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pcl_Recognition_172.pxd
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pcl_Recognition_172.pxd
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from libc.stddef cimport size_t
from libcpp.vector cimport vector
from libcpp.string cimport string
from libcpp cimport bool
# main
cimport pcl_defs as cpp
# boost
from boost_shared_ptr cimport shared_ptr
###############################################################################
# Types
###############################################################################
### base class ###
# quantizable_modality.h
# namespace pcl
# {
# /** \brief Interface for a quantizable modality.
# * \author Stefan Holzer
# */
# class PCL_EXPORTS QuantizableModality
cdef extern from "pcl/Recognition/quantizable_modality.h" namespace "pcl":
cdef cppclass Feature[In, Out](cpp.PCLBase[In]):
QuantizableModality ()
# /** \brief Destructor. */
# virtual ~QuantizableModality ();
# /** \brief Returns a reference to the internally computed quantized map. */
# virtual QuantizedMap & getQuantizedMap () = 0;
# /** \brief Returns a reference to the internally computed spreaded quantized map. */
# virtual QuantizedMap & getSpreadedQuantizedMap () = 0;
# /** \brief Extracts features from this modality within the specified mask.
# * \param[in] mask defines the areas where features are searched in.
# * \param[in] nr_features defines the number of features to be extracted
# * (might be less if not sufficient information is present in the modality).
# * \param[in] modality_index the index which is stored in the extracted features.
# * \param[out] features the destination for the extracted features.
# */
# virtual void extractFeatures (
# const MaskMap & mask, size_t nr_features, size_t modality_index,
# std::vector<QuantizedMultiModFeature> & features) const = 0;
#
# /** \brief Extracts all possible features from the modality within the specified mask.
# * \param[in] mask defines the areas where features are searched in.
# * \param[in] nr_features IGNORED (TODO: remove this parameter).
# * \param[in] modality_index the index which is stored in the extracted features.
# * \param[out] features the destination for the extracted features.
# */
# virtual void extractAllFeatures (const MaskMap & mask, size_t nr_features, size_t modality_index,
# std::vector<QuantizedMultiModFeature> & features) const = 0;
###
### Inheritance class ###
# auxiliary.h
# #include <pcl/recognition/ransac_based/auxiliary.h>
###
# boost.h
# #include <boost/unordered_map.hpp>
# #include <boost/graph/graph_traits.hpp>
###
# bvh.h
# #include <pcl/recognition/ransac_based/bvh.h>
###
# color_gradient_dot_modality.h
# namespace pcl
# {
#
# /** \brief A point structure for representing RGB color
# * \ingroup common
# */
# struct EIGEN_ALIGN16 PointRGB
# {
# union
# {
# union
# {
# struct
# {
# uint8_t b;
# uint8_t g;
# uint8_t r;
# uint8_t _unused;
# };
# float rgb;
# };
# uint32_t rgba;
# };
#
# inline PointRGB ()
# {}
#
# inline PointRGB (const uint8_t b, const uint8_t g, const uint8_t r)
# : b (b), g (g), r (r), _unused (0)
# {}
#
# EIGEN_MAKE_ALIGNED_OPERATOR_NEW
# };
#
#
# /** \brief A point structure representing Euclidean xyz coordinates, and the intensity value.
