pyramidal_klt.hpp
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#ifndef PCL_TRACKING_IMPL_PYRAMIDAL_KLT_HPP
#define PCL_TRACKING_IMPL_PYRAMIDAL_KLT_HPP
#include <pcl/common/time.h>
#include <pcl/common/utils.h>
#include <pcl/common/io.h>
#include <pcl/common/utils.h>
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT> inline void
pcl::tracking::PyramidalKLTTracker<PointInT, IntensityT>::setTrackingWindowSize (int width, int height)
{
track_width_ = width;
track_height_ = height;
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT> inline void
pcl::tracking::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->points[i]);
keypoints_ = p;
}
keypoints_status_.reset (new pcl::PointIndices);
keypoints_status_->indices.resize (keypoints_->size (), 0);
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT> inline void
pcl::tracking::PyramidalKLTTracker<PointInT, IntensityT>::setPointsToTrack (const pcl::PointIndicesConstPtr& points)
{
assert ((input_ || ref_) && "[pcl::tracking::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
pcl::tracking::PyramidalKLTTracker<PointInT, IntensityT>::initCompute ()
{
// std::cout << ">>> [PyramidalKLTTracker::initCompute]" << std::endl;
if (!PCLBase<PointInT>::initCompute ())
{
PCL_ERROR ("[pcl::tracking::%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!",
tracker_name_.c_str ());
return (false);
}
if (!keypoints_ || keypoints_->empty ())
{
PCL_ERROR ("[pcl::tracking::%s::initCompute] No keypoints aborting!",
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!",
tracker_name_.c_str ());
return (false);
}
if (nb_levels_ > 5)
{
PCL_ERROR ("[pcl::tracking::%s::initCompute] Number of pyramid levels should not exceed 5!",
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
pcl::tracking::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.points[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.points[y * width]);
float* grad_y_row = &(grad_y.points[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;
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename IntensityT> void
pcl::tracking::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));
#ifdef _OPENMP
#pragma omp parallel for shared (output) firstprivate (ii) num_threads (threads_)
#endif
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
pcl::tracking::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
pcl::tracking::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
pcl::tracking::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;
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads (threads_)
#endif
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
pcl::tracking::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;
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads (threads_)
#endif
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
pcl::tracking::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));
#ifdef _OPENMP
#pragma omp parallel for num_threads (threads_)
#endif
for (int i = 0; i < static_cast<int> (input->size ()); ++i)
tmp->points[i] = intensity_ (input->points[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
pcl::tracking::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.points[0]) + (y + location[1])*step + location[0];
const float* grad_x_ptr = &(grad_x.points[0]) + (y + location[1])*step + location[0];
const float* grad_y_ptr = &(grad_y.points[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
pcl::tracking::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.points[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
pcl::tracking::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->points[ptidx].u * ratio,
prev_keypoints->points[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->points[ptidx].u);
iprev_point[1] = std::floor (prev_keypoints->points[ptidx].v);
const PointInT& prev_pt = prev_input->points[iprev_point[1]*prev_input->width + iprev_point[0]];
const PointInT& next_pt = input->points[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
pcl::tracking::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_->indices = status;
}
#endif
pcl::PointUV
A 2D point structure representing pixel image coordinates.
Definition: point_types.hpp:749
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:443
pcl::uint32_t
std::uint32_t uint32_t
Definition: pcl_macros.h:96
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:402
pcl::tracking::PyramidalKLTTracker::convolveCols
void convolveCols(const FloatImageConstPtr &input, FloatImage &output) const
Convolve image columns.
Definition: pyramidal_klt.hpp:302
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:397
pcl::PointUV::u
float u
Definition: point_types.hpp:751
pcl::tracking::PyramidalKLTTracker::downsample
void downsample(const FloatImageConstPtr &input, FloatImageConstPtr &output) const
downsample input
Definition: pyramidal_klt.hpp:219
pcl::PointUV::v
float v
Definition: point_types.hpp:752
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:164
pcl::PointCloud::resize
void resize(std::size_t n)
Resize the cloud.
Definition: point_cloud.h:442
pcl::copyPointCloud
PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out)
Extract the indices of a given point cloud as a new point cloud.
pcl::BORDER_CONSTANT
Definition: io.h:256
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::tracking::PyramidalKLTTracker::initCompute
bool initCompute() override
This method should get called before starting the actual computation.
Definition: pyramidal_klt.hpp:93
pcl::PointCloud< pcl::PointUV >
pcl::TransformationFromCorrespondences
Calculates a transformation based on corresponding 3D points.
Definition: transformation_from_correspondences.h:48
pcl::BORDER_REFLECT_101
Definition: io.h:258
pcl::tracking::PyramidalKLTTracker::FloatImageConstPtr
FloatImage::ConstPtr FloatImageConstPtr
Definition: pyramidal_klt.h:72
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:400
pcl::tracking::PyramidalKLTTracker::computeTracking
void computeTracking() override
Abstract tracking method.
Definition: pyramidal_klt.hpp:622
pcl::PointIndicesConstPtr
PointIndices::ConstPtr PointIndicesConstPtr
Definition: PointIndices.h:27
pcl::tracking::PyramidalKLTTracker::convolveRows
void convolveRows(const FloatImageConstPtr &input, FloatImage &output) const
Convolve image rows.
Definition: pyramidal_klt.hpp:271
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:334
pcl::tracking::PyramidalKLTTracker::setTrackingWindowSize
void setTrackingWindowSize(int width, int height)
set the tracking window size
Definition: pyramidal_klt.hpp:48
pcl::tracking::PyramidalKLTTracker::convolve
void convolve(const FloatImageConstPtr &input, FloatImage &output) const
Separately convolve image with decomposable convolution kernel.
Definition: pyramidal_klt.hpp:262
pcl::PointCloud::reserve
void reserve(std::size_t n)
Definition: point_cloud.h:436
pcl::tracking::PyramidalKLTTracker::PointCloudInConstPtr
typename PointCloudIn::ConstPtr PointCloudInConstPtr
Definition: pyramidal_klt.h:69
pcl::InterpolationType
InterpolationType
Definition: io.h:254
pcl::tracking::PyramidalKLTTracker::FloatImagePtr
FloatImage::Ptr FloatImagePtr
Definition: pyramidal_klt.h:71
pcl::PointIndices
Definition: PointIndices.h:12
pcl::TransformationFromCorrespondences::getTransformation
Eigen::Affine3f getTransformation()
Calculate the transformation that will best transform the points into their correspondences.
Definition: transformation_from_correspondences.hpp:72
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:435
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:472
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:53
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:415
time.h
pcl::tracking::PyramidalKLTTracker::setPointsToTrack
void setPointsToTrack(const pcl::PointIndicesConstPtr &points)
Provide a pointer to points to track.
Definition: pyramidal_klt.hpp:75
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:416
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:467
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 alocation while...
Definition: pyramidal_klt.hpp:402