Bruno Lopes's profile - activity

I discovered the problem. I changed the initialization of the two Mat variables (points and centers). Now I use the following way to initialize them:

Mat points = Mat(total_rows, 128, CV_32F, descritores).clone(); Mat centers = Mat(clusterCount, 128, CV_32F).clone();

I found this way in the Opencv 2.4 Cheat Sheet (docs.opencv.org/trunk/opencv_cheatsheet.pdf).

Thanks everybody.

I debugged the code and the problem occurs in the line 2659 of the file matrix.cpp: box[j] = Vec2f(sample[j], sample[j]);

I don't understand why it is not working. These are the values of some variables in the kmeans function: N = 950173 isrow = false dims = 128 type = 5 attempts = 10 criteria.type = 3 criteria.epsilon = 1 criteria.maxCount = 100

Is the type (data.depth()) wrong? If it is the case, how can I proceed in order to get rid of this error?

Thanks in advance.

Sorry guys, Ben is right. In my text I mixed up rows and cols. So I have 950173 samples with a lenght of 128. I'm working with SIFT descriptors. I edited the post. I also put the centers variable initialization. I tested this code with a smaller data sample, and the algorithm worked as expected. But when I try the real sample, the program just stops.

Hi everyone,

I'm using OpenCV 2.4.3 in Ubuntu (12.10) in the following way:

Mat points = Mat(total_rows, 128, CV_32F, descritores);    
Mat labels;
int clusterCount = k;
int dimensions = points.cols;
int sampleCount = points.rows;
int num_iterations = 2;
Mat centers(clusterCount, 128, CV_32F/*points.type()*/);
kmeans(points, clusterCount, labels, TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, num_iterations, 1.0), 10, KMEANS_PP_CENTERS, centers);

But suddenly the program stops when it is executing kmeans, without showing an exception or error. Descritores is a float pointer, and I verified that points is being initialized in a correct way. Points.cols = 128 and points.rows = 950173. My pc has 16 GB of ram, so I don't think is a memory problem. Could you please help me to understand the error? Is there any limit to the size of the data kmeans can handle? If some more details are needed, just ask me. Thanks.