Ajout des tests sur HaarCascade

2018-08-13 11:29:33 +02:00 · 2018-08-13 11:29:33 +02:00 · 5ea3bd93c8
commit 5ea3bd93c8
parent 406e6d9b5d
54 changed files with 8111 additions and 1 deletions
--- a/ongoing/HaarCascade_tests/LICENSE
+++ b/ongoing/HaarCascade_tests/LICENSE
@ -0,0 +1,25 @@
+`createsamples.pl`: Copyright (c) 2008, Naotoshi Seo
+From: https://code.google.com/p/tutorial-haartraining/
+`mergevec.py`: Copyright (c) 2014, Blake Wulfe
+From: https://github.com/wulfebw/mergevec
+
+Copyright (c) 2013, Thorsten Ball. All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
--- a/ongoing/HaarCascade_tests/README.md
+++ b/ongoing/HaarCascade_tests/README.md
@ -0,0 +1,119 @@
+# Train your own OpenCV Haar classifier
+
+**Important**: This guide assumes you work with OpenCV 2.4.x. Since I no longer work with OpenCV, and don't have the time to keep up with changes and fixes, this guide is **unmaintained**. Pull requests will be merged of course, and if someone else wants commit access, feel free to ask!
+
+This repository aims to provide tools and information on training your own
+OpenCV Haar classifier.  Use it in conjunction with this blog post: [Train your own OpenCV Haar
+classifier](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html).
+
+
+
+## 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 this repository
+
+        git clone https://github.com/mrnugget/opencv-haar-classifier-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:
+
+         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. Use your finished classifier!
+
+        cd ~/opencv-2.4.9/samples/c
+        chmod +x build_all.sh
+        ./build_all.sh
+        ./facedetect --cascade="~/finished_classifier.xml"
+
+
+## Acknowledgements
+
+A huge thanks goes to Naotoshi Seo, who wrote the `mergevec.cpp` and
+`createsamples.cpp` tools and released them under the MIT licencse. His notes
+on OpenCV Haar training were a huge help. Thank you, Naotoshi!
+
+## References & Links:
+
+- [Naotoshi Seo - Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features)](http://note.sonots.com/SciSoftware/haartraining.html)
+- [Material for Naotoshi Seo's tutorial](https://code.google.com/p/tutorial-haartraining/)
+- [OpenCV Documentation - Cascade Classifier Training](http://docs.opencv.org/doc/user_guide/ug_traincascade.html)
--- a/ongoing/HaarCascade_tests/bin/createsamples.cpp
+++ b/ongoing/HaarCascade_tests/bin/createsamples.cpp
@ -0,0 +1,243 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+/*
+ * createsamples.cpp
+ *
+ * Create test/training samples
+ */
+
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h>
+
+#include <cvhaartraining.h>
+
+int main( int argc, char* argv[] )
+{
+    srand(time(NULL));
+    int i = 0;
+    char* nullname   = "(NULL)";
+    char* vecname    = NULL; /* .vec file name */
+    char* infoname   = NULL; /* file name with marked up image descriptions */
+    char* imagename  = NULL; /* single sample image */
+    char* bgfilename = NULL; /* background */
+    int num = 1000;
+    int bgcolor = 0;
+    int bgthreshold = 80;
+    int invert = 0;
+    int maxintensitydev = 40;
+    double maxxangle = 1.1;
+    double maxyangle = 1.1;
+    double maxzangle = 0.5;
+    int showsamples = 0;
+    /* the samples are adjusted to this scale in the sample preview window */
+    double scale = 4.0;
+    int width  = 24;
+    int height = 24;
+
+    if( argc == 1 )
+    {
+        printf( "Usage: %s\n  [-info <collection_file_name>]\n"
+                "  [-img <image_file_name>]\n"
+                "  [-vec <vec_file_name>]\n"
+                "  [-bg <background_file_name>]\n  [-num <number_of_samples = %d>]\n"
+                "  [-bgcolor <background_color = %d>]\n"
+                "  [-inv] [-randinv] [-bgthresh <background_color_threshold = %d>]\n"
+                "  [-maxidev <max_intensity_deviation = %d>]\n"
+                "  [-maxxangle <max_x_rotation_angle = %f>]\n"
+                "  [-maxyangle <max_y_rotation_angle = %f>]\n"
+                "  [-maxzangle <max_z_rotation_angle = %f>]\n"
+                "  [-show [<scale = %f>]]\n"
+                "  [-w <sample_width = %d>]\n  [-h <sample_height = %d>]\n",
+                argv[0], num, bgcolor, bgthreshold, maxintensitydev,
+                maxxangle, maxyangle, maxzangle, scale, width, height );
+        
+        return 0;
+    }
+
+    for( i = 1; i < argc; ++i )
+    {
+        if( !strcmp( argv[i], "-info" ) )
+        {
+            infoname = argv[++i];
+        }
+        else if( !strcmp( argv[i], "-img" ) )
+        {
+            imagename = argv[++i];
+        }
+        else if( !strcmp( argv[i], "-vec" ) )
+        {
+            vecname = argv[++i];
+        }
+        else if( !strcmp( argv[i], "-bg" ) )
+        {
+            bgfilename = argv[++i];
+        }
+        else if( !strcmp( argv[i], "-num" ) )
+        {
+            num = atoi( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-bgcolor" ) )
+        {
+            bgcolor = atoi( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-bgthresh" ) )
+        {
+            bgthreshold = atoi( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-inv" ) )
+        {
+            invert = 1;
+        }
+        else if( !strcmp( argv[i], "-randinv" ) )
+        {
+            invert = CV_RANDOM_INVERT;
+        }
+        else if( !strcmp( argv[i], "-maxidev" ) )
+        {
+            maxintensitydev = atoi( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-maxxangle" ) )
+        {
+            maxxangle = atof( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-maxyangle" ) )
+        {
+            maxyangle = atof( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-maxzangle" ) )
+        {
+            maxzangle = atof( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-show" ) )
+        {
+            showsamples = 1;
+            if( i+1 < argc && strlen( argv[i+1] ) > 0 && argv[i+1][0] != '-' )
+            {
+                double d;
+                d = strtod( argv[i+1], 0 );
+                if( d != -HUGE_VAL && d != HUGE_VAL && d > 0 ) scale = d;
+                ++i;
+            }
+        }
+        else if( !strcmp( argv[i], "-w" ) )
+        {
+            width = atoi( argv[++i] );
+        }
+        else if( !strcmp( argv[i], "-h" ) )
+        {
+            height = atoi( argv[++i] );
+        }
+    }
+    
+    printf( "Info file name: %s\n", ((infoname == NULL) ?   nullname : infoname ) );
+    printf( "Img file name: %s\n",  ((imagename == NULL) ?  nullname : imagename ) );
+    printf( "Vec file name: %s\n",  ((vecname == NULL) ?    nullname : vecname ) );
