diff --git a/VirtualRobot/CMakeLists.txt b/VirtualRobot/CMakeLists.txt
index 8b96818067c5d661b29f374e3f83364b285cda54..513182ca01f4964af32465ff4cbc870c30dcf212 100644
--- a/VirtualRobot/CMakeLists.txt
+++ b/VirtualRobot/CMakeLists.txt
@@ -246,6 +246,8 @@ VirtualRobotTest.h
ManipulationObject.h
BoundingBox.h
RuntimeEnvironment.h
+DataStructures/nanoflann.hpp
+DataStructures/KdTreePointCloud.h
Compression/CompressionRLE.h
Compression/CompressionBZip2.h
SphereApproximator.h
diff --git a/VirtualRobot/CollisionDetection/CollisionModel.cpp b/VirtualRobot/CollisionDetection/CollisionModel.cpp
index ec72ba0694eb2871dae4ea9eabd77852f14c595d..be073751967658482681d3b71c94cb5573cfad66 100644
--- a/VirtualRobot/CollisionDetection/CollisionModel.cpp
+++ b/VirtualRobot/CollisionDetection/CollisionModel.cpp
@@ -11,13 +11,13 @@
namespace VirtualRobot
{
- CollisionModel::CollisionModel(VisualizationNodePtr visu, const std::string& name, CollisionCheckerPtr colChecker, int id)
+ CollisionModel::CollisionModel(VisualizationNodePtr visu, const std::string& name, CollisionCheckerPtr colChecker, int id, float margin)
{
globalPose = Eigen::Matrix4f::Identity();
this->id = id;
this->name = name;
-
+ this->margin = margin;
this->colChecker = colChecker;
if (!this->colChecker)
@@ -31,12 +31,12 @@ namespace VirtualRobot
}
updateVisualization = true;
-
setVisualization(visu);
}
CollisionModel::CollisionModel(VisualizationNodePtr visu, const std::string &name, CollisionCheckerPtr colChecker, int id, InternalCollisionModelPtr collisionModel)
{
+ margin = 0.0;
globalPose = Eigen::Matrix4f::Identity();
this->id = id;
@@ -59,17 +59,7 @@ namespace VirtualRobot
VR_WARNING << "internal collision model is NULL for " << name << endl;
collisionModelImplementation = boost::dynamic_pointer_cast<InternalCollisionModel>(collisionModel->clone(false));
VR_ASSERT(collisionModelImplementation->getPQPModel());
- visualization = visu;
- if (visu)
- {
- this->model = visu->getTriMeshModel();
-
- if (this->model)
- {
- bbox = this->model->boundingBox;
- }
- }
-
+ setVisualization(visu);
}
@@ -83,6 +73,36 @@ namespace VirtualRobot
{
}
+ float CollisionModel::getMargin() const
+ {
+ return margin;
+ }
+
+ void CollisionModel::inflateModel(float value)
+ {
+ if(margin != value || origVisualization && !model)
+ {
+ visualization = origVisualization->clone(true);
+ visualization->shrinkFatten(value);
+ model = visualization->getTriMeshModel();
+ if (model)
+ {
+ bbox = model->boundingBox;
+ }
+
+
+#if defined(VR_COLLISION_DETECTION_PQP)
+ collisionModelImplementation.reset(new CollisionModelPQP(model, colChecker, id));
+#else
+ collisionModelImplementation.reset(new CollisionModelDummy(colChecker));
+#endif
+ }
+ if(!origVisualization)
+ margin = 0.0;
+ else
+ margin = value;
+ }
+
std::string CollisionModel::getName()
{
@@ -102,21 +122,20 @@ namespace VirtualRobot
VirtualRobot::CollisionModelPtr CollisionModel::clone(CollisionCheckerPtr colChecker, float scaling, bool deepVisuMesh)
{
- VisualizationNodePtr visuNew;
+ VisualizationNodePtr visuOrigNew;
- if (visualization)
- {
- visuNew = visualization->clone(deepVisuMesh, scaling);
- }
+ if(origVisualization)
+ visuOrigNew = origVisualization->clone(deepVisuMesh, scaling);
std::string nameNew = name;
int idNew = id;
CollisionModelPtr p;
if(deepVisuMesh || !this->collisionModelImplementation)
- p.reset(new CollisionModel(visuNew, nameNew, colChecker, idNew));
+ p.reset(new CollisionModel(visuOrigNew, nameNew, colChecker, idNew, margin));
else
- p.reset(new CollisionModel(visuNew, nameNew, colChecker, idNew, this->collisionModelImplementation));
+ p.reset(new CollisionModel(visuOrigNew, nameNew, colChecker, idNew, this->collisionModelImplementation));
+ p->margin = margin;
p->setGlobalPose(getGlobalPose());
p->setUpdateVisualization(getUpdateVisualizationStatus());
return p;
@@ -125,24 +144,11 @@ namespace VirtualRobot
void CollisionModel::setVisualization(const VisualizationNodePtr visu)
{
visualization = visu;
+ origVisualization = visu;
model.reset();
bbox.clear();
- if (visu)
- {
- model = visu->getTriMeshModel();
-
- if (model)
- {
- bbox = model->boundingBox;
- }
- }
-
-#if defined(VR_COLLISION_DETECTION_PQP)
- collisionModelImplementation.reset(new CollisionModelPQP(model, colChecker, id));
-#else
- collisionModelImplementation.reset(new CollisionModelDummy(colChecker));
-#endif
+ inflateModel(margin); // updates the model
}
int CollisionModel::getId()
diff --git a/VirtualRobot/CollisionDetection/CollisionModel.h b/VirtualRobot/CollisionDetection/CollisionModel.h
index f37952cb80b89ce1f8a3f85d25dd18efec7e73e0..ecfd9d5eac160663c81f0a608dc3179cb1097d37 100644
--- a/VirtualRobot/CollisionDetection/CollisionModel.h
+++ b/VirtualRobot/CollisionDetection/CollisionModel.h
@@ -65,8 +65,7 @@ namespace VirtualRobot
\param colChecker If not specified, the global singleton instance is used. Only useful, when parallel collision checks should be performed.
\param id A user id.
*/
- CollisionModel(const VisualizationNodePtr visu, const std::string& name = "", CollisionCheckerPtr colChecker = CollisionCheckerPtr(), int id = 666);
- CollisionModel(VisualizationNodePtr visu, const std::string& name, CollisionCheckerPtr colChecker, int id, InternalCollisionModelPtr collisionModel);
+ CollisionModel(const VisualizationNodePtr visu, const std::string& name = "", CollisionCheckerPtr colChecker = CollisionCheckerPtr(), int id = 666, float margin = 0.0f);
/*!Standard Destructor
*/
virtual ~CollisionModel();
@@ -163,15 +162,26 @@ namespace VirtualRobot
virtual void scale(Eigen::Vector3f& scaleFactor);
+ float getMargin() const;
+
+ /**
+ * @brief in/deflates the model. If value is <0 the model is deflated.
+ * @param margin Each vertex of the model is moved about this margin along its normal.
+ */
+ void inflateModel(float margin);
+
protected:
+ // internal constructor needed for flat copy of internal collision model
+ CollisionModel(VisualizationNodePtr visu, const std::string& name, CollisionCheckerPtr colChecker, int id, InternalCollisionModelPtr collisionModel);
//! delete all data
void destroyData();
- VisualizationNodePtr visualization; // this is the original visualization
+ VisualizationNodePtr visualization; // this is the modified visualization
+ VisualizationNodePtr origVisualization; // this is the original visualization
VisualizationNodePtr modelVisualization; // this is the visualization of the trimeshmodel
bool updateVisualization;
TriMeshModelPtr model;
-
+ float margin; // inflates the model with this margin (in mm)
BoundingBox bbox;
//! the name
diff --git a/VirtualRobot/DataStructures/KdTreePointCloud.h b/VirtualRobot/DataStructures/KdTreePointCloud.h
new file mode 100644
index 0000000000000000000000000000000000000000..502008a5d2bf0cd5b368ebf53bfb1474dfa532db
--- /dev/null
+++ b/VirtualRobot/DataStructures/KdTreePointCloud.h
@@ -0,0 +1,72 @@
+/*
+ * This file is part of ArmarX.
+ *
+ * Copyright (C) 2011-2016, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology (KIT), all rights reserved.
+ *
+ * ArmarX is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License version 2 as
+ * published by the Free Software Foundation.
+ *
+ * ArmarX is distributed in the hope that it will be useful, but
+ * WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program. If not, see <http://www.gnu.org/licenses/>.
+ *
+ * @package ArmarX
+ * @author Mirko Waechter( mirko.waechter at kit dot edu)
+ * @date 2016
+ * @copyright http://www.gnu.org/licenses/gpl-2.0.txt
+ * GNU General Public License
+ */
+#ifndef _VirtualRobot_KDTREEPOINTCLOUD_H
+#define _VirtualRobot_KDTREEPOINTCLOUD_H
+
+namespace VirtualRobot
+{
+ /**
+ * Pointcloud for usage with nanoflann kd tree.
+ */
+ template <typename T>
+ struct PointCloud
+ {
+ struct Point
+ {
+ T x,y,z;
+ };
+
+ std::vector<Point> pts;
+ // Must return the number of data points
+ inline size_t kdtree_get_point_count() const { return pts.size(); }
+
+ // Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
+ inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
+ {
+ const T d0=p1[0]-pts[idx_p2].x;
+ const T d1=p1[1]-pts[idx_p2].y;
+ const T d2=p1[2]-pts[idx_p2].z;
+ return d0*d0+d1*d1+d2*d2;
+ }
+
+ // Returns the dim'th component of the idx'th point in the class:
+ // Since this is inlined and the "dim" argument is typically an immediate value, the
+ // "if/else's" are actually solved at compile time.
