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Replace '->points.' with just '->' #4218

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b7bc60c
Replace '->points.' with just '->'
kunaltyagi Jun 23, 2020
16903a2
Applying formatting
kunaltyagi Jun 23, 2020
52bb988
PointCloud can't remove usage of points
kunaltyagi Jun 24, 2020
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8 changes: 4 additions & 4 deletions ...ework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/colorshot_local_estimator.h
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Expand Up @@ -55,7 +55,7 @@ class ColorSHOTLocalEstimation : public LocalEstimator<PointInT, FeatureT> {
// compute keypoints
computeKeypoints(processed, keypoints, normals);

if (keypoints->points.size() == 0) {
if (keypoints->size() == 0) {
PCL_WARN("ColorSHOTLocalEstimation :: No keypoints were found\n");
return false;
}
Expand All @@ -73,13 +73,13 @@ class ColorSHOTLocalEstimation : public LocalEstimator<PointInT, FeatureT> {
shot_estimate.setSearchSurface(processed);
shot_estimate.setRadiusSearch(support_radius_);
shot_estimate.compute(*shots);
signatures->resize(shots->points.size());
signatures->width = static_cast<int>(shots->points.size());
signatures->resize(shots->size());
signatures->width = static_cast<int>(shots->size());
signatures->height = 1;

int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);

for (std::size_t k = 0; k < shots->points.size(); k++)
for (std::size_t k = 0; k < shots->size(); k++)
for (int i = 0; i < size_feat; i++)
signatures->points[k].histogram[i] = shots->points[k].descriptor[i];

Expand Down
13 changes: 6 additions & 7 deletions ..._framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator.h
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Expand Up @@ -50,15 +50,14 @@ class FPFHLocalEstimation : public LocalEstimator<PointInT, FeatureT> {
normal_estimator_->estimate(in, processed, normals);

this->computeKeypoints(processed, keypoints, normals);
std::cout << " " << normals->points.size() << " " << processed->points.size()
<< std::endl;
std::cout << " " << normals->size() << " " << processed->size() << std::endl;

if (keypoints->points.empty()) {
if (keypoints->empty()) {
PCL_WARN("FPFHLocalEstimation :: No keypoints were found\n");
return false;
}

assert(processed->points.size() == normals->points.size());
assert(processed->size() == normals->size());

// compute signatures
using FPFHEstimator =
Expand All @@ -75,12 +74,12 @@ class FPFHLocalEstimation : public LocalEstimator<PointInT, FeatureT> {
fpfh_estimate.setRadiusSearch(support_radius_);
fpfh_estimate.compute(*fpfhs);

signatures->resize(fpfhs->points.size());
signatures->width = static_cast<int>(fpfhs->points.size());
signatures->resize(fpfhs->size());
signatures->width = static_cast<int>(fpfhs->size());
signatures->height = 1;

int size_feat = 33;
for (std::size_t k = 0; k < fpfhs->points.size(); k++)
for (std::size_t k = 0; k < fpfhs->size(); k++)
for (int i = 0; i < size_feat; i++)
signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];

Expand Down
13 changes: 6 additions & 7 deletions ...mework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/fpfh_local_estimator_omp.h
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Expand Up @@ -51,15 +51,14 @@ class FPFHLocalEstimationOMP : public LocalEstimator<PointInT, FeatureT> {

// compute keypoints
computeKeypoints(processed, keypoints, normals);
std::cout << " " << normals->points.size() << " " << processed->points.size()
<< std::endl;
std::cout << " " << normals->size() << " " << processed->size() << std::endl;

if (keypoints->points.size() == 0) {
if (keypoints->size() == 0) {
PCL_WARN("FPFHLocalEstimation :: No keypoints were found\n");
return false;
}

assert(processed->points.size() == normals->points.size());
assert(processed->size() == normals->size());

