Example of use:
===============

python v1like_extract_fromcsv.py -i ./test_imageset config/v1like_a.py ./test_imageset/train5test5_split01.csv .v1like_a  --nprocessors=$(cat /proc/cpuinfo | grep processor | wc -l)


Steps to reproduce the results from our PLoS 2008 paper:
========================================================

"Why is Real-World Visual Object Recognition Hard?"
By Nicolas Pinto, David D. Cox and James J. DiCarlo (2008)
Published in PLoS Comput Biol 4(1): e27.
doi:10.1371/journal.pcbi.0040027
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0040027

Instructions:
-------------

# -- set up
export PJT=$HOME/plos08_reprod
export NPROCS=$(cat /proc/cpuinfo | grep processor | wc -l)
mkdir -p $PJT/{src,data}

cd $PJT/src
git clone https://github.com/npinto/v1like.git
git clone https://github.com/npinto/sclas.git
export V1LIKE=$PJT/src/v1like
export SCLAS=$PJT/src/sclas

# -- don't forget to install shogun
# see e.g.: https://github.com/npinto/np-toolbox/blob/master/install_scripts/install_shogun0.9.3_Ubuntu9.10.bash

# ----------------------------
# -- #Caltech101: get image set
cd $PJT/data
wget http://www.vision.caltech.edu/Image_Datasets/#Caltech101/101_ObjectCategories.tar.gz
tar xzvf 101_ObjectCategories.tar.gz

# -- #Caltech101: create splits
export ntrain=15 ntest=15
for i in `seq -w 1 10`; do
    python $SCLAS/create_traintest_split.py --rseed=$i --ntrain=$ntrain --ntest=$ntest $PJT/data/101_ObjectCategories/{,train${ntrain}test${ntest}_split_${i}.csv};
done;

# -- #Caltech101: generate v1like features
for conf in v1like_a{,_plus}; do
    for i in `seq -w 1 10`; do
        python $V1LIKE/v1like_extract_fromcsv.py --nprocessors=$NPROCS -i $PJT/data/101_ObjectCategories/ $V1LIKE/config/$conf.py $PJT/data/101_ObjectCategories/train${ntrain}test${ntest}_split_${i}.csv $conf.mat;
    done;
done;

# -- #Caltech101: generate kernels
for conf in v1like_a{,_plus}; do
    for csv in $PJT/data/101_ObjectCategories/train${ntrain}test${ntest}_split_??.csv; do
        python $SCLAS/kernel_generate_fromcsv.py -i $(dirname $csv) $csv $conf.mat $csv.kernel.$conf.mat;
    done;
done;

# -- #Caltech101: run SVMs
for conf in v1like_a{,_plus}; do
    for csv in $PJT/data/101_ObjectCategories/train${ntrain}test${ntest}_split_??.csv; do
        python $SCLAS/svm_ova_fromfilenames.py $csv.kernel.$conf.mat -o $csv.svm_ova_results.$conf.mat;
    done;
done;

# -- #Caltech101: average classification results (crudely ;-)
for conf in v1like_a{,_plus}; do
    echo $conf;
    for i in `seq -w 1 10`; do
        python $SCLAS/print_mat.py $PJT/data/101_ObjectCategories/train${ntrain}test${ntest}_split_${i}.csv.svm_ova_results.$conf.mat accuracy;
    done | awk '{sum+=$2} END {print sum/NR}';
done;
# v1like_a
# 58.0392
# v1like_a_plus
# 61.4183

# ----------------------------------------
# -- Controlled Invariance: get image sets
cd $PJT/data
wget http://s3.amazonaws.com/PLoS08_ControlSets/PLoS08_ControlSet_Cars_Planes_v01.tar.gz http://s3.amazonaws.com/PLoS08_ControlSets/PLoS08_ControlSet_Cars_Planes_v01.tar.gz.md5
md5sum -c PLoS08_ControlSet_Cars_Planes_v01.tar.gz.md5
tar xzvf PLoS08_ControlSet_Cars_Planes_v01.tar.gz

# -- Controlled Invariance: create splits
export ntrain=100 ntest=30
for dir in $(ls -d $PJT/data/PLoS08_ControlSet_Cars_Planes_v01/*/); do
    for i in `seq -w 1 10`; do
        python $SCLAS/create_traintest_split.py --rseed=$i --ntrain=$ntrain --ntest=$ntest $dir/{,train${ntrain}test${ntest}_split_${i}.csv};
    done;
done;