# * \ingroup common
# */
# struct EIGEN_ALIGN16 GradientXY
# {
# union
# {
# struct
# {
# float x;
# float y;
# float angle;
# float magnitude;
# };
# float data[4];
# };
# EIGEN_MAKE_ALIGNED_OPERATOR_NEW
#
# inline bool operator< (const GradientXY & rhs)
# {
# return (magnitude > rhs.magnitude);
# }
# };
# inline std::ostream & operator << (std::ostream & os, const GradientXY & p)
# {
# os << "(" << p.x << "," << p.y << " - " << p.magnitude << ")";
# return (os);
# }
#
# // --------------------------------------------------------------------------
# template <typename PointInT>
# class ColorGradientDOTModality : public DOTModality, public PCLBase<PointInT>
# {
# protected:
# using PCLBase<PointInT>::input_;
#
# struct Candidate
# {
# GradientXY gradient;
#
# int x;
# int y;
#
# bool operator< (const Candidate & rhs)
# {
# return (gradient.magnitude > rhs.gradient.magnitude);
# }
# };
#
# public:
# typedef typename pcl::PointCloud<PointInT> PointCloudIn;
#
# ColorGradientDOTModality (size_t bin_size);
#
# virtual ~ColorGradientDOTModality ();
#
# inline void
# setGradientMagnitudeThreshold (const float threshold)
# {
# gradient_magnitude_threshold_ = threshold;
# }
#
# //inline QuantizedMap &
# //getDominantQuantizedMap ()
# //{
# // return (dominant_quantized_color_gradients_);
# //}
#
# inline QuantizedMap &
# getDominantQuantizedMap ()
# {
# return (dominant_quantized_color_gradients_);
# }
#
# QuantizedMap
# computeInvariantQuantizedMap (const MaskMap & mask,
# const RegionXY & region);
#
# /** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
# * \param cloud the const boost shared pointer to a PointCloud message
# */
# virtual void
# setInputCloud (const typename PointCloudIn::ConstPtr & cloud)
# {
# input_ = cloud;
# //processInputData ();
# }
#
# virtual void
# processInputData ();
#
# protected:
#
# void
# computeMaxColorGradients ();
#
# void
# computeDominantQuantizedGradients ();
#
# //void
# //computeInvariantQuantizedGradients ();
#
# private:
# size_t bin_size_;
#
# float gradient_magnitude_threshold_;
# pcl::PointCloud<pcl::GradientXY> color_gradients_;
#
# pcl::QuantizedMap dominant_quantized_color_gradients_;
# //pcl::QuantizedMap invariant_quantized_color_gradients_;
#
# };
#
# }
# template <typename PointInT>
# pcl::ColorGradientDOTModality<PointInT>::ColorGradientDOTModality (const size_t bin_size)
# : bin_size_ (bin_size), gradient_magnitude_threshold_ (80.0f), color_gradients_ (), dominant_quantized_color_gradients_ ()
