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2019-04-05 09:03:42 -0500 asked a question Downsampling and Lanczos

Downsampling and Lanczos If I'm not understanding it wrong, according to this Lanczos should generate good results for d

2017-02-09 02:12:10 -0500 commented answer How to match 2 HOG for object detection?

This is a great explanation, many things more clear for me. Thank you very much.

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2017-01-19 06:38:21 -0500 commented question Cannot make EmguCV-OpenCV BOW Categorization work properly

Thanks for respose. I have already send my question to that forum. I wish someone show me what I am doing wrong. I am sure that all train, template and test images are OK. And they are loaded without any problems. I have tried with many different images, many different things. As I have mentioned, the AccessViolation error occurs sometimes on the classification stage. It is sometimes sucessful with many large amout and size of images, but sometimes not. SVM class behavior difference when loaded from file is also very anoying. Thanks again.

2017-01-19 05:59:06 -0500 asked a question Cannot make EmguCV-OpenCV BOW Categorization work properly

I have already asked here, but no one answered: http: //

I am trying to learn BOW object categorization. I have tried to implement the example given in the book "Practical OpenCV, Samarth Brahmbhatt" Chapter 8 (page 148)

  • When I save the SVM's to file on the training stage and read them on the categorization stage, the result is comletely different. (If the line svm = notFromFile[category]; is removed, the results are wrong; if not, it is successful with the dataset provided by the book.)
  • When I try this code with some larger datasets, I sometimes get this exception: System.AccessViolationException' in Emgu.CV.World.dll for the line bowDescriptorExtractor.Compute(frame_g, kp, img); and the application closes. It cannot be handled.

I have tried many things but could not figure them out. Any suggestions why these are happening, and how to solve, is very appreciated.

I am using emgucv-windesktop

My implementation:

internal class Categorizer3 : ICategorizer
    public string Name
            return "Categorizer3";

    public bool Train()
            Feature2D descriptorExtractor;
            Feature2D featureDetector;
            List<Mat> templates;
            BOWKMeansTrainer bowtrainer;
            BOWImgDescriptorExtractor bowDescriptorExtractor;
            init(out descriptorExtractor, out featureDetector, out templates, out bowtrainer, out bowDescriptorExtractor);

            List<Tuple<string, Mat>> train_set;
            List<string> category_names;
            make_train_set(out train_set, out category_names);

            Mat vocab;
            build_vocab(descriptorExtractor, featureDetector, templates, bowtrainer, out vocab);


            Dictionary<string, Mat> positive_data;
            Dictionary<string, Mat> negative_data;
            make_pos_neg(train_set, bowDescriptorExtractor, featureDetector, category_names, out positive_data, out negative_data);

            this.train_classifiers(category_names, positive_data, negative_data);

            return true;
        catch (Exception)
            return false;
    public event TrainedEventHandler Trained;
    protected void OnTrained(string fn)
        if (this.Trained != null)
    public Categorizer3()
    private Feature2D create_FeatureDetector()
        return new SURF(500);
        //return new KAZE();
        //return new SIFT();
        //return new Freak();
    private BOWImgDescriptorExtractor create_bowDescriptorExtractor(Feature2D descriptorExtractor)
        LinearIndexParams ip = new LinearIndexParams();
        SearchParams sp = new SearchParams();
        var descriptorMatcher = new FlannBasedMatcher(ip, sp);

        return new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
    private void init(out Feature2D descriptorExtractor, out Feature2D featureDetector, out List<Mat> templates, out BOWKMeansTrainer bowtrainer, out BOWImgDescriptorExtractor bowDescriptorExtractor)
        int clusters = 1000;
        featureDetector = create_FeatureDetector();

        MCvTermCriteria term = new MCvTermCriteria(10000, 0.0001d);
        term.Type = TermCritType.Iter | TermCritType.Eps;
        bowtrainer = new BOWKMeansTrainer(clusters, term, 5, Emgu.CV.CvEnum.KMeansInitType.PPCenters);//****

        BFMatcher matcher = new BFMatcher(DistanceType.L1);//****
        descriptorExtractor = featureDetector;//******

        bowDescriptorExtractor = create_bowDescriptorExtractor(descriptorExtractor);

        templates = new List<Mat>();
        string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\templates";
        //string TEMPLATE_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";
        foreach (var filename in Directory.GetFiles(TEMPLATE_FOLDER, "*", SearchOption.AllDirectories))
            templates.Add(GetMat(filename, true));

    void make_train_set(out List<Tuple<string, Mat>> train_set, out List<string> category_names)
        string TRAIN_FOLDER = "C:\\Emgu\\book\\practical-opencv\\code\\src\\chapter8\\code8-5\\data\\train_images";

        category_names = new List<string>();
        train_set = new List<Tuple<string, Mat>>();
        foreach (var dir in Directory.GetDirectories(TRAIN_FOLDER))
            // Get category name from name of the folder
            string category = new DirectoryInfo(dir).Name;
            foreach (var filename in Directory.GetFiles(dir))
                train_set.Add(new Tuple<string, Mat>(category, GetMat(filename, true)));

    void build_vocab(Feature2D descriptorExtractor, Feature2D featureDetector, List<Mat> templates ...