generic-library/opencv
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Merge branch 'master' into tvl1_ali

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This commit is contained in:
Ernest Galbrun 2014-07-01 10:17:07 +02:00
commit c45e645d6c
682 changed files with 72006 additions and 154976 deletions
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2
3rdparty/ippicv/.gitignore vendored Normal file
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@ -0,0 +1,2 @@
downloads/
unpack/

108
3rdparty/ippicv/downloader.cmake vendored Normal file
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@ -0,0 +1,108 @@
#
# The script downloads ICV package
#
# On return this will define:
# OPENCV_ICV_PATH - path to unpacked downloaded package
#
function(_icv_downloader)
# Define actual ICV versions
if(APPLE)
set(OPENCV_ICV_PACKAGE_NAME "ippicv_macosx_20140429.tgz")
set(OPENCV_ICV_PACKAGE_HASH "f2195a60829899983acd4a45794e1717")
set(OPENCV_ICV_PLATFORM "macosx")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_osx")
elseif(UNIX)
if(ANDROID AND (NOT ANDROID_ABI STREQUAL x86))
return()
endif()
set(OPENCV_ICV_PACKAGE_NAME "ippicv_linux_20140513.tgz")
set(OPENCV_ICV_PACKAGE_HASH "d80cb24f3a565113a9d6dc56344142f6")
set(OPENCV_ICV_PLATFORM "linux")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_lnx")
elseif(WIN32 AND NOT ARM)
set(OPENCV_ICV_PACKAGE_NAME "ippicv_windows_20140429.zip")
set(OPENCV_ICV_PACKAGE_HASH "b5028a92224ec1fbc554010c52eb3ec8")
set(OPENCV_ICV_PLATFORM "windows")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_win")
else()
return() # Not supported
endif()
set(OPENCV_ICV_UNPACK_PATH "${CMAKE_CURRENT_LIST_DIR}/unpack")
set(OPENCV_ICV_PATH "${OPENCV_ICV_UNPACK_PATH}${OPENCV_ICV_PACKAGE_SUBDIR}")
if(DEFINED OPENCV_ICV_PACKAGE_DOWNLOADED
AND OPENCV_ICV_PACKAGE_DOWNLOADED STREQUAL OPENCV_ICV_PACKAGE_HASH
AND EXISTS ${OPENCV_ICV_PATH})
# Package has been downloaded and checked by the previous build
set(OPENCV_ICV_PATH "${OPENCV_ICV_PATH}" PARENT_SCOPE)
return()
else()
if(EXISTS ${OPENCV_ICV_UNPACK_PATH})
message(STATUS "ICV: Removing previous unpacked package: ${OPENCV_ICV_UNPACK_PATH}")
file(REMOVE_RECURSE ${OPENCV_ICV_UNPACK_PATH})
endif()
endif()
unset(OPENCV_ICV_PACKAGE_DOWNLOADED CACHE)
set(OPENCV_ICV_PACKAGE_ARCHIVE "${CMAKE_CURRENT_LIST_DIR}/downloads/${OPENCV_ICV_PLATFORM}-${OPENCV_ICV_PACKAGE_HASH}/${OPENCV_ICV_PACKAGE_NAME}")
get_filename_component(OPENCV_ICV_PACKAGE_ARCHIVE_DIR "${OPENCV_ICV_PACKAGE_ARCHIVE}" PATH)
if(EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
file(MD5 "${OPENCV_ICV_PACKAGE_ARCHIVE}" archive_md5)
if(NOT archive_md5 STREQUAL OPENCV_ICV_PACKAGE_HASH)
message(WARNING "ICV: Local copy of ICV package has invalid MD5 hash: ${archive_md5} (expected: ${OPENCV_ICV_PACKAGE_HASH})")
file(REMOVE "${OPENCV_ICV_PACKAGE_ARCHIVE}")
file(REMOVE_RECURSE "${OPENCV_ICV_PACKAGE_ARCHIVE_DIR}")
endif()
endif()
if(NOT EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
if(NOT DEFINED OPENCV_ICV_URL)
if(DEFINED ENV{OPENCV_ICV_URL})
set(OPENCV_ICV_URL $ENV{OPENCV_ICV_URL})
else()
set(OPENCV_ICV_URL "http://sourceforge.net/projects/opencvlibrary/files/3rdparty/ippicv")
endif()
endif()
file(MAKE_DIRECTORY ${OPENCV_ICV_PACKAGE_ARCHIVE_DIR})
message(STATUS "ICV: Downloading ${OPENCV_ICV_PACKAGE_NAME}...")
file(DOWNLOAD "${OPENCV_ICV_URL}/${OPENCV_ICV_PACKAGE_NAME}" "${OPENCV_ICV_PACKAGE_ARCHIVE}"
TIMEOUT 600 STATUS __status
EXPECTED_MD5 ${OPENCV_ICV_PACKAGE_HASH})
if(NOT __status EQUAL 0)
message(FATAL_ERROR "ICV: Failed to download ICV package: ${OPENCV_ICV_PACKAGE_NAME}. Status=${__status}")
else()
# Don't remove this code, because EXPECTED_MD5 parameter doesn't fail "file(DOWNLOAD)" step
# on wrong hash
file(MD5 "${OPENCV_ICV_PACKAGE_ARCHIVE}" archive_md5)
if(NOT archive_md5 STREQUAL OPENCV_ICV_PACKAGE_HASH)
message(FATAL_ERROR "ICV: Downloaded copy of ICV package has invalid MD5 hash: ${archive_md5} (expected: ${OPENCV_ICV_PACKAGE_HASH})")
endif()
endif()
endif()
ocv_assert(EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
ocv_assert(NOT EXISTS "${OPENCV_ICV_UNPACK_PATH}")
file(MAKE_DIRECTORY ${OPENCV_ICV_UNPACK_PATH})
ocv_assert(EXISTS "${OPENCV_ICV_UNPACK_PATH}")
message(STATUS "ICV: Unpacking ${OPENCV_ICV_PACKAGE_NAME} to ${OPENCV_ICV_UNPACK_PATH}...")
execute_process(COMMAND ${CMAKE_COMMAND} -E tar xz "${OPENCV_ICV_PACKAGE_ARCHIVE}"
WORKING_DIRECTORY "${OPENCV_ICV_UNPACK_PATH}"
RESULT_VARIABLE __result)
if(NOT __result EQUAL 0)
message(FATAL_ERROR "ICV: Failed to unpack ICV package from ${OPENCV_ICV_PACKAGE_ARCHIVE} to ${OPENCV_ICV_UNPACK_PATH} with error ${__result}")
endif()
ocv_assert(EXISTS "${OPENCV_ICV_PATH}")
set(OPENCV_ICV_PACKAGE_DOWNLOADED "${OPENCV_ICV_PACKAGE_HASH}" CACHE INTERNAL "ICV package hash")
message(STATUS "ICV: Package successfully downloaded")
set(OPENCV_ICV_PATH "${OPENCV_ICV_PATH}" PARENT_SCOPE)
endfunction()
_icv_downloader()

13
CMakeLists.txt
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@ -127,6 +127,7 @@ OCV_OPTION(WITH_FFMPEG "Include FFMPEG support" ON
OCV_OPTION(WITH_GSTREAMER "Include Gstreamer support" ON IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_GSTREAMER_0_10 "Enable Gstreamer 0.10 support (instead of 1.x)" OFF )
OCV_OPTION(WITH_GTK "Include GTK support" ON IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_GTK_2_X "Use GTK version 2" OFF IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_IPP "Include Intel IPP support" ON IF (NOT IOS) )
OCV_OPTION(WITH_JASPER "Include JPEG2K support" ON IF (NOT IOS) )
OCV_OPTION(WITH_JPEG "Include JPEG support" ON)
@ -174,6 +175,7 @@ OCV_OPTION(BUILD_WITH_STATIC_CRT "Enables use of staticaly linked CRT for sta
OCV_OPTION(BUILD_FAT_JAVA_LIB "Create fat java wrapper containing the whole OpenCV library" ON IF NOT BUILD_SHARED_LIBS AND CMAKE_COMPILER_IS_GNUCXX )
OCV_OPTION(BUILD_ANDROID_SERVICE "Build OpenCV Manager for Google Play" OFF IF ANDROID AND ANDROID_SOURCE_TREE )
OCV_OPTION(BUILD_ANDROID_PACKAGE "Build platform-specific package for Google Play" OFF IF ANDROID )
OCV_OPTION(BUILD_CUDA_STUBS "Build CUDA modules stubs when no CUDA SDK" OFF IF (NOT IOS) )
# 3rd party libs
OCV_OPTION(BUILD_ZLIB "Build zlib from source" WIN32 OR APPLE )
@ -215,6 +217,7 @@ OCV_OPTION(ENABLE_NOISY_WARNINGS "Show all warnings even if they are too no
OCV_OPTION(OPENCV_WARNINGS_ARE_ERRORS "Treat warnings as errors" OFF )
OCV_OPTION(ENABLE_WINRT_MODE "Build with Windows Runtime support" OFF IF WIN32 )
OCV_OPTION(ENABLE_WINRT_MODE_NATIVE "Build with Windows Runtime native C++ support" OFF IF WIN32 )
OCV_OPTION(ANDROID_EXAMPLES_WITH_LIBS "Build binaries of Android examples with native libraries" OFF IF ANDROID )
# ----------------------------------------------------------------------------
@ -747,8 +750,14 @@ else()
status(" Cocoa:" YES)
endif()
else()
status(" GTK+ 2.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-2.0_VERSION})" ELSE NO)
status(" GThread :" HAVE_GTHREAD THEN "YES (ver ${ALIASOF_gthread-2.0_VERSION})" ELSE NO)
if(HAVE_GTK3)
status(" GTK+ 3.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-3.0_VERSION})" ELSE NO)
elseif(HAVE_GTK)
status(" GTK+ 2.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-2.0_VERSION})" ELSE NO)
else()
status(" GTK+:" NO)
endif()
status(" GThread :" HAVE_GTHREAD THEN "YES (ver ${ALIASOF_gthread-2.0_VERSION})" ELSE NO)
status(" GtkGlExt:" HAVE_GTKGLEXT THEN "YES (ver ${ALIASOF_gtkglext-1.0_VERSION})" ELSE NO)
endif()
endif()

2
apps/CMakeLists.txt
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@ -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
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@ -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
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@ -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
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@ -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_ */

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@ -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
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729
apps/haartraining/cvclassifier.h
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@ -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
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@ -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
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apps/haartraining/cvhaartraining.h
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/*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
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/*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}"

10
cmake/OpenCVDetectAndroidSDK.cmake
View File

@ -335,6 +335,16 @@ macro(add_android_project target path)
add_dependencies(${target} ${android_proj_native_deps})
endif()
if(ANDROID_EXAMPLES_WITH_LIBS)
add_custom_target(
${target}_copy_libs
COMMAND ${CMAKE_COMMAND} -DSRC_DIR=${OpenCV_BINARY_DIR}/lib -DDST_DIR=${android_proj_bin_dir}/libs -P ${OpenCV_SOURCE_DIR}/cmake/copyAndroidLibs.cmake
WORKING_DIRECTORY ${OpenCV_BINARY_DIR}/lib
DEPENDS "${OpenCV_BINARY_DIR}/bin/classes.jar.dephelper" opencv_java
)
add_dependencies(${target} ${target}_copy_libs)
endif()
if(__android_project_chain)
add_dependencies(${target} ${__android_project_chain})
endif()

2
cmake/OpenCVDetectTBB.cmake
View File

@ -63,6 +63,8 @@ if(NOT HAVE_TBB)
set(_TBB_LIB_PATH "${_TBB_LIB_PATH}/vc10")
elseif(MSVC11)
set(_TBB_LIB_PATH "${_TBB_LIB_PATH}/vc11")
elseif(MSVC12)
set(_TBB_LIB_PATH "${_TBB_LIB_PATH}/vc12")
endif()
set(TBB_LIB_DIR "${_TBB_LIB_PATH}" CACHE PATH "Full path of TBB library directory")
link_directories("${TBB_LIB_DIR}")

