haar-training

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 current repository

git clone https://github.com/techmagic-team/haar-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 (NB! results will be not so ggod as with regular training: 20-30% lower accuracy, x-times faster):

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. Done

You can test trained cascade using FACE DETECTION sample coming with OpenCV distirbution:

cd ~/opencv-2.4.9/samples/c

chmod +x build_all.sh

./build_all.sh

./facedetect --cascade="~/finished_classifier.xml"