使用 C++ 读取 .las 版本 1.3，并使用 pcl 库显示结果
Reading .las version 1.3 using C++, and displaying the results using pcl library
我对点云世界还很陌生。此外，我在 C++ 方面经验不足。
我需要读取 .las 文件并使用 pcl 库处理它们。 This is a sample file from the dataset that I need to read. I followed this youtube video. However, since the file I'm trying to read is of version 1.3, I followed the corresponding spec file 来定义 header 字段，我使用 'Data Record Format 3' 这是文件 header.
中提到的数据记录格式
这是我对header和数据记录格式的定义：
#pragma once
#include <string>
#include <vector>
struct float4
{
float x, y, z, intensity;
};
class PointCloud
{
public:
uint32_t getVertsCount();
float4* getVertsData();
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr read(const std::string& path);//void read(const std::string &path);
private:
std::vector<float4> verts;
#pragma pack(1)
struct Header
{
char magic[4];
uint16_t fileSourceID;
uint16_t globalEncoding;
uint32_t guidData1;
uint16_t guidData2;
uint16_t guidData3;
uint8_t guidData4[8];
uint8_t versionMaj, versionMin;
char systemIdentifier[32];
char genSoftware[32];
uint16_t creationDay, creationYear;
uint16_t headerSize;
uint32_t pointDataOffset;
uint32_t numVarLenRecords;
uint8_t pointDataRecordFormat;
uint16_t pointDataRecordLen;
uint32_t numberOfPoints;
uint32_t numPointsByReturn[5];
double scaleX, scaleY, scaleZ;
double offsetX, offsetY, offsetZ;
double maxX, minX, maxY, minY, maxZ, minZ;
uint64_t waveform;
};
//#pragma pack(1)
struct PointRecord3
{
uint32_t x, y, z;
uint16_t intensity;
uint8_t flags;
uint8_t classification;
uint8_t scanAngleRank;
uint8_t userData;
uint16_t pointSourceId;
double gpsTime;
uint16_t red;
uint16_t green;
uint16_t blue;
};
};
我用下面的代码读取了点数据，但是没有得到正确的点：
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr PointCloud::read(const string& path)
{
ifstream inf(path, ios::binary);
typename pcl::PointCloud<PointT>::Ptr lasCloud(new pcl::PointCloud<PointT>);
if (inf.is_open())
{
Header header;
inf.read((char*)&header, sizeof(header));
cout << "Signature: " << header.magic << endl;
cout << "Source ID: " << int(header.fileSourceID) << endl;
cout << "Global Encoding: " << int(header.globalEncoding) << endl;
cout << "Guid 1: " << int(header.guidData1) << endl;
cout << "Guid 2: " << int(header.guidData2) << endl;
cout << "Guid 3: " << int(header.guidData3) << endl;
cout << "Guid 4: " << header.guidData4 << endl;
cout << (int)header.versionMaj << '.' << (int)header.versionMin << endl;
cout << "Sys Identifier: " << header.systemIdentifier << endl;
cout << "Gen Software: " << header.genSoftware << endl;
cout << "Creation Day: " << header.creationDay << endl;
cout << "Creation Year: " << header.creationYear << endl;
cout << header.headerSize << " == " << sizeof(header) << endl;
cout << "Point Data Offset: " << header.pointDataOffset << endl;
cout << "Number of Variable Len Records: " << header.numVarLenRecords << endl;
cout << "point Data Record Format: " << header.pointDataRecordFormat << endl;
cout << "point Data Record Len: " << header.pointDataRecordLen << endl;
cout << "Number of Points: " << header.numberOfPoints << endl;
cout << "Number of Points by Return: " << header.numPointsByReturn << endl;
cout << "Scales: " << header.scaleX << ", " << header.scaleY << ", " << header.scaleZ << endl;
cout << "Offsets: " << header.offsetX << ", " << header.offsetY << ", " << header.offsetZ << endl;
cout << "Xmin = " << header.minX << ", Ymin = " << header.minY << ", Zmin = " << header.minZ << endl;
cout << "Xmax = " << header.maxX << ", Ymax = " << header.maxY << ", Zmax = " << header.maxZ << endl;
cout << "Waveform: "<<header.waveform << endl;
assert(header.versionMaj == 1 && header.versionMin == 3);
//assert(header.headerSize == sizeof(header));
