#!/bin/bash

ans=`dpkg-query  -W p7zip-full`

if [ -z "$ans" ]; then

    echo "Please, install p7zip-full by running: sudo apt-get install p7zip-full"

    exit -

fi

for i in `cat semantic3D_files.csv`

do

    output_file=`basename $i`

    echo Downloading ${output_file} ...

    #把wget $i改成：wget -c -N $i

    wget -c -N $i

    7z x ${output_file} -y

done

mv station1_xyz_intensity_rgb.txt neugasse_station1_xyz_intensity_rgb.txt

exit 

import os

import subprocess

import shutil

import open3d

from dataset.semantic_dataset import all_file_prefixes

def wc(file_name):

    out = subprocess.Popen(

        ["wc", "-l", file_name], stdout=subprocess.PIPE, stderr=subprocess.STDOUT

    ).communicate()[0]

    return int(out.partition(b" ")[0])

def prepend_line(file_name, line):

    with open(file_name, "r+") as f:

        content = f.read()

        f.seek(0, 0)

        f.write(line.rstrip("\r\n") + "\n" + content)

def point_cloud_txt_to_pcd(raw_dir, file_prefix):

    # File names

    txt_file = os.path.join(raw_dir, file_prefix + ".txt")

    pts_file = os.path.join(raw_dir, file_prefix + ".pts")

    pcd_file = os.path.join(raw_dir, file_prefix + ".pcd")

    # Skip if already done

    if os.path.isfile(pcd_file):

        print("pcd {} exists, skipped".format(pcd_file))

        return

    # .txt to .pts

    # We could just prepend the line count, however, there are some intensity value

    # which are non-integers.

    print("[txt->pts]")

    print("txt: {}".format(txt_file))

    print("pts: {}".format(pts_file))

    with open(txt_file, "r") as txt_f, open(pts_file, "w") as pts_f:

        for line in txt_f:

            # x, y, z, i, r, g, b

            tokens = line.split()

            tokens[3] = str(int(float(tokens[3])))

            line = " ".join(tokens)

            pts_f.write(line + "\n")

    prepend_line(pts_file, str(wc(txt_file)))

    # .pts -> .pcd

    print("[pts->pcd]")

    print("pts: {}".format(pts_file))

    print("pcd: {}".format(pcd_file))

    """

    point_cloud = open3d.read_point_cloud(pts_file)

    open3d.write_point_cloud(pcd_file, point_cloud)

    改成：

    point_cloud = open3d.io.read_point_cloud(pts_file)

    open3d.io.write_point_cloud(pcd_file, point_cloud)

    """

    point_cloud = open3d.io.read_point_cloud(pts_file)

    open3d.io.write_point_cloud(pcd_file, point_cloud)

    os.remove(pts_file)

if __name__ == "__main__":

    # By default

    # raw data: "dataset/semantic_raw"

    current_dir = os.path.dirname(os.path.realpath(__file__))

    dataset_dir = os.path.join(current_dir, "dataset")

    raw_dir = os.path.join(dataset_dir, "semantic_raw")

    for file_prefix in all_file_prefixes:

        point_cloud_txt_to_pcd(raw_dir, file_prefix)

python preprocess.py

open3d.utility.Vector3dVector()

open3d.geometry.PointCloud.voxel_down_sample_and_trace(

        dense_pcd, voxel_size, min_bound, max_bound, False

    )

python train.py

• 7. Predict

Pick a checkpoint and run the predict.py script. The prediction dataset is configured by --set. Since PointNet2 only takes a few thousand points per forward pass, we need to sample from the prediction dataset multiple times to get a good coverage of the points. Each sample contains the few thousand points required by PointNet2. To specify the number of such samples per scene, use the --num_samples flag.

python predict.py --ckpt log/semantic/best_model_epoch_040.ckpt \

                  --set=validation \

                  --num_samples=500

The prediction results will be written to result/sparse.