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pyramidal_klt.hpp
/*
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#ifndef PCL_TRACKING_IMPL_PYRAMIDAL_KLT_HPP
#define PCL_TRACKING_IMPL_PYRAMIDAL_KLT_HPP
#include <pcl/common/io.h>
#include <pcl/common/time.h>
#include <pcl/common/utils.h>
namespace pcl {
namespace tracking {
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
inline void
PyramidalKLTTracker<PointInT, IntensityT>::setTrackingWindowSize(int width, int height)
{
track_width_ = width;
track_height_ = height;
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
inline void
PyramidalKLTTracker<PointInT, IntensityT>::setPointsToTrack(
const pcl::PointCloud<pcl::PointUV>::ConstPtr& keypoints)
{
if (keypoints->size() <= keypoints_nbr_)
keypoints_ = keypoints;
else {
pcl::PointCloud<pcl::PointUV>::Ptr p(new pcl::PointCloud<pcl::PointUV>);
p->reserve(keypoints_nbr_);
for (std::size_t i = 0; i < keypoints_nbr_; ++i)
p->push_back((*keypoints)[i]);
keypoints_ = p;
}
keypoints_status_.reset(new std::vector<int>);
keypoints_status_->resize(keypoints_->size(), 0);
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
inline void
PyramidalKLTTracker<PointInT, IntensityT>::setPointsToTrack(
const pcl::PointIndicesConstPtr& points)
{
assert((input_ || ref_) && "[PyramidalKLTTracker] CALL setInputCloud FIRST!");
pcl::PointCloud<pcl::PointUV>::Ptr keypoints(new pcl::PointCloud<pcl::PointUV>);
keypoints->reserve(keypoints_nbr_);
for (std::size_t i = 0; i < keypoints_nbr_; ++i) {
pcl::PointUV uv;
uv.u = points->indices[i] % input_->width;
uv.v = points->indices[i] / input_->width;
keypoints->push_back(uv);
}
setPointsToTrack(keypoints);
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
bool
PyramidalKLTTracker<PointInT, IntensityT>::initCompute()
{
// std::cout << ">>> [PyramidalKLTTracker::initCompute]" << std::endl;
if (!PCLBase<PointInT>::initCompute()) {
PCL_ERROR("[%s::initCompute] PCLBase::Init failed.\n", tracker_name_.c_str());
return (false);
}
if (!input_->isOrganized()) {
PCL_ERROR(
"[pcl::tracking::%s::initCompute] Need an organized point cloud to proceed!\n",
tracker_name_.c_str());
return (false);
}
if (!keypoints_ || keypoints_->empty()) {
PCL_ERROR("[pcl::tracking::%s::initCompute] No keypoints aborting!\n",
tracker_name_.c_str());
return (false);
}
// This is the first call
if (!ref_) {
ref_ = input_;
// std::cout << "First run!!!" << std::endl;
if ((track_height_ * track_width_) % 2 == 0) {
PCL_ERROR(
"[pcl::tracking::%s::initCompute] Tracking window (%dx%d) must be odd!\n",
tracker_name_.c_str(),
track_width_,
track_height_);
return (false);
}
if (track_height_ < 3 || track_width_ < 3) {
PCL_ERROR(
"[pcl::tracking::%s::initCompute] Tracking window (%dx%d) must be >= 3x3!\n",
tracker_name_.c_str(),
track_width_,
track_height_);
return (false);
}
track_width_2_ = track_width_ / 2;
track_height_2_ = track_height_ / 2;
if (nb_levels_ < 2) {
PCL_ERROR("[pcl::tracking::%s::initCompute] Number of pyramid levels should be "
"at least 2!\n",
tracker_name_.c_str());
return (false);
}
if (nb_levels_ > 5) {
PCL_ERROR("[pcl::tracking::%s::initCompute] Number of pyramid levels should not "
"exceed 5!\n",
tracker_name_.c_str());
return (false);
}
computePyramids(ref_, ref_pyramid_, pcl::BORDER_REFLECT_101);
return (true);
}
initialized_ = true;
return (true);
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::derivatives(const FloatImage& src,
FloatImage& grad_x,
FloatImage& grad_y) const
{