+    printf( "BG  file name: %s\n",  ((bgfilename == NULL) ? nullname : bgfilename ) );
+    printf( "Num: %d\n", num );
+    printf( "BG color: %d\n", bgcolor );
+    printf( "BG threshold: %d\n", bgthreshold );
+    printf( "Invert: %s\n", (invert == CV_RANDOM_INVERT) ? "RANDOM"
+                                : ( (invert) ? "TRUE" : "FALSE" ) );
+    printf( "Max intensity deviation: %d\n", maxintensitydev );
+    printf( "Max x angle: %g\n", maxxangle );
+    printf( "Max y angle: %g\n", maxyangle );
+    printf( "Max z angle: %g\n", maxzangle );
+    printf( "Show samples: %s\n", (showsamples) ? "TRUE" : "FALSE" );
+    if( showsamples )
+    {
+        printf( "Scale: %g\n", scale );
+    }
+    printf( "Width: %d\n", width );
+    printf( "Height: %d\n", height );
+
+    /* determine action */
+    if( imagename && vecname )
+    {
+        printf( "Create training samples from single image applying distortions...\n" );
+
+        cvCreateTrainingSamples( vecname, imagename, bgcolor, bgthreshold, bgfilename,
+                                 num, invert, maxintensitydev,
+                                 maxxangle, maxyangle, maxzangle,
+                                 showsamples, width, height );
+
+        printf( "Done\n" );
+    }
+    else if( imagename && bgfilename && infoname )
+    {
+        printf( "Create test samples from single image applying distortions...\n" );
+
+        cvCreateTestSamples( infoname, imagename, bgcolor, bgthreshold, bgfilename, num,
+            invert, maxintensitydev,
+            maxxangle, maxyangle, maxzangle, showsamples, width, height );
+
+        printf( "Done\n" );
+    }
+    else if( infoname && vecname )
+    {
+        int total;
+
+        printf( "Create training samples from images collection...\n" );
+
+        total = cvCreateTrainingSamplesFromInfo( infoname, vecname, num, showsamples,
+                                                 width, height );
+
+        printf( "Done. Created %d samples\n", total );
+    }
+    else if( vecname )
+    {
+        printf( "View samples from vec file (press ESC to exit)...\n" );
+
+        cvShowVecSamples( vecname, width, height, scale );
+
+        printf( "Done\n" );
+    }
+    else
+    {
+        printf( "Nothing to do\n" );
+    }
+
+    return 0;
+}
+
--- a/ongoing/HaarCascade_tests/bin/createsamples.pl
+++ b/ongoing/HaarCascade_tests/bin/createsamples.pl
@ -0,0 +1,79 @@
+#!/usr/bin/perl
+use File::Basename;
+use strict;
+##########################################################################
+# Create samples from an image applying distortions repeatedly 
+# (create many many samples from many images applying distortions)
+#
+#  perl createtrainsamples.pl <positives.dat> <negatives.dat> <vec_output_dir>
+#      [<totalnum = 7000>] [<createsample_command_options = ./createsamples -w 20 -h 20...>]
+#  ex) perl createtrainsamples.pl positives.dat negatives.dat samples
+#
+# Author: Naotoshi Seo
+# Date  : 09/12/2008 Add <totalnum> and <createsample_command_options> options
+# Date  : 06/02/2007
+# Date  : 03/12/2006
+#########################################################################
+my $cmd = './createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1 -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 20 -h 20';
+my $totalnum = 7000;
+my $tmpfile  = 'tmp';
+
+if ($#ARGV < 2) {
+    print "Usage: perl createtrainsamples.pl\n";
+    print "  <positives_collection_filename>\n";
+    print "  <negatives_collection_filename>\n";
+    print "  <output_dirname>\n";
+    print "  [<totalnum = " . $totalnum . ">]\n";
+    print "  [<createsample_command_options = '" . $cmd . "'>]\n";
+    exit;
+}
+my $positive  = $ARGV[0];
+my $negative  = $ARGV[1];
+my $outputdir = $ARGV[2];
+$totalnum     = $ARGV[3] if ($#ARGV > 2);
+$cmd          = $ARGV[4] if ($#ARGV > 3);
+
+open(POSITIVE, "< $positive");
+my @positives = <POSITIVE>;
+close(POSITIVE);
+
+open(NEGATIVE, "< $negative");
+my @negatives = <NEGATIVE>;
+close(NEGATIVE);
+
+# number of generated images from one image so that total will be $totalnum
+my $numfloor  = int($totalnum / $#positives);
+my $numremain = $totalnum - $numfloor * $#positives;
+
+# Get the directory name of positives
+my $first = $positives[0];
+my $last  = $positives[$#positives];
+while ($first ne $last) {
+    $first = dirname($first);
+    $last  = dirname($last);
+    if ( $first eq "" ) { last; }
+}
+my $imgdir = $first;
+my $imgdirlen = length($first);
+
+for (my $k = 0; $k < $#positives; $k++ ) {
+    my $img = $positives[$k];
+    my $num = ($k < $numremain) ? $numfloor + 1 : $numfloor;
+
+    # Pick up negative images randomly
+    my @localnegatives = ();
+    for (my $i = 0; $i < $num; $i++) {
+        my $ind = int(rand($#negatives));
+        push(@localnegatives, $negatives[$ind]);
+    }
+    open(TMP, "> $tmpfile");
+    print TMP @localnegatives;
+    close(TMP);
+    #system("cat $tmpfile");
+
+    !chomp($img);
+    my $vec = $outputdir . substr($img, $imgdirlen) . ".vec" ;
+    print "$cmd -img $img -bg $tmpfile -vec $vec -num $num" . "\n";
+    system("$cmd -img $img -bg $tmpfile -vec $vec -num $num");
+}
+unlink($tmpfile);
--- a/ongoing/HaarCascade_tests/classifier/.gitkeep
+++ b/ongoing/HaarCascade_tests/classifier/.gitkeep
--- a/ongoing/HaarCascade_tests/classifier/cascade.xml
+++ b/ongoing/HaarCascade_tests/classifier/cascade.xml
--- a/ongoing/HaarCascade_tests/classifier/params.xml
+++ b/ongoing/HaarCascade_tests/classifier/params.xml
@ -0,0 +1,19 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<params>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>40</height>
+  <width>80</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9900001287460327e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams></params>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage0.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage0.xml
@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage0>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-1.0557038784027100e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 5334943 -6.1255004256963730e-03</internalNodes>
+      <leafValues>
+        7.7242046594619751e-01 -7.1225577592849731e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 3406097 -9.1979682445526123e-02</internalNodes>
+      <leafValues>
+        8.5950666666030884e-01 -5.6599384546279907e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 5464794 -7.4063672218471766e-04</internalNodes>
+      <leafValues>
+        8.4704905748367310e-01 -5.1533699035644531e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 1824321 6.4185885712504387e-03</internalNodes>