+ inline T kdtree_get_pt(const size_t idx, int dim) const
+ {
+ if (dim==0) return pts[idx].x;
+ else if (dim==1) return pts[idx].y;
+ else return pts[idx].z;
+ }
+
+ // Optional bounding-box computation: return false to default to a standard bbox computation loop.
+ // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
+ // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
+ template <class BBOX>
+ bool kdtree_get_bbox(BBOX& /*bb*/) const { return false; }
+
+ };
+
+}
+#endif
diff --git a/VirtualRobot/DataStructures/nanoflann.hpp b/VirtualRobot/DataStructures/nanoflann.hpp
new file mode 100755
index 0000000000000000000000000000000000000000..445aa2998c37f387e30e72c6d4fa27606c7100be
--- /dev/null
+++ b/VirtualRobot/DataStructures/nanoflann.hpp
@@ -0,0 +1,1398 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ * Copyright 2011-2016 Jose Luis Blanco (joseluisblancoc@gmail.com).
+ * All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ * notice, this list of conditions and the following disclaimer in the
+ * documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/** \mainpage nanoflann C++ API documentation
+ * nanoflann is a C++ header-only library for building KD-Trees, mostly
+ * optimized for 2D or 3D point clouds.
+ *
+ * nanoflann does not require compiling or installing, just an
+ * #include <nanoflann.hpp> in your code.
+ *
+ * See:
+ * - <a href="modules.html" >C++ API organized by modules</a>
+ * - <a href="https://github.com/jlblancoc/nanoflann" >Online README</a>
+ * - <a href="http://jlblancoc.github.io/nanoflann/" >Doxygen documentation</a>
+ */
+
+#ifndef NANOFLANN_HPP_
+#define NANOFLANN_HPP_
+
+#include <vector>
+#include <cassert>
+#include <algorithm>
+#include <stdexcept>
+#include <cstdio> // for fwrite()
+#include <cmath> // for abs()
+#include <cstdlib> // for abs()
+#include <limits>
+
+// Avoid conflicting declaration of min/max macros in windows headers
+#if !defined(NOMINMAX) && (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
+# define NOMINMAX
+# ifdef max
+# undef max
+# undef min
+# endif
+#endif
+
+namespace nanoflann
+{
+/** @addtogroup nanoflann_grp nanoflann C++ library for ANN
+ * @{ */
+
+ /** Library version: 0xMmP (M=Major,m=minor,P=patch) */
+ #define NANOFLANN_VERSION 0x123
+
+ /** @addtogroup result_sets_grp Result set classes
+ * @{ */
+ template <typename DistanceType, typename IndexType = size_t, typename CountType = size_t>
+ class KNNResultSet
+ {
+ IndexType * indices;
+ DistanceType* dists;
+ CountType capacity;
+ CountType count;
+
+ public:
+ inline KNNResultSet(CountType capacity_) : indices(0), dists(0), capacity(capacity_), count(0)
+ {
+ }
+
+ inline void init(IndexType* indices_, DistanceType* dists_)
+ {
+ indices = indices_;
+ dists = dists_;
+ count = 0;
+ if (capacity)
+ dists[capacity-1] = (std::numeric_limits<DistanceType>::max)();
+ }
+
+ inline CountType size() const
+ {
+ return count;
+ }
+
+ inline bool full() const
+ {
+ return count == capacity;
+ }
+
+
+ inline void addPoint(DistanceType dist, IndexType index)
+ {
+ CountType i;
+ for (i=count; i>0; --i) {
+#ifdef NANOFLANN_FIRST_MATCH // If defined and two points have the same distance, the one with the lowest-index will be returned first.
+ if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) {
+#else
+ if (dists[i-1]>dist) {
+#endif
+ if (i<capacity) {
+ dists[i] = dists[i-1];
+ indices[i] = indices[i-1];
+ }
+ }
+ else break;
+ }
+ if (i<capacity) {
+ dists[i] = dist;
+ indices[i] = index;
+ }
+ if (count<capacity) count++;
+ }
+
+ inline DistanceType worstDist() const
+ {
+ return dists[capacity-1];
+ }
+ };
+
+
+ /**
+ * A result-set class used when performing a radius based search.
+ */
+ template <typename DistanceType, typename IndexType = size_t>
+ class RadiusResultSet
+ {
+ public:
+ const DistanceType radius;
+
+ std::vector<std::pair<IndexType,DistanceType> >& m_indices_dists;
+
+ inline RadiusResultSet(DistanceType radius_, std::vector<std::pair<IndexType,DistanceType> >& indices_dists) : radius(radius_), m_indices_dists(indices_dists)
+ {
+ init();
+ }
+
+ inline ~RadiusResultSet() { }
+
+ inline void init() { clear(); }
+ inline void clear() { m_indices_dists.clear(); }
+
+ inline size_t size() const { return m_indices_dists.size(); }
+
+ inline bool full() const { return true; }
+
+ inline void addPoint(DistanceType dist, IndexType index)
+ {
+ if (dist<radius)
+ m_indices_dists.push_back(std::make_pair(index,dist));
+ }
+
+ inline DistanceType worstDist() const { return radius; }
+
+ /** Clears the result set and adjusts the search radius. */
+ inline void set_radius_and_clear( const DistanceType r )
+ {
+ radius = r;
+ clear();
+ }
+
+ /**
+ * Find the worst result (furtherest neighbor) without copying or sorting
+ * Pre-conditions: size() > 0
+ */
+ std::pair<IndexType,DistanceType> worst_item() const
+ {
+ if (m_indices_dists.empty()) throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on an empty list of results.");
+ typedef typename std::vector<std::pair<IndexType,DistanceType> >::const_iterator DistIt;
+ DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end());
+ return *it;
+ }
+ };
+
+ /** operator "<" for std::sort() */
+ struct IndexDist_Sorter
+ {
+ /** PairType will be typically: std::pair<IndexType,DistanceType> */
+ template <typename PairType>
+ inline bool operator()(const PairType &p1, const PairType &p2) const {
+ return p1.second < p2.second;
+ }
+ };
+
+ /** @} */
+
+
+ /** @addtogroup loadsave_grp Load/save auxiliary functions
+ * @{ */
+ template<typename T>
+ void save_value(FILE* stream, const T& value, size_t count = 1)
+ {
+ fwrite(&value, sizeof(value),count, stream);
+ }
+
+ template<typename T>
+ void save_value(FILE* stream, const std::vector<T>& value)
+ {
+ size_t size = value.size();
+ fwrite(&size, sizeof(size_t), 1, stream);
+ fwrite(&value[0], sizeof(T), size, stream);
+ }
+
+ template<typename T>
+ void load_value(FILE* stream, T& value, size_t count = 1)
+ {
+ size_t read_cnt = fread(&value, sizeof(value), count, stream);
+ if (read_cnt != count) {
+ throw std::runtime_error("Cannot read from file");
+ }
+ }
+
+
+ template<typename T>
+ void load_value(FILE* stream, std::vector<T>& value)
+ {
+ size_t size;
+ size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
+ if (read_cnt!=1) {
+ throw std::runtime_error("Cannot read from file");
+ }
+ value.resize(size);
+ read_cnt = fread(&value[0], sizeof(T), size, stream);
+ if (read_cnt!=size) {
+ throw std::runtime_error("Cannot read from file");
+ }
+ }
+ /** @} */
+
+
+ /** @addtogroup metric_grp Metric (distance) classes
+ * @{ */
+
+ /** Manhattan distance functor (generic version, optimized for high-dimensionality data sets).
+ * Corresponding distance traits: nanoflann::metric_L1
+ * \tparam T Type of the elements (e.g. double, float, uint8_t)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
+ */
+ template<class T, class DataSource, typename _DistanceType = T>
+ struct L1_Adaptor
+ {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
+
+ inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
+ {
+ DistanceType result = DistanceType();
+ const T* last = a + size;
+ const T* lastgroup = last - 3;
+ size_t d = 0;
+
+ /* Process 4 items with each loop for efficiency. */
+ while (a < lastgroup) {
+ const DistanceType diff0 = std::abs(a[0] - data_source.kdtree_get_pt(b_idx,d++));
+ const DistanceType diff1 = std::abs(a[1] - data_source.kdtree_get_pt(b_idx,d++));
+ const DistanceType diff2 = std::abs(a[2] - data_source.kdtree_get_pt(b_idx,d++));
+ const DistanceType diff3 = std::abs(a[3] - data_source.kdtree_get_pt(b_idx,d++));
+ result += diff0 + diff1 + diff2 + diff3;
+ a += 4;
+ if ((worst_dist>0)&&(result>worst_dist)) {
+ return result;
+ }
+ }
+ /* Process last 0-3 components. Not needed for standard vector lengths. */
+ while (a < last) {
+ result += std::abs( *a++ - data_source.kdtree_get_pt(b_idx,d++) );
+ }
+ return result;
+ }
+
+ template <typename U, typename V>
+ inline DistanceType accum_dist(const U a, const V b, int ) const
+ {
+ return std::abs(a-b);
+ }
+ };
+
+ /** Squared Euclidean distance functor (generic version, optimized for high-dimensionality data sets).