// compute signatures
using FPFHEstimator =
Expand All @@ -77,12 +76,12 @@ class FPFHLocalEstimationOMP : public LocalEstimator<PointInT, FeatureT> {
fpfh_estimate.setRadiusSearch(support_radius_);
fpfh_estimate.compute(*fpfhs);

signatures->resize(fpfhs->points.size());
signatures->width = static_cast<int>(fpfhs->points.size());
signatures->resize(fpfhs->size());
signatures->width = static_cast<int>(fpfhs->size());
signatures->height = 1;

int size_feat = 33;
for (std::size_t k = 0; k < fpfhs->points.size(); k++)
for (std::size_t k = 0; k < fpfhs->size(); k++)
for (int i = 0; i < size_feat; i++)
signatures->points[k].histogram[i] = fpfhs->points[k].histogram[i];

Expand Down
20 changes: 10 additions & 10 deletions ...d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/local_estimator.h
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Expand Up @@ -93,14 +93,14 @@ class UniformSamplingExtractor : public KeypointExtractor<PointInT> {
tree->setInputCloud(input);

neighborhood_indices_.reset(new std::vector<std::vector<int>>);
neighborhood_indices_->resize(keypoints_cloud->points.size());
neighborhood_indices_->resize(keypoints_cloud->size());
neighborhood_dist_.reset(new std::vector<std::vector<float>>);
neighborhood_dist_->resize(keypoints_cloud->points.size());
neighborhood_dist_->resize(keypoints_cloud->size());

filtered_keypoints.points.resize(keypoints_cloud->points.size());
filtered_keypoints.points.resize(keypoints_cloud->size());
int good = 0;

for (std::size_t i = 0; i < keypoints_cloud->points.size(); i++) {
for (std::size_t i = 0; i < keypoints_cloud->size(); i++) {

if (tree->radiusSearch(keypoints_cloud->points[i],
radius_,
Expand Down Expand Up @@ -133,7 +133,7 @@ class UniformSamplingExtractor : public KeypointExtractor<PointInT> {

neighborhood_indices_->resize(good);
neighborhood_dist_->resize(good);
keypoints_cloud->points.resize(good);
keypoints_cloud->resize(good);

neighborhood_indices_->clear();
neighborhood_dist_->clear();
Expand Down Expand Up @@ -215,7 +215,7 @@ class SIFTSurfaceKeypointExtractor : public KeypointExtractor<PointInT> {
void
compute(PointInTPtr& keypoints)
{
if (normals_ == 0 || (normals_->points.size() != input_->points.size()))
if (normals_ == 0 || (normals_->size() != input_->size()))
PCL_WARN("SIFTSurfaceKeypointExtractor -- Normals are not valid\n");

keypoints.reset(new pcl::PointCloud<PointInT>);
Expand All @@ -224,8 +224,8 @@ class SIFTSurfaceKeypointExtractor : public KeypointExtractor<PointInT> {
new pcl::PointCloud<pcl::PointNormal>);
input_cloud->width = input_->width;
input_cloud->height = input_->height;
input_cloud->points.resize(input_->width * input_->height);
for (std::size_t i = 0; i < input_->points.size(); i++) {
input_cloud->resize(input_->width * input_->height);
for (std::size_t i = 0; i < input_->size(); i++) {
input_cloud->points[i].getVector3fMap() = input_->points[i].getVector3fMap();
input_cloud->points[i].getNormalVector3fMap() =
normals_->points[i].getNormalVector3fMap();
Expand Down Expand Up @@ -298,7 +298,7 @@ class HarrisKeypointExtractor : public KeypointExtractor<PointInT> {
{
keypoints.reset(new pcl::PointCloud<PointInT>);

if (normals_ == 0 || (normals_->points.size() != input_->points.size()))
if (normals_ == 0 || (normals_->size() != input_->size()))
PCL_WARN("HarrisKeypointExtractor -- Normals are not valid\n");

