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pcl mls upsampling test revised from https://github.com/PointCloudLibrary/pcl/issues/1958 for PCL 1.12
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set (CMAKE_CXX_STANDARD 14) | |
cmake_minimum_required(VERSION 2.6 FATAL_ERROR) | |
project(MY_GRAND_PROJECT) | |
# specify the components required | |
#find_package(PCL 1.10 REQUIRED COMPONENTS common io) | |
# or use all available components | |
find_package(PCL 1.12 PATHS "$ENV{HOME}/Documents/pcl/build") | |
#find_package(PCL 1.10 REQUIRED) | |
include_directories("/usr/include/eigen3") | |
include_directories("$ENV{HOME}/Documents/pcl") | |
include_directories("$ENV{HOME}/Documents/pcl/common/include") | |
include_directories("$ENV{HOME}/Documents/pcl/surface/include") | |
include_directories("$ENV{HOME}/Documents/pcl/io/include") | |
include_directories("$ENV{HOME}/Documents/pcl/kdtree/include") | |
include_directories("$ENV{HOME}/Documents/pcl/search/include") | |
include_directories("$ENV{HOME}/Documents/pcl/build/include") | |
link_directories("$ENV{HOME}/Documents/pcl/build/lib") | |
add_definitions(${PCL_DEFINITIONS}) | |
add_definitions("-DDISABLE_PCAP -DDISABLE_PNG -DDISABLE_LIBUSB_1_0") | |
message(STATUS "include: ${PCL_INCLUDE_DIRS}") | |
message(STATUS "def: ${PCL_DEFINITIONS}") | |
message(STATUS "lib: ${PCL_LIBRARIES}") | |
add_executable(random_upsampling random_upsampling.cpp) | |
target_link_libraries(random_upsampling pcl_common pcl_surface pcl_io pcl_kdtree pcl_search ${PCL_LIBRARIES}) |
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#include <pcl/point_types.h> | |
#include <pcl/io/pcd_io.h> | |
#include <pcl/io/vtk_io.h> | |
#include <pcl/surface/mls.h> | |
#include <pcl/search/kdtree.h> | |
using namespace pcl; | |
using namespace pcl::io; | |
int | |
main (int argc, char** argv) | |
{ | |
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>); | |
PointCloud<PointXYZ>::Ptr cloud2 (new PointCloud<PointXYZ>); | |
search::KdTree<PointXYZ>::Ptr tree; | |
int gridSize = 50; | |
for(unsigned int x = 0; x < gridSize; x++) | |
{ | |
for(unsigned int y = 0; y < gridSize; y++) | |
{ | |
double d = 0.001 * ( (double)rand() / (double)RAND_MAX ); | |
pcl::PointXYZ pt(x / 10.0 , y / 10.0 , 0.5 * cos(double(x)/10.0) - 0.5 * sin(double(y)/10.0) + d); | |
cloud->push_back(pt); | |
if(std::abs((int)x+(int)y-50)<=10){ | |
pcl::PointXYZ pt2(x / 10.0 , y / 10.0 , 0.5 * sin(double(x)/10.0) - 0.5 * cos(double(y)/10.0) + d); | |
cloud2->push_back(pt2); | |
} | |
} | |
} | |
// cloud->is_dense = false; | |
if (!cloud->empty()) | |
{ | |
std::string file_path = "cloud.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
if (!cloud2->empty()) | |
{ | |
std::string file_path = "cloud2.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud2); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
// Create search tree | |
tree.reset (new search::KdTree<PointXYZ> (false)); | |
tree->setInputCloud (cloud); | |
// Init objects | |
PointCloud<PointXYZ> mls_points; | |
PointCloud<PointNormal>::Ptr mls_normals (new PointCloud<PointNormal> ()); | |
MovingLeastSquares<PointXYZ, PointNormal> mls; | |
// Set parameters | |
mls.setInputCloud (cloud); | |
mls.setComputeNormals (true); | |
mls.setPolynomialOrder (2); | |
mls.setSearchMethod (tree); | |
mls.setSearchRadius (0.5); | |
// Reconstruct | |
mls.process (*mls_normals); | |
std::cout << "processed size: " << mls_normals->size() << std::endl; | |
if (!mls_normals->empty()) | |
{ | |
std::string file_path = "none.pcd"; | |
pcl::io::savePCDFile(file_path, *mls_normals); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
// Testing upsampling | |
MovingLeastSquares<PointXYZ, PointNormal> mls_sample_local_plane; | |
// Set parameters | |
mls_sample_local_plane.setInputCloud (cloud); | |
mls_sample_local_plane.setComputeNormals (true); | |
