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2013-10-16 08:13:50 -0500 answered a question Is there a minimal source download for OpenCV?

When deploying you actually do not need any source at all, you need to either deploy dynamic library files or just link your app with static OpenCV libraries.

If you build OpenCV from source there are also some options where you can skip building docs, samples etc., but it concerns only development and not deployment.

2013-10-14 10:21:37 -0500 commented question Problem with FLANN in a multithreaded application

Well, Moster, thank you. Your comments helped me a lot.

The exception arise at this line

matcher.match(features_from.descriptors.row(point_ind), features_to.descriptors.row(indicies[rind]), matches); (the only actually where some submatrices are used).

And the reason for that is that in a call for flann radius search

cur_result_num = grid_of_flann_[inds.first][inds.second].radiusSearch(query, indicies, dists, radius, num_results);

the number of results returned (cur_result_num) could be greater than the maximum number of results specified (num_results). I misunderstood this point.

It is not yet clear for me why this exeption doesn't show up in a single thread, but nevertheless the problem is solved for now.

Thank you again

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2013-10-14 08:06:52 -0500 answered a question getting error: expected expression before ‘IplImage’ in attempt to create alias for IplImage

There are #define and typedef that can make it work, but it seems that you need to study at least some basic c/c++ before trying to do something with opencv.

2013-10-14 07:45:56 -0500 answered a question Why is the blob tracking code 'legacy'? What's replaced it?

As far as I know if any code become legacy it is neither maintained nor developed further any more.

But you may take a look at SimpleBlobDetector at features2d module, this might be what you are looking for.

2013-10-14 05:41:17 -0500 asked a question Problem with FLANN in a multithreaded application


I have a problem with running opencv code in multiple threads. When I do not use flann, the program runs ok even in multiple treads. But when I am adding flann based functions I get the exception "OpenCV Error: Assertion failed (0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows) in Mat, file /tmp/opencv-KNTZ/opencv-2.4.4/modules/core/src/matrix.cpp, line 284" at some moment (different each time) during multithreaded execution.

Is flann implemetation known to be not thread-safe, or there might be a problem with something else?

Thank you in advance, Artem

P. S. In the folowing I provide the code, where the problem occurs. But there is actually no matrix operations involved, at least explicitly.

float getError(float max_dist,  const std::vector<cv::Point2f>& initial_points, const cv::detail::ImageFeatures& features_from,  const cv::detail::ImageFeatures& features_to, const cv::Mat& homography, Grid* grid) {

    std::vector<cv::Point2f> transformed_points;
    cv::perspectiveTransform(initial_points, transformed_points, homography);

    const float kRadius = 4.0;

    const float kMaxDescriptorDistance = max_dist;
    const float kGamma = kRadius + 2.0;

    cv::BFMatcher matcher(cv::NORM_HAMMING2);

    int matches_count = 0;
    double error = 0.0;

    cv::Rect referenceRect = cv::Rect(cv::Point(0,0), features_to.img_size);

    std::vector<int> indicies;
    std::vector<float> distanses;

    for (int point_ind = 0; point_ind < (int)transformed_points.size(); ++point_ind) {
        if (!referenceRect.contains(transformed_points[point_ind])) {

        grid->radiusSearch(transformed_points[point_ind], &indicies, &distanses);

        if (!indicies.empty()) {
            int min_ind = -1;
            float min_dist = std::numeric_limits<float>::max();
            for (int rind = 0; rind < (int)indicies.size(); ++rind) {
                std::vector<cv::DMatch> matches;
                matcher.match(features_from.descriptors.row(point_ind), features_to.descriptors.row(indicies[rind]), matches);

                if (matches[0].distance < kMaxDescriptorDistance) {
                    if (matches[0].distance < min_dist) {
                        min_dist = matches[0].distance;
                        min_ind = rind;


            if (min_ind >= 0) {

                std::vector<cv::DMatch> matches;
                matcher.match(features_from.descriptors.row(point_ind), features_to.descriptors.row(indicies[min_ind]), matches);

                error += distanses[min_ind];

    error += kGamma * (initial_points.size() - matches_count);

    return error;


And here is the code for class grid, which make use of flann

 class Grid {
                    Grid(unsigned int rows, unsigned int cols, unsigned int row_bins = 7, unsigned int col_bins = 5);
                    std::pair<unsigned int, unsigned int> getGridPoint(cv::Point2f pt);
                    void addKeyPoint(const cv::KeyPoint& kp);
                    void buildIndecies();
                    void radiusSearch(const cv::Point2f& pt, std::vector<int>* result, std::vector<float>* distanses, float radius = 2, unsigned int num_results = 4);
                    unsigned int rows_;
                    unsigned int cols_;
                    unsigned int row_bins_;
                    unsigned int col_bins_;
                    std::vector<float> upper_row_limits_;
                    std::vector<float> upper_col_limits_;
                    std::vector<std::vector<std::vector<cv::Point> > > grid_of_points_;
                    std::vector<std::vector<cv::flann::Index> > grid_of_flann_;

void Grid::radiusSearch(const cv::Point2f& pt, std::vector<int>* result, std::vector<float>* distanses, float radius, unsigned int num_results) {
            std::vector<float> query(2);
            std::vector<int> indicies(num_results, -1);
            std::vector<float> dists(num_results, -1.0);

            std::pair<unsigned int, unsigned int> inds = this->getGridPoint(pt);

            query[0] = pt.x;
            query[1] = pt.y;

            int cur_result_num = 0;

            if (grid_of_points_[inds.first][inds.second].size() > 4) {

                 cur_result_num =  grid_of_flann_[inds.first][inds.second].radiusSearch(query, indicies, dists ...