94
cmake/OpenCVFindIPP.cmake
View File

@ -35,7 +35,7 @@ unset(IPP_VERSION_MINOR)
unset(IPP_VERSION_BUILD)
set(IPP_LIB_PREFIX ${CMAKE_STATIC_LIBRARY_PREFIX})
set(IPP_LIB_SUFFIX ${CMAKE_STATIC_LIBRARY_SUFFIX})
set(IPP_LIB_SUFFIX ${CMAKE_STATIC_LIBRARY_SUFFIX})
set(IPP_X64 0)
if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
@ -88,23 +88,18 @@ macro(ipp_detect_version)
set(IPP_INCLUDE_DIRS ${IPP_ROOT_DIR}/include)
set(__msg)
if(EXISTS ${IPP_ROOT_DIR}/ippicv.h)
if(EXISTS ${IPP_ROOT_DIR}/include/ippicv_redefs.h)
set(__msg " (ICV version)")
set(HAVE_IPP_ICV_ONLY 1)
if(EXISTS ${IPP_ROOT_DIR}/ippversion.h)
_ipp_not_supported("Can't resolve IPP directory: ${IPP_ROOT_DIR}")
else()
ipp_get_version(${IPP_ROOT_DIR}/ippicv.h)
endif()
ocv_assert(IPP_VERSION_STR VERSION_GREATER "8.0")
set(IPP_INCLUDE_DIRS ${IPP_ROOT_DIR}/)
elseif(EXISTS ${IPP_ROOT_DIR}/include/ipp.h)
ipp_get_version(${IPP_ROOT_DIR}/include/ippversion.h)
ocv_assert(IPP_VERSION_STR VERSION_GREATER "1.0")
# nothing
else()
_ipp_not_supported("Can't resolve IPP directory: ${IPP_ROOT_DIR}")
endif()
ipp_get_version(${IPP_INCLUDE_DIRS}/ippversion.h)
ocv_assert(IPP_VERSION_STR VERSION_GREATER "1.0")
message(STATUS "found IPP${__msg}: ${_MAJOR}.${_MINOR}.${_BUILD} [${IPP_VERSION_STR}]")
message(STATUS "at: ${IPP_ROOT_DIR}")
@ -113,11 +108,6 @@ macro(ipp_detect_version)
endif()
set(HAVE_IPP 1)
if(EXISTS ${IPP_INCLUDE_DIRS}/ipp_redefine.h)
set(HAVE_IPP_REDEFINE 1)
else()
unset(HAVE_IPP_REDEFINE)
endif()
macro(_ipp_set_library_dir DIR)
if(NOT EXISTS ${DIR})
@ -126,32 +116,30 @@ macro(ipp_detect_version)
set(IPP_LIBRARY_DIR ${DIR})
endmacro()
if(NOT HAVE_IPP_ICV_ONLY)
if(APPLE)
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib)
elseif(IPP_X64)
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib/intel64)
else()
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib/ia32)
endif()
if(APPLE)
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib)
elseif(IPP_X64)
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib/intel64)
else()
if(EXISTS ${IPP_ROOT_DIR}/lib)
set(IPP_LIBRARY_DIR ${IPP_ROOT_DIR}/lib)
else()
_ipp_not_supported("IPP ${IPP_VERSION_STR} at ${IPP_ROOT_DIR} is not supported")
endif()
if(X86_64)
_ipp_set_library_dir(${IPP_LIBRARY_DIR}/intel64)
else()
_ipp_set_library_dir(${IPP_LIBRARY_DIR}/ia32)
endif()
_ipp_set_library_dir(${IPP_ROOT_DIR}/lib/ia32)
endif()
macro(_ipp_add_library name)
if (EXISTS ${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX})
list(APPEND IPP_LIBRARIES ${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX})
add_library(ipp${name} STATIC IMPORTED)
set_target_properties(ipp${name} PROPERTIES
IMPORTED_LINK_INTERFACE_LIBRARIES ""
IMPORTED_LOCATION ${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX}
)
list(APPEND IPP_LIBRARIES ipp${name})
# CMake doesn't support "install(TARGETS ipp${name} " command with imported targets
install(FILES ${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX}
DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
string(TOUPPER ${name} uname)
set(IPP${uname}_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/${OPENCV_3P_LIB_INSTALL_PATH}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX}" CACHE INTERNAL "" FORCE)
set(IPP${uname}_LOCATION_PATH "${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX}" CACHE INTERNAL "" FORCE)
else()
message(STATUS "Can't find IPP library: ${name}")
message(STATUS "Can't find IPP library: ${name} at ${IPP_LIBRARY_DIR}/${IPP_LIB_PREFIX}${IPP_PREFIX}${name}${IPP_SUFFIX}${IPP_LIB_SUFFIX}")
endif()
endmacro()
@ -221,35 +209,17 @@ if(DEFINED ENV{OPENCV_IPP_PATH} AND NOT DEFINED IPPROOT)
set(IPPROOT "$ENV{OPENCV_IPP_PATH}")
endif()
if(NOT DEFINED IPPROOT)
set(IPPROOT "${OpenCV_SOURCE_DIR}/3rdparty/ippicv")
endif()
# Try ICV
find_path(
IPP_ICV_H_PATH
NAMES ippicv.h
PATHS ${IPPROOT}
DOC "The path to Intel(R) IPP ICV header files"
NO_DEFAULT_PATH
NO_CMAKE_PATH)
set(IPP_ROOT_DIR ${IPP_ICV_H_PATH})
if(NOT IPP_ICV_H_PATH)
# Try standalone IPP
find_path(
IPP_H_PATH
NAMES ippversion.h
PATHS ${IPPROOT}
PATH_SUFFIXES include
DOC "The path to Intel(R) IPP header files"
NO_DEFAULT_PATH
NO_CMAKE_PATH)
if(IPP_H_PATH)
get_filename_component(IPP_ROOT_DIR ${IPP_H_PATH} PATH)
include("${OpenCV_SOURCE_DIR}/3rdparty/ippicv/downloader.cmake")
if(DEFINED OPENCV_ICV_PATH)
set(IPPROOT "${OPENCV_ICV_PATH}")
else()
return()
endif()
endif()
if(IPP_ROOT_DIR)
file(TO_CMAKE_PATH "${IPPROOT}" __IPPROOT)
if(EXISTS "${__IPPROOT}/include/ippversion.h")
set(IPP_ROOT_DIR ${__IPPROOT})
ipp_detect_version()
endif()

21
cmake/OpenCVFindLibsGUI.cmake
View File

@ -39,11 +39,26 @@ if(WITH_QT)
endif()
# --- GTK ---
ocv_clear_vars(HAVE_GTK HAVE_GTHREAD HAVE_GTKGLEXT)
ocv_clear_vars(HAVE_GTK HAVE_GTK3 HAVE_GTHREAD HAVE_GTKGLEXT)
if(WITH_GTK AND NOT HAVE_QT)
CHECK_MODULE(gtk+-2.0 HAVE_GTK)
if(NOT WITH_GTK_2_X)
CHECK_MODULE(gtk+-3.0 HAVE_GTK3)
if(HAVE_GTK3)
set(HAVE_GTK TRUE)
endif()
endif()
if(NOT HAVE_GTK)
CHECK_MODULE(gtk+-2.0 HAVE_GTK)
if(HAVE_GTK AND (ALIASOF_gtk+-2.0_VERSION VERSION_LESS MIN_VER_GTK))
message (FATAL_ERROR "GTK support requires a minimum version of ${MIN_VER_GTK} (${ALIASOF_gtk+-2.0_VERSION} found)")
set(HAVE_GTK FALSE)
endif()
endif()
CHECK_MODULE(gthread-2.0 HAVE_GTHREAD)
if(WITH_OPENGL)
if(HAVE_GTK AND NOT HAVE_GTHREAD)
message(FATAL_ERROR "gthread not found. This library is required when building with GTK support")
endif()
if(WITH_OPENGL AND NOT HAVE_GTK3)
CHECK_MODULE(gtkglext-1.0 HAVE_GTKGLEXT)
endif()
endif()

27
cmake/OpenCVGenConfig.cmake
View File

@ -83,6 +83,14 @@ endif()
export(TARGETS ${OpenCVModules_TARGETS} FILE "${CMAKE_BINARY_DIR}/OpenCVModules${modules_file_suffix}.cmake")
if(TARGET ippicv)
set(USE_IPPICV TRUE)
file(RELATIVE_PATH INSTALL_PATH_RELATIVE_IPPICV ${CMAKE_BINARY_DIR} ${IPPICV_LOCATION_PATH})
else()
set(USE_IPPICV FALSE)
set(INSTALL_PATH_RELATIVE_IPPICV "non-existed-path")
endif()
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig.cmake.in" "${CMAKE_BINARY_DIR}/OpenCVConfig.cmake" @ONLY)
#support for version checking when finding opencv. find_package(OpenCV 2.3.1 EXACT) should now work.
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig-version.cmake.in" "${CMAKE_BINARY_DIR}/OpenCVConfig-version.cmake" @ONLY)
@ -98,9 +106,6 @@ if(INSTALL_TO_MANGLED_PATHS)
set(OpenCV_3RDPARTY_LIB_DIRS_CONFIGCMAKE "\"\${OpenCV_INSTALL_PATH}/${OpenCV_3RDPARTY_LIB_DIRS_CONFIGCMAKE}\"")
endif()
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig.cmake.in" "${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig.cmake" @ONLY)
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig-version.cmake.in" "${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig-version.cmake" @ONLY)
if(UNIX) # ANDROID configuration is created here also
#http://www.vtk.org/Wiki/CMake/Tutorials/Packaging reference
# For a command "find_package(<name> [major[.minor]] [EXACT] [REQUIRED|QUIET])"
@ -108,6 +113,15 @@ if(UNIX) # ANDROID configuration is created here also
# <prefix>/(share|lib)/cmake/<name>*/ (U)
# <prefix>/(share|lib)/<name>*/ (U)
# <prefix>/(share|lib)/<name>*/(cmake|CMake)/ (U)
if(USE_IPPICV)
if(INSTALL_TO_MANGLED_PATHS)
file(RELATIVE_PATH INSTALL_PATH_RELATIVE_IPPICV "${CMAKE_INSTALL_PREFIX}/${OPENCV_CONFIG_INSTALL_PATH}-${OPENCV_VERSION}/" ${IPPICV_INSTALL_PATH})
else()
file(RELATIVE_PATH INSTALL_PATH_RELATIVE_IPPICV "${CMAKE_INSTALL_PREFIX}/${OPENCV_CONFIG_INSTALL_PATH}/" ${IPPICV_INSTALL_PATH})
endif()
endif()
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig.cmake.in" "${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig.cmake" @ONLY)
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig-version.cmake.in" "${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig-version.cmake" @ONLY)
if(INSTALL_TO_MANGLED_PATHS)
install(FILES ${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig.cmake DESTINATION ${OPENCV_CONFIG_INSTALL_PATH}-${OPENCV_VERSION}/ COMPONENT dev)
install(FILES ${CMAKE_BINARY_DIR}/unix-install/OpenCVConfig-version.cmake DESTINATION ${OPENCV_CONFIG_INSTALL_PATH}-${OPENCV_VERSION}/ COMPONENT dev)
@ -131,6 +145,13 @@ if(WIN32)
set(OpenCV2_INCLUDE_DIRS_CONFIGCMAKE "\"\"")
exec_program(mkdir ARGS "-p \"${CMAKE_BINARY_DIR}/win-install/\"" OUTPUT_VARIABLE RET_VAL)
if(USE_IPPICV)
if(BUILD_SHARED_LIBS)
file(RELATIVE_PATH INSTALL_PATH_RELATIVE_IPPICV "${CMAKE_INSTALL_PREFIX}/${OpenCV_INSTALL_BINARIES_PREFIX}lib" ${IPPICV_INSTALL_PATH})
else()
file(RELATIVE_PATH INSTALL_PATH_RELATIVE_IPPICV "${CMAKE_INSTALL_PREFIX}/${OpenCV_INSTALL_BINARIES_PREFIX}staticlib" ${IPPICV_INSTALL_PATH})
endif()
endif()
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig.cmake.in" "${CMAKE_BINARY_DIR}/win-install/OpenCVConfig.cmake" @ONLY)
configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/OpenCVConfig-version.cmake.in" "${CMAKE_BINARY_DIR}/win-install/OpenCVConfig-version.cmake" @ONLY)
if(BUILD_SHARED_LIBS)

