rtabmap/corelib/src/util3d_correspondences.cpp

/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

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    * Redistributions in binary form must reproduce the above copyright
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      documentation and/or other materials provided with the distribution.
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

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*/

#include "rtabmap/core/util3d_correspondences.h"
#include "rtabmap/core/util3d.h"

#include <rtabmap/utilite/UStl.h>
#include <rtabmap/core/EpipolarGeometry.h>
#if CV_MAJOR_VERSION < 5
#include <opencv2/calib3d/calib3d.hpp>
#else
#include <opencv2/geometry.hpp>
#endif
#include <pcl/search/kdtree.h>
#include <pcl/common/point_tests.h>

namespace rtabmap
{

namespace util3d
{


void extractXYZCorrespondences(const std::multimap<int, pcl::PointXYZ> & words1,
									  const std::multimap<int, pcl::PointXYZ> & words2,
									  pcl::PointCloud<pcl::PointXYZ> & cloud1,
									  pcl::PointCloud<pcl::PointXYZ> & cloud2)
{
	const std::list<int> & ids = uUniqueKeys(words1);
	for(std::list<int>::const_iterator i=ids.begin(); i!=ids.end(); ++i)
	{
		if(words1.count(*i) == 1 && words2.count(*i) == 1)
		{
			const pcl::PointXYZ & pt1 = words1.find(*i)->second;
			const pcl::PointXYZ & pt2 = words2.find(*i)->second;
			if(pcl::isFinite(pt1) && pcl::isFinite(pt2))
			{
				cloud1.push_back(pt1);
				cloud2.push_back(pt2);
			}
		}
	}
}

void extractXYZCorrespondencesRANSAC(const std::multimap<int, pcl::PointXYZ> & words1,
									  const std::multimap<int, pcl::PointXYZ> & words2,
									  pcl::PointCloud<pcl::PointXYZ> & cloud1,
									  pcl::PointCloud<pcl::PointXYZ> & cloud2)
{
	std::list<std::pair<pcl::PointXYZ, pcl::PointXYZ> > pairs;
	const std::list<int> & ids = uUniqueKeys(words1);
	for(std::list<int>::const_iterator i=ids.begin(); i!=ids.end(); ++i)
	{
		if(words1.count(*i) == 1 && words2.count(*i) == 1)
		{
			const pcl::PointXYZ & pt1 = words1.find(*i)->second;
			const pcl::PointXYZ & pt2 = words2.find(*i)->second;
			if(pcl::isFinite(pt1) && pcl::isFinite(pt2))
			{
				pairs.push_back(std::pair<pcl::PointXYZ, pcl::PointXYZ>(pt1, pt2));
			}
		}
	}

	if(pairs.size() > 7)
	{
		// Remove outliers using fundamental matrix RANSAC
		std::vector<uchar> status(pairs.size(), 0);
		//Convert Keypoints to a structure that OpenCV understands
		//3 dimensions (Homogeneous vectors)
		cv::Mat points1(1, (int)pairs.size(), CV_32FC2);
		cv::Mat points2(1, (int)pairs.size(), CV_32FC2);

		float * points1data = points1.ptr<float>(0);
		float * points2data = points2.ptr<float>(0);

		// Fill the points here ...
		int i=0;
		for(std::list<std::pair<pcl::PointXYZ, pcl::PointXYZ> >::const_iterator iter = pairs.begin();
			iter != pairs.end();
			++iter )
		{
			points1data[i*2] = (*iter).first.x;
			points1data[i*2+1] = (*iter).first.y;

			points2data[i*2] = (*iter).second.x;
			points2data[i*2+1] = (*iter).second.y;