// std::cout << ">>> derivatives" << std::endl;
////////////////////////////////////////////////////////
// Use Shcarr operator to compute derivatives. //
// Vertical kernel +3 +10 +3 = [1 0 -1]T * [3 10 3] //
// 0 0 0 //
// -3 -10 -3 //
// Horizontal kernel +3 0 -3 = [3 10 3]T * [1 0 -1] //
// +10 0 -10 //
// +3 0 -3 //
////////////////////////////////////////////////////////
if (grad_x.size() != src.size() || grad_x.width != src.width ||
grad_x.height != src.height)
grad_x = FloatImage(src.width, src.height);
if (grad_y.size() != src.size() || grad_y.width != src.width ||
grad_y.height != src.height)
grad_y = FloatImage(src.width, src.height);
int height = src.height, width = src.width;
float* row0 = new float[src.width + 2];
float* row1 = new float[src.width + 2];
float* trow0 = row0;
++trow0;
float* trow1 = row1;
++trow1;
const float* src_ptr = &(src[0]);
for (int y = 0; y < height; y++) {
const float* srow0 = src_ptr + (y > 0 ? y - 1 : height > 1 ? 1 : 0) * width;
const float* srow1 = src_ptr + y * width;
const float* srow2 =
src_ptr + (y < height - 1 ? y + 1 : (height > 1 ? height - 2 : 0)) * width;
float* grad_x_row = &(grad_x[y * width]);
float* grad_y_row = &(grad_y[y * width]);
// do vertical convolution
for (int x = 0; x < width; x++) {
trow0[x] = (srow0[x] + srow2[x]) * 3 + srow1[x] * 10;
trow1[x] = srow2[x] - srow0[x];
}
// make border
int x0 = width > 1 ? 1 : 0, x1 = width > 1 ? width - 2 : 0;
trow0[-1] = trow0[x0];
trow0[width] = trow0[x1];
trow1[-1] = trow1[x0];
trow1[width] = trow1[x1];
// do horizontal convolution and store results
for (int x = 0; x < width; x++) {
grad_x_row[x] = trow0[x + 1] - trow0[x - 1];
grad_y_row[x] = (trow1[x + 1] + trow1[x - 1]) * 3 + trow1[x] * 10;
}
}
delete[] row0;
delete[] row1;
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::downsample(const FloatImageConstPtr& input,
FloatImageConstPtr& output) const
{
FloatImage smoothed(input->width, input->height);
convolve(input, smoothed);
int width = (smoothed.width + 1) / 2;
int height = (smoothed.height + 1) / 2;
std::vector<int> ii(width);
for (int i = 0; i < width; ++i)
ii[i] = 2 * i;
FloatImagePtr down(new FloatImage(width, height));
// clang-format off
#pragma omp parallel for \
default(none) \
shared(down, height, output, smoothed, width) \
firstprivate(ii) \
num_threads(threads_)
// clang-format on
for (int j = 0; j < height; ++j) {
int jj = 2 * j;
for (int i = 0; i < width; ++i)
(*down)(i, j) = smoothed(ii[i], jj);
}
output = down;
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::downsample(
const FloatImageConstPtr& input,
FloatImageConstPtr& output,
FloatImageConstPtr& output_grad_x,
FloatImageConstPtr& output_grad_y) const
{
downsample(input, output);
FloatImagePtr grad_x(new FloatImage(input->width, input->height));
FloatImagePtr grad_y(new FloatImage(input->width, input->height));
derivatives(*output, *grad_x, *grad_y);
output_grad_x = grad_x;
output_grad_y = grad_y;
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::convolve(
const FloatImageConstPtr& input, FloatImage& output) const
{
FloatImagePtr tmp(new FloatImage(input->width, input->height));
convolveRows(input, *tmp);
convolveCols(tmp, output);
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::convolveRows(
const FloatImageConstPtr& input, FloatImage& output) const
{
int width = input->width;
int height = input->height;
int last = input->width - kernel_size_2_;
int w = last - 1;
// clang-format off
#pragma omp parallel for \
default(none) \