+      <leafValues>
+        -5.3197431564331055e-01 7.3788273334503174e-01</leafValues></_></weakClassifiers></stage0>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage1.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage1.xml
@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage1>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.1398526430130005e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 6021714 -1.7777483910322189e-02</internalNodes>
+      <leafValues>
+        8.6206895112991333e-01 -4.7945204377174377e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 4145756 2.3743551224470139e-02</internalNodes>
+      <leafValues>
+        -4.3439662456512451e-01 8.2215088605880737e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 6265710 -6.0039879754185677e-03</internalNodes>
+      <leafValues>
+        7.5790786743164062e-01 -4.6309715509414673e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 5464794 -1.3670222833752632e-03</internalNodes>
+      <leafValues>
+        8.6037290096282959e-01 -4.0961334109306335e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 5134522 -9.9409371614456177e-03</internalNodes>
+      <leafValues>
+        7.8303009271621704e-01 -5.7429856061935425e-01</leafValues></_></weakClassifiers></stage1>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage10.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage10.xml
@ -0,0 +1,57 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage10>
+  <maxWeakCount>10</maxWeakCount>
+  <stageThreshold>-1.1790993213653564e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 19850 -1.0749980807304382e-01</internalNodes>
+      <leafValues>
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+        7.9198676347732544e-01 -2.6785668730735779e-01</leafValues></_>
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+      <leafValues>
+        -3.5887122154235840e-01 5.7739567756652832e-01</leafValues></_>
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+      <leafValues>
+        -2.0818111300468445e-01 8.6457198858261108e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -5.0708097219467163e-01 3.9419853687286377e-01</leafValues></_>
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+      <leafValues>
+        -2.5683295726776123e-01 7.1523237228393555e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        4.4412004947662354e-01 -4.7601011395454407e-01</leafValues></_>
+    <_>
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+    <_>
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+      <leafValues>
+        -2.9500880837440491e-01 7.6856881380081177e-01</leafValues></_></weakClassifiers></stage10>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage11.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage11.xml
@ -0,0 +1,62 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage11>
+  <maxWeakCount>11</maxWeakCount>
+  <stageThreshold>-1.2136906385421753e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+        -2.6329115033149719e-01 7.5061726570129395e-01</leafValues></_>
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+        4.5392939448356628e-01 -5.2305990457534790e-01</leafValues></_>
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+      <leafValues>
+        -3.0884775519371033e-01 7.6920127868652344e-01</leafValues></_>
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+        6.6684818267822266e-01 -3.6782276630401611e-01</leafValues></_>
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+      <leafValues>
+        -2.5124689936637878e-01 7.3933559656143188e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.9734661579132080e-01 6.2891435623168945e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        2.1867121756076813e-01 -8.0109864473342896e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.5235512256622314e-01 6.1455774307250977e-01</leafValues></_>
+    <_>
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+        0 -1 3247423 1.1386678088456392e-03</internalNodes>
+      <leafValues>
+        -2.6051363348960876e-01 6.1844825744628906e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        8.1104975938796997e-01 -2.4966773390769958e-01</leafValues></_></weakClassifiers></stage11>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage12.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage12.xml
@ -0,0 +1,47 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage12>
+  <maxWeakCount>8</maxWeakCount>
+  <stageThreshold>-8.9855605363845825e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 4446480 -2.1266389638185501e-02</internalNodes>
+      <leafValues>
+        7.5962662696838379e-01 -3.3781963586807251e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -7.4217349290847778e-01 3.7758478522300720e-01</leafValues></_>
+    <_>
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+        0 -1 7270666 2.3525862488895655e-03</internalNodes>
+      <leafValues>
+        -2.7755615115165710e-01 8.4097403287887573e-01</leafValues></_>
+    <_>
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+        0 -1 4469812 -1.0660489089787006e-02</internalNodes>
+      <leafValues>
+        8.9270591735839844e-01 -2.4468661844730377e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.1090390682220459e-01 6.7939525842666626e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        8.7543725967407227e-01 -1.7979109287261963e-01</leafValues></_>
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+      <leafValues>
+        5.6206727027893066e-01 -3.6763671040534973e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        5.2464032173156738e-01 -3.5799154639244080e-01</leafValues></_></weakClassifiers></stage12>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage13.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage13.xml
@ -0,0 +1,57 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage13>
+  <maxWeakCount>10</maxWeakCount>
+  <stageThreshold>-1.2344558238983154e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 64580 5.7471403852105141e-04</internalNodes>
+      <leafValues>
+        4.3063583970069885e-01 -9.0740740299224854e-01</leafValues></_>
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+        5.3049582242965698e-01 -3.5649526119232178e-01</leafValues></_>
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+        5.5134570598602295e-01 -3.3935609459877014e-01</leafValues></_>
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+        8.3912080526351929e-01 -2.0921953022480011e-01</leafValues></_>
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+      <leafValues>
