.layout.addWidget(self.gamma_label, self.current_row, 2)
self.current_row += 1
self.noise_title = QLabel("spread of relative noise")
self.noise_title.setAlignment(Qt.AlignRight)
self.layout.addWidget(self.noise_title, self.current_row, 0)
self.noise_slider = QSlider(Qt.Horizontal)
self.noise_slider.setMinimum(self.noise_min)
self.noise_slider.setMaximum(self.noise_max)
self.noise_slider.setValue(self.noise)
self.layout.addWidget(self.noise_slider, self.current_row, 1)
self.noise_slider.valueChanged.connect(self.update_labels)
self.noise_label = QLabel(f"{self.noise}m")
self.noise_label.setAlignment(Qt.AlignLeft)
self.layout.addWidget(self.noise_label, self.current_row, 2)
self.current_row += 1
self.num_info = QLabel("")
self.num_info.setAlignment(Qt.AlignLeft)
self.num_info.setMaximumSize(self.monitor.width(), self.row_height)
self.layout.addWidget(self.num_info, self.current_row, 0)
self.log_info = QLabel("")
self.log_info.setAlignment(Qt.AlignLeft)
self.log_info.setMaximumSize(self.monitor.width(), self.row_height)
self.layout.addWidget(self.log_info, self.current_row, 1, 1, 3)
if self.extracted_fog:
self.threshold_slider.setEnabled(True)
else:
self.threshold_slider.setEnabled(False)
if self.simulated_fog:
self.toggle_simulated_fog_btn.setText('remove our fog simulation')
self.alpha_slider.setEnabled(True)
self.beta_slider.setEnabled(True)
self.gamma_slider.setEnabled(True)
# self.noise_slider.setEnabled(True)
else:
self.toggle_simulated_fog_btn.setText('add our fog simulation')
self.alpha_slider.setEnabled(False)
self.beta_slider.setEnabled(False)
self.gamma_slider.setEnabled(False)
# self.noise_slider.setEnabled(False)
self.dense_split_paths = []
self.cb = QComboBox()
# self.cb.setEditable(True)
# self.cb.lineEdit().setReadOnly(True)
# self.cb.lineEdit().setAlignment(Qt.AlignCenter)
self.cb.addItems(self.populate_dense_splits())
self.cb.currentIndexChanged.connect(self.selection_change)
self.cb.setEnabled(False)
self.layout.addWidget(self.cb, self.current_row, 3, 1, 2)
self.current_row += 1
if self.simulated_fog_dense:
self.toggle_simulated_fog_dense_btn = QPushButton("remove STF fog simulation")
else:
self.toggle_simulated_fog_dense_btn = QPushButton("add STF fog simulation")
self.toggle_simulated_fog_dense_btn.clicked.connect(self.toggle_simulated_fog_dense)
self.layout.addWidget(self.toggle_simulated_fog_dense_btn, self.current_row, 3, 1, 2)
self.toggle_simulated_fog_dense_btn.setEnabled(False)
# Create textbox
if self.show_predictions:
self.visualize_predictions_path_btn = QPushButton('hide predictions', self)
else:
self.visualize_predictions_path_btn = QPushButton('show predictions', self)
self.visualize_predictions_path_btn.setEnabled(False)
self.layout.addWidget(self.visualize_predictions_path_btn, self.current_row, 0, 1, 1)
self.visualize_predictions_path_btn.clicked.connect(self.toggle_predictions)
self.experiment_path_box = QLineEdit(self)
self.experiment_path_box.setText('dense_models/pv_rcnn/'
'2021-03-13_01-29-41_98d97b57_dense_models_batch_size_24_pv_rcnn')
self.layout.addWidget(self.experiment_path_box, self.current_row, 1, 1, 1)
self.load_experiment_path_btn = QPushButton('load results', self)
self.layout.addWidget(self.load_experiment_path_btn, self.current_row, 2, 1, 1)
self.load_experiment_path_btn.clicked.connect(self.load_results)
self.current_row += 1
self.prediction_threshold_title = QLabel("prediction confidence")
self.prediction_threshold_title.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.prediction_threshold_title, self.current_row, 0)
self.prediction_threshold_label = QLabel(str(self.prediction_threshold))
self.prediction_threshold_label.setAlignment(Qt.AlignCenter)
