python png_to_jpg.py

echo "Create vector file from positives, negatives and starts the training"
echo "Parameters: posivies size, number of posties, "


# Number of training stages
nrStages=20

# detection types [HAAR,LBP]
# fn=HAAR
# object size, width, height
imgw=24
imgh=24

# number of images containg the object to detect (see pos directory content)
nrf=0
FILES=./pos/*.jpg
for f in $FILES; do
  nrf=$((nrf+1))
done

nrPos=$nrf
nrPosThreeTimes=$(($nrPos * 3))

# number of images NOT containg the object to detect (see neg directory content)
nrf=0
FILES=./neg/*.jpg
for f in $FILES; do
  nrf=$((nrf+1))
done

nrNeg=$nrf

# # create positive dat file and negative txt file
python3 ./prepare_data.py \
  -posWidthX 24 \
  -posWidthY 24 \
  -negWidthX 64 \
  -negWidthY 64


# create vector file from positives
opencv_createsamples \
  -info info.dat \
  -vec positives.vec \
  -bg neg.txt \
  -maxxangle 1.1 \
  -maxyangle 1.1 \
  -maxzangle 1.1 \
  -w $imgw -h $imgh \
  -num $nrPos

# create positives from a single image
#opencv_createsamples \
  # -img object.jpg \
  # -bg neg.txt \
  # -info info/info.lst \
  # -pngoutput info \
  # -maxxangle 1.1 \
  # -maxyangle 1.1 \
  # -maxzangle 1.1 \
  # -num $nrPos \
  # -w $imgw -h $imgh


# train cascade
opencv_traincascade \
  -data data \
  -vec positives.vec \
  -bg neg.txt \
  -numPos $nrPos \
  -numNeg $nrNeg \
  -numStages $nrStages \
  -w $imgw \
  -h $imgh \
  -minHitRate 0.999 \
  -maxFalseAlarmRate 0.5 \
  -mode ALL \
  -numThreads 4 \
  -precalcValBufSize 8192  -precalcIdxBufSize 8192 
# -featureType $fn \