+        6.9258832931518555e-01 -2.9845607280731201e-01</leafValues></_>
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+      <leafValues>
+        -2.2013919055461884e-01 7.1406865119934082e-01</leafValues></_>
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+        0 -1 3248862 5.8889649808406830e-03</internalNodes>
+      <leafValues>
+        3.0268713831901550e-01 -6.7810201644897461e-01</leafValues></_>
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+      <leafValues>
+        6.4427900314331055e-01 -2.9659023880958557e-01</leafValues></_></weakClassifiers></stage13>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage14.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage14.xml
@ -0,0 +1,62 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage14>
+  <maxWeakCount>11</maxWeakCount>
+  <stageThreshold>-1.6017471551895142e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+        0 -1 2407103 6.3523324206471443e-03</internalNodes>
+      <leafValues>
+        -1.3568985462188721e-01 7.1784234046936035e-01</leafValues></_>
+    <_>
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+        0 -1 5321682 -4.0579847991466522e-03</internalNodes>
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+        3.7964171171188354e-01 -5.0524675846099854e-01</leafValues></_>
+    <_>
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+        -2.3831744492053986e-01 6.9320493936538696e-01</leafValues></_>
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+        -4.3946433067321777e-01 4.4063070416450500e-01</leafValues></_>
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+        5.6555390357971191e-01 -2.9760795831680298e-01</leafValues></_>
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+        7.6978367567062378e-01 -2.0543955266475677e-01</leafValues></_>
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+        0 -1 5358305 -5.2268290892243385e-04</internalNodes>
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+        6.2546741962432861e-01 -2.8095382452011108e-01</leafValues></_>
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+        6.9004940986633301e-01 -2.2914317250251770e-01</leafValues></_>
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+        4.0621888637542725e-01 -4.1578367352485657e-01</leafValues></_>
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+        5.0994277000427246e-01 -3.8216280937194824e-01</leafValues></_>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage15.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage15.xml
@ -0,0 +1,72 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage15>
+  <maxWeakCount>13</maxWeakCount>
+  <stageThreshold>-1.6141852140426636e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+        -3.7164750695228577e-01 5.5102038383483887e-01</leafValues></_>
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+        7.6413309574127197e-01 -1.8512850999832153e-01</leafValues></_>
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+        -1.2920907139778137e-01 9.3113201856613159e-01</leafValues></_>
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+        5.3158652782440186e-01 -2.8859263658523560e-01</leafValues></_>
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+        2.5252798199653625e-01 -9.6875768899917603e-01</leafValues></_>
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+        0 -1 4080349 -2.7588799595832825e-02</internalNodes>
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+        6.8124520778656006e-01 -2.3295682668685913e-01</leafValues></_>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage16.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage16.xml
@ -0,0 +1,52 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage16>
+  <maxWeakCount>9</maxWeakCount>
+  <stageThreshold>-1.1815985441207886e+00</stageThreshold>
+  <weakClassifiers>
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+        5.7512277364730835e-01 -3.7197539210319519e-01</leafValues></_>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage17.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage17.xml
@ -0,0 +1,67 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage17>
+  <maxWeakCount>12</maxWeakCount>
+  <stageThreshold>-9.4175177812576294e-01</stageThreshold>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage18.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage18.xml
@ -0,0 +1,62 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage18>
+  <maxWeakCount>11</maxWeakCount>
+  <stageThreshold>-7.9091215133666992e-01</stageThreshold>
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+        4.5807263255119324e-01 -3.0947107076644897e-01</leafValues></_>
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+        6.8682104349136353e-01 -2.1287234127521515e-01</leafValues></_>
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+      <leafValues>
+        -2.1554158627986908e-01 6.7852944135665894e-01</leafValues></_>
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+      <leafValues>
+        -2.2545242309570312e-01 6.4780122041702271e-01</leafValues></_>
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+      <leafValues>
+        -1.8552508950233459e-01 7.5173664093017578e-01</leafValues></_>
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+      <leafValues>
+        6.8883955478668213e-01 -2.2326049208641052e-01</leafValues></_>
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+      <leafValues>
+        -1.8941468000411987e-01 7.8270876407623291e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -4.0309405326843262e-01 4.2827814817428589e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        1.8223567306995392e-01 -8.4566026926040649e-01</leafValues></_></weakClassifiers></stage18>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage19.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage19.xml
@ -0,0 +1,72 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage19>
+  <maxWeakCount>13</maxWeakCount>
+  <stageThreshold>-1.2151402235031128e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+      <leafValues>
+        -1.8851435184478760e-01 6.8961203098297119e-01</leafValues></_>
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+      <leafValues>
+        6.8834054470062256e-01 -2.2686544060707092e-01</leafValues></_>
+    <_>
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+        0 -1 5435622 -7.2470661252737045e-03</internalNodes>
+      <leafValues>
+        3.1112310290336609e-01 -6.0889953374862671e-01</leafValues></_>
+    <_>
