[Haar Classifier] Train your own OpenCV Haar classifier

I just keep a record for myself because there are a lot of documents teaching how to train your haar classifier and almost of them seem to don't work well. The following 2 items are clear and easy to understand.

The Data Image Source (cars) I use.

1. Train your own OpenCV Haar classifier

find ./positive_images -iname "*.pgm" > positives.txt
find ./negative_images -iname "*.pgm" > negatives.txt

perl bin/createsamples.pl positives.txt negatives.txt samples 550\
  "opencv_createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1\
  -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 48 -h 24"

python ./tools/mergevec.py -v samples/ -o samples.vec

opencv_traincascade -data classifier -vec samples.vec -bg negatives.txt\
  -numStages 10 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 1000\
  -numNeg 600 -w 48 -h 24 -mode ALL -precalcValBufSize 1024\
  -precalcIdxBufSize 1024

2. OpenCV Tutorial: Training your own detector | packtpub.com
find pos/ -name '*.pgm' -exec echo \{\} 1 0 0 100 40 \; > cars.info
find neg/ -name '*.pgm' > bg.txt

opencv_createsamples -info cars.info -num 550 -w 48 -h 24 -vec cars.vec
opencv_createsamples -w 48 -h 24 -vec cars.vec

opencv_traincascade -data data -vec cars.vec -bg bg.txt \
 -numPos 500 -numNeg 500 -numStages 10 -w 48 -h 24 -featureType LBP

P.S: Which one is best? I don't know...


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