Point Cloud Library (PCL)
1.7.0
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IN NO EVENT SHALL THE 00028 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00029 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00030 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00031 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00032 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00033 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00034 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00035 * POSSIBILITY OF SUCH DAMAGE. 00036 * 00037 * $Id$ 00038 * 00039 */ 00040 00041 #ifndef PCL_FEATURES_IMPL_INTENSITY_GRADIENT_H_ 00042 #define PCL_FEATURES_IMPL_INTENSITY_GRADIENT_H_ 00043 00044 #include <pcl/features/intensity_gradient.h> 00045 00046 ////////////////////////////////////////////////////////////////////////////////////////////// 00047 template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void 00048 pcl::IntensityGradientEstimation <PointInT, PointNT, PointOutT, IntensitySelectorT>::computePointIntensityGradient ( 00049 const pcl::PointCloud <PointInT> &cloud, const std::vector <int> &indices, 00050 const Eigen::Vector3f &point, float mean_intensity, const Eigen::Vector3f &normal, Eigen::Vector3f &gradient) 00051 { 00052 if (indices.size () < 3) 00053 { 00054 gradient[0] = gradient[1] = gradient[2] = std::numeric_limits<float>::quiet_NaN (); 00055 return; 00056 } 00057 00058 Eigen::Matrix3f A = Eigen::Matrix3f::Zero (); 00059 Eigen::Vector3f b = Eigen::Vector3f::Zero (); 00060 00061 for (size_t i_point = 0; i_point < indices.size (); ++i_point) 00062 { 00063 PointInT p = cloud.points[indices[i_point]]; 00064 if (!pcl_isfinite (p.x) || 00065 !pcl_isfinite (p.y) || 00066 !pcl_isfinite (p.z) || 00067 !pcl_isfinite (intensity_ (p))) 00068 continue; 00069 00070 p.x -= point[0]; 00071 p.y -= point[1]; 00072 p.z -= point[2]; 00073 intensity_.demean (p, mean_intensity); 00074 00075 A (0, 0) += p.x * p.x; 00076 A (0, 1) += p.x * p.y; 00077 A (0, 2) += p.x * p.z; 00078 00079 A (1, 1) += p.y * p.y; 00080 A (1, 2) += p.y * p.z; 00081 00082 A (2, 2) += p.z * p.z; 00083 00084 b[0] += p.x * intensity_ (p); 00085 b[1] += p.y * intensity_ (p); 00086 b[2] += p.z * intensity_ (p); 00087 } 00088 // Fill in the lower triangle of A 00089 A (1, 0) = A (0, 1); 00090 A (2, 0) = A (0, 2); 00091 A (2, 1) = A (1, 2); 00092 00093 //* 00094 Eigen::Vector3f x = A.colPivHouseholderQr ().solve (b); 00095 /*/ 00096 00097 Eigen::Vector3f eigen_values; 00098 Eigen::Matrix3f eigen_vectors; 00099 eigen33 (A, eigen_vectors, eigen_values); 00100 00101 b = eigen_vectors.transpose () * b; 00102 00103 if ( eigen_values (0) != 0) 00104 b (0) /= eigen_values (0); 00105 else 00106 b (0) = 0; 00107 00108 if ( eigen_values (1) != 0) 00109 b (1) /= eigen_values (1); 00110 else 00111 b (1) = 0; 00112 00113 if ( eigen_values (2) != 0) 00114 b (2) /= eigen_values (2); 00115 else 00116 b (2) = 0; 00117 00118 00119 Eigen::Vector3f x = eigen_vectors * b; 00120 00121 // if (A.col (0).squaredNorm () != 0) 00122 // x [0] /= A.col (0).squaredNorm (); 00123 // b -= x [0] * A.col (0); 00124 // 00125 // 00126 // if (A.col (1).squaredNorm () != 0) 00127 // x [1] /= A.col (1).squaredNorm (); 00128 // b -= x[1] * A.col (1); 00129 // 00130 // x [2] = b.dot (A.col (2)); 00131 // if (A.col (2).squaredNorm () != 0) 00132 // x[2] /= A.col (2).squaredNorm (); 00133 // Fit a hyperplane to the data 00134 00135 //*/ 00136 // std::cout << A << "\n*\n" << bb << "\n=\n" << x << "\nvs.\n" << x2 << "\n\n"; 00137 // std::cout << A * x << "\nvs.