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fixes in accordance to style guide (tabs replaced by two spaces)
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Marc Gronle committed Feb 8, 2016
1 parent feafab9 commit 0c9885e
Showing 1 changed file with 161 additions and 161 deletions.
322 changes: 161 additions & 161 deletions examples/features/example_difference_of_normals.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -39,200 +39,200 @@ typedef pcl::search::Search<PointT>::Ptr SearchPtr;

int main (int argc, char *argv[])
{
///The smallest scale to use in the DoN filter.
double scale1 = 0.2;
///The smallest scale to use in the DoN filter.
double scale1 = 0.2;

///The largest scale to use in the DoN filter.
double scale2 = 2.0;
///The largest scale to use in the DoN filter.
double scale2 = 2.0;

///The minimum DoN magnitude to threshold by
double threshold = 0.25;
///The minimum DoN magnitude to threshold by
double threshold = 0.25;

///segment scene into clusters with given distance tolerance using euclidean clustering
double segradius = 0.2;
///segment scene into clusters with given distance tolerance using euclidean clustering
double segradius = 0.2;

//voxelization factor of pointcloud to use in approximation of normals
bool approx = false;
double decimation = 100;
//voxelization factor of pointcloud to use in approximation of normals
bool approx = false;
double decimation = 100;

if(argc < 2){
cerr << "Expected 2 arguments: inputfile outputfile" << endl;
}
if(argc < 2){
cerr << "Expected 2 arguments: inputfile outputfile" << endl;
}

///The file to read from.
string infile = argv[1];
///The file to read from.
string infile = argv[1];

///The file to output to.
string outfile = argv[2];
///The file to output to.
string outfile = argv[2];

// Load cloud in blob format
pcl::PCLPointCloud2 blob;
pcl::io::loadPCDFile (infile.c_str(), blob);
// Load cloud in blob format
pcl::PCLPointCloud2 blob;
pcl::io::loadPCDFile (infile.c_str(), blob);

pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
cout << "Loading point cloud...";
pcl::fromPCLPointCloud2 (blob, *cloud);
cout << "done." << endl;

SearchPtr tree;
SearchPtr tree;

if (cloud->isOrganized ())
{
tree.reset (new pcl::search::OrganizedNeighbor<PointT> ());
}
else
{
if (cloud->isOrganized ())
{
tree.reset (new pcl::search::OrganizedNeighbor<PointT> ());
}
else
{
tree.reset (new pcl::search::KdTree<PointT> (false));
}

tree->setInputCloud (cloud);

PointCloud<PointT>::Ptr small_cloud_downsampled;
PointCloud<PointT>::Ptr large_cloud_downsampled;

// If we are using approximation
if(approx){
cout << "Downsampling point cloud for approximation" << endl;

// Create the downsampling filtering object
pcl::VoxelGrid<PointT> sor;
sor.setDownsampleAllData (false);
sor.setInputCloud (cloud);

// Create downsampled point cloud for DoN NN search with small scale
small_cloud_downsampled = PointCloud<PointT>::Ptr(new pcl::PointCloud<PointT>);
float smalldownsample = static_cast<float> (scale1 / decimation);
sor.setLeafSize (smalldownsample, smalldownsample, smalldownsample);
sor.filter (*small_cloud_downsampled);
cout << "Using leaf size of " << smalldownsample << " for small scale, " << small_cloud_downsampled->size() << " points" << endl;

// Create downsampled point cloud for DoN NN search with large scale
large_cloud_downsampled = PointCloud<PointT>::Ptr(new pcl::PointCloud<PointT>);
const float largedownsample = float (scale2/decimation);
sor.setLeafSize (largedownsample, largedownsample, largedownsample);
sor.filter (*large_cloud_downsampled);
cout << "Using leaf size of " << largedownsample << " for large scale, " << large_cloud_downsampled->size() << " points" << endl;
}

// Compute normals using both small and large scales at each point
pcl::NormalEstimationOMP<PointT, PointNT> ne;
ne.setInputCloud (cloud);
}

tree->setInputCloud (cloud);

PointCloud<PointT>::Ptr small_cloud_downsampled;
PointCloud<PointT>::Ptr large_cloud_downsampled;

// If we are using approximation
if(approx){
cout << "Downsampling point cloud for approximation" << endl;

// Create the downsampling filtering object
pcl::VoxelGrid<PointT> sor;
sor.setDownsampleAllData (false);
sor.setInputCloud (cloud);

// Create downsampled point cloud for DoN NN search with small scale
small_cloud_downsampled = PointCloud<PointT>::Ptr(new pcl::PointCloud<PointT>);
float smalldownsample = static_cast<float> (scale1 / decimation);
sor.setLeafSize (smalldownsample, smalldownsample, smalldownsample);
sor.filter (*small_cloud_downsampled);
cout << "Using leaf size of " << smalldownsample << " for small scale, " << small_cloud_downsampled->size() << " points" << endl;