typename pcl::PointCloud<pcl::PointXYZI>::Ptr intensity_keypoints(
Expand Down Expand Up @@ -347,7 +347,7 @@ class SUSANKeypointExtractor : public KeypointExtractor<PointInT> {
{
keypoints.reset(new pcl::PointCloud<PointInT>);

if (normals_ == 0 || (normals_->points.size() != input_->points.size()))
if (normals_ == 0 || (normals_->size() != input_->size()))
PCL_WARN("SUSANKeypointExtractor -- Normals are not valid\n");

typename pcl::PointCloud<pcl::PointXYZI>::Ptr intensity_keypoints(
Expand Down
15 changes: 7 additions & 8 deletions ..._framework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/shot_local_estimator.h
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Expand Up @@ -84,16 +84,15 @@ class SHOTLocalEstimation : public LocalEstimator<PointInT, FeatureT> {

// compute keypoints
this->computeKeypoints(processed, keypoints, normals);
std::cout << " " << normals->points.size() << " " << processed->points.size()
<< std::endl;
std::cout << " " << normals->size() << " " << processed->size() << std::endl;

if (keypoints->points.empty()) {
if (keypoints->empty()) {
PCL_WARN("SHOTLocalEstimation :: No keypoints were found\n");
return false;
}

std::cout << keypoints->points.size() << " " << normals->points.size() << " "
<< processed->points.size() << std::endl;
std::cout << keypoints->size() << " " << normals->size() << " " << processed->size()
<< std::endl;
// compute signatures
using SHOTEstimator = pcl::SHOTEstimation<PointInT, pcl::Normal, pcl::SHOT352>;
typename pcl::search::KdTree<PointInT>::Ptr tree(new pcl::search::KdTree<PointInT>);
Expand All @@ -107,13 +106,13 @@ class SHOTLocalEstimation : public LocalEstimator<PointInT, FeatureT> {
shot_estimate.setInputNormals(normals);
shot_estimate.setRadiusSearch(support_radius_);
shot_estimate.compute(*shots);
signatures->resize(shots->points.size());
signatures->width = static_cast<int>(shots->points.size());
signatures->resize(shots->size());
signatures->width = static_cast<int>(shots->size());
signatures->height = 1;

int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);

for (std::size_t k = 0; k < shots->points.size(); k++)
for (std::size_t k = 0; k < shots->size(); k++)
for (int i = 0; i < size_feat; i++)
signatures->points[k].histogram[i] = shots->points[k].descriptor[i];

Expand Down
9 changes: 4 additions & 5 deletions ...mework/include/pcl/apps/3d_rec_framework/feature_wrapper/local/shot_local_estimator_omp.h
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Expand Up @@ -85,10 +85,9 @@ class SHOTLocalEstimationOMP : public LocalEstimator<PointInT, FeatureT> {
}

this->computeKeypoints(processed, keypoints, normals);
std::cout << " " << normals->points.size() << " " << processed->points.size()
<< std::endl;
std::cout << " " << normals->size() << " " << processed->size() << std::endl;

if (keypoints->points.empty()) {
if (keypoints->empty()) {
PCL_WARN("SHOTLocalEstimationOMP :: No keypoints were found\n");
return false;
}
Expand All @@ -112,8 +111,8 @@ class SHOTLocalEstimationOMP : public LocalEstimator<PointInT, FeatureT> {
shot_estimate.compute(*shots);
}

signatures->resize(shots->points.size());
signatures->width = static_cast<int>(shots->points.size());
signatures->resize(shots->size());
signatures->width = static_cast<int>(shots->size());
signatures->height = 1;

int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);
Expand Down
22 changes: 11 additions & 11 deletions apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/feature_wrapper/normal_estimator.h
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Expand Up @@ -34,9 +34,9 @@ class PreProcessorAndNormalEstimator {
std::vector<int> src_indices;

float sum_distances = 0.0;
std::vector<float> avg_distances(input->points.size());
std::vector<float> avg_distances(input->size());
// Iterate through the source data set
for (std::size_t i = 0; i < input->points.size(); ++i) {
for (std::size_t i = 0; i < input->size(); ++i) {
tree->nearestKSearch(input->points[i], 9, nn_indices, nn_distances);

