Train your own OpenCV Haar classifier

Important: This guide assumes you work with OpenCV 3.1

Instructions

1. Install OpenCV & get OpenCV source

2. Clone this repository

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 =====
   <BEGIN
   POS count : consumed   1000 : 1000
   NEG count : acceptanceRatio    600 : 1
   Precalculation time: 11
   +----+---------+---------+
   |  N |    HR   |    FA   |
   +----+---------+---------+
   |   1|        1|        1|
   +----+---------+---------+
   |   2|        1|        1|
   +----+---------+---------+
   |   3|        1|        1|
   +----+---------+---------+
   |   4|        1|        1|
   +----+---------+---------+
   |   5|        1|        1|
   +----+---------+---------+
   |   6|        1|        1|
   +----+---------+---------+
   |   7|        1| 0.711667|
   +----+---------+---------+
   |   8|        1|     0.54|
   +----+---------+---------+
   |   9|        1|    0.305|
   +----+---------+---------+
   END>
   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)
• Material for Naotoshi Seo's tutorial
• OpenCV Documentation - Cascade Classifier Training
• Train your own OpenCV Haar classifier.