assert(header.pointDataRecordFormat == 3);
//inf.seekg(header.pointDataOffset);
inf.seekg(sizeof(header));
//inf.seekg(header.pointDataOffset+sizeof(header.waveform));
for (uint32_t i = 0; i < header.numberOfPoints; i++)
{
//PointRecord1* points = new PointRecord1[header.numberOfPoints];
PointRecord3 point;
//inf.read((char*)(points + i), sizeof(PointRecord1));
//inf.read((char*)&point, sizeof(PointRecord1));
inf.read((char*)&point, sizeof(PointRecord3));
PointT cloudPoint;
cloudPoint.x = (float)(point.x * header.scaleX) + header.offsetX;
cloudPoint.y = (float)(point.y * header.scaleY) + header.offsetY;
cloudPoint.z = (float)(point.z * header.scaleZ) + header.offsetZ;
cloudPoint.intensity = (float)(point.intensity) / 65536.0;
lasCloud->points.push_back(cloudPoint);
}
if (!inf.good())
throw runtime_error("Reading went wrong!");
}
else
{
throw runtime_error("Can't find any!");
}
lasCloud->width = lasCloud->points.size();
lasCloud->height = 1;
lasCloud->is_dense = true;
std::cout << "Cloud size = " << lasCloud->points.size() << endl;
return lasCloud;
}
int main (int argc, char** argv)
{
std::cout << "starting enviroment" << std::endl;
pcl::visualization::PCLVisualizer::Ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
CameraAngle setAngle = FPS; //XY, FPS, Side, TopDown
initCamera(setAngle, viewer);
pcl::PointCloud<pcl::PointXYZI>::Ptr inputCloudI; //
PointCloud pcd;
inputCloudI=pcd.read<pcl::PointXYZI>("C:/Users/hedey/OneDrive/Documents/Research_papers/STDF/10_4231_MFQF-Q141/I-65/LiDAR/RoadSurface/NB/20180524_I65_NB_RoadSurface_1_50.5.las");
std::cout << "Cloud size = " << inputCloudI->points.size() << endl;
renderPointCloud(viewer, inputCloudI, "lasCloud");
while (!viewer->wasStopped())
{
viewer->spinOnce();
}
}
我注意到一个问题。 header 大小定义为 227（这是 header 大小字段的值）。但是，header 末尾有一个名为 'Start of Waveform Data Packet Record' 的 8 字节字段，如果包含在 header 定义中，将使 header 大小为 235 字节。此外，用于在 youtube 视频中查找点数据的 pointDataOffset 字段指向 227 字节。当我用它来查找点数据时，我得到了不合理的点值。
我的目标是处理这个点云并使用 pcl 云显示它，但我未能正确读取这些点。
我不知道激光雷达和 *.las 文件，但我注意到输入文件是使用 libLAS 创建的
> file 20180524_I65_NB_RoadSurface_1_50.5.las
20180524_I65_NB_RoadSurface_1_50.5.las: LIDAR point data records, version 1.3, SYSID libLAS, Generating Software libLAS 1.6.0
那么，为什么不用libLAS读入数据呢？ https://liblas.org/
libLAS 带有方便的 CLI 实用程序来处理 las 文件，例如：
lasinfo 20180524_I65_NB_RoadSurface_1_50.5.las
---------------------------------------------------------
Header Summary
---------------------------------------------------------
Version: 1.3
Source ID: 0
Reserved: 0
Project ID/GUID: '00000000-0000-0000-0000-000000000000'
System ID: 'libLAS'
Generating Software: 'libLAS 1.6.0'
File Creation Day/Year: 144/2018
Header Byte Size 227
Data Offset: 227
Header Padding: 0
Number Var. Length Records: None
Point Data Format: 3
Number of Point Records: 22017565
Compressed: False
Number of Points by Return: 0 0 0 0 0
Scale Factor X Y Z: 0.00100000000000 0.00100000000000 0.00100000000000
Offset X Y Z: 590284.000 4339456.000 157.000
Min X Y Z: 589879.772 4338728.975 149.667
Max X Y Z: 590334.248 4339568.021 178.397
Spatial Reference: None
---------------------------------------------------------
Schema Summary
---------------------------------------------------------
Point Format ID: 3
Number of dimensions: 16
Custom schema?: false
Size in bytes: 34
Dimensions
---------------------------------------------------------
'X' -- size: 32 offset: 0
'Y' -- size: 32 offset: 4
'Z' -- size: 32 offset: 8
'Intensity' -- size: 16 offset: 12
'Return Number' -- size: 3 offset: 14
'Number of Returns' -- size: 3 offset: 14
'Scan Direction' -- size: 1 offset: 14
'Flightline Edge' -- size: 1 offset: 14
'Classification' -- size: 8 offset: 15
'Scan Angle Rank' -- size: 8 offset: 16