shared(input, height, last, output, w, width) \
num_threads(threads_)
// clang-format on
for (int j = 0; j < height; ++j) {
for (int i = kernel_size_2_; i < last; ++i) {
double result = 0;
for (int k = kernel_last_, l = i - kernel_size_2_; k > -1; --k, ++l)
result += kernel_[k] * (*input)(l, j);
output(i, j) = static_cast<float>(result);
}
for (int i = last; i < width; ++i)
output(i, j) = output(w, j);
for (int i = 0; i < kernel_size_2_; ++i)
output(i, j) = output(kernel_size_2_, j);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::convolveCols(const FloatImageConstPtr& input,
FloatImage& output) const
{
output = FloatImage(input->width, input->height);
int width = input->width;
int height = input->height;
int last = input->height - kernel_size_2_;
int h = last - 1;
// clang-format off
#pragma omp parallel for \
default(none) \
shared(input, h, height, last, output, width) \
num_threads(threads_)
// clang-format on
for (int i = 0; i < width; ++i) {
for (int j = kernel_size_2_; j < last; ++j) {
double result = 0;
for (int k = kernel_last_, l = j - kernel_size_2_; k > -1; --k, ++l)
result += kernel_[k] * (*input)(i, l);
output(i, j) = static_cast<float>(result);
}
for (int j = last; j < height; ++j)
output(i, j) = output(i, h);
for (int j = 0; j < kernel_size_2_; ++j)
output(i, j) = output(i, kernel_size_2_);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::computePyramids(
const PointCloudInConstPtr& input,
std::vector<FloatImageConstPtr>& pyramid,
pcl::InterpolationType border_type) const
{
int step = 3;
pyramid.resize(step * nb_levels_);
FloatImageConstPtr previous;
FloatImagePtr tmp(new FloatImage(input->width, input->height));
// clang-format off
#pragma omp parallel for \
default(none) \
shared(input, tmp) \
num_threads(threads_)
// clang-format on
for (int i = 0; i < static_cast<int>(input->size()); ++i)
(*tmp)[i] = intensity_((*input)[i]);
previous = tmp;
FloatImagePtr img(new FloatImage(previous->width + 2 * track_width_,
previous->height + 2 * track_height_));
pcl::copyPointCloud(*tmp,
*img,
track_height_,
track_height_,
track_width_,
track_width_,
border_type,
0.f);
pyramid[0] = img;
// compute first level gradients
FloatImagePtr g_x(new FloatImage(input->width, input->height));
FloatImagePtr g_y(new FloatImage(input->width, input->height));
derivatives(*img, *g_x, *g_y);
// copy to bigger clouds
FloatImagePtr grad_x(new FloatImage(previous->width + 2 * track_width_,
previous->height + 2 * track_height_));
pcl::copyPointCloud(*g_x,
*grad_x,
track_height_,
track_height_,
track_width_,
track_width_,
pcl::BORDER_CONSTANT,
0.f);
pyramid[1] = grad_x;
FloatImagePtr grad_y(new FloatImage(previous->width + 2 * track_width_,
previous->height + 2 * track_height_));
pcl::copyPointCloud(*g_y,
*grad_y,
track_height_,
track_height_,
track_width_,
track_width_,
pcl::BORDER_CONSTANT,
0.f);
pyramid[2] = grad_y;
for (int level = 1; level < nb_levels_; ++level) {
// compute current level and current level gradients
FloatImageConstPtr current;
FloatImageConstPtr g_x;
FloatImageConstPtr g_y;
downsample(previous, current, g_x, g_y);
// copy to bigger clouds
FloatImagePtr image(new FloatImage(current->width + 2 * track_width_,
current->height + 2 * track_height_));
pcl::copyPointCloud(*current,
*image,
track_height_,
track_height_,
track_width_,
track_width_,
border_type,
0.f);
pyramid[level * step] = image;
FloatImagePtr gradx(
new FloatImage(g_x->width + 2 * track_width_, g_x->height + 2 * track_height_));
pcl::copyPointCloud(*g_x,
*gradx,