+ * Corresponding distance traits: nanoflann::metric_L2
+ * \tparam T Type of the elements (e.g. double, float, uint8_t)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
+ */
+ template<class T, class DataSource, typename _DistanceType = T>
+ struct L2_Adaptor
+ {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
+
+ inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
+ {
+ DistanceType result = DistanceType();
+ const T* last = a + size;
+ const T* lastgroup = last - 3;
+ size_t d = 0;
+
+ /* Process 4 items with each loop for efficiency. */
+ while (a < lastgroup) {
+ const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx,d++);
+ const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx,d++);
+ const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx,d++);
+ const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx,d++);
+ result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+ a += 4;
+ if ((worst_dist>0)&&(result>worst_dist)) {
+ return result;
+ }
+ }
+ /* Process last 0-3 components. Not needed for standard vector lengths. */
+ while (a < last) {
+ const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx,d++);
+ result += diff0 * diff0;
+ }
+ return result;
+ }
+
+ template <typename U, typename V>
+ inline DistanceType accum_dist(const U a, const V b, int ) const
+ {
+ return (a-b)*(a-b);
+ }
+ };
+
+ /** Squared Euclidean (L2) distance functor (suitable for low-dimensionality datasets, like 2D or 3D point clouds)
+ * Corresponding distance traits: nanoflann::metric_L2_Simple
+ * \tparam T Type of the elements (e.g. double, float, uint8_t)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
+ */
+ template<class T, class DataSource, typename _DistanceType = T>
+ struct L2_Simple_Adaptor
+ {
+ typedef T ElementType;
+ typedef _DistanceType DistanceType;
+
+ const DataSource &data_source;
+
+ L2_Simple_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
+
+ inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
+ return data_source.kdtree_distance(a,b_idx,size);
+ }
+
+ template <typename U, typename V>
+ inline DistanceType accum_dist(const U a, const V b, int ) const
+ {
+ return (a-b)*(a-b);
+ }
+ };
+
+ /** Metaprogramming helper traits class for the L1 (Manhattan) metric */
+ struct metric_L1 {
+ template<class T, class DataSource>
+ struct traits {
+ typedef L1_Adaptor<T,DataSource> distance_t;
+ };
+ };
+ /** Metaprogramming helper traits class for the L2 (Euclidean) metric */
+ struct metric_L2 {
+ template<class T, class DataSource>
+ struct traits {
+ typedef L2_Adaptor<T,DataSource> distance_t;
+ };
+ };
+ /** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
+ struct metric_L2_Simple {
+ template<class T, class DataSource>
+ struct traits {
+ typedef L2_Simple_Adaptor<T,DataSource> distance_t;
+ };
+ };
+
+ /** @} */
+
+ /** @addtogroup param_grp Parameter structs
+ * @{ */
+
+ /** Parameters (see README.md) */
+ struct KDTreeSingleIndexAdaptorParams
+ {
+ KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10) :
+ leaf_max_size(_leaf_max_size)
+ {}
+
+ size_t leaf_max_size;
+ };
+
+ /** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
+ struct SearchParams
+ {
+ /** Note: The first argument (checks_IGNORED_) is ignored, but kept for compatibility with the FLANN interface */
+ SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true ) :
+ checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
+
+ int checks; //!< Ignored parameter (Kept for compatibility with the FLANN interface).
+ float eps; //!< search for eps-approximate neighbours (default: 0)
+ bool sorted; //!< only for radius search, require neighbours sorted by distance (default: true)
+ };
+ /** @} */
+
+
+ /** @addtogroup memalloc_grp Memory allocation
+ * @{ */
+
+ /**
+ * Allocates (using C's malloc) a generic type T.
+ *
+ * Params:
+ * count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+ template <typename T>
+ inline T* allocate(size_t count = 1)
+ {
+ T* mem = static_cast<T*>( ::malloc(sizeof(T)*count));
+ return mem;
+ }
+
+
+ /**
+ * Pooled storage allocator
+ *
+ * The following routines allow for the efficient allocation of storage in
+ * small chunks from a specified pool. Rather than allowing each structure
+ * to be freed individually, an entire pool of storage is freed at once.
+ * This method has two advantages over just using malloc() and free(). First,
+ * it is far more efficient for allocating small objects, as there is
+ * no overhead for remembering all the information needed to free each
+ * object or consolidating fragmented memory. Second, the decision about
+ * how long to keep an object is made at the time of allocation, and there
+ * is no need to track down all the objects to free them.
+ *
+ */
+
+ const size_t WORDSIZE=16;
+ const size_t BLOCKSIZE=8192;
+
+ class PooledAllocator
+ {
+ /* We maintain memory alignment to word boundaries by requiring that all
+ allocations be in multiples of the machine wordsize. */
+ /* Size of machine word in bytes. Must be power of 2. */
+ /* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */
+
+
+ size_t remaining; /* Number of bytes left in current block of storage. */
+ void* base; /* Pointer to base of current block of storage. */
+ void* loc; /* Current location in block to next allocate memory. */
+
+ void internal_init()
+ {
+ remaining = 0;
+ base = NULL;
+ usedMemory = 0;
+ wastedMemory = 0;
+ }
+
+ public:
+ size_t usedMemory;
+ size_t wastedMemory;
+
+ /**
+ Default constructor. Initializes a new pool.
+ */
+ PooledAllocator() {
+ internal_init();
+ }
+
+ /**
+ * Destructor. Frees all the memory allocated in this pool.
+ */
+ ~PooledAllocator() {
+ free_all();
+ }
+
+ /** Frees all allocated memory chunks */
+ void free_all()
+ {
+ while (base != NULL) {
+ void *prev = *(static_cast<void**>( base)); /* Get pointer to prev block. */
+ ::free(base);
+ base = prev;
+ }
+ internal_init();
+ }
+
+ /**
+ * Returns a pointer to a piece of new memory of the given size in bytes
+ * allocated from the pool.
+ */
+ void* malloc(const size_t req_size)
+ {
+ /* Round size up to a multiple of wordsize. The following expression
+ only works for WORDSIZE that is a power of 2, by masking last bits of
+ incremented size to zero.
+ */
+ const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
+
+ /* Check whether a new block must be allocated. Note that the first word
+ of a block is reserved for a pointer to the previous block.
+ */
+ if (size > remaining) {
+
+ wastedMemory += remaining;
+
+ /* Allocate new storage. */
+ const size_t blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
+ size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
+
+ // use the standard C malloc to allocate memory
+ void* m = ::malloc(blocksize);
+ if (!m) {
+ fprintf(stderr,"Failed to allocate memory.\n");
+ return NULL;
+ }
+
+ /* Fill first word of new block with pointer to previous block. */
+ static_cast<void**>(m)[0] = base;
+ base = m;
+
+ size_t shift = 0;
+ //int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
+
+ remaining = blocksize - sizeof(void*) - shift;
+ loc = (static_cast<char*>(m) + sizeof(void*) + shift);
+ }
+ void* rloc = loc;
+ loc = static_cast<char*>(loc) + size;
+ remaining -= size;
+
+ usedMemory += size;
+
+ return rloc;
+ }
+
+ /**
+ * Allocates (using this pool) a generic type T.
+ *
+ * Params:
+ * count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+ template <typename T>
+ T* allocate(const size_t count = 1)
+ {
+ T* mem = static_cast<T*>(this->malloc(sizeof(T)*count));
+ return mem;
+ }
+
+ };
+ /** @} */
+
+ /** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
+ * @{ */
+
+ // ---------------- CArray -------------------------
+ /** A STL container (as wrapper) for arrays of constant size defined at compile time (class imported from the MRPT project)
+ * This code is an adapted version from Boost, modifed for its integration
+ * within MRPT (JLBC, Dec/2009) (Renamed array -> CArray to avoid possible potential conflicts).
+ * See
+ * http://www.josuttis.com/cppcode
+ * for details and the latest version.
+ * See
+ * http://www.boost.org/libs/array for Documentation.
+ * for documentation.
+ *
+ * (C) Copyright Nicolai M. Josuttis 2001.
+ * Permission to copy, use, modify, sell and distribute this software
+ * is granted provided this copyright notice appears in all copies.
+ * This software is provided "as is" without express or implied
+ * warranty, and with no claim as to its suitability for any purpose.
+ *
+ * 29 Jan 2004 - minor fixes (Nico Josuttis)
+ * 04 Dec 2003 - update to synch with library TR1 (Alisdair Meredith)
+ * 23 Aug 2002 - fix for Non-MSVC compilers combined with MSVC libraries.