			++i;
		}

		// Find the fundamental matrix
		cv::Mat fundamentalMatrix = cv::findFundamentalMat(
					points1,
					points2,
					status,
					cv::FM_RANSAC,
					3.0,
					0.99);

		if(!fundamentalMatrix.empty())
		{
			int i = 0;
			for(std::list<std::pair<pcl::PointXYZ, pcl::PointXYZ> >::iterator iter=pairs.begin(); iter!=pairs.end(); ++iter)
			{
				if(status[i])
				{
					cloud1.push_back(iter->first);
					cloud2.push_back(iter->second);
				}
				++i;
			}
		}
	}
}

void extractXYZCorrespondences(const std::list<std::pair<cv::Point2f, cv::Point2f> > & correspondences,
									   const cv::Mat & depthImage1,
									   const cv::Mat & depthImage2,
									   float cx, float cy,
									   float fx, float fy,
									   float maxDepth,
									   pcl::PointCloud<pcl::PointXYZ> & cloud1,
									   pcl::PointCloud<pcl::PointXYZ> & cloud2)
{
	cloud1.resize(correspondences.size());
	cloud2.resize(correspondences.size());
	int oi=0;
	for(std::list<std::pair<cv::Point2f, cv::Point2f> >::const_iterator iter = correspondences.begin();
		iter!=correspondences.end();
		++iter)
	{
		pcl::PointXYZ pt1 = projectDepthTo3D(depthImage1, iter->first.x, iter->first.y, cx, cy, fx, fy, true);
		pcl::PointXYZ pt2 = projectDepthTo3D(depthImage2, iter->second.x, iter->second.y, cx, cy, fx, fy, true);
		if(pcl::isFinite(pt1) && pcl::isFinite(pt2) &&
		   (maxDepth <= 0 || (pt1.z <= maxDepth && pt2.z<=maxDepth)))
		{
			cloud1[oi] = pt1;
			cloud2[oi] = pt2;
			++oi;
		}
	}
	cloud1.resize(oi);
	cloud2.resize(oi);
}

template<typename PointT>
inline void extractXYZCorrespondencesImpl(const std::list<std::pair<cv::Point2f, cv::Point2f> > & correspondences,
							   const pcl::PointCloud<PointT> & cloud1,
							   const pcl::PointCloud<PointT> & cloud2,
							   pcl::PointCloud<pcl::PointXYZ> & inliers1,
							   pcl::PointCloud<pcl::PointXYZ> & inliers2,
							   char depthAxis)
{
	for(std::list<std::pair<cv::Point2f, cv::Point2f> >::const_iterator iter = correspondences.begin();
		iter!=correspondences.end();
		++iter)
	{
		PointT pt1 = cloud1.at(int(iter->first.y+0.5f) * cloud1.width + int(iter->first.x+0.5f));
		PointT pt2 = cloud2.at(int(iter->second.y+0.5f) * cloud2.width + int(iter->second.x+0.5f));
		if(pcl::isFinite(pt1) &&
		   pcl::isFinite(pt2))
		{
			inliers1.push_back(pcl::PointXYZ(pt1.x, pt1.y, pt1.z));
			inliers2.push_back(pcl::PointXYZ(pt2.x, pt2.y, pt2.z));
		}
	}
}

void extractXYZCorrespondences(const std::list<std::pair<cv::Point2f, cv::Point2f> > & correspondences,
							   const pcl::PointCloud<pcl::PointXYZ> & cloud1,
							   const pcl::PointCloud<pcl::PointXYZ> & cloud2,
							   pcl::PointCloud<pcl::PointXYZ> & inliers1,
							   pcl::PointCloud<pcl::PointXYZ> & inliers2,
							   char depthAxis)
{
	extractXYZCorrespondencesImpl(correspondences, cloud1, cloud2, inliers1, inliers2, depthAxis);
}
void extractXYZCorrespondences(const std::list<std::pair<cv::Point2f, cv::Point2f> > & correspondences,
							   const pcl::PointCloud<pcl::PointXYZRGB> & cloud1,
							   const pcl::PointCloud<pcl::PointXYZRGB> & cloud2,
							   pcl::PointCloud<pcl::PointXYZ> & inliers1,
							   pcl::PointCloud<pcl::PointXYZ> & inliers2,
							   char depthAxis)
{
	extractXYZCorrespondencesImpl(correspondences, cloud1, cloud2, inliers1, inliers2, depthAxis);
}

