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extract_fog.py
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extract_fog.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/)"
import os
import csv
import argparse
import numpy as np
from tqdm import tqdm
from typing import List
from pathlib import Path
MIN_DIST = 1.75 # in m
MAX_DIST = 10 # in m
MIN_HEIGHT = -1 # in m
MAX_HEIGHT = np.inf # in m
SPLIT = 'test_dense_fog_night'
TOPIC = 'lidar_hdl64_strongest'
def get_recordings(split: str) -> List[str]:
splits_folder = Path(__file__).parent.absolute() / 'SeeingThroughFog' / 'splits'
splits = sorted(os.listdir(splits_folder))
assert f'{split}.txt' in splits, f'{split} is undefined'
recordings = []
with open(f'{splits_folder / split}.txt') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
recordings.append(f'{row[0]}_{row[1]}.png')
return sorted(recordings)
def extract_fog(arguments: argparse.Namespace, recordings: List[str]) -> None:
points_before_sum = 0
points_after_sum = 0
avg_num_point_before = 0
avg_num_point_after = 0
prog_bar = tqdm(recordings)
save_dir = Path(arguments.root_path) / f'{arguments.topic}_fog_extraction'
save_dir.mkdir(parents=True, exist_ok=True)
for i, recording in enumerate(prog_bar):
file_name = recording.replace('.png', '.bin')
path = Path(arguments.root_path) / arguments.topic / file_name
pc = np.fromfile(path, dtype=np.float32)
pc = pc.reshape((-1, 5))
points_before = len(pc)
pc = filter_ego_point(pc)
pc = filter_by_distance(pc)
pc = filter_by_height(pc)
points_after = len(pc)
points_before_sum += points_before
points_after_sum += points_after
avg_num_point_before = (avg_num_point_before * i + points_before) / (i + 1)
avg_num_point_after = (avg_num_point_after * i + points_after) / (i + 1)
save_path = save_dir / file_name
pc.astype(np.float32).tofile(save_path)
prog_bar.set_description(f'{int(avg_num_point_after)}/{int(avg_num_point_before)}')
num_recordings = len(recordings)
avg_num_point_before = points_before_sum / num_recordings
avg_num_point_after = points_after_sum / num_recordings
print(f'average points before: {avg_num_point_before}')
print(f'average points after: {avg_num_point_after}')
def filter_ego_point(pc: np.ndarray, l: float = 5.116, w: float = 1.899, h: float = 1.496) -> np.ndarray:
# default dimensions are dimensions of W222 taken from wikipedia
x_mask_lower = -l / 2 < pc[:, 0]
x_mask_upper = pc[:, 0] < l / 2
x_mask = (x_mask_lower == 1) & (x_mask_upper == 1)
y_mask_lower = -w / 2 < pc[:, 1]
y_mask_upper = pc[:, 1] < w / 2
y_mask = (y_mask_lower == 1) & (y_mask_upper == 1)
z_mask_lower = -h < pc[:, 2]
z_mask_upper = pc[:, 2] < -h / 2
z_mask = (z_mask_lower == 1) & (z_mask_upper == 1)
inside_mask = (x_mask == 1) & (y_mask == 1) & (z_mask == 1)
outside_mask = ~inside_mask
pc = pc[outside_mask, :]
return pc
def filter_by_distance(pc: np.ndarray, min_dist: float = MIN_DIST, max_dist: float = MAX_DIST) -> np.ndarray:
min_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) > min_dist
pc = pc[min_dist_mask, :]
max_dist_mask = np.linalg.norm(pc[:, 0:3], axis=1) < max_dist
pc = pc[max_dist_mask, :]
return pc
def filter_by_height(pc: np.ndarray, min_height: float = MIN_HEIGHT, max_height: float = MAX_HEIGHT) -> np.ndarray:
min_height_mask = pc[:, 2] > min_height
pc = pc[min_height_mask, :]
max_height_mask = pc[:, 2] < max_height
pc = pc[max_height_mask, :]
return pc
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--path', type=str, default=str(Path.home() / 'datasets/DENSE/SeeingThroughFog'),
help='path to SeeingThroughFog dataset')
parser.add_argument('-s', '--split', type=str, default=SPLIT)
parser.add_argument('-t', '--topic', type=str, default=TOPIC)
args = parser.parse_args()
recs = get_recordings(args.split)
extract_fog(args, recs)