# {
# }
#
# //////////////////////////////////////////////////////////////////////////////////////////////
# template <typename PointInT>
# pcl::ColorGradientDOTModality<PointInT>::
# ~ColorGradientDOTModality ()
# {
# }
#
# //////////////////////////////////////////////////////////////////////////////////////////////
# template <typename PointInT>
# void
# pcl::ColorGradientDOTModality<PointInT>::
# processInputData ()
# {
# // extract color gradients
# computeMaxColorGradients ();
#
# // compute dominant quantized gradient map
# computeDominantQuantizedGradients ();
#
# // compute invariant quantized gradient map
# //computeInvariantQuantizedGradients ();
# }
#
# //////////////////////////////////////////////////////////////////////////////////////////////
# template <typename PointInT>
# void
# pcl::ColorGradientDOTModality<PointInT>::
# computeMaxColorGradients ()
# {
# const int width = input_->width;
# const int height = input_->height;
#
# color_gradients_.points.resize (width*height);
# color_gradients_.width = width;
# color_gradients_.height = height;
#
# const float pi = tan(1.0f)*4;
# for (int row_index = 0; row_index < height-2; ++row_index)
# {
# for (int col_index = 0; col_index < width-2; ++col_index)
# {
# const int index0 = row_index*width+col_index;
# const int index_c = row_index*width+col_index+2;
# const int index_r = (row_index+2)*width+col_index;
#
# //const int index_d = (row_index+1)*width+col_index+1;
#
# const unsigned char r0 = input_->points[index0].r;
# const unsigned char g0 = input_->points[index0].g;
# const unsigned char b0 = input_->points[index0].b;
#
# const unsigned char r_c = input_->points[index_c].r;
# const unsigned char g_c = input_->points[index_c].g;
# const unsigned char b_c = input_->points[index_c].b;
#
# const unsigned char r_r = input_->points[index_r].r;
# const unsigned char g_r = input_->points[index_r].g;
# const unsigned char b_r = input_->points[index_r].b;
#
# const float r_dx = static_cast<float> (r_c) - static_cast<float> (r0);
# const float g_dx = static_cast<float> (g_c) - static_cast<float> (g0);
# const float b_dx = static_cast<float> (b_c) - static_cast<float> (b0);
#
# const float r_dy = static_cast<float> (r_r) - static_cast<float> (r0);
# const float g_dy = static_cast<float> (g_r) - static_cast<float> (g0);
# const float b_dy = static_cast<float> (b_r) - static_cast<float> (b0);
#
# const float sqr_mag_r = r_dx*r_dx + r_dy*r_dy;
# const float sqr_mag_g = g_dx*g_dx + g_dy*g_dy;
# const float sqr_mag_b = b_dx*b_dx + b_dy*b_dy;
#
# GradientXY gradient;
# gradient.x = col_index;
# gradient.y = row_index;
# if (sqr_mag_r > sqr_mag_g && sqr_mag_r > sqr_mag_b)
# {
# gradient.magnitude = sqrt (sqr_mag_r);
# gradient.angle = atan2 (r_dy, r_dx) * 180.0f / pi;
# }
# else if (sqr_mag_g > sqr_mag_b)
# {
# //GradientXY gradient;
# gradient.magnitude = sqrt (sqr_mag_g);
# gradient.angle = atan2 (g_dy, g_dx) * 180.0f / pi;
# //gradient.x = col_index;
# //gradient.y = row_index;
#
# //color_gradients_ (col_index+1, row_index+1) = gradient;
# }
# else
# {
# //GradientXY gradient;
# gradient.magnitude = sqrt (sqr_mag_b);
# gradient.angle = atan2 (b_dy, b_dx) * 180.0f / pi;
# //gradient.x = col_index;
# //gradient.y = row_index;
#
# //color_gradients_ (col_index+1, row_index+1) = gradient;
# }
#
# assert (color_gradients_ (col_index+1, row_index+1).angle >= -180 &&
# color_gradients_ (col_index+1, row_index+1).angle <= 180);
#
# color_gradients_ (col_index+1, row_index+1) = gradient;
# }
# }
#
# return;
# }
# //////////////////////////////////////////////////////////////////////////////////////////////