1
cmake/OpenCVMinDepVersions.cmake
View File

@ -2,3 +2,4 @@ set(MIN_VER_CMAKE 2.8.7)
set(MIN_VER_CUDA 4.2)
set(MIN_VER_PYTHON 2.6)
set(MIN_VER_ZLIB 1.2.3)
set(MIN_VER_GTK 2.18.0)

8
cmake/copyAndroidLibs.cmake Normal file
View File

@ -0,0 +1,8 @@
# helper file for Android samples build
file(GLOB_RECURSE LIBS RELATIVE ${SRC_DIR} "*.so")
foreach(l ${LIBS})
message(STATUS " Copying: ${l} ...")
execute_process(COMMAND ${CMAKE_COMMAND} -E copy_if_different ${SRC_DIR}/${l} ${DST_DIR}/${l})
endforeach()

22
cmake/templates/OpenCVConfig.cmake.in
View File

@ -49,6 +49,18 @@ if(NOT DEFINED OpenCV_MODULES_SUFFIX)
endif()
endif()
if(@USE_IPPICV@) # value is defined by package builder
if(NOT TARGET ippicv)
if(EXISTS "${CMAKE_CURRENT_LIST_DIR}/@INSTALL_PATH_RELATIVE_IPPICV@")
add_library(ippicv STATIC IMPORTED)
set_target_properties(ippicv PROPERTIES
IMPORTED_LINK_INTERFACE_LIBRARIES ""
IMPORTED_LOCATION "${CMAKE_CURRENT_LIST_DIR}/@INSTALL_PATH_RELATIVE_IPPICV@"
)
endif()
endif()
endif()
if(NOT TARGET opencv_core)
include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake)
endif()
@ -64,7 +76,11 @@ set(OpenCV_USE_CUFFT @HAVE_CUFFT@)
set(OpenCV_USE_NVCUVID @HAVE_NVCUVID@)
# Android API level from which OpenCV has been compiled is remembered
set(OpenCV_ANDROID_NATIVE_API_LEVEL @OpenCV_ANDROID_NATIVE_API_LEVEL_CONFIGCMAKE@)
if(ANDROID)
set(OpenCV_ANDROID_NATIVE_API_LEVEL @OpenCV_ANDROID_NATIVE_API_LEVEL_CONFIGCMAKE@)
else()
set(OpenCV_ANDROID_NATIVE_API_LEVEL 0)
endif()
# Some additional settings are required if OpenCV is built as static libs
set(OpenCV_SHARED @BUILD_SHARED_LIBS@)
@ -75,8 +91,8 @@ set(OpenCV_USE_MANGLED_PATHS @OpenCV_USE_MANGLED_PATHS_CONFIGCMAKE@)
# Extract the directory where *this* file has been installed (determined at cmake run-time)
get_filename_component(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_FILE}" PATH CACHE)
if(NOT WIN32 OR OpenCV_ANDROID_NATIVE_API_LEVEL GREATER 0)
if(OpenCV_ANDROID_NATIVE_API_LEVEL GREATER 0)
if(NOT WIN32 OR ANDROID)
if(ANDROID)
set(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../..")
else()
set(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../..")

1404
data/haarcascades/haarcascade_licence_plate_rus_16stages.xml Normal file
View File

File diff suppressed because it is too large Load Diff

195
data/haarcascades/haarcascade_mcs_eyepair_big.xml
View File

@ -1,86 +1,123 @@
<?xml version="1.0"?>
<!--
45x11 Eye pair detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
45x11 Eye pair detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

193
data/haarcascades/haarcascade_mcs_eyepair_small.xml
View File

@ -1,85 +1,122 @@
<?xml version="1.0"?>
<!--
22x5 Eye pair detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
22x5 Eye pair detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

176
data/haarcascades/haarcascade_mcs_leftear.xml
View File

@ -1,66 +1,122 @@
<?xml version="1.0"?>
<!----------------------------------------------------------------------------
12x20 Left ear (in the image) detector computed with 5000 positive and 15000
negative samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2011, Modesto Castrillon-Santana (IUSIANI, Universidad de
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite this paper:
@INPROCEEDINGS{Castrillon11-caepia,
author = "Castrill\'on Santana, M. and Lorenzo Navarro, J. and Hern\'andez Sosa, D. ",
title = "An Study on Ear Detection and its Applications to Face Detection",
booktitle = "Conferencia de la AsociaciÛn EspaÒola para la Inteligencia Artificial (CAEPIA)",
year = "2011",
month = "November",
address = "La Laguna, Spain",
file = F
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto.html or in the paper.
<!--
12x20 Left ear (in the image) detector computed with 5000 positive and 15000 negative samples
2011-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
------------------------------------------------------------------------>
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@INPROCEEDINGS{Castrillon11-caepia,
author = "Castrill\'on Santana, M. and Lorenzo Navarro, J. and Hern\'andez Sosa, D. ",
title = "An Study on Ear Detection and its Applications to Face Detection",
booktitle = "Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA)",
year = "2011",
month = "November",
address = "La Laguna, Spain",
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
<featureType>HAAR</featureType>

193
data/haarcascades/haarcascade_mcs_lefteye.xml
View File

@ -1,85 +1,122 @@
<?xml version="1.0"?>
<!--
18x12 Left eye (in the image) detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
18x12 Left eye (in the image) detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

24071
data/haarcascades/haarcascade_mcs_lefteye_alt.xml Normal file
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194
data/haarcascades/haarcascade_mcs_mouth.xml
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@ -1,85 +1,123 @@
<?xml version="1.0"?>
<!--
25x15 Mouth detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
25x15 Mouth detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

193
data/haarcascades/haarcascade_mcs_nose.xml
View File

@ -1,85 +1,122 @@
<?xml version="1.0"?>
<!--
18x15 Nose detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2008, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
18x15 Nose detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

177
data/haarcascades/haarcascade_mcs_rightear.xml
View File

@ -1,66 +1,123 @@
<?xml version="1.0"?>
<!----------------------------------------------------------------------------
12x20 Right ear (in the image) detector computed with 5000 positive and 15000
negative samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2011, Modesto Castrillon-Santana (IUSIANI, Universidad de
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite this paper:
@INPROCEEDINGS{Castrillon11-caepia,
author = "Castrill\'on Santana, M. and Lorenzo Navarro, J. and Hern\'andez Sosa, D. ",
title = "An Study on Ear Detection and its Applications to Face Detection",
booktitle = "Conferencia de la AsociaciÛn EspaÒola para la Inteligencia Artificial (CAEPIA)",
year = "2011",
month = "November",
address = "La Laguna, Spain",
file = F
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto.html or in the paper.
<!--
12x20 Right ear (in the image) detector computed with 5000 positive and 15000 negative samples
2011-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
------------------------------------------------------------------------>
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@INPROCEEDINGS{Castrillon11-caepia,
author = "Castrill\'on Santana, M. and Lorenzo Navarro, J. and Hern\'andez Sosa, D. ",
title = "An Study on Ear Detection and its Applications to Face Detection",
booktitle = "Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA)",
year = "2011",
month = "November",
address = "La Laguna, Spain",
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You.
To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
<featureType>HAAR</featureType>

193
data/haarcascades/haarcascade_mcs_righteye.xml
View File

@ -1,85 +1,122 @@
<?xml version="1.0"?>
<!--
18x12 Right eye (in the image) detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions 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 Contributor may not 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
| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
@INPROCEEDINGS{Castrillon07-swb,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
title = "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
booktitle = "1st Spanish Workshop on Biometrics",
year = "2007",
month = "June",
address = "Girona, Spain",
file = F
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
18x12 Right eye (in the image) detector computed with 7000 positive samples
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International
Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these
terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and
conditions.
Section 1 – Definitions.
Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public
License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.
Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public
License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
in any publication cite the following paper:
@ARTICLE{Castrillon11-mva,
author = {Modesto Castrill\'on and Oscar D\'eniz and Daniel Hern\'andez and Javier Lorenzo},
title = {A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework},
journal = {Machine Vision and Applications},
year = {2011},
volume = {22},
pages = {481-494},
number = {3},
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
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-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

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@ -1,83 +1,120 @@
<?xml version="1.0"?>
<!--
22x20 Head and shoulders detector
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| 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.
|
| Copyright (c) 2006, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions in binary form must reproduce the above
| copyright notice, this list of conditions and the following
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| with the distribution.
| * The name of Contributor may not used to endorse or promote products
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| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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| 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. Back to
| Top
//////////////////////////////////////////////////////////////////////////
RESEARCH USE:
If you are using this particular detector or involved ideas please cite one of these papers:
@InProceedings{Kruppa03-pets,
author = "Hannes Kruppa, Modesto Castrill\'on-Santana and Bernt Schiele",
title = "Fast and Robust Face Finding via Local Context."
booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
year = "2003",
month = "October"
}
@ARTICLE{Castrillon07-jvci,
author = "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
title = "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
journal = "Journal of Visual Communication and Image Representation",
year = "2007",
vol = "18",
issue = "2",
month = "April",
pages = "130-140"
}
A comparison of this and other face related classifiers can be found in:
@InProceedings{Castrillon08a-visapp,
'athor = "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
title = "Face and Facial Feature Detection Evaluation"
booktitle = "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
year = "2008",
month = "January"
}
More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.
22x20 Head and shoulders detector
2006-present, Modesto Castrillon-Santana (SIANI, Universidad de Las Palmas de Gran Canaria, Spain.
COMMERCIAL USE:
If you have any commercial interest in this work please contact
mcastrillon@iusiani.ulpgc.es
If you have any commercial interest in this work contact mcastrillon@iusiani.ulpgc.es
Creative Commons Attribution-NonCommercial 4.0 International Public License
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in any publication cite the following paper:
@InProceedings{Kruppa03-pets,
author = "Hannes Kruppa, Modesto Castrill\'on-Santana and Bernt Schiele",
title = "Fast and Robust Face Finding via Local Context."
booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
year = "2003",
month = "October"
}
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
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for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and
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-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>

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2
doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst
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@ -16,7 +16,7 @@ Basics of Brute-Force Matcher
Brute-Force matcher is simple. It takes the descriptor of one feature in first set and is matched with all other features in second set using some distance calculation. And the closest one is returned.
For BF matcher, first we have to create the BFMatcher object using **cv2.BFMatcher()**. It takes two optional params. First one is ``normType``. It specifies the distance measurement to be used. By default, it is ``cv2.NORM_L2``. It is good for SIFT, SURF etc (``cv2.NORM_L1`` is also there). For binary string based descriptors like ORB, BRIEF, BRISK etc, ``cv2.NORM_HAMMING`` should be used, which used Hamming distance as measurement. If ORB is using ``VTA_K == 3 or 4``, ``cv2.NORM_HAMMING2`` should be used.
For BF matcher, first we have to create the BFMatcher object using **cv2.BFMatcher()**. It takes two optional params. First one is ``normType``. It specifies the distance measurement to be used. By default, it is ``cv2.NORM_L2``. It is good for SIFT, SURF etc (``cv2.NORM_L1`` is also there). For binary string based descriptors like ORB, BRIEF, BRISK etc, ``cv2.NORM_HAMMING`` should be used, which used Hamming distance as measurement. If ORB is using ``WTA_K == 3 or 4``, ``cv2.NORM_HAMMING2`` should be used.
Second param is boolean variable, ``crossCheck`` which is false by default. If it is true, Matcher returns only those matches with value (i,j) such that i-th descriptor in set A has j-th descriptor in set B as the best match and vice-versa. That is, the two features in both sets should match each other. It provides consistant result, and is a good alternative to ratio test proposed by D.Lowe in SIFT paper.

