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:

• Naotoshi Seo - Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features)
• Material for Naotoshi Seo's tutorial
• OpenCV Documentation - Cascade Classifier Training