float avg_dist_neighbours = 0.0;
Expand Down Expand Up @@ -132,7 +132,7 @@ class PreProcessorAndNormalEstimator {
out = in;
}

if (out->points.empty()) {
if (out->empty()) {
PCL_WARN("NORMAL estimator: Cloud has no points after voxel grid, "
"won't be able to compute normals!\n");
return;
Expand All @@ -159,7 +159,7 @@ class PreProcessorAndNormalEstimator {
out = out2;
}

if (out->points.empty()) {
if (out->empty()) {
PCL_WARN("NORMAL estimator: Cloud has no points after removing outliers...!\n");
return;
}
Expand Down Expand Up @@ -188,7 +188,7 @@ class PreProcessorAndNormalEstimator {
{
pcl::ScopeTime t("check nans...");
int j = 0;
for (std::size_t i = 0; i < out->points.size(); ++i) {
for (std::size_t i = 0; i < out->size(); ++i) {
if (!std::isfinite(out->points[i].x) || !std::isfinite(out->points[i].y) ||
!std::isfinite(out->points[i].z))
continue;
Expand All @@ -197,11 +197,11 @@ class PreProcessorAndNormalEstimator {
j++;
}

if (j != static_cast<int>(out->points.size())) {
if (j != static_cast<int>(out->size())) {
PCL_ERROR("Contain nans...");
}

out->points.resize(j);
out->resize(j);
out->width = j;
out->height = 1;
}
Expand All @@ -223,7 +223,7 @@ class PreProcessorAndNormalEstimator {
if (!out->isOrganized()) {
pcl::ScopeTime t("check nans...");
int j = 0;
for (std::size_t i = 0; i < normals->points.size(); ++i) {
for (std::size_t i = 0; i < normals->size(); ++i) {
if (!std::isfinite(normals->points[i].normal_x) ||
!std::isfinite(normals->points[i].normal_y) ||
!std::isfinite(normals->points[i].normal_z))
Expand All @@ -234,19 +234,19 @@ class PreProcessorAndNormalEstimator {
j++;
}

normals->points.resize(j);
normals->resize(j);
normals->width = j;
normals->height = 1;

out->points.resize(j);
out->resize(j);
out->width = j;
out->height = 1;
}
else {
// is is organized, we set the xyz points to NaN
pcl::ScopeTime t("check nans organized...");
bool NaNs = false;
for (std::size_t i = 0; i < normals->points.size(); ++i) {
for (std::size_t i = 0; i < normals->size(); ++i) {
if (std::isfinite(normals->points[i].normal_x) &&
std::isfinite(normals->points[i].normal_y) &&
std::isfinite(normals->points[i].normal_z))
Expand Down
2 changes: 1 addition & 1 deletion ...ec_framework/include/pcl/apps/3d_rec_framework/pipeline/impl/global_nn_recognizer_crh.hpp
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Expand Up @@ -396,7 +396,7 @@ pcl::rec_3d_framework::GlobalNNCRHRecognizer<Distance, PointInT, FeatureT>::init
std::mt19937 rng(rd());
std::normal_distribution<float> nd(0.0f, noise_);
// Noisify each point in the dataset
for (std::size_t cp = 0; cp < view->points.size(); ++cp)
for (std::size_t cp = 0; cp < view->size(); ++cp)
view->points[cp].z += nd(rng);
}