2
doc/tutorials/core/basic_linear_transform/basic_linear_transform.rst
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@ -187,7 +187,7 @@ Explanation
image.convertTo(new_image, -1, alpha, beta);
where :convert_to:`convertTo <>` would effectively perform *new_image = a*image + beta*. However, we wanted to show you how to access each pixel. In any case, both methods give the same result.
where :convert_to:`convertTo <>` would effectively perform *new_image = a*image + beta*. However, we wanted to show you how to access each pixel. In any case, both methods give the same result but convertTo is more optimized and works a lot faster.
Result
=======

10
doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.rst
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@ -31,15 +31,15 @@ Here's a sample code of how to achieve all the stuff enumerated at the goal list
Explanation
===========
Here we talk only about XML and YAML file inputs. Your output (and its respective input) file may have only one of these extensions and the structure coming from this. They are two kinds of data structures you may serialize: *mappings* (like the STL map) and *element sequence* (like the STL vector>. The difference between these is that in a map every element has a unique name through what you may access it. For sequences you need to go through them to query a specific item.
Here we talk only about XML and YAML file inputs. Your output (and its respective input) file may have only one of these extensions and the structure coming from this. They are two kinds of data structures you may serialize: *mappings* (like the STL map) and *element sequence* (like the STL vector). The difference between these is that in a map every element has a unique name through what you may access it. For sequences you need to go through them to query a specific item.
1. **XML\\YAML File Open and Close.** Before you write any content to such file you need to open it and at the end to close it. The XML\YAML data structure in OpenCV is :xmlymlpers:`FileStorage <filestorage>`. To specify that this structure to which file binds on your hard drive you can use either its constructor or the *open()* function of this:
1. **XML/YAML File Open and Close.** Before you write any content to such file you need to open it and at the end to close it. The XML/YAML data structure in OpenCV is :xmlymlpers:`FileStorage <filestorage>`. To specify that this structure to which file binds on your hard drive you can use either its constructor or the *open()* function of this:
.. code-block:: cpp
string filename = "I.xml";
FileStorage fs(filename, FileStorage::WRITE);
\\...
//...
fs.open(filename, FileStorage::READ);
Either one of this you use the second argument is a constant specifying the type of operations you'll be able to on them: WRITE, READ or APPEND. The extension specified in the file name also determinates the output format that will be used. The output may be even compressed if you specify an extension such as *.xml.gz*.
@ -64,7 +64,7 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
fs["iterationNr"] >> itNr;
itNr = (int) fs["iterationNr"];
#. **Input\\Output of OpenCV Data structures.** Well these behave exactly just as the basic C++ types:
#. **Input/Output of OpenCV Data structures.** Well these behave exactly just as the basic C++ types:
.. code-block:: cpp
@ -77,7 +77,7 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
fs["R"] >> R; // Read cv::Mat
fs["T"] >> T;
#. **Input\\Output of vectors (arrays) and associative maps.** As I mentioned beforehand we can output maps and sequences (array, vector) too. Again we first print the name of the variable and then we have to specify if our output is either a sequence or map.
#. **Input/Output of vectors (arrays) and associative maps.** As I mentioned beforehand, we can output maps and sequences (array, vector) too. Again we first print the name of the variable and then we have to specify if our output is either a sequence or map.
For sequence before the first element print the "[" character and after the last one the "]" character:

2
doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.rst
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@ -113,7 +113,7 @@ Although *Mat* works really well as an image container, it is also a general mat
For instance, *CV_8UC3* means we use unsigned char types that are 8 bit long and each pixel has three of these to form the three channels. This are predefined for up to four channel numbers. The :basicstructures:`Scalar <scalar>` is four element short vector. Specify this and you can initialize all matrix points with a custom value. If you need more you can create the type with the upper macro, setting the channel number in parenthesis as you can see below.
+ Use C\\C++ arrays and initialize via constructor
+ Use C/C++ arrays and initialize via constructor
.. literalinclude:: ../../../../samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp
:language: cpp

28
doc/tutorials/introduction/linux_install/linux_install.rst
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@ -7,22 +7,24 @@ These steps have been tested for Ubuntu 10.04 but should work with other distros
Required Packages
=================
* GCC 4.4.x or later. This can be installed with:
* GCC 4.4.x or later
* CMake 2.8.7 or higher
* Git
* GTK+2.x or higher, including headers (libgtk2.0-dev)
* pkg-config
* Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy)
* ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev
* [optional] libtbb2 libtbb-dev
* [optional] libdc1394 2.x
* [optional] libjpeg-dev, libpng-dev, libtiff-dev, libjasper-dev, libdc1394-22-dev
The packages can be installed using a terminal and the following commands or by using Synaptic Manager:
.. code-block:: bash
sudo apt-get install build-essential
* CMake 2.8.7 or higher;
* Git;
* GTK+2.x or higher, including headers (libgtk2.0-dev);
* pkg-config;
* Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy);
* ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev;
* [optional] libdc1394 2.x;
* [optional] libjpeg-dev, libpng-dev, libtiff-dev, libjasper-dev.
All the libraries above can be installed via Terminal or by using Synaptic Manager.
[compiler] sudo apt-get install build-essential
[required] sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
[optional] sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
Getting OpenCV Source Code
==========================

2
doc/tutorials/introduction/windows_install/windows_install.rst
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@ -55,7 +55,7 @@ Building the OpenCV library from scratch requires a couple of tools installed be
.. |TortoiseGit| replace:: TortoiseGit
.. _TortoiseGit: http://code.google.com/p/tortoisegit/wiki/Download
.. |Python_Libraries| replace:: Python libraries
.. _Python_Libraries: http://www.python.org/getit/
.. _Python_Libraries: http://www.python.org/downloads/
.. |Numpy| replace:: Numpy
.. _Numpy: http://numpy.scipy.org/
.. |IntelTBB| replace:: Intel |copy| Threading Building Blocks (*TBB*)

30
doc/tutorials/introduction/windows_visual_studio_Opencv/windows_visual_studio_Opencv.rst
View File

@ -90,17 +90,25 @@ A full list, for the latest version would contain:
.. code-block:: bash
opencv_core231d.lib
opencv_imgproc231d.lib
opencv_highgui231d.lib
opencv_ml231d.lib
opencv_video231d.lib
opencv_features2d231d.lib
opencv_calib3d231d.lib
opencv_objdetect231d.lib
opencv_contrib231d.lib
opencv_legacy231d.lib
opencv_flann231d.lib
opencv_calib3d249d.lib
opencv_contrib249d.lib
opencv_core249d.lib
opencv_features2d249d.lib
opencv_flann249d.lib
opencv_gpu249d.lib
opencv_highgui249d.lib
opencv_imgproc249d.lib
opencv_legacy249d.lib
opencv_ml249d.lib
opencv_nonfree249d.lib
opencv_objdetect249d.lib
opencv_ocl249d.lib
opencv_photo249d.lib
opencv_stitching249d.lib
opencv_superres249d.lib
opencv_ts249d.lib
opencv_video249d.lib
opencv_videostab249d.lib
The letter *d* at the end just indicates that these are the libraries required for the debug. Now click ok to save and do the same with a new property inside the Release rule section. Make sure to omit the *d* letters from the library names and to save the property sheets with the save icon above them.

2
doc/tutorials/introduction/windows_visual_studio_image_watch/windows_visual_studio_image_watch.rst
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@ -78,6 +78,8 @@ Make sure your active solution configuration (:menuselection:`Build --> Configur
Build your solution (:menuselection:`Build --> Build Solution`, or press *F7*).
Before continuing, do not forget to add the command line argument of your input image to your project (:menuselection:`Right click on project --> Properties --> Configuration Properties --> Debugging` and then set the field ``Command Arguments`` with the location of the image).
Now set a breakpoint on the source line that says
.. code-block:: c++

4
doc/tutorials/ml/introduction_to_svm/introduction_to_svm.rst
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@ -105,8 +105,8 @@ Explanation
.. code-block:: cpp
Mat trainingDataMat(3, 2, CV_32FC1, trainingData);
Mat labelsMat (3, 1, CV_32FC1, labels);
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
Mat labelsMat (4, 1, CV_32FC1, labels);
2. **Set up SVM's parameters**

2
include/opencv/cv.h
View File

@ -65,8 +65,6 @@
#include "opencv2/photo/photo_c.h"
#include "opencv2/video/tracking_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#if !defined(CV_IMPL)
#define CV_IMPL extern "C"

1
include/opencv2/opencv.hpp
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@ -51,7 +51,6 @@
#include "opencv2/objdetect.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/contrib.hpp"
#include "opencv2/ml.hpp"
#endif

32
modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
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@ -224,9 +224,9 @@ Computes useful camera characteristics from the camera matrix.
:param imageSize: Input image size in pixels.
:param apertureWidth: Physical width of the sensor.
:param apertureWidth: Physical width in mm of the sensor.
:param apertureHeight: Physical height of the sensor.
:param apertureHeight: Physical height in mm of the sensor.
:param fovx: Output field of view in degrees along the horizontal sensor axis.
@ -234,13 +234,15 @@ Computes useful camera characteristics from the camera matrix.
:param focalLength: Focal length of the lens in mm.
:param principalPoint: Principal point in pixels.
:param principalPoint: Principal point in mm.
:param aspectRatio: :math:`f_y/f_x`
The function computes various useful camera characteristics from the previously estimated camera matrix.
.. note::
Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).
composeRT
-------------
@ -582,15 +584,15 @@ Finds an object pose from 3D-2D point correspondences.
:param flags: Method for solving a PnP problem:
* **CV_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In this case the function finds such a pose that minimizes reprojection error, that is the sum of squared distances between the observed projections ``imagePoints`` and the projected (using :ocv:func:`projectPoints` ) ``objectPoints`` .
* **CV_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem". In this case the function requires exactly four object and image points.
* **CV_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".
* **ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In this case the function finds such a pose that minimizes reprojection error, that is the sum of squared distances between the observed projections ``imagePoints`` and the projected (using :ocv:func:`projectPoints` ) ``objectPoints`` .
* **P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem". In this case the function requires exactly four object and image points.
* **EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".
The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients.
.. note::
* An example of how to use solvePNP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py
* An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python2/plane_ar.py
solvePnPRansac
------------------
@ -707,8 +709,8 @@ Calculates an essential matrix from the corresponding points in two images.
:param method: Method for computing a fundamental matrix.
* **CV_RANSAC** for the RANSAC algorithm.
* **CV_LMEDS** for the LMedS algorithm.
* **RANSAC** for the RANSAC algorithm.
* **MEDS** for the LMedS algorithm.
:param threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
@ -807,7 +809,7 @@ In this scenario, ``points1`` and ``points2`` are the same input for ``findEssen
cv::Point2d pp(0.0, 0.0);
Mat E, R, t, mask;
E = findEssentialMat(points1, points2, focal, pp, CV_RANSAC, 0.999, 1.0, mask);
E = findEssentialMat(points1, points2, focal, pp, RANSAC, 0.999, 1.0, mask);
recoverPose(E, points1, points2, R, t, focal, pp, mask);
@ -830,9 +832,9 @@ Finds a perspective transformation between two planes.
* **0** - a regular method using all the points
* **CV_RANSAC** - RANSAC-based robust method
* **RANSAC** - RANSAC-based robust method
* **CV_LMEDS** - Least-Median robust method
* **LMEDS** - Least-Median robust method
:param ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier (used in the RANSAC method only). That is, if
@ -842,7 +844,7 @@ Finds a perspective transformation between two planes.
then the point :math:`i` is considered an outlier. If ``srcPoints`` and ``dstPoints`` are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.
:param mask: Optional output mask set by a robust method ( ``CV_RANSAC`` or ``CV_LMEDS`` ). Note that the input mask values are ignored.
:param mask: Optional output mask set by a robust method ( ``RANSAC`` or ``LMEDS`` ). Note that the input mask values are ignored.
The functions find and return the perspective transformation :math:`H` between the source and the destination planes:
@ -1490,6 +1492,10 @@ Reconstructs points by triangulation.
The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. Projections matrices can be obtained from :ocv:func:`stereoRectify`.
.. note::
Keep in mind that all input data should be of float type in order for this function to work.
.. seealso::
:ocv:func:`reprojectImageTo3D`

