template <typename PointInT, typename PointNT, typename PointOutT> void
pcl16::PPFRGBRegionEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
PCL16_INFO ("before computing output size: %u\n", output.size ());
output.resize (indices_->size ());
for (int index_i = 0; index_i < static_cast<int> (indices_->size ()); ++index_i)
{
int i = (*indices_)[index_i];
std::vector<int> nn_indices;
std::vector<float> nn_distances;
tree_->radiusSearch (i, static_cast<float> (search_radius_), nn_indices, nn_distances);
PointOutT average_feature_nn;
average_feature_nn.alpha_m = 0;
average_feature_nn.f1 = average_feature_nn.f2 = average_feature_nn.f3 = average_feature_nn.f4 =
average_feature_nn.r_ratio = average_feature_nn.g_ratio = average_feature_nn.b_ratio = 0.0f;
for (std::vector<int>::iterator nn_it = nn_indices.begin (); nn_it != nn_indices.end (); ++nn_it)
{
int j = *nn_it;
if (i != j)
{
float f1, f2, f3, f4, r_ratio, g_ratio, b_ratio;
if (pcl16::computeRGBPairFeatures
(input_->points[i].getVector4fMap (), normals_->points[i].getNormalVector4fMap (), input_->points[i].getRGBVector4i (),
input_->points[j].getVector4fMap (), normals_->points[j].getNormalVector4fMap (), input_->points[j].getRGBVector4i (),
f1, f2, f3, f4, r_ratio, g_ratio, b_ratio))
{
average_feature_nn.f1 += f1;
average_feature_nn.f2 += f2;
average_feature_nn.f3 += f3;
average_feature_nn.f4 += f4;
average_feature_nn.r_ratio += r_ratio;
average_feature_nn.g_ratio += g_ratio;
average_feature_nn.b_ratio += b_ratio;
}
else
{
PCL16_ERROR ("[pcl16::%s::computeFeature] Computing pair feature vector between points %zu and %zu went wrong.\n", getClassName ().c_str (), i, j);
}
}
}
float normalization_factor = static_cast<float> (nn_indices.size ());
average_feature_nn.f1 /= normalization_factor;
average_feature_nn.f2 /= normalization_factor;
average_feature_nn.f3 /= normalization_factor;
average_feature_nn.f4 /= normalization_factor;
average_feature_nn.r_ratio /= normalization_factor;
average_feature_nn.g_ratio /= normalization_factor;
average_feature_nn.b_ratio /= normalization_factor;
output.points[index_i] = average_feature_nn;
}
PCL16_INFO ("Output size: %u\n", output.points.size ());
}
template <typename PointInT, typename PointOutT, typename IntensityT> void
pcl::BriskKeypoint2D<PointInT, PointOutT, IntensityT>::detectKeypoints (PointCloudOut &output)
{
// image size
const int width = int (input_->width);
const int height = int (input_->height);
// destination for intensity data; will be forwarded to BRISK
std::vector<unsigned char> image_data (width*height);
for (size_t i = 0; i < image_data.size (); ++i)
image_data[i] = static_cast<unsigned char> (intensity_ ((*input_)[i]));
pcl::keypoints::brisk::ScaleSpace brisk_scale_space (octaves_);
brisk_scale_space.constructPyramid (image_data, width, height);
// Check if the template types are the same. If true, avoid a copy.
// The PointOutT MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT macro!
