41 #ifndef PCL_FEATURES_IMPL_SPIN_IMAGE_H_
42 #define PCL_FEATURES_IMPL_SPIN_IMAGE_H_
45 #include <pcl/point_cloud.h>
46 #include <pcl/point_types.h>
47 #include <pcl/exceptions.h>
48 #include <pcl/kdtree/kdtree_flann.h>
49 #include <pcl/features/spin_image.h>
53 template <
typename Po
intInT,
typename Po
intNT,
typename Po
intOutT>
55 unsigned int image_width,
double support_angle_cos,
unsigned int min_pts_neighb) :
56 input_normals_ (), rotation_axes_cloud_ (),
57 is_angular_ (false), rotation_axis_ (), use_custom_axis_(false), use_custom_axes_cloud_ (false),
58 is_radial_ (false), image_width_ (image_width), support_angle_cos_ (support_angle_cos),
59 min_pts_neighb_ (min_pts_neighb)
61 assert (support_angle_cos_ <= 1.0 && support_angle_cos_ >= 0.0);
68 template <
typename Po
intInT,
typename Po
intNT,
typename Po
intOutT> Eigen::ArrayXXd
71 assert (image_width_ > 0);
72 assert (support_angle_cos_ <= 1.0 && support_angle_cos_ >= 0.0);
74 const Eigen::Vector3f origin_point (input_->points[index].getVector3fMap ());
76 Eigen::Vector3f origin_normal;
79 input_normals_->points[index].getNormalVector3fMap () :
82 const Eigen::Vector3f rotation_axis = use_custom_axis_ ?
83 rotation_axis_.getNormalVector3fMap () :
84 use_custom_axes_cloud_ ?
85 rotation_axes_cloud_->points[index].getNormalVector3fMap () :
88 Eigen::ArrayXXd m_matrix (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
89 Eigen::ArrayXXd m_averAngles (Eigen::ArrayXXd::Zero (image_width_+1, 2*image_width_+1));
96 double bin_size = 0.0;
98 bin_size = search_radius_ / image_width_;
100 bin_size = search_radius_ / image_width_ / sqrt(2.0);
102 std::vector<int> nn_indices;
103 std::vector<float> nn_sqr_dists;
104 const int neighb_cnt = this->searchForNeighbors (index, search_radius_, nn_indices, nn_sqr_dists);
105 if (neighb_cnt < static_cast<int> (min_pts_neighb_))
108 "Too few points for spin image, use setMinPointCountInNeighbourhood() to decrease the threshold or use larger feature radius",
109 "spin_image.hpp",
"computeSiForPoint");
113 for (
int i_neigh = 0; i_neigh < neighb_cnt ; i_neigh++)
116 double cos_between_normals = -2.0;
117 if (support_angle_cos_ > 0.0 || is_angular_)
119 cos_between_normals = origin_normal.dot (input_normals_->points[nn_indices[i_neigh]].getNormalVector3fMap ());
120 if (fabs (cos_between_normals) > (1.0 + 10*std::numeric_limits<float>::epsilon ()))
122 PCL_ERROR (
"[pcl::%s::computeSiForPoint] Normal for the point %d and/or the point %d are not normalized, dot ptoduct is %f.\n",
123 getClassName ().c_str (), nn_indices[i_neigh], index, cos_between_normals);
125 "spin_image.hpp",
"computeSiForPoint");
127 cos_between_normals = std::max (-1.0, std::min (1.0, cos_between_normals));
129 if (fabs (cos_between_normals) < support_angle_cos_ )
134 if (cos_between_normals < 0.0)
136 cos_between_normals = -cos_between_normals;
141 const Eigen::Vector3f direction (
142 surface_->points[nn_indices[i_neigh]].getVector3fMap () - origin_point);
143 const double direction_norm = direction.norm ();
144 if (fabs(direction_norm) < 10*std::numeric_limits<double>::epsilon ())
146 assert (direction_norm > 0.0);
149 double cos_dir_axis = direction.dot(rotation_axis) / direction_norm;
150 if (fabs(cos_dir_axis) > (1.0 + 10*std::numeric_limits<float>::epsilon()))
152 PCL_ERROR (
"[pcl::%s::computeSiForPoint] Rotation axis for the point %d are not normalized, dot ptoduct is %f.\n",
153 getClassName ().c_str (), index, cos_dir_axis);
154 throw PCLException (
"Some rotation axis is not normalized",
155 "spin_image.hpp",
"computeSiForPoint");
157 cos_dir_axis = std::max (-1.0, std::min (1.0, cos_dir_axis));
160 double beta = std::numeric_limits<double>::signaling_NaN ();
161 double alpha = std::numeric_limits<double>::signaling_NaN ();
164 beta = asin (cos_dir_axis);
165 alpha = direction_norm;
169 beta = direction_norm * cos_dir_axis;
170 alpha = direction_norm * sqrt (1.0 - cos_dir_axis*cos_dir_axis);
172 if (fabs (beta) >= bin_size * image_width_ || alpha >= bin_size * image_width_)
178 assert (alpha >= 0.0);
179 assert (alpha <= bin_size * image_width_ + 20 * std::numeric_limits<float>::epsilon () );
183 double beta_bin_size = is_radial_ ? (M_PI / 2 / image_width_) : bin_size;
184 int beta_bin = int(std::floor (beta / beta_bin_size)) + int(image_width_);