2
modules/calib3d/src/solvepnp.cpp
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@ -94,7 +94,7 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints,
return true;
}
else
CV_Error(CV_StsBadArg, "The flags argument must be one of CV_ITERATIVE or CV_EPNP");
CV_Error(CV_StsBadArg, "The flags argument must be one of CV_ITERATIVE, CV_P3P or CV_EPNP");
return false;
}

58
modules/calib3d/src/stereosgbm.cpp
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@ -1029,18 +1029,6 @@ void filterSpecklesImpl(cv::Mat& img, int newVal, int maxSpeckleSize, int maxDif
T dp = *dpp;
int* lpp = labels + width*p.y + p.x;
if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff )
{
lpp[+1] = curlabel;
*ws++ = Point2s(p.x+1, p.y);
}
if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff )
{
lpp[-1] = curlabel;
*ws++ = Point2s(p.x-1, p.y);
}
if( p.y < height-1 && !lpp[+width] && dpp[+dstep] != newVal && std::abs(dp - dpp[+dstep]) <= maxDiff )
{
lpp[+width] = curlabel;
@ -1053,6 +1041,18 @@ void filterSpecklesImpl(cv::Mat& img, int newVal, int maxSpeckleSize, int maxDif
*ws++ = Point2s(p.x, p.y-1);
}
if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff )
{
lpp[+1] = curlabel;
*ws++ = Point2s(p.x+1, p.y);
}
if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff )
{
lpp[-1] = curlabel;
*ws++ = Point2s(p.x-1, p.y);
}
// pop most recent and propagate
// NB: could try least recent, maybe better convergence
p = *--ws;
@ -1078,13 +1078,39 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi
double _maxDiff, InputOutputArray __buf )
{
Mat img = _img.getMat();
int type = img.type();
Mat temp, &_buf = __buf.needed() ? __buf.getMatRef() : temp;
CV_Assert( img.type() == CV_8UC1 || img.type() == CV_16SC1 );
CV_Assert( type == CV_8UC1 || type == CV_16SC1 );
int newVal = cvRound(_newval);
int maxDiff = cvRound(_maxDiff);
int newVal = cvRound(_newval), maxDiff = cvRound(_maxDiff);
if (img.type() == CV_8UC1)
#if IPP_VERSION_X100 >= 801
Ipp32s bufsize = 0;
IppiSize roisize = { img.cols, img.rows };
IppDataType datatype = type == CV_8UC1 ? ipp8u : ipp16s;
if (!__buf.needed() && (type == CV_8UC1 || type == CV_16SC1))
{
IppStatus status = ippiMarkSpecklesGetBufferSize(roisize, datatype, CV_MAT_CN(type), &bufsize);
Ipp8u * buffer = ippsMalloc_8u(bufsize);
if ((int)status >= 0)
{
if (type == CV_8UC1)
status = ippiMarkSpeckles_8u_C1IR((Ipp8u *)img.data, (int)img.step, roisize,
(Ipp8u)newVal, maxSpeckleSize, (Ipp8u)maxDiff, ippiNormL1, buffer);
else
status = ippiMarkSpeckles_16s_C1IR((Ipp16s *)img.data, (int)img.step, roisize,
(Ipp16s)newVal, maxSpeckleSize, (Ipp16s)maxDiff, ippiNormL1, buffer);
}
if (status >= 0)
return;
setIppErrorStatus();
}
#endif
if (type == CV_8UC1)
filterSpecklesImpl<uchar>(img, newVal, maxSpeckleSize, maxDiff, _buf);
else
filterSpecklesImpl<short>(img, newVal, maxSpeckleSize, maxDiff, _buf);

2
modules/calib3d/test/test_cameracalibration.cpp
View File

@ -1608,7 +1608,7 @@ void CV_StereoCalibrationTest::run( int )
Mat _M1, _M2, _D1, _D2;
vector<Mat> _R1, _R2, _T1, _T2;
calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 );
calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T1, 0 );
calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T2, 0 );
undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 );
undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 );

1
modules/contrib/CMakeLists.txt
View File

@ -1 +0,0 @@
ocv_define_module(contrib opencv_imgproc opencv_calib3d opencv_ml opencv_video opencv_objdetect OPTIONAL opencv_highgui opencv_nonfree)

12
modules/contrib/doc/contrib.rst
View File

@ -1,12 +0,0 @@
***************************************
contrib. Contributed/Experimental Stuff
***************************************
The module contains some recently added functionality that has not been stabilized, or functionality that is considered optional.
.. toctree::
:maxdepth: 2
stereo
FaceRecognizer Documentation <facerec/index>
openfabmap

107
modules/contrib/doc/facerec/colormaps.rst
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@ -1,107 +0,0 @@
ColorMaps in OpenCV
===================
applyColorMap
---------------------
Applies a GNU Octave/MATLAB equivalent colormap on a given image.
.. ocv:function:: void applyColorMap(InputArray src, OutputArray dst, int colormap)
:param src: The source image, grayscale or colored does not matter.
:param dst: The result is the colormapped source image. Note: :ocv:func:`Mat::create` is called on dst.
:param colormap: The colormap to apply, see the list of available colormaps below.
Currently the following GNU Octave/MATLAB equivalent colormaps are implemented:
.. code-block:: cpp
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11
}
Description
-----------
The human perception isn't built for observing fine changes in grayscale images. Human eyes are more sensitive to observing changes between colors, so you often need to recolor your grayscale images to get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your computer vision application.
In OpenCV 2.4 you only need :ocv:func:`applyColorMap` to apply a colormap on a given image. The following sample code reads the path to an image from command line, applies a Jet colormap on it and shows the result:
.. code-block:: cpp
#include <opencv2/contrib.hpp>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
int main(int argc, const char *argv[]) {
// Get the path to the image, if it was given
// if no arguments were given.
String filename;
if (argc > 1) {
filename = String(argv[1]);
}
// The following lines show how to apply a colormap on a given image
// and show it with cv::imshow example with an image. An exception is
// thrown if the path to the image is invalid.
if(!filename.empty()) {
Mat img0 = imread(filename);
// Throw an exception, if the image can't be read:
if(img0.empty()) {
CV_Error(CV_StsBadArg, "Sample image is empty. Please adjust your path, so it points to a valid input image!");
}
// Holds the colormap version of the image:
Mat cm_img0;
// Apply the colormap:
applyColorMap(img0, cm_img0, COLORMAP_JET);
// Show the result:
imshow("cm_img0", cm_img0);
waitKey(0);
}
return 0;
}
And here are the color scales for each of the available colormaps:
+-----------------------+---------------------------------------------------+
| Class | Scale |
+=======================+===================================================+
| COLORMAP_AUTUMN | .. image:: img/colormaps/colorscale_autumn.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_BONE | .. image:: img/colormaps/colorscale_bone.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_COOL | .. image:: img/colormaps/colorscale_cool.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_HOT | .. image:: img/colormaps/colorscale_hot.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_HSV | .. image:: img/colormaps/colorscale_hsv.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_JET | .. image:: img/colormaps/colorscale_jet.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_OCEAN | .. image:: img/colormaps/colorscale_ocean.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_PINK | .. image:: img/colormaps/colorscale_pink.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_RAINBOW | .. image:: img/colormaps/colorscale_rainbow.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_SPRING | .. image:: img/colormaps/colorscale_spring.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_SUMMER | .. image:: img/colormaps/colorscale_summer.jpg |
+-----------------------+---------------------------------------------------+
| COLORMAP_WINTER | .. image:: img/colormaps/colorscale_winter.jpg |
+-----------------------+---------------------------------------------------+