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+        6.6673213243484497e-01 -2.5371745228767395e-01</leafValues></_>
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+      <leafValues>
+        -3.4340032935142517e-01 4.5103815197944641e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.9627179503440857e-01 5.1694852113723755e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -2.1723985671997070e-01 6.9305503368377686e-01</leafValues></_>
+    <_>
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+        0 -1 6619242 3.6975359544157982e-03</internalNodes>
+      <leafValues>
+        -3.1726816296577454e-01 4.9445587396621704e-01</leafValues></_>
+    <_>
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+        0 -1 6315032 -5.5430391803383827e-03</internalNodes>
+      <leafValues>
+        5.7705235481262207e-01 -3.7490817904472351e-01</leafValues></_>
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+      <leafValues>
+        -3.6123612523078918e-01 4.7430756688117981e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        1.8285752832889557e-01 -8.8321834802627563e-01</leafValues></_>
+    <_>
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+        0 -1 7089734 1.2511686654761434e-03</internalNodes>
+      <leafValues>
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+    <_>
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+        6.4223343133926392e-01 -2.9110759496688843e-01</leafValues></_></weakClassifiers></stage19>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage2.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage2.xml
@ -0,0 +1,47 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage2>
+  <maxWeakCount>8</maxWeakCount>
+  <stageThreshold>-9.4922059774398804e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 2396840 1.0431748814880848e-02</internalNodes>
+      <leafValues>
+        -3.4243175387382507e-01 8.5138541460037231e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.1060457229614258e-01 7.9508054256439209e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -2.9358071088790894e-01 9.7165352106094360e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        6.3514846563339233e-01 -4.9989247322082520e-01</leafValues></_>
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+        6.4996314048767090e-01 -4.6733775734901428e-01</leafValues></_>
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+      <leafValues>
+        6.5466618537902832e-01 -5.3771013021469116e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.3722141385078430e-01 7.8262948989868164e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        7.3387306928634644e-01 -3.6649671196937561e-01</leafValues></_></weakClassifiers></stage2>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage3.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage3.xml
@ -0,0 +1,42 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage3>
+  <maxWeakCount>7</maxWeakCount>
+  <stageThreshold>-1.4982649087905884e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+      <leafValues>
+        -4.5937961339950562e-01 7.7031421661376953e-01</leafValues></_>
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+        8.5675507783889771e-01 -3.0365657806396484e-01</leafValues></_>
+    <_>
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+        6.2530416250228882e-01 -4.0575742721557617e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -5.4163134098052979e-01 5.3285014629364014e-01</leafValues></_>
+    <_>
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+        0 -1 5363514 1.7069303430616856e-03</internalNodes>
+      <leafValues>
+        -3.0198717117309570e-01 9.2841923236846924e-01</leafValues></_>
+    <_>
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+        0 -1 4468048 1.9969230517745018e-02</internalNodes>
+      <leafValues>
+        -3.6788323521614075e-01 7.8370809555053711e-01</leafValues></_>
+    <_>
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+        0 -1 5363514 -1.0842503979802132e-03</internalNodes>
+      <leafValues>
+        8.2461130619049072e-01 -2.7838116884231567e-01</leafValues></_></weakClassifiers></stage3>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage4.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage4.xml
@ -0,0 +1,42 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage4>
+  <maxWeakCount>7</maxWeakCount>
+  <stageThreshold>-1.4942907094955444e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 6097684 1.9832536578178406e-02</internalNodes>
+      <leafValues>
+        -5.1601421833038330e-01 6.6473990678787231e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        5.6847703456878662e-01 -5.2866727113723755e-01</leafValues></_>
+    <_>
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+        0 -1 3484578 1.0051997378468513e-02</internalNodes>
+      <leafValues>
+        -2.5192338228225708e-01 7.9386711120605469e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -3.6736944317817688e-01 5.9266370534896851e-01</leafValues></_>
+    <_>
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+        0 -1 5239092 -1.2895530089735985e-02</internalNodes>
+      <leafValues>
+        6.8578672409057617e-01 -3.1832396984100342e-01</leafValues></_>
+    <_>
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+        0 -1 5467791 4.8367399722337723e-04</internalNodes>
+      <leafValues>
+        -3.4358265995979309e-01 7.5980979204177856e-01</leafValues></_>
+    <_>
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+        0 -1 4923376 -2.5067629758268595e-04</internalNodes>
+      <leafValues>
+        7.2300755977630615e-01 -2.7180227637290955e-01</leafValues></_></weakClassifiers></stage4>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage5.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage5.xml
@ -0,0 +1,42 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage5>
+  <maxWeakCount>7</maxWeakCount>
+  <stageThreshold>-8.9513111114501953e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+        0 -1 2422096 7.1755228564143181e-03</internalNodes>
+      <leafValues>
+        -3.4664765000343323e-01 7.1523916721343994e-01</leafValues></_>
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+      <leafValues>
+        4.4286754727363586e-01 -6.5785908699035645e-01</leafValues></_>
+    <_>
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+        0 -1 4192168 -3.6444932222366333e-02</internalNodes>
+      <leafValues>
+        6.2337446212768555e-01 -4.2781525850296021e-01</leafValues></_>
+    <_>
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+        0 -1 5579292 -1.3623120030388236e-03</internalNodes>