int countUniquePairs(const std::multimap<int, pcl::PointXYZ> & wordsA,
					 const std::multimap<int, pcl::PointXYZ> & wordsB)
{
	const std::list<int> & ids = uUniqueKeys(wordsA);
	int pairs = 0;
	for(std::list<int>::const_iterator i=ids.begin(); i!=ids.end(); ++i)
	{
		std::list<pcl::PointXYZ> ptsA = uValues(wordsA, *i);
		std::list<pcl::PointXYZ> ptsB = uValues(wordsB, *i);
		if(ptsA.size() == 1 && ptsB.size() == 1)
		{
			++pairs;
		}
	}
	return pairs;
}

void filterMaxDepth(pcl::PointCloud<pcl::PointXYZ> & inliers1,
					pcl::PointCloud<pcl::PointXYZ> & inliers2,
					float maxDepth,
					char depthAxis,
					bool removeDuplicates)
{
	std::list<pcl::PointXYZ> addedPts;
	if(maxDepth > 0.0f && inliers1.size() && inliers1.size() == inliers2.size())
	{
		pcl::PointCloud<pcl::PointXYZ> tmpInliers1;
		pcl::PointCloud<pcl::PointXYZ> tmpInliers2;
		for(unsigned int i=0; i<inliers1.size(); ++i)
		{
			if((depthAxis == 'x' && inliers1.at(i).x < maxDepth && inliers2.at(i).x < maxDepth) ||
			   (depthAxis == 'y' && inliers1.at(i).y < maxDepth && inliers2.at(i).y < maxDepth) ||
			   (depthAxis == 'z' && inliers1.at(i).z < maxDepth && inliers2.at(i).z < maxDepth))
			{
				bool dup = false;
				if(removeDuplicates)
				{
					for(std::list<pcl::PointXYZ>::iterator iter = addedPts.begin(); iter!=addedPts.end(); ++iter)
					{
						if(iter->x == inliers1.at(i).x &&
						   iter->y == inliers1.at(i).y &&
						   iter->z == inliers1.at(i).z)
						{
							dup = true;
						}
					}
					if(!dup)
					{
						addedPts.push_back(inliers1.at(i));
					}
				}

				if(!dup)
				{
					tmpInliers1.push_back(inliers1.at(i));
					tmpInliers2.push_back(inliers2.at(i));
				}
			}
		}
		inliers1 = tmpInliers1;
		inliers2 = tmpInliers2;
	}
}


// a kdtree is constructed with cloud_target, then nearest neighbor
// is computed for each cloud_source points.
int getCorrespondencesCount(const pcl::PointCloud<pcl::PointXYZ>::ConstPtr & cloud_source,
							const pcl::PointCloud<pcl::PointXYZ>::ConstPtr & cloud_target,
							float maxDistance)
{
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdTree(new pcl::search::KdTree<pcl::PointXYZ>);
	kdTree->setInputCloud(cloud_target);
	int count = 0;
	float sqrdMaxDistance = maxDistance * maxDistance;
	for(unsigned int i=0; i<cloud_source->size(); ++i)
	{
		std::vector<int> ind(1);
		std::vector<float> dist(1);
		if(kdTree->nearestKSearch(cloud_source->at(i), 1, ind, dist) && dist[0] < sqrdMaxDistance)
		{
			++count;
		}
	}
	return count;
}