# template <typename PointInT>
# void
# pcl::ColorGradientDOTModality<PointInT>::
# computeDominantQuantizedGradients ()
# {
# const size_t input_width = input_->width;
# const size_t input_height = input_->height;
#
# const size_t output_width = input_width / bin_size_;
# const size_t output_height = input_height / bin_size_;
#
# dominant_quantized_color_gradients_.resize (output_width, output_height);
#
# //size_t offset_x = 0;
# //size_t offset_y = 0;
#
# const size_t num_gradient_bins = 7;
# const size_t max_num_of_gradients = 1;
#
# const float divisor = 180.0f / (num_gradient_bins - 1.0f);
#
# float global_max_gradient = 0.0f;
# float local_max_gradient = 0.0f;
#
# unsigned char * peak_pointer = dominant_quantized_color_gradients_.getData ();
# memset (peak_pointer, 0, output_width*output_height);
#
# //int tmpCounter = 0;
# for (size_t row_bin_index = 0; row_bin_index < output_height; ++row_bin_index)
# {
# for (size_t col_bin_index = 0; col_bin_index < output_width; ++col_bin_index)
# {
# const size_t x_position = col_bin_index * bin_size_;
# const size_t y_position = row_bin_index * bin_size_;
#
# //std::vector<int> x_coordinates;
# //std::vector<int> y_coordinates;
# //std::vector<float> values;
#
# // iteratively search for the largest gradients, set it to -1, search the next largest ... etc.
# //while (counter < max_num_of_gradients)
# {
# float max_gradient;
# size_t max_gradient_pos_x;
# size_t max_gradient_pos_y;
#
# // find next location and value of maximum gradient magnitude in current region
# {
# max_gradient = 0.0f;
# for (size_t row_sub_index = 0; row_sub_index < bin_size_; ++row_sub_index)
# {
# for (size_t col_sub_index = 0; col_sub_index < bin_size_; ++col_sub_index)
# {
# const float magnitude = color_gradients_ (col_sub_index + x_position, row_sub_index + y_position).magnitude;
#
# if (magnitude > max_gradient)
# {
# max_gradient = magnitude;
# max_gradient_pos_x = col_sub_index;
# max_gradient_pos_y = row_sub_index;
# }
# }
# }
# }
#
# if (max_gradient >= gradient_magnitude_threshold_)
# {
# const size_t angle = static_cast<size_t> (180 + color_gradients_ (max_gradient_pos_x + x_position, max_gradient_pos_y + y_position).angle + 0.5f);
# const size_t bin_index = static_cast<size_t> ((angle >= 180 ? angle-180 : angle)/divisor);
#
# *peak_pointer |= 1 << bin_index;
# }
#
# //++counter;
#
# //x_coordinates.push_back (max_gradient_pos_x + x_position);
# //y_coordinates.push_back (max_gradient_pos_y + y_position);
# //values.push_back (max_gradient);
#
# //color_gradients_ (max_gradient_pos_x + x_position, max_gradient_pos_y + y_position).magnitude = -1.0f;
# }
#
# //// reset values which have been set to -1
# //for (size_t value_index = 0; value_index < values.size (); ++value_index)
# //{
# // color_gradients_ (x_coordinates[value_index], y_coordinates[value_index]).magnitude = values[value_index];
# //}
#
#
# if (*peak_pointer == 0)
# {
# *peak_pointer |= 1 << 7;
# }
#
# //if (*peakPointer != 0)
# //{
# // ++tmpCounter;
# //}
#
# //++stringPointer;
# ++peak_pointer;
#
# //offset_x += bin_size;
# }
#
# //offset_y += bin_size;
# //offset_x = bin_size/2+1;
# }
# }
# //////////////////////////////////////////////////////////////////////////////////////////////
# template <typename PointInT>
# pcl::QuantizedMap
# pcl::ColorGradientDOTModality<PointInT>::
# computeInvariantQuantizedMap (const MaskMap & mask,
# const RegionXY & region)
# {
# const size_t input_width = input_->width;
# const size_t input_height = input_->height;
#
# const size_t