// Create downsampled point cloud for DoN NN search with large scale
large_cloud_downsampled = PointCloud<PointT>::Ptr(new pcl::PointCloud<PointT>);
const float largedownsample = float (scale2/decimation);
sor.setLeafSize (largedownsample, largedownsample, largedownsample);
sor.filter (*large_cloud_downsampled);
cout << "Using leaf size of " << largedownsample << " for large scale, " << large_cloud_downsampled->size() << " points" << endl;
}

// Compute normals using both small and large scales at each point
pcl::NormalEstimationOMP<PointT, PointNT> ne;
ne.setInputCloud (cloud);
ne.setSearchMethod (tree);

/**
* NOTE: setting viewpoint is very important, so that we can ensure
* normals are all pointed in the same direction!
*/
ne.setViewPoint(std::numeric_limits<float>::max(),std::numeric_limits<float>::max(),std::numeric_limits<float>::max());
/**
* NOTE: setting viewpoint is very important, so that we can ensure
* normals are all pointed in the same direction!
*/
ne.setViewPoint(std::numeric_limits<float>::max(),std::numeric_limits<float>::max(),std::numeric_limits<float>::max());

if(scale1 >= scale2){
cerr << "Error: Large scale must be > small scale!" << endl;
exit(EXIT_FAILURE);
}
if(scale1 >= scale2){
cerr << "Error: Large scale must be > small scale!" << endl;
exit(EXIT_FAILURE);
}

//the normals calculated with the small scale
cout << "Calculating normals for scale..." << scale1 << endl;
pcl::PointCloud<PointNT>::Ptr normals_small_scale (new pcl::PointCloud<PointNT>);
//the normals calculated with the small scale
cout << "Calculating normals for scale..." << scale1 << endl;
pcl::PointCloud<PointNT>::Ptr normals_small_scale (new pcl::PointCloud<PointNT>);

if(approx){
ne.setSearchSurface(small_cloud_downsampled);
if(approx){
ne.setSearchSurface(small_cloud_downsampled);
}

ne.setRadiusSearch (scale1);
ne.compute (*normals_small_scale);
ne.setRadiusSearch (scale1);
ne.compute (*normals_small_scale);

cout << "Calculating normals for scale..." << scale2 << endl;
//the normals calculated with the large scale
pcl::PointCloud<PointNT>::Ptr normals_large_scale (new pcl::PointCloud<PointNT>);
cout << "Calculating normals for scale..." << scale2 << endl;
//the normals calculated with the large scale
pcl::PointCloud<PointNT>::Ptr normals_large_scale (new pcl::PointCloud<PointNT>);

if(approx){
ne.setSearchSurface(large_cloud_downsampled);
}
ne.setRadiusSearch (scale2);
ne.compute (*normals_large_scale);
if(approx){
ne.setSearchSurface(large_cloud_downsampled);
}
ne.setRadiusSearch (scale2);
ne.compute (*normals_large_scale);

// Create output cloud for DoN results
PointCloud<PointOutT>::Ptr doncloud (new pcl::PointCloud<PointOutT>);
copyPointCloud<PointT, PointOutT>(*cloud, *doncloud);
// Create output cloud for DoN results
PointCloud<PointOutT>::Ptr doncloud (new pcl::PointCloud<PointOutT>);
copyPointCloud<PointT, PointOutT>(*cloud, *doncloud);

cout << "Calculating DoN... " << endl;
// Create DoN operator
pcl::DifferenceOfNormalsEstimation<PointT, PointNT, PointOutT> don;
don.setInputCloud (cloud);
don.setNormalScaleLarge(normals_large_scale);
don.setNormalScaleSmall(normals_small_scale);
cout << "Calculating DoN... " << endl;
// Create DoN operator
pcl::DifferenceOfNormalsEstimation<PointT, PointNT, PointOutT> don;
don.setInputCloud (cloud);
don.setNormalScaleLarge(normals_large_scale);
don.setNormalScaleSmall(normals_small_scale);

if(!don.initCompute ()){
std::cerr << "Error: Could not intialize DoN feature operator" << std::endl;
exit(EXIT_FAILURE);
}
if(!don.initCompute ()){
std::cerr << "Error: Could not intialize DoN feature operator" << std::endl;
exit(EXIT_FAILURE);
}

//Compute DoN
don.computeFeature(*doncloud);
//Compute DoN
don.computeFeature(*doncloud);

pcl::PCDWriter writer;

// Save DoN features
writer.write<PointOutT> (outfile.c_str (), *doncloud, false);

//Filter by magnitude
cout << "Filtering out DoN mag <= "<< threshold << "..." << endl;