+ * 05 Aug 2001 - minor update (Nico Josuttis)
+ * 20 Jan 2001 - STLport fix (Beman Dawes)
+ * 29 Sep 2000 - Initial Revision (Nico Josuttis)
+ *
+ * Jan 30, 2004
+ */
+ template <typename T, std::size_t N>
+ class CArray {
+ public:
+ T elems[N]; // fixed-size array of elements of type T
+
+ public:
+ // type definitions
+ typedef T value_type;
+ typedef T* iterator;
+ typedef const T* const_iterator;
+ typedef T& reference;
+ typedef const T& const_reference;
+ typedef std::size_t size_type;
+ typedef std::ptrdiff_t difference_type;
+
+ // iterator support
+ inline iterator begin() { return elems; }
+ inline const_iterator begin() const { return elems; }
+ inline iterator end() { return elems+N; }
+ inline const_iterator end() const { return elems+N; }
+
+ // reverse iterator support
+#if !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION) && !defined(BOOST_MSVC_STD_ITERATOR) && !defined(BOOST_NO_STD_ITERATOR_TRAITS)
+ typedef std::reverse_iterator<iterator> reverse_iterator;
+ typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
+#elif defined(_MSC_VER) && (_MSC_VER == 1300) && defined(BOOST_DINKUMWARE_STDLIB) && (BOOST_DINKUMWARE_STDLIB == 310)
+ // workaround for broken reverse_iterator in VC7
+ typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, iterator,
+ reference, iterator, reference> > reverse_iterator;
+ typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, const_iterator,
+ const_reference, iterator, reference> > const_reverse_iterator;
+#else
+ // workaround for broken reverse_iterator implementations
+ typedef std::reverse_iterator<iterator,T> reverse_iterator;
+ typedef std::reverse_iterator<const_iterator,T> const_reverse_iterator;
+#endif
+
+ reverse_iterator rbegin() { return reverse_iterator(end()); }
+ const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
+ reverse_iterator rend() { return reverse_iterator(begin()); }
+ const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
+ // operator[]
+ inline reference operator[](size_type i) { return elems[i]; }
+ inline const_reference operator[](size_type i) const { return elems[i]; }
+ // at() with range check
+ reference at(size_type i) { rangecheck(i); return elems[i]; }
+ const_reference at(size_type i) const { rangecheck(i); return elems[i]; }
+ // front() and back()
+ reference front() { return elems[0]; }
+ const_reference front() const { return elems[0]; }
+ reference back() { return elems[N-1]; }
+ const_reference back() const { return elems[N-1]; }
+ // size is constant
+ static inline size_type size() { return N; }
+ static bool empty() { return false; }
+ static size_type max_size() { return N; }
+ enum { static_size = N };
+ /** This method has no effects in this class, but raises an exception if the expected size does not match */
+ inline void resize(const size_t nElements) { if (nElements!=N) throw std::logic_error("Try to change the size of a CArray."); }
+ // swap (note: linear complexity in N, constant for given instantiation)
+ void swap (CArray<T,N>& y) { std::swap_ranges(begin(),end(),y.begin()); }
+ // direct access to data (read-only)
+ const T* data() const { return elems; }
+ // use array as C array (direct read/write access to data)
+ T* data() { return elems; }
+ // assignment with type conversion
+ template <typename T2> CArray<T,N>& operator= (const CArray<T2,N>& rhs) {
+ std::copy(rhs.begin(),rhs.end(), begin());
+ return *this;
+ }
+ // assign one value to all elements
+ inline void assign (const T& value) { for (size_t i=0;i<N;i++) elems[i]=value; }
+ // assign (compatible with std::vector's one) (by JLBC for MRPT)
+ void assign (const size_t n, const T& value) { assert(N==n); for (size_t i=0;i<N;i++) elems[i]=value; }
+ private:
+ // check range (may be private because it is static)
+ static void rangecheck (size_type i) { if (i >= size()) { throw std::out_of_range("CArray<>: index out of range"); } }
+ }; // end of CArray
+
+ /** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors when DIM=-1.
+ * Fixed size version for a generic DIM:
+ */
+ template <int DIM, typename T>
+ struct array_or_vector_selector
+ {
+ typedef CArray<T,DIM> container_t;
+ };
+ /** Dynamic size version */
+ template <typename T>
+ struct array_or_vector_selector<-1,T> {
+ typedef std::vector<T> container_t;
+ };
+ /** @} */
+
+ /** @addtogroup kdtrees_grp KD-tree classes and adaptors
+ * @{ */
+
+ /** kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ *
+ * The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):
+ *
+ * \code
+ * // Must return the number of data poins
+ * inline size_t kdtree_get_point_count() const { ... }
+ *
+ * // [Only if using the metric_L2_Simple type] Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
+ * inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
+ *
+ * // Must return the dim'th component of the idx'th point in the class:
+ * inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
+ *
+ * // Optional bounding-box computation: return false to default to a standard bbox computation loop.
+ * // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
+ * // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
+ * template <class BBOX>
+ * bool kdtree_get_bbox(BBOX &bb) const
+ * {
+ * bb[0].low = ...; bb[0].high = ...; // 0th dimension limits
+ * bb[1].low = ...; bb[1].high = ...; // 1st dimension limits
+ * ...
+ * return true;
+ * }
+ *
+ * \endcode
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ * \tparam DIM Dimensionality of data points (e.g. 3 for 3D points)
+ * \tparam IndexType Will be typically size_t or int
+ */
+ template <typename Distance, class DatasetAdaptor,int DIM = -1, typename IndexType = size_t>
+ class KDTreeSingleIndexAdaptor
+ {
+ private:
+ /** Hidden copy constructor, to disallow copying indices (Not implemented) */
+ KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor<Distance,DatasetAdaptor,DIM,IndexType>&);
+ public:
+ typedef typename Distance::ElementType ElementType;
+ typedef typename Distance::DistanceType DistanceType;
+ protected:
+
+ /**
+ * Array of indices to vectors in the dataset.
+ */
+ std::vector<IndexType> vind;
+
+ size_t m_leaf_max_size;
+
+
+ /**
+ * The dataset used by this index
+ */
+ const DatasetAdaptor &dataset; //!< The source of our data
+
+ const KDTreeSingleIndexAdaptorParams index_params;
+
+ size_t m_size; //!< Number of current poins in the dataset
+ size_t m_size_at_index_build; //!< Number of points in the dataset when the index was built
+ int dim; //!< Dimensionality of each data point
+
+
+ /*--------------------- Internal Data Structures --------------------------*/
+ struct Node
+ {
+ /** Union used because a node can be either a LEAF node or a non-leaf node, so both data fields are never used simultaneously */
+ union {
+ struct leaf
+ {
+ IndexType left, right; //!< Indices of points in leaf node
+ } lr;
+ struct nonleaf
+ {
+ int divfeat; //!< Dimension used for subdivision.
+ DistanceType divlow, divhigh; //!< The values used for subdivision.
+ } sub;
+ } node_type;
+ Node* child1, * child2; //!< Child nodes (both=NULL mean its a leaf node)
+ };
+ typedef Node* NodePtr;
+
+
+ struct Interval
+ {
+ ElementType low, high;
+ };
+
+ /** Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM" */
+ typedef typename array_or_vector_selector<DIM,Interval>::container_t BoundingBox;
+
+ /** Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM" */
+ typedef typename array_or_vector_selector<DIM,DistanceType>::container_t distance_vector_t;
+
+ /** The KD-tree used to find neighbours */
+ NodePtr root_node;
+ BoundingBox root_bbox;
+
+ /**
+ * Pooled memory allocator.
+ *
+ * Using a pooled memory allocator is more efficient
+ * than allocating memory directly when there is a large
+ * number small of memory allocations.
+ */
+ PooledAllocator pool;
+
+ public:
+
+ Distance distance;
+
+ /**
+ * KDTree constructor
+ *
+ * Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann
+ *
+ * The KD-Tree point dimension (the length of each point in the datase, e.g. 3 for 3D points)
+ * is determined by means of:
+ * - The \a DIM template parameter if >0 (highest priority)
+ * - Otherwise, the \a dimensionality parameter of this constructor.
+ *
+ * @param inputData Dataset with the input features
+ * @param params Basically, the maximum leaf node size
+ */
+ KDTreeSingleIndexAdaptor(const int dimensionality, const DatasetAdaptor& inputData, const KDTreeSingleIndexAdaptorParams& params = KDTreeSingleIndexAdaptorParams() ) :
+ dataset(inputData), index_params(params), root_node(NULL), distance(inputData)
+ {
+ m_size = dataset.kdtree_get_point_count();
+ m_size_at_index_build = m_size;
+ dim = dimensionality;
+ if (DIM>0) dim=DIM;
+ m_leaf_max_size = params.leaf_max_size;
+
+ // Create a permutable array of indices to the input vectors.
+ init_vind();
+ }
+
+ /** Standard destructor */
+ ~KDTreeSingleIndexAdaptor() { }
+
+ /** Frees the previously-built index. Automatically called within buildIndex(). */
+ void freeIndex()
+ {
+ pool.free_all();
+ root_node=NULL;
+ m_size_at_index_build = 0;
+ }
+
+ /**
+ * Builds the index
+ */
+ void buildIndex()
+ {
+ init_vind();
+ freeIndex();
+ m_size_at_index_build = m_size;
+ if(m_size == 0) return;
+ computeBoundingBox(root_bbox);
+ root_node = divideTree(0, m_size, root_bbox ); // construct the tree
+ }
+
+ /** Returns number of points in dataset */
+ size_t size() const { return m_size; }
+
+ /** Returns the length of each point in the dataset */
+ size_t veclen() const {
+ return static_cast<size_t>(DIM>0 ? DIM : dim);
+ }
+
+ /**
+ * Computes the inde memory usage
+ * Returns: memory used by the index
+ */
+ size_t usedMemory() const
+ {
+ return pool.usedMemory+pool.wastedMemory+dataset.kdtree_get_point_count()*sizeof(IndexType); // pool memory and vind array memory
+ }
+
+ /** \name Query methods
+ * @{ */
+
+ /**
+ * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside
+ * the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the nearest-neighbors are stored
+ * vec = the vector for which to search the nearest neighbors
+ *
+ * \tparam RESULTSET Should be any ResultSet<DistanceType>
+ * \return True if the requested neighbors could be found.
+ * \sa knnSearch, radiusSearch
+ */
+ template <typename RESULTSET>
+ bool findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
+ {
+ assert(vec);
+ if (size() == 0)
+ return false;
+ if (!root_node)
+ throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
+ float epsError = 1+searchParams.eps;
+
+ distance_vector_t dists; // fixed or variable-sized container (depending on DIM)
+ dists.assign((DIM>0 ? DIM : dim) ,0); // Fill it with zeros.
