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Train Classifier
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#!/bin/sh | |
find traffic/positive/ -type f -name '*.jpg' > positives.dat | |
find traffic/negative/ -type f -name '*.jpg' > negatives.dat | |
[ -f collection.dat ] && rm -f collection.dat | |
# get width and height of each image, output to collection.dat file | |
for f in $(cat positives.dat); do | |
dim=$(identify "$f" | cut -d ' ' -f 3 | tr 'x' ' ') | |
echo "$f 1 0 0 $dim" | |
done > collection.dat | |
NUMPOS=$(wc -l collection.dat) | |
[ -f samples.vec ] && rm -f samples.vec | |
# create samples.vec file needed by classifier | |
opencv_createsamples -info collection.dat -bgcolor 0 -bgthresh 0 \ | |
-vec samples.vec -num $NUMPOS -w 20 -h 20 | |
# train classifier to detect positive images | |
# for precalculation you can set the amount of memory to use | |
# set sample size of positive images, 20 x 20 seems to work well | |
opencv_traincascade -data classifier -featureType HAAR -vec samples.vec \ | |
-bg negatives.dat -numPos 600 -numNeg 470 -numStages 20 \ | |
-precalcValBufSize 6000 -precalcIdxBufSize 6000 \ | |
-minHitRate 0.999 -maxFalseAlarmRate 0.5 -mode ALL \ | |
-w 20 -h 20 |
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FYI, you need to have a count of neg and positive:
NUMNEG=$(wc -l negatives.dat)
Then opencv_traincascade can use these vars:
opencv_traincascade -data classifier -featureType HAAR -vec samples.vec
-bg negatives.dat -numPos $NUMPOS -numNeg $NUMNEG -numStages 20
-precalcValBufSize 6000 -precalcIdxBufSize 6000
-minHitRate 0.999 -maxFalseAlarmRate 0.5 -mode ALL
-w 20 -h 20