387 lines
15 KiB
C++
387 lines
15 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) 2010-2011, Willow Garage, 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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* $Id$
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*
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*/
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#pragma once
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#include <pcl/search/organized.h>
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#include <pcl/common/point_tests.h> // for pcl::isFinite
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#include <pcl/common/projection_matrix.h> // for getCameraMatrixFromProjectionMatrix, ...
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#include <Eigen/Eigenvalues>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointT> int
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pcl::search::OrganizedNeighbor<PointT>::radiusSearch (const PointT &query,
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const double radius,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances,
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unsigned int max_nn) const
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{
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// NAN test
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assert (isFinite (query) && "Invalid (NaN, Inf) point coordinates given to nearestKSearch!");
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// search window
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unsigned left, right, top, bottom;
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//unsigned x, y, idx;
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float squared_distance;
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const float squared_radius = radius * radius;
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k_indices.clear ();
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k_sqr_distances.clear ();
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this->getProjectedRadiusSearchBox (query, squared_radius, left, right, top, bottom);
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// iterate over search box
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if (max_nn == 0 || max_nn >= static_cast<unsigned int> (input_->size ()))
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max_nn = static_cast<unsigned int> (input_->size ());
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k_indices.reserve (max_nn);
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k_sqr_distances.reserve (max_nn);
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unsigned yEnd = (bottom + 1) * input_->width + right + 1;
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unsigned idx = top * input_->width + left;
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unsigned skip = input_->width - right + left - 1;
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unsigned xEnd = idx - left + right + 1;
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for (; xEnd != yEnd; idx += skip, xEnd += input_->width)
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{
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for (; idx < xEnd; ++idx)
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{
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if (!mask_[idx] || !isFinite ((*input_)[idx]))
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continue;
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float dist_x = (*input_)[idx].x - query.x;
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float dist_y = (*input_)[idx].y - query.y;
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float dist_z = (*input_)[idx].z - query.z;
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squared_distance = dist_x * dist_x + dist_y * dist_y + dist_z * dist_z;
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//squared_distance = ((*input_)[idx].getVector3fMap () - query.getVector3fMap ()).squaredNorm ();
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if (squared_distance <= squared_radius)
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{
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k_indices.push_back (idx);
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k_sqr_distances.push_back (squared_distance);
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// already done ?
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if (k_indices.size () == max_nn)
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{
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if (sorted_results_)
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this->sortResults (k_indices, k_sqr_distances);
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return (max_nn);
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}
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}
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}
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}
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if (sorted_results_)
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this->sortResults (k_indices, k_sqr_distances);
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return (static_cast<int> (k_indices.size ()));
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointT> int
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pcl::search::OrganizedNeighbor<PointT>::nearestKSearch (const PointT &query,
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int k,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances) const
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{
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assert (isFinite (query) && "Invalid (NaN, Inf) point coordinates given to nearestKSearch!");
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if (k < 1)
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{
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k_indices.clear ();
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k_sqr_distances.clear ();
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return (0);
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}
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Eigen::Vector3f queryvec (query.x, query.y, query.z);
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// project query point on the image plane
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//Eigen::Vector3f q = KR_ * query.getVector3fMap () + projection_matrix_.block <3, 1> (0, 3);
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Eigen::Vector3f q (KR_ * queryvec + projection_matrix_.block <3, 1> (0, 3));
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int xBegin = int(q [0] / q [2] + 0.5f);
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int yBegin = int(q [1] / q [2] + 0.5f);
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int xEnd = xBegin + 1; // end is the pixel that is not used anymore, like in iterators
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int yEnd = yBegin + 1;
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// the search window. This is supposed to shrink within the iterations
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unsigned left = 0;
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unsigned right = input_->width - 1;
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unsigned top = 0;
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unsigned bottom = input_->height - 1;
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std::vector <Entry> results; // sorted from smallest to largest distance
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results.reserve (k);
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// add point laying on the projection of the query point.
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if (xBegin >= 0 &&
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xBegin < static_cast<int> (input_->width) &&
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yBegin >= 0 &&
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yBegin < static_cast<int> (input_->height))
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testPoint (query, k, results, yBegin * input_->width + xBegin);
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else // point lys
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{
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// find the box that touches the image border -> don't waste time evaluating boxes that are completely outside the image!
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int dist = std::numeric_limits<int>::max ();
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if (xBegin < 0)
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dist = -xBegin;
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else if (xBegin >= static_cast<int> (input_->width))
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dist = xBegin - static_cast<int> (input_->width);
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if (yBegin < 0)
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dist = std::min (dist, -yBegin);
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else if (yBegin >= static_cast<int> (input_->height))
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dist = std::min (dist, yBegin - static_cast<int> (input_->height));
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xBegin -= dist;
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xEnd += dist;
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yBegin -= dist;
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yEnd += dist;
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}
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// stop used as isChanged as well as stop.