+ DistanceType distsq = computeInitialDistances(vec, dists);
+ searchLevel(result, vec, root_node, distsq, dists, epsError); // "count_leaf" parameter removed since was neither used nor returned to the user.
+ return result.full();
+ }
+
+ /**
+ * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1]. Their indices are stored inside
+ * the result object.
+ * \sa radiusSearch, findNeighbors
+ * \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
+ * \return Number `N` of valid points in the result set. Only the first `N` entries in `out_indices` and `out_distances_sq` will be valid.
+ * Return may be less than `num_closest` only if the number of elements in the tree is less than `num_closest`.
+ */
+ size_t knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int /* nChecks_IGNORED */ = 10) const
+ {
+ nanoflann::KNNResultSet<DistanceType,IndexType> resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ return resultSet.size();
+ }
+
+ /**
+ * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
+ * The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance.
+ * Previous contents of \a IndicesDists are cleared.
+ *
+ * If searchParams.sorted==true, the output list is sorted by ascending distances.
+ *
+ * For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.
+ *
+ * \sa knnSearch, findNeighbors, radiusSearchCustomCallback
+ * \return The number of points within the given radius (i.e. indices.size() or dists.size() )
+ */
+ size_t radiusSearch(const ElementType *query_point,const DistanceType &radius, std::vector<std::pair<IndexType,DistanceType> >& IndicesDists, const SearchParams& searchParams) const
+ {
+ RadiusResultSet<DistanceType,IndexType> resultSet(radius,IndicesDists);
+ const size_t nFound = radiusSearchCustomCallback(query_point,resultSet,searchParams);
+ if (searchParams.sorted)
+ std::sort(IndicesDists.begin(),IndicesDists.end(), IndexDist_Sorter() );
+ return nFound;
+ }
+
+ /**
+ * Just like radiusSearch() but with a custom callback class for each point found in the radius of the query.
+ * See the source of RadiusResultSet<> as a start point for your own classes.
+ * \sa radiusSearch
+ */
+ template <class SEARCH_CALLBACK>
+ size_t radiusSearchCustomCallback(const ElementType *query_point,SEARCH_CALLBACK &resultSet, const SearchParams& searchParams = SearchParams() ) const
+ {
+ this->findNeighbors(resultSet, query_point, searchParams);
+ return resultSet.size();
+ }
+
+ /** @} */
+
+ private:
+ /** Make sure the auxiliary list \a vind has the same size than the current dataset, and re-generate if size has changed. */
+ void init_vind()
+ {
+ // Create a permutable array of indices to the input vectors.
+ m_size = dataset.kdtree_get_point_count();
+ if (vind.size()!=m_size) vind.resize(m_size);
+ for (size_t i = 0; i < m_size; i++) vind[i] = i;
+ }
+
+ /// Helper accessor to the dataset points:
+ inline ElementType dataset_get(size_t idx, int component) const {
+ return dataset.kdtree_get_pt(idx,component);
+ }
+
+
+ void save_tree(FILE* stream, NodePtr tree)
+ {
+ save_value(stream, *tree);
+ if (tree->child1!=NULL) {
+ save_tree(stream, tree->child1);
+ }
+ if (tree->child2!=NULL) {
+ save_tree(stream, tree->child2);
+ }
+ }
+
+
+ void load_tree(FILE* stream, NodePtr& tree)
+ {
+ tree = pool.allocate<Node>();
+ load_value(stream, *tree);
+ if (tree->child1!=NULL) {
+ load_tree(stream, tree->child1);
+ }
+ if (tree->child2!=NULL) {
+ load_tree(stream, tree->child2);
+ }
+ }
+
+
+ void computeBoundingBox(BoundingBox& bbox)
+ {
+ bbox.resize((DIM>0 ? DIM : dim));
+ if (dataset.kdtree_get_bbox(bbox))
+ {
+ // Done! It was implemented in derived class
+ }
+ else
+ {
+ const size_t N = dataset.kdtree_get_point_count();
+ if (!N) throw std::runtime_error("[nanoflann] computeBoundingBox() called but no data points found.");
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ bbox[i].low =
+ bbox[i].high = dataset_get(0,i);
+ }
+ for (size_t k=1; k<N; ++k) {
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ if (dataset_get(k,i)<bbox[i].low) bbox[i].low = dataset_get(k,i);
+ if (dataset_get(k,i)>bbox[i].high) bbox[i].high = dataset_get(k,i);
+ }
+ }
+ }
+ }
+
+
+ /**
+ * Create a tree node that subdivides the list of vecs from vind[first]
+ * to vind[last]. The routine is called recursively on each sublist.
+ *
+ * @param left index of the first vector
+ * @param right index of the last vector
+ */
+ NodePtr divideTree(const IndexType left, const IndexType right, BoundingBox& bbox)
+ {
+ NodePtr node = pool.allocate<Node>(); // allocate memory
+
+ /* If too few exemplars remain, then make this a leaf node. */
+ if ( (right-left) <= static_cast<IndexType>(m_leaf_max_size) ) {
+ node->child1 = node->child2 = NULL; /* Mark as leaf node. */
+ node->node_type.lr.left = left;
+ node->node_type.lr.right = right;
+
+ // compute bounding-box of leaf points
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ bbox[i].low = dataset_get(vind[left],i);
+ bbox[i].high = dataset_get(vind[left],i);
+ }
+ for (IndexType k=left+1; k<right; ++k) {
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ if (bbox[i].low>dataset_get(vind[k],i)) bbox[i].low=dataset_get(vind[k],i);
+ if (bbox[i].high<dataset_get(vind[k],i)) bbox[i].high=dataset_get(vind[k],i);
+ }
+ }
+ }
+ else {
+ IndexType idx;
+ int cutfeat;
+ DistanceType cutval;
+ middleSplit_(&vind[0]+left, right-left, idx, cutfeat, cutval, bbox);
+
+ node->node_type.sub.divfeat = cutfeat;
+
+ BoundingBox left_bbox(bbox);
+ left_bbox[cutfeat].high = cutval;
+ node->child1 = divideTree(left, left+idx, left_bbox);
+
+ BoundingBox right_bbox(bbox);
+ right_bbox[cutfeat].low = cutval;
+ node->child2 = divideTree(left+idx, right, right_bbox);
+
+ node->node_type.sub.divlow = left_bbox[cutfeat].high;
+ node->node_type.sub.divhigh = right_bbox[cutfeat].low;
+
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+ bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+ }
+ }
+
+ return node;
+ }
+
+
+ void computeMinMax(IndexType* ind, IndexType count, int element, ElementType& min_elem, ElementType& max_elem)
+ {
+ min_elem = dataset_get(ind[0],element);
+ max_elem = dataset_get(ind[0],element);
+ for (IndexType i=1; i<count; ++i) {
+ ElementType val = dataset_get(ind[i],element);
+ if (val<min_elem) min_elem = val;
+ if (val>max_elem) max_elem = val;
+ }
+ }
+
+ void middleSplit_(IndexType* ind, IndexType count, IndexType& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+ {
+ const DistanceType EPS=static_cast<DistanceType>(0.00001);
+ ElementType max_span = bbox[0].high-bbox[0].low;
+ for (int i=1; i<(DIM>0 ? DIM : dim); ++i) {
+ ElementType span = bbox[i].high-bbox[i].low;
+ if (span>max_span) {
+ max_span = span;
+ }
+ }
+ ElementType max_spread = -1;
+ cutfeat = 0;
+ for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
+ ElementType span = bbox[i].high-bbox[i].low;
+ if (span>(1-EPS)*max_span) {
+ ElementType min_elem, max_elem;
+ computeMinMax(ind, count, i, min_elem, max_elem);
+ ElementType spread = max_elem-min_elem;;
+ if (spread>max_spread) {
+ cutfeat = i;
+ max_spread = spread;
+ }
+ }
+ }
+ // split in the middle
+ DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
+ ElementType min_elem, max_elem;
+ computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+
+ if (split_val<min_elem) cutval = min_elem;
+ else if (split_val>max_elem) cutval = max_elem;
+ else cutval = split_val;
+
+ IndexType lim1, lim2;
+ planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+ if (lim1>count/2) index = lim1;
+ else if (lim2<count/2) index = lim2;
+ else index = count/2;
+ }
+
+
+ /**
+ * Subdivide the list of points by a plane perpendicular on axe corresponding
+ * to the 'cutfeat' dimension at 'cutval' position.
+ *
+ * On return:
+ * dataset[ind[0..lim1-1]][cutfeat]<cutval
+ * dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+ * dataset[ind[lim2..count]][cutfeat]>cutval
+ */
+ void planeSplit(IndexType* ind, const IndexType count, int cutfeat, DistanceType &cutval, IndexType& lim1, IndexType& lim2)
+ {
+ /* Move vector indices for left subtree to front of list. */
+ IndexType left = 0;
+ IndexType right = count-1;
+ for (;; ) {
+ while (left<=right && dataset_get(ind[left],cutfeat)<cutval) ++left;
+ while (right && left<=right && dataset_get(ind[right],cutfeat)>=cutval) --right;
+ if (left>right || !right) break; // "!right" was added to support unsigned Index types
+ std::swap(ind[left], ind[right]);
+ ++left;
+ --right;
+ }
+ /* If either list is empty, it means that all remaining features
+ * are identical. Split in the middle to maintain a balanced tree.