if (isSamePointType<PointOutT, pcl::PointWithScale> ())
brisk_scale_space.getKeypoints (threshold_, output.points);
else
{
pcl::PointCloud<pcl::PointWithScale> output_temp;
brisk_scale_space.getKeypoints (threshold_, output_temp.points);
pcl::copyPointCloud<pcl::PointWithScale, PointOutT> (output_temp, output);
}
// we do not change the denseness
output.width = int (output.points.size ());
output.height = 1;
output.is_dense = false; // set to false to be sure
// 2nd pass to remove the invalid set of 3D keypoints
if (remove_invalid_3D_keypoints_)
{
PointCloudOut output_clean;
for (size_t i = 0; i < output.size (); ++i)
{
PointOutT pt;
// Interpolate its position in 3D, as the "u" and "v" are subpixel accurate
bilinearInterpolation (input_, output[i].x, output[i].y, pt);
// Check if the point is finite
if (pcl::isFinite (pt))
output_clean.push_back (output[i]);
}
output = output_clean;
output.is_dense = true; // set to true as there's no keypoint at an invalid XYZ
}
}
template <typename PointInT, typename PointOutT, typename NormalT> void
pcl::TrajkovicKeypoint3D<PointInT, PointOutT, NormalT>::detectKeypoints (PointCloudOut &output)
{
response_.reset (new pcl::PointCloud<float> (input_->width, input_->height));
const Normals &normals = *normals_;
const PointCloudIn &input = *input_;
pcl::PointCloud<float>& response = *response_;
const int w = static_cast<int> (input_->width) - half_window_size_;
const int h = static_cast<int> (input_->height) - half_window_size_;
if (method_ == FOUR_CORNERS)
{
#ifdef _OPENMP
#pragma omp parallel for num_threads (threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT ¢er = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
// Get rid of isolated points
if (!count) continue;
float sn1 = squaredNormalsDiff (up, center);
float sn2 = squaredNormalsDiff (down, center);
float r1 = sn1 + sn2;
float r2 = squaredNormalsDiff (right, center) + squaredNormalsDiff (left, center);
float d = std::min (r1, r2);
if (d < first_threshold_) continue;
sn1 = sqrt (sn1);
sn2 = sqrt (sn2);
float b1 = normalsDiff (right, up) * sn1;
b1+= normalsDiff (left, down) * sn2;
float b2 = normalsDiff (right, down) * sn2;
b2+= normalsDiff (left, up) * sn1;
float B = std::min (b1, b2);
float A = r2 - r1 - 2*B;
response (i,j) = ((B < 0) && ((B + A) > 0)) ? r1 - ((B*B)/A) : d;
}
}
}
else
{
#ifdef _OPENMP
#pragma omp parallel for num_threads (threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT ¢er = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
const NormalT &upleft = getNormalOrNull (i-half_window_size_, j-half_window_size_, count);
const NormalT &upright = getNormalOrNull (i+half_window_size_, j-half_window_size_, count);
const NormalT &downleft = getNormalOrNull (i-half_window_size_, j+half_window_size_, count);
const NormalT &downright = getNormalOrNull (i+half_window_size_, j+half_window_size_, count);
// Get rid of isolated points
if (!count) continue;
std::vector<float> r (4,0);
r[0] = squaredNormalsDiff (up, center);
r[0]+= squaredNormalsDiff (down, center);
r[1] = squaredNormalsDiff (upright, center);
r[1]+= squaredNormalsDiff (downleft, center);
r[2] = squaredNormalsDiff (right, center);
r[2]+= squaredNormalsDiff (left, center);
r[3] = squaredNormalsDiff (downright, center);
r[3]+= squaredNormalsDiff (upleft, center);
float d = *(std::min_element (r.begin (), r.end ()));
if (d < first_threshold_) continue;
std::vector<float> B (4,0);
std::vector<float> A (4,0);
std::vector<float> sumAB (4,0);
B[0] = normalsDiff (upright, up) * normalsDiff (up, center);
B[0]+= normalsDiff (downleft, down) * normalsDiff (down, center);
B[1] = normalsDiff (right, upright) * normalsDiff (upright, center);
B[1]+= normalsDiff (left, downleft) * normalsDiff (downleft, center);
B[2] = normalsDiff (downright, right) * normalsDiff (downright, center);
B[2]+= normalsDiff (upleft, left) * normalsDiff (upleft, center);