'User Data' -- size: 8 offset: 17
'Point Source ID' -- size: 16 offset: 18
'Time' -- size: 64 offset: 20
'Red' -- size: 16 offset: 28
'Green' -- size: 16 offset: 30
'Blue' -- size: 16 offset: 32
---------------------------------------------------------
Point Inspection Summary
---------------------------------------------------------
Header Point Count: 22017565
Actual Point Count: 22017565
Minimum and Maximum Attributes (min,max)
---------------------------------------------------------
Min X, Y, Z: 600191.027, 4313816.564, 148.621
Max X, Y, Z: 600212.594, 4314678.007, 156.632
Bounding Box: 600191.027, 4313816.564, 600212.594, 4314678.007
Time: 55449082.421688, 55488872.904376
Return Number: 0, 0
Return Count: 0, 0
Flightline Edge: 0, 0
Intensity: 0, 255
Scan Direction Flag: 0, 0
Scan Angle Rank: 0, 0
Classification: 1, 3
Point Source Id: 0, 31
User Data: 0, 0
Minimum Color (RGB): 0 0 0
Maximum Color (RGB): 0 0 0
Number of Points by Return
---------------------------------------------------------
(1) 22017565
Number of Returns by Pulse
---------------------------------------------------------
(0) 22017565
Point Classifications
---------------------------------------------------------
7187055 Unclassified (1)
8128678 Ground (2)
6701832 Low Vegetation (3)
-------------------------------------------------------
0 withheld
0 keypoint
0 synthetic
-------------------------------------------------------
和
las2txt 20180524_I65_NB_RoadSurface_1_50.5.las qq.txt && head qq.txt
600209.243,4313837.086,155.155
600209.342,4313839.620,155.191
600209.232,4313836.806,155.154
600209.338,4313839.516,155.197
600209.221,4313836.523,155.165
600209.333,4313839.398,155.194
600209.206,4313836.177,155.158
600209.328,4313839.285,155.200
600209.189,4313835.778,155.145
600209.322,4313839.152,155.193
这意味着文件没问题，库可以工作，您可以节省 很多 的时间来学习基本的库用法，而不是尝试自己重新实现它（想想各种数据格式等，错误处理等，测试等，遇到问题时获得反馈等，以及您的时间）
我对点云世界还很陌生。此外，我在 C++ 方面经验不足。 我需要读取 .las 文件并使用 pcl 库处理它们。 This is a sample file from the dataset that I need to read. I followed this youtube video. However, since the file I'm trying to read is of version 1.3, I followed the corresponding spec file 来定义 header 字段，我使用 'Data Record Format 3' 这是文件 header.
中提到的数据记录格式这是我对header和数据记录格式的定义：
#pragma once
#include <string>
#include <vector>
struct float4
{
float x, y, z, intensity;
};
class PointCloud
{
public:
uint32_t getVertsCount();
float4* getVertsData();
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr read(const std::string& path);//void read(const std::string &path);
private:
std::vector<float4> verts;
#pragma pack(1)
struct Header
{
char magic[4];
uint16_t fileSourceID;
uint16_t globalEncoding;
uint32_t guidData1;
uint16_t guidData2;
uint16_t guidData3;
uint8_t guidData4[8];
uint8_t versionMaj, versionMin;
char systemIdentifier[32];
char genSoftware[32];
uint16_t creationDay, creationYear;
uint16_t headerSize;
uint32_t pointDataOffset;
uint32_t numVarLenRecords;
uint8_t pointDataRecordFormat;
uint16_t pointDataRecordLen;
uint32_t numberOfPoints;
uint32_t numPointsByReturn[5];
double scaleX, scaleY, scaleZ;
double offsetX, offsetY, offsetZ;
double maxX, minX, maxY, minY, maxZ, minZ;
uint64_t waveform;
};
//#pragma pack(1)
struct PointRecord3
{
uint32_t x, y, z;
uint16_t intensity;
uint8_t flags;
uint8_t classification;
uint8_t scanAngleRank;
uint8_t userData;
uint16_t pointSourceId;
double gpsTime;
uint16_t red;
uint16_t green;
uint16_t blue;
};
};
我用下面的代码读取了点数据，但是没有得到正确的点：
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr PointCloud::read(const string& path)
{
ifstream inf(path, ios::binary);
typename pcl::PointCloud<PointT>::Ptr lasCloud(new pcl::PointCloud<PointT>);
if (inf.is_open())
{
Header header;
