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.
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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
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Clone this repository
git clone https://github.com/mrnugget/opencv-haar-classifier-training
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Put your positive images in the ./positive_images folder and create a list
of them:
find ./positive_images -iname "*.jpg" > positives.txt
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Put the negative images in the ./negative_images folder and create a list of them:
find ./negative_images -iname "*.jpg" > negatives.txt
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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"
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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.
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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
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.
-
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).
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Use your finished classifier!
cd ~/opencv-2.4.9/samples/c
chmod +x build_all.sh
./build_all.sh
./facedetect --cascade="~/finished_classifier.xml"