output_width = input_width / bin_size_;
# const size_t output_height = input_height / bin_size_;
#
# const size_t sub_start_x = region.x / bin_size_;
# const size_t sub_start_y = region.y / bin_size_;
# const size_t sub_width = region.width / bin_size_;
# const size_t sub_height = region.height / bin_size_;
#
# QuantizedMap map;
# map.resize (sub_width, sub_height);
#
# //size_t offset_x = 0;
# //size_t offset_y = 0;
#
# const size_t num_gradient_bins = 7;
# const size_t max_num_of_gradients = 7;
#
# const float divisor = 180.0f / (num_gradient_bins - 1.0f);
#
# float global_max_gradient = 0.0f;
# float local_max_gradient = 0.0f;
#
# unsigned char * peak_pointer = map.getData ();
#
# //int tmpCounter = 0;
# for (size_t row_bin_index = 0; row_bin_index < sub_height; ++row_bin_index)
# {
# for (size_t col_bin_index = 0; col_bin_index < sub_width; ++col_bin_index)
# {
# std::vector<size_t> x_coordinates;
# std::vector<size_t> y_coordinates;
# std::vector<float> values;
#
# for (int row_pixel_index = -static_cast<int> (bin_size_)/2;
# row_pixel_index <= static_cast<int> (bin_size_)/2;
# row_pixel_index += static_cast<int> (bin_size_)/2)
# {
# const size_t y_position = /*offset_y +*/ row_pixel_index + (sub_start_y + row_bin_index)*bin_size_;
#
# if (y_position < 0 || y_position >= input_height)
# continue;
#
# for (int col_pixel_index = -static_cast<int> (bin_size_)/2;
# col_pixel_index <= static_cast<int> (bin_size_)/2;
# col_pixel_index += static_cast<int> (bin_size_)/2)
# {
# const size_t x_position = /*offset_x +*/ col_pixel_index + (sub_start_x + col_bin_index)*bin_size_;
# size_t counter = 0;
#
# if (x_position < 0 || x_position >= input_width)
# continue;
#
# // find maximum gradient magnitude in current bin
# {
# local_max_gradient = 0.0f;
# for (size_t row_sub_index = 0; row_sub_index < bin_size_; ++row_sub_index)
# {
# for (size_t col_sub_index = 0; col_sub_index < bin_size_; ++col_sub_index)
# {
# const float magnitude = color_gradients_ (col_sub_index + x_position, row_sub_index + y_position).magnitude;
#
# if (magnitude > local_max_gradient)
# local_max_gradient = magnitude;
# }
# }
# }
#
# //*stringPointer += localMaxGradient;
#
# if (local_max_gradient > global_max_gradient)
# {
# global_max_gradient = local_max_gradient;
# }
#
# // iteratively search for the largest gradients, set it to -1, search the next largest ... etc.
# while (true)
# {
# float max_gradient;
# size_t max_gradient_pos_x;
# size_t max_gradient_pos_y;
#
# // find next location and value of maximum gradient magnitude in current region
# {
# max_gradient = 0.0f;
# for (size_t row_sub_index = 0; row_sub_index < bin_size_; ++row_sub_index)
# {
# for (size_t col_sub_index = 0; col_sub_index < bin_size_; ++col_sub_index)
# {
# const float magnitude = color_gradients_ (col_sub_index + x_position, row_sub_index + y_position).magnitude;
#
# if (magnitude > max_gradient)
# {
# max_gradient = magnitude;
# max_gradient_pos_x = col_sub_index;
# max_gradient_pos_y = row_sub_index;
# }
# }
# }
# }
#
# // TODO: really localMaxGradient and not maxGradient???
# if (local_max_gradient < gradient_magnitude_threshold_)
# {
# //*peakPointer |= 1 << (numOfGradientBins-1);
# break;
# }
#
# // TODO: replace gradient_magnitude_threshold_ here by a fixed ratio?
# if (/*max_gradient < (local_max_gradient * gradient_magnitude_threshold_) ||*/
# counter >= max_num_of_gradients)
# {
# break;
# }
#
# ++counter;
#
# const size_t angle = static_cast<size_t> (180 + color_gradients_ (max_gradient_pos_x + x_position, max_gradient_pos_y + y_position).angle + 0.5f);