mls_sample_local_plane.setPolynomialOrder (2); | |
mls_sample_local_plane.setSearchMethod (tree); | |
mls_sample_local_plane.setSearchRadius (0.5); | |
mls_sample_local_plane.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::SAMPLE_LOCAL_PLANE); | |
mls_sample_local_plane.setUpsamplingRadius (0.1); | |
mls_sample_local_plane.setUpsamplingStepSize (0.05); | |
mls_normals->clear (); | |
PointCloud<PointNormal>::Ptr cloud_sample_local_plane (new PointCloud<PointNormal> ()); | |
mls_sample_local_plane.process (*cloud_sample_local_plane); | |
std::cout << "processed size: " << cloud_sample_local_plane->size() << std::endl; | |
if (!cloud_sample_local_plane->empty()) | |
{ | |
std::string file_path = "sample_local_plane.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud_sample_local_plane); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
MovingLeastSquares<PointXYZ, PointNormal> mls_voxel_grid_dilation; | |
PointCloud<PointNormal>::Ptr cloud_voxel_grid_dilation (new PointCloud<PointNormal> ()); | |
mls_voxel_grid_dilation.setInputCloud (cloud); | |
mls_voxel_grid_dilation.setComputeNormals (true); | |
mls_voxel_grid_dilation.setPolynomialOrder (2); | |
mls_voxel_grid_dilation.setSearchMethod (tree); | |
mls_voxel_grid_dilation.setSearchRadius (0.5); | |
mls_voxel_grid_dilation.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::VOXEL_GRID_DILATION); | |
mls_voxel_grid_dilation.setDilationIterations (0); | |
mls_voxel_grid_dilation.setDilationVoxelSize (0.2f); | |
mls_voxel_grid_dilation.process (*cloud_voxel_grid_dilation); | |
std::cout << "processed size: " << cloud_voxel_grid_dilation->size() << std::endl; | |
if (!cloud_voxel_grid_dilation->empty()) | |
{ | |
std::string file_path = "voxel_grid_dilation.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud_voxel_grid_dilation); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
PointCloud<PointNormal>::Ptr cloud_distinct_cloud (new PointCloud<PointNormal> ()); | |
MovingLeastSquares<PointXYZ, PointNormal> mls_distinct_cloud; | |
mls_distinct_cloud.setInputCloud (cloud); | |
mls_distinct_cloud.setComputeNormals (true); | |
mls_distinct_cloud.setPolynomialOrder (2); | |
mls_distinct_cloud.setSearchMethod (tree); | |
mls_distinct_cloud.setSearchRadius (0.5); | |
mls_distinct_cloud.setUpsamplingMethod(pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal>::DISTINCT_CLOUD); | |
mls_distinct_cloud.setDistinctCloud(cloud2); | |
mls_distinct_cloud.process(*cloud_distinct_cloud); | |
std::cout << "processed size: " << cloud_distinct_cloud->size() << std::endl; | |
if (!cloud_distinct_cloud->empty()) | |
{ | |
std::string file_path = "distinct_cloud.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud_distinct_cloud); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
MovingLeastSquares<PointXYZ, PointNormal> mls_random_uniform_density; | |
PointCloud<PointNormal>::Ptr cloud_random_uniform_density (new PointCloud<PointNormal> ()); | |
mls_random_uniform_density.setComputeNormals(true); | |
mls_random_uniform_density.setInputCloud(cloud); | |
mls_random_uniform_density.setPolynomialOrder(2); | |
mls_random_uniform_density.setSearchMethod(tree); | |
mls_random_uniform_density.setSearchRadius(0.5); | |
mls_random_uniform_density.setUpsamplingMethod(pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal>::RANDOM_UNIFORM_DENSITY); | |
mls_random_uniform_density.setPointDensity(500); | |
mls_random_uniform_density.process(*cloud_random_uniform_density); | |
std::cout << "processed size: " << cloud_random_uniform_density->size() << std::endl; | |
if (!cloud_random_uniform_density->empty()) | |
{ | |
std::string file_path = "random_uniform_density.pcd"; | |
pcl::io::savePCDFile(file_path, *cloud_random_uniform_density); | |
PCL_INFO("Wrote file: %s\n", file_path.c_str()); | |
} | |
} |
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