B[3] = normalsDiff (down, downright) * normalsDiff (downright, center);
B[3]+= normalsDiff (up, upleft) * normalsDiff (upleft, center);
A[0] = r[1] - r[0] - B[0] - B[0];
A[1] = r[2] - r[1] - B[1] - B[1];
A[2] = r[3] - r[2] - B[2] - B[2];
A[3] = r[0] - r[3] - B[3] - B[3];
sumAB[0] = A[0] + B[0];
sumAB[1] = A[1] + B[1];
sumAB[2] = A[2] + B[2];
sumAB[3] = A[3] + B[3];
if ((*std::max_element (B.begin (), B.end ()) < 0) &&
(*std::min_element (sumAB.begin (), sumAB.end ()) > 0))
{
std::vector<float> D (4,0);
D[0] = B[0] * B[0] / A[0];
D[1] = B[1] * B[1] / A[1];
D[2] = B[2] * B[2] / A[2];
D[3] = B[3] * B[3] / A[3];
response (i,j) = *(std::min (D.begin (), D.end ()));
}
else
response (i,j) = d;
}
}
}
// Non maximas suppression
std::vector<int> indices = *indices_;
std::sort (indices.begin (), indices.end (),
boost::bind (&TrajkovicKeypoint3D::greaterCornernessAtIndices, this, _1, _2));
output.clear ();
output.reserve (input_->size ());
std::vector<bool> occupency_map (indices.size (), false);
const int width (input_->width);
const int height (input_->height);
const int occupency_map_size (indices.size ());
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads (threads_)
#endif
for (int i = 0; i < indices.size (); ++i)
{
int idx = indices[i];
if ((response_->points[idx] < second_threshold_) || occupency_map[idx])
continue;
PointOutT p;
p.getVector3fMap () = input_->points[idx].getVector3fMap ();
p.intensity = response_->points [idx];
#ifdef _OPENMP
#pragma omp critical
#endif
{
output.push_back (p);
keypoints_indices_->indices.push_back (idx);
}
const int x = idx % width;
const int y = idx / width;
const int u_end = std::min (width, x + half_window_size_);
const int v_end = std::min (height, y + half_window_size_);
for(int v = std::max (0, y - half_window_size_); v < v_end; ++v)
for(int u = std::max (0, x - half_window_size_); u < u_end; ++u)
occupency_map[v*width + u] = true;
}
output.height = 1;
output.width = static_cast<uint32_t> (output.size());
// we don not change the denseness
output.is_dense = true;
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::process (PointCloudOut &output)
{
// Check if normals have to be computed/saved
if (compute_normals_)
{
normals_.reset (new NormalCloud);
// Copy the header
normals_->header = input_->header;
// Clear the fields in case the method exits before computation
normals_->width = normals_->height = 0;
normals_->points.clear ();
}
// Copy the header
output.header = input_->header;
output.width = output.height = 0;
output.points.clear ();
if (search_radius_ <= 0 || sqr_gauss_param_ <= 0)
{
PCL_ERROR ("[pcl::%s::reconstruct] Invalid search radius (%f) or Gaussian parameter (%f)!\n", getClassName ().c_str (), search_radius_, sqr_gauss_param_);
return;
}
if (!initCompute ())
return;
// Initialize the spatial locator
if (!tree_)
{
KdTreePtr tree;
if (input_->isOrganized ())
tree.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
else
tree.reset (new pcl::search::KdTree<PointInT> (false));
setSearchMethod (tree);
}
// Send the surface dataset to the spatial locator
tree_->setInputCloud (input_, indices_);
// Initialize random number generator if necessary
switch (upsample_method_)
{
case (RANDOM_UNIFORM_DENSITY):
{
boost::mt19937 *rng = new boost::mt19937 (static_cast<unsigned int>(std::time(0)));
float tmp = static_cast<float> (search_radius_ / 2.0f);
boost::uniform_real<float> *uniform_distrib = new boost::uniform_real<float> (-tmp, tmp);
rng_uniform_distribution_ = new boost::variate_generator<boost::mt19937, boost::uniform_real<float> > (*rng, *uniform_distrib);
break;
}
case (VOXEL_GRID_DILATION):
{
mls_results_.resize (input_->size ());
break;
}
default:
break;
}
// Perform the actual surface reconstruction
performProcessing (output);
if (compute_normals_)
{
normals_->height = 1;
normals_->width = static_cast<uint32_t> (normals_->size ());
// TODO!!! MODIFY TO PER-CLOUD COPYING - much faster than per-point
