automatic commit

diff --git a/README.txt b/README.txt
index 9f2d329..3bca7f0 100644 (file)
--- a/README.txt
+++ b/README.txt
@@ -1,5 +1,6 @@
 
 

-INTRODUCTION
+######################################################################
+## INTRODUCTION

 
   This is the C++ implementation of the folded hierarchy of
   classifiers for cat detection described in
 
   This is the C++ implementation of the folded hierarchy of
   classifiers for cat detection described in


@@ -9,7 +10,8 @@ INTRODUCTION
 
   Please cite this paper when referring to this software.
 
 
   Please cite this paper when referring to this software.
 

-INSTALLATION
+######################################################################
+## INSTALLATION

 
   This program was developed on Debian GNU/Linux computers with the
   following main tool versions
 
   This program was developed on Debian GNU/Linux computers with the
   following main tool versions


@@ -36,12 +38,12 @@ INSTALLATION
   You can also run the full thing with the following commands if you
   have wget installed
 
   You can also run the full thing with the following commands if you
   have wget installed
 

-     wget http://www.idiap.ch/folded-ctf/not-public-yet/data/folding-gpl.tgz
-     tar zxvf folding-gpl.tgz
-     cd folding
-     wget http://www.idiap.ch/folded-ctf/not-public-yet/data/rmk.tgz
-     tar zxvf rmk.tgz
-     ./run.sh
+   > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/folding-gpl.tgz
+   > tar zxvf folding-gpl.tgz
+   > cd folding
+   > wget http://www.idiap.ch/folded-ctf/not-public-yet/data/rmk.tgz
+   > tar zxvf rmk.tgz
+   > ./run.sh

 
   Note that every one of the twenty rounds of training/testing takes
   more than three days on a powerful PC. However, the script detects
 
   Note that every one of the twenty rounds of training/testing takes
   more than three days on a powerful PC. However, the script detects


@@ -54,7 +56,8 @@ INSTALLATION
 
   You are welcome to send bug reports and comments to fleuret@idiap.ch
 
 
   You are welcome to send bug reports and comments to fleuret@idiap.ch
 

-PARAMETERS
+######################################################################
+## PARAMETERS

 
   To set the value of a parameter during an experiment, just add an
   argument of the form --parameter-name=value before the commands that
 
   To set the value of a parameter during an experiment, just add an
   argument of the form --parameter-name=value before the commands that


@@ -73,7 +76,7 @@ PARAMETERS
 
   * pictures-for-article ("no")
 
 
   * pictures-for-article ("no")
 

-    Should the pictures be generated to be clear in b&w
+    Should the pictures be generated for printing in black and white.

 
   * pool-name (no default)
 
 
   * pool-name (no default)
 


@@ -90,14 +93,14 @@ PARAMETERS
 
   * result-path ("/tmp/")
 
 
   * result-path ("/tmp/")
 

-    In what directory should we save all the produced file during the
+    In what directory should we save all the produced files during the

     computation.
 
   * loss-type ("exponential")
 
     computation.
 
   * loss-type ("exponential")
 

-    What kind of loss to use for the boosting. While different loss are
-    implementer in the code, only the exponential has been thoroughly
-    tested.
+    What kind of loss to use for the boosting. While different losses
+    are implemented in the code, only the exponential has been
+    thoroughly tested.

 
   * nb-images (-1)
 
 
   * nb-images (-1)
 


@@ -107,7 +110,7 @@ PARAMETERS
   * tree-depth-max (1)
 
     Maximum depth of the decision trees used as weak learners in the
   * tree-depth-max (1)
 
     Maximum depth of the decision trees used as weak learners in the

-    classifier.
+    classifier. The default value corresponds to stumps.