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bool stop = false;
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do
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{
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// increment box size
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--xBegin;
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++xEnd;
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--yBegin;
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++yEnd;
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// the range in x-direction which intersects with the image width
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int xFrom = xBegin;
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int xTo = xEnd;
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clipRange (xFrom, xTo, 0, input_->width);
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// if x-extend is not 0
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if (xTo > xFrom)
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{
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// if upper line of the rectangle is visible and x-extend is not 0
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if (yBegin >= 0 && yBegin < static_cast<int> (input_->height))
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{
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index_t idx = yBegin * input_->width + xFrom;
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index_t idxTo = idx + xTo - xFrom;
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for (; idx < idxTo; ++idx)
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stop = testPoint (query, k, results, idx) || stop;
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}
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// the row yEnd does NOT belong to the box -> last row = yEnd - 1
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// if lower line of the rectangle is visible
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if (yEnd > 0 && yEnd <= static_cast<int> (input_->height))
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{
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index_t idx = (yEnd - 1) * input_->width + xFrom;
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index_t idxTo = idx + xTo - xFrom;
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for (; idx < idxTo; ++idx)
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stop = testPoint (query, k, results, idx) || stop;
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}
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// skip first row and last row (already handled above)
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int yFrom = yBegin + 1;
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int yTo = yEnd - 1;
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clipRange (yFrom, yTo, 0, input_->height);
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// if we have lines in between that are also visible
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if (yFrom < yTo)
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{
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if (xBegin >= 0 && xBegin < static_cast<int> (input_->width))
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{
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index_t idx = yFrom * input_->width + xBegin;
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index_t idxTo = yTo * input_->width + xBegin;
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for (; idx < idxTo; idx += input_->width)
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stop = testPoint (query, k, results, idx) || stop;
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}
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if (xEnd > 0 && xEnd <= static_cast<int> (input_->width))
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{
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index_t idx = yFrom * input_->width + xEnd - 1;
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index_t idxTo = yTo * input_->width + xEnd - 1;
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for (; idx < idxTo; idx += input_->width)
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stop = testPoint (query, k, results, idx) || stop;
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}
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}
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// stop here means that the k-nearest neighbor changed -> recalculate bounding box of ellipse.
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if (stop)
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getProjectedRadiusSearchBox (query, results.back ().distance, left, right, top, bottom);
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}
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// now we use it as stop flag -> if bounding box is completely within the already examined search box were done!
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stop = (static_cast<int> (left) >= xBegin && static_cast<int> (left) < xEnd &&
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static_cast<int> (right) >= xBegin && static_cast<int> (right) < xEnd &&
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static_cast<int> (top) >= yBegin && static_cast<int> (top) < yEnd &&
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static_cast<int> (bottom) >= yBegin && static_cast<int> (bottom) < yEnd);
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} while (!stop);
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const auto results_size = results.size ();
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k_indices.resize (results_size);
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k_sqr_distances.resize (results_size);
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std::size_t idx = 0;
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for(const auto& result : results)
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{
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k_indices [idx] = result.index;
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k_sqr_distances [idx] = result.distance;
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++idx;
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}
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return (static_cast<int> (results_size));
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}
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////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointT> void
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pcl::search::OrganizedNeighbor<PointT>::getProjectedRadiusSearchBox (const PointT& point,
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float squared_radius,
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unsigned &minX,
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unsigned &maxX,
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unsigned &minY,
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unsigned &maxY) const
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{
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Eigen::Vector3f queryvec (point.x, point.y, point.z);
