Revision history [back]

Hi there, I wanna use cv::ml::EM module. However, when I get covariance after estimating GMM in debug mode, there is an error. Error message is "Exception thrown at 0x00007FFBA36AD225 (opencv_world310.dll) in Ex_gmm_v1.exe: 0xC0000005: Access violation reading location 0xFFFFFFFFFFFFFFFF." Same code works in release mode. My environment is windows10 based visual studio V.14. How can I solve this error? Please give me your answer. Thanks in advance.

================================================================================= // Ex_gmm.cpp : Defines the entry point for the console application. //

'#include "stdafx.h"

'#include <iostream> '#include <set>

'#include <opencv2\highgui.hpp> '#include <opencv2\imgproc.hpp> '#include <opencv2\ml.hpp>

'#include <stdio.h> '#include <cxcore.h> '#include <fstream> '#include <string.h> '#include <highgui.h> '#include <cv.h> '#define M_PI 3.14

using namespace std; using namespace cv; using namespace cv::ml;

int main() { const int N = 4; const int N1 = (int)sqrt((double)N); const Scalar colors[] = { Scalar(0,0,255), Scalar(0,255,0), Scalar(0,255,255),Scalar(255,255,0) };

int i, j;
int nsamples = 100;
Mat samples(nsamples, 2, CV_32FC1);
Mat labels;
Mat img = Mat::zeros(Size(500, 500), CV_8UC3);
Mat sample(1, 2, CV_32FC1);

samples = samples.reshape(2, 0);
for (i = 0; i < N; i++)
{
    // form the training samples
    Mat samples_part = samples.rowRange(i*nsamples / N, (i + 1)*nsamples / N);

    Scalar mean(((i%N1) + 1)*img.rows / (N1 + 1),
        ((i / N1) + 1)*img.rows / (N1 + 1));
    Scalar sigma(30, 30);
    randn(samples_part, mean, sigma);
}
samples = samples.reshape(1, 0);

// cluster the data
Ptr<EM> em_model = EM::create();
em_model->setClustersNumber(N);
em_model->setCovarianceMatrixType(EM::COV_MAT_GENERIC);
em_model->setTermCriteria(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 300, 0.1));
em_model->trainEM(samples, noArray(), labels, noArray());

cv::Mat Means = em_model->getMeans();
cv::Mat Weights = em_model->getWeights();
std::vector<cv::Mat> covs;
**em_model->getCovs(covs);  // stop here**

// classify every image pixel
for (i = 0; i < img.rows; i++)
{
    for (j = 0; j < img.cols; j++)
    {
        sample.at<float>(0) = (float)j;
        sample.at<float>(1) = (float)i;
        int response = cvRound(em_model->predict2(sample, noArray())[1]);
        Scalar c = colors[response];

        circle(img, Point(j, i), 1, c*0.75, FILLED);
    }
}

//draw the clustered samples
for (i = 0; i < nsamples; i++)
{
    Point pt(cvRound(samples.at<float>(i, 0)), cvRound(samples.at<float>(i, 1)));
    circle(img, pt, 1, colors[labels.at<int>(i)], FILLED);
}

imshow("EM-clustering result", img);
waitKey(0);

return 0;

}