Exemple #1
0
/*!
	\fn generateClusterMembershipForFeatures
Params:
	@param1: cv::flann::Index flann_index
			FLANN Index trained before
	@param2: cv::Mat featureVector
			new feature vector needed to find nearest neighbor points
	@param3: int k (default value is 1)
			number of nearest neighbor of each new feature vector
return 
	cv::Mat indices
		nearest neighbor index
description:
	Find the nearest neighbor index of each new feature vector
*/
cv::Mat WordModel::generateClusterMembershipForFeatures(cv::flann::Index flann_index, cv::Mat featureVector, int k/* =1 */)
{
	cv::Mat indices = cv::Mat::zeros(featureVector.rows, k, CV_32S);
	cv::Mat distances = cv::Mat::zeros(featureVector.rows, k, CV_32F);

	int numbreOfCheckers = 100;

	flann_index.knnSearch(featureVector, indices, distances, k);
	return indices;
}
double IterativeTranslationFitter::getModelFitScore(const std::vector<cv::Vec3f>& cloud, const cv::Point3f& position,
    boost::function<double(double)> kernel,
    cv::flann::Index &search) const
{
  double inlier_count = 0;
  std::vector<int> indices(1);
  std::vector<float> distances(1);
  cv::Mat_<float> points(1, 3);
  for (std::vector<cv::Point3f>::const_iterator mIt = model_points_.begin(); mIt != model_points_.end(); ++mIt) {
    points(0, 0) = mIt->x + position.x;
    points(0, 1) = mIt->y + position.y;
    points(0, 2) = mIt->z + position.z;

    search.knnSearch(points, indices, distances, 1);
    inlier_count += kernel(sqrt(distances[0]));
  }
  return inlier_count / model_points_.size();
}
Exemple #3
0
double Util::GetSearchRadius(cv::flann::Index &myKdTree, const cv::Mat &features, int nMaxSearch, float percent = 0.02f)
{
    //3.5 计算radius,目标2%
    double maxDistanceSum = 0.0;
    int numpts = features.rows;
    //int DIMENSION = features.cols;
    int knn = numpts*percent;
    if(knn > nMaxSearch)
        knn = nMaxSearch;

    int nchecks = 2*knn > nMaxSearch ? 2*knn : nMaxSearch;
    for(int i = 0; i <  numpts; i++){
        cv::Mat indices;
        cv::Mat dists;
        myKdTree.knnSearch(features.row(i), indices, dists, knn, cv::flann::SearchParams(nchecks));
        //std::cout << distances << std::endl;
        float localMax = dists.at<float>(0, knn-1);

        //std::cout << localMax << std::endl;
        maxDistanceSum += localMax;
    }
    double search_radius = maxDistanceSum / numpts; //average
    return search_radius;
}
 inline void nearestKSearch(cv::flann::Index &index, const Model &model, int k, std::vector<int> &indices, std::vector<float> &distances)
 {
   indices.resize(k);
   distances.resize(k);
   index.knnSearch(model.second, indices, distances, k, cv::flann::SearchParams(512));
 }