haar培训OpenCV断言失败

Question

I am trying to train a haar-like classifier for pedestrians in OpenCV using 3340 positive images and 1224 negative images. (in a .txt file I keep the negative image names ie negatives(1).bmp, and in a txt file I keep the positives ie picture(1).bmp 1 0 0 64 128. Actually positive examples are already cropped images of pedestrians so I only need specify one positive sample per image).

At some point during the training process it stops and says :

"Opencv Error: Assertion failed (elements_read==1)in unknown function, file c:\\path\\cvhaartraining.cpp, line 1858"

Any ideas as to what is causing this ?

Answer 1

this issue was answered by creater of the utility on the OpenCV DevZone site in June 2012.

To quote Maria:

The problem is that your vec-file has exactly the same samples count that you passed in command line -numPos 979. 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.

It worked for me. I also had same problem, I was following the famous tutorial on HAAR training but wanted to try the newer training utility with -npos 7000 -nneg 2973

so i did following calcs:

vec-file has to contain >= (numPos + (numStages-1) * (1 - minHitRate) * numPos) + S

7000 >= (numPos + (20-1) * (1 - 0.999) * numPos) + 2973

(7000 - 2973)/(1 + 19*0.001) >= numPos

numPos <= 4027/1.019

numPos <= 3951 ~~ 3950

and used:

-npos 3950 -nneg 2973

It works. I also noticed that others have also had success with reducing numPos : here