181 lines
8.0 KiB
C++
181 lines
8.0 KiB
C++
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*/
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#ifndef PCL_FEATURES_IMPL_PPFRGB_H_
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#define PCL_FEATURES_IMPL_PPFRGB_H_
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#include <pcl/features/ppfrgb.h>
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#include <pcl/features/pfhrgb.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT>
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pcl::PPFRGBEstimation<PointInT, PointNT, PointOutT>::PPFRGBEstimation ()
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: FeatureFromNormals <PointInT, PointNT, PointOutT> ()
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{
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feature_name_ = "PPFRGBEstimation";
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// Slight hack in order to pass the check for the presence of a search method in Feature::initCompute ()
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Feature<PointInT, PointOutT>::tree_.reset (new pcl::search::KdTree <PointInT> ());
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Feature<PointInT, PointOutT>::search_radius_ = 1.0f;
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PPFRGBEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// Initialize output container - overwrite the sizes done by Feature::initCompute ()
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output.resize (indices_->size () * input_->size ());
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output.height = 1;
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output.width = output.size ();
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// Compute point pair features for every pair of points in the cloud
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for (std::size_t index_i = 0; index_i < indices_->size (); ++index_i)
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{
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std::size_t i = (*indices_)[index_i];
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for (std::size_t j = 0 ; j < input_->size (); ++j)
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{
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PointOutT p;
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if (i != j)
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{
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if (pcl::computeRGBPairFeatures
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((*input_)[i].getVector4fMap (), (*normals_)[i].getNormalVector4fMap (), (*input_)[i].getRGBVector4i (),
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(*input_)[j].getVector4fMap (), (*normals_)[j].getNormalVector4fMap (), (*input_)[j].getRGBVector4i (),
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p.f1, p.f2, p.f3, p.f4, p.r_ratio, p.g_ratio, p.b_ratio))
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{
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// Calculate alpha_m angle
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Eigen::Vector3f model_reference_point = (*input_)[i].getVector3fMap (),
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model_reference_normal = (*normals_)[i].getNormalVector3fMap (),
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model_point = (*input_)[j].getVector3fMap ();
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Eigen::AngleAxisf rotation_mg (std::acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ())),
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model_reference_normal.cross (Eigen::Vector3f::UnitX ()).normalized ());
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Eigen::Affine3f transform_mg = Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg;
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Eigen::Vector3f model_point_transformed = transform_mg * model_point;
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float angle = std::atan2 ( -model_point_transformed(2), model_point_transformed(1));
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if (std::sin (angle) * model_point_transformed(2) < 0.0f)
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angle *= (-1);
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p.alpha_m = -angle;
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}
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else
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{
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PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
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p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = p.r_ratio = p.g_ratio = p.b_ratio = 0.f;
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}
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}
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// Do not calculate the feature for identity pairs (i, i) as they are not used
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// in the following computations
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else
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p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = p.r_ratio = p.g_ratio = p.b_ratio = 0.f;
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output[index_i*input_->size () + j] = p;
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT>
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pcl::PPFRGBRegionEstimation<PointInT, PointNT, PointOutT>::PPFRGBRegionEstimation ()
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: FeatureFromNormals <PointInT, PointNT, PointOutT> ()
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{
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feature_name_ = "PPFRGBEstimation";
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::PPFRGBRegionEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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PCL_INFO ("before computing output size: %u\n", output.size ());
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output.resize (indices_->size ());
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for (std::size_t index_i = 0; index_i < indices_->size (); ++index_i)
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{
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auto i = (*indices_)[index_i];
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pcl::Indices nn_indices;
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std::vector<float> nn_distances;
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tree_->radiusSearch (i, static_cast<float> (search_radius_), nn_indices, nn_distances);
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PointOutT average_feature_nn;
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average_feature_nn.alpha_m = 0;
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average_feature_nn.f1 = average_feature_nn.f2 = average_feature_nn.f3 = average_feature_nn.f4 =
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average_feature_nn.r_ratio = average_feature_nn.g_ratio = average_feature_nn.b_ratio = 0.0f;
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for (const auto &j : nn_indices)
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{
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if (i != j)
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{
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float f1, f2, f3, f4, r_ratio, g_ratio, b_ratio;
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if (pcl::computeRGBPairFeatures
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((*input_)[i].getVector4fMap (), (*normals_)[i].getNormalVector4fMap (), (*input_)[i].getRGBVector4i (),
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(*input_)[j].getVector4fMap (), (*normals_)[j].getNormalVector4fMap (), (*input_)[j].getRGBVector4i (),
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f1, f2, f3, f4, r_ratio, g_ratio, b_ratio))
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{
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average_feature_nn.f1 += f1;
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average_feature_nn.f2 += f2;
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average_feature_nn.f3 += f3;
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average_feature_nn.f4 += f4;
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average_feature_nn.r_ratio += r_ratio;
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average_feature_nn.g_ratio += g_ratio;
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average_feature_nn.b_ratio += b_ratio;
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}
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else
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{
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PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
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}
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}
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}
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float normalization_factor = static_cast<float> (nn_indices.size ());
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average_feature_nn.f1 /= normalization_factor;
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average_feature_nn.f2 /= normalization_factor;
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average_feature_nn.f3 /= normalization_factor;
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average_feature_nn.f4 /= normalization_factor;
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average_feature_nn.r_ratio /= normalization_factor;
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average_feature_nn.g_ratio /= normalization_factor;
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average_feature_nn.b_ratio /= normalization_factor;
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output[index_i] = average_feature_nn;
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}
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PCL_INFO ("Output size: %zu\n", static_cast<std::size_t>(output.size ()));
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}
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#define PCL_INSTANTIATE_PPFRGBEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PPFRGBEstimation<T,NT,OutT>;
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#define PCL_INSTANTIATE_PPFRGBRegionEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PPFRGBRegionEstimation<T,NT,OutT>;
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#endif // PCL_FEATURES_IMPL_PPFRGB_H_
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