185 assert (0 <= beta_bin && beta_bin < m_matrix.cols ());
186 int alpha_bin = int(std::floor (alpha / bin_size));
187 assert (0 <= alpha_bin && alpha_bin < m_matrix.rows ());
189 if (alpha_bin == static_cast<int> (image_width_))
193 alpha = bin_size * (alpha_bin + 1) - std::numeric_limits<double>::epsilon ();
195 if (beta_bin ==
int(2*image_width_) )
199 beta = beta_bin_size * (beta_bin - int(image_width_) + 1) - std::numeric_limits<double>::epsilon ();
202 double a = alpha/bin_size - double(alpha_bin);
203 double b = beta/beta_bin_size - double(beta_bin-
int(image_width_));
205 assert (0 <= a && a <= 1);
206 assert (0 <= b && b <= 1);
208 m_matrix (alpha_bin, beta_bin) += (1-a) * (1-b);
209 m_matrix (alpha_bin+1, beta_bin) += a * (1-b);
210 m_matrix (alpha_bin, beta_bin+1) += (1-a) * b;
211 m_matrix (alpha_bin+1, beta_bin+1) += a * b;
215 m_averAngles (alpha_bin, beta_bin) += (1-a) * (1-b) * acos (cos_between_normals);
216 m_averAngles (alpha_bin+1, beta_bin) += a * (1-b) * acos (cos_between_normals);
217 m_averAngles (alpha_bin, beta_bin+1) += (1-a) * b * acos (cos_between_normals);
218 m_averAngles (alpha_bin+1, beta_bin+1) += a * b * acos (cos_between_normals);
225 m_matrix = m_averAngles / (m_matrix + std::numeric_limits<double>::epsilon ());
227 else if (neighb_cnt > 1)
230 m_matrix /= m_matrix.sum();
238 template <
typename Po
intInT,
typename Po
intNT,
typename Po
intOutT>
bool
243 PCL_ERROR (
"[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
250 PCL_ERROR (
"[pcl::%s::initCompute] No input dataset containing normals was given!\n", getClassName ().c_str ());
256 if (input_normals_->points.size () != input_->points.size ())
258 PCL_ERROR (
"[pcl::%s::initCompute] ", getClassName ().c_str ());
259 PCL_ERROR (
"The number of points in the input dataset differs from ");
260 PCL_ERROR (
"the number of points in the dataset containing the normals!\n");
266 if (search_radius_ == 0)
268 PCL_ERROR (
"[pcl::%s::initCompute] Need a search radius different than 0!\n", getClassName ().c_str ());
274 PCL_ERROR (
"[pcl::%s::initCompute] K-nearest neighbor search for spin images not implemented. Used a search radius instead!\n", getClassName ().c_str ());
283 fake_surface_ =
true;
289 assert(!(use_custom_axis_ && use_custom_axes_cloud_));
291 if (!use_custom_axis_ && !use_custom_axes_cloud_
294 PCL_ERROR (
"[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
300 if ((is_angular_ || support_angle_cos_ > 0.0)
303 PCL_ERROR (
"[pcl::%s::initCompute] No normals for input cloud were given!\n", getClassName ().c_str ());
309 if (use_custom_axes_cloud_
310 && rotation_axes_cloud_->size () == input_->size ())
312 PCL_ERROR (
"[pcl::%s::initCompute] Rotation axis cloud have different size from input!\n", getClassName ().c_str ());
323 template <
typename Po
intInT,
typename Po
intNT,
typename Po
intOutT>
void
326 for (
int i_input = 0; i_input < static_cast<int> (indices_->size ()); ++i_input)
328 Eigen::ArrayXXd res = computeSiForPoint (indices_->at (i_input));
331 for (
int iRow = 0; iRow < res.rows () ; iRow++)
333 for (
int iCol = 0; iCol < res.cols () ; iCol++)
335 output.
points[i_input].histogram[ iRow*res.cols () + iCol ] =
static_cast<float> (res (iRow, iCol));
341 #define PCL_INSTANTIATE_SpinImageEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::SpinImageEstimation<T,NT,OutT>;
343 #endif // PCL_FEATURES_IMPL_SPIN_IMAGE_H_
Eigen::ArrayXXd computeSiForPoint(int index) const
Computes a spin-image for the point of the scan.
SpinImageEstimation(unsigned int image_width=8, double support_angle_cos=0.0, unsigned int min_pts_neighb=0)
Constructs empty spin image estimator.
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
std::string feature_name_
The feature name.
virtual bool initCompute()
initializes computations specific to spin-image.
virtual bool deinitCompute()
This method should get called after ending the actual computation.
Feature represents the base feature class.
A base class for all pcl exceptions which inherits from std::runtime_error.
PointCloud represents the base class in PCL for storing collections of 3D points. ...
virtual void computeFeature(PointCloudOut &output)
Estimate the Spin Image descriptors at a set of points given by setInputWithNormals() using the surfa...