for (unsigned int i = 0; i < output.size (); ++i)
{
typedef typename pcl::traits::fieldList<PointOutT>::type FieldList;
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_x", normals_->points[i].normal_x));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_y", normals_->points[i].normal_y));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_z", normals_->points[i].normal_z));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "curvature", normals_->points[i].curvature));
}
}
// Set proper widths and heights for the clouds
output.height = 1;
output.width = static_cast<uint32_t> (output.size ());
deinitCompute ();
}
template <typename PointInT, typename PointOutT, typename IntensityT> void
pcl::HarrisKeypoint2D<PointInT, PointOutT, IntensityT>::detectKeypoints (PointCloudOut &output)
{
derivatives_cols_.resize (input_->width, input_->height);
derivatives_rows_.resize (input_->width, input_->height);
//Compute cloud intensities first derivatives along columns and rows
//!!! nsallem 20120220 : we don't test here for density so if one term in nan the result is nan
int w = static_cast<int> (input_->width) - 1;
int h = static_cast<int> (input_->height) - 1;
// j = 0 --> j-1 out of range ; use 0
// i = 0 --> i-1 out of range ; use 0
derivatives_cols_(0,0) = (intensity_ ((*input_) (0,1)) - intensity_ ((*input_) (0,0))) * 0.5;
derivatives_rows_(0,0) = (intensity_ ((*input_) (1,0)) - intensity_ ((*input_) (0,0))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int i = 1; i < w; ++i)
{
derivatives_cols_(i,0) = (intensity_ ((*input_) (i,1)) - intensity_ ((*input_) (i,0))) * 0.5;
}
derivatives_rows_(w,0) = (intensity_ ((*input_) (w,0)) - intensity_ ((*input_) (w-1,0))) * 0.5;
derivatives_cols_(w,0) = (intensity_ ((*input_) (w,1)) - intensity_ ((*input_) (w,0))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, derivatives_rows_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int j = 1; j < h; ++j)
{
// i = 0 --> i-1 out of range ; use 0
derivatives_rows_(0,j) = (intensity_ ((*input_) (1,j)) - intensity_ ((*input_) (0,j))) * 0.5;
for(int i = 1; i < w; ++i)
{
// derivative with respect to rows
derivatives_rows_(i,j) = (intensity_ ((*input_) (i+1,j)) - intensity_ ((*input_) (i-1,j))) * 0.5;
// derivative with respect to cols
derivatives_cols_(i,j) = (intensity_ ((*input_) (i,j+1)) - intensity_ ((*input_) (i,j-1))) * 0.5;
}
// i = w --> w+1 out of range ; use w
derivatives_rows_(w,j) = (intensity_ ((*input_) (w,j)) - intensity_ ((*input_) (w-1,j))) * 0.5;
}
// j = h --> j+1 out of range use h
derivatives_cols_(0,h) = (intensity_ ((*input_) (0,h)) - intensity_ ((*input_) (0,h-1))) * 0.5;
derivatives_rows_(0,h) = (intensity_ ((*input_) (1,h)) - intensity_ ((*input_) (0,h))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int i = 1; i < w; ++i)
{
derivatives_cols_(i,h) = (intensity_ ((*input_) (i,h)) - intensity_ ((*input_) (i,h-1))) * 0.5;
}
derivatives_rows_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w-1,h))) * 0.5;
derivatives_cols_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w,h-1))) * 0.5;
float highest_response_;
switch (method_)
{
case HARRIS:
responseHarris(*response_, highest_response_);
break;
case NOBLE:
responseNoble(*response_, highest_response_);
break;
case LOWE:
responseLowe(*response_, highest_response_);
break;
case TOMASI:
responseTomasi(*response_, highest_response_);
break;
}
if (!nonmax_)
output = *response_;
else
{
threshold_*= highest_response_;
std::sort (indices_->begin (), indices_->end (),
boost::bind (&HarrisKeypoint2D::greaterIntensityAtIndices, this, _1, _2));
output.clear ();
output.reserve (response_->size());
std::vector<bool> occupency_map (response_->size (), false);
int width (response_->width);
int height (response_->height);
const int occupency_map_size (occupency_map.size ());
#ifdef _OPENMP
#pragma omp parallel for shared (output, occupency_map) private (width, height) num_threads(threads_)
#endif
