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2014-12-09 13:43:38 -0600 | marked best answer | Do all opencv functions support in-place mode for their arguments? in cases where this question arises. For example, should I write: (not in-place variant) or: (in-place variant)? If some functions do not support in-place mode, how can I know about this? |
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2013-02-10 14:09:27 -0600 | answered a question | Convert opencv_traincascade intermediate output into xml There are two applications to train cascade classifiers in OpenCV now, see http://answers.opencv.org/question/757/haartraining-vs-traincascade-object-detection/#759. c-example-convert_cascade is for cascades trained by haartraining application and it does not support a format of cascades trained by traincascade application. This is why you got the exception. For the conversion in your case you should run opencv_traincascade again with the same " Please also keep in mind that when you kill the application the last stage*.xml can be broken (partially saved). In this case you'll get an exception with the suggested conversion too because the broken xml can not be read. You can just remove the last stage xml or set " |
2013-01-30 13:18:06 -0600 | commented answer | Haartraining vs Traincascade : Object Detection Of course, more numPos and numSet you set, more time is need. It's hard to say exactly how training time depends on a number of samples because it's also highly depends on your training datasets and other parameters. From my experience the most time-consuming part of traincascade is selecting the negative examples to train each new stage because they have to be recognized as positive samples by all previous (already trained) stages. I.e. traincascade spends significant time in searching the samples of negative base that are very similar to positives (faces). |
2013-01-30 13:06:22 -0600 | commented answer | Haartraining vs Traincascade : Object Detection Yes, a couple of trained LBP cascades can be found here http://code.opencv.org/projects/opencv/repository/revisions/master/show/data/lbpcascades. I don't know how lbpcascade_profileface.xml was trained (e.g. which vec-file was used). lbpcascade_frontalface.xml was trained by me on vec-file http://code.opencv.org/projects/opencv/repository/revisions/master/changes/data/vec_files/trainingfaces_24-24.vec; the used parameters of traincascade app are listed in the cascade xml. At that time I did not have a goal to train as good as possible LBP cascade, so that cascade is just an example (but working well !) and I'm sure you can train even better LBP cascade for faces. |
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2012-11-25 10:29:30 -0600 | answered a question | Traincascade Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file. I guess moderators will recommend you to open new question and not to ask more questions in the answer on your own question :) Here it's said that 0.9999999... is not a good value of
About wasting hours of work.. I also don't recommend you to downgrade to 2.2. I did not find in the Git history my commit (due to files reorganization), but I fixed the following problem of traincascade: when traincascade tries samples from vec-file one by one and reaches the end of the file it have to finish the training, otherwise it will use duplicate samples. This was the bug. The answer on the question about 2000 positives. To be sure that you can train a good cascade, try to use traincascade with default parameters on well-tried vec-file. Maybe you should start to play with parameters on this vec-file (not your) and definitely with LBP features (LBPs decrease wasting the time). For the choosing numPos, I think you can follow something like this About tips on studing the traincascade code. As usual, from top to more details.. Here classical Haar is the best feature to get understanding faster (especially where features are processed by ADABoost because Haar is ordered (not categorical)). For an optimization the integral images are intensively used in cascades (keep it in mind). Don ... (more) |
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2012-11-23 05:50:08 -0600 | commented answer | Traincascade Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file. Yes, you still need to keep in mind this formula. That fix was only about to throw an exception with error message for a user if there are not enough positive samples for the next stage training, because there was an assertion in that point of the code before. |
2012-11-23 02:52:58 -0600 | commented answer | Error in parameter of traincascade? @icedecker You wrote that you prefer to use the formula, but a bit confused with it. I tried to describe it in more details here (http://answers.opencv.org/question/4368/traincascade-error-bad-argument-can-not-get-new/). Please check this if you're still interested. |
2012-11-23 00:06:24 -0600 | answered a question | Traincascade Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file. Hi, First of all, I have to note that you copied my formula description incompletely. I wrote at that issue: " For the document you asked.. I don't remember that I wrote this formula anywhere except the issue. The formula is not from any paper of course, it just follows from how traincascade application selects a set of positive samples to train each stage of a cascade classifier. Ok, I'll describe my formula in more details as you ask.
If some positive samples ( One more important note: to train next Now we are ready to derive the formula. For the 0-stage training we just get numPose positive samples from vec-file. In the worse case |
2012-11-22 10:55:55 -0600 | answered a question | FeatureDetector giving conversion error on image set You have to pass
for a vector of images (see the part of the documentation you mentioned above). |
2012-11-20 11:39:30 -0600 | marked best answer | What is the most effective way to access cv::Mat elements in a loop? I need to process cv::Mat elements and there is not special OpenCV function that performs my task. So I have to iterate the elements in loop. What is the more effective way of elementwise access? cv::Mat iterators, cv::Mat::at() method, maybe pointer arithmetic by hand or something else? |