# const size_t bin_index = static_cast<size_t> ((angle >= 180 ? angle-180 : angle)/divisor);
#
# *peak_pointer |= 1 << bin_index;
#
# x_coordinates.push_back (max_gradient_pos_x + x_position);
# y_coordinates.push_back (max_gradient_pos_y + y_position);
# values.push_back (max_gradient);
#
# color_gradients_ (max_gradient_pos_x + x_position, max_gradient_pos_y + y_position).magnitude = -1.0f;
# }
#
# // reset values which have been set to -1
# for (size_t value_index = 0; value_index < values.size (); ++value_index)
# {
# color_gradients_ (x_coordinates[value_index], y_coordinates[value_index]).magnitude = values[value_index];
# }
#
# x_coordinates.clear ();
# y_coordinates.clear ();
# values.clear ();
# }
# }
#
# if (*peak_pointer == 0)
# {
# *peak_pointer |= 1 << 7;
# }
#
# //if (*peakPointer != 0)
# //{
# // ++tmpCounter;
# //}
#
# //++stringPointer;
# ++peak_pointer;
#
# //offset_x += bin_size;
# }
#
# //offset_y += bin_size;
# //offset_x = bin_size/2+1;
# }
#
# return map;
# }
#
# #endif
# color_gradient_modality.h
# namespace pcl
#
# /** \brief Modality based on max-RGB gradients.
# * \author Stefan Holzer
# */
# template <typename PointInT>
# class ColorGradientModality : public QuantizableModality, public PCLBase<PointInT>
{
protected:
using PCLBase<PointInT>::input_;
/** \brief Candidate for a feature (used in feature extraction methods). */
struct Candidate
{
/** \brief The gradient. */
GradientXY gradient;
/** \brief The x-position. */
int x;
/** \brief The y-position. */
int y;
/** \brief Operator for comparing to candidates (by magnitude of the gradient).
* \param[in] rhs the candidate to compare with.
*/
bool operator< (const Candidate & rhs) const
{
return (gradient.magnitude > rhs.gradient.magnitude);
}
};
public:
typedef typename pcl::PointCloud<PointInT> PointCloudIn;
/** \brief Different methods for feature selection/extraction. */
enum FeatureSelectionMethod
{
MASK_BORDER_HIGH_GRADIENTS,
MASK_BORDER_EQUALLY, // this gives templates most equally to the OpenCV implementation
DISTANCE_MAGNITUDE_SCORE
};
/** \brief Constructor. */
ColorGradientModality ();
/** \brief Destructor. */
virtual ~ColorGradientModality ();
/** \brief Sets the threshold for the gradient magnitude which is used when quantizing the data.
* Gradients with a smaller magnitude are ignored.
* \param[in] threshold the new gradient magnitude threshold.
*/
inline void
setGradientMagnitudeThreshold (const float threshold)
{
gradient_magnitude_threshold_ = threshold;
}
/** \brief Sets the threshold for the gradient magnitude which is used for feature extraction.
* Gradients with a smaller magnitude are ignored.
* \param[in] threshold the new gradient magnitude threshold.
*/
inline void
setGradientMagnitudeThresholdForFeatureExtraction (const float threshold)
{
gradient_magnitude_threshold_feature_extraction_ = threshold;
}
/** \brief Sets the feature selection method.
* \param[in] method the feature selection method.
*/
inline void
setFeatureSelectionMethod (const FeatureSelectionMethod method)
{
feature_selection_method_ = method;
}
/** \brief Sets the spreading size for spreading the quantized data. */
inline void
setSpreadingSize (const size_t spreading_size)
{
spreading_size_ = spreading_size;
}
/** \brief Sets whether variable feature numbers for feature extraction is enabled.
* \param[in] enabled enables/disables variable feature numbers for feature extraction.
*/
inline void
setVariableFeatureNr (const bool enabled)
{
variable_feature_nr_ = enabled;
}
/** \brief Returns a reference to the internally computed quantized map. */
inline QuantizedMap &
getQuantizedMap ()
{
return (filtered_quantized_color_gradients_);
}
/** \brief Returns a reference to the internally computed spreaded quantized map. */
inline QuantizedMap &
getSpreadedQuantizedMap ()
{
return (spreaded_filtered_quantized_color_gradients_);
}
/** \brief Returns a point cloud containing the max-RGB gradients. */
inline pcl::PointCloud<pcl::GradientXY> &
getMaxColorGradients ()
{
return (color_gradients_);
}
/** \brief Extracts features from this modality within the specified mask.
* \param[in] mask defines the areas where features are searched in.
* \param[in] nr_features defines the number of features to be extracted
* (might be less if not sufficient information is present in the modality).
* \param[in] modalityIndex the index which is stored in the extracted features.
* \param[out] features the destination for the extracted features.
*/
void
extractFeatures (const MaskMap & mask, size_t nr_features, size_t modalityIndex,
std::vector<QuantizedMultiModFeature> & features) const;
/** \brief Extracts all possible features from the modality within the specified mask.
* \param[in] mask defines the areas where features are searched in.
* \param[in] nr_features IGNORED (TODO: remove this parameter).
* \param[in] modalityIndex the index which is stored in the extracted features.
* \param[out] features the destination for the extracted features.
*/
void
extractAllFeatures (const MaskMap & mask, size_t nr_features, size_t modalityIndex,
std::vector<QuantizedMultiModFeature> & features) const;
/** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
* \param cloud the const boost shared pointer to a PointCloud message
*/
virtual void
setInputCloud (const typename PointCloudIn::ConstPtr & cloud)
{
input_ = cloud;
}
/** \brief Processes the input data (smoothing, computing gradients, quantizing, filtering, spreading). */
virtual void
processInputData ();
/** \brief Processes the input data assuming that everything up to filtering is already done/available
* (so only spreading is performed). */
virtual void
processInputDataFromFiltered ();
protected:
/** \brief Computes the Gaussian kernel used for smoothing.
* \param[in] kernel_size the size of the Gaussian kernel.
* \param[in] sigma the sigma.
* \param[out] kernel_values the destination for the values of the kernel. */
void
computeGaussianKernel (const size_t kernel_size, const float sigma, std::vector <float> & kernel_values);
/** \brief Computes the max-RGB gradients for the specified cloud.
* \param[in] cloud the cloud for which the gradients are computed.
*/
void
computeMaxColorGradients (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud);
/** \brief Computes the max-RGB gradients for the specified cloud using sobel.
* \param[in] cloud the cloud for which the gradients are computed.
*/
void
computeMaxColorGradientsSobel (const typename pcl::PointCloud<pcl::RGB>::ConstPtr & cloud);
/** \brief Quantizes the color gradients. */
void
quantizeColorGradients ();
/** \brief Filters the quantized gradients. */
void
filterQuantizedColorGradients ();
/** \brief Erodes a mask.
* \param[in] mask_in the mask which will be eroded.
* \param[out] mask_out the destination for the eroded mask.
*/
static void
erode (const pcl::MaskMap & mask_in, pcl::MaskMap & mask_out);
private:
/** \brief Determines whether variable numbers of features are extracted or not. */
bool variable_feature_nr_;
/** \brief Stores a smoothed verion of the input cloud. */
pcl::PointCloud<pcl::RGB>::Ptr smoothed_input_;
/** \brief Defines which feature selection method is used. */
FeatureSelectionMethod feature_selection_method_;
/** \brief The threshold applied on the gradient magnitudes (for quantization). */
float gradient_magnitude_threshold_;
/** \brief The threshold applied on the gradient magnitudes for feature extraction. */
float gradient_magnitude_threshold_feature_extraction_;
/** \brief The point cloud which holds the max-RGB gradients. */
pcl::PointCloud<pcl::GradientXY> color_gradients_;
/** \brief The spreading size. */
size_t spreading_size_;
/** \brief The map which holds the quantized max-RGB gradients. */
pcl::QuantizedMap quantized_color_gradients_;
/** \brief The map which holds the filtered quantized data. */
pcl::QuantizedMap filtered_quantized_color_gradients_;
/** \brief The map which holds the spreaded quantized data. */
pcl::QuantizedMap spreaded_filtered_quantized_color_gradients_;
};
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
pcl::ColorGradientModality<PointInT>::
ColorGradientModality ()
: variable_feature_nr_ (false)
, smoothed_input_ (new pcl::PointCloud<pcl::RGB> ())
, feature_selection_method_ (DISTANCE_MAGNITUDE_SCORE)
, gradient_magnitude_threshold_ (10.0f)
, gradient_magnitude_threshold_feature_extraction_ (55.0f)
, color_gradients_ ()
, spreading_size_ (8)
, quantized_color_gradients_ ()
, filtered_quantized_color_gradients_ ()