400
modules/contrib/doc/facerec/etc/at.txt
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@ -1,400 +0,0 @@
/home/philipp/facerec/data/at/s13/2.pgm;12
/home/philipp/facerec/data/at/s13/7.pgm;12
/home/philipp/facerec/data/at/s13/6.pgm;12
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/home/philipp/facerec/data/at/s13/3.pgm;12
/home/philipp/facerec/data/at/s13/4.pgm;12
/home/philipp/facerec/data/at/s13/10.pgm;12
/home/philipp/facerec/data/at/s13/8.pgm;12
/home/philipp/facerec/data/at/s13/1.pgm;12
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/home/philipp/facerec/data/at/s17/6.pgm;16
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/home/philipp/facerec/data/at/s17/3.pgm;16
/home/philipp/facerec/data/at/s17/4.pgm;16
/home/philipp/facerec/data/at/s17/10.pgm;16
/home/philipp/facerec/data/at/s17/8.pgm;16
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/home/philipp/facerec/data/at/s10/8.pgm;9
/home/philipp/facerec/data/at/s10/1.pgm;9
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/home/philipp/facerec/data/at/s27/6.pgm;26
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/home/philipp/facerec/data/at/s27/5.pgm;26
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/home/philipp/facerec/data/at/s5/2.pgm;4
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/home/philipp/facerec/data/at/s20/5.pgm;19
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/home/philipp/facerec/data/at/s23/3.pgm;22
/home/philipp/facerec/data/at/s23/4.pgm;22
/home/philipp/facerec/data/at/s23/10.pgm;22
/home/philipp/facerec/data/at/s23/8.pgm;22
/home/philipp/facerec/data/at/s23/1.pgm;22
/home/philipp/facerec/data/at/s4/2.pgm;3
/home/philipp/facerec/data/at/s4/7.pgm;3
/home/philipp/facerec/data/at/s4/6.pgm;3
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/home/philipp/facerec/data/at/s4/5.pgm;3
/home/philipp/facerec/data/at/s4/3.pgm;3
/home/philipp/facerec/data/at/s4/4.pgm;3
/home/philipp/facerec/data/at/s4/10.pgm;3
/home/philipp/facerec/data/at/s4/8.pgm;3
/home/philipp/facerec/data/at/s4/1.pgm;3
/home/philipp/facerec/data/at/s9/2.pgm;8
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@ -1,377 +0,0 @@
FaceRecognizer
==============
.. highlight:: cpp
.. Sample code::
* An example using the FaceRecognizer class can be found at opencv_source_code/samples/cpp/facerec_demo.cpp
* (Python) An example using the FaceRecognizer class can be found at opencv_source_code/samples/python2/facerec_demo.py
FaceRecognizer
--------------
.. ocv:class:: FaceRecognizer : public Algorithm
All face recognition models in OpenCV are derived from the abstract base class :ocv:class:`FaceRecognizer`, which provides
a unified access to all face recongition algorithms in OpenCV. ::
class FaceRecognizer : public Algorithm
{
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
virtual void train(InputArray src, InputArray labels) = 0;
// Updates a FaceRecognizer.
virtual void update(InputArrayOfArrays src, InputArray labels);
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
virtual void predict(InputArray src, int &label, double &confidence) const = 0;
// Serializes this object to a given filename.
virtual void save(const String& filename) const;
// Deserializes this object from a given filename.
virtual void load(const String& filename);
// Serializes this object to a given cv::FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
};
Description
+++++++++++
I'll go a bit more into detail explaining :ocv:class:`FaceRecognizer`, because it doesn't look like a powerful interface at first sight. But: Every :ocv:class:`FaceRecognizer` is an :ocv:class:`Algorithm`, so you can easily get/set all model internals (if allowed by the implementation). :ocv:class:`Algorithm` is a relatively new OpenCV concept, which is available since the 2.4 release. I suggest you take a look at its description.
:ocv:class:`Algorithm` provides the following features for all derived classes:
* So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see :ocv:func:`Algorithm::create`). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms.
* Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with :ocv:cfunc:`cvSetCaptureProperty`, :ocv:cfunc:`cvGetCaptureProperty`, :ocv:func:`VideoCapture::set` and :ocv:func:`VideoCapture::get`. :ocv:class:`Algorithm` provides similar method where instead of integer id's you specify the parameter names as text Strings. See :ocv:func:`Algorithm::set` and :ocv:func:`Algorithm::get` for details.
* Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time.
Moreover every :ocv:class:`FaceRecognizer` supports the:
* **Training** of a :ocv:class:`FaceRecognizer` with :ocv:func:`FaceRecognizer::train` on a given set of images (your face database!).
* **Prediction** of a given sample image, that means a face. The image is given as a :ocv:class:`Mat`.
* **Loading/Saving** the model state from/to a given XML or YAML.
.. note:: When using the FaceRecognizer interface in combination with Python, please stick to Python 2. Some underlying scripts like create_csv will not work in other versions, like Python 3.
Setting the Thresholds
+++++++++++++++++++++++
Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in :ocv:class:`FaceRecognizer` to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* :ocv:class:`FaceRecognizer` algorithm. The appropriate place to set the thresholds is in the constructor of the specific :ocv:class:`FaceRecognizer` and since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm` (see above), you can get/set the thresholds at runtime!
Here is an example of setting a threshold for the Eigenfaces method, when creating the model:
.. code-block:: cpp
// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0
int num_components = 10;
double threshold = 10.0;
// Then if you want to have a cv::FaceRecognizer with a confidence threshold,
// create the concrete implementation with the appropiate parameters:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer(num_components, threshold);
Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to :ocv:class:`Algorithm` it's possible to set internal model thresholds during runtime. Let's see how we would set/get the prediction for the Eigenface model, we've created above:
.. code-block:: cpp
// The following line reads the threshold from the Eigenfaces model:
double current_threshold = model->getDouble("threshold");
// And this line sets the threshold to 0.0:
model->set("threshold", 0.0);
If you've set the threshold to ``0.0`` as we did above, then:
.. code-block:: cpp
//
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// Get a prediction from the model. Note: We've set a threshold of 0.0 above,
// since the distance is almost always larger than 0.0, you'll get -1 as
// label, which indicates, this face is unknown
int predicted_label = model->predict(img);
// ...
is going to yield ``-1`` as predicted label, which states this face is unknown.
Getting the name of a FaceRecognizer
+++++++++++++++++++++++++++++++++++++
Since every :ocv:class:`FaceRecognizer` is a :ocv:class:`Algorithm`, you can use :ocv:func:`Algorithm::name` to get the name of a :ocv:class:`FaceRecognizer`:
.. code-block:: cpp
// Create a FaceRecognizer:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
// And here's how to get its name:
String name = model->name();
FaceRecognizer::train
---------------------
Trains a FaceRecognizer with given data and associated labels.
.. ocv:function:: void FaceRecognizer::train( InputArrayOfArrays src, InputArray labels ) = 0
:param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector<Mat>``.
:param labels: The labels corresponding to the images have to be given either as a ``vector<int>`` or a
The following source code snippet shows you how to learn a Fisherfaces model on a given set of images. The images are read with :ocv:func:`imread` and pushed into a ``std::vector<Mat>``. The labels of each image are stored within a ``std::vector<int>`` (you could also use a :ocv:class:`Mat` of type `CV_32SC1`). Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. For the available :ocv:class:`FaceRecognizer` you don't have to pay any attention to the order of the labels, just make sure same persons have the same label:
.. code-block:: cpp
// holds images and labels
vector<Mat> images;
vector<int> labels;
// images for first person
images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
// images for second person
images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
Now that you have read some images, we can create a new :ocv:class:`FaceRecognizer`. In this example I'll create a Fisherfaces model and decide to keep all of the possible Fisherfaces:
.. code-block:: cpp
// Create a new Fisherfaces model and retain all available Fisherfaces,
// this is the most common usage of this specific FaceRecognizer:
//
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
And finally train it on the given dataset (the face images and labels):
.. code-block:: cpp
// This is the common interface to train all of the available cv::FaceRecognizer
// implementations:
//
model->train(images, labels);
FaceRecognizer::update
----------------------
Updates a FaceRecognizer with given data and associated labels.
.. ocv:function:: void FaceRecognizer::update( InputArrayOfArrays src, InputArray labels )
:param src: The training images, that means the faces you want to learn. The data has to be given as a ``vector<Mat>``.
:param labels: The labels corresponding to the images have to be given either as a ``vector<int>`` or a
This method updates a (probably trained) :ocv:class:`FaceRecognizer`, but only if the algorithm supports it. The Local Binary Patterns Histograms (LBPH) recognizer (see :ocv:func:`createLBPHFaceRecognizer`) can be updated. For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to re-estimate the model with :ocv:func:`FaceRecognizer::train`. In any case, a call to train empties the existing model and learns a new model, while update does not delete any model data.
.. code-block:: cpp
// Create a new LBPH model (it can be updated) and use the default parameters,
// this is the most common usage of this specific FaceRecognizer:
//
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
// This is the common interface to train all of the available cv::FaceRecognizer
// implementations:
//
model->train(images, labels);
// Some containers to hold new image:
vector<Mat> newImages;
vector<int> newLabels;
// You should add some images to the containers:
//
// ...
//
// Now updating the model is as easy as calling:
model->update(newImages,newLabels);
// This will preserve the old model data and extend the existing model
// with the new features extracted from newImages!
Calling update on an Eigenfaces model (see :ocv:func:`createEigenFaceRecognizer`), which doesn't support updating, will throw an error similar to:
.. code-block:: none
OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305
terminate called after throwing an instance of 'cv::Exception'
Please note: The :ocv:class:`FaceRecognizer` does not store your training images, because this would be very memory intense and it's not the responsibility of te :ocv:class:`FaceRecognizer` to do so. The caller is responsible for maintaining the dataset, he want to work with.
FaceRecognizer::predict
-----------------------
.. ocv:function:: int FaceRecognizer::predict( InputArray src ) const = 0
.. ocv:function:: void FaceRecognizer::predict( InputArray src, int & label, double & confidence ) const = 0
Predicts a label and associated confidence (e.g. distance) for a given input image.
:param src: Sample image to get a prediction from.
:param label: The predicted label for the given image.
:param confidence: Associated confidence (e.g. distance) for the predicted label.
The suffix ``const`` means that prediction does not affect the internal model
state, so the method can be safely called from within different threads.
The following example shows how to get a prediction from a trained model:
.. code-block:: cpp
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
// Read in a sample image:
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// And get a prediction from the cv::FaceRecognizer:
int predicted = model->predict(img);
Or to get a prediction and the associated confidence (e.g. distance):
.. code-block:: cpp
using namespace cv;
// Do your initialization here (create the cv::FaceRecognizer model) ...
// ...
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// Some variables for the predicted label and associated confidence (e.g. distance):
int predicted_label = -1;
double predicted_confidence = 0.0;
// Get the prediction and associated confidence from the model
model->predict(img, predicted_label, predicted_confidence);
FaceRecognizer::save
--------------------
Saves a :ocv:class:`FaceRecognizer` and its model state.
.. ocv:function:: void FaceRecognizer::save(const String& filename) const
Saves this model to a given filename, either as XML or YAML.
:param filename: The filename to store this :ocv:class:`FaceRecognizer` to (either XML/YAML).
.. ocv:function:: void FaceRecognizer::save(FileStorage& fs) const
Saves this model to a given :ocv:class:`FileStorage`.
:param fs: The :ocv:class:`FileStorage` to store this :ocv:class:`FaceRecognizer` to.
Every :ocv:class:`FaceRecognizer` overwrites ``FaceRecognizer::save(FileStorage& fs)``
to save the internal model state. ``FaceRecognizer::save(const String& filename)`` saves
the state of a model to the given filename.
The suffix ``const`` means that prediction does not affect the internal model
state, so the method can be safely called from within different threads.
FaceRecognizer::load
--------------------
Loads a :ocv:class:`FaceRecognizer` and its model state.
.. ocv:function:: void FaceRecognizer::load( const String& filename )
.. ocv:function:: void FaceRecognizer::load( const FileStorage& fs ) = 0
Loads a persisted model and state from a given XML or YAML file . Every
:ocv:class:`FaceRecognizer` has to overwrite ``FaceRecognizer::load(FileStorage& fs)``
to enable loading the model state. ``FaceRecognizer::load(FileStorage& fs)`` in
turn gets called by ``FaceRecognizer::load(const String& filename)``, to ease
saving a model.
createEigenFaceRecognizer
-------------------------
.. ocv:function:: Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX)
:param num_components: The number of components (read: Eigenfaces) kept for this Prinicpal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
:param threshold: The threshold applied in the prediciton.
Notes:
++++++
* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images.
* This model does not support updating.
Model internal data:
++++++++++++++++++++
* ``num_components`` see :ocv:func:`createEigenFaceRecognizer`.
* ``threshold`` see :ocv:func:`createEigenFaceRecognizer`.
* ``eigenvalues`` The eigenvalues for this Principal Component Analysis (ordered descending).
* ``eigenvectors`` The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
* ``mean`` The sample mean calculated from the training data.
* ``projections`` The projections of the training data.
* ``labels`` The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
createFisherFaceRecognizer
--------------------------
.. ocv:function:: Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX)
:param num_components: The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes ``c`` (read: subjects, persons you want to recognize). If you leave this at the default (``0``) or set it to a value less-equal ``0`` or greater ``(c-1)``, it will be set to the correct number ``(c-1)`` automatically.
:param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
Notes:
++++++
* Training and prediction must be done on grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use :ocv:func:`resize` to resize the images.
* This model does not support updating.
Model internal data:
++++++++++++++++++++
* ``num_components`` see :ocv:func:`createFisherFaceRecognizer`.
* ``threshold`` see :ocv:func:`createFisherFaceRecognizer`.
* ``eigenvalues`` The eigenvalues for this Linear Discriminant Analysis (ordered descending).
* ``eigenvectors`` The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
* ``mean`` The sample mean calculated from the training data.
* ``projections`` The projections of the training data.
* ``labels`` The labels corresponding to the projections.
createLBPHFaceRecognizer
-------------------------
.. ocv:function:: Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX)
:param radius: The radius used for building the Circular Local Binary Pattern. The greater the radius, the
:param neighbors: The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost.
:param grid_x: The number of cells in the horizontal direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
:param grid_y: The number of cells in the vertical direction, ``8`` is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
:param threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
Notes:
++++++
* The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use :ocv:func:`cvtColor` to convert between the color spaces.
* This model supports updating.
Model internal data:
++++++++++++++++++++
* ``radius`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``neighbors`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``grid_x`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``grid_y`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``threshold`` see :ocv:func:`createLBPHFaceRecognizer`.
* ``histograms`` Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
* ``labels`` Labels corresponding to the calculated Local Binary Patterns Histograms.

