Esempio n. 1
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/*!
	\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;
}
Esempio n. 2
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  TyErrorId initialize(AnnotatorContext &ctx)
  {
    outInfo("initialize");
    resourcesPath = ros::package::getPath("rs_resources") + '/';

    ctx.extractValue("DeCafH5File", h5_file);
    ctx.extractValue("DeCafListFile", list_file);
    ctx.extractValue("DeCafKDTreeIndices", kdtree_file);
    ctx.extractValue("DeCafKNeighbors", k);

    ctx.extractValue("caffe_model_file", caffe_model_file);
    ctx.extractValue("caffe_trained_file", caffe_trained_file);
    ctx.extractValue("caffe_mean_file", caffe_mean_file);
    ctx.extractValue("caffe_label_file", caffe_label_file);

    ctx.extractValue("asGT", asGT);

    outInfo(h5_file);
    outInfo(list_file);
    outInfo(kdtree_file);
    outInfo(caffe_model_file);
    outInfo(caffe_trained_file);
    outInfo(caffe_mean_file);
    outInfo(caffe_label_file);

    // Check if the data has already been saved to disk
    if(!boost::filesystem::exists(resourcesPath + h5_file) ||
       !boost::filesystem::exists(resourcesPath + list_file) ||
       !boost::filesystem::exists(resourcesPath + kdtree_file) ||
       !boost::filesystem::exists(resourcesPath + caffe_model_file) ||
       !boost::filesystem::exists(resourcesPath + caffe_trained_file) ||
       !boost::filesystem::exists(resourcesPath + caffe_mean_file) ||
       !boost::filesystem::exists(resourcesPath + caffe_label_file))
    {
      outError("files not found!");
      return UIMA_ERR_USER_ANNOTATOR_COULD_NOT_INIT;
    }
    caffeProxyObj = std::make_shared<CaffeProxy>(resourcesPath + caffe_model_file,
                    resourcesPath + caffe_trained_file,
                    resourcesPath + caffe_mean_file,
                    resourcesPath + caffe_label_file);

    flann::Matrix<float> data;
    loadFileList(models, resourcesPath + list_file);
    flann::load_from_file(data, resourcesPath + h5_file, "training_data");
    outInfo("Training data found. Loaded " << data.rows << " models from " << h5_file << "/" << list_file);

    this->data = cv::Mat(data.rows, data.cols, CV_32F, data.ptr()).clone();
    index.build(this->data, cv::flann::KDTreeIndexParams());

    return UIMA_ERR_NONE;
  }
Esempio n. 3
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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();
}
Esempio n. 4
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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;
}
Esempio n. 5
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 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));
 }