track_height_,
track_height_,
track_width_,
track_width_,
pcl::BORDER_CONSTANT,
0.f);
pyramid[level * step + 1] = gradx;
FloatImagePtr grady(
new FloatImage(g_y->width + 2 * track_width_, g_y->height + 2 * track_height_));
pcl::copyPointCloud(*g_y,
*grady,
track_height_,
track_height_,
track_width_,
track_width_,
pcl::BORDER_CONSTANT,
0.f);
pyramid[level * step + 2] = grady;
// set the new level
previous = current;
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::spatialGradient(
const FloatImage& img,
const FloatImage& grad_x,
const FloatImage& grad_y,
const Eigen::Array2i& location,
const Eigen::Array4f& weight,
Eigen::ArrayXXf& win,
Eigen::ArrayXXf& grad_x_win,
Eigen::ArrayXXf& grad_y_win,
Eigen::Array3f& covariance) const
{
const int step = img.width;
covariance.setZero();
for (int y = 0; y < track_height_; y++) {
const float* img_ptr = &(img[0]) + (y + location[1]) * step + location[0];
const float* grad_x_ptr = &(grad_x[0]) + (y + location[1]) * step + location[0];
const float* grad_y_ptr = &(grad_y[0]) + (y + location[1]) * step + location[0];
float* win_ptr = win.data() + y * win.cols();
float* grad_x_win_ptr = grad_x_win.data() + y * grad_x_win.cols();
float* grad_y_win_ptr = grad_y_win.data() + y * grad_y_win.cols();
for (int x = 0; x < track_width_; ++x, ++grad_x_ptr, ++grad_y_ptr) {
*win_ptr++ = img_ptr[x] * weight[0] + img_ptr[x + 1] * weight[1] +
img_ptr[x + step] * weight[2] + img_ptr[x + step + 1] * weight[3];
float ixval = grad_x_ptr[0] * weight[0] + grad_x_ptr[1] * weight[1] +
grad_x_ptr[step] * weight[2] + grad_x_ptr[step + 1] * weight[3];
float iyval = grad_y_ptr[0] * weight[0] + grad_y_ptr[1] * weight[1] +
grad_y_ptr[step] * weight[2] + grad_y_ptr[step + 1] * weight[3];
//!!! store those
*grad_x_win_ptr++ = ixval;
*grad_y_win_ptr++ = iyval;
// covariance components
covariance[0] += ixval * ixval;
covariance[1] += ixval * iyval;
covariance[2] += iyval * iyval;
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::mismatchVector(
const Eigen::ArrayXXf& prev,
const Eigen::ArrayXXf& prev_grad_x,
const Eigen::ArrayXXf& prev_grad_y,
const FloatImage& next,
const Eigen::Array2i& location,
const Eigen::Array4f& weight,
Eigen::Array2f& b) const
{
const int step = next.width;
b.setZero();
for (int y = 0; y < track_height_; y++) {
const float* next_ptr = &(next[0]) + (y + location[1]) * step + location[0];
const float* prev_ptr = prev.data() + y * prev.cols();
const float* prev_grad_x_ptr = prev_grad_x.data() + y * prev_grad_x.cols();
const float* prev_grad_y_ptr = prev_grad_y.data() + y * prev_grad_y.cols();
for (int x = 0; x < track_width_; ++x, ++prev_grad_y_ptr, ++prev_grad_x_ptr) {
float diff = next_ptr[x] * weight[0] + next_ptr[x + 1] * weight[1] +
next_ptr[x + step] * weight[2] + next_ptr[x + step + 1] * weight[3] -
prev_ptr[x];
b[0] += *prev_grad_x_ptr * diff;
b[1] += *prev_grad_y_ptr * diff;
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::track(
const PointCloudInConstPtr& prev_input,
const PointCloudInConstPtr& input,
const std::vector<FloatImageConstPtr>& prev_pyramid,
const std::vector<FloatImageConstPtr>& pyramid,
const pcl::PointCloud<pcl::PointUV>::ConstPtr& prev_keypoints,
pcl::PointCloud<pcl::PointUV>::Ptr& keypoints,
std::vector<int>& status,
Eigen::Affine3f& motion) const
{
std::vector<Eigen::Array2f, Eigen::aligned_allocator<Eigen::Array2f>> next_pts(
prev_keypoints->size());
Eigen::Array2f half_win((track_width_ - 1) * 0.5f, (track_height_ - 1) * 0.5f);
