openCV k-means call assertion failed - c

Question

I'm have read c++ sample from samples folder of openCV source distribution, and, if omit random picture generation, kmeans call looks pretty simple – author even doesn't allocate centers/labels arrays (you can find it here). However, I can't do the same in C. If I don't allocate labels, I get assertion error:
OpenCV Error: Assertion failed (labels.isContinuous() && labels.type()
== CV_32S && (labels.cols == 1 || labels.rows == 1) && labels.cols + labels.rows - 1 == data.rows) in cvKMeans2, file
/tmp/opencv-xiht/opencv-2.4.9/modules/core/src/matrix.cpp, line 3094
Ok, I tried to create empty labels matrix, but assertion message don't changes at all.
IplImage* image = cvLoadImage("test.jpg", -1);
IplImage* normal = cvCreateImage(cvGetSize(image), IPL_DEPTH_32F, image->nChannels);
cvConvertScale(image, normal, 1/255.0, 0);
CvMat* points = cvCreateMat(image->width, image->height, CV_32F);
points->data.fl = normal->imageData;
CvMat* labels = cvCreateMat(1, points->cols, CV_32S);
CvMat* centers = NULL;
CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0);
// KMEANS_PP_CENTERS is undefined
int KMEANS_PP_CENTERS = 2;
cvKMeans2(points, 4, labels, criteria, 3, NULL, KMEANS_PP_CENTERS, centers, 0);
The thing that drives me nuts:
CvMat* labels = cvCreateMat(1, points->cols, CV_32S);
int good = labels->type == CV_32S; // FALSE here
It's obviously one (not sure if the only) issue that causes assertion fail. How this supposed to work? I can't use С++ API since whole application is in plain C.

Answer 1

the assertion tells you:
type must be CV_32S which seems to be the case in your code, maybe your if-statement is false because the type is changed to CV_32SC1 automatically? no idea...
you can either place each point in a row or in a column, so rows/cols is set to 1 and the other dimension must be set to data.rows which indicates that data holds the points you want to cluster in the format that each point is placed in a row, leading to #points rows. So your error seems to be CvMat* labels = cvCreateMat(1, points->cols, CV_32S); which should be CvMat* labels = cvCreateMat(1, points->rows, CV_32S); instead, to make the assertion go away, but your use of points seems to be conceptually wrong.
You probably have to hold your points (you want to cluster) in a cvMat with n rows and 2 cols of type CV_32FC1 or 1 col and type CV_32FC2 (maybe both versions work, maybe only one, or maybe I'm wrong there at all).
edit: I've written a short code snippet that works for me:
// here create the data array where your input points will be hold:
CvMat* points = cvCreateMat( numberOfPoints , 2 /* 2D points*/ , CV_32F);
// this is a float array of the
float* pointsDataPtr = points->data.fl;
// fill the mat:
for(unsigned int r=0; r<samples.size(); ++r)
{
pointsDataPtr[2*r] = samples.at(r).x; // this is the x coordinate of your r-th point
pointsDataPtr[2*r+1] = samples.at(r).y; // this is the y coordinate of your r-th point
}
// this is the data array for the labels, which will be the output of the method.
CvMat* labels = cvCreateMat(1, points->rows, CV_32S);
// this is the quit criteria, which I did neither check nor modify, just used your version here.
CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0);
// call the method for 2 cluster
cvKMeans2(points, 2, labels, criteria);
// now labels holds numberOfPoints labels which have either value 0 or 1 since we searched for 2 cluster
int* labelData = labels->data.i; // array to the labels
for(unsigned int r=0; r<samples.size(); ++r)
{
int labelOfPointR = labelData[r]; // this is the value of the label of point number r
// here I use c++ API to draw the points, do whatever else you want to do with the label information (in C API). I choose different color for different labels.
cv::Scalar outputColor;
switch(labelOfPointR)
{
case 0: outputColor = cv::Scalar(0,255,0); break;
case 1: outputColor = cv::Scalar(0,0,255); break;
default: outputColor = cv::Scalar(255,0,255); break; // this should never happen for 2 clusters...
}
cv::circle(outputMat, samples.at(r), 2, outputColor);
}
giving me this result for some generated point data:
Maybe you need the centers too, the C API gives you the option to return them, but didnt check how it works.