2015-01-06 08:20:46 -0600 | received badge | ● Nice Answer (source) |
2014-10-30 12:02:27 -0600 | commented answer | CvSVM::EPS_SVR train_auto assertion sv_count != 0 failed The other thing it could possibly be, is that the subset of training samples within that fold (for the k-fold) has no samples in it. If you look at line 1982 in the modules/ml/src/svm.cpp file you'll see where that comes in. Unfortunately, I'm not entirely sure exactly how the alpha value is calculated (it is done by the solver for the algorithm type from what I understand), but it's more likely the k-fold sample is the problem I think. |
2014-10-30 11:10:13 -0600 | commented answer | Beginner guide for SVM +1 for the StackOverflow post on images, probably one of the most useful openCv posts I've found on SVM's on StackOverflow |
2014-10-30 10:50:03 -0600 | answered a question | CvSVM::EPS_SVR train_auto assertion sv_count != 0 failed Using your sample code provided I don't get any issue at all, though I did change the maximum number of iterations down to 1000 so I could run it in a reasonable amount of time. Looking at the source code near that assertion I spy the following: So you either have 0 samples and the sv_count is never increasing (it is initialized to 0) or every alpha value is equal to 0 (since the use of |
2014-10-30 10:29:09 -0600 | commented question | How to use opencv_haartraining to train a haarcascade yes, I suppose you could look at each script (everything is in a git repository https://github.com/mrnugget/opencv-haar-classifier-training) and convert it to do whatever you need it to do, but that would be a lot of work I think. The perl scripts are just for getting the samples and vector format you need to use the opencv_traincascade. Since you mention opencv_haartraining, I think this tutorial goes really far in depth on that http://note.sonots.com/SciSoftware/haartraining.html and is what the first one I linked to is based on. Either way I think you need to use perl though |
2014-10-27 13:40:50 -0600 | commented question | How to use opencv_haartraining to train a haarcascade coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html Try out this tutorial? They do a really good job of explaining. |
2014-10-08 12:04:17 -0600 | commented question | Install opencv in Ubuntu 64 bits posting the errors you got will help people help you. |
2014-10-07 14:25:20 -0600 | commented question | Cascade Training Error also make sure numNeg is less than the number of negatives as well! I got this error and then got it working again by checking |
2014-10-01 14:28:27 -0600 | answered a question | Are there specific times when cv2.waitKey() is required? I know that it is required if you want to show changes in an image during a loop, or even the loop itself. When you call HighGui cannot process windows events like redraw, resizing, input event unless you call As far as when it is recommended, I'd say anytime you need to update display changes to a user you should call it, or if you're waiting for a user to give you some input. Hope this helps. The documentation here: http://docs.opencv.org/modules/highgui/doc/user_interface.html#imshow should tell you pretty much everything you want to know about why this all is, but to qoute:
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2014-10-01 14:21:34 -0600 | commented question | Histogram Comparison: Sensitive to Illumination? Have you considered using a different colorspace? Like YCrCb? |
2014-10-01 13:20:48 -0600 | commented question | Response is not integral I am getting this error if I save a SVM, then try to load it and keep training on it. Is this not possible? |
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2014-09-30 12:54:06 -0600 | answered a question | How to get a probability for a SVM prediction? The output of predict will be the label which you have set the class with. For example, if you have set up a binary SVM then you will have likely also set up 2 labels (say -1 and 1). If the SVM decides that the The sign is dependent entirely on the labels you set, if you don't use a sign then there will be none. All that said, If you look at the [documentation] then you'll find that there is a second parameter to the predict function:
In simple terms, if you pass true for the second parameter you don't get back your class label, but the distance from the margin, and here the sign matters as it will be the direction away from the hyperplane. But only if you are doing a binary classification! I'm not sure about your last question about the probability for non-binary problems, but in the case of a binary problem then the distance is your probability (Or can be converted, see the comments below), the farther from the margin the more likely the object belongs to the class on that side of the cutting plane. The negative/positive will tell you which class it is. The documentation for predict is here: http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-predict |
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2014-09-17 11:40:28 -0600 | commented question | Best way to integrate mutiple classifiers into a single system If you have multiple descriptors for items, perhaps you run each of those descriptors on your image, possibly within a ROI created by a sliding window? To deal with the angles thing, perhaps you can just run each of the classifiers you have for each angle, and have each classifier say yes or no, and if X classifiers say yes then you can say you have that object? These are just suggestions, so I figured I'd put them in a comment rather than an answer. I don't know anything about convolutional NN or voc. trees sorry. |
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2014-09-02 09:18:56 -0600 | commented question | Trying to calculate histogram on Android and find the median. Unsure how to access histogram data from Mat hist.at<float>(num), is the same as Mat.get I believe. The links in your question are broken otherwise I would try to answer a bit more |
2014-09-02 09:16:46 -0600 | answered a question | *java* api Histogram calculation I just ran into this problem and solved it. The important things to note are the following areas within the native library:
to now of course you will get an assertion error on the second assertion in the native code above, so you should then notice that your Hope this helps Edit: Also, you should think about using 256.0f instead of 255.0f in your ranges since it is exclusive on the top (it says it somewhere in the documentation but I don't have a link) |