pcl::TransformationFromCorrespondences transformation_computer;
const int nb_points = prev_keypoints->size();
for (int level = nb_levels_ - 1; level >= 0; --level) {
const FloatImage& prev = *(prev_pyramid[level * 3]);
const FloatImage& next = *(pyramid[level * 3]);
const FloatImage& grad_x = *(prev_pyramid[level * 3 + 1]);
const FloatImage& grad_y = *(prev_pyramid[level * 3 + 2]);
Eigen::ArrayXXf prev_win(track_height_, track_width_);
Eigen::ArrayXXf grad_x_win(track_height_, track_width_);
Eigen::ArrayXXf grad_y_win(track_height_, track_width_);
float ratio(1. / (1 << level));
for (int ptidx = 0; ptidx < nb_points; ptidx++) {
Eigen::Array2f prev_pt((*prev_keypoints)[ptidx].u * ratio,
(*prev_keypoints)[ptidx].v * ratio);
Eigen::Array2f next_pt;
if (level == nb_levels_ - 1)
next_pt = prev_pt;
else
next_pt = next_pts[ptidx] * 2.f;
next_pts[ptidx] = next_pt;
Eigen::Array2i iprev_point;
prev_pt -= half_win;
iprev_point[0] = std::floor(prev_pt[0]);
iprev_point[1] = std::floor(prev_pt[1]);
if (iprev_point[0] < -track_width_ ||
(std::uint32_t)iprev_point[0] >= grad_x.width ||
iprev_point[1] < -track_height_ ||
(std::uint32_t)iprev_point[1] >= grad_y.height) {
if (level == 0)
status[ptidx] = -1;
continue;
}
float a = prev_pt[0] - iprev_point[0];
float b = prev_pt[1] - iprev_point[1];
Eigen::Array4f weight;
weight[0] = (1.f - a) * (1.f - b);
weight[1] = a * (1.f - b);
weight[2] = (1.f - a) * b;
weight[3] = 1 - weight[0] - weight[1] - weight[2];
Eigen::Array3f covar = Eigen::Array3f::Zero();
spatialGradient(prev,
grad_x,
grad_y,
iprev_point,
weight,
prev_win,
grad_x_win,
grad_y_win,
covar);
float det = covar[0] * covar[2] - covar[1] * covar[1];
float min_eigenvalue = (covar[2] + covar[0] -
std::sqrt((covar[0] - covar[2]) * (covar[0] - covar[2]) +
4.f * covar[1] * covar[1])) /
2.f;
if (min_eigenvalue < min_eigenvalue_threshold_ ||
det < std::numeric_limits<float>::epsilon()) {
status[ptidx] = -2;
continue;
}
det = 1.f / det;
next_pt -= half_win;
Eigen::Array2f prev_delta(0, 0);
for (unsigned int j = 0; j < max_iterations_; j++) {
Eigen::Array2i inext_pt = next_pt.floor().cast<int>();
if (inext_pt[0] < -track_width_ || (std::uint32_t)inext_pt[0] >= next.width ||
inext_pt[1] < -track_height_ || (std::uint32_t)inext_pt[1] >= next.height) {
if (level == 0)
status[ptidx] = -1;
break;
}
a = next_pt[0] - inext_pt[0];
b = next_pt[1] - inext_pt[1];
weight[0] = (1.f - a) * (1.f - b);
weight[1] = a * (1.f - b);
weight[2] = (1.f - a) * b;
weight[3] = 1 - weight[0] - weight[1] - weight[2];
// compute mismatch vector
Eigen::Array2f beta = Eigen::Array2f::Zero();
mismatchVector(prev_win, grad_x_win, grad_y_win, next, inext_pt, weight, beta);
// optical flow resolution
Eigen::Vector2f delta((covar[1] * beta[1] - covar[2] * beta[0]) * det,
(covar[1] * beta[0] - covar[0] * beta[1]) * det);
// update position
next_pt[0] += delta[0];
next_pt[1] += delta[1];
next_pts[ptidx] = next_pt + half_win;
if (delta.squaredNorm() <= epsilon_)
break;
if (j > 0 && std::abs(delta[0] + prev_delta[0]) < 0.01 &&
std::abs(delta[1] + prev_delta[1]) < 0.01) {
next_pts[ptidx][0] -= delta[0] * 0.5f;
next_pts[ptidx][1] -= delta[1] * 0.5f;
break;
}
// update delta
prev_delta = delta;
}
// update tracked points
if (level == 0 && !status[ptidx]) {
Eigen::Array2f next_point = next_pts[ptidx] - half_win;
Eigen::Array2i inext_point;
inext_point[0] = std::floor(next_point[0]);
inext_point[1] = std::floor(next_point[1]);
if (inext_point[0] < -track_width_ ||
(std::uint32_t)inext_point[0] >= next.width ||