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Changelog
=========
Release 0.05
------------
This library is now included in the official OpenCV distribution (from 2.4 on).
The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall
OpenCV API.
To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
have been synchronized and are now based on the same interfaces and implementation.
The library now has an extensive documentation:
* The API is explained in detail and with a lot of code examples.
* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
* A tutorial for gender classification with Fisherfaces.
* A tutorial for face recognition in videos (e.g. webcam).
Release highlights
++++++++++++++++++
* There are no single highlights to pick from, this release is a highlight itself.
Release 0.04
------------
This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
bugfixes, but none influenced the recognition rate.
Release highlights
++++++++++++++++++
* A whole lot of exceptions with meaningful error messages.
* A tutorial for Windows users: `http://bytefish.de/blog/opencv_visual_studio_and_libfacerec <http://bytefish.de/blog/opencv_visual_studio_and_libfacerec>`_
Release 0.03
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
++++++++++++++++++
* New Unit Tests (for LBP Histograms) make the library more robust.
* Added more documentation.
Release 0.02
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
++++++++++++++++++
* New Unit Tests (for LBP Histograms) make the library more robust.
* Added a documentation and changelog in reStructuredText.
Release 0.01
------------
Initial release as header-only library.
Release highlights
++++++++++++++++++
* Colormaps for OpenCV to enhance the visualization.
* Face Recognition algorithms implemented:
* Eigenfaces [TP91]_
* Fisherfaces [BHK97]_
* Local Binary Patterns Histograms [AHP04]_
* Added persistence facilities to store the models with a common API.
* Unit Tests (using `gtest <http://code.google.com/p/googletest/>`_).
* Providing a CMakeLists.txt to enable easy cross-platform building.

