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https://github.com/alievk/dd_haartraining

Haar training for defect detection problem (OBACHT, DESY)
https://github.com/alievk/dd_haartraining

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Haar training for defect detection problem (OBACHT, DESY)

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README 

        
# 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](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html).



## 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/2.4.5/opencv-2.4.5.tar.gz

        tar xvzf opencv-2.4.5.tar.gz



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-2.4.5/apps/haartraining

        cd ~/opencv-2.4.5/apps/haartraining

        g++ `pkg-config --libs --cflags opencv` -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-2.4.5/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)](http://note.sonots.com/SciSoftware/haartraining.html)

- [Material for Naotoshi Seo's tutorial](https://code.google.com/p/tutorial-haartraining/)

- [OpenCV Documentation - Cascade Classifier Training](http://docs.opencv.org/doc/user_guide/ug_traincascade.html)