+ */
+ lim1 = left;
+ right = count-1;
+ for (;; ) {
+ while (left<=right && dataset_get(ind[left],cutfeat)<=cutval) ++left;
+ while (right && left<=right && dataset_get(ind[right],cutfeat)>cutval) --right;
+ if (left>right || !right) break; // "!right" was added to support unsigned Index types
+ std::swap(ind[left], ind[right]);
+ ++left;
+ --right;
+ }
+ lim2 = left;
+ }
+
+ DistanceType computeInitialDistances(const ElementType* vec, distance_vector_t& dists) const
+ {
+ assert(vec);
+ DistanceType distsq = DistanceType();
+
+ for (int i = 0; i < (DIM>0 ? DIM : dim); ++i) {
+ if (vec[i] < root_bbox[i].low) {
+ dists[i] = distance.accum_dist(vec[i], root_bbox[i].low, i);
+ distsq += dists[i];
+ }
+ if (vec[i] > root_bbox[i].high) {
+ dists[i] = distance.accum_dist(vec[i], root_bbox[i].high, i);
+ distsq += dists[i];
+ }
+ }
+
+ return distsq;
+ }
+
+ /**
+ * Performs an exact search in the tree starting from a node.
+ * \tparam RESULTSET Should be any ResultSet<DistanceType>
+ */
+ template <class RESULTSET>
+ void searchLevel(RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
+ distance_vector_t& dists, const float epsError) const
+ {
+ /* If this is a leaf node, then do check and return. */
+ if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+ //count_leaf += (node->lr.right-node->lr.left); // Removed since was neither used nor returned to the user.
+ DistanceType worst_dist = result_set.worstDist();
+ for (IndexType i=node->node_type.lr.left; i<node->node_type.lr.right; ++i) {
+ const IndexType index = vind[i];// reorder... : i;
+ DistanceType dist = distance(vec, index, (DIM>0 ? DIM : dim));
+ if (dist<worst_dist) {
+ result_set.addPoint(dist,vind[i]);
+ }
+ }
+ return;
+ }
+
+ /* Which child branch should be taken first? */
+ int idx = node->node_type.sub.divfeat;
+ ElementType val = vec[idx];
+ DistanceType diff1 = val - node->node_type.sub.divlow;
+ DistanceType diff2 = val - node->node_type.sub.divhigh;
+
+ NodePtr bestChild;
+ NodePtr otherChild;
+ DistanceType cut_dist;
+ if ((diff1+diff2)<0) {
+ bestChild = node->child1;
+ otherChild = node->child2;
+ cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
+ }
+ else {
+ bestChild = node->child2;
+ otherChild = node->child1;
+ cut_dist = distance.accum_dist( val, node->node_type.sub.divlow, idx);
+ }
+
+ /* Call recursively to search next level down. */
+ searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+ DistanceType dst = dists[idx];
+ mindistsq = mindistsq + cut_dist - dst;
+ dists[idx] = cut_dist;
+ if (mindistsq*epsError<=result_set.worstDist()) {
+ searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+ }
+ dists[idx] = dst;
+ }
+
+ public:
+ /** Stores the index in a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when loading the index object it must be constructed associated to the same source of data points used while building it.
+ * See the example: examples/saveload_example.cpp
+ * \sa loadIndex */
+ void saveIndex(FILE* stream)
+ {
+ save_value(stream, m_size);
+ save_value(stream, dim);
+ save_value(stream, root_bbox);
+ save_value(stream, m_leaf_max_size);
+ save_value(stream, vind);
+ save_tree(stream, root_node);
+ }
+
+ /** Loads a previous index from a binary file.
+ * IMPORTANT NOTE: The set of data points is NOT stored in the file, so the index object must be constructed associated to the same source of data points used while building the index.
+ * See the example: examples/saveload_example.cpp
+ * \sa loadIndex */
+ void loadIndex(FILE* stream)
+ {
+ load_value(stream, m_size);
+ load_value(stream, dim);
+ load_value(stream, root_bbox);
+ load_value(stream, m_leaf_max_size);
+ load_value(stream, vind);
+ load_tree(stream, root_node);
+ }
+
+ }; // class KDTree
+
+
+ /** An L2-metric KD-tree adaptor for working with data directly stored in an Eigen Matrix, without duplicating the data storage.
+ * Each row in the matrix represents a point in the state space.
+ *
+ * Example of usage:
+ * \code
+ * Eigen::Matrix<num_t,Dynamic,Dynamic> mat;
+ * // Fill out "mat"...
+ *
+ * typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> > my_kd_tree_t;
+ * const int max_leaf = 10;
+ * my_kd_tree_t mat_index(dimdim, mat, max_leaf );
+ * mat_index.index->buildIndex();
+ * mat_index.index->...
+ * \endcode
+ *
+ * \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ */
+ template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
+ struct KDTreeEigenMatrixAdaptor
+ {
+ typedef KDTreeEigenMatrixAdaptor<MatrixType,DIM,Distance> self_t;
+ typedef typename MatrixType::Scalar num_t;
+ typedef typename MatrixType::Index IndexType;
+ typedef typename Distance::template traits<num_t,self_t>::distance_t metric_t;
+ typedef KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType> index_t;
+
+ index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index.
+
+ /// Constructor: takes a const ref to the matrix object with the data points
+ KDTreeEigenMatrixAdaptor(const int dimensionality, const MatrixType &mat, const int leaf_max_size = 10) : m_data_matrix(mat)
+ {
+ const IndexType dims = mat.cols();
+ if (dims!=dimensionality) throw std::runtime_error("Error: 'dimensionality' must match column count in data matrix");
+ if (DIM>0 && static_cast<int>(dims)!=DIM)
+ throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
+ index = new index_t( dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size ) );
+ index->buildIndex();
+ }
+ private:
+ /** Hidden copy constructor, to disallow copying this class (Not implemented) */
+ KDTreeEigenMatrixAdaptor(const self_t&);
+ public:
+
+ ~KDTreeEigenMatrixAdaptor() {
+ delete index;
+ }
+
+ const MatrixType &m_data_matrix;
+
+ /** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]).
+ * Note that this is a short-cut method for index->findNeighbors().
+ * The user can also call index->... methods as desired.
+ * \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
+ */
+ inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int /* nChecks_IGNORED */ = 10) const
+ {
+ nanoflann::KNNResultSet<num_t,IndexType> resultSet(num_closest);
+ resultSet.init(out_indices, out_distances_sq);
+ index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+ }
+
+ /** @name Interface expected by KDTreeSingleIndexAdaptor
+ * @{ */
+
+ const self_t & derived() const {
+ return *this;
+ }
+ self_t & derived() {
+ return *this;
+ }
+
+ // Must return the number of data points
+ inline size_t kdtree_get_point_count() const {
+ return m_data_matrix.rows();
+ }
+
+ // Returns the L2 distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
+ inline num_t kdtree_distance(const num_t *p1, const IndexType idx_p2,IndexType size) const
+ {
+ num_t s=0;
+ for (IndexType i=0; i<size; i++) {
+ const num_t d= p1[i]-m_data_matrix.coeff(idx_p2,i);
+ s+=d*d;
+ }
+ return s;
+ }
+
+ // Returns the dim'th component of the idx'th point in the class:
+ inline num_t kdtree_get_pt(const IndexType idx, int dim) const {
+ return m_data_matrix.coeff(idx,IndexType(dim));
+ }
+
+ // Optional bounding-box computation: return false to default to a standard bbox computation loop.
+ // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
+ // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
+ template <class BBOX>
+ bool kdtree_get_bbox(BBOX& /*bb*/) const {
+ return false;
+ }
+
+ /** @} */
+
+ }; // end of KDTreeEigenMatrixAdaptor
+ /** @} */
+
+/** @} */ // end of grouping
+} // end of NS
+
+
+#endif /* NANOFLANN_HPP_ */
diff --git a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationFactory.h b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationFactory.h
index 81dd1166b8fab062dde0ed5883d299fbbfbe9fba..dc6627a892ccde009732bd6d02c82bc9099e20f7 100644
--- a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationFactory.h
+++ b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationFactory.h
@@ -354,7 +354,7 @@ namespace VirtualRobot
* \param zNear The near plane's distance.
* \param zFar The far plane's distance
* \param vertFov The fov in rad. (vertical)
- * \param nanValue All values above this value will be mapped on this value (usually nan or 0)
+ * \param nanValue All values above the zFar value will be mapped on this value (usually nan or 0)
* \return true on success
*/
static bool renderOffscreenRgbDepthPointcloud
@@ -454,4 +454,4 @@ namespace VirtualRobot
} // namespace VirtualRobot
-#endif // _VirtualRobot_CoinVisualizationFactory_h_
+#endif // _VirtualRobot_CoinVisualizationFactory_h_
\ No newline at end of file
diff --git a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.cpp b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.cpp
index 63d9a333a8bc570e9f9a69a7d8bead172b2d0c00..1b602aeb6daadeb081ba0586f4fcbd7b1844b758 100644
--- a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.cpp
+++ b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.cpp
@@ -37,7 +37,7 @@ namespace VirtualRobot
* CoinVisualizationNode::visualization.
* If \p visualizationNode is a valid object call SoNode::ref() on it.
*/
- CoinVisualizationNode::CoinVisualizationNode(SoNode* visualizationNode) :
+ CoinVisualizationNode::CoinVisualizationNode(SoNode* visualizationNode, float margin) :
visualization(visualizationNode)
{
visualizationAtGlobalPose = new SoSeparator();
@@ -142,7 +142,7 @@ namespace VirtualRobot
return triMeshModel;
}
-
+ typedef std::map<const SoPrimitiveVertex*,int> CoinVertexIndexMap;
/**
* This method constructs an instance of TriMeshModel and stores it in
* CoinVisualizationNode::triMeshModel.
