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

Learn how to train your own OpenCV Haar classifier
https://github.com/mrnugget/opencv-haar-classifier-training

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Learn how to train your own OpenCV Haar classifier

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README 

        
# Train your own OpenCV Haar classifier



**Important**: This guide assumes you work with OpenCV 2.4.x. Since I no longer work with OpenCV, and don't have the time to keep up with changes and fixes, this guide is **unmaintained**. Pull requests will be merged of course, and if someone else wants commit access, feel free to ask!



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/2.4.9/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. Use `tools/mergevec.py` to merge the samples in `./samples` into one file:



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



   Note: If you get the error `struct.error: unpack requires a string argument of length 12`

   then go into your **samples** directory and delete all files of length 0.



7. 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

          

    If you want to train it faster, configure feature type option with LBP:



         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 -featureType LBP



    After starting the training program it will print back its parameters and then start training. Each stage will print out some analysis as it is trained:



      ```

      ===== TRAINING 0-stage =====

      

      Training until now has taken 0 days 3 hours 19 minutes 16 seconds.

      ```



    Each row represents a feature that is being trained and contains some output about its HitRatio and FalseAlarm ratio. If a training stage only selects a few features (e.g. N = 2) then its possible something is wrong with your training data.



    At the end of each stage the classifier is saved to a file and the process can be stopped and restarted. This is useful if you are tweaking a machine/settings to optimize training speed.



8. 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).



9. 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)](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)