self.layout.addWidget(self.prediction_threshold_label, self.current_row, 2)
self.prediction_threshold_slider = QSlider(Qt.Horizontal)
self.prediction_threshold_slider.setMinimum(0)
self.prediction_threshold_slider.setMaximum(100)
self.prediction_threshold_slider.setValue(self.prediction_threshold)
self.prediction_threshold_slider.setTickPosition(QSlider.TicksBelow)
self.prediction_threshold_slider.setTickInterval(10)
self.prediction_threshold_slider.setEnabled(False)
self.layout.addWidget(self.prediction_threshold_slider, self.current_row, 1)
self.prediction_threshold_slider.valueChanged.connect(self.prediction_threshold_slider_change)
self.current_row += 1
# hide broken functionality for now
self.toggle_extracted_fog_btn.setVisible(False)
self.load_fog_btn.setVisible(False)
self.threshold_title.setVisible(False)
self.threshold_slider.setVisible(False)
self.threshold_label.setVisible(False)
self.noise_title.setVisible(False)
self.noise_slider.setVisible(False)
self.noise_label.setVisible(False)
def load_results(self) -> None:
exp_dir = EXPERIMENTS_ROOT / self.experiment_path_box.text()
test_folders = [x[0] for x in os.walk(exp_dir) if 'epoch' in x[0] and 'test' in x[0]]
self.result_dict = {}
for test_folder in test_folders:
key = test_folder.split('/')[-1]
pkl_path = Path(test_folder) / 'result.pkl'
with open(pkl_path, 'rb') as f:
self.result_dict[key] = pkl.load(f)
if self.result_dict is not None:
self.visualize_predictions_path_btn.setEnabled(True)
self.prediction_threshold_slider.setEnabled(True)
def visualize_predictions(self) -> None:
split = self.cb.currentText()
if 'test' in split:
pred_dict = self.result_dict[split][self.index]
assert self.file_name.split('/')[-1].split('.')[0] == pred_dict['frame_id'], f'frame missmatch ' \
f"{self.file_name.split('/')[-1].split('.')[0]} != {pred_dict['frame_id']}"
lookup = {'Car': 0,
'Pedestrian': 1,
'Cyclist': 2}
predictions = np.zeros((pred_dict['boxes_lidar'].shape[0], 9))
predictions[:, 0:-2] = pred_dict['boxes_lidar']
predictions[:, 7] = np.array([lookup[name] for name in pred_dict['name']])
predictions[:, 8] = pred_dict['score']
for prediction in predictions:
x, y, z, w, l, h, rotation, category, score = prediction
if score*100 > self.prediction_threshold:
dist = np.sqrt(x**2 + y**2 + z**2)
rotation = np.rad2deg(rotation) + 90
color = (255, 255, 255, 255) # white
box = gl.GLBoxItem(QtGui.QVector3D(1, 1, 1), color=color)
box.setSize(l, w, h)
box.translate(-l / 2, -w / 2, -h / 2)
box.rotate(angle=rotation, x=0, y=0, z=1)
box.translate(x, y, z)
self.viewer.addItem(box)
self.predictions[dist] = box
def populate_dense_splits(self) -> List[str]:
split_folder = Path(__file__).parent.absolute() / 'SeeingThroughFog' / 'splits'
splits = []
for file in os.listdir(split_folder):
if file.endswith('.txt'):
splits.append(file.replace('.txt', ''))
self.dense_split_paths.append(split_folder / file)
self.dense_split_paths = sorted(self.dense_split_paths)
return sorted(splits)
def selection_change(self) -> None:
self.reset_fog_buttons()
self.file_list = []
split = self.cb.currentText()
# open file and read the content in a list
with open(f'SeeingThroughFog/splits/{split}.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(DENSE) / f'{line[:-1].replace(",", "_")}.bin'
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_dense()
self.show_pointcloud(self.file_list[self.index])
def update_labels(self) -> None:
self.p.alpha = self.alpha_slider.value() / self.p.alpha_scale
self.alpha_label.setText(f"\u03B1 = {self.p.alpha}")
self.p.mor = np.log(20) / self.p.alpha
self.mor_label.setText(f'meteorological optical range (MOR) = {round(self.p.mor, 2)}m')
self.p.beta_scale = 1000 * self.p.mor