 
   * proportion-negative-cells-for-training (0.025)
 
 
   * proportion-negative-cells-for-training (0.025)
 


@@ -116,14 +119,14 @@ PARAMETERS
 
   * nb-negative-samples-per-positive (10)
 
 
   * nb-negative-samples-per-positive (10)
 

-    How many negative cell to sample for every positive cell during
+    How many negative cells to sample for every positive cell during

     training.
 
   * nb-features-for-boosting-optimization (10000)
 
     How many pose-indexed features to use at every step of boosting.
 
     training.
 
   * nb-features-for-boosting-optimization (10000)
 
     How many pose-indexed features to use at every step of boosting.
 

-  * force-head-belly-independence (no)
+  * force-head-belly-independence ("no")

 
     Should we force the independence between the two levels of the
     detector (i.e. make an H+B detector)
 
     Should we force the independence between the two levels of the
     detector (i.e. make an H+B detector)


@@ -139,8 +142,8 @@ PARAMETERS
 
   * nb-levels (1)
 
 
   * nb-levels (1)
 

-    How many levels in the hierarchy, this is 2 for the JMLR paper
-    experiments.
+    How many levels in the hierarchy. This should be 2 for the JMLR
+    paper experiments.

 
   * proportion-for-train (0.5)
 
 
   * proportion-for-train (0.5)
 


@@ -207,7 +210,8 @@ PARAMETERS
 
     Should we display a progress bar.
 
 
     Should we display a progress bar.
 

-COMMANDS
+######################################################################
+## COMMANDS

 
    * open-pool
 
 
    * open-pool
 


@@ -245,6 +249,6 @@ COMMANDS
 
      Write PNG images of the scenes in the pool.
 
 
      Write PNG images of the scenes in the pool.
 

-  --
-  Francois Fleuret
-  October 2008
+--
+Francois Fleuret
+October 2008

diff --git a/detector.cc b/detector.cc
index 1c3ef23..358c66b 100644 (file)
--- a/detector.cc
+++ b/detector.cc
@@ -446,7 +446,6 @@ void Detector::read(istream *is) {
   _thresholds = new scalar_t[_nb_classifiers];
 
   for(int q = 0; q < _nb_classifiers; q++) {
   _thresholds = new scalar_t[_nb_classifiers];
 
   for(int q = 0; q < _nb_classifiers; q++) {

-    cout << "Read classifier " << q << endl;

     _pi_feature_families[q] = new PiFeatureFamily();
     _pi_feature_families[q]->read(is);
     _classifiers[q] = read_classifier(is);
     _pi_feature_families[q] = new PiFeatureFamily();
     _pi_feature_families[q]->read(is);
     _classifiers[q] = read_classifier(is);

diff --git a/materials.cc b/materials.cc
index 21f6790..64729db 100644 (file)
--- a/materials.cc
+++ b/materials.cc
@@ -171,7 +171,7 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool,
       if(detector) {
         int nb_features = 100;
         for(int f = 0; f < nb_features; f++) 
       if(detector) {
         int nb_features = 100;
         for(int f = 0; f < nb_features; f++) 

-          if(f == 0 || f ==50 || f  == 53) {
+          if(f == 0 || f == 50 || f  == 53) {

             int n_family, n_feature;
             if(f < nb_features/2) {
               n_family = 0;
             int n_family, n_feature;
             if(f < nb_features/2) {
               n_family = 0;


@@ -192,17 +192,6 @@ void write_pool_images_with_poses_and_referentials(LabelledImagePool *pool,
       u++;
     }
 
       u++;
     }
 

-    //         sprintf(buffer, "/tmp/image-%05d.png", i);
-    //         cout << "Writing " << buffer << endl;
-    //         result_sp.write_png(buffer);
-
-    //         if(global.write_tag_images) {
-    //           sprintf(buffer, "/tmp/image-%05d_tags.png", i);
-    //           cout << "Writing " << buffer << endl;
-    //           image->compute_rich_structure();
-    //           image->write_tag_png(buffer);
-    //         }
-

     pool->release_image(i);
   }
 
     pool->release_image(i);
   }