@@ -180,8 +180,7 @@ namespace VirtualRobot
const SoPrimitiveVertex* v2,
const SoPrimitiveVertex* v3)
{
- TriMeshModel* triangleMeshModel = static_cast<TriMeshModel*>(data);
-
+ TriMeshModel* triangleMeshModel = static_cast<TriMeshModel*>(data);
if (!triangleMeshModel)
{
VR_INFO << ": Internal error, NULL data" << endl;
@@ -213,8 +212,10 @@ namespace VirtualRobot
b << triangle[1][0], triangle[1][1], triangle[1][2];
c << triangle[2][0], triangle[2][1], triangle[2][2];
n << normal[0][0], normal[0][1], normal[0][2];
- // add new triangle to the model
+
+ // add new triangle to the model
triangleMeshModel->addTriangleWithFace(a, b, c, n);
+
}
@@ -498,6 +499,23 @@ namespace VirtualRobot
return true;
}
+ void CoinVisualizationNode::shrinkFatten(float offset)
+ {
+ if(offset != 0.0f)
+ {
+ triMeshModel.reset();
+ getTriMeshModel()->mergeVertices();
+ getTriMeshModel()->fattenShrink(offset);
+
+ scaledVisualization->removeChild(scaledVisualization->findChild(visualization));
+ visualization->unref();
+ visualization = CoinVisualizationFactory::getCoinVisualization(getTriMeshModel());
+ visualization->ref();
+ scaledVisualization->addChild(visualization);
+ }
+
+ }
+
void CoinVisualizationNode::scale(Eigen::Vector3f& scaleFactor)
{
scaling->scaleFactor.setValue(scaleFactor(0), scaleFactor(1), scaleFactor(2));
diff --git a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.h b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.h
index 190b30490e2f60078a83739221be7665120c3a85..b8485b596922d93357b02bd63abe36f961d31fd7 100644
--- a/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.h
+++ b/VirtualRobot/Visualization/CoinVisualization/CoinVisualizationNode.h
@@ -44,7 +44,7 @@ namespace VirtualRobot
{
friend class CoinVisualizationFactory;
public:
- CoinVisualizationNode(SoNode* visualizationNode);
+ CoinVisualizationNode(SoNode* visualizationNode, float margin = 0.0f);
~CoinVisualizationNode();
virtual TriMeshModelPtr getTriMeshModel();
@@ -94,7 +94,7 @@ namespace VirtualRobot
\param filename The new filename. If filename extension is ".iv", the file is stored in Open Inventor format. Otherwise the file is stored in VRML2 format (.wrl).
*/
virtual bool saveModel(const std::string& modelPath, const std::string& filename);
-
+ virtual void shrinkFatten(float offset);
virtual void createTriMeshModel();
protected:
@@ -115,7 +115,7 @@ namespace VirtualRobot
SoMatrixTransform* globalPoseTransform;
TriMeshModelPtr triMeshModel;
SoScale* scaling;
-
+ float margin = 0.0f;
static void InventorTriangleCB(void* data, SoCallbackAction* action,
const SoPrimitiveVertex* v1,
const SoPrimitiveVertex* v2,
diff --git a/VirtualRobot/Visualization/TriMeshModel.cpp b/VirtualRobot/Visualization/TriMeshModel.cpp
index 8897224719b47487119bbd22aa80ddc8be65a52e..1888e5fd3fe9affd975770dc4639a1d8179e87ce 100644
--- a/VirtualRobot/Visualization/TriMeshModel.cpp
+++ b/VirtualRobot/Visualization/TriMeshModel.cpp
@@ -6,12 +6,15 @@
#include "TriMeshModel.h"
#include "../VirtualRobot.h"
-
+#include "../DataStructures/nanoflann.hpp"
+#include "../DataStructures/KdTreePointCloud.h"
#include<Eigen/Geometry>
#include <algorithm>
#include <fstream>
#include <iomanip>
+
+
namespace VirtualRobot
{
@@ -148,48 +151,53 @@ namespace VirtualRobot
/**
* This method adds a vertex to the internal data structure TriMeshModel::vertices.
*/
- unsigned int TriMeshModel::addVertex(const Eigen::Vector3f& vertex)
+ int TriMeshModel::addVertex(const Eigen::Vector3f& vertex)
{
if (std::isnan(vertex[0]) || std::isnan(vertex[1]) || std::isnan(vertex[2]))
{
VR_ERROR << "NAN vertex added!!!" << endl;
- return 0;
+ return -1;
}
vertices.push_back(vertex);
boundingBox.addPoint(vertex);
- return vertices.size()-1;
+ return vertices.size() - 1;
}
/**
* This method adds a normal to the internal data structure TriMeshModel::normals.
*/
- void TriMeshModel::addNormal(const Eigen::Vector3f& normal)
+ int TriMeshModel::addNormal(const Eigen::Vector3f& normal)
{
normals.push_back(normal);
+ return normals.size() - 1;
+
}
/**
* This method adds a color to the internal data structure TriMeshModel::colors
*/
- void TriMeshModel::addColor(const VisualizationFactory::Color& color)
+ int TriMeshModel::addColor(const VisualizationFactory::Color& color)
{
colors.push_back(color);
+ return colors.size() - 1;
+
}
/**
* This method converts and adds a color to the internal data structure TriMeshModel::colors
*/
- void TriMeshModel::addColor(const Eigen::Vector4f& color)
+ int TriMeshModel::addColor(const Eigen::Vector4f& color)
{
- addColor(VisualizationFactory::Color(color(0), color(1), color(2), color(3)));
+ return addColor(VisualizationFactory::Color(color(0), color(1), color(2), color(3)));
}
/**
* This method converts and adds a color to the internal data structure TriMeshModel::materials
*/
- void TriMeshModel::addMaterial(const VisualizationFactory::PhongMaterial& material)
+ int TriMeshModel::addMaterial(const VisualizationFactory::PhongMaterial& material)
{
materials.push_back(material);
+ return materials.size() - 1;
}
@@ -216,6 +224,191 @@ namespace VirtualRobot
std::for_each(faces.begin(), faces.end(), std::mem_fun_ref(&MathTools::TriangleFace::flipOrientation));
}
+
+
+
+ void TriMeshModel::mergeVertices(float mergeThreshold)
+ {
+ int size = vertices.size();
+ int faceCount = faces.size();
+ std::vector<std::set<MathTools::TriangleFace*>> vertex2FaceMap(size);
+ for (int j = 0; j < faceCount; ++j)
+ {
+ MathTools::TriangleFace& face = faces.at(j);
+ vertex2FaceMap[face.id1].insert(&faces.at(j));
+ vertex2FaceMap[face.id2].insert(&faces.at(j));
+ vertex2FaceMap[face.id3].insert(&faces.at(j));
+ }
+#if 1
+ PointCloud<float> cloud;
+ cloud.pts.reserve(size);
+ for (int i = 0; i < size; ++i)
+ {
+ cloud.pts.emplace_back(PointCloud<float>::Point{vertices.at(i)[0],
+ vertices.at(i)[1],
+ vertices.at(i)[2]});
+ }
+ typedef float num_t;
+ // construct a kd-tree index:
+ typedef nanoflann::KDTreeSingleIndexAdaptor<
+ nanoflann::L2_Simple_Adaptor<num_t, PointCloud<num_t> > ,
+ PointCloud<num_t>,
+ 3 /* dim */
+ > my_kd_tree_t;
+
+ my_kd_tree_t index(3 /*dim*/, cloud, nanoflann::KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
+ index.buildIndex();
+
+
+
+ const num_t search_radius = static_cast<num_t>(mergeThreshold);
+ std::vector<std::pair<size_t,num_t> > ret_matches;
+
+ nanoflann::SearchParams params;
+ num_t query_pt[3];
+ params.sorted = false;
+ for (int i = 0; i < size; ++i)
+ {
+ auto& p1 = vertices.at(i);
+ query_pt[0] = p1[0];
+ query_pt[1] = p1[1];
+ query_pt[2] = p1[2];
+ const size_t nMatches = index.radiusSearch(&query_pt[0],search_radius, ret_matches, params);
+ for (int k = 0; k < nMatches; ++k)
+ {
+ int foundIndex = ret_matches.at(k).first;
+ auto& faces = vertex2FaceMap[foundIndex];
+ for(MathTools::TriangleFace* facePtr : faces)
+ {
+ bool found = false;
+ if(facePtr->id1 == foundIndex)
+ {
+ facePtr->id1 = i;
+ found = true;
+ }
+ if(facePtr->id2 == foundIndex)
+ {
+ facePtr->id2 = i;
+ found = true;
+ }
+ if(facePtr->id3 == foundIndex)
+ {
+ facePtr->id3 = i;
+ found = true;
+ }
+ if(found)
+ vertex2FaceMap[i].insert(facePtr);
+ }
+ }
+ }
+
+#else
+ std::vector<bool> deleted(size, false);
+ for (int i = 0; i < size; ++i)
+ {
+ auto& p1 = vertices.at(i);
+ for (int k = 0; k < size; ++k)
+ {
+ if(k == i || deleted.at(k))
+ continue;
+ auto &p2 = vertices.at(k);
+ if((p1-p2).norm() < mergeThreshold)
+ {
+// deleted.at(k) = true;
+ for(MathTools::TriangleFace* facePtr : vertex2FaceMap[k])
+ {
+ bool found = false;
+ if(facePtr->id1 == k)
+ {