self.p.beta = self.beta_slider.value() / self.p.beta_scale
self.beta_label.setText(f"\u03B2 = {round(self.p.beta * self.p.mor, 3)} / MOR")
self.p.gamma = self.gamma_slider.value() / self.p.gamma_scale
self.gamma_label.setText(f"\u0393 = {self.p.gamma}")
self.p.beta_0 = self.p.gamma / np.pi
self.noise = self.noise_slider.value()
self.noise_label.setText(f"{self.noise}m")
if self.file_list:
self.show_pointcloud(self.file_list[self.index])
def reset_fog_buttons(self) -> None:
self.boxes = {}
self.threshold_slider.setEnabled(False)
self.alpha_slider.setEnabled(False)
self.beta_slider.setEnabled(False)
self.gamma_slider.setEnabled(False)
self.noise_slider.setEnabled(False)
self.simulated_fog = False
self.simulated_fog_dense = False
self.toggle_simulated_fog_btn.setText('add our fog simulation')
self.toggle_simulated_fog_dense_btn.setText('add STF fog simulation')
self.extracted_fog = False
self.toggle_extracted_fog_btn.setText('add extraced fog')
def reset(self) -> None:
self.reset_viewer()
self.reset_fog_buttons()
self.reset_custom_values()
self.cb.setEnabled(False)
self.toggle_extracted_fog_btn.setEnabled(False)
self.toggle_simulated_fog_btn.setEnabled(False)
self.toggle_simulated_fog_dense_btn.setEnabled(False)
self.file_list = None
self.current_pc = None
self.current_mesh = None
self.simulated_fog = False
self.simulated_fog_dense = False
self.extracted_fog = False
self.extracted_fog_pc = None
self.extracted_fog_mesh = None
self.extracted_fog_index = -1
def reset_custom_values(self) -> None:
self.min_value = 0
self.max_value = 63
self.num_features = 5
self.dataset = None
self.success = False
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_name = self.color_dict[self.color_feature]
def threshold_slider_change(self) -> None:
self.threshold = self.threshold_slider.value()
self.threshold_label.setText(str(self.threshold))
if self.current_mesh and Path(self.file_name).suffix != '.pickle':
self.show_pointcloud(self.file_name)
def prediction_threshold_slider_change(self) -> None:
self.prediction_threshold = self.prediction_threshold_slider.value()
self.prediction_threshold_label.setText(str(self.prediction_threshold))
if self.file_list:
self.show_pointcloud(self.file_list[self.index])
def color_slider_change(self) -> None:
self.color_feature = self.color_slider.value()
self.color_name = self.color_dict[self.color_feature]
self.color_label.setText(self.color_name)
if self.current_mesh:
if Path(self.file_name).suffix == '.pickle':
self.show_pcdet_dict(self.file_name)
else:
self.show_pointcloud(self.file_name)
def check_index_overflow(self) -> None:
if self.index == -1:
self.index = len(self.file_list) - 1
if self.index >= len(self.file_list):
self.index = 0
def decrement_index(self) -> None:
if self.index != -1:
self.index -= 1
self.check_index_overflow()
if Path(self.file_list[self.index]).suffix == ".pickle":
self.show_pcdet_dict(self.file_list[self.index])
else:
self.show_pointcloud(self.file_list[self.index])
def increment_index(self) -> None:
if self.index != -1:
self.index += 1
self.check_index_overflow()
if Path(self.file_list[self.index]).suffix == ".pickle":
self.show_pcdet_dict(self.file_list[self.index])
else:
self.show_pointcloud(self.file_list[self.index])
def set_pc_det(self, before: bool) -> None:
self.min_value = 0
self.max_value = 63
self.extension = 'pickle'
self.d_type = np.float32
self.intensity_multiplier = 1
if before:
self.dataset = 'before'
self.num_features = 5
self.color_dict[6] = 'channel'
else:
self.dataset = 'after'
self.num_features = 4
self.color_dict[6] = 'not available'
def set_extracted_fog_samples(self):
self.threshold_slider.setEnabled(True)
self.dataset = 'FOG'
self.min_value = 0
self.max_value = 63