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//Eigen::Vector3f q = KR_ * point.getVector3fMap () + projection_matrix_.block <3, 1> (0, 3);
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Eigen::Vector3f q (KR_ * queryvec + projection_matrix_.block <3, 1> (0, 3));
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float a = squared_radius * KR_KRT_.coeff (8) - q [2] * q [2];
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float b = squared_radius * KR_KRT_.coeff (7) - q [1] * q [2];
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float c = squared_radius * KR_KRT_.coeff (4) - q [1] * q [1];
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int min, max;
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// a and c are multiplied by two already => - 4ac -> - ac
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float det = b * b - a * c;
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if (det < 0)
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{
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minY = 0;
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maxY = input_->height - 1;
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}
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else
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{
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float y1 = static_cast<float> ((b - std::sqrt (det)) / a);
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float y2 = static_cast<float> ((b + std::sqrt (det)) / a);
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min = std::min (static_cast<int> (std::floor (y1)), static_cast<int> (std::floor (y2)));
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max = std::max (static_cast<int> (std::ceil (y1)), static_cast<int> (std::ceil (y2)));
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minY = static_cast<unsigned> (std::min (static_cast<int> (input_->height) - 1, std::max (0, min)));
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maxY = static_cast<unsigned> (std::max (std::min (static_cast<int> (input_->height) - 1, max), 0));
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}
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b = squared_radius * KR_KRT_.coeff (6) - q [0] * q [2];
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c = squared_radius * KR_KRT_.coeff (0) - q [0] * q [0];
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det = b * b - a * c;
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if (det < 0)
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{
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minX = 0;
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maxX = input_->width - 1;
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}
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else
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{
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float x1 = static_cast<float> ((b - std::sqrt (det)) / a);
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float x2 = static_cast<float> ((b + std::sqrt (det)) / a);
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min = std::min (static_cast<int> (std::floor (x1)), static_cast<int> (std::floor (x2)));
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max = std::max (static_cast<int> (std::ceil (x1)), static_cast<int> (std::ceil (x2)));
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minX = static_cast<unsigned> (std::min (static_cast<int> (input_->width)- 1, std::max (0, min)));
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maxX = static_cast<unsigned> (std::max (std::min (static_cast<int> (input_->width) - 1, max), 0));
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointT> void
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pcl::search::OrganizedNeighbor<PointT>::computeCameraMatrix (Eigen::Matrix3f& camera_matrix) const
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{
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pcl::getCameraMatrixFromProjectionMatrix (projection_matrix_, camera_matrix);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointT> void
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pcl::search::OrganizedNeighbor<PointT>::estimateProjectionMatrix ()
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{
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// internally we calculate with double but store the result into float matrices.
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projection_matrix_.setZero ();
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if (input_->height == 1 || input_->width == 1)
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{
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PCL_ERROR ("[pcl::%s::estimateProjectionMatrix] Input dataset is not organized!\n", this->getName ().c_str ());
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return;
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}
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const unsigned ySkip = (std::max) (input_->height >> pyramid_level_, unsigned (1));
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const unsigned xSkip = (std::max) (input_->width >> pyramid_level_, unsigned (1));
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Indices indices;
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indices.reserve (input_->size () >> (pyramid_level_ << 1));
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|
||
|
|
for (unsigned yIdx = 0, idx = 0; yIdx < input_->height; yIdx += ySkip, idx += input_->width * ySkip)
|
||
|
|
{
|
||
|
|
for (unsigned xIdx = 0, idx2 = idx; xIdx < input_->width; xIdx += xSkip, idx2 += xSkip)
|
||
|
|
{
|
||
|
|
if (!mask_ [idx2])
|
||
|
|
continue;
|
||
|
|
|
||
|
|
indices.push_back (idx2);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
double residual_sqr = pcl::estimateProjectionMatrix<PointT> (input_, projection_matrix_, indices);
|
||
|
|
|
||
|
|
if (std::abs (residual_sqr) > eps_ * float (indices.size ()))
|
||
|
|
{
|
||
|
|
PCL_ERROR ("[pcl::%s::estimateProjectionMatrix] Input dataset is not from a projective device!\nResidual (MSE) %f, using %d valid points\n", this->getName ().c_str (), residual_sqr / double (indices.size()), indices.size ());
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
// get left 3x3 sub matrix, which contains K * R, with K = camera matrix = [[fx s cx] [0 fy cy] [0 0 1]]
|
||
|
|
// and R being the rotation matrix
|
||
|
|
KR_ = projection_matrix_.topLeftCorner <3, 3> ();
|
||
|
|
|
||
|
|
// precalculate KR * KR^T needed by calculations during nn-search
|
||
|
|
KR_KRT_ = KR_ * KR_.transpose ();
|
||
|
|
}
|
||
|
|
|
||
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
template<typename PointT> bool
|
||
|
|
pcl::search::OrganizedNeighbor<PointT>::projectPoint (const PointT& point, pcl::PointXY& q) const
|
||
|
|
{
|
||
|
|
Eigen::Vector3f projected = KR_ * point.getVector3fMap () + projection_matrix_.block <3, 1> (0, 3);
|
||
|
|
q.x = projected [0] / projected [2];
|
||
|
|
q.y = projected [1] / projected [2];
|
||
|
|
return (projected[2] != 0);
|
||
|
|
}
|
||
|
|
#define PCL_INSTANTIATE_OrganizedNeighbor(T) template class PCL_EXPORTS pcl::search::OrganizedNeighbor<T>;
|
||
|
|
|