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+        7.0154279470443726e-01 -3.5026982426643372e-01</leafValues></_>
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+        0 -1 7273277 -7.4735237285494804e-03</internalNodes>
+      <leafValues>
+        7.3942059278488159e-01 -2.7020177245140076e-01</leafValues></_>
+    <_>
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+        0 -1 5462082 -2.7533981483429670e-04</internalNodes>
+      <leafValues>
+        5.3899711370468140e-01 -4.1663372516632080e-01</leafValues></_>
+    <_>
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+        0 -1 5989817 -7.2895870544016361e-03</internalNodes>
+      <leafValues>
+        5.0333309173583984e-01 -4.8206123709678650e-01</leafValues></_></weakClassifiers></stage5>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage6.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage6.xml
@ -0,0 +1,42 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage6>
+  <maxWeakCount>7</maxWeakCount>
+  <stageThreshold>-8.8963723182678223e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+        0 -1 3406097 -8.9364483952522278e-02</internalNodes>
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+        7.3221755027770996e-01 -4.6583852171897888e-01</leafValues></_>
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+      <leafValues>
+        -4.1563612222671509e-01 6.9622164964675903e-01</leafValues></_>
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+        7.4425798654556274e-01 -3.7180989980697632e-01</leafValues></_>
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+        6.4965981245040894e-01 -3.6152300238609314e-01</leafValues></_>
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+        0 -1 5462082 -4.6658696373924613e-04</internalNodes>
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+        6.6287922859191895e-01 -3.7917545437812805e-01</leafValues></_>
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+        0 -1 5426769 3.5744084743782878e-04</internalNodes>
+      <leafValues>
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+        -7.3581212759017944e-01 3.5141283273696899e-01</leafValues></_></weakClassifiers></stage6>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage7.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage7.xml
@ -0,0 +1,57 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage7>
+  <maxWeakCount>10</maxWeakCount>
+  <stageThreshold>-1.5281771421432495e+00</stageThreshold>
+  <weakClassifiers>
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+        -3.9255014061927795e-01 7.4722838401794434e-01</leafValues></_>
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+        5.4928964376449585e-01 -3.9800989627838135e-01</leafValues></_>
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+        -2.4620300531387329e-01 8.8145399093627930e-01</leafValues></_>
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+        -7.3674768209457397e-01 3.2037591934204102e-01</leafValues></_>
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+        6.4166831970214844e-01 -3.6269333958625793e-01</leafValues></_>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage8.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage8.xml
@ -0,0 +1,42 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage8>
+  <maxWeakCount>7</maxWeakCount>
+  <stageThreshold>-8.3137303590774536e-01</stageThreshold>
+  <weakClassifiers>
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+        6.9363540410995483e-01 -3.6106982827186584e-01</leafValues></_>
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+        5.5970847606658936e-01 -4.2648985981941223e-01</leafValues></_>
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+        0 -1 6923954 -2.5097420439124107e-04</internalNodes>
+      <leafValues>
+        5.0482910871505737e-01 -3.6784309148788452e-01</leafValues></_>
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+        0 -1 5161851 -1.9997386261820793e-03</internalNodes>
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+        5.9490412473678589e-01 -3.5654053092002869e-01</leafValues></_>
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+</opencv_storage>
--- a/ongoing/HaarCascade_tests/classifier/stage9.xml
+++ b/ongoing/HaarCascade_tests/classifier/stage9.xml
@ -0,0 +1,52 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage9>
+  <maxWeakCount>9</maxWeakCount>
+  <stageThreshold>-1.0093878507614136e+00</stageThreshold>
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+    <_>
+      <internalNodes>
+        0 -1 2157790 1.7119385302066803e-02</internalNodes>
+      <leafValues>
+        -3.2808634638786316e-01 6.9305676221847534e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 5464817 -1.4041164831724018e-04</internalNodes>
+      <leafValues>
+        5.0532776117324829e-01 -5.0529742240905762e-01</leafValues></_></weakClassifiers></stage9>
+</opencv_storage>
--- a/ongoing/HaarCascade_tests/haar.py~
+++ b/ongoing/HaarCascade_tests/haar.py~
--- a/ongoing/HaarCascade_tests/negative_images/.gitkeep
+++ b/ongoing/HaarCascade_tests/negative_images/.gitkeep
--- a/ongoing/HaarCascade_tests/negatives.txt
+++ b/ongoing/HaarCascade_tests/negatives.txt
@ -0,0 +1,134 @@
+./negative_images/train_image_004.png
+./negative_images/train_image_000.png
+./negative_images/train_image_001.png
+./negative_images/train_image_005.png
+./negative_images/train_image_003.png
+./negative_images/train_image_002.png
+./negative_images/105.jpg
+./negative_images/246.jpg
+./negative_images/178.jpg
+./negative_images/159.jpg
+./negative_images/108.jpg
+./negative_images/258.jpg
+./negative_images/172.jpg
+./negative_images/186.jpg
+./negative_images/099.jpg
+./negative_images/128.jpg
+./negative_images/175.jpg
+./negative_images/153.jpg
+./negative_images/093.jpg
+./negative_images/254.jpg
+./negative_images/147.jpg
+./negative_images/173.jpg
+./negative_images/185.jpg
+./negative_images/126.jpg
+./negative_images/158.jpg
+./negative_images/084.jpg
+./negative_images/156.jpg
+./negative_images/103.jpg
+./negative_images/091.jpg
+./negative_images/167.jpg
+./negative_images/100.jpg
+./negative_images/116.jpg
+./negative_images/123.jpg
+./negative_images/134.jpg
+./negative_images/252.jpg
+./negative_images/155.jpg
+./negative_images/127.jpg
+./negative_images/145.jpg
+./negative_images/109.jpg
+./negative_images/144.jpg
+./negative_images/242.jpg
+./negative_images/149.jpg
+./negative_images/251.jpg
+./negative_images/141.jpg
+./negative_images/255.jpg
+./negative_images/179.jpg
+./negative_images/132.jpg
+./negative_images/110.jpg
+./negative_images/096.jpg
+./negative_images/140.jpg
+./negative_images/092.jpg
+./negative_images/148.jpg
+./negative_images/261.jpg
+./negative_images/163.jpg
+./negative_images/165.jpg
+./negative_images/090.jpg
+./negative_images/138.jpg
+./negative_images/130.jpg
+./negative_images/174.jpg
+./negative_images/131.jpg
+./negative_images/170.jpg
+./negative_images/243.jpg
+./negative_images/168.jpg
+./negative_images/082.jpg
+./negative_images/087.jpg
+./negative_images/259.jpg
+./negative_images/248.jpg
+./negative_images/094.jpg
+./negative_images/121.jpg
+./negative_images/135.jpg
+./negative_images/169.jpg
+./negative_images/136.jpg
+./negative_images/102.jpg
+./negative_images/098.jpg
+./negative_images/187.jpg
+./negative_images/256.jpg
+./negative_images/146.jpg
+./negative_images/177.jpg
+./negative_images/088.jpg
+./negative_images/161.jpg
+./negative_images/097.jpg
+./negative_images/095.jpg
+./negative_images/083.jpg
+./negative_images/085.jpg
+./negative_images/112.jpg
+./negative_images/143.jpg
+./negative_images/152.jpg
+./negative_images/117.jpg
+./negative_images/180.jpg
+./negative_images/107.jpg
+./negative_images/247.jpg
+./negative_images/114.jpg
+./negative_images/124.jpg
+./negative_images/253.jpg
+./negative_images/244.jpg
+./negative_images/249.jpg
+./negative_images/142.jpg
+./negative_images/133.jpg
+./negative_images/250.jpg
+./negative_images/151.jpg
+./negative_images/106.jpg
+./negative_images/157.jpg
+./negative_images/162.jpg
+./negative_images/241.jpg
+./negative_images/101.jpg
+./negative_images/160.jpg
+./negative_images/137.jpg
+./negative_images/176.jpg
+./negative_images/115.jpg
+./negative_images/164.jpg
+./negative_images/245.jpg
+./negative_images/113.jpg
+./negative_images/118.jpg
+./negative_images/262.jpg
+./negative_images/183.jpg
+./negative_images/119.jpg
+./negative_images/150.jpg
+./negative_images/086.jpg
+./negative_images/122.jpg
+./negative_images/104.jpg
+./negative_images/125.jpg
+./negative_images/184.jpg
+./negative_images/257.jpg
+./negative_images/171.jpg
+./negative_images/260.jpg
+./negative_images/139.jpg
+./negative_images/182.jpg
+./negative_images/111.jpg
+./negative_images/166.jpg
+./negative_images/129.jpg
+./negative_images/120.jpg
+./negative_images/154.jpg
+./negative_images/181.jpg
+./negative_images/089.jpg
--- a/ongoing/HaarCascade_tests/negatives.txt~
+++ b/ongoing/HaarCascade_tests/negatives.txt~
@ -0,0 +1,6 @@
+./negative_images/train_image_004.png
+./negative_images/train_image_000.png
+./negative_images/train_image_001.png
+./negative_images/train_image_005.png
+./negative_images/train_image_003.png
+./negative_images/train_image_002.png
--- a/ongoing/HaarCascade_tests/opencv-haar-classifier-training
+++ b/ongoing/HaarCascade_tests/opencv-haar-classifier-training
@ -1 +0,0 @@
-Subproject commit e357b150ad98cc975e65d24b6e866c2d269ad29e
--- a/ongoing/HaarCascade_tests/positive_images/.gitkeep
+++ b/ongoing/HaarCascade_tests/positive_images/.gitkeep
--- a/ongoing/HaarCascade_tests/positives.txt
+++ b/ongoing/HaarCascade_tests/positives.txt
@ -0,0 +1,14 @@
+./positive_images/192.png
+./positive_images/224.png
+./positive_images/160.png
+./positive_images/000.png
+./positive_images/128.png
+./positive_images/256.png
+./positive_images/032.png
+./positive_images/288.png
+./positive_images/064.png
+./positive_images/096.png
+./positive_images/012.jpg
+./positive_images/011.jpg
+./positive_images/001.jpg
+./positive_images/000.jpg
--- a/ongoing/HaarCascade_tests/positives.txt~
+++ b/ongoing/HaarCascade_tests/positives.txt~
@ -0,0 +1,10 @@
+./positive_images/192.png
+./positive_images/224.png
+./positive_images/160.png
+./positive_images/000.png
+./positive_images/128.png
+./positive_images/256.png
+./positive_images/032.png
+./positive_images/288.png
+./positive_images/064.png
+./positive_images/096.png
--- a/ongoing/HaarCascade_tests/samples.vec
+++ b/ongoing/HaarCascade_tests/samples.vec
--- a/ongoing/HaarCascade_tests/samples/.gitkeep
+++ b/ongoing/HaarCascade_tests/samples/.gitkeep
--- a/ongoing/HaarCascade_tests/samples/000.png.vec
+++ b/ongoing/HaarCascade_tests/samples/000.png.vec
--- a/ongoing/HaarCascade_tests/samples/001.jpg.vec
+++ b/ongoing/HaarCascade_tests/samples/001.jpg.vec
--- a/ongoing/HaarCascade_tests/samples/011.jpg.vec
+++ b/ongoing/HaarCascade_tests/samples/011.jpg.vec
--- a/ongoing/HaarCascade_tests/samples/012.jpg.vec
+++ b/ongoing/HaarCascade_tests/samples/012.jpg.vec
--- a/ongoing/HaarCascade_tests/samples/032.png.vec
+++ b/ongoing/HaarCascade_tests/samples/032.png.vec
--- a/ongoing/HaarCascade_tests/samples/064.png.vec
+++ b/ongoing/HaarCascade_tests/samples/064.png.vec
--- a/ongoing/HaarCascade_tests/samples/096.png.vec
+++ b/ongoing/HaarCascade_tests/samples/096.png.vec
--- a/ongoing/HaarCascade_tests/samples/128.png.vec
+++ b/ongoing/HaarCascade_tests/samples/128.png.vec
--- a/ongoing/HaarCascade_tests/samples/160.png.vec
+++ b/ongoing/HaarCascade_tests/samples/160.png.vec
--- a/ongoing/HaarCascade_tests/samples/192.png.vec
+++ b/ongoing/HaarCascade_tests/samples/192.png.vec
--- a/ongoing/HaarCascade_tests/samples/224.png.vec
+++ b/ongoing/HaarCascade_tests/samples/224.png.vec
--- a/ongoing/HaarCascade_tests/samples/256.png.vec
+++ b/ongoing/HaarCascade_tests/samples/256.png.vec
--- a/ongoing/HaarCascade_tests/samples/288.png.vec
+++ b/ongoing/HaarCascade_tests/samples/288.png.vec
--- a/ongoing/HaarCascade_tests/tools/mergevec.py
+++ b/ongoing/HaarCascade_tests/tools/mergevec.py
@ -0,0 +1,171 @@
+###############################################################################
+# Copyright (c) 2014, Blake Wulfe
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+###############################################################################
+
+"""
+File: mergevec.py 
+Author: blake.w.wulfe@gmail.com 
+Date: 6/13/2014
+File Description:
+
+	This file contains a function that merges .vec files called "merge_vec_files". 
+	I made it as a replacement for mergevec.cpp (created by Naotoshi Seo. 
+	See: http://note.sonots.com/SciSoftware/haartraining/mergevec.cpp.html) 
+	in order to avoid recompiling openCV with mergevec.cpp. 
+
+	To use the function:
+	(1) Place all .vec files to be merged in a single directory (vec_directory).
+	(2) Navigate to this file in your CLI (terminal or cmd) and type "python mergevec.py -v your_vec_directory -o your_output_filename".
+
+		The first argument (-v) is the name of the directory containing the .vec files
+		The second argument (-o) is the name of the output file
+
+	To test the output of the function:
+	(1) Install openCV.
+	(2) Navigate to the output file in your CLI (terminal or cmd).
+	(2) Type "opencv_createsamples -w img_width -h img_height -vec output_filename". 
+		This should show the .vec files in sequence.
+
+"""
+
+import sys
+import glob
+import struct
+import argparse
+import traceback
+
+
+def exception_response(e):
+	exc_type, exc_value, exc_traceback = sys.exc_info()
+	lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
+	for line in lines:
+		print(line)
+
+def get_args():
+	parser = argparse.ArgumentParser()
+	parser.add_argument('-v', dest='vec_directory')
+	parser.add_argument('-o', dest='output_filename')
+	args = parser.parse_args()
+	return (args.vec_directory, args.output_filename)
+
+def merge_vec_files(vec_directory, output_vec_file):
+	"""
+	Iterates throught the .vec files in a directory and combines them. 
+
+	(1) Iterates through files getting a count of the total images in the .vec files
+	(2) checks that the image sizes in all files are the same
+
+	The format of a .vec file is:
+
+	4 bytes denoting number of total images (int)
+	4 bytes denoting size of images (int)
+	2 bytes denoting min value (short)
+	2 bytes denoting max value (short)
+
+	ex: 	6400 0000 4605 0000 0000 0000
+
+		hex		6400 0000  	4605 0000 		0000 		0000
+			   	# images  	size of h * w		min		max
+		dec	    	100     	1350			0 		0
+	
+	:type vec_directory: string
+	:param vec_directory: Name of the directory containing .vec files to be combined. 
+				Do not end with slash. Ex: '/Users/username/Documents/vec_files'
+
+	:type output_vec_file: string
+	:param output_vec_file: Name of aggregate .vec file for output. 
+		Ex: '/Users/username/Documents/aggregate_vec_file.vec'
+
+	"""
+	
+	# Check that the .vec directory does not end in '/' and if it does, remove it.
+	if vec_directory.endswith('/'):
+		vec_directory = vec_directory[:-1]
+	# Get .vec files
+	files = glob.glob('{0}/*.vec'.format(vec_directory))
+
+	# Check to make sure there are .vec files in the directory
+	if len(files) <= 0:
+		print('Vec files to be merged could not be found from directory: {0}'.format(vec_directory))
+		sys.exit(1)
+	# Check to make sure there are more than one .vec files
+	if len(files) == 1:
+		print('Only 1 vec file was found in directory: {0}. Cannot merge a single file.'.format(vec_directory))
+		sys.exit(1)
+		
+
+	# Get the value for the first image size
+	prev_image_size = 0
+	try:
+		with open(files[0], 'rb') as vecfile:
+			content = ''.join(str(line) for line in vecfile.readlines())
+			val = struct.unpack('<iihh', content[:12])
+			prev_image_size = val[1]
+	except IOError as e:
+		print('An IO error occured while processing the file: {0}'.format(f))
+		exception_response(e)
+
+
+	# Get the total number of images
+	total_num_images = 0
+	for f in files:
+		try:
+			with open(f, 'rb') as vecfile:	
+				content = ''.join(str(line) for line in vecfile.readlines())
+				val = struct.unpack('<iihh', content[:12])
+				num_images = val[0]
+				image_size = val[1]
+				if image_size != prev_image_size:
+					err_msg = """The image sizes in the .vec files differ. These values must be the same. \n The image size of file {0}: {1}\n 
+						The image size of previous files: {0}""".format(f, image_size, prev_image_size)
+					sys.exit(err_msg)
+
+				total_num_images += num_images
+		except IOError as e:
+			print('An IO error occured while processing the file: {0}'.format(f))
+			exception_response(e)
+
+	
+	# Iterate through the .vec files, writing their data (not the header) to the output file
+	# '<iihh' means 'little endian, int, int, short, short'
+	header = struct.pack('<iihh', total_num_images, image_size, 0, 0)
+	try:
+		with open(output_vec_file, 'wb') as outputfile:
+			outputfile.write(header)
+
+			for f in files:
+				with open(f, 'rb') as vecfile:
+					content = ''.join(str(line) for line in vecfile.readlines())
+					data = content[12:]
+					outputfile.write(data)
+	except Exception as e:
+		exception_response(e)
+
+
+if __name__ == '__main__':
+	vec_directory, output_filename = get_args()
+	if not vec_directory:
+		sys.exit('mergvec requires a directory of vec files. Call mergevec.py with -v /your_vec_directory')
+	if not output_filename:
+		sys.exit('mergevec requires an output filename. Call mergevec.py with -o your_output_filename')
+
+	merge_vec_files(vec_directory, output_filename)
+
--- a/ongoing/HaarCascade_tests/trained_classifiers/banana_classifier.xml
+++ b/ongoing/HaarCascade_tests/trained_classifiers/banana_classifier.xml
--- a/ongoing/HaarCascade_tests/trained_classifiers/haar.py
+++ b/ongoing/HaarCascade_tests/trained_classifiers/haar.py
@ -0,0 +1,14 @@
+import numpy as np
+import cv2 as cv
+face_cascade = cv.CascadeClassifier('banana_classifier.xml')
+img = cv.imread('000.jpg')
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+for (x,y,w,h) in faces:
+    print("hello")
+    cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
+    roi_gray = gray[y:y+h, x:x+w]
+    roi_color = img[y:y+h, x:x+w]
+cv.imshow('img',img)
+cv.waitKey(0)
+cv.destroyAllWindows()
--- a/ongoing/HaarCascade_tests/trained_classifiers/haar.py~
+++ b/ongoing/HaarCascade_tests/trained_classifiers/haar.py~
@ -0,0 +1,13 @@
+import numpy as np
+import cv2 as cv
+face_cascade = cv.CascadeClassifier('banana_classifier.xml')
+img = cv.imread('000.jpg')
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+for (x,y,w,h) in faces:
+    cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
+    roi_gray = gray[y:y+h, x:x+w]
+    roi_color = img[y:y+h, x:x+w]
+cv.imshow('img',img)
+cv.waitKey(0)
+cv.destroyAllWindows()
				`@ -1 +0,0 @@`
				`Subproject commit e357b150ad98cc975e65d24b6e866c2d269ad29e`