inf.read((char*)&header, sizeof(header));
cout << "Signature: " << header.magic << endl;
cout << "Source ID: " << int(header.fileSourceID) << endl;
cout << "Global Encoding: " << int(header.globalEncoding) << endl;
cout << "Guid 1: " << int(header.guidData1) << endl;
cout << "Guid 2: " << int(header.guidData2) << endl;
cout << "Guid 3: " << int(header.guidData3) << endl;
cout << "Guid 4: " << header.guidData4 << endl;
cout << (int)header.versionMaj << '.' << (int)header.versionMin << endl;
cout << "Sys Identifier: " << header.systemIdentifier << endl;
cout << "Gen Software: " << header.genSoftware << endl;
cout << "Creation Day: " << header.creationDay << endl;
cout << "Creation Year: " << header.creationYear << endl;
cout << header.headerSize << " == " << sizeof(header) << endl;
cout << "Point Data Offset: " << header.pointDataOffset << endl;
cout << "Number of Variable Len Records: " << header.numVarLenRecords << endl;
cout << "point Data Record Format: " << header.pointDataRecordFormat << endl;
cout << "point Data Record Len: " << header.pointDataRecordLen << endl;
cout << "Number of Points: " << header.numberOfPoints << endl;
cout << "Number of Points by Return: " << header.numPointsByReturn << endl;
cout << "Scales: " << header.scaleX << ", " << header.scaleY << ", " << header.scaleZ << endl;
cout << "Offsets: " << header.offsetX << ", " << header.offsetY << ", " << header.offsetZ << endl;
cout << "Xmin = " << header.minX << ", Ymin = " << header.minY << ", Zmin = " << header.minZ << endl;
cout << "Xmax = " << header.maxX << ", Ymax = " << header.maxY << ", Zmax = " << header.maxZ << endl;
cout << "Waveform: "<<header.waveform << endl;
assert(header.versionMaj == 1 && header.versionMin == 3);
//assert(header.headerSize == sizeof(header));
assert(header.pointDataRecordFormat == 3);
//inf.seekg(header.pointDataOffset);
inf.seekg(sizeof(header));
//inf.seekg(header.pointDataOffset+sizeof(header.waveform));
for (uint32_t i = 0; i < header.numberOfPoints; i++)
{
//PointRecord1* points = new PointRecord1[header.numberOfPoints];
PointRecord3 point;
//inf.read((char*)(points + i), sizeof(PointRecord1));
//inf.read((char*)&point, sizeof(PointRecord1));
inf.read((char*)&point, sizeof(PointRecord3));
PointT cloudPoint;
cloudPoint.x = (float)(point.x * header.scaleX) + header.offsetX;
cloudPoint.y = (float)(point.y * header.scaleY) + header.offsetY;
cloudPoint.z = (float)(point.z * header.scaleZ) + header.offsetZ;
cloudPoint.intensity = (float)(point.intensity) / 65536.0;
lasCloud->points.push_back(cloudPoint);
}
if (!inf.good())
throw runtime_error("Reading went wrong!");
}
else
{
throw runtime_error("Can't find any!");
}
lasCloud->width = lasCloud->points.size();
lasCloud->height = 1;
lasCloud->is_dense = true;
std::cout << "Cloud size = " << lasCloud->points.size() << endl;
return lasCloud;
}
int main (int argc, char** argv)
{
std::cout << "starting enviroment" << std::endl;
pcl::visualization::PCLVisualizer::Ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
CameraAngle setAngle = FPS; //XY, FPS, Side, TopDown
initCamera(setAngle, viewer);
pcl::PointCloud<pcl::PointXYZI>::Ptr inputCloudI; //
PointCloud pcd;
inputCloudI=pcd.read<pcl::PointXYZI>("C:/Users/hedey/OneDrive/Documents/Research_papers/STDF/10_4231_MFQF-Q141/I-65/LiDAR/RoadSurface/NB/20180524_I65_NB_RoadSurface_1_50.5.las");
std::cout << "Cloud size = " << inputCloudI->points.size() << endl;
renderPointCloud(viewer, inputCloudI, "lasCloud");
while (!viewer->wasStopped())
{
viewer->spinOnce();
}
}
我注意到一个问题。 header 大小定义为 227（这是 header 大小字段的值）。但是，header 末尾有一个名为 'Start of Waveform Data Packet Record' 的 8 字节字段，如果包含在 header 定义中，将使 header 大小为 235 字节。此外，用于在 youtube 视频中查找点数据的 pointDataOffset 字段指向 227 字节。当我用它来查找点数据时，我得到了不合理的点值。 我的目标是处理这个点云并使用 pcl 云显示它，但我未能正确读取这些点。