// build the condition
pcl::ConditionOr<PointOutT>::Ptr range_cond (new
pcl::ConditionOr<PointOutT> ());
range_cond->addComparison (pcl::FieldComparison<PointOutT>::ConstPtr (new
pcl::FieldComparison<PointOutT> ("curvature", pcl::ComparisonOps::GT, threshold)));
// build the filter
pcl::ConditionalRemoval<PointOutT> condrem;
condrem.setCondition(range_cond);
condrem.setInputCloud (doncloud);

pcl::PointCloud<PointOutT>::Ptr doncloud_filtered (new pcl::PointCloud<PointOutT>);

// apply filter
condrem.filter (*doncloud_filtered);

doncloud = doncloud_filtered;

// Save filtered output
std::cout << "Filtered Pointcloud: " << doncloud->points.size () << " data points." << std::endl;
std::stringstream ss;
ss << outfile.substr(0,outfile.length()-4) << "_threshold_"<< threshold << "_.pcd";
writer.write<PointOutT> (ss.str (), *doncloud, false);

//Filter by magnitude
cout << "Clustering using EuclideanClusterExtraction with tolerance <= "<< segradius << "..." << endl;

pcl::search::KdTree<PointOutT>::Ptr segtree (new pcl::search::KdTree<PointOutT>);
segtree->setInputCloud (doncloud);

std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<PointOutT> ec;

ec.setClusterTolerance (segradius);
ec.setMinClusterSize (50);
ec.setMaxClusterSize (100000);
ec.setSearchMethod (segtree);
ec.setInputCloud (doncloud);
ec.extract (cluster_indices);

int j = 0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it, j++)
{
pcl::PointCloud<PointOutT>::Ptr cloud_cluster_don (new pcl::PointCloud<PointOutT>);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit){
cloud_cluster_don->points.push_back (doncloud->points[*pit]);
}

cloud_cluster_don->width = int (cloud_cluster_don->points.size ());
cloud_cluster_don->height = 1;
cloud_cluster_don->is_dense = true;

std::cout << "PointCloud representing the Cluster: " << cloud_cluster_don->points.size () << " data points." << std::endl;
std::stringstream ss;
ss << outfile.substr(0,outfile.length()-4) << "_threshold_"<< threshold << "_cluster_" << j << ".pcd";
writer.write<PointOutT> (ss.str (), *cloud_cluster_don, false);
}
//Filter by magnitude
cout << "Filtering out DoN mag <= "<< threshold << "..." << endl;

// build the condition
pcl::ConditionOr<PointOutT>::Ptr range_cond (new
pcl::ConditionOr<PointOutT> ());
range_cond->addComparison (pcl::FieldComparison<PointOutT>::ConstPtr (new
pcl::FieldComparison<PointOutT> ("curvature", pcl::ComparisonOps::GT, threshold)));
// build the filter
pcl::ConditionalRemoval<PointOutT> condrem;
condrem.setCondition (range_cond);
condrem.setInputCloud (doncloud);

pcl::PointCloud<PointOutT>::Ptr doncloud_filtered (new pcl::PointCloud<PointOutT>);

// apply filter
condrem.filter (*doncloud_filtered);

doncloud = doncloud_filtered;

// Save filtered output
std::cout << "Filtered Pointcloud: " << doncloud->points.size () << " data points." << std::endl;
std::stringstream ss;
ss << outfile.substr(0,outfile.length()-4) << "_threshold_"<< threshold << "_.pcd";
writer.write<PointOutT> (ss.str (), *doncloud, false);

//Filter by magnitude
cout << "Clustering using EuclideanClusterExtraction with tolerance <= "<< segradius << "..." << endl;

pcl::search::KdTree<PointOutT>::Ptr segtree (new pcl::search::KdTree<PointOutT>);
segtree->setInputCloud (doncloud);

std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<PointOutT> ec;

ec.setClusterTolerance (segradius);
ec.setMinClusterSize (50);
ec.setMaxClusterSize (100000);
ec.setSearchMethod (segtree);
ec.setInputCloud (doncloud);
ec.extract (cluster_indices);

int j = 0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it, j++)
{
pcl::PointCloud<PointOutT>::Ptr cloud_cluster_don (new pcl::PointCloud<PointOutT>);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit){
cloud_cluster_don->points.push_back (doncloud->points[*pit]);
}

cloud_cluster_don->width = int (cloud_cluster_don->points.size ());
cloud_cluster_don->height = 1;
cloud_cluster_don->is_dense = true;

std::cout << "PointCloud representing the Cluster: " << cloud_cluster_don->points.size () << " data points." << std::endl;
std::stringstream ss;
ss << outfile.substr(0,outfile.length()-4) << "_threshold_"<< threshold << "_cluster_" << j << ".pcd";
writer.write<PointOutT> (ss.str (), *cloud_cluster_don, false);
}
}

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