, spreaded_filtered_quantized_color_gradients_ ()
{
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
pcl::ColorGradientModality<PointInT>::
~ColorGradientModality ()
{
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT> void
pcl::ColorGradientModality<PointInT>::
computeGaussianKernel (const size_t kernel_size, const float sigma, std::vector <float> & kernel_values)
{
// code taken from OpenCV
const int n = int (kernel_size);
const int SMALL_GAUSSIAN_SIZE = 7;
static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
{
{1.f},
{0.25f, 0.5f, 0.25f},
{0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
{0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
};
const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
small_gaussian_tab[n>>1] : 0;
//CV_Assert( ktype == CV_32F || ktype == CV_64F );
/*Mat kernel(n, 1, ktype);*/
kernel_values.resize (n);
float* cf = &(kernel_values[0]);
//double* cd = (double*)kernel.data;
double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
double scale2X = -0.5/(sigmaX*sigmaX);
double sum = 0;
int i;
for( i = 0; i < n; i++ )
{
double x = i - (n-1)*0.5;
double t = fixed_kernel ? double (fixed_kernel[i]) : std::exp (scale2X*x*x);
cf[i] = float (t);
sum += cf[i];
}
sum = 1./sum;
for (i = 0; i < n; i++ )
{
cf[i] = float (cf[i]*sum);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
processInputData ()
{
// compute gaussian kernel values
const size_t kernel_size = 7;
std::vector<float> kernel_values;
computeGaussianKernel (kernel_size, 0.0f, kernel_values);
// smooth input
pcl::filters::Convolution<pcl::RGB, pcl::RGB> convolution;
Eigen::ArrayXf gaussian_kernel(kernel_size);
//gaussian_kernel << 1.f/16, 1.f/8, 3.f/16, 2.f/8, 3.f/16, 1.f/8, 1.f/16;
//gaussian_kernel << 16.f/1600.f, 32.f/1600.f, 64.f/1600.f, 128.f/1600.f, 256.f/1600.f, 128.f/1600.f, 64.f/1600.f, 32.f/1600.f, 16.f/1600.f;
gaussian_kernel << kernel_values[0], kernel_values[1], kernel_values[2], kernel_values[3], kernel_values[4], kernel_values[5], kernel_values[6];
pcl::PointCloud<pcl::RGB>::Ptr rgb_input_ (new pcl::PointCloud<pcl::RGB>());
const uint32_t width = input_->width;
const uint32_t height = input_->height;
rgb_input_->resize (width*height);
rgb_input_->width = width;
rgb_input_->height = height;
rgb_input_->is_dense = input_->is_dense;
for (size_t row_index = 0; row_index < height; ++row_index)
{
for (size_t col_index = 0; col_index < width; ++col_index)
{
(*rgb_input_) (col_index, row_index).r = (*input_) (col_index, row_index).r;
(*rgb_input_) (col_index, row_index).g = (*input_) (col_index, row_index).g;
(*rgb_input_) (col_index, row_index).b = (*input_) (col_index, row_index).b;
}
}
convolution.setInputCloud (rgb_input_);
convolution.setKernel (gaussian_kernel);
convolution.convolve (*smoothed_input_);
// extract color gradients
computeMaxColorGradientsSobel (smoothed_input_);
// quantize gradients
quantizeColorGradients ();
// filter quantized gradients to get only dominants one + thresholding
filterQuantizedColorGradients ();
// spread filtered quantized gradients
//spreadFilteredQunatizedColorGradients ();
pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
spreaded_filtered_quantized_color_gradients_,
spreading_size_);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void
pcl::ColorGradientModality<PointInT>::
processInputDataFromFiltered ()
{
// spread filtered quantized gradients
//spreadFilteredQunatizedColorGradients ();
pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
spreaded_filtered_quantized_color_gradients_,
spreading_size_);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT>
void pcl::ColorGradientModality<PointInT>::
extractFeatures (const MaskMap & mask, const size_t nr_features, const size_t modality_index,
std::vector<QuantizedMultiModFeature> & features) const