我不知道激光雷达和 *.las 文件，但我注意到输入文件是使用 libLAS 创建的
> file 20180524_I65_NB_RoadSurface_1_50.5.las
20180524_I65_NB_RoadSurface_1_50.5.las: LIDAR point data records, version 1.3, SYSID libLAS, Generating Software libLAS 1.6.0
那么，为什么不用libLAS读入数据呢？ https://liblas.org/
libLAS 带有方便的 CLI 实用程序来处理 las 文件，例如：
lasinfo 20180524_I65_NB_RoadSurface_1_50.5.las
---------------------------------------------------------
Header Summary
---------------------------------------------------------
Version: 1.3
Source ID: 0
Reserved: 0
Project ID/GUID: '00000000-0000-0000-0000-000000000000'
System ID: 'libLAS'
Generating Software: 'libLAS 1.6.0'
File Creation Day/Year: 144/2018
Header Byte Size 227
Data Offset: 227
Header Padding: 0
Number Var. Length Records: None
Point Data Format: 3
Number of Point Records: 22017565
Compressed: False
Number of Points by Return: 0 0 0 0 0
Scale Factor X Y Z: 0.00100000000000 0.00100000000000 0.00100000000000
Offset X Y Z: 590284.000 4339456.000 157.000
Min X Y Z: 589879.772 4338728.975 149.667
Max X Y Z: 590334.248 4339568.021 178.397
Spatial Reference: None
---------------------------------------------------------
Schema Summary
---------------------------------------------------------
Point Format ID: 3
Number of dimensions: 16
Custom schema?: false
Size in bytes: 34
Dimensions
---------------------------------------------------------
'X' -- size: 32 offset: 0
'Y' -- size: 32 offset: 4
'Z' -- size: 32 offset: 8
'Intensity' -- size: 16 offset: 12
'Return Number' -- size: 3 offset: 14
'Number of Returns' -- size: 3 offset: 14
'Scan Direction' -- size: 1 offset: 14
'Flightline Edge' -- size: 1 offset: 14
'Classification' -- size: 8 offset: 15
'Scan Angle Rank' -- size: 8 offset: 16
'User Data' -- size: 8 offset: 17
'Point Source ID' -- size: 16 offset: 18
'Time' -- size: 64 offset: 20
'Red' -- size: 16 offset: 28
'Green' -- size: 16 offset: 30
'Blue' -- size: 16 offset: 32
---------------------------------------------------------
Point Inspection Summary
---------------------------------------------------------
Header Point Count: 22017565
Actual Point Count: 22017565
Minimum and Maximum Attributes (min,max)
---------------------------------------------------------
Min X, Y, Z: 600191.027, 4313816.564, 148.621
Max X, Y, Z: 600212.594, 4314678.007, 156.632
Bounding Box: 600191.027, 4313816.564, 600212.594, 4314678.007
Time: 55449082.421688, 55488872.904376
Return Number: 0, 0
Return Count: 0, 0
Flightline Edge: 0, 0
Intensity: 0, 255
Scan Direction Flag: 0, 0
Scan Angle Rank: 0, 0
Classification: 1, 3
Point Source Id: 0, 31
User Data: 0, 0
Minimum Color (RGB): 0 0 0
Maximum Color (RGB): 0 0 0
Number of Points by Return
---------------------------------------------------------
(1) 22017565
Number of Returns by Pulse
---------------------------------------------------------
(0) 22017565
Point Classifications
---------------------------------------------------------
7187055 Unclassified (1)
8128678 Ground (2)
6701832 Low Vegetation (3)
-------------------------------------------------------
0 withheld
0 keypoint
0 synthetic
-------------------------------------------------------
和
las2txt 20180524_I65_NB_RoadSurface_1_50.5.las qq.txt && head qq.txt
600209.243,4313837.086,155.155
600209.342,4313839.620,155.191
600209.232,4313836.806,155.154
600209.338,4313839.516,155.197
600209.221,4313836.523,155.165
600209.333,4313839.398,155.194
600209.206,4313836.177,155.158
600209.328,4313839.285,155.200
600209.189,4313835.778,155.145
600209.322,4313839.152,155.193
这意味着文件没问题，库可以工作，您可以节省 很多 的时间来学习基本的库用法，而不是尝试自己重新实现它（想想各种数据格式等，错误处理等，测试等，遇到问题时获得反馈等，以及您的时间）