# -- Controlled Invariance: generate v1like features
for conf in v1like_a{,_plus}; do
    for dir in $(ls -d $PJT/data/PLoS08_ControlSet_Cars_Planes_v01/*/); do
        for i in `seq -w 1 10`; do
            python $V1LIKE/v1like_extract_fromcsv.py --nprocessors=$NPROCS -i $dir/ $V1LIKE/config/$conf.py $dir/train${ntrain}test${ntest}_split_${i}.csv $conf.mat;
        done;
    done;
done;

# -- Controlled Invariance: generate kernels
for conf in v1like_a{,_plus}; do
    for dir in $(ls -d $PJT/data/PLoS08_ControlSet_Cars_Planes_v01/*/); do
        for csv in $dir/train${ntrain}test${ntest}_split_??.csv; do
            python $SCLAS/kernel_generate_fromcsv.py -i $(dirname $csv) $csv $conf.mat $csv.kernel.$conf.mat;
        done;
    done;
done;

# -- Controlled Invariance: run SVMs
for conf in v1like_a{,_plus}; do
    for dir in $(ls -d $PJT/data/PLoS08_ControlSet_Cars_Planes_v01/*/); do
        for csv in $dir/train${ntrain}test${ntest}_split_??.csv; do
            python $SCLAS/svm_ova_fromfilenames.py $csv.kernel.$conf.mat -o $csv.svm_ova_results.$conf.mat;
        done;
    done;
done;

# -- Controlled Invariance: average classification results (crudely ;-)
for conf in v1like_a{,_plus}; do
    for dir in $(ls -d $PJT/data/PLoS08_ControlSet_Cars_Planes_v01/*/); do
        echo $conf $dir;
        for i in `seq -w 1 10`; do
            python $SCLAS/print_mat.py $dir/train${ntrain}test${ntest}_split_${i}.csv.svm_ova_results.$conf.mat accuracy;
        done | awk '{sum+=$2} END {print sum/NR}';
    done;
done;
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_NaturalBg_n130_200x200_GS/
#97.1667
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_NoiseBg_n130_200x200_GS/
#100
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_PhaseScrambledBg_n130_200x200_GS/
#99.8333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_NaturalBg_n130_200x200_GS/
#87.3333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_NoiseBg_n130_200x200_GS/
#97.8333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_PhaseScrambledBg_n130_200x200_GS/
#92.5
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_NaturalBg_n130_200x200_GS/
#68.3333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_NoiseBg_n130_200x200_GS/
#84
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_PhaseScrambledBg_n130_200x200_GS/
#81.3333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_NaturalBg_n130_200x200_GS/
#65.5
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_NoiseBg_n130_200x200_GS/
#74.1667
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_PhaseScrambledBg_n130_200x200_GS/
#65.6667
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_NaturalBg_n130_200x200_GS/
#54.8333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_NoiseBg_n130_200x200_GS/
#68
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_PhaseScrambledBg_n130_200x200_GS/
#62.5
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_NaturalBg_n130_200x200_GS/
#51.3333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_NoiseBg_n130_200x200_GS/
#61.8333
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_PhaseScrambledBg_n130_200x200_GS/
#57.5
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_NaturalBg_n130_200x200_GS/
#45.5
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_NoiseBg_n130_200x200_GS/
#55
#v1like_a plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_PhaseScrambledBg_n130_200x200_GS/
#50.6667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_NaturalBg_n130_200x200_GS/
#97.3333
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_NoiseBg_n130_200x200_GS/
#100
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation00_PhaseScrambledBg_n130_200x200_GS/
#99.6667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_NaturalBg_n130_200x200_GS/
#86
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_NoiseBg_n130_200x200_GS/
#98.3333
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation01_PhaseScrambledBg_n130_200x200_GS/
#92.3333
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_NaturalBg_n130_200x200_GS/
#67.1667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_NoiseBg_n130_200x200_GS/
#88.6667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation02_PhaseScrambledBg_n130_200x200_GS/
#80.6667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_NaturalBg_n130_200x200_GS/
#63.8333
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_NoiseBg_n130_200x200_GS/
#78.5
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation03_PhaseScrambledBg_n130_200x200_GS/
#69.1667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_NaturalBg_n130_200x200_GS/
#51.5
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_NoiseBg_n130_200x200_GS/
#70.6667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation04_PhaseScrambledBg_n130_200x200_GS/
#64.5
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_NaturalBg_n130_200x200_GS/
#51.1667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_NoiseBg_n130_200x200_GS/
#64.8333
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation05_PhaseScrambledBg_n130_200x200_GS/
#58.1667
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_NaturalBg_n130_200x200_GS/
#47
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_NoiseBg_n130_200x200_GS/
#57.5
#v1like_a_plus plos08_reprod/data/PLoS08_ControlSet_Cars_Planes_v01/ControlSet_Cars_Planes_Variation06_PhaseScrambledBg_n130_200x200_GS/
#55.3333