Issues with OpenCV train_HOG c++ sample code? [closed]

asked 2017-08-04 04:49:39 -0500

yogeshmurthy_ gravatar image

updated 2017-08-07 02:47:13 -0500

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 );

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Closed for the following reason the question is answered, right answer was accepted by sturkmen
close date 2017-09-02 11:27:40.379491

Comments

i don't think, you're allowed to change the train params. (kernel or svm type), else the prediction later (which is just a hardcoded dot-product) will crash, because your "compressed support vector" will have the wrong shape.

berak gravatar imageberak ( 2017-08-06 02:18:41 -0500 )edit

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?

yogeshmurthy_ gravatar imageyogeshmurthy_ ( 2017-08-07 00:10:05 -0500 )edit

yes, exactly. the setSVMDetector() method sets the single (compressed) support vector for a linear SVM.

berak gravatar imageberak ( 2017-08-07 00:35:10 -0500 )edit

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?

yogeshmurthy_ gravatar imageyogeshmurthy_ ( 2017-08-07 01:41:39 -0500 )edit

please add some lines of code to your question

berak gravatar imageberak ( 2017-08-07 01:49:45 -0500 )edit

the problem is still the same: you cannot use any other kernel than LINEAR

berak gravatar imageberak ( 2017-08-07 02:38:57 -0500 )edit

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?

yogeshmurthy_ gravatar imageyogeshmurthy_ ( 2017-08-07 02:48:18 -0500 )edit

ha- wait: you're restricted to LINEAR and SVR if you want to train a support vector for the HOGDescriptor (using detectMultiScale() later). this is, what train_HOG.cpp does.

ofc. you can use any kernel, if you use the SVM on its own, e.g. make it a multi-class classification, and use svm->predict() , and hog.compute() to make the train/test features from small image patches

berak gravatar imageberak ( 2017-08-07 05:30:29 -0500 )edit

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?

yogeshmurthy_ gravatar imageyogeshmurthy_ ( 2017-08-07 06:19:16 -0500 )edit

the assertion is from train_HOG.cpp, not from getDecisionFunction() and it checks, if you have a single 1d support vector, and 1d indices/alpha. those are required for the dot product in detectMultiScale() later.

berak gravatar imageberak ( 2017-08-07 06:55:45 -0500 )edit