opencv error: assertion failed (samples.cols == var_count && samples.type() == 5) in cv::ml:: etc.

edit

@berak : i have 74 images. How can i get the size of the each image ?

Size s = img.size();

@berak: i correct the code as you mentioned. place the code resize(img, img, Size(64, 64)); above hog.compute(img, featureVector, Size(32, 32), Size(64, 64)); in both training and testing section. I created Features.xml files in the training section . But test section showing the same error.

^please see update !

@berak: yes. its working now..Thank you

@berak : But the result is always showing -1 for every test images (once i tried input from my positive training database, but the result is always -1 )

yea, expected, that's where the REAL work starts ;)

• read up on SVM's and their params. try a different kernel. (LINEAR should work better)
• 80 images are a laugh. having one image per feature is a good rule of thumb.
• don't take 1 hog feature per image, but make a grid of say 4x4 squares, take seperate hog features for each, concatenate the resulting 16 vectors to a single one
• vary the hog params
• read up on cross-fold validation

@berak : I wrote the training section in the following section , Is there any error in my code? is there any error while training the positive (labelled as 1) image hog features into the svm train method?

` int main(){ for (size_t i = 1; i <= 37; ++i)   
{  //reading positive images
           resize(img, img, Size(64, 64));
    hog.compute(img, featureVector, Size(32, 32), Size(64, 64));
            Mat feature = Mat(featureVector).reshape(1, 1);
        trainData.push_back(feature);
        trainLabels.push_back(1);  } for (size_t i = 1; i <= 37; ++i)    {
 //do the same above for negative images }Ptr<ml::SVM> svm = ml::SVM::create();
svm->train(trainData, ml::ROW_SAMPLE, trainLabels);

svm->save("Features.xml");

} `

no, i don't think, there's an error. again, see the comment above.

@berak : I read some stuffs. I realized that RBF is better choice if training dataset is large; if not Linear would be sufficient. what do you think?