Point Cloud Library (PCL)
1.7.0
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IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 * 00034 */ 00035 00036 #ifndef PCL_SIFT_KEYPOINT_H_ 00037 #define PCL_SIFT_KEYPOINT_H_ 00038 00039 #include <pcl/keypoints/keypoint.h> 00040 00041 namespace pcl 00042 { 00043 template<typename PointT> 00044 struct SIFTKeypointFieldSelector 00045 { 00046 inline float 00047 operator () (const PointT & p) const 00048 { 00049 return p.intensity; 00050 } 00051 }; 00052 template<> 00053 struct SIFTKeypointFieldSelector<PointNormal> 00054 { 00055 inline float 00056 operator () (const PointNormal & p) const 00057 { 00058 return p.curvature; 00059 } 00060 }; 00061 template<> 00062 struct SIFTKeypointFieldSelector<PointXYZRGB> 00063 { 00064 inline float 00065 operator () (const PointXYZRGB & p) const 00066 { 00067 return (static_cast<float> (299*p.r + 587*p.g + 114*p.b) / 1000.0f); 00068 } 00069 }; 00070 template<> 00071 struct SIFTKeypointFieldSelector<PointXYZRGBA> 00072 { 00073 inline float 00074 operator () (const PointXYZRGBA & p) const 00075 { 00076 return (static_cast<float> (299*p.r + 587*p.g + 114*p.b) / 1000.0f); 00077 } 00078 }; 00079 00080 /** \brief @b SIFTKeypoint detects the Scale Invariant Feature Transform 00081 * keypoints for a given point cloud dataset containing points and intensity. 00082 * This implementation adapts the original algorithm from images to point 00083 * clouds. 00084 * 00085 * For more information about the image-based SIFT interest operator, see: 00086 * 00087 * David G. Lowe, "Distinctive image features from scale-invariant keypoints," 00088 * International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. 00089 * 00090 * \author Michael Dixon 00091 * \ingroup keypoints 00092 */ 00093 template <typename PointInT, typename PointOutT> 00094 class SIFTKeypoint : public Keypoint<PointInT, PointOutT> 00095 { 00096 public: 00097 typedef boost::shared_ptr<SIFTKeypoint<PointInT, PointOutT> > Ptr; 00098 typedef boost::shared_ptr<const SIFTKeypoint<PointInT, PointOutT> > ConstPtr; 00099 00100 typedef typename Keypoint<PointInT, PointOutT>::PointCloudIn PointCloudIn; 00101 typedef typename Keypoint<PointInT, PointOutT>::PointCloudOut PointCloudOut; 00102 typedef typename Keypoint<PointInT, PointOutT>::KdTree KdTree; 00103 00104 using Keypoint<PointInT, PointOutT>::name_; 00105 using Keypoint<PointInT, PointOutT>::input_; 00106 using Keypoint<PointInT, PointOutT>::indices_; 00107 using Keypoint<PointInT, PointOutT>::surface_; 00108 using Keypoint<PointInT, PointOutT>::tree_; 00109 using Keypoint<PointInT, PointOutT>::initCompute; 00110 00111 /** \brief Empty constructor. */ 00112 SIFTKeypoint () : min_scale_ (0.0), nr_octaves_ (0), nr_scales_per_octave_ (0), 00113 min_contrast_ (-std::numeric_limits<float>::max ()), scale_idx_ (-1), 00114 out_fields_ (), getFieldValue_ () 00115 { 00116 name_ = "SIFTKeypoint"; 00117 } 00118 00119 /** \brief Specify the range of scales over which to search for keypoints 00120 * \param min_scale the standard deviation of the smallest scale in the scale space 00121 * \param nr_octaves the number of octaves (i.e. doublings of scale) to compute 00122 * \param nr_scales_per_octave the number of scales to compute within each octave 00123 */ 00124 void 00125 setScales (float min_scale, int nr_octaves, int nr_scales_per_octave); 00126 00127 /** \brief Provide a threshold to limit detection of keypoints without sufficient contrast 00128 * \param min_contrast the minimum contrast required for detection 00129 */ 00130 void 00131 setMinimumContrast (float min_contrast); 00132 00133 protected: 00134 bool 00135 initCompute (); 00136 00137 /** \brief Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in 00138 * setSearchMethod (). 00139 * \param output the resultant cloud of keypoints 00140 */ 00141 void 00142 detectKeypoints (PointCloudOut &output); 00143 00144 private: 00145 /** \brief Detect the SIFT keypoints for a given point cloud for a single octave. 00146 * \param input the point cloud to detect keypoints in 00147 * \param tree a k-D tree of the points in \a input 00148 * \param base_scale the first (smallest) scale in the octave 00149 * \param nr_scales_per_octave the number of scales to to compute 00150 * \param output the resultant point cloud containing the SIFT keypoints 00151 */ 00152 void 00153 detectKeypointsForOctave (const PointCloudIn &input, KdTree &tree, 00154 float base_scale, int nr_scales_per_octave, 00155 PointCloudOut &output); 00156 00157 /** \brief Compute the difference-of-Gaussian (DoG) scale space for the given input and scales 00158 * \param input the point cloud for which the DoG scale space will be computed 00159 * \param tree a k-D tree of the points in \a input 00160 * \param scales a vector containing the scales over which to compute the DoG scale space 00161 * \param diff_of_gauss the resultant DoG scale space (in a number-of-points by number-of-scales matrix) 00162 */ 00163 void 00164 computeScaleSpace (const PointCloudIn &input, KdTree &tree, 00165 const std::vector<float> &scales, 00166 Eigen::MatrixXf &diff_of_gauss); 00167 00168 /** \brief Find the local minima and maxima in the provided difference-of-Gaussian (DoG) scale space 00169 * \param input the input point cloud 00170 * \param tree a k-D tree of the points in \a input 00171 * \param diff_of_gauss the DoG scale space (in a number-of-points by number-of-scales matrix) 00172 * \param extrema_indices the resultant vector containing the point indices of each keypoint 00173 * \param extrema_scales the resultant vector containing the scale indices of each keypoint 00174 */ 00175 void 00176 findScaleSpaceExtrema (const PointCloudIn &input, KdTree &tree, 00177 const Eigen::MatrixXf &diff_of_gauss, 00178 std::vector<int> &extrema_indices, std::vector<int> &extrema_scales); 00179 00180 00181 /** \brief The standard deviation of the smallest scale in the scale space.*/ 00182 float min_scale_; 00183 00184 /** \brief The number of octaves (i.e. doublings of scale) over which to search for keypoints.*/ 00185 int nr_octaves_; 00186 00187 /** \brief The number of scales to be computed for each octave.*/ 00188 int nr_scales_per_octave_; 00189 00190 /** \brief The minimum contrast required for detection.*/ 00191 float min_contrast_; 00192 00193 /** \brief Set to a value different than -1 if the output cloud has a "scale" field and we have to save 00194 * the keypoints scales. */ 00195 int scale_idx_; 00196 00197 /** \brief The list of fields present in the output point cloud data. */ 00198 std::vector<pcl::PCLPointField> out_fields_; 00199 00200 SIFTKeypointFieldSelector<PointInT> getFieldValue_; 00201 }; 00202 } 00203 00204 #include <pcl/keypoints/impl/sift_keypoint.hpp> 00205 00206 #endif // #ifndef PCL_SIFT_KEYPOINT_H_