inext_point[1] < -track_height_ ||
(std::uint32_t)inext_point[1] >= next.height) {
status[ptidx] = -1;
continue;
}
// insert valid keypoint
pcl::PointUV n;
n.u = next_pts[ptidx][0];
n.v = next_pts[ptidx][1];
keypoints->push_back(n);
// add points pair to compute transformation
inext_point[0] = std::floor(next_pts[ptidx][0]);
inext_point[1] = std::floor(next_pts[ptidx][1]);
iprev_point[0] = std::floor((*prev_keypoints)[ptidx].u);
iprev_point[1] = std::floor((*prev_keypoints)[ptidx].v);
const PointInT& prev_pt =
(*prev_input)[iprev_point[1] * prev_input->width + iprev_point[0]];
const PointInT& next_pt =
(*input)[inext_point[1] * input->width + inext_point[0]];
transformation_computer.add(
prev_pt.getVector3fMap(), next_pt.getVector3fMap(), 1.0);
}
}
}
motion = transformation_computer.getTransformation();
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT>
void
PyramidalKLTTracker<PointInT, IntensityT>::computeTracking()
{
if (!initialized_)
return;
std::vector<FloatImageConstPtr> pyramid;
computePyramids(input_, pyramid, pcl::BORDER_REFLECT_101);
pcl::PointCloud<pcl::PointUV>::Ptr keypoints(new pcl::PointCloud<pcl::PointUV>);
keypoints->reserve(keypoints_->size());
std::vector<int> status(keypoints_->size(), 0);
track(ref_, input_, ref_pyramid_, pyramid, keypoints_, keypoints, status, motion_);
// swap reference and input
ref_ = input_;
ref_pyramid_ = pyramid;
keypoints_ = keypoints;
*keypoints_status_ = status;
}
} // namespace tracking
} // namespace pcl
#endif
pcl::PCLBase
PCL base class.
Definition pcl_base.h:70
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::push_back
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
Definition point_cloud.h:663
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition point_cloud.h:462
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition point_cloud.h:398
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition point_cloud.h:400
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::PointCloud::reserve
void reserve(std::size_t n)
Definition point_cloud.h:445
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition point_cloud.h:413
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition point_cloud.h:414
pcl::TransformationFromCorrespondences
Calculates a transformation based on corresponding 3D points.
Definition transformation_from_correspondences.h:50
pcl::TransformationFromCorrespondences::getTransformation
Eigen::Affine3f getTransformation()
Calculate the transformation that will best transform the points into their correspondences.
Definition transformation_from_correspondences.hpp:77
pcl::TransformationFromCorrespondences::add
void add(const Eigen::Vector3f &point, const Eigen::Vector3f &corresponding_point, float weight=1.0)
Add a new sample.
Definition transformation_from_correspondences.hpp:58
pcl::tracking::PyramidalKLTTracker::mismatchVector
void mismatchVector(const Eigen::ArrayXXf &prev, const Eigen::ArrayXXf &prev_grad_x, const Eigen::ArrayXXf &prev_grad_y, const FloatImage &next, const Eigen::Array2i &location, const Eigen::Array4f &weights, Eigen::Array2f &b) const
Definition pyramidal_klt.hpp:518
pcl::tracking::PyramidalKLTTracker::derivatives
void derivatives(const FloatImage &src, FloatImage &grad_x, FloatImage &grad_y) const
compute Scharr derivatives of a source cloud.
Definition pyramidal_klt.hpp:171
pcl::tracking::PyramidalKLTTracker::initCompute
bool initCompute() override
This method should get called before starting the actual computation.