+ facePtr->id1 = i;
+ found = true;
+ }
+ if(facePtr->id2 == k)
+ {
+ facePtr->id2 = i;
+ found = true;
+ }
+ if(facePtr->id3 == k)
+ {
+ facePtr->id3 = i;
+ found = true;
+ }
+ if(found)
+ vertex2FaceMap[i].insert(facePtr);
+ }
+ }
+ }
+ }
+#endif
+ }
+
+ void TriMeshModel::fattenShrink(float offset)
+ {
+ int i;
+
+
+ int size = this->faces.size();
+ std::vector<bool> visited(vertices.size(), false);
+ std::vector<std::pair<Eigen::Vector3f,int>> offsets(vertices.size(), std::make_pair(Eigen::Vector3f::Zero(), 0));
+ for (i = 0; i < vertices.size(); ++i)
+ {
+ offsets.at(i) = std::make_pair(Eigen::Vector3f::Zero(), 0);
+ }
+ for (i = 0; i < size; ++i)
+ {
+ MathTools::TriangleFace& face = faces.at(i);
+ auto& p1 = vertices.at(face.id1);
+ auto& p2 = vertices.at(face.id2);
+ auto& p3 = vertices.at(face.id3);
+ auto normal1 = face.idNormal1 < normals.size() ? normals.at(face.idNormal1) : face.normal;
+ auto normal2 = face.idNormal2 < normals.size() ? normals.at(face.idNormal2) : face.normal;
+ auto normal3 = face.idNormal3 < normals.size() ? normals.at(face.idNormal3) : face.normal;
+ if(std::isnan(face.normal[0]) || std::isnan(face.normal[1]) || std::isnan(face.normal[2]))
+ std::cout << "NAN in face " << i << " : " << face.normal << std::endl;
+ if(std::isnan(normal1[0]) || std::isnan(normal1[1]) || std::isnan(normal1[2]))
+ std::cout << "NAN in normal1 " << i << " : " << face.normal << std::endl;
+ if(std::isnan(normal1[0]) || std::isnan(normal2[1]) || std::isnan(normal2[2]))
+ std::cout << "NAN in normal2 " << i << " : " << face.normal << std::endl;
+ if(std::isnan(normal3[0]) || std::isnan(normal3[1]) || std::isnan(normal3[2]))
+ std::cout << "NAN in normal3 " << i << " : " << face.normal << std::endl;
+
+// if(!visited.at(face.id1))
+ if(normal1.norm() > 0)
+ {
+ offsets.at(face.id1).first += normal1.normalized();
+ offsets.at(face.id1).second ++;
+ }
+// if(!visited.at(face.id2))
+ if(normal2.norm() > 0)
+ {
+ offsets.at(face.id2).first += normal2.normalized();
+// offsets.at(face.id2).second ++;
+ }
+// if(!visited.at(face.id3))
+ if(normal3.norm() > 0)
+ {
+ offsets.at(face.id3).first += normal3.normalized();
+// offsets.at(face.id3).second ++;
+ }
+ visited.at(face.id1) = true;
+ visited.at(face.id2) = true;
+ visited.at(face.id3) = true;
+ }
+
+ for (i = 0; i < vertices.size(); ++i)
+ {
+ auto& p = vertices.at(i);
+ auto& pair = offsets.at(i);
+ if(offsets.at(i).first.norm() > 0)
+ {
+ if(std::isnan(pair.first[0]) || std::isnan(pair.first[1]) || std::isnan(pair.first[2]))
+ std::cout << "NAN in " << i << " : " << pair.first << std::endl;
+ p += pair.first.normalized() * offset;
+ }
+ }
+
+ }
+
void TriMeshModel::setColor(VisualizationFactory::Color color)
{
colors.clear();
@@ -478,14 +671,14 @@ namespace VirtualRobot
boundingBox.scale(scaleFactor);
}
- VirtualRobot::TriMeshModelPtr TriMeshModel::clone()
+ VirtualRobot::TriMeshModelPtr TriMeshModel::clone() const
{
Eigen::Vector3f scaleFactor;
scaleFactor << 1.0f, 1.0f, 1.0f;
return clone(scaleFactor);
}
- VirtualRobot::TriMeshModelPtr TriMeshModel::clone(Eigen::Vector3f& scaleFactor)
+ VirtualRobot::TriMeshModelPtr TriMeshModel::clone(Eigen::Vector3f& scaleFactor) const
{
TriMeshModelPtr r(new TriMeshModel());
r->vertices = vertices;
@@ -496,4 +689,4 @@ namespace VirtualRobot
}
-} // namespace VirtualRobot
+} // namespace VirtualRobot
\ No newline at end of file
diff --git a/VirtualRobot/Visualization/TriMeshModel.h b/VirtualRobot/Visualization/TriMeshModel.h
index 1f577aa98567605a7be0def539c71f265057a8df..6848f0744c8f91eac160977b7dda5aa4bd2b30f2 100644
--- a/VirtualRobot/Visualization/TriMeshModel.h
+++ b/VirtualRobot/Visualization/TriMeshModel.h
@@ -57,20 +57,29 @@ namespace VirtualRobot
void addTriangleWithFace(Eigen::Vector3f& vertex1, Eigen::Vector3f& vertex2, Eigen::Vector3f& vertex3, Eigen::Vector4f& vertexColor1, Eigen::Vector4f& vertexColor2, Eigen::Vector4f& vertexColor3);
static Eigen::Vector3f CreateNormal(Eigen::Vector3f& vertex1, Eigen::Vector3f& vertex2, Eigen::Vector3f& vertex3);
void addFace(const MathTools::TriangleFace& face);
+ int addVertex(const Eigen::Vector3f& vertex);
+ int addNormal(const Eigen::Vector3f& normal);
+ int addColor(const VisualizationFactory::Color& color);
+ int addColor(const Eigen::Vector4f& color);
+ int addMaterial(const VisualizationFactory::PhongMaterial& material);
void addFace(unsigned int id0, unsigned int id1, unsigned int id2);
- /*!
- * \brief addVertex Adds a vertex to this object
- * \param vertex
- * \return The vertex id (== position in vertex array)
- */
- unsigned int addVertex(const Eigen::Vector3f& vertex);
- void addNormal(const Eigen::Vector3f& normal);
- void addColor(const VisualizationFactory::Color& color);
- void addColor(const Eigen::Vector4f& color);
- void addMaterial(const VisualizationFactory::PhongMaterial& material);
void clear();
void flipVertexOrientations();
+ /**
+ * @brief Merges vertices that are close together (mergeThreshold).
+ * Usually, vertices that are close together should be one vertex. Otherwise the mesh
+ * could consist of many individual triangles.
+ * All vertex ids stored in faces are updated. This function is quite efficient due to a kd-tree and an inverted face-vertex mapping.
+ * @param mergeThreshold If squared Euclidan distance of two points is belong this threshold, two vertices are merged.
+ */
+ void mergeVertices(float mergeThreshold = 0.0001);
+ /**
+ * @brief fatten or shrink this trimesh. Done by moving a vertex along a normal calculated from the normals
+ * of all the faces the vertex is used in.
+ * @param offset All vertexes are moved about this offset in mm.
+ */
+ void fattenShrink(float offset);
// Overwrite all colors
void setColor(VisualizationFactory::Color color);
@@ -84,15 +93,15 @@ namespace VirtualRobot
unsigned int checkAndCorrectNormals(bool inverted);
virtual void scale(Eigen::Vector3f& scaleFactor);
- TriMeshModelPtr clone();
- TriMeshModelPtr clone(Eigen::Vector3f& scaleFactor);
+ TriMeshModelPtr clone() const;
+ TriMeshModelPtr clone(Eigen::Vector3f& scaleFactor) const;
std::vector<Eigen::Vector3f> normals;
std::vector<Eigen::Vector3f> vertices;
std::vector<VisualizationFactory::Color> colors;
std::vector<MathTools::TriangleFace> faces;
std::vector<VisualizationFactory::PhongMaterial> materials;
- BoundingBox boundingBox;
+ BoundingBox boundingBox;
};
} // namespace VirtualRobot
diff --git a/VirtualRobot/Visualization/VisualizationNode.cpp b/VirtualRobot/Visualization/VisualizationNode.cpp
index 9fe52fc641cc55631e096270df3105f86c09d9b1..97a25cea3cca0399400b44d1f541adb70e0837d6 100644
--- a/VirtualRobot/Visualization/VisualizationNode.cpp
+++ b/VirtualRobot/Visualization/VisualizationNode.cpp
@@ -245,6 +245,11 @@ namespace VirtualRobot
VR_WARNING << "not implemented..." << endl;
}
+ void VisualizationNode::shrinkFatten(float offset)
+ {
+ VR_WARNING << "not implemented..." << endl;
+ }
+
void VisualizationNode::createTriMeshModel()
{
diff --git a/VirtualRobot/Visualization/VisualizationNode.h b/VirtualRobot/Visualization/VisualizationNode.h
index 3cca726e155a6e8d2c6282ba02ac00917d9ed340..4b7bc5f38624d7b4fd1fe0d9229f7da061461130 100644
--- a/VirtualRobot/Visualization/VisualizationNode.h
+++ b/VirtualRobot/Visualization/VisualizationNode.h
@@ -155,7 +155,7 @@ namespace VirtualRobot
virtual bool saveModel(const std::string& modelPath, const std::string& filename);
virtual void scale(Eigen::Vector3f& scaleFactor);
-
+ virtual void shrinkFatten(float offset);
//! update trimesh model
virtual void createTriMeshModel();