/opencv-haar-classifier-training

Learn how to train your own OpenCV Haar classifier

Primary LanguageC++OtherNOASSERTION

Train your own OpenCV Haar classifier

This repository aims to provide tools and information on training your own OpenCV Haar classifier. Use it in conjunction with this blog post: Train your own OpenCV Haar classifier.

Instructions

  1. Install OpenCV & get OpenCV source

     brew tap homebrew/science
 brew install --with-tbb opencv
 wget http://downloads.sourceforge.net/project/opencvlibrary/opencv-unix/2.4.9/opencv-2.4.9.zip
 unzip opencv-2.4.9.zip

  2. Clone this repository

     git clone https://github.com/mrnugget/opencv-haar-classifier-training

  3. Put your positive images in the ./positive_images folder and create a list of them:

     find ./positive_images -iname "*.jpg" > positives.txt

  4. Put the negative images in the ./negative_images folder and create a list of them:

     find ./negative_images -iname "*.jpg" > negatives.txt

  5. Create positive samples with the bin/createsamples.pl script and save them to the ./samples folder:

     perl bin/createsamples.pl positives.txt negatives.txt samples 1500\
   "opencv_createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1\
   -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 80 -h 40"

  6. Compile the mergevec.cpp file in the ./src directory:

     cp src/mergevec.cpp ~/opencv-2.4.9/apps/haartraining
 cd ~/opencv-2.4.9/apps/haartraining
 g++ `pkg-config --libs --cflags opencv | sed 's/libtbb\.dylib/tbb/'`\
   -I. -o mergevec mergevec.cpp\
   cvboost.cpp cvcommon.cpp cvsamples.cpp cvhaarclassifier.cpp\
   cvhaartraining.cpp\
   -lopencv_core -lopencv_calib3d -lopencv_imgproc -lopencv_highgui -lopencv_objdetect

  7. Use the compiled executable mergevec to merge the samples in ./samples into one file:

     find ./samples -name '*.vec' > samples.txt
 ./mergevec samples.txt samples.vec

  8. Start training the classifier with opencv_traincascade, which comes with OpenCV, and save the results to ./classifier:

     opencv_traincascade -data classifier -vec samples.vec -bg negatives.txt\
   -numStages 20 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 1000\
   -numNeg 600 -w 80 -h 40 -mode ALL -precalcValBufSize 1024\
   -precalcIdxBufSize 1024

  9. Wait until the process is finished (which takes a long time — a couple of days probably, depending on the computer you have and how big your images are).

  10. Use your finished classifier!

    cd ~/opencv-2.4.9/samples/c
chmod +x build_all.sh
./build_all.sh
./facedetect --cascade="~/finished_classifier.xml"

Acknowledgements

A huge thanks goes to Naotoshi Seo, who wrote the mergevec.cpp and createsamples.cpp tools and released them under the MIT licencse. His notes on OpenCV Haar training were a huge help. Thank you, Naotoshi!

References & Links: