Administrator/opencv-MIRROR
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-09-12 21:37:09 -05:00
Code Issues Packages Projects Releases Wiki Activity

removed contrib, legacy and softcsscade modules; removed latentsvm and datamatrix detector from objdetect. removed haartraining and sft apps.

Browse Source 
some of the stuff will be moved to opencv_contrib module.
in order to make this PR pass buildbot, please, comment off opencv_legacy, opencv_contrib and opencv_softcascade test runs.
This commit is contained in:
Vadim Pisarevsky
2014-06-24 19:18:51 +04:00
parent 6d4c4dcd94
commit 3858f2291d
398 changed files with 12 additions and 150885 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
apps/CMakeLists.txt
View File

@@ -1,6 +1,4 @@
add_definitions(-D__OPENCV_BUILD=1)
link_libraries(${OPENCV_LINKER_LIBS})
add_subdirectory(haartraining)
add_subdirectory(traincascade)
add_subdirectory(sft)

89
apps/haartraining/CMakeLists.txt
View File

@@ -1,89 +0,0 @@
SET(OPENCV_HAARTRAINING_DEPS opencv_core opencv_imgproc opencv_photo opencv_ml opencv_highgui opencv_objdetect opencv_calib3d opencv_video opencv_features2d opencv_flann opencv_legacy)
ocv_check_dependencies(${OPENCV_HAARTRAINING_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(haartraining)
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_include_modules(${OPENCV_HAARTRAINING_DEPS})
if(WIN32)
link_directories(${CMAKE_CURRENT_BINARY_DIR})
endif()
link_libraries(${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
# -----------------------------------------------------------
# Library
# -----------------------------------------------------------
set(cvhaartraining_lib_src
_cvcommon.h
cvclassifier.h
_cvhaartraining.h
cvhaartraining.h
cvboost.cpp
cvcommon.cpp
cvhaarclassifier.cpp
cvhaartraining.cpp
cvsamples.cpp
)
add_library(opencv_haartraining_engine STATIC ${cvhaartraining_lib_src})
set_target_properties(opencv_haartraining_engine PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
)
# -----------------------------------------------------------
# haartraining
# -----------------------------------------------------------
add_executable(opencv_haartraining cvhaartraining.h haartraining.cpp)
set_target_properties(opencv_haartraining PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_haartraining")
# -----------------------------------------------------------
# createsamples
# -----------------------------------------------------------
add_executable(opencv_createsamples cvhaartraining.h createsamples.cpp)
set_target_properties(opencv_createsamples PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_createsamples")
# -----------------------------------------------------------
# performance
# -----------------------------------------------------------
add_executable(opencv_performance performance.cpp)
set_target_properties(opencv_performance PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_performance")
# -----------------------------------------------------------
# Install part
# -----------------------------------------------------------
if(INSTALL_CREATE_DISTRIB)
if(BUILD_SHARED_LIBS)
install(TARGETS opencv_haartraining RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
install(TARGETS opencv_createsamples RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
install(TARGETS opencv_performance RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
endif()
else()
install(TARGETS opencv_haartraining RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
install(TARGETS opencv_createsamples RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
install(TARGETS opencv_performance RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
endif()
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(opencv_performance PROPERTIES FOLDER "applications")
set_target_properties(opencv_createsamples PROPERTIES FOLDER "applications")
set_target_properties(opencv_haartraining PROPERTIES FOLDER "applications")
set_target_properties(opencv_haartraining_engine PROPERTIES FOLDER "applications")
endif()

92
apps/haartraining/_cvcommon.h
View File

@@ -1,92 +0,0 @@
/*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*/
#ifndef __CVCOMMON_H_
#define __CVCOMMON_H_
#include "opencv2/core.hpp"
#include "cxcore.h"
#include "cv.h"
#include "cxmisc.h"
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#ifndef PATH_MAX
#define PATH_MAX 512
#endif /* PATH_MAX */
int icvMkDir( const char* filename );
/* returns index at specified position from index matrix of any type.
if matrix is NULL, then specified position is returned */
CV_INLINE
int icvGetIdxAt( CvMat* idx, int pos );
CV_INLINE
int icvGetIdxAt( CvMat* idx, int pos )
{
if( idx == NULL )
{
return pos;
}
else
{
CvScalar sc;
int type;
type = CV_MAT_TYPE( idx->type );
cvRawDataToScalar( idx->data.ptr + pos *
( (idx->rows == 1) ? CV_ELEM_SIZE( type ) : idx->step ), type, &sc );
return (int) sc.val[0];
}
}
/* debug functions */
#define CV_DEBUG_SAVE( ptr ) icvSave( ptr, __FILE__, __LINE__ );
void icvSave( const CvArr* ptr, const char* filename, int line );
#endif /* __CVCOMMON_H_ */

414
apps/haartraining/_cvhaartraining.h
View File

@@ -1,414 +0,0 @@
/*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*/
/*
* _cvhaartraining.h
*
* training of cascade of boosted classifiers based on haar features
*/
#ifndef __CVHAARTRAINING_H_
#define __CVHAARTRAINING_H_
#include "_cvcommon.h"
#include "cvclassifier.h"
#include <cstring>
#include <cstdio>
/* parameters for tree cascade classifier training */
/* max number of clusters */
#define CV_MAX_CLUSTERS 3
/* term criteria for K-Means */
#define CV_TERM_CRITERIA() cvTermCriteria( CV_TERMCRIT_EPS, 1000, 1E-5 )
/* print statistic info */
#define CV_VERBOSE 1
#define CV_STAGE_CART_FILE_NAME "AdaBoostCARTHaarClassifier.txt"
#define CV_HAAR_FEATURE_MAX 3
#define CV_HAAR_FEATURE_DESC_MAX 20
typedef int sum_type;
typedef double sqsum_type;
typedef short idx_type;
#define CV_SUM_MAT_TYPE CV_32SC1
#define CV_SQSUM_MAT_TYPE CV_64FC1
#define CV_IDX_MAT_TYPE CV_16SC1
#define CV_STUMP_TRAIN_PORTION 100
#define CV_THRESHOLD_EPS (0.00001F)
typedef struct CvTHaarFeature
{
char desc[CV_HAAR_FEATURE_DESC_MAX];
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvTHaarFeature;
typedef struct CvFastHaarFeature
{
int tilted;
struct
{
int p0, p1, p2, p3;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvFastHaarFeature;
typedef struct CvIntHaarFeatures
{
CvSize winsize;
int count;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
} CvIntHaarFeatures;
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2 CV_DEFAULT( 0 ), int y2 CV_DEFAULT( 0 ),
int w2 CV_DEFAULT( 0 ), int h2 CV_DEFAULT( 0 ),
float wt2 CV_DEFAULT( 0.0F ) );
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2, int y2, int w2, int h2, float wt2 )
{
CvTHaarFeature hf;
assert( CV_HAAR_FEATURE_MAX >= 3 );
assert( strlen( desc ) < CV_HAAR_FEATURE_DESC_MAX );
strcpy( &(hf.desc[0]), desc );
hf.tilted = ( hf.desc[0] == 't' );
hf.rect[0].r.x = x0;
hf.rect[0].r.y = y0;
hf.rect[0].r.width = w0;
hf.rect[0].r.height = h0;
hf.rect[0].weight = wt0;
hf.rect[1].r.x = x1;
hf.rect[1].r.y = y1;
hf.rect[1].r.width = w1;
hf.rect[1].r.height = h1;
hf.rect[1].weight = wt1;
hf.rect[2].r.x = x2;
hf.rect[2].r.y = y2;
hf.rect[2].r.width = w2;
hf.rect[2].r.height = h2;
hf.rect[2].weight = wt2;
return hf;
}
/* Prepared for training samples */
typedef struct CvHaarTrainingData
{
CvSize winsize; /* training image size */
int maxnum; /* maximum number of samples */
CvMat sum; /* sum images (each row represents image) */
CvMat tilted; /* tilted sum images (each row represents image) */
CvMat normfactor; /* normalization factor */
CvMat cls; /* classes. 1.0 - object, 0.0 - background */
CvMat weights; /* weights */
CvMat* valcache; /* precalculated feature values (CV_32FC1) */
CvMat* idxcache; /* presorted indices (CV_IDX_MAT_TYPE) */
} CvHaarTrainigData;
/* Passed to callback functions */
typedef struct CvUserdata
{
CvHaarTrainingData* trainingData;
CvIntHaarFeatures* haarFeatures;
} CvUserdata;
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures );
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures )
{
CvUserdata userdata;
userdata.trainingData = trainingData;
userdata.haarFeatures = haarFeatures;
return userdata;
}
#define CV_INT_HAAR_CLASSIFIER_FIELDS() \
float (*eval)( CvIntHaarClassifier*, sum_type*, sum_type*, float ); \
void (*save)( CvIntHaarClassifier*, FILE* file ); \
void (*release)( CvIntHaarClassifier** );
/* internal weak classifier*/
typedef struct CvIntHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
} CvIntHaarClassifier;
/*
* CART classifier
*/
typedef struct CvCARTHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
int* compidx;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
float* threshold;
int* left;
int* right;
float* val;
} CvCARTHaarClassifier;
/* internal stage classifier */
typedef struct CvStageHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
float threshold;
CvIntHaarClassifier** classifier;
} CvStageHaarClassifier;
/* internal cascade classifier */
typedef struct CvCascadeHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
CvIntHaarClassifier** classifier;
} CvCascadeHaarClassifier;
/* internal tree cascade classifier node */
typedef struct CvTreeCascadeNode
{
CvStageHaarClassifier* stage;
struct CvTreeCascadeNode* next;
struct CvTreeCascadeNode* child;
struct CvTreeCascadeNode* parent;
struct CvTreeCascadeNode* next_same_level;
struct CvTreeCascadeNode* child_eval;
int idx;
int leaf;
} CvTreeCascadeNode;
/* internal tree cascade classifier */
typedef struct CvTreeCascadeClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
CvTreeCascadeNode* root; /* root of the tree */
CvTreeCascadeNode* root_eval; /* root node for the filtering */
int next_idx;
} CvTreeCascadeClassifier;
CV_INLINE float cvEvalFastHaarFeature( const CvFastHaarFeature* feature,
const sum_type* sum, const sum_type* tilted )
{
const sum_type* img = feature->tilted ? tilted : sum;
float ret = feature->rect[0].weight*
(img[feature->rect[0].p0] - img[feature->rect[0].p1] -
img[feature->rect[0].p2] + img[feature->rect[0].p3]) +
feature->rect[1].weight*
(img[feature->rect[1].p0] - img[feature->rect[1].p1] -
img[feature->rect[1].p2] + img[feature->rect[1].p3]);
if( feature->rect[2].weight != 0.0f )
ret += feature->rect[2].weight *
( img[feature->rect[2].p0] - img[feature->rect[2].p1] -
img[feature->rect[2].p2] + img[feature->rect[2].p3] );
return ret;
}
typedef struct CvSampleDistortionData
{
IplImage* src;
IplImage* erode;
IplImage* dilate;
IplImage* mask;
IplImage* img;
IplImage* maskimg;
int dx;
int dy;
int bgcolor;
} CvSampleDistortionData;
/*
* icvConvertToFastHaarFeature
*
* Convert to fast representation of haar features
*
* haarFeature - input array
* fastHaarFeature - output array
* size - size of arrays
* step - row step for the integral image
*/
void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature,
CvFastHaarFeature* fastHaarFeature,
int size, int step );
void icvWriteVecHeader( FILE* file, int count, int width, int height );
void icvWriteVecSample( FILE* file, CvArr* sample );
void icvPlaceDistortedSample( CvArr* background,
int inverse, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int inscribe, double maxshiftf, double maxscalef,
CvSampleDistortionData* data );
void icvEndSampleDistortion( CvSampleDistortionData* data );
int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
CvSampleDistortionData* data );
typedef int (*CvGetHaarTrainingDataCallback)( CvMat* img, void* userdata );
typedef struct CvVecFile
{
FILE* input;
int count;
int vecsize;
int last;
short* vector;
} CvVecFile;
int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata );
/*
* icvGetHaarTrainingDataFromVec
*
* Fill <data> with samples from .vec file, passed <cascade>
int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
CvIntHaarClassifier* cascade,
const char* filename,
int* consumed );
*/
CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count );
void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier );
void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
CvIntHaarFeatures* intHaarFeatures );
float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold );
void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count );
void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step );
void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step );
/* tree cascade classifier */
float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSetLeafNode( CvTreeCascadeClassifier* tree, CvTreeCascadeNode* leaf );
float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
sum_type* tilted, float normfactor );
CvTreeCascadeNode* icvCreateTreeCascadeNode();
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node );
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier );
/* Prints out current tree structure to <stdout> */
void icvPrintTreeCascade( CvTreeCascadeNode* root );
/* Loads tree cascade classifier */
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
int* splits );
/* Finds leaves belonging to maximal level and connects them via leaf->next_same_level */
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tree );
#endif /* __CVHAARTRAINING_H_ */

245
apps/haartraining/createsamples.cpp
View File

@@ -1,245 +0,0 @@
/*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 <cstdio>
#include <cstring>
#include <cstdlib>
#include <cmath>
#include <ctime>
using namespace std;
#include "cvhaartraining.h"
int main( int argc, char* argv[] )
{
int i = 0;
char* nullname = (char*)"(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;
srand((unsigned int)time(0));
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;
}

3789
apps/haartraining/cvboost.cpp
View File

File diff suppressed because it is too large Load Diff

729
apps/haartraining/cvclassifier.h
View File

@@ -1,729 +0,0 @@
/*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*/
/*
* File cvclassifier.h
*
* Classifier types
*/
#ifndef _CVCLASSIFIER_H_
#define _CVCLASSIFIER_H_
#include <cmath>
#include "cxcore.h"
#define CV_BOOST_API
/* Convert matrix to vector */
#define CV_MAT2VEC( mat, vdata, vstep, num ) \
assert( (mat).rows == 1 || (mat).cols == 1 ); \
(vdata) = ((mat).data.ptr); \
if( (mat).rows == 1 ) \
{ \
(vstep) = CV_ELEM_SIZE( (mat).type ); \
(num) = (mat).cols; \
} \
else \
{ \
(vstep) = (mat).step; \
(num) = (mat).rows; \
}
/* Set up <sample> matrix header to be <num> sample of <trainData> samples matrix */
#define CV_GET_SAMPLE( trainData, tdflags, num, sample ) \
if( CV_IS_ROW_SAMPLE( tdflags ) ) \
{ \
cvInitMatHeader( &(sample), 1, (trainData).cols, \
CV_MAT_TYPE( (trainData).type ), \
((trainData).data.ptr + (num) * (trainData).step), \
(trainData).step ); \
} \
else \
{ \
cvInitMatHeader( &(sample), (trainData).rows, 1, \
CV_MAT_TYPE( (trainData).type ), \
((trainData).data.ptr + (num) * CV_ELEM_SIZE( (trainData).type )), \
(trainData).step ); \
}
#define CV_GET_SAMPLE_STEP( trainData, tdflags, sstep ) \
(sstep) = ( ( CV_IS_ROW_SAMPLE( tdflags ) ) \
? (trainData).step : CV_ELEM_SIZE( (trainData).type ) );
#define CV_LOGRATIO_THRESHOLD 0.00001F
/* log( val / (1 - val ) ) */
CV_INLINE float cvLogRatio( float val );
CV_INLINE float cvLogRatio( float val )
{
float tval;
tval = MAX(CV_LOGRATIO_THRESHOLD, MIN( 1.0F - CV_LOGRATIO_THRESHOLD, (val) ));
return logf( tval / (1.0F - tval) );
}
/* flags values for classifier consturctor flags parameter */
/* each trainData matrix column is a sample */
#define CV_COL_SAMPLE 0
/* each trainData matrix row is a sample */
#define CV_ROW_SAMPLE 1
#ifndef CV_IS_ROW_SAMPLE
# define CV_IS_ROW_SAMPLE( flags ) ( ( flags ) & CV_ROW_SAMPLE )
#endif
/* Classifier supports tune function */
#define CV_TUNABLE (1 << 1)
#define CV_IS_TUNABLE( flags ) ( (flags) & CV_TUNABLE )
/* classifier fields common to all classifiers */
#define CV_CLASSIFIER_FIELDS() \
int flags; \
float(*eval)( struct CvClassifier*, CvMat* ); \
void (*tune)( struct CvClassifier*, CvMat*, int flags, CvMat*, CvMat*, CvMat*, \
CvMat*, CvMat* ); \
int (*save)( struct CvClassifier*, const char* file_name ); \
void (*release)( struct CvClassifier** );
typedef struct CvClassifier
{
CV_CLASSIFIER_FIELDS()
} CvClassifier;
#define CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
typedef struct CvClassifierTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
} CvClassifierTrainParams;
/*
Common classifier constructor:
CvClassifier* cvCreateMyClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask CV_DEFAULT(0),
CvCompIdx* compIdx CV_DEFAULT(0),
CvMat* sampleIdx CV_DEFAULT(0),
CvMat* weights CV_DEFAULT(0),
CvClassifierTrainParams* trainParams CV_DEFAULT(0)
)
*/
typedef CvClassifier* (*CvClassifierConstructor)( CvMat*, int, CvMat*, CvMat*, CvMat*,
CvMat*, CvMat*, CvMat*,
CvClassifierTrainParams* );
typedef enum CvStumpType
{
CV_CLASSIFICATION = 0,
CV_CLASSIFICATION_CLASS = 1,
CV_REGRESSION = 2
} CvStumpType;
typedef enum CvStumpError
{
CV_MISCLASSIFICATION = 0,
CV_GINI = 1,
CV_ENTROPY = 2,
CV_SQUARE = 3
} CvStumpError;
typedef struct CvStumpTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvStumpType type;
CvStumpError error;
} CvStumpTrainParams;
typedef struct CvMTStumpTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvStumpType type;
CvStumpError error;
int portion; /* number of components calculated in each thread */
int numcomp; /* total number of components */
/* callback which fills <mat> with components [first, first+num[ */
void (*getTrainData)( CvMat* mat, CvMat* sampleIdx, CvMat* compIdx,
int first, int num, void* userdata );
CvMat* sortedIdx; /* presorted samples indices */
void* userdata; /* passed to callback */
} CvMTStumpTrainParams;
typedef struct CvStumpClassifier
{
CV_CLASSIFIER_FIELDS()
int compidx;
float lerror; /* impurity of the right node */
float rerror; /* impurity of the left node */
float threshold;
float left;
float right;
} CvStumpClassifier;
typedef struct CvCARTTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
/* desired number of internal nodes */
int count;
CvClassifierTrainParams* stumpTrainParams;
CvClassifierConstructor stumpConstructor;
/*
* Split sample indices <idx>
* on the "left" indices <left> and "right" indices <right>
* according to samples components <compidx> values and <threshold>.
*
* NOTE: Matrices <left> and <right> must be allocated using cvCreateMat function
* since they are freed using cvReleaseMat function
*
* If it is NULL then the default implementation which evaluates training
* samples from <trainData> passed to classifier constructor is used
*/
void (*splitIdx)( int compidx, float threshold,
CvMat* idx, CvMat** left, CvMat** right,
void* userdata );
void* userdata;
} CvCARTTrainParams;
typedef struct CvCARTClassifier
{
CV_CLASSIFIER_FIELDS()
/* number of internal nodes */
int count;
/* internal nodes (each array of <count> elements) */
int* compidx;
float* threshold;
int* left;
int* right;
/* leaves (array of <count>+1 elements) */
float* val;
} CvCARTClassifier;
CV_BOOST_API
void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols CV_DEFAULT( 0 ) );
CV_BOOST_API
void cvReleaseStumpClassifier( CvClassifier** classifier );
CV_BOOST_API
float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample );
CV_BOOST_API
CvClassifier* cvCreateStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask CV_DEFAULT(0),
CvMat* compIdx CV_DEFAULT(0),
CvMat* sampleIdx CV_DEFAULT(0),
CvMat* weights CV_DEFAULT(0),
CvClassifierTrainParams* trainParams CV_DEFAULT(0) );
/*
* cvCreateMTStumpClassifier
*
* Multithreaded stump classifier constructor
* Includes huge train data support through callback function
*/
CV_BOOST_API
CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
/*
* cvCreateCARTClassifier
*
* CART classifier constructor
*/
CV_BOOST_API
CvClassifier* cvCreateCARTClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
CV_BOOST_API
void cvReleaseCARTClassifier( CvClassifier** classifier );
CV_BOOST_API
float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample );
/****************************************************************************************\
* Boosting *
\****************************************************************************************/
/*
* CvBoostType
*
* The CvBoostType enumeration specifies the boosting type.
*
* Remarks
* Four different boosting variants for 2 class classification problems are supported:
* Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
* The L2 (2 class classification problems) and LK (K class classification problems)
* algorithms are close to LogitBoost but more numerically stable than last one.
* For regression three different loss functions are supported:
* Least square, least absolute deviation and huber loss.
*/
typedef enum CvBoostType
{
CV_DABCLASS = 0, /* 2 class Discrete AdaBoost */
CV_RABCLASS = 1, /* 2 class Real AdaBoost */
CV_LBCLASS = 2, /* 2 class LogitBoost */
CV_GABCLASS = 3, /* 2 class Gentle AdaBoost */
CV_L2CLASS = 4, /* classification (2 class problem) */
CV_LKCLASS = 5, /* classification (K class problem) */
CV_LSREG = 6, /* least squares regression */
CV_LADREG = 7, /* least absolute deviation regression */
CV_MREG = 8 /* M-regression (Huber loss) */
} CvBoostType;
/****************************************************************************************\
* Iterative training functions *
\****************************************************************************************/
/*
* CvBoostTrainer
*
* The CvBoostTrainer structure represents internal boosting trainer.
*/
typedef struct CvBoostTrainer CvBoostTrainer;
/*
* cvBoostStartTraining
*
* The cvBoostStartTraining function starts training process and calculates
* response values and weights for the first weak classifier training.
*
* Parameters
* trainClasses
* Vector of classes of training samples classes. Each element must be 0 or 1 and
* of type CV_32FC1.
* weakTrainVals
* Vector of response values for the first trained weak classifier.
* Must be of type CV_32FC1.
* weights
* Weight vector of training samples for the first trained weak classifier.
* Must be of type CV_32FC1.
* type
* Boosting type. CV_DABCLASS, CV_RABCLASS, CV_LBCLASS, CV_GABCLASS
* types are supported.
*
* Return Values
* The return value is a pointer to internal trainer structure which is used
* to perform next training iterations.
*
* Remarks
* weakTrainVals and weights must be allocated before calling the function
* and of the same size as trainingClasses. Usually weights should be initialized
* with 1.0 value.
* The function calculates response values and weights for the first weak
* classifier training and stores them into weakTrainVals and weights
* respectively.
* Note, the training of the weak classifier using weakTrainVals, weight,
* trainingData is outside of this function.
*/
CV_BOOST_API
CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type );
/*
* cvBoostNextWeakClassifier
*
* The cvBoostNextWeakClassifier function performs next training
* iteration and caluclates response values and weights for the next weak
* classifier training.
*
* Parameters
* weakEvalVals
* Vector of values obtained by evaluation of each sample with
* the last trained weak classifier (iteration i). Must be of CV_32FC1 type.
* trainClasses
* Vector of classes of training samples. Each element must be 0 or 1,
* and of type CV_32FC1.
* weakTrainVals
* Vector of response values for the next weak classifier training
* (iteration i+1). Must be of type CV_32FC1.
* weights
* Weight vector of training samples for the next weak classifier training
* (iteration i+1). Must be of type CV_32FC1.
* trainer
* A pointer to internal trainer returned by the cvBoostStartTraining
* function call.
*
* Return Values
* The return value is the coefficient for the last trained weak classifier.
*
* Remarks
* weakTrainVals and weights must be exactly the same vectors as used in
* the cvBoostStartTraining function call and should not be modified.
* The function calculates response values and weights for the next weak
* classifier training and stores them into weakTrainVals and weights
* respectively.
* Note, the training of the weak classifier of iteration i+1 using
* weakTrainVals, weight, trainingData is outside of this function.
*/
CV_BOOST_API
float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* trainer );
/*
* cvBoostEndTraining
*
* The cvBoostEndTraining function finishes training process and releases
* internally allocated memory.
*
* Parameters
* trainer
* A pointer to a pointer to internal trainer returned by the cvBoostStartTraining
* function call.
*/
CV_BOOST_API
void cvBoostEndTraining( CvBoostTrainer** trainer );
/****************************************************************************************\
* Boosted tree models *
\****************************************************************************************/
/*
* CvBtClassifier
*
* The CvBtClassifier structure represents boosted tree model.
*
* Members
* flags
* Flags. If CV_IS_TUNABLE( flags ) != 0 then the model supports tuning.
* eval
* Evaluation function. Returns sample predicted class (0, 1, etc.)
* for classification or predicted value for regression.
* tune
* Tune function. If the model supports tuning then tune call performs
* one more boosting iteration if passed to the function flags parameter
* is CV_TUNABLE otherwise releases internally allocated for tuning memory
* and makes the model untunable.
* NOTE: Since tuning uses the pointers to parameters,
* passed to the cvCreateBtClassifier function, they should not be modified
* or released between tune calls.
* save
* This function stores the model into given file.
* release
* This function releases the model.
* type
* Boosted tree model type.
* numclasses
* Number of classes for CV_LKCLASS type or 1 for all other types.
* numiter
* Number of iterations. Number of weak classifiers is equal to number
* of iterations for all types except CV_LKCLASS. For CV_LKCLASS type
* number of weak classifiers is (numiter * numclasses).
* numfeatures
* Number of features in sample.
* trees
* Stores weak classifiers when the model does not support tuning.
* seq
* Stores weak classifiers when the model supports tuning.
* trainer
* Pointer to internal tuning parameters if the model supports tuning.
*/
typedef struct CvBtClassifier
{
CV_CLASSIFIER_FIELDS()
CvBoostType type;
int numclasses;
int numiter;
int numfeatures;
union
{
CvCARTClassifier** trees;
CvSeq* seq;
};
void* trainer;
} CvBtClassifier;
/*
* CvBtClassifierTrainParams
*
* The CvBtClassifierTrainParams structure stores training parameters for
* boosted tree model.
*
* Members
* type
* Boosted tree model type.
* numiter
* Desired number of iterations.
* param
* Parameter Model Type Parameter Meaning
* param[0] Any Shrinkage factor
* param[1] CV_MREG alpha. (1-alpha) determines "break-down" point of
* the training procedure, i.e. the fraction of samples
* that can be arbitrary modified without serious
* degrading the quality of the result.
* CV_DABCLASS, Weight trimming factor.
* CV_RABCLASS,
* CV_LBCLASS,
* CV_GABCLASS,
* CV_L2CLASS,
* CV_LKCLASS
* numsplits
* Desired number of splits in each tree.
*/
typedef struct CvBtClassifierTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvBoostType type;
int numiter;
float param[2];
int numsplits;
} CvBtClassifierTrainParams;
/*
* cvCreateBtClassifier
*
* The cvCreateBtClassifier function creates boosted tree model.
*
* Parameters
* trainData
* Matrix of feature values. Must have CV_32FC1 type.
* flags
* Determines how samples are stored in trainData.
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE.
* Optionally may be combined with CV_TUNABLE to make tunable model.
* trainClasses
* Vector of responses for regression or classes (0, 1, 2, etc.) for classification.
* typeMask,
* missedMeasurementsMask,
* compIdx
* Not supported. Must be NULL.
* sampleIdx
* Indices of samples used in training. If NULL then all samples are used.
* For CV_DABCLASS, CV_RABCLASS, CV_LBCLASS and CV_GABCLASS must be NULL.
* weights
* Not supported. Must be NULL.
* trainParams
* A pointer to CvBtClassifierTrainParams structure. Training parameters.
* See CvBtClassifierTrainParams description for details.
*
* Return Values
* The return value is a pointer to created boosted tree model of type CvBtClassifier.
*
* Remarks
* The function performs trainParams->numiter training iterations.
* If CV_TUNABLE flag is specified then created model supports tuning.
* In this case additional training iterations may be performed by
* tune function call.
*/
CV_BOOST_API
CvClassifier* cvCreateBtClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
/*
* cvCreateBtClassifierFromFile
*
* The cvCreateBtClassifierFromFile function restores previously saved
* boosted tree model from file.
*
* Parameters
* filename
* The name of the file with boosted tree model.
*
* Remarks
* The restored model does not support tuning.
*/
CV_BOOST_API
CvClassifier* cvCreateBtClassifierFromFile( const char* filename );
/****************************************************************************************\
* Utility functions *
\****************************************************************************************/
/*
* cvTrimWeights
*
* The cvTrimWeights function performs weight trimming.
*
* Parameters
* weights
* Weights vector.
* idx
* Indices vector of weights that should be considered.
* If it is NULL then all weights are used.
* factor
* Weight trimming factor. Must be in [0, 1] range.
*
* Return Values
* The return value is a vector of indices. If all samples should be used then
* it is equal to idx. In other case the cvReleaseMat function should be called
* to release it.
*
* Remarks
*/
CV_BOOST_API
CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor );
/*
* cvReadTrainData
*
* The cvReadTrainData function reads feature values and responses from file.
*
* Parameters
* filename
* The name of the file to be read.
* flags
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
* will be stored.
* trainData
* A pointer to a pointer to created matrix with feature values.
* cvReleaseMat function should be used to destroy created matrix.
* trainClasses
* A pointer to a pointer to created matrix with response values.
* cvReleaseMat function should be used to destroy created matrix.
*
* Remarks
* File format:
* ============================================
* m n
* value_1_1 value_1_2 ... value_1_n response_1
* value_2_1 value_2_2 ... value_2_n response_2
* ...
* value_m_1 value_m_2 ... value_m_n response_m
* ============================================
* m
* Number of samples
* n
* Number of features in each sample
* value_i_j
* Value of j-th feature of i-th sample
* response_i
* Response value of i-th sample
* For classification problems responses represent classes (0, 1, etc.)
* All values and classes are integer or real numbers.
*/
CV_BOOST_API
void cvReadTrainData( const char* filename,
int flags,
CvMat** trainData,
CvMat** trainClasses );
/*
* cvWriteTrainData
*
* The cvWriteTrainData function stores feature values and responses into file.
*
* Parameters
* filename
* The name of the file.
* flags
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
* are stored.
* trainData
* Feature values matrix.
* trainClasses
* Response values vector.
* sampleIdx
* Vector of idicies of the samples that should be stored. If it is NULL
* then all samples will be stored.
*
* Remarks
* See the cvReadTrainData function for file format description.
*/
CV_BOOST_API
void cvWriteTrainData( const char* filename,
int flags,
CvMat* trainData,
CvMat* trainClasses,
CvMat* sampleIdx );
/*
* cvRandShuffle
*
* The cvRandShuffle function perfroms random shuffling of given vector.
*
* Parameters
* vector
* Vector that should be shuffled.
* Must have CV_8UC1, CV_16SC1, CV_32SC1 or CV_32FC1 type.
*/
CV_BOOST_API
void cvRandShuffleVec( CvMat* vector );
#endif /* _CVCLASSIFIER_H_ */

125
apps/haartraining/cvcommon.cpp
View File

@@ -1,125 +0,0 @@
/*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*/
#include "_cvcommon.h"
#include <cstring>
#include <ctime>
#include <sys/stat.h>
#include <sys/types.h>
#ifdef _WIN32
#include <direct.h>
#endif /* _WIN32 */
int icvMkDir( const char* filename )
{
char path[PATH_MAX];
char* p;
int pos;
#ifdef _WIN32
struct _stat st;
#else /* _WIN32 */
struct stat st;
mode_t mode;
mode = 0755;
#endif /* _WIN32 */
strcpy( path, filename );
p = path;
for( ; ; )
{
pos = (int)strcspn( p, "/\\" );
if( pos == (int) strlen( p ) ) break;
if( pos != 0 )
{
p[pos] = '\0';
#ifdef _WIN32
if( p[pos-1] != ':' )
{
if( _stat( path, &st ) != 0 )
{
if( _mkdir( path ) != 0 ) return 0;
}
}
#else /* _WIN32 */
if( stat( path, &st ) != 0 )
{
if( mkdir( path, mode ) != 0 ) return 0;
}
#endif /* _WIN32 */
}
p[pos] = '/';
p += pos + 1;
}
return 1;
}
#if 0
/* debug functions */
void icvSave( const CvArr* ptr, const char* filename, int line )
{
CvFileStorage* fs;
char buf[PATH_MAX];
const char* name;
name = strrchr( filename, '\\' );
if( !name ) name = strrchr( filename, '/' );
if( !name ) name = filename;
else name++; /* skip '/' or '\\' */
sprintf( buf, "%s-%d-%d", name, line, time( NULL ) );
fs = cvOpenFileStorage( buf, NULL, CV_STORAGE_WRITE_TEXT );
if( !fs ) return;
cvWrite( fs, "debug", ptr );
cvReleaseFileStorage( &fs );
}
#endif // #if 0
/* End of file. */

835
apps/haartraining/cvhaarclassifier.cpp
View File

@@ -1,835 +0,0 @@
/*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*/
/*
* cvhaarclassifier.cpp
*
* haar classifiers (stump, CART, stage, cascade)
*/
#include "_cvhaartraining.h"
CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count )
{
CvCARTHaarClassifier* cart;
size_t datasize;
datasize = sizeof( *cart ) +
( sizeof( int ) +
sizeof( CvTHaarFeature ) + sizeof( CvFastHaarFeature ) +
sizeof( float ) + sizeof( int ) + sizeof( int ) ) * count +
sizeof( float ) * (count + 1);
cart = (CvCARTHaarClassifier*) cvAlloc( datasize );
memset( cart, 0, datasize );
cart->feature = (CvTHaarFeature*) (cart + 1);
cart->fastfeature = (CvFastHaarFeature*) (cart->feature + count);
cart->threshold = (float*) (cart->fastfeature + count);
cart->left = (int*) (cart->threshold + count);
cart->right = (int*) (cart->left + count);
cart->val = (float*) (cart->right + count);
cart->compidx = (int*) (cart->val + count + 1 );
cart->count = count;
cart->eval = icvEvalCARTHaarClassifier;
cart->save = icvSaveCARTHaarClassifier;
cart->release = icvReleaseHaarClassifier;
return (CvIntHaarClassifier*) cart;
}
void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier )
{
cvFree( classifier );
*classifier = NULL;
}
void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
CvIntHaarFeatures* intHaarFeatures )
{
int i;
for( i = 0; i < cart->count; i++ )
{
carthaar->feature[i] = intHaarFeatures->feature[cart->compidx[i]];
carthaar->fastfeature[i] = intHaarFeatures->fastfeature[cart->compidx[i]];
carthaar->threshold[i] = cart->threshold[i];
carthaar->left[i] = cart->left[i];
carthaar->right[i] = cart->right[i];
carthaar->val[i] = cart->val[i];
carthaar->compidx[i] = cart->compidx[i];
}
carthaar->count = cart->count;
carthaar->val[cart->count] = cart->val[cart->count];
}
float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int idx = 0;
do
{
if( cvEvalFastHaarFeature(
((CvCARTHaarClassifier*) classifier)->fastfeature + idx, sum, tilted )
< (((CvCARTHaarClassifier*) classifier)->threshold[idx] * normfactor) )
{
idx = ((CvCARTHaarClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTHaarClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
return ((CvCARTHaarClassifier*) classifier)->val[-idx];
}
CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold )
{
CvStageHaarClassifier* stage;
size_t datasize;
datasize = sizeof( *stage ) + sizeof( CvIntHaarClassifier* ) * count;
stage = (CvStageHaarClassifier*) cvAlloc( datasize );
memset( stage, 0, datasize );
stage->count = count;
stage->threshold = threshold;
stage->classifier = (CvIntHaarClassifier**) (stage + 1);
stage->eval = icvEvalStageHaarClassifier;
stage->save = icvSaveStageHaarClassifier;
stage->release = icvReleaseStageHaarClassifier;
return (CvIntHaarClassifier*) stage;
}
void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier )
{
int i;
for( i = 0; i < ((CvStageHaarClassifier*) *classifier)->count; i++ )
{
if( ((CvStageHaarClassifier*) *classifier)->classifier[i] != NULL )
{
((CvStageHaarClassifier*) *classifier)->classifier[i]->release(
&(((CvStageHaarClassifier*) *classifier)->classifier[i]) );
}
}
cvFree( classifier );
*classifier = NULL;
}
float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int i;
float stage_sum;
stage_sum = 0.0F;
for( i = 0; i < ((CvStageHaarClassifier*) classifier)->count; i++ )
{
stage_sum +=
((CvStageHaarClassifier*) classifier)->classifier[i]->eval(
((CvStageHaarClassifier*) classifier)->classifier[i],
sum, tilted, normfactor );
}
return stage_sum;
}
CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count )
{
CvCascadeHaarClassifier* ptr;
size_t datasize;
datasize = sizeof( *ptr ) + sizeof( CvIntHaarClassifier* ) * count;
ptr = (CvCascadeHaarClassifier*) cvAlloc( datasize );
memset( ptr, 0, datasize );
ptr->count = count;
ptr->classifier = (CvIntHaarClassifier**) (ptr + 1);
ptr->eval = icvEvalCascadeHaarClassifier;
ptr->save = NULL;
ptr->release = icvReleaseCascadeHaarClassifier;
return (CvIntHaarClassifier*) ptr;
}
void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier )
{
int i;
for( i = 0; i < ((CvCascadeHaarClassifier*) *classifier)->count; i++ )
{
if( ((CvCascadeHaarClassifier*) *classifier)->classifier[i] != NULL )
{
((CvCascadeHaarClassifier*) *classifier)->classifier[i]->release(
&(((CvCascadeHaarClassifier*) *classifier)->classifier[i]) );
}
}
cvFree( classifier );
*classifier = NULL;
}
float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int i;
for( i = 0; i < ((CvCascadeHaarClassifier*) classifier)->count; i++ )
{
if( ((CvCascadeHaarClassifier*) classifier)->classifier[i]->eval(
((CvCascadeHaarClassifier*) classifier)->classifier[i],
sum, tilted, normfactor )
< ( ((CvStageHaarClassifier*)
((CvCascadeHaarClassifier*) classifier)->classifier[i])->threshold
- CV_THRESHOLD_EPS) )
{
return 0.0;
}
}
return 1.0;
}
void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file )
{
fprintf( file, "%d\n", ( ( feature->rect[2].weight == 0.0F ) ? 2 : 3) );
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[0].r.x,
feature->rect[0].r.y,
feature->rect[0].r.width,
feature->rect[0].r.height,
0,
(int) (feature->rect[0].weight) );
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[1].r.x,
feature->rect[1].r.y,
feature->rect[1].r.width,
feature->rect[1].r.height,
0,
(int) (feature->rect[1].weight) );
if( feature->rect[2].weight != 0.0F )
{
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[2].r.x,
feature->rect[2].r.y,
feature->rect[2].r.width,
feature->rect[2].r.height,
0,
(int) (feature->rect[2].weight) );
}
fprintf( file, "%s\n", &(feature->desc[0]) );
}
void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file )
{
int nrect;
int j;
int tmp;
int weight;
nrect = 0;
int values_read = fscanf( file, "%d", &nrect );
CV_Assert(values_read == 1);
assert( nrect <= CV_HAAR_FEATURE_MAX );
for( j = 0; j < nrect; j++ )
{
values_read = fscanf( file, "%d %d %d %d %d %d",
&(feature->rect[j].r.x),
&(feature->rect[j].r.y),
&(feature->rect[j].r.width),
&(feature->rect[j].r.height),
&tmp, &weight );
CV_Assert(values_read == 6);
feature->rect[j].weight = (float) weight;
}
for( j = nrect; j < CV_HAAR_FEATURE_MAX; j++ )
{
feature->rect[j].r.x = 0;
feature->rect[j].r.y = 0;
feature->rect[j].r.width = 0;
feature->rect[j].r.height = 0;
feature->rect[j].weight = 0.0f;
}
values_read = fscanf( file, "%s", &(feature->desc[0]) );
CV_Assert(values_read == 1);
feature->tilted = ( feature->desc[0] == 't' );
}
void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
{
int i;
int count;
count = ((CvCARTHaarClassifier*) classifier)->count;
fprintf( file, "%d\n", count );
for( i = 0; i < count; i++ )
{
icvSaveHaarFeature( &(((CvCARTHaarClassifier*) classifier)->feature[i]), file );
fprintf( file, "%e %d %d\n",
((CvCARTHaarClassifier*) classifier)->threshold[i],
((CvCARTHaarClassifier*) classifier)->left[i],
((CvCARTHaarClassifier*) classifier)->right[i] );
}
for( i = 0; i <= count; i++ )
{
fprintf( file, "%e ", ((CvCARTHaarClassifier*) classifier)->val[i] );
}
fprintf( file, "\n" );
}
CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step )
{
CvCARTHaarClassifier* ptr;
int i;
int count;
ptr = NULL;
int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 )
{
ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count );
for( i = 0; i < count; i++ )
{
icvLoadHaarFeature( &(ptr->feature[i]), file );
values_read = fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]),
&(ptr->right[i]) );
CV_Assert(values_read == 3);
}
for( i = 0; i <= count; i++ )
{
values_read = fscanf( file, "%f", &(ptr->val[i]) );
CV_Assert(values_read == 1);
}
icvConvertToFastHaarFeature( ptr->feature, ptr->fastfeature, ptr->count, step );
}
return (CvIntHaarClassifier*) ptr;
}
void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
{
int count;
int i;
float threshold;
count = ((CvStageHaarClassifier*) classifier)->count;
fprintf( file, "%d\n", count );
for( i = 0; i < count; i++ )
{
((CvStageHaarClassifier*) classifier)->classifier[i]->save(
((CvStageHaarClassifier*) classifier)->classifier[i], file );
}
threshold = ((CvStageHaarClassifier*) classifier)->threshold;
/* to be compatible with the previous implementation */
/* threshold = 2.0F * ((CvStageHaarClassifier*) classifier)->threshold - count; */
fprintf( file, "%e\n", threshold );
}
static CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
{
CvStageHaarClassifier* ptr = NULL;
//CV_FUNCNAME( "icvLoadCARTStageHaarClassifierF" );
__BEGIN__;
if( file != NULL )
{
int count;
int i;
float threshold;
count = 0;
int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 )
{
ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F );
for( i = 0; i < count; i++ )
{
ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step );
}
values_read = fscanf( file, "%f", &threshold );
CV_Assert(values_read == 1);
ptr->threshold = threshold;
/* to be compatible with the previous implementation */
/* ptr->threshold = 0.5F * (threshold + count); */
}
if( feof( file ) )
{
ptr->release( (CvIntHaarClassifier**) &ptr );
ptr = NULL;
}
}
__END__;
return (CvIntHaarClassifier*) ptr;
}
CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step )
{
CvIntHaarClassifier* ptr = NULL;
CV_FUNCNAME( "icvLoadCARTStageHaarClassifier" );
__BEGIN__;
FILE* file;
file = fopen( filename, "r" );
if( file )
{
CV_CALL( ptr = icvLoadCARTStageHaarClassifierF( file, step ) );
fclose( file );
}
__END__;
return ptr;
}
/* tree cascade classifier */
/* evaluates a tree cascade classifier */
float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
CvTreeCascadeNode* ptr;
ptr = ((CvTreeCascadeClassifier*) classifier)->root;
while( ptr )
{
if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
sum, tilted, normfactor )
>= ptr->stage->threshold - CV_THRESHOLD_EPS )
{
ptr = ptr->child;
}
else
{
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr == NULL ) return 0.0F;
ptr = ptr->next;
}
}
return 1.0F;
}
/* sets path int the tree form the root to the leaf node */
void icvSetLeafNode( CvTreeCascadeClassifier* tcc, CvTreeCascadeNode* leaf )
{
CV_FUNCNAME( "icvSetLeafNode" );
__BEGIN__;
CvTreeCascadeNode* ptr;
ptr = NULL;
while( leaf )
{
leaf->child_eval = ptr;
ptr = leaf;
leaf = leaf->parent;
}
leaf = tcc->root;
while( leaf && leaf != ptr ) leaf = leaf->next;
if( leaf != ptr )
CV_ERROR( CV_StsError, "Invalid tcc or leaf node." );
tcc->root_eval = ptr;
__END__;
}
/* evaluates a tree cascade classifier. used in filtering */
float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
sum_type* tilted, float normfactor )
{
CvTreeCascadeNode* ptr;
//CvTreeCascadeClassifier* tree;
//tree = (CvTreeCascadeClassifier*) classifier;
ptr = ((CvTreeCascadeClassifier*) classifier)->root_eval;
while( ptr )
{
if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
sum, tilted, normfactor )
< ptr->stage->threshold - CV_THRESHOLD_EPS )
{
return 0.0F;
}
ptr = ptr->child_eval;
}
return 1.0F;
}
/* creates tree cascade node */
CvTreeCascadeNode* icvCreateTreeCascadeNode()
{
CvTreeCascadeNode* ptr = NULL;
CV_FUNCNAME( "icvCreateTreeCascadeNode" );
__BEGIN__;
size_t data_size;
data_size = sizeof( *ptr );
CV_CALL( ptr = (CvTreeCascadeNode*) cvAlloc( data_size ) );
memset( ptr, 0, data_size );
__END__;
return ptr;
}
/* releases all tree cascade nodes accessible via links */
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node )
{
//CV_FUNCNAME( "icvReleaseTreeCascadeNodes" );
__BEGIN__;
if( node && *node )
{
CvTreeCascadeNode* ptr;
CvTreeCascadeNode* ptr_;
ptr = *node;
while( ptr )
{
while( ptr->child ) ptr = ptr->child;
if( ptr->stage ) ptr->stage->release( (CvIntHaarClassifier**) &ptr->stage );
ptr_ = ptr;
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr ) ptr = ptr->next;
cvFree( &ptr_ );
}
}
__END__;
}
/* releases tree cascade classifier */
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier )
{
if( classifier && *classifier )
{
icvReleaseTreeCascadeNodes( &((CvTreeCascadeClassifier*) *classifier)->root );
cvFree( classifier );
*classifier = NULL;
}
}
void icvPrintTreeCascade( CvTreeCascadeNode* root )
{
//CV_FUNCNAME( "icvPrintTreeCascade" );
__BEGIN__;
CvTreeCascadeNode* node;
CvTreeCascadeNode* n;
char buf0[256];
char buf[256];
int level;
int i;
int max_level;
node = root;
level = max_level = 0;
while( node )
{
while( node->child ) { node = node->child; level++; }
if( level > max_level ) { max_level = level; }
while( node && !node->next ) { node = node->parent; level--; }
if( node ) node = node->next;
}
printf( "\nTree Classifier\n" );
printf( "Stage\n" );
for( i = 0; i <= max_level; i++ ) printf( "+---" );
printf( "+\n" );
for( i = 0; i <= max_level; i++ ) printf( "|%3d", i );
printf( "|\n" );
for( i = 0; i <= max_level; i++ ) printf( "+---" );
printf( "+\n\n" );
node = root;
buf[0] = 0;
while( node )
{
sprintf( buf + strlen( buf ), "%3d", node->idx );
while( node->child )
{
node = node->child;
sprintf( buf + strlen( buf ),
((node->idx < 10) ? "---%d" : ((node->idx < 100) ? "--%d" : "-%d")),
node->idx );
}
printf( " %s\n", buf );
while( node && !node->next ) { node = node->parent; }
if( node )
{
node = node->next;
n = node->parent;
buf[0] = 0;
while( n )
{
if( n->next )
sprintf( buf0, " | %s", buf );
else
sprintf( buf0, " %s", buf );
strcpy( buf, buf0 );
n = n->parent;
}
printf( " %s |\n", buf );
}
}
printf( "\n" );
fflush( stdout );
__END__;
}
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
int* splits )
{
CvTreeCascadeClassifier* ptr = NULL;
CvTreeCascadeNode** nodes = NULL;
CV_FUNCNAME( "icvLoadTreeCascadeClassifier" );
__BEGIN__;
size_t data_size;
CvStageHaarClassifier* stage;
char stage_name[PATH_MAX];
char* suffix;
int i, num;
FILE* f;
int result, parent=0, next=0;
int stub;
if( !splits ) splits = &stub;
*splits = 0;
data_size = sizeof( *ptr );
CV_CALL( ptr = (CvTreeCascadeClassifier*) cvAlloc( data_size ) );
memset( ptr, 0, data_size );
ptr->eval = icvEvalTreeCascadeClassifier;
ptr->release = icvReleaseTreeCascadeClassifier;
sprintf( stage_name, "%s/", filename );
suffix = stage_name + strlen( stage_name );
for( i = 0; ; i++ )
{
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
f = fopen( stage_name, "r" );
if( !f ) break;
fclose( f );
}
num = i;
if( num < 1 ) EXIT;
data_size = sizeof( *nodes ) * num;
CV_CALL( nodes = (CvTreeCascadeNode**) cvAlloc( data_size ) );
for( i = 0; i < num; i++ )
{
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
f = fopen( stage_name, "r" );
CV_CALL( stage = (CvStageHaarClassifier*)
icvLoadCARTStageHaarClassifierF( f, step ) );
result = ( f && stage ) ? fscanf( f, "%d%d", &parent, &next ) : 0;
if( f ) fclose( f );
if( result != 2 )
{
num = i;
break;
}
printf( "Stage %d loaded\n", i );
if( parent >= i || (next != -1 && next != i + 1) )
CV_ERROR( CV_StsError, "Invalid tree links" );
CV_CALL( nodes[i] = icvCreateTreeCascadeNode() );
nodes[i]->stage = stage;
nodes[i]->idx = i;
nodes[i]->parent = (parent != -1 ) ? nodes[parent] : NULL;
nodes[i]->next = ( next != -1 ) ? nodes[i] : NULL;
nodes[i]->child = NULL;
}
for( i = 0; i < num; i++ )
{
if( nodes[i]->next )
{
(*splits)++;
nodes[i]->next = nodes[i+1];
}
if( nodes[i]->parent && nodes[i]->parent->child == NULL )
{
nodes[i]->parent->child = nodes[i];
}
}
ptr->root = nodes[0];
ptr->next_idx = num;
__END__;
cvFree( &nodes );
return (CvIntHaarClassifier*) ptr;
}
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tcc )
{
CvTreeCascadeNode* leaves;
//CV_FUNCNAME( "icvFindDeepestLeaves" );
__BEGIN__;
int level, cur_level;
CvTreeCascadeNode* ptr;
CvTreeCascadeNode* last;
leaves = last = NULL;
ptr = tcc->root;
level = -1;
cur_level = 0;
/* find leaves with maximal level */
while( ptr )
{
if( ptr->child ) { ptr = ptr->child; cur_level++; }
else
{
if( cur_level == level )
{
last->next_same_level = ptr;
ptr->next_same_level = NULL;
last = ptr;
}
if( cur_level > level )
{
level = cur_level;
leaves = last = ptr;
ptr->next_same_level = NULL;
}
while( ptr && ptr->next == NULL ) { ptr = ptr->parent; cur_level--; }
if( ptr ) ptr = ptr->next;
}
}
__END__;
return leaves;
}
/* End of file. */

3060
apps/haartraining/cvhaartraining.cpp
View File

File diff suppressed because it is too large Load Diff

192
apps/haartraining/cvhaartraining.h
View File

@@ -1,192 +0,0 @@
/*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*/
/*
* cvhaartraining.h
*
* haar training functions
*/
#ifndef _CVHAARTRAINING_H_
#define _CVHAARTRAINING_H_
/*
* cvCreateTrainingSamples
*
* Create training samples applying random distortions to sample image and
* store them in .vec file
*
* filename - .vec file name
* imgfilename - sample image file name
* bgcolor - background color for sample image
* bgthreshold - background color threshold. Pixels those colors are in range
* [bgcolor-bgthreshold, bgcolor+bgthreshold] are considered as transparent
* bgfilename - background description file name. If not NULL samples
* will be put on arbitrary background
* count - desired number of samples
* invert - if not 0 sample foreground pixels will be inverted
* if invert == CV_RANDOM_INVERT then samples will be inverted randomly
* maxintensitydev - desired max intensity deviation of foreground samples pixels
* maxxangle - max rotation angles
* maxyangle
* maxzangle
* showsamples - if not 0 samples will be shown
* winwidth - desired samples width
* winheight - desired samples height
*/
#define CV_RANDOM_INVERT 0x7FFFFFFF
void cvCreateTrainingSamples( const char* filename,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert = 0, int maxintensitydev = 40,
double maxxangle = 1.1,
double maxyangle = 1.1,
double maxzangle = 0.5,
int showsamples = 0,
int winwidth = 24, int winheight = 24 );
void cvCreateTestSamples( const char* infoname,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int showsamples,
int winwidth, int winheight );
/*
* cvCreateTrainingSamplesFromInfo
*
* Create training samples from a set of marked up images and store them into .vec file
* infoname - file in which marked up image descriptions are stored
* num - desired number of samples
* showsamples - if not 0 samples will be shown
* winwidth - sample width
* winheight - sample height
*
* Return number of successfully created samples
*/
int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilename,
int num,
int showsamples,
int winwidth, int winheight );
/*
* cvShowVecSamples
*
* Shows samples stored in .vec file
*
* filename
* .vec file name
* winwidth
* sample width
* winheight
* sample height
* scale
* the scale each sample is adjusted to
*/
void cvShowVecSamples( const char* filename, int winwidth, int winheight, double scale );
/*
* cvCreateCascadeClassifier
*
* Create cascade classifier
* dirname - directory name in which cascade classifier will be created.
* It must exist and contain subdirectories 0, 1, 2, ... (nstages-1).
* vecfilename - name of .vec file with object's images
* bgfilename - name of background description file
* bg_vecfile - true if bgfilename represents a vec file with discrete negatives
* npos - number of positive samples used in training of each stage
* nneg - number of negative samples used in training of each stage
* nstages - number of stages
* numprecalculated - number of features being precalculated. Each precalculated feature
* requires (number_of_samples*(sizeof( float ) + sizeof( short ))) bytes of memory
* numsplits - number of binary splits in each weak classifier
* 1 - stumps, 2 and more - trees.
* minhitrate - desired min hit rate of each stage
* maxfalsealarm - desired max false alarm of each stage
* weightfraction - weight trimming parameter
* mode - 0 - BASIC = Viola
* 1 - CORE = All upright
* 2 - ALL = All features
* symmetric - if not 0 vertical symmetry is assumed
* equalweights - if not 0 initial weights of all samples will be equal
* winwidth - sample width
* winheight - sample height
* boosttype - type of applied boosting algorithm
* 0 - Discrete AdaBoost
* 1 - Real AdaBoost
* 2 - LogitBoost
* 3 - Gentle AdaBoost
* stumperror - type of used error if Discrete AdaBoost algorithm is applied
* 0 - misclassification error
* 1 - gini error
* 2 - entropy error
*/
void cvCreateCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate = 0.995F, float maxfalsealarm = 0.5F,
float weightfraction = 0.95F,
int mode = 0, int symmetric = 1,
int equalweights = 1,
int winwidth = 24, int winheight = 24,
int boosttype = 3, int stumperror = 0 );
void cvCreateTreeCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate, float maxfalsealarm,
float weightfraction,
int mode, int symmetric,
int equalweights,
int winwidth, int winheight,
int boosttype, int stumperror,
int maxtreesplits, int minpos, bool bg_vecfile = false );
#endif /* _CVHAARTRAINING_H_ */

953
apps/haartraining/cvsamples.cpp
View File

@@ -1,953 +0,0 @@
/*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*/
/*
* cvsamples.cpp
*
* support functions for training and test samples creation.
*/
#include "cvhaartraining.h"
#include "_cvhaartraining.h"
/* if ipl.h file is included then iplWarpPerspectiveQ function
is used for image transformation during samples creation;
otherwise internal cvWarpPerspective function is used */
//#include <ipl.h>
#include "cv.h"
#include "highgui.h"
/* Calculates coefficients of perspective transformation
* which maps <quad> into rectangle ((0,0), (w,0), (w,h), (h,0)):
*
* c00*xi + c01*yi + c02
* ui = ---------------------
* c20*xi + c21*yi + c22
*
* c10*xi + c11*yi + c12
* vi = ---------------------
* c20*xi + c21*yi + c22
*
* Coefficients are calculated by solving linear system:
* / x0 y0 1 0 0 0 -x0*u0 -y0*u0 \ /c00\ /u0\
* | x1 y1 1 0 0 0 -x1*u1 -y1*u1 | |c01| |u1|
* | x2 y2 1 0 0 0 -x2*u2 -y2*u2 | |c02| |u2|
* | x3 y3 1 0 0 0 -x3*u3 -y3*u3 |.|c10|=|u3|,
* | 0 0 0 x0 y0 1 -x0*v0 -y0*v0 | |c11| |v0|
* | 0 0 0 x1 y1 1 -x1*v1 -y1*v1 | |c12| |v1|
* | 0 0 0 x2 y2 1 -x2*v2 -y2*v2 | |c20| |v2|
* \ 0 0 0 x3 y3 1 -x3*v3 -y3*v3 / \c21/ \v3/
*
* where:
* (xi, yi) = (quad[i][0], quad[i][1])
* cij - coeffs[i][j], coeffs[2][2] = 1
* (ui, vi) - rectangle vertices
*/
static void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2],
double coeffs[3][3] )
{
//CV_FUNCNAME( "cvWarpPerspective" );
__BEGIN__;
double a[8][8];
double b[8];
CvMat A = cvMat( 8, 8, CV_64FC1, a );
CvMat B = cvMat( 8, 1, CV_64FC1, b );
CvMat X = cvMat( 8, 1, CV_64FC1, coeffs );
int i;
for( i = 0; i < 4; ++i )
{
a[i][0] = quad[i][0]; a[i][1] = quad[i][1]; a[i][2] = 1;
a[i][3] = a[i][4] = a[i][5] = a[i][6] = a[i][7] = 0;
b[i] = 0;
}
for( i = 4; i < 8; ++i )
{
a[i][3] = quad[i-4][0]; a[i][4] = quad[i-4][1]; a[i][5] = 1;
a[i][0] = a[i][1] = a[i][2] = a[i][6] = a[i][7] = 0;
b[i] = 0;
}
int u = src_size.width - 1;
int v = src_size.height - 1;
a[1][6] = -quad[1][0] * u; a[1][7] = -quad[1][1] * u;
a[2][6] = -quad[2][0] * u; a[2][7] = -quad[2][1] * u;
b[1] = b[2] = u;
a[6][6] = -quad[2][0] * v; a[6][7] = -quad[2][1] * v;
a[7][6] = -quad[3][0] * v; a[7][7] = -quad[3][1] * v;
b[6] = b[7] = v;
cvSolve( &A, &B, &X );
coeffs[2][2] = 1;
__END__;
}
/* Warps source into destination by a perspective transform */
static void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] )
{
CV_FUNCNAME( "cvWarpPerspective" );
__BEGIN__;
#ifdef __IPL_H__
IplImage src_stub, dst_stub;
IplImage* src_img;
IplImage* dst_img;
CV_CALL( src_img = cvGetImage( src, &src_stub ) );
CV_CALL( dst_img = cvGetImage( dst, &dst_stub ) );
iplWarpPerspectiveQ( src_img, dst_img, quad, IPL_WARP_R_TO_Q,
IPL_INTER_CUBIC | IPL_SMOOTH_EDGE );
#else
int fill_value = 0;
double c[3][3]; /* transformation coefficients */
double q[4][2]; /* rearranged quad */
int left = 0;
int right = 0;
int next_right = 0;
int next_left = 0;
double y_min = 0;
double y_max = 0;
double k_left, b_left, k_right, b_right;
uchar* src_data;
int src_step;
CvSize src_size;
uchar* dst_data;
int dst_step;
CvSize dst_size;
double d = 0;
int direction = 0;
int i;
if( !src || (!CV_IS_IMAGE( src ) && !CV_IS_MAT( src )) ||
cvGetElemType( src ) != CV_8UC1 ||
cvGetDims( src ) != 2 )
{
CV_ERROR( CV_StsBadArg,
"Source must be two-dimensional array of CV_8UC1 type." );
}
if( !dst || (!CV_IS_IMAGE( dst ) && !CV_IS_MAT( dst )) ||
cvGetElemType( dst ) != CV_8UC1 ||
cvGetDims( dst ) != 2 )
{
CV_ERROR( CV_StsBadArg,
"Destination must be two-dimensional array of CV_8UC1 type." );
}
CV_CALL( cvGetRawData( src, &src_data, &src_step, &src_size ) );
CV_CALL( cvGetRawData( dst, &dst_data, &dst_step, &dst_size ) );
CV_CALL( cvGetPerspectiveTransform( src_size, quad, c ) );
/* if direction > 0 then vertices in quad follow in a CW direction,
otherwise they follow in a CCW direction */
direction = 0;
for( i = 0; i < 4; ++i )
{
int ni = i + 1; if( ni == 4 ) ni = 0;
int pi = i - 1; if( pi == -1 ) pi = 3;
d = (quad[i][0] - quad[pi][0])*(quad[ni][1] - quad[i][1]) -
(quad[i][1] - quad[pi][1])*(quad[ni][0] - quad[i][0]);
int cur_direction = CV_SIGN(d);
if( direction == 0 )
{
direction = cur_direction;
}
else if( direction * cur_direction < 0 )
{
direction = 0;
break;
}
}
if( direction == 0 )
{
CV_ERROR( CV_StsBadArg, "Quadrangle is nonconvex or degenerated." );
}
/* <left> is the index of the topmost quad vertice
if there are two such vertices <left> is the leftmost one */
left = 0;
for( i = 1; i < 4; ++i )
{
if( (quad[i][1] < quad[left][1]) ||
((quad[i][1] == quad[left][1]) && (quad[i][0] < quad[left][0])) )
{
left = i;
}
}
/* rearrange <quad> vertices in such way that they follow in a CW
direction and the first vertice is the topmost one and put them
into <q> */
if( direction > 0 )
{
for( i = left; i < 4; ++i )
{
q[i-left][0] = quad[i][0];
q[i-left][1] = quad[i][1];
}
for( i = 0; i < left; ++i )
{
q[4-left+i][0] = quad[i][0];
q[4-left+i][1] = quad[i][1];
}
}
else
{
for( i = left; i >= 0; --i )
{
q[left-i][0] = quad[i][0];
q[left-i][1] = quad[i][1];
}
for( i = 3; i > left; --i )
{
q[4+left-i][0] = quad[i][0];
q[4+left-i][1] = quad[i][1];
}
}
left = right = 0;
/* if there are two topmost points, <right> is the index of the rightmost one
otherwise <right> */
if( q[left][1] == q[left+1][1] )
{
right = 1;
}
/* <next_left> follows <left> in a CCW direction */
next_left = 3;
/* <next_right> follows <right> in a CW direction */
next_right = right + 1;
/* subtraction of 1 prevents skipping of the first row */
y_min = q[left][1] - 1;
/* left edge equation: y = k_left * x + b_left */
k_left = (q[left][0] - q[next_left][0]) /
(q[left][1] - q[next_left][1]);
b_left = (q[left][1] * q[next_left][0] -
q[left][0] * q[next_left][1]) /
(q[left][1] - q[next_left][1]);
/* right edge equation: y = k_right * x + b_right */
k_right = (q[right][0] - q[next_right][0]) /
(q[right][1] - q[next_right][1]);
b_right = (q[right][1] * q[next_right][0] -
q[right][0] * q[next_right][1]) /
(q[right][1] - q[next_right][1]);
for(;;)
{
int x, y;
y_max = MIN( q[next_left][1], q[next_right][1] );
int iy_min = MAX( cvRound(y_min), 0 ) + 1;
int iy_max = MIN( cvRound(y_max), dst_size.height - 1 );
double x_min = k_left * iy_min + b_left;
double x_max = k_right * iy_min + b_right;
/* walk through the destination quadrangle row by row */
for( y = iy_min; y <= iy_max; ++y )
{
int ix_min = MAX( cvRound( x_min ), 0 );
int ix_max = MIN( cvRound( x_max ), dst_size.width - 1 );
for( x = ix_min; x <= ix_max; ++x )
{
/* calculate coordinates of the corresponding source array point */
double div = (c[2][0] * x + c[2][1] * y + c[2][2]);
double src_x = (c[0][0] * x + c[0][1] * y + c[0][2]) / div;
double src_y = (c[1][0] * x + c[1][1] * y + c[1][2]) / div;
int isrc_x = cvFloor( src_x );
int isrc_y = cvFloor( src_y );
double delta_x = src_x - isrc_x;
double delta_y = src_y - isrc_y;
uchar* s = src_data + isrc_y * src_step + isrc_x;
int i00, i10, i01, i11;
i00 = i10 = i01 = i11 = (int) fill_value;
/* linear interpolation using 2x2 neighborhood */
if( isrc_x >= 0 && isrc_x <= src_size.width &&
isrc_y >= 0 && isrc_y <= src_size.height )
{
i00 = s[0];
}
if( isrc_x >= -1 && isrc_x < src_size.width &&
isrc_y >= 0 && isrc_y <= src_size.height )
{
i10 = s[1];
}
if( isrc_x >= 0 && isrc_x <= src_size.width &&
isrc_y >= -1 && isrc_y < src_size.height )
{
i01 = s[src_step];
}
if( isrc_x >= -1 && isrc_x < src_size.width &&
isrc_y >= -1 && isrc_y < src_size.height )
{
i11 = s[src_step+1];
}
double i0 = i00 + (i10 - i00)*delta_x;
double i1 = i01 + (i11 - i01)*delta_x;
((uchar*)(dst_data + y * dst_step))[x] = (uchar) (i0 + (i1 - i0)*delta_y);
}
x_min += k_left;
x_max += k_right;
}
if( (next_left == next_right) ||
(next_left+1 == next_right && q[next_left][1] == q[next_right][1]) )
{
break;
}
if( y_max == q[next_left][1] )
{
left = next_left;
next_left = left - 1;
k_left = (q[left][0] - q[next_left][0]) /
(q[left][1] - q[next_left][1]);
b_left = (q[left][1] * q[next_left][0] -
q[left][0] * q[next_left][1]) /
(q[left][1] - q[next_left][1]);
}
if( y_max == q[next_right][1] )
{
right = next_right;
next_right = right + 1;
k_right = (q[right][0] - q[next_right][0]) /
(q[right][1] - q[next_right][1]);
b_right = (q[right][1] * q[next_right][0] -
q[right][0] * q[next_right][1]) /
(q[right][1] - q[next_right][1]);
}
y_min = y_max;
}
#endif /* #ifndef __IPL_H__ */
__END__;
}
static
void icvRandomQuad( int width, int height, double quad[4][2],
double maxxangle,
double maxyangle,
double maxzangle )
{
double distfactor = 3.0;
double distfactor2 = 1.0;
double halfw, halfh;
int i;
double rotVectData[3];
double vectData[3];
double rotMatData[9];
CvMat rotVect;
CvMat rotMat;
CvMat vect;
double d;
rotVect = cvMat( 3, 1, CV_64FC1, &rotVectData[0] );
rotMat = cvMat( 3, 3, CV_64FC1, &rotMatData[0] );
vect = cvMat( 3, 1, CV_64FC1, &vectData[0] );
rotVectData[0] = maxxangle * (2.0 * rand() / RAND_MAX - 1.0);
rotVectData[1] = ( maxyangle - fabs( rotVectData[0] ) )
* (2.0 * rand() / RAND_MAX - 1.0);
rotVectData[2] = maxzangle * (2.0 * rand() / RAND_MAX - 1.0);
d = (distfactor + distfactor2 * (2.0 * rand() / RAND_MAX - 1.0)) * width;
/*
rotVectData[0] = maxxangle;
rotVectData[1] = maxyangle;
rotVectData[2] = maxzangle;
d = distfactor * width;
*/
cvRodrigues2( &rotVect, &rotMat );
halfw = 0.5 * width;
halfh = 0.5 * height;
quad[0][0] = -halfw;
quad[0][1] = -halfh;
quad[1][0] = halfw;
quad[1][1] = -halfh;
quad[2][0] = halfw;
quad[2][1] = halfh;
quad[3][0] = -halfw;
quad[3][1] = halfh;
for( i = 0; i < 4; i++ )
{
rotVectData[0] = quad[i][0];
rotVectData[1] = quad[i][1];
rotVectData[2] = 0.0;
cvMatMulAdd( &rotMat, &rotVect, 0, &vect );
quad[i][0] = vectData[0] * d / (d + vectData[2]) + halfw;
quad[i][1] = vectData[1] * d / (d + vectData[2]) + halfh;
/*
quad[i][0] += halfw;
quad[i][1] += halfh;
*/
}
}
int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
CvSampleDistortionData* data )
{
memset( data, 0, sizeof( *data ) );
data->src = cvLoadImage( imgfilename, 0 );
if( data->src != NULL && data->src->nChannels == 1
&& data->src->depth == IPL_DEPTH_8U )
{
int r, c;
uchar* pmask;
uchar* psrc;
uchar* perode;
uchar* pdilate;
uchar dd, de;
data->dx = data->src->width / 2;
data->dy = data->src->height / 2;
data->bgcolor = bgcolor;
data->mask = cvCloneImage( data->src );
data->erode = cvCloneImage( data->src );
data->dilate = cvCloneImage( data->src );
/* make mask image */
for( r = 0; r < data->mask->height; r++ )
{
for( c = 0; c < data->mask->width; c++ )
{
pmask = ( (uchar*) (data->mask->imageData + r * data->mask->widthStep)
+ c );
if( bgcolor - bgthreshold <= (int) (*pmask) &&
(int) (*pmask) <= bgcolor + bgthreshold )
{
*pmask = (uchar) 0;
}
else
{
*pmask = (uchar) 255;
}
}
}
/* extend borders of source image */
cvErode( data->src, data->erode, 0, 1 );
cvDilate( data->src, data->dilate, 0, 1 );
for( r = 0; r < data->mask->height; r++ )
{
for( c = 0; c < data->mask->width; c++ )
{
pmask = ( (uchar*) (data->mask->imageData + r * data->mask->widthStep)
+ c );
if( (*pmask) == 0 )
{
psrc = ( (uchar*) (data->src->imageData + r * data->src->widthStep)
+ c );
perode =
( (uchar*) (data->erode->imageData + r * data->erode->widthStep)
+ c );
pdilate =
( (uchar*)(data->dilate->imageData + r * data->dilate->widthStep)
+ c );
de = (uchar)(bgcolor - (*perode));
dd = (uchar)((*pdilate) - bgcolor);
if( de >= dd && de > bgthreshold )
{
(*psrc) = (*perode);
}
if( dd > de && dd > bgthreshold )
{
(*psrc) = (*pdilate);
}
}
}
}
data->img = cvCreateImage( cvSize( data->src->width + 2 * data->dx,
data->src->height + 2 * data->dy ),
IPL_DEPTH_8U, 1 );
data->maskimg = cvCloneImage( data->img );
return 1;
}
return 0;
}
void icvPlaceDistortedSample( CvArr* background,
int inverse, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int inscribe, double maxshiftf, double maxscalef,
CvSampleDistortionData* data )
{
double quad[4][2];
int r, c;
uchar* pimg;
uchar* pbg;
uchar* palpha;
uchar chartmp;
int forecolordev;
float scale;
IplImage* img;
IplImage* maskimg;
CvMat stub;
CvMat* bgimg;
CvRect cr;
CvRect roi;
double xshift, yshift, randscale;
icvRandomQuad( data->src->width, data->src->height, quad,
maxxangle, maxyangle, maxzangle );
quad[0][0] += (double) data->dx;
quad[0][1] += (double) data->dy;
quad[1][0] += (double) data->dx;
quad[1][1] += (double) data->dy;
quad[2][0] += (double) data->dx;
quad[2][1] += (double) data->dy;
quad[3][0] += (double) data->dx;
quad[3][1] += (double) data->dy;
cvSet( data->img, cvScalar( data->bgcolor ) );
cvSet( data->maskimg, cvScalar( 0.0 ) );
cvWarpPerspective( data->src, data->img, quad );
cvWarpPerspective( data->mask, data->maskimg, quad );
cvSmooth( data->maskimg, data->maskimg, CV_GAUSSIAN, 3, 3 );
bgimg = cvGetMat( background, &stub );
cr.x = data->dx;
cr.y = data->dy;
cr.width = data->src->width;
cr.height = data->src->height;
if( inscribe )
{
/* quad's circumscribing rectangle */
cr.x = (int) MIN( quad[0][0], quad[3][0] );
cr.y = (int) MIN( quad[0][1], quad[1][1] );
cr.width = (int) (MAX( quad[1][0], quad[2][0] ) + 0.5F ) - cr.x;
cr.height = (int) (MAX( quad[2][1], quad[3][1] ) + 0.5F ) - cr.y;
}
xshift = maxshiftf * rand() / RAND_MAX;
yshift = maxshiftf * rand() / RAND_MAX;
cr.x -= (int) ( xshift * cr.width );
cr.y -= (int) ( yshift * cr.height );
cr.width = (int) ((1.0 + maxshiftf) * cr.width );
cr.height = (int) ((1.0 + maxshiftf) * cr.height);
randscale = maxscalef * rand() / RAND_MAX;
cr.x -= (int) ( 0.5 * randscale * cr.width );
cr.y -= (int) ( 0.5 * randscale * cr.height );
cr.width = (int) ((1.0 + randscale) * cr.width );
cr.height = (int) ((1.0 + randscale) * cr.height);
scale = MAX( ((float) cr.width) / bgimg->cols, ((float) cr.height) / bgimg->rows );
roi.x = (int) (-0.5F * (scale * bgimg->cols - cr.width) + cr.x);
roi.y = (int) (-0.5F * (scale * bgimg->rows - cr.height) + cr.y);
roi.width = (int) (scale * bgimg->cols);
roi.height = (int) (scale * bgimg->rows);
img = cvCreateImage( cvSize( bgimg->cols, bgimg->rows ), IPL_DEPTH_8U, 1 );
maskimg = cvCreateImage( cvSize( bgimg->cols, bgimg->rows ), IPL_DEPTH_8U, 1 );
cvSetImageROI( data->img, roi );
cvResize( data->img, img );
cvResetImageROI( data->img );
cvSetImageROI( data->maskimg, roi );
cvResize( data->maskimg, maskimg );
cvResetImageROI( data->maskimg );
forecolordev = (int) (maxintensitydev * (2.0 * rand() / RAND_MAX - 1.0));
for( r = 0; r < img->height; r++ )
{
for( c = 0; c < img->width; c++ )
{
pimg = (uchar*) img->imageData + r * img->widthStep + c;
pbg = (uchar*) bgimg->data.ptr + r * bgimg->step + c;
palpha = (uchar*) maskimg->imageData + r * maskimg->widthStep + c;
chartmp = (uchar) MAX( 0, MIN( 255, forecolordev + (*pimg) ) );
if( inverse )
{
chartmp ^= 0xFF;
}
*pbg = (uchar) (( chartmp*(*palpha )+(255 - (*palpha) )*(*pbg) ) / 255);
}
}
cvReleaseImage( &img );
cvReleaseImage( &maskimg );
}
void icvEndSampleDistortion( CvSampleDistortionData* data )
{
if( data->src )
{
cvReleaseImage( &data->src );
}
if( data->mask )
{
cvReleaseImage( &data->mask );
}
if( data->erode )
{
cvReleaseImage( &data->erode );
}
if( data->dilate )
{
cvReleaseImage( &data->dilate );
}
if( data->img )
{
cvReleaseImage( &data->img );
}
if( data->maskimg )
{
cvReleaseImage( &data->maskimg );
}
}
void icvWriteVecHeader( FILE* file, int count, int width, int height )
{
int vecsize;
short tmp;
/* number of samples */
fwrite( &count, sizeof( count ), 1, file );
/* vector size */
vecsize = width * height;
fwrite( &vecsize, sizeof( vecsize ), 1, file );
/* min/max values */
tmp = 0;
fwrite( &tmp, sizeof( tmp ), 1, file );
fwrite( &tmp, sizeof( tmp ), 1, file );
}
void icvWriteVecSample( FILE* file, CvArr* sample )
{
CvMat* mat, stub;
int r, c;
short tmp;
uchar chartmp;
mat = cvGetMat( sample, &stub );
chartmp = 0;
fwrite( &chartmp, sizeof( chartmp ), 1, file );
for( r = 0; r < mat->rows; r++ )
{
for( c = 0; c < mat->cols; c++ )
{
tmp = (short) (CV_MAT_ELEM( *mat, uchar, r, c ));
fwrite( &tmp, sizeof( tmp ), 1, file );
}
}
}
int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilename,
int num,
int showsamples,
int winwidth, int winheight )
{
char fullname[PATH_MAX];
char* filename;
FILE* info;
FILE* vec;
IplImage* src=0;
IplImage* sample;
int line;
int error;
int i;
int x, y, width, height;
int total;
assert( infoname != NULL );
assert( vecfilename != NULL );
total = 0;
if( !icvMkDir( vecfilename ) )
{
#if CV_VERBOSE
fprintf( stderr, "Unable to create directory hierarchy: %s\n", vecfilename );
#endif /* CV_VERBOSE */
return total;
}
info = fopen( infoname, "r" );
if( info == NULL )
{
#if CV_VERBOSE
fprintf( stderr, "Unable to open file: %s\n", infoname );
#endif /* CV_VERBOSE */
return total;
}
vec = fopen( vecfilename, "wb" );
if( vec == NULL )
{
#if CV_VERBOSE
fprintf( stderr, "Unable to open file: %s\n", vecfilename );
#endif /* CV_VERBOSE */
fclose( info );
return total;
}
sample = cvCreateImage( cvSize( winwidth, winheight ), IPL_DEPTH_8U, 1 );
icvWriteVecHeader( vec, num, sample->width, sample->height );
if( showsamples )
{
cvNamedWindow( "Sample", CV_WINDOW_AUTOSIZE );
}
strcpy( fullname, infoname );
filename = strrchr( fullname, '\\' );
if( filename == NULL )
{
filename = strrchr( fullname, '/' );
}
if( filename == NULL )
{
filename = fullname;
}
else
{
filename++;
}
for( line = 1, error = 0, total = 0; total < num ;line++ )
{
int count;
error = ( fscanf( info, "%s %d", filename, &count ) != 2 );
if( !error )
{
src = cvLoadImage( fullname, 0 );
error = ( src == NULL );
if( error )
{
#if CV_VERBOSE
fprintf( stderr, "Unable to open image: %s\n", fullname );
#endif /* CV_VERBOSE */
}
}
for( i = 0; (i < count) && (total < num); i++, total++ )
{
error = ( fscanf( info, "%d %d %d %d", &x, &y, &width, &height ) != 4 );
if( error ) break;
cvSetImageROI( src, cvRect( x, y, width, height ) );
cvResize( src, sample, width >= sample->width &&
height >= sample->height ? CV_INTER_AREA : CV_INTER_LINEAR );
if( showsamples )
{
cvShowImage( "Sample", sample );
if( cvWaitKey( 0 ) == 27 )
{
showsamples = 0;
}
}
icvWriteVecSample( vec, sample );
}
if( src )
{
cvReleaseImage( &src );
}
if( error )
{
#if CV_VERBOSE
fprintf( stderr, "%s(%d) : parse error", infoname, line );
#endif /* CV_VERBOSE */
break;
}
}
if( sample )
{
cvReleaseImage( &sample );
}
fclose( vec );
fclose( info );
return total;
}
void cvShowVecSamples( const char* filename, int winwidth, int winheight,
double scale )
{
CvVecFile file;
short tmp;
int i;
CvMat* sample;
tmp = 0;
file.input = fopen( filename, "rb" );
if( file.input != NULL )
{
size_t elements_read1 = fread( &file.count, sizeof( file.count ), 1, file.input );
size_t elements_read2 = fread( &file.vecsize, sizeof( file.vecsize ), 1, file.input );
size_t elements_read3 = fread( &tmp, sizeof( tmp ), 1, file.input );
size_t elements_read4 = fread( &tmp, sizeof( tmp ), 1, file.input );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
if( file.vecsize != winwidth * winheight )
{
int guessed_w = 0;
int guessed_h = 0;
fprintf( stderr, "Warning: specified sample width=%d and height=%d "
"does not correspond to .vec file vector size=%d.\n",
winwidth, winheight, file.vecsize );
if( file.vecsize > 0 )
{
guessed_w = cvFloor( sqrt( (float) file.vecsize ) );
if( guessed_w > 0 )
{
guessed_h = file.vecsize / guessed_w;
}
}
if( guessed_w <= 0 || guessed_h <= 0 || guessed_w * guessed_h != file.vecsize)
{
fprintf( stderr, "Error: failed to guess sample width and height\n" );
fclose( file.input );
return;
}
else
{
winwidth = guessed_w;
winheight = guessed_h;
fprintf( stderr, "Guessed width=%d, guessed height=%d\n",
winwidth, winheight );
}
}
if( !feof( file.input ) && scale > 0 )
{
CvMat* scaled_sample = 0;
file.last = 0;
file.vector = (short*) cvAlloc( sizeof( *file.vector ) * file.vecsize );
sample = scaled_sample = cvCreateMat( winheight, winwidth, CV_8UC1 );
if( scale != 1.0 )
{
scaled_sample = cvCreateMat( MAX( 1, cvCeil( scale * winheight ) ),
MAX( 1, cvCeil( scale * winwidth ) ),
CV_8UC1 );
}
cvNamedWindow( "Sample", CV_WINDOW_AUTOSIZE );
for( i = 0; i < file.count; i++ )
{
icvGetHaarTraininDataFromVecCallback( sample, &file );
if( scale != 1.0 ) cvResize( sample, scaled_sample, CV_INTER_LINEAR);
cvShowImage( "Sample", scaled_sample );
if( cvWaitKey( 0 ) == 27 ) break;
}
if( scaled_sample && scaled_sample != sample ) cvReleaseMat( &scaled_sample );
cvReleaseMat( &sample );
cvFree( &file.vector );
}
fclose( file.input );
}
}
/* End of file. */

284
apps/haartraining/haartraining.cpp
View File

@@ -1,284 +0,0 @@
/*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>
using namespace std;
#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;
}

377
apps/haartraining/performance.cpp
View File

@@ -1,377 +0,0 @@
/*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*/
/*
* performance.cpp
*
* Measure performance of classifier
*/
#include "opencv2/core.hpp"
#include "cv.h"
#include "highgui.h"
#include <cstdio>
#include <cmath>
#include <ctime>
#ifdef _WIN32
/* use clock() function insted of time() */
#define time( arg ) (((double) clock()) / CLOCKS_PER_SEC)
#endif /* _WIN32 */
#ifndef PATH_MAX
#define PATH_MAX 512
#endif /* PATH_MAX */
typedef struct HidCascade
{
int size;
int count;
} HidCascade;
typedef struct ObjectPos
{
float x;
float y;
float width;
int found; /* for reference */
int neghbors;
} ObjectPos;
int main( int argc, char* argv[] )
{
int i, j;
char* classifierdir = NULL;
//char* samplesdir = NULL;
int saveDetected = 1;
double scale_factor = 1.2;
float maxSizeDiff = 1.5F;
float maxPosDiff = 0.3F;
/* number of stages. if <=0 all stages are used */
int nos = -1, nos0;
int width = 24;
int height = 24;
int rocsize;
FILE* info;
char* infoname;
char fullname[PATH_MAX];
char detfilename[PATH_MAX];
char* filename;
char detname[] = "det-";
CvHaarClassifierCascade* cascade;
CvMemStorage* storage;
CvSeq* objects;
double totaltime;
infoname = (char*)"";
rocsize = 40;
if( argc == 1 )
{
printf( "Usage: %s\n -data <classifier_directory_name>\n"
" -info <collection_file_name>\n"
" [-maxSizeDiff <max_size_difference = %f>]\n"
" [-maxPosDiff <max_position_difference = %f>]\n"
" [-sf <scale_factor = %f>]\n"
" [-ni]\n"
" [-nos <number_of_stages = %d>]\n"
" [-rs <roc_size = %d>]\n"
" [-w <sample_width = %d>]\n"
" [-h <sample_height = %d>]\n",
argv[0], maxSizeDiff, maxPosDiff, scale_factor, nos, rocsize,
width, height );
return 0;
}
for( i = 1; i < argc; i++ )
{
if( !strcmp( argv[i], "-data" ) )
{
classifierdir = argv[++i];
}
else if( !strcmp( argv[i], "-info" ) )
{
infoname = argv[++i];
}
else if( !strcmp( argv[i], "-maxSizeDiff" ) )
{
maxSizeDiff = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-maxPosDiff" ) )
{
maxPosDiff = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-sf" ) )
{
scale_factor = atof( argv[++i] );
}
else if( !strcmp( argv[i], "-ni" ) )
{
saveDetected = 0;
}
else if( !strcmp( argv[i], "-nos" ) )
{
nos = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-rs" ) )
{
rocsize = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-w" ) )
{
width = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-h" ) )
{
height = atoi( argv[++i] );
}
}
cascade = cvLoadHaarClassifierCascade( classifierdir, cvSize( width, height ) );
if( cascade == NULL )
{
printf( "Unable to load classifier from %s\n", classifierdir );
return 1;
}
int* numclassifiers = new int[cascade->count];
numclassifiers[0] = cascade->stage_classifier[0].count;
for( i = 1; i < cascade->count; i++ )
{
numclassifiers[i] = numclassifiers[i-1] + cascade->stage_classifier[i].count;
}
storage = cvCreateMemStorage();
nos0 = cascade->count;
if( nos <= 0 )
nos = nos0;
strcpy( fullname, infoname );
filename = strrchr( fullname, '\\' );
if( filename == NULL )
{
filename = strrchr( fullname, '/' );
}
if( filename == NULL )
{
filename = fullname;
}
else
{
filename++;
}
info = fopen( infoname, "r" );
totaltime = 0.0;
if( info != NULL )
{
int x, y;
IplImage* img;
int hits, missed, falseAlarms;
int totalHits, totalMissed, totalFalseAlarms;
int found;
float distance;
int refcount;
ObjectPos* ref;
int detcount;
ObjectPos* det;
int error=0;
int* pos;
int* neg;
pos = (int*) cvAlloc( rocsize * sizeof( *pos ) );
neg = (int*) cvAlloc( rocsize * sizeof( *neg ) );
for( i = 0; i < rocsize; i++ ) { pos[i] = neg[i] = 0; }
printf( "+================================+======+======+======+\n" );
printf( "| File Name | Hits |Missed| False|\n" );
printf( "+================================+======+======+======+\n" );
totalHits = totalMissed = totalFalseAlarms = 0;
while( !feof( info ) )
{
if( fscanf( info, "%s %d", filename, &refcount ) != 2 || refcount <= 0 ) break;
img = cvLoadImage( fullname );
if( !img ) continue;
ref = (ObjectPos*) cvAlloc( refcount * sizeof( *ref ) );
for( i = 0; i < refcount; i++ )
{
int w, h;
error = (fscanf( info, "%d %d %d %d", &x, &y, &w, &h ) != 4);
if( error ) break;
ref[i].x = 0.5F * w + x;
ref[i].y = 0.5F * h + y;
ref[i].width = sqrtf( 0.5F * (w * w + h * h) );
ref[i].found = 0;
ref[i].neghbors = 0;
}
if( !error )
{
cvClearMemStorage( storage );
cascade->count = nos;
totaltime -= time( 0 );
objects = cvHaarDetectObjects( img, cascade, storage, scale_factor, 1 );
totaltime += time( 0 );
cascade->count = nos0;
detcount = ( objects ? objects->total : 0);
det = (detcount > 0) ?
( (ObjectPos*)cvAlloc( detcount * sizeof( *det )) ) : NULL;
hits = missed = falseAlarms = 0;
for( i = 0; i < detcount; i++ )
{
CvAvgComp r = *((CvAvgComp*) cvGetSeqElem( objects, i ));
det[i].x = 0.5F * r.rect.width + r.rect.x;
det[i].y = 0.5F * r.rect.height + r.rect.y;
det[i].width = sqrtf( 0.5F * (r.rect.width * r.rect.width +
r.rect.height * r.rect.height) );
det[i].neghbors = r.neighbors;
if( saveDetected )
{
cvRectangle( img, cvPoint( r.rect.x, r.rect.y ),
cvPoint( r.rect.x + r.rect.width, r.rect.y + r.rect.height ),
CV_RGB( 255, 0, 0 ), 3 );
}
found = 0;
for( j = 0; j < refcount; j++ )
{
distance = sqrtf( (det[i].x - ref[j].x) * (det[i].x - ref[j].x) +
(det[i].y - ref[j].y) * (det[i].y - ref[j].y) );
if( (distance < ref[j].width * maxPosDiff) &&
(det[i].width > ref[j].width / maxSizeDiff) &&
(det[i].width < ref[j].width * maxSizeDiff) )
{
ref[j].found = 1;
ref[j].neghbors = MAX( ref[j].neghbors, det[i].neghbors );
found = 1;
}
}
if( !found )
{
falseAlarms++;
neg[MIN(det[i].neghbors, rocsize - 1)]++;
}
}
for( j = 0; j < refcount; j++ )
{
if( ref[j].found )
{
hits++;
pos[MIN(ref[j].neghbors, rocsize - 1)]++;
}
else
{
missed++;
}
}
totalHits += hits;
totalMissed += missed;
totalFalseAlarms += falseAlarms;
printf( "|%32.32s|%6d|%6d|%6d|\n", filename, hits, missed, falseAlarms );
printf( "+--------------------------------+------+------+------+\n" );
fflush( stdout );
if( saveDetected )
{
strcpy( detfilename, detname );
strcat( detfilename, filename );
strcpy( filename, detfilename );
cvvSaveImage( fullname, img );
}
if( det ) { cvFree( &det ); det = NULL; }
} /* if( !error ) */
cvReleaseImage( &img );
cvFree( &ref );
}
fclose( info );
printf( "|%32.32s|%6d|%6d|%6d|\n", "Total",
totalHits, totalMissed, totalFalseAlarms );
printf( "+================================+======+======+======+\n" );
printf( "Number of stages: %d\n", nos );
printf( "Number of weak classifiers: %d\n", numclassifiers[nos - 1] );
printf( "Total time: %f\n", totaltime );
/* print ROC to stdout */
for( i = rocsize - 1; i > 0; i-- )
{
pos[i-1] += pos[i];
neg[i-1] += neg[i];
}
fprintf( stderr, "%d\n", nos );
for( i = 0; i < rocsize; i++ )
{
fprintf( stderr, "\t%d\t%d\t%f\t%f\n", pos[i], neg[i],
((float)pos[i]) / (totalHits + totalMissed),
((float)neg[i]) / (totalHits + totalMissed) );
}
cvFree( &pos );
cvFree( &neg );
}
delete[] numclassifiers;
cvReleaseHaarClassifierCascade( &cascade );
cvReleaseMemStorage( &storage );
return 0;
}

33
apps/sft/CMakeLists.txt
View File

@@ -1,33 +0,0 @@
set(name sft)
set(the_target opencv_${name})
set(OPENCV_${the_target}_DEPS opencv_core opencv_softcascade opencv_highgui opencv_imgproc opencv_ml)
ocv_check_dependencies(${OPENCV_${the_target}_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(${the_target})
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/include" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_include_modules(${OPENCV_${the_target}_DEPS})
file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
add_executable(${the_target} ${${the_target}_SOURCES})
target_link_libraries(${the_target} ${OPENCV_${the_target}_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
OUTPUT_NAME "opencv_trainsoftcascade")
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "applications")
endif()
install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)

162
apps/sft/config.cpp
View File

@@ -1,162 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
#include <sft/config.hpp>
#include <iomanip>
sft::Config::Config(): seed(0) {}
void sft::Config::write(cv::FileStorage& fs) const
{
fs << "{"
<< "trainPath" << trainPath
<< "testPath" << testPath
<< "modelWinSize" << modelWinSize
<< "offset" << offset
<< "octaves" << octaves
<< "positives" << positives
<< "negatives" << negatives
<< "btpNegatives" << btpNegatives
<< "shrinkage" << shrinkage
<< "treeDepth" << treeDepth
<< "weaks" << weaks
<< "poolSize" << poolSize
<< "cascadeName" << cascadeName
<< "outXmlPath" << outXmlPath
<< "seed" << seed
<< "featureType" << featureType
<< "}";
}
void sft::Config::read(const cv::FileNode& node)
{
trainPath = (string)node["trainPath"];
testPath = (string)node["testPath"];
cv::FileNodeIterator nIt = node["modelWinSize"].end();
modelWinSize = cv::Size((int)*(--nIt), (int)*(--nIt));
nIt = node["offset"].end();
offset = cv::Point2i((int)*(--nIt), (int)*(--nIt));
node["octaves"] >> octaves;
positives = (int)node["positives"];
negatives = (int)node["negatives"];
btpNegatives = (int)node["btpNegatives"];
shrinkage = (int)node["shrinkage"];
treeDepth = (int)node["treeDepth"];
weaks = (int)node["weaks"];
poolSize = (int)node["poolSize"];
cascadeName = (std::string)node["cascadeName"];
outXmlPath = (std::string)node["outXmlPath"];
seed = (int)node["seed"];
featureType = (std::string)node["featureType"];
}
void sft::write(cv::FileStorage& fs, const string&, const Config& x)
{
x.write(fs);
}
void sft::read(const cv::FileNode& node, Config& x, const Config& default_value)
{
x = default_value;
if(!node.empty())
x.read(node);
}
namespace {
struct Out
{
Out(std::ostream& _out): out(_out) {}
template<typename T>
void operator ()(const T a) const {out << a << " ";}
std::ostream& out;
private:
Out& operator=(Out const& other);
};
}
std::ostream& sft::operator<<(std::ostream& out, const Config& m)
{
out << std::setw(14) << std::left << "trainPath" << m.trainPath << std::endl
<< std::setw(14) << std::left << "testPath" << m.testPath << std::endl
<< std::setw(14) << std::left << "modelWinSize" << m.modelWinSize << std::endl
<< std::setw(14) << std::left << "offset" << m.offset << std::endl
<< std::setw(14) << std::left << "octaves";
Out o(out);
for_each(m.octaves.begin(), m.octaves.end(), o);
out << std::endl
<< std::setw(14) << std::left << "positives" << m.positives << std::endl
<< std::setw(14) << std::left << "negatives" << m.negatives << std::endl
<< std::setw(14) << std::left << "btpNegatives" << m.btpNegatives << std::endl
<< std::setw(14) << std::left << "shrinkage" << m.shrinkage << std::endl
<< std::setw(14) << std::left << "treeDepth" << m.treeDepth << std::endl
<< std::setw(14) << std::left << "weaks" << m.weaks << std::endl
<< std::setw(14) << std::left << "poolSize" << m.poolSize << std::endl
<< std::setw(14) << std::left << "cascadeName" << m.cascadeName << std::endl
<< std::setw(14) << std::left << "outXmlPath" << m.outXmlPath << std::endl
<< std::setw(14) << std::left << "seed" << m.seed << std::endl
<< std::setw(14) << std::left << "featureType" << m.featureType << std::endl;
return out;
}

77
apps/sft/dataset.cpp
View File

@@ -1,77 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
#include <sft/dataset.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <queue>
// in the default case data folders should be aligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
sft::ScaledDataset::ScaledDataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
dprintf("%s\n", "Positives globing...");
cv::glob(path + "/pos/octave_" + cv::format("%d", oct) + "/*.png", pos);
dprintf("%s\n", "Negatives globing...");
cv::glob(path + "/neg/octave_" + cv::format("%d", oct) + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}
cv::Mat sft::ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int sft::ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
sft::ScaledDataset::~ScaledDataset(){}

74
apps/sft/include/sft/common.hpp
View File

@@ -1,74 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
#ifndef __SFT_COMMON_HPP__
#define __SFT_COMMON_HPP__
#include <opencv2/core.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/softcascade.hpp>
namespace cv {using namespace softcascade;}
namespace sft
{
using cv::Mat;
struct ICF;
typedef cv::String string;
typedef std::vector<ICF> Icfvector;
typedef std::vector<sft::string> svector;
typedef std::vector<int> ivector;
}
// used for noisy printfs
//#define WITH_DEBUG_OUT
#if defined WITH_DEBUG_OUT
# include <stdio.h>
# define dprintf(format, ...) printf(format, ##__VA_ARGS__)
#else
# define dprintf(format, ...)
#endif
#endif

138
apps/sft/include/sft/config.hpp
View File

@@ -1,138 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
#ifndef __SFT_CONFIG_HPP__
#define __SFT_CONFIG_HPP__
#include <sft/common.hpp>
#include <ostream>
namespace sft {
struct Config
{
Config();
void write(cv::FileStorage& fs) const;
void read(const cv::FileNode& node);
// Scaled and shrunk model size.
cv::Size model(ivector::const_iterator it) const
{
float octave = powf(2.f, (float)(*it));
return cv::Size( cvRound(modelWinSize.width * octave) / shrinkage,
cvRound(modelWinSize.height * octave) / shrinkage );
}
// Scaled but, not shrunk bounding box for object in sample image.
cv::Rect bbox(ivector::const_iterator it) const
{
float octave = powf(2.f, (float)(*it));
return cv::Rect( cvRound(offset.x * octave), cvRound(offset.y * octave),
cvRound(modelWinSize.width * octave), cvRound(modelWinSize.height * octave));
}
string resPath(ivector::const_iterator it) const
{
return cv::format("%s%d.xml",cascadeName.c_str(), *it);
}
// Paths to a rescaled data
string trainPath;
string testPath;
// Original model size.
cv::Size modelWinSize;
// example offset into positive image
cv::Point2i offset;
// List of octaves for which have to be trained cascades (a list of powers of two)
ivector octaves;
// Maximum number of positives that should be used during training
int positives;
// Initial number of negatives used during training.
int negatives;
// Number of weak negatives to add each bootstrapping step.
int btpNegatives;
// Inverse of scale for feature resizing
int shrinkage;
// Depth on weak classifier's decision tree
int treeDepth;
// Weak classifiers number in resulted cascade
int weaks;
// Feature random pool size
int poolSize;
// file name to store cascade
string cascadeName;
// path to resulting cascade
string outXmlPath;
// seed for random generation
int seed;
// channel feature type
string featureType;
// // bounding rectangle for actual example into example window
// cv::Rect exampleWindow;
};
// required for cv::FileStorage serialization
void write(cv::FileStorage& fs, const string&, const Config& x);
void read(const cv::FileNode& node, Config& x, const Config& default_value);
std::ostream& operator<<(std::ostream& out, const Config& m);
}
#endif

67
apps/sft/include/sft/dataset.hpp
View File

@@ -1,67 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
#ifndef __SFT_OCTAVE_HPP__
#define __SFT_OCTAVE_HPP__
#include <sft/common.hpp>
namespace sft
{
using cv::softcascade::Dataset;
class ScaledDataset : public Dataset
{
public:
ScaledDataset(const sft::string& path, const int octave);
virtual cv::Mat get(SampleType type, int idx) const;
virtual int available(SampleType type) const;
virtual ~ScaledDataset();
private:
svector pos;
svector neg;
};
}
#endif

168
apps/sft/sft.cpp
View File

@@ -1,168 +0,0 @@
/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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 the copyright holders 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*/
// Training application for Soft Cascades.
#include <sft/common.hpp>
#include <iostream>
#include <sft/dataset.hpp>
#include <sft/config.hpp>
#include <opencv2/core/core_c.h>
int main(int argc, char** argv)
{
using namespace sft;
const string keys =
"{help h usage ? | | print this message }"
"{config c | | path to configuration xml }"
;
cv::CommandLineParser parser(argc, argv, keys);
parser.about("Soft cascade training application.");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
string configPath = parser.get<string>("config");
if (configPath.empty())
{
std::cout << "Configuration file is missing or empty. Could not start training." << std::endl;
return 0;
}
std::cout << "Read configuration from file " << configPath << std::endl;
cv::FileStorage fs(configPath, cv::FileStorage::READ);
if(!fs.isOpened())
{
std::cout << "Configuration file " << configPath << " can't be opened." << std::endl;
return 1;
}
// 1. load config
sft::Config cfg;
fs["config"] >> cfg;
std::cout << std::endl << "Training will be executed for configuration:" << std::endl << cfg << std::endl;
// 2. check and open output file
cv::FileStorage fso(cfg.outXmlPath, cv::FileStorage::WRITE);
if(!fso.isOpened())
{
std::cout << "Training stopped. Output classifier Xml file " << cfg.outXmlPath << " can't be opened." << std::endl;
return 1;
}
fso << cfg.cascadeName
<< "{"
<< "stageType" << "BOOST"
<< "featureType" << cfg.featureType
<< "octavesNum" << (int)cfg.octaves.size()
<< "width" << cfg.modelWinSize.width
<< "height" << cfg.modelWinSize.height
<< "shrinkage" << cfg.shrinkage
<< "octaves" << "[";
// 3. Train all octaves
for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
{
// a. create random feature pool
int nfeatures = cfg.poolSize;
cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl;
int nchannels = (cfg.featureType == "HOG6MagLuv") ? 10: 8;
std::cout << "number of feature channels is " << nchannels << std::endl;
cv::Ptr<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures, nchannels);
nfeatures = pool->size();
int npositives = cfg.positives;
int nnegatives = cfg.negatives;
int shrinkage = cfg.shrinkage;
cv::Rect boundingBox = cfg.bbox(it);
std::cout << "Object bounding box" << boundingBox << std::endl;
typedef cv::Octave Octave;
cv::Ptr<cv::ChannelFeatureBuilder> builder = cv::ChannelFeatureBuilder::create(cfg.featureType);
std::cout << "Channel builder " << builder->info()->name() << std::endl;
cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);
std::string path = cfg.trainPath;
sft::ScaledDataset dataset(path, *it);
if (boost->train(&dataset, pool, cfg.weaks, cfg.treeDepth))
{
CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost->write(fout, cfg.cascadeName);
cvReleaseFileStorage( &fout);
cv::Mat thresholds;
boost->setRejectThresholds(thresholds);
boost->write(fso, pool, thresholds);
cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
tfs << "thresholds" << thresholds;
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
}
}
fso << "]" << "}";
fso.release();
std::cout << "Training complete..." << std::endl;
return 0;
}

4
apps/traincascade/CMakeLists.txt
View File

@@ -1,4 +1,4 @@
set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_ml opencv_imgproc opencv_photo opencv_objdetect opencv_highgui opencv_calib3d opencv_video opencv_features2d opencv_flann opencv_legacy)
set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_ml opencv_imgproc opencv_photo opencv_objdetect opencv_highgui opencv_calib3d opencv_video opencv_features2d)
ocv_check_dependencies(${OPENCV_TRAINCASCADE_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
@@ -20,7 +20,7 @@ set(traincascade_files traincascade.cpp
set(the_target opencv_traincascade)
add_executable(${the_target} ${traincascade_files})
target_link_libraries(${the_target} ${OPENCV_TRAINCASCADE_DEPS} opencv_haartraining_engine)
target_link_libraries(${the_target} ${OPENCV_TRAINCASCADE_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"