self.num_features = 5
self.extension = 'bin'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
def set_kitti(self) -> None:
self.dataset = 'KITTI'
self.min_value = -1
self.max_value = -1
self.num_features = 4
self.extension = 'bin'
self.d_type = np.float32
self.intensity_multiplier = 255
self.color_dict[6] = 'not available'
def load_kitti(self) -> None:
self.reset_fog_buttons()
self.file_list = []
# open file and read the content in a list
with open('file_lists/KITTI.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(KITTI) / line[:-1]
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_kitti()
self.show_pointcloud(self.file_list[self.index])
def set_audi(self) -> None:
self.dataset = 'A2D2'
self.min_value = 0
self.max_value = 4
self.num_features = 5
self.extension = 'npz'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_dict[6] = 'lidar_id'
def load_audi(self) -> None:
self.reset_fog_buttons()
self.file_list = []
# open file and read the content in a list
with open('file_lists/A2D2.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(AUDI) / line[:-1]
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_audi()
self.show_pointcloud(self.file_list[self.index])
def set_honda(self) -> None:
self.dataset = 'Honda3D'
self.min_value = 0
self.max_value = 63
self.num_features = 5
self.extension = 'ply'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
def load_honda(self) -> None:
self.reset_fog_buttons()
self.file_list = []
# open file and read the content in a list
with open('file_lists/Honda3D.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(HONDA) / line[:-1]
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_honda()
self.show_pointcloud(self.file_list[self.index])
def set_argo(self) -> None:
self.dataset = 'Argoverse'
self.min_value = 0
self.max_value = 31
self.num_features = 5
self.extension = 'ply'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
def load_argo(self) -> None:
self.reset_fog_buttons()
self.file_list = []
# open file and read the content in a list
with open('file_lists/Argoverse.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(ARGO) / line[:-1]
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_argo()
self.show_pointcloud(self.file_list[self.index])
def set_dense(self) -> None:
self.dataset = 'DENSE'
self.min_value = 0
self.max_value = 63
self.num_features = 5
self.extension = 'bin'
self.d_type = np.float32
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
self.cb.setEnabled(True)
def load_dense(self) -> None:
self.reset_fog_buttons()
self.file_list = []
# open file and read the content in a list
with open('file_lists/DENSE.txt', 'r') as filehandle:
for line in filehandle:
# remove linebreak which is the last character of the string
file_path = Path(DENSE) / line[:-1]
# add item to the list
self.file_list.append(str(file_path))
self.index = 0
self.set_dense()
self.cb.setCurrentText('all')
self.show_pointcloud(self.file_list[self.index])
def set_nuscenes(self) -> None:
self.dataset = 'nuScenes'
self.min_value = 0
self.max_value = 31
self.num_features = 5
self.extension = 'bin'
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
def load_nuscenes(self) -> None:
self.reset_fog_buttons()
self.file_list = []
with open('file_lists/nuScenes.pkl', 'rb') as f:
file_list = pkl.load(f)
for file in file_list:
self.file_list.append(NUSCENES + file)
self.index = 0
self.set_nuscenes()
self.show_pointcloud(self.file_list[self.index])
def set_lyft(self) -> None:
self.dataset = 'LyftL5'
self.min_value = 0
self.max_value = 16
self.num_features = 5
self.extension = 'bin'
self.intensity_multiplier = 1
self.color_dict[6] = 'channel'
def load_lyft(self) -> None:
self.reset_fog_buttons()