Expand Down
4 changes: 2 additions & 2 deletions ...c_framework/include/pcl/apps/3d_rec_framework/pipeline/impl/global_nn_recognizer_cvfh.hpp
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Expand Up @@ -583,15 +583,15 @@ pcl::rec_3d_framework::GlobalNNCVFHRecognizer<Distance, PointInT, FeatureT>::ini
PointInTPtr processed(new pcl::PointCloud<PointInT>);
PointInTPtr view = models->at(i).views_->at(v);

if (view->points.empty())
if (view->empty())
PCL_WARN("View has no points!!!\n");

if (noisify_) {
std::random_device rd;
std::mt19937 rng(rd());
std::normal_distribution<float> nd(0.0f, noise_);
// Noisify each point in the dataset
for (std::size_t cp = 0; cp < view->points.size(); ++cp)
for (std::size_t cp = 0; cp < view->size(); ++cp)
view->points[cp].z += nd(rng);
}

Expand Down
15 changes: 7 additions & 8 deletions apps/3d_rec_framework/include/pcl/apps/3d_rec_framework/pipeline/impl/local_recognizer.hpp
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Expand Up @@ -67,7 +67,7 @@ pcl::rec_3d_framework::LocalRecognitionPipeline<Distance, PointInT, FeatureT>::

int size_feat = sizeof(signature->points[0].histogram) / sizeof(float);

for (std::size_t dd = 0; dd < signature->points.size(); dd++) {
for (std::size_t dd = 0; dd < signature->size(); dd++) {
descr_model.keypoint_id = static_cast<int>(dd);
descr_model.descr.resize(size_feat);

Expand Down Expand Up @@ -208,7 +208,7 @@ pcl::rec_3d_framework::LocalRecognitionPipeline<Distance, PointInT, FeatureT>::
PointInTPtr keypoints_pointcloud;

if (signatures_ != nullptr && processed_ != nullptr &&
(signatures_->size() == keypoints_pointcloud->points.size())) {
(signatures_->size() == keypoints_pointcloud->size())) {
keypoints_pointcloud = keypoints_input_;
signatures = signatures_;
processed = processed_;
Expand All @@ -228,15 +228,14 @@ pcl::rec_3d_framework::LocalRecognitionPipeline<Distance, PointInT, FeatureT>::
processed_ = processed;
}

std::cout << "Number of keypoints:" << keypoints_pointcloud->points.size()
<< std::endl;
std::cout << "Number of keypoints:" << keypoints_pointcloud->size() << std::endl;

int size_feat = sizeof(signatures->points[0].histogram) / sizeof(float);

// feature matching and object hypotheses
std::map<std::string, ObjectHypothesis> object_hypotheses;
{
for (std::size_t idx = 0; idx < signatures->points.size(); idx++) {
for (std::size_t idx = 0; idx < signatures->size(); idx++) {
float* hist = signatures->points[idx].histogram;
std::vector<float> std_hist(hist, hist + size_feat);
flann_model histogram;
Expand Down Expand Up @@ -269,9 +268,9 @@ pcl::rec_3d_framework::LocalRecognitionPipeline<Distance, PointInT, FeatureT>::
flann_models_.at(indices[0][0]).model.id_)) != object_hypotheses.end()) {
// if the object hypothesis already exists, then add information
ObjectHypothesis oh = (*it_map).second;
oh.correspondences_pointcloud->points.push_back(model_keypoint);
oh.correspondences_pointcloud->push_back(model_keypoint);
oh.correspondences_to_inputcloud->push_back(pcl::Correspondence(
static_cast<int>(oh.correspondences_pointcloud->points.size() - 1),
static_cast<int>(oh.correspondences_pointcloud->size() - 1),
static_cast<int>(idx),
distances[0][0]));
oh.feature_distances_->push_back(distances[0][0]);
Expand All @@ -282,7 +281,7 @@ pcl::rec_3d_framework::LocalRecognitionPipeline<Distance, PointInT, FeatureT>::

typename pcl::PointCloud<PointInT>::Ptr correspondences_pointcloud(
new pcl::PointCloud<PointInT>());
correspondences_pointcloud->points.push_back(model_keypoint);
correspondences_pointcloud->push_back(model_keypoint);

oh.model_ = flann_models_.at(indices[0][0]).model;
oh.correspondences_pointcloud = correspondences_pointcloud;
Expand Down
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