/**
 * if a=[1 2 3 4 6 6], b=[1 1 2 4 5 6 6], results= [(2,2) (4,4)]
 * realPairsCount = 5
 */
void findCorrespondences(
		const std::multimap<int, cv::KeyPoint> & wordsA,
		const std::multimap<int, cv::KeyPoint> & wordsB,
		std::list<std::pair<cv::Point2f, cv::Point2f> > & pairs)
{
	const std::list<int> & ids = uUniqueKeys(wordsA);
	pairs.clear();
	for(std::list<int>::const_iterator i=ids.begin(); i!=ids.end(); ++i)
	{
		std::list<cv::KeyPoint> ptsA = uValues(wordsA, *i);
		std::list<cv::KeyPoint> ptsB = uValues(wordsB, *i);
		if(ptsA.size() == 1 && ptsB.size() == 1)
		{
			pairs.push_back(std::pair<cv::Point2f, cv::Point2f>(ptsA.front().pt, ptsB.front().pt));
		}
	}
}

void findCorrespondences(
		const std::multimap<int, cv::Point3f> & words1,
		const std::multimap<int, cv::Point3f> & words2,
		std::vector<cv::Point3f> & inliers1,
		std::vector<cv::Point3f> & inliers2,
		float maxDepth,
		std::vector<int> * uniqueCorrespondences)
{
	std::list<int> ids = uUniqueKeys(words1);
	// Find pairs
	inliers1.resize(ids.size());
	inliers2.resize(ids.size());
	if(uniqueCorrespondences)
	{
		uniqueCorrespondences->resize(ids.size());
	}

	int oi=0;
	for(std::list<int>::iterator iter=ids.begin(); iter!=ids.end(); ++iter)
	{
		if(words1.count(*iter) == 1 && words2.count(*iter) == 1)
		{
			inliers1[oi] = words1.find(*iter)->second;
			inliers2[oi] = words2.find(*iter)->second;
			if(util3d::isFinite(inliers1[oi]) &&
			   util3d::isFinite(inliers2[oi]) &&
			   (inliers1[oi].x != 0 || inliers1[oi].y != 0 || inliers1[oi].z != 0) &&
			   (inliers2[oi].x != 0 || inliers2[oi].y != 0 || inliers2[oi].z != 0) &&
			   (maxDepth <= 0 || (inliers1[oi].x > 0 && inliers1[oi].x <= maxDepth && inliers2[oi].x>0 &&inliers2[oi].x<=maxDepth)))
			{
				if(uniqueCorrespondences)
				{
					uniqueCorrespondences->at(oi) = *iter;
				}
				++oi;
			}
		}
	}
	inliers1.resize(oi);
	inliers2.resize(oi);
	if(uniqueCorrespondences)
	{
		uniqueCorrespondences->resize(oi);
	}
}

void findCorrespondences(
		const std::map<int, cv::Point3f> & words1,
		const std::map<int, cv::Point3f> & words2,
		std::vector<cv::Point3f> & inliers1,
		std::vector<cv::Point3f> & inliers2,
		float maxDepth,
		std::vector<int> * correspondences)
{
	std::vector<int> ids = uKeys(words1);
	// Find pairs
	inliers1.resize(ids.size());
	inliers2.resize(ids.size());
	if(correspondences)
	{
		correspondences->resize(ids.size());
	}

	int oi=0;
	for(std::vector<int>::iterator iter=ids.begin(); iter!=ids.end(); ++iter)
	{
		if(words2.find(*iter) != words2.end())
		{
			inliers1[oi] = words1.find(*iter)->second;
			inliers2[oi] = words2.find(*iter)->second;
			if(util3d::isFinite(inliers1[oi]) &&
			   util3d::isFinite(inliers2[oi]) &&
			   (inliers1[oi].x != 0 || inliers1[oi].y != 0 || inliers1[oi].z != 0) &&
			   (inliers2[oi].x != 0 || inliers2[oi].y != 0 || inliers2[oi].z != 0) &&
			   (maxDepth <= 0 || (inliers1[oi].x > 0 && inliers1[oi].x <= maxDepth && inliers2[oi].x>0 &&inliers2[oi].x<=maxDepth)))
			{
				if(correspondences)
				{
					correspondences->at(oi) = *iter;
				}
				++oi;
			}
		}
	}
	inliers1.resize(oi);
	inliers2.resize(oi);
	if(correspondences)
	{
		correspondences->resize(oi);
	}
}

}

}