yogeshmurthy_'s profile - activity

Yes, I've got crops already, just need to know if the object is present or not. I need something that runs fast because

@berak By recent OpenCV, are you referring to version 3.x or 4.x? The tools to train Haar cascades still seem to exist i

@berak By recent OpenCV, are you referring to version 3.x or 4.x? The tools to train Haar cascades still seem to exist i

If you are trying to detect vehicles, your training set should contain multiple images of different vehicles. After trai

If you are trying to detect vehicles, your training set should contain multiple images of different vehicles. After trai

Since the error is in the cvtColor function, it means that your source image, img is empty. Before calling cvtColor, ple

Is there a way to use a trained CascadeClassifier for just classification in OpenCV 3.x? I have trained a cascade classi

Using mean shift algorithm?

@StevenPuttemans How is the merge done?

Cool. No issues. Thanks a ton for your help. I'd have upvoted your comments but I don't have enough points yet. Sorry :(

Thanks. Can you tell me why and how regression is used for a classification problem? Is there any developer manual/documentation for OpenCV from where I can get this information?

Thanks for clarifying that. Can you tell me in which part of the code the restriction of SVR+LINEAR kernel is enforced? Also, the assertion fails at getSupportVectors() and getDecisionFunction(), much before setting the SVM detector for a HOGDescriptor object. Printing the values gives alpha.total( ) = 200, svidx.total( ) = 200 and sv_total = 200. Can you help me understand what's happening here?

Do you mean I cannot use any kernel other than LINEAR on HOG data? How do I experiment with other kernels and different parameters for HOG+SVM? Can you point me to appropriate resources? Are you aware of what dataset and method has been used to train the defaultPeopleDetector?

The issue I am facing is before the prediction phase. It happens when I am trying to get the support vectors and decision function from the trained SVM. Here's the code snippet where the assertion is failing.

Mat sv = svm->getSupportVectors(); const int sv_total = sv.rows; Mat alpha, svidx; double rho = svm->getDecisionFunction(0, alpha, svidx); CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );

Can you help me pinpoint the exact issue?

For prediction, the SVM associated with a HOGDescriptor object is set explicitly using the setSVMDetector method of the class. I imagine as long as the vectors, in both training and test sets, are of the same size, this should work, right?

I am trying to train a HOG+SVM on the INRIA dataset using the OpenCV sample code train_HOG.cpp. Firstly, I am confused why Support Vector Regression (EPS_SVR) is used for what is clearly a classification problem. I've tried changing it to C_SVC but am getting the following runtime error with all kernels other than linear (this happens for the regression case as well) while testing on the training set itself:

OpenCV Error: Assertion failed (alpha.total() == 1 && svidx.total() == 1 && sv_total == 1) in get_svm_detector terminate called after throwing an instance of 'cv::Exception' what(): HOGsvm.cpp:41: error: (-215) alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 in function get_svm_detector Aborted (core dumped)

Any idea on why it is happening and how to resolve it?

EDIT: Here's the code snippet that's raising the error

Ptr<svm> svm = SVM::create() ;

svm->setTermCriteria(TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-3 ));

svm->setType(SVM::C_SVC);

svm->setKernel(SVM::RBF);

svm->setC(0.01);

svm->setGamma(0.1);

svm->train(train_data, ROW_SAMPLE, Mat(labels));

HOGDescriptor myHog ;

myHog.winSize = Size(64, 128) ;

vector<float> hogDetector ;

Mat sv = svm->getSupportVectors();

const int sv_total = sv.rows;

Mat alpha, svidx;

double rho = svm->getDecisionFunction(0, alpha, svidx);

CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );