--- /dev/null
+/*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*/
+
+/*
+ * haartraining.cpp
+ *
+ * Train cascade classifier
+ */
+
+#include <cstdio>
+#include <cstring>
+#include <cstdlib>
+
+#include "cvhaartraining.h"
+
+int main( int argc, char* argv[] )
+{
+ int i = 0;
+ char* nullname = (char*)"(NULL)";
+
+ char* vecname = NULL;
+ char* dirname = NULL;
+ char* bgname = NULL;
+
+ bool bg_vecfile = false;
+ int npos = 2000;
+ int nneg = 2000;
+ int nstages = 14;
+ int mem = 200;
+ int nsplits = 1;
+ float minhitrate = 0.995F;
+ float maxfalsealarm = 0.5F;
+ float weightfraction = 0.95F;
+ int mode = 0;
+ int symmetric = 1;
+ int equalweights = 0;
+ int width = 24;
+ int height = 24;
+ const char* boosttypes[] = { "DAB", "RAB", "LB", "GAB" };
+ int boosttype = 3;
+ const char* stumperrors[] = { "misclass", "gini", "entropy" };
+ int stumperror = 0;
+ int maxtreesplits = 0;
+ int minpos = 500;
+
+ if( argc == 1 )
+ {
+ printf( "Usage: %s\n -data <dir_name>\n"
+ " -vec <vec_file_name>\n"
+ " -bg <background_file_name>\n"
+ " [-bg-vecfile]\n"
+ " [-npos <number_of_positive_samples = %d>]\n"
+ " [-nneg <number_of_negative_samples = %d>]\n"
+ " [-nstages <number_of_stages = %d>]\n"
+ " [-nsplits <number_of_splits = %d>]\n"
+ " [-mem <memory_in_MB = %d>]\n"
+ " [-sym (default)] [-nonsym]\n"
+ " [-minhitrate <min_hit_rate = %f>]\n"
+ " [-maxfalsealarm <max_false_alarm_rate = %f>]\n"
+ " [-weighttrimming <weight_trimming = %f>]\n"
+ " [-eqw]\n"
+ " [-mode <BASIC (default) | CORE | ALL>]\n"
+ " [-w <sample_width = %d>]\n"
+ " [-h <sample_height = %d>]\n"
+ " [-bt <DAB | RAB | LB | GAB (default)>]\n"
+ " [-err <misclass (default) | gini | entropy>]\n"
+ " [-maxtreesplits <max_number_of_splits_in_tree_cascade = %d>]\n"
+ " [-minpos <min_number_of_positive_samples_per_cluster = %d>]\n",
+ argv[0], npos, nneg, nstages, nsplits, mem,
+ minhitrate, maxfalsealarm, weightfraction, width, height,
+ maxtreesplits, minpos );
+
+ return 0;
+ }
+
+ for( i = 1; i < argc; i++ )
+ {
+ if( !strcmp( argv[i], "-data" ) )
+ {
+ dirname = argv[++i];
+ }
+ else if( !strcmp( argv[i], "-vec" ) )
+ {
+ vecname = argv[++i];
+ }
+ else if( !strcmp( argv[i], "-bg" ) )
+ {
+ bgname = argv[++i];
+ }
+ else if( !strcmp( argv[i], "-bg-vecfile" ) )
+ {
+ bg_vecfile = true;
+ }
+ else if( !strcmp( argv[i], "-npos" ) )
+ {
+ npos = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-nneg" ) )
+ {
+ nneg = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-nstages" ) )
+ {
+ nstages = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-nsplits" ) )
+ {
+ nsplits = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-mem" ) )
+ {
+ mem = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-sym" ) )
+ {
+ symmetric = 1;
+ }
+ else if( !strcmp( argv[i], "-nonsym" ) )
+ {
+ symmetric = 0;
+ }
+ else if( !strcmp( argv[i], "-minhitrate" ) )
+ {
+ minhitrate = (float) atof( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-maxfalsealarm" ) )