\n" << A * x2 << "\n\n------\n"; 00138 // Project the gradient vector, x, onto the tangent plane 00139 gradient = (Eigen::Matrix3f::Identity () - normal*normal.transpose ()) * x; 00140 } 00141 00142 ////////////////////////////////////////////////////////////////////////////////////////////// 00143 template <typename PointInT, typename PointNT, typename PointOutT, typename IntensitySelectorT> void 00144 pcl::IntensityGradientEstimation<PointInT, PointNT, PointOutT, IntensitySelectorT>::computeFeature (PointCloudOut &output) 00145 { 00146 // Allocate enough space to hold the results 00147 // \note This resize is irrelevant for a radiusSearch (). 00148 std::vector<int> nn_indices (k_); 00149 std::vector<float> nn_dists (k_); 00150 output.is_dense = true; 00151 00152 // If the data is dense, we don't need to check for NaN 00153 if (surface_->is_dense) 00154 { 00155 #ifdef _OPENMP 00156 #pragma omp parallel for shared (output) private (nn_indices, nn_dists) num_threads(threads_) 00157 #endif 00158 // Iterating over the entire index vector 00159 for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx) 00160 { 00161 PointOutT &p_out = output.points[idx]; 00162 00163 if (!this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists)) 00164 { 00165 p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN (); 00166 output.is_dense = false; 00167 continue; 00168 } 00169 00170 Eigen::Vector3f centroid; 00171 float mean_intensity = 0; 00172 // Initialize to 0 00173 centroid.setZero (); 00174 for (size_t i = 0; i < nn_indices.size (); ++i) 00175 { 00176 centroid += surface_->points[nn_indices[i]].getVector3fMap (); 00177 mean_intensity += intensity_ (surface_->points[nn_indices[i]]); 00178 } 00179 centroid /= static_cast<float> (nn_indices.size ()); 00180 mean_intensity /= static_cast<float> (nn_indices.size ()); 00181 00182 Eigen::Vector3f normal = Eigen::Vector3f::Map (normals_->points[(*indices_) [idx]].normal); 00183 Eigen::Vector3f gradient; 00184 computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient); 00185 00186 p_out.gradient[0] = gradient[0]; 00187 p_out.gradient[1] = gradient[1]; 00188 p_out.gradient[2] = gradient[2]; 00189 } 00190 } 00191 else 00192 { 00193 #ifdef _OPENMP 00194 #pragma omp parallel for shared (output) private (nn_indices, nn_dists) num_threads(threads_) 00195 #endif 00196 // Iterating over the entire index vector 00197 for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx) 00198 { 00199 PointOutT &p_out = output.points[idx]; 00200 if (!isFinite ((*surface_) [(*indices_)[idx]]) || 00201 !this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists)) 00202 { 00203 p_out.gradient[0] = p_out.gradient[1] = p_out.gradient[2] = std::numeric_limits<float>::quiet_NaN (); 00204 output.is_dense = false; 00205 continue; 00206 } 00207 Eigen::Vector3f centroid; 00208 float mean_intensity = 0; 00209 // Initialize to 0 00210 centroid.setZero (); 00211 unsigned cp = 0; 00212 for (size_t i = 0; i < nn_indices.size (); ++i) 00213 { 00214 // Check if the point is invalid 00215 if (!isFinite ((*surface_) [nn_indices[i]])) 00216 continue; 00217 00218 centroid += surface_->points [nn_indices[i]].getVector3fMap (); 00219 mean_intensity += intensity_ (surface_->points [nn_indices[i]]); 00220 ++cp; 00221 } 00222 centroid /= static_cast<float> (cp); 00223 mean_intensity /= static_cast<float> (cp); 00224 Eigen::Vector3f normal = Eigen::Vector3f::Map (normals_->points[(*indices_) [idx]].normal); 00225 Eigen::Vector3f gradient; 00226 computePointIntensityGradient (*surface_, nn_indices, centroid, mean_intensity, normal, gradient); 00227 00228 p_out.gradient[0] = gradient[0]; 00229 p_out.gradient[1] = gradient[1]; 00230 p_out.gradient[2] = gradient[2]; 00231 } 00232 } 00233 } 00234 00235 #define PCL_INSTANTIATE_IntensityGradientEstimation(InT,NT,OutT) template class PCL_EXPORTS pcl::IntensityGradientEstimation<InT,NT,OutT>; 00236 00237 #endif // PCL_FEATURES_IMPL_INTENSITY_GRADIENT_H_