Definition pyramidal_klt.hpp:98
pcl::tracking::PyramidalKLTTracker::PointCloudInConstPtr
typename PointCloudIn::ConstPtr PointCloudInConstPtr
Definition pyramidal_klt.h:69
pcl::tracking::PyramidalKLTTracker::FloatImagePtr
FloatImage::Ptr FloatImagePtr
Definition pyramidal_klt.h:71
pcl::tracking::PyramidalKLTTracker::setPointsToTrack
void setPointsToTrack(const pcl::PointIndicesConstPtr &points)
Provide a pointer to points to track.
Definition pyramidal_klt.hpp:79
pcl::tracking::PyramidalKLTTracker::computeTracking
void computeTracking() override
Abstract tracking method.
Definition pyramidal_klt.hpp:713
pcl::tracking::PyramidalKLTTracker::setTrackingWindowSize
void setTrackingWindowSize(int width, int height)
set the tracking window size
Definition pyramidal_klt.hpp:50
pcl::tracking::PyramidalKLTTracker::computePyramids
virtual void computePyramids(const PointCloudInConstPtr &input, std::vector< FloatImageConstPtr > &pyramid, pcl::InterpolationType border_type) const
Compute the pyramidal representation of an image.
Definition pyramidal_klt.hpp:364
pcl::tracking::PyramidalKLTTracker::downsample
void downsample(const FloatImageConstPtr &input, FloatImageConstPtr &output) const
downsample input
Definition pyramidal_klt.hpp:235
pcl::tracking::PyramidalKLTTracker::track
virtual void track(const PointCloudInConstPtr &previous_input, const PointCloudInConstPtr &current_input, const std::vector< FloatImageConstPtr > &previous_pyramid, const std::vector< FloatImageConstPtr > &current_pyramid, const pcl::PointCloud< pcl::PointUV >::ConstPtr &previous_keypoints, pcl::PointCloud< pcl::PointUV >::Ptr &current_keypoints, std::vector< int > &status, Eigen::Affine3f &motion) const
Definition pyramidal_klt.hpp:548
pcl::tracking::PyramidalKLTTracker::spatialGradient
virtual void spatialGradient(const FloatImage &img, const FloatImage &grad_x, const FloatImage &grad_y, const Eigen::Array2i &location, const Eigen::Array4f &weights, Eigen::ArrayXXf &win, Eigen::ArrayXXf &grad_x_win, Eigen::ArrayXXf &grad_y_win, Eigen::Array3f &covariance) const
extract the patch from the previous image, previous image gradients surrounding pixel allocation whil...
Definition pyramidal_klt.hpp:474
pcl::tracking::PyramidalKLTTracker::FloatImageConstPtr
FloatImage::ConstPtr FloatImageConstPtr
Definition pyramidal_klt.h:72
pcl::tracking::PyramidalKLTTracker::convolveRows
void convolveRows(const FloatImageConstPtr &input, FloatImage &output) const
Convolve image rows.
Definition pyramidal_klt.hpp:295
pcl::tracking::PyramidalKLTTracker::convolveCols
void convolveCols(const FloatImageConstPtr &input, FloatImage &output) const
Convolve image columns.
Definition pyramidal_klt.hpp:329
pcl::tracking::PyramidalKLTTracker::convolve
void convolve(const FloatImageConstPtr &input, FloatImage &output) const
Separately convolve image with decomposable convolution kernel.
Definition pyramidal_klt.hpp:284
time.h
Define methods for measuring time spent in code blocks.
pcl::copyPointCloud
void copyPointCloud(const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out)
Copy all the fields from a given point cloud into a new point cloud.
Definition io.hpp:142
pcl
Definition convolution.h:46
pcl::InterpolationType
InterpolationType
Definition io.h:255
pcl::BORDER_REFLECT_101
@ BORDER_REFLECT_101
Definition io.h:258
pcl::BORDER_CONSTANT
@ BORDER_CONSTANT
Definition io.h:256
pcl::PointIndicesConstPtr
PointIndices::ConstPtr PointIndicesConstPtr
Definition PointIndices.h:24
pcl::PointUV
A 2D point structure representing pixel image coordinates.
Definition point_types.hpp:772
pcl::PointUV::u
float u
Definition point_types.hpp:773
pcl::PointUV::v
float v
Definition point_types.hpp:774