Opencv问题：Bad argument can not get new positive sample

迈不过友情╰ 2022-09-18 07:50 25阅读 0赞

# 出现的问题如下： #

# Traincascade Error: Bad argument (Can not get new positive sample. The most possible reason is insufficient count of samples in given vec-file. #

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**解决办法：**

The problem is that your vec-file has exactly the same samples count that you passed in command line -numPos. Training application used all samples from the vec-file to train 0-stage and it can not get new positive samples for the next stage training because vec-file is over.The bug of traincascade is that it had assert() in such cases, but it has to throw an exception with error message for a user. It was fixed in r8913.

***-numPose*** is a samples count that is used to train each stage. Some already used samples can be filtered by each previous stage (ie recognized as background), but no more than (1 - minHitRate) \* numPose on each stage.  So vec-file has to contain >= (numPose + (numStages-1) \* (1 - minHitRate) \* numPose) + S,

where S is a count of samples from vec-file that can be recognized as background right away. I hope it can help you to create vec-file of correct size and chose right numPos value.

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First of all, I have to note that you copied my formula description incompletely. I wrote at that issue: "S is a count of samples from vec-file that can be recognized as background right away". With the partial description of S from the question, the formula does not make sense at all :)

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.

***numPose*** \- a count of positive samples which is used to train each stage (do not confuse it with a count of all samples in vec-file!).

***numStages*** \- a stages count which a cascade classifier will have after the training.

**minHitRate** \- training constraint for each stage which means the following. Suppose a positive samples subset of size numPose was selected to train current i\-stage (i is a zero-based index). After the training of current stage, at least minHitRate \* numPose samples from this subset have to pass this stage, i.e. current cascade classifier with i+1 stages has to recognize this part (minHitRate) of the selected samples as positive.

If some positive samples (falseNegativeCount pieces) from the set (of size numPose) which was used to train i\-stage were recognized as negative (i.e. background) after the stage training, then numPose - falseNegativeCount pieces of correctly recognized positive samples will be kept to train i+1\-stage and falseNegativeCount pices of new positive samples (unused before) will be selected from vec-file to get a set of size numPose again.

One more important note: to train next i+1\-stage we select only the samples that are passed a current cascade classifier with i+1 stages.

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 (1 - minHitRate) \* numPose of these samples are recognized as negative by the cascade with 0-stage only. So in this case to get a training set of positive samples for the 1-stage training we have to select (1 - minHitRate) \* numPose new samples from vec-file that are recognized as positive by the cascade with 0-stage only. While the selection, some new positive samples from vec-file can be recognized as background right away by the current cascade and we skip such samples. The count of skipped sample depends on your vec-file (how different samples are in it) and other training parameters.

By analogy, for each i\-stage training (i=1,..numStages-1) in the worse case we have to select (1 - minHitRate) \* numPose new positive samples and several positive samples will be skipped in the process.

As result to train all stages we need numPose + (numStages - 1) \* (1 - minHitRate) \* numPose + S positive samples, where S is a count of all the skipped samples from vec-file (for all stages).

Of course this formula does not allow to know an exact size of vec-file (samples count) we must have, because S depends on vec-file samples quality. But the formula gives an estimation of this size and understanding its reason. I hope it is so :) If you disagree or have more questions, please let me know.

r8913 was a link to the SVN revision 8913, but we migrated on GIT. The corresponding fix is in OpenCV >= 2.4.2. And yes, 2.4.3 is actual version (you can always check this [here][]).

[SouthEast]: /images/20220828/e85abf24ad9642729007be38e01df27f.png
[here]: http://opencv.org/category/news