for (int idx = 0; idx < occupency_map_size; ++idx)
{
if (occupency_map[idx] || response_->points [indices_->at (idx)].intensity < threshold_ || !isFinite (response_->points[idx]))
continue;
#ifdef _OPENMP
#pragma omp critical
#endif
output.push_back (response_->at (indices_->at (idx)));
int u_end = std::min (width, indices_->at (idx) % width + min_distance_);
int v_end = std::min (height, indices_->at (idx) / width + min_distance_);
for(int u = std::max (0, indices_->at (idx) % width - min_distance_); u < u_end; ++u)
for(int v = std::max (0, indices_->at (idx) / width - min_distance_); v < v_end; ++v)
occupency_map[v*input_->width+u] = true;
}
// if (refine_)
// refineCorners (output);
output.height = 1;
output.width = static_cast<uint32_t> (output.size());
}
// we don not change the denseness
output.is_dense = input_->is_dense;
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::process (PointCloudOut &output)
{
// Reset or initialize the collection of indices
corresponding_input_indices_.reset (new PointIndices);
// Check if normals have to be computed/saved
if (compute_normals_)
{
normals_.reset (new NormalCloud);
// Copy the header
normals_->header = input_->header;
// Clear the fields in case the method exits before computation
normals_->width = normals_->height = 0;
normals_->points.clear ();
}
// Copy the header
output.header = input_->header;
output.width = output.height = 0;
output.points.clear ();
if (search_radius_ <= 0 || sqr_gauss_param_ <= 0)
{
PCL_ERROR ("[pcl::%s::process] Invalid search radius (%f) or Gaussian parameter (%f)!\n", getClassName ().c_str (), search_radius_, sqr_gauss_param_);
return;
}
// Check if distinct_cloud_ was set
if (upsample_method_ == DISTINCT_CLOUD && !distinct_cloud_)
{
PCL_ERROR ("[pcl::%s::process] Upsample method was set to DISTINCT_CLOUD, but no distinct cloud was specified.\n", getClassName ().c_str ());
return;
}
if (!initCompute ())
return;
// Initialize the spatial locator
if (!tree_)
{
KdTreePtr tree;
if (input_->isOrganized ())
tree.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
else
tree.reset (new pcl::search::KdTree<PointInT> (false));
setSearchMethod (tree);
}
// Send the surface dataset to the spatial locator
tree_->setInputCloud (input_);
switch (upsample_method_)
{
// Initialize random number generator if necessary
case (RANDOM_UNIFORM_DENSITY):
{
rng_alg_.seed (static_cast<unsigned> (std::time (0)));
float tmp = static_cast<float> (search_radius_ / 2.0f);
boost::uniform_real<float> uniform_distrib (-tmp, tmp);
rng_uniform_distribution_.reset (new boost::variate_generator<boost::mt19937&, boost::uniform_real<float> > (rng_alg_, uniform_distrib));
break;
}
case (VOXEL_GRID_DILATION):
case (DISTINCT_CLOUD):
{
if (!cache_mls_results_)
PCL_WARN ("The cache mls results is forced when using upsampling method VOXEL_GRID_DILATION or DISTINCT_CLOUD.\n");
cache_mls_results_ = true;
break;
}
default:
break;
}
if (cache_mls_results_)
{
mls_results_.resize (input_->size ());
}
else
{
mls_results_.resize (1); // Need to have a reference to a single dummy result.
}
// Perform the actual surface reconstruction
performProcessing (output);
if (compute_normals_)
{
normals_->height = 1;
normals_->width = static_cast<uint32_t> (normals_->size ());
for (unsigned int i = 0; i < output.size (); ++i)
{
typedef typename pcl::traits::fieldList<PointOutT>::type FieldList;
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_x", normals_->points[i].normal_x));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_y", normals_->points[i].normal_y));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "normal_z", normals_->points[i].normal_z));
pcl::for_each_type<FieldList> (SetIfFieldExists<PointOutT, float> (output.points[i], "curvature", normals_->points[i].curvature));
}
}
// Set proper widths and heights for the clouds
output.height = 1;
output.width = static_cast<uint32_t> (output.size ());
deinitCompute ();
}