+ {
+ maxfalsealarm = (float) atof( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-weighttrimming" ) )
+ {
+ weightfraction = (float) atof( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-eqw" ) )
+ {
+ equalweights = 1;
+ }
+ else if( !strcmp( argv[i], "-mode" ) )
+ {
+ char* tmp = argv[++i];
+
+ if( !strcmp( tmp, "CORE" ) )
+ {
+ mode = 1;
+ }
+ else if( !strcmp( tmp, "ALL" ) )
+ {
+ mode = 2;
+ }
+ else
+ {
+ mode = 0;
+ }
+ }
+ else if( !strcmp( argv[i], "-w" ) )
+ {
+ width = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-h" ) )
+ {
+ height = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-bt" ) )
+ {
+ i++;
+ if( !strcmp( argv[i], boosttypes[0] ) )
+ {
+ boosttype = 0;
+ }
+ else if( !strcmp( argv[i], boosttypes[1] ) )
+ {
+ boosttype = 1;
+ }
+ else if( !strcmp( argv[i], boosttypes[2] ) )
+ {
+ boosttype = 2;
+ }
+ else
+ {
+ boosttype = 3;
+ }
+ }
+ else if( !strcmp( argv[i], "-err" ) )
+ {
+ i++;
+ if( !strcmp( argv[i], stumperrors[0] ) )
+ {
+ stumperror = 0;
+ }
+ else if( !strcmp( argv[i], stumperrors[1] ) )
+ {
+ stumperror = 1;
+ }
+ else
+ {
+ stumperror = 2;
+ }
+ }
+ else if( !strcmp( argv[i], "-maxtreesplits" ) )
+ {
+ maxtreesplits = atoi( argv[++i] );
+ }
+ else if( !strcmp( argv[i], "-minpos" ) )
+ {
+ minpos = atoi( argv[++i] );
+ }
+ }
+
+ printf( "Data dir name: %s\n", ((dirname == NULL) ? nullname : dirname ) );
+ printf( "Vec file name: %s\n", ((vecname == NULL) ? nullname : vecname ) );
+ printf( "BG file name: %s, is a vecfile: %s\n", ((bgname == NULL) ? nullname : bgname ), bg_vecfile ? "yes" : "no" );
+ printf( "Num pos: %d\n", npos );
+ printf( "Num neg: %d\n", nneg );
+ printf( "Num stages: %d\n", nstages );
+ printf( "Num splits: %d (%s as weak classifier)\n", nsplits,
+ (nsplits == 1) ? "stump" : "tree" );
+ printf( "Mem: %d MB\n", mem );
+ printf( "Symmetric: %s\n", (symmetric) ? "TRUE" : "FALSE" );
+ printf( "Min hit rate: %f\n", minhitrate );
+ printf( "Max false alarm rate: %f\n", maxfalsealarm );
+ printf( "Weight trimming: %f\n", weightfraction );
+ printf( "Equal weights: %s\n", (equalweights) ? "TRUE" : "FALSE" );
+ printf( "Mode: %s\n", ( (mode == 0) ? "BASIC" : ( (mode == 1) ? "CORE" : "ALL") ) );
+ printf( "Width: %d\n", width );
+ printf( "Height: %d\n", height );
+ //printf( "Max num of precalculated features: %d\n", numprecalculated );
+ printf( "Applied boosting algorithm: %s\n", boosttypes[boosttype] );
+ printf( "Error (valid only for Discrete and Real AdaBoost): %s\n",
+ stumperrors[stumperror] );
+
+ printf( "Max number of splits in tree cascade: %d\n", maxtreesplits );
+ printf( "Min number of positive samples per cluster: %d\n", minpos );
+
+ cvCreateTreeCascadeClassifier( dirname, vecname, bgname,
+ npos, nneg, nstages, mem,
+ nsplits,
+ minhitrate, maxfalsealarm, weightfraction,
+ mode, symmetric,
+ equalweights, width, height,
+ boosttype, stumperror,
+ maxtreesplits, minpos, bg_vecfile );
+
+ return 0;
+}
projects / opencv / blobdiff