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Face Recognition with OpenCV
############################
.. contents:: Table of Contents
:depth: 3
Introduction
============
`OpenCV (Open Source Computer Vision) <http://opencv.org>`_ is a popular computer vision library started by `Intel <http://www.intel.com>`_ in 1999. The cross-platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. In 2008 `Willow Garage <http://www.willowgarage.com>`_ took over support and OpenCV 2.3.1 now comes with a programming interface to C, C++, `Python <http://www.python.org>`_ and `Android <http://www.android.com>`_. OpenCV is released under a BSD license so it is used in academic projects and commercial products alike.
OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition, so you can start experimenting with face recognition right away. This document is the guide I've wished for, when I was working myself into face recognition. It shows you how to perform face recognition with :ocv:class:`FaceRecognizer` in OpenCV (with full source code listings) and gives you an introduction into the algorithms behind. I'll also show how to create the visualizations you can find in many publications, because a lot of people asked for.
The currently available algorithms are:
* Eigenfaces (see :ocv:func:`createEigenFaceRecognizer`)
* Fisherfaces (see :ocv:func:`createFisherFaceRecognizer`)
* Local Binary Patterns Histograms (see :ocv:func:`createLBPHFaceRecognizer`)
You don't need to copy and paste the source code examples from this page, because they are available in the ``src`` folder coming with this documentation. If you have built OpenCV with the samples turned on, chances are good you have them compiled already! Although it might be interesting for very advanced users, I've decided to leave the implementation details out as I am afraid they confuse new users.
All code in this document is released under the `BSD license <http://www.opensource.org/licenses/bsd-license>`_, so feel free to use it for your projects.
Face Recognition
================
Face recognition is an easy task for humans. Experiments in [Tu06]_ have shown, that even one to three day old babies are able to distinguish between known faces. So how hard could it be for a computer? It turns out we know little about human recognition to date. Are inner features (eyes, nose, mouth) or outer features (head shape, hairline) used for a successful face recognition? How do we analyze an image and how does the brain encode it? It was shown by `David Hubel <http://en.wikipedia.org/wiki/David_H._Hubel>`_ and `Torsten Wiesel <http://en.wikipedia.org/wiki/Torsten_Wiesel>`_, that our brain has specialized nerve cells responding to specific local features of a scene, such as lines, edges, angles or movement. Since we don't see the world as scattered pieces, our visual cortex must somehow combine the different sources of information into useful patterns. Automatic face recognition is all about extracting those meaningful features from an image, putting them into a useful representation and performing some kind of classification on them.
Face recognition based on the geometric features of a face is probably the most intuitive approach to face recognition. One of the first automated face recognition systems was described in [Kanade73]_: marker points (position of eyes, ears, nose, ...) were used to build a feature vector (distance between the points, angle between them, ...). The recognition was performed by calculating the euclidean distance between feature vectors of a probe and reference image. Such a method is robust against changes in illumination by its nature, but has a huge drawback: the accurate registration of the marker points is complicated, even with state of the art algorithms. Some of the latest work on geometric face recognition was carried out in [Bru92]_. A 22-dimensional feature vector was used and experiments on large datasets have shown, that geometrical features alone my not carry enough information for face recognition.
The Eigenfaces method described in [TP91]_ took a holistic approach to face recognition: A facial image is a point from a high-dimensional image space and a lower-dimensional representation is found, where classification becomes easy. The lower-dimensional subspace is found with Principal Component Analysis, which identifies the axes with maximum variance. While this kind of transformation is optimal from a reconstruction standpoint, it doesn't take any class labels into account. Imagine a situation where the variance is generated from external sources, let it be light. The axes with maximum variance do not necessarily contain any discriminative information at all, hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant Analysis was applied to face recognition in [BHK97]_. The basic idea is to minimize the variance within a class, while maximizing the variance between the classes at the same time.
Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of the input data only local regions of an image are described, the extracted features are (hopefully) more robust against partial occlusion, illumation and small sample size. Algorithms used for a local feature extraction are Gabor Wavelets ([Wiskott97]_), Discrete Cosinus Transform ([Messer06]_) and Local Binary Patterns ([AHP04]_). It's still an open research question what's the best way to preserve spatial information when applying a local feature extraction, because spatial information is potentially useful information.
Face Database
==============
Let's get some data to experiment with first. I don't want to do a toy example here. We are doing face recognition, so you'll need some face images! You can either create your own dataset or start with one of the available face databases, `http://face-rec.org/databases/ <http://face-rec.org/databases>`_ gives you an up-to-date overview. Three interesting databases are (parts of the description are quoted from `http://face-rec.org <http://face-rec.org>`_):
* `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ The AT&T Facedatabase, sometimes also referred to as *ORL Database of Faces*, contains ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
* `Yale Facedatabase A <http://vision.ucsd.edu/content/yale-face-database>`_, also known as Yalefaces. The AT&T Facedatabase is good for initial tests, but it's a fairly easy database. The Eigenfaces method already has a 97% recognition rate on it, so you won't see any great improvements with other algorithms. The Yale Facedatabase A (also known as Yalefaces) is a more appropriate dataset for initial experiments, because the recognition problem is harder. The database consists of 15 people (14 male, 1 female) each with 11 grayscale images sized :math:`320 \times 243` pixel. There are changes in the light conditions (center light, left light, right light), facial expressions (happy, normal, sad, sleepy, surprised, wink) and glasses (glasses, no-glasses).
The original images are not cropped and aligned. Please look into the :ref:`appendixft` for a Python script, that does the job for you.
* `Extended Yale Facedatabase B <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html>`_ The Extended Yale Facedatabase B contains 2414 images of 38 different people in its cropped version. The focus of this database is set on extracting features that are robust to illumination, the images have almost no variation in emotion/occlusion/... . I personally think, that this dataset is too large for the experiments I perform in this document. You better use the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ for intial testing. A first version of the Yale Facedatabase B was used in [BHK97]_ to see how the Eigenfaces and Fisherfaces method perform under heavy illumination changes. [Lee05]_ used the same setup to take 16128 images of 28 people. The Extended Yale Facedatabase B is the merge of the two databases, which is now known as Extended Yalefacedatabase B.
Preparing the data
-------------------
Once we have acquired some data, we'll need to read it in our program. In the demo applications I have decided to read the images from a very simple CSV file. Why? Because it's the simplest platform-independent approach I can think of. However, if you know a simpler solution please ping me about it. Basically all the CSV file needs to contain are lines composed of a ``filename`` followed by a ``;`` followed by the ``label`` (as *integer number*), making up a line like this:
.. code-block:: none
/path/to/image.ext;0
Let's dissect the line. ``/path/to/image.ext`` is the path to an image, probably something like this if you are in Windows: ``C:/faces/person0/image0.jpg``. Then there is the separator ``;`` and finally we assign the label ``0`` to the image. Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label.
Download the AT&T Facedatabase from AT&T Facedatabase and the corresponding CSV file from at.txt, which looks like this (file is without ... of course):
.. code-block:: none
./at/s1/1.pgm;0
./at/s1/2.pgm;0
...
./at/s2/1.pgm;1
./at/s2/2.pgm;1
...
./at/s40/1.pgm;39
./at/s40/2.pgm;39
Imagine I have extracted the files to ``D:/data/at`` and have downloaded the CSV file to ``D:/data/at.txt``. Then you would simply need to Search & Replace ``./`` with ``D:/data/``. You can do that in an editor of your choice, every sufficiently advanced editor can do this. Once you have a CSV file with valid filenames and labels, you can run any of the demos by passing the path to the CSV file as parameter:
.. code-block:: none
facerec_demo.exe D:/data/at.txt
Creating the CSV File
+++++++++++++++++++++
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call create_csv.py with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Please see the :ref:`appendixft` for additional informations.
Eigenfaces
==========
The problem with the image representation we are given is its high dimensionality. Two-dimensional :math:`p \times q` grayscale images span a :math:`m = pq`-dimensional vector space, so an image with :math:`100 \times 100` pixels lies in a :math:`10,000`-dimensional image space already. The question is: Are all dimensions equally useful for us? We can only make a decision if there's any variance in data, so what we are looking for are the components that account for most of the information. The Principal Component Analysis (PCA) was independently proposed by `Karl Pearson <http://en.wikipedia.org/wiki/Karl_Pearson>`_ (1901) and `Harold Hotelling <http://en.wikipedia.org/wiki/Harold_Hotelling>`_ (1933) to turn a set of possibly correlated variables into a smaller set of uncorrelated variables. The idea is, that a high-dimensional dataset is often described by correlated variables and therefore only a few meaningful dimensions account for most of the information. The PCA method finds the directions with the greatest variance in the data, called principal components.
Algorithmic Description
-----------------------
Let :math:`X = \{ x_{1}, x_{2}, \ldots, x_{n} \}` be a random vector with observations :math:`x_i \in R^{d}`.
1. Compute the mean :math:`\mu`
.. math::
\mu = \frac{1}{n} \sum_{i=1}^{n} x_{i}
2. Compute the the Covariance Matrix `S`
.. math::
S = \frac{1}{n} \sum_{i=1}^{n} (x_{i} - \mu) (x_{i} - \mu)^{T}`
3. Compute the eigenvalues :math:`\lambda_{i}` and eigenvectors :math:`v_{i}` of :math:`S`
.. math::
S v_{i} = \lambda_{i} v_{i}, i=1,2,\ldots,n
4. Order the eigenvectors descending by their eigenvalue. The :math:`k` principal components are the eigenvectors corresponding to the :math:`k` largest eigenvalues.
The :math:`k` principal components of the observed vector :math:`x` are then given by:
.. math::
y = W^{T} (x - \mu)
where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`.
The reconstruction from the PCA basis is given by:
.. math::
x = W y + \mu
where :math:`W = (v_{1}, v_{2}, \ldots, v_{k})`.
The Eigenfaces method then performs face recognition by:
* Projecting all training samples into the PCA subspace.
* Projecting the query image into the PCA subspace.
* Finding the nearest neighbor between the projected training images and the projected query image.
Still there's one problem left to solve. Imagine we are given :math:`400` images sized :math:`100 \times 100` pixel. The Principal Component Analysis solves the covariance matrix :math:`S = X X^{T}`, where :math:`{size}(X) = 10000 \times 400` in our example. You would end up with a :math:`10000 \times 10000` matrix, roughly :math:`0.8 GB`. Solving this problem isn't feasible, so we'll need to apply a trick. From your linear algebra lessons you know that a :math:`M \times N` matrix with :math:`M > N` can only have :math:`N - 1` non-zero eigenvalues. So it's possible to take the eigenvalue decomposition :math:`S = X^{T} X` of size :math:`N \times N` instead:
.. math::
X^{T} X v_{i} = \lambda_{i} v{i}
and get the original eigenvectors of :math:`S = X X^{T}` with a left multiplication of the data matrix:
.. math::
X X^{T} (X v_{i}) = \lambda_{i} (X v_{i})
The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be normalized to unit length. I don't want to turn this into a publication, so please look into [Duda01]_ for the derivation and proof of the equations.
Eigenfaces in OpenCV
--------------------
For the first source code example, I'll go through it with you. I am first giving you the whole source code listing, and after this we'll look at the most important lines in detail. Please note: every source code listing is commented in detail, so you should have no problems following it.
.. literalinclude:: src/facerec_eigenfaces.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_eigenfaces.cpp <src/facerec_eigenfaces.cpp>`
I've used the jet colormap, so you can see how the grayscale values are distributed within the specific Eigenfaces. You can see, that the Eigenfaces do not only encode facial features, but also the illumination in the images (see the left light in Eigenface \#4, right light in Eigenfaces \#5):
.. image:: img/eigenfaces_opencv.png
:align: center
We've already seen, that we can reconstruct a face from its lower dimensional approximation. So let's see how many Eigenfaces are needed for a good reconstruction. I'll do a subplot with :math:`10,30,\ldots,310` Eigenfaces:
.. code-block:: cpp
// Display or save the image reconstruction at some predefined steps:
for(int num_components = 10; num_components < 300; num_components+=15) {
// slice the eigenvectors from the model
Mat evs = Mat(W, Range::all(), Range(0, num_components));
Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(evs, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
} else {
imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
}
}
10 Eigenvectors are obviously not sufficient for a good image reconstruction, 50 Eigenvectors may already be sufficient to encode important facial features. You'll get a good reconstruction with approximately 300 Eigenvectors for the AT&T Facedatabase. There are rule of thumbs how many Eigenfaces you should choose for a successful face recognition, but it heavily depends on the input data. [Zhao03]_ is the perfect point to start researching for this:
.. image:: img/eigenface_reconstruction_opencv.png
:align: center
Fisherfaces
============
The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear combination of features that maximizes the total variance in data. While this is clearly a powerful way to represent data, it doesn't consider any classes and so a lot of discriminative information *may* be lost when throwing components away. Imagine a situation where the variance in your data is generated by an external source, let it be the light. The components identified by a PCA do not necessarily contain any discriminative information at all, so the projected samples are smeared together and a classification becomes impossible (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave <http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>`_ for an example).
The Linear Discriminant Analysis performs a class-specific dimensionality reduction and was invented by the great statistician `Sir R. A. Fisher <http://en.wikipedia.org/wiki/Ronald_Fisher>`_. He successfully used it for classifying flowers in his 1936 paper *The use of multiple measurements in taxonomic problems* [Fisher36]_. In order to find the combination of features that separates best between classes the Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation. This was also recognized by `Belhumeur <http://www.cs.columbia.edu/~belhumeur/>`_, `Hespanha <http://www.ece.ucsb.edu/~hespanha/>`_ and `Kriegman <http://cseweb.ucsd.edu/~kriegman/>`_ and so they applied a Discriminant Analysis to face recognition in [BHK97]_.
Algorithmic Description
-----------------------
Let :math:`X` be a random vector with samples drawn from :math:`c` classes:
.. math::
:nowrap:
\begin{align*}
X & = & \{X_1,X_2,\ldots,X_c\} \\
X_i & = & \{x_1, x_2, \ldots, x_n\}
\end{align*}
The scatter matrices :math:`S_{B}` and `S_{W}` are calculated as:
.. math::
:nowrap:
\begin{align*}
S_{B} & = & \sum_{i=1}^{c} N_{i} (\mu_i - \mu)(\mu_i - \mu)^{T} \\
S_{W} & = & \sum_{i=1}^{c} \sum_{x_{j} \in X_{i}} (x_j - \mu_i)(x_j - \mu_i)^{T}
\end{align*}
, where :math:`\mu` is the total mean:
.. math::
\mu = \frac{1}{N} \sum_{i=1}^{N} x_i
And :math:`\mu_i` is the mean of class :math:`i \in \{1,\ldots,c\}`:
.. math::
\mu_i = \frac{1}{|X_i|} \sum_{x_j \in X_i} x_j
Fisher's classic algorithm now looks for a projection :math:`W`, that maximizes the class separability criterion:
.. math::
W_{opt} = \operatorname{arg\,max}_{W} \frac{|W^T S_B W|}{|W^T S_W W|}
Following [BHK97]_, a solution for this optimization problem is given by solving the General Eigenvalue Problem:
.. math::
:nowrap:
\begin{align*}
S_{B} v_{i} & = & \lambda_{i} S_w v_{i} \nonumber \\
S_{W}^{-1} S_{B} v_{i} & = & \lambda_{i} v_{i}
\end{align*}
There's one problem left to solve: The rank of :math:`S_{W}` is at most :math:`(N-c)`, with :math:`N` samples and :math:`c` classes. In pattern recognition problems the number of samples :math:`N` is almost always samller than the dimension of the input data (the number of pixels), so the scatter matrix :math:`S_{W}` becomes singular (see [RJ91]_). In [BHK97]_ this was solved by performing a Principal Component Analysis on the data and projecting the samples into the :math:`(N-c)`-dimensional space. A Linear Discriminant Analysis was then performed on the reduced data, because :math:`S_{W}` isn't singular anymore.
The optimization problem can then be rewritten as:
.. math::
:nowrap:
\begin{align*}
W_{pca} & = & \operatorname{arg\,max}_{W} |W^T S_T W| \\
W_{fld} & = & \operatorname{arg\,max}_{W} \frac{|W^T W_{pca}^T S_{B} W_{pca} W|}{|W^T W_{pca}^T S_{W} W_{pca} W|}
\end{align*}
The transformation matrix :math:`W`, that projects a sample into the :math:`(c-1)`-dimensional space is then given by:
.. math::
W = W_{fld}^{T} W_{pca}^{T}
Fisherfaces in OpenCV
---------------------
.. literalinclude:: src/facerec_fisherfaces.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_fisherfaces.cpp <src/facerec_fisherfaces.cpp>`
For this example I am going to use the Yale Facedatabase A, just because the plots are nicer. Each Fisherface has the same length as an original image, thus it can be displayed as an image. The demo shows (or saves) the first, at most 16 Fisherfaces:
.. image:: img/fisherfaces_opencv.png
:align: center
The Fisherfaces method learns a class-specific transformation matrix, so the they do not capture illumination as obviously as the Eigenfaces method. The Discriminant Analysis instead finds the facial features to discriminate between the persons. It's important to mention, that the performance of the Fisherfaces heavily depends on the input data as well. Practically said: if you learn the Fisherfaces for well-illuminated pictures only and you try to recognize faces in bad-illuminated scenes, then method is likely to find the wrong components (just because those features may not be predominant on bad illuminated images). This is somewhat logical, since the method had no chance to learn the illumination.
The Fisherfaces allow a reconstruction of the projected image, just like the Eigenfaces did. But since we only identified the features to distinguish between subjects, you can't expect a nice reconstruction of the original image. For the Fisherfaces method we'll project the sample image onto each of the Fisherfaces instead. So you'll have a nice visualization, which feature each of the Fisherfaces describes:
.. code-block:: cpp
// Display or save the image reconstruction at some predefined steps:
for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
// Slice the Fisherface from the model:
Mat ev = W.col(num_component);
Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(ev, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
if(argc == 2) {
imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
} else {
imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
}
}
The differences may be subtle for the human eyes, but you should be able to see some differences:
.. image:: img/fisherface_reconstruction_opencv.png
:align: center
Local Binary Patterns Histograms
================================
Eigenfaces and Fisherfaces take a somewhat holistic approach to face recognition. You treat your data as a vector somewhere in a high-dimensional image space. We all know high-dimensionality is bad, so a lower-dimensional subspace is identified, where (probably) useful information is preserved. The Eigenfaces approach maximizes the total scatter, which can lead to problems if the variance is generated by an external source, because components with a maximum variance over all classes aren't necessarily useful for classification (see `http://www.bytefish.de/wiki/pca_lda_with_gnu_octave <http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>`_). So to preserve some discriminative information we applied a Linear Discriminant Analysis and optimized as described in the Fisherfaces method. The Fisherfaces method worked great... at least for the constrained scenario we've assumed in our model.
Now real life isn't perfect. You simply can't guarantee perfect light settings in your images or 10 different images of a person. So what if there's only one image for each person? Our covariance estimates for the subspace *may* be horribly wrong, so will the recognition. Remember the Eigenfaces method had a 96% recognition rate on the AT&T Facedatabase? How many images do we actually need to get such useful estimates? Here are the Rank-1 recognition rates of the Eigenfaces and Fisherfaces method on the AT&T Facedatabase, which is a fairly easy image database:
.. image:: img/at_database_small_sample_size.png
:scale: 60%
:align: center
So in order to get good recognition rates you'll need at least 8(+-1) images for each person and the Fisherfaces method doesn't really help here. The above experiment is a 10-fold cross validated result carried out with the facerec framework at: `https://github.com/bytefish/facerec <https://github.com/bytefish/facerec>`_. This is not a publication, so I won't back these figures with a deep mathematical analysis. Please have a look into [KM01]_ for a detailed analysis of both methods, when it comes to small training datasets.
So some research concentrated on extracting local features from images. The idea is to not look at the whole image as a high-dimensional vector, but describe only local features of an object. The features you extract this way will have a low-dimensionality implicitly. A fine idea! But you'll soon observe the image representation we are given doesn't only suffer from illumination variations. Think of things like scale, translation or rotation in images - your local description has to be at least a bit robust against those things. Just like :ocv:class:`SIFT`, the Local Binary Patterns methodology has its roots in 2D texture analysis. The basic idea of Local Binary Patterns is to summarize the local structure in an image by comparing each pixel with its neighborhood. Take a pixel as center and threshold its neighbors against. If the intensity of the center pixel is greater-equal its neighbor, then denote it with 1 and 0 if not. You'll end up with a binary number for each pixel, just like 11001111. So with 8 surrounding pixels you'll end up with 2^8 possible combinations, called *Local Binary Patterns* or sometimes referred to as *LBP codes*. The first LBP operator described in literature actually used a fixed 3 x 3 neighborhood just like this:
.. image:: img/lbp/lbp.png
:scale: 80%
:align: center
Algorithmic Description
-----------------------
A more formal description of the LBP operator can be given as:
.. math::
LBP(x_c, y_c) = \sum_{p=0}^{P-1} 2^p s(i_p - i_c)
, with :math:`(x_c, y_c)` as central pixel with intensity :math:`i_c`; and :math:`i_n` being the intensity of the the neighbor pixel. :math:`s` is the sign function defined as:
.. math::
:nowrap:
\begin{equation}
s(x) =
\begin{cases}
1 & \text{if $x \geq 0$}\\
0 & \text{else}
\end{cases}
\end{equation}
This description enables you to capture very fine grained details in images. In fact the authors were able to compete with state of the art results for texture classification. Soon after the operator was published it was noted, that a fixed neighborhood fails to encode details differing in scale. So the operator was extended to use a variable neighborhood in [AHP04]_. The idea is to align an abritrary number of neighbors on a circle with a variable radius, which enables to capture the following neighborhoods:
.. image:: img/lbp/patterns.png
:scale: 80%
:align: center
For a given Point :math:`(x_c,y_c)` the position of the neighbor :math:`(x_p,y_p), p \in P` can be calculated by:
.. math::
:nowrap:
\begin{align*}
x_{p} & = & x_c + R \cos({\frac{2\pi p}{P}})\\
y_{p} & = & y_c - R \sin({\frac{2\pi p}{P}})
\end{align*}
Where :math:`R` is the radius of the circle and :math:`P` is the number of sample points.
The operator is an extension to the original LBP codes, so it's sometimes called *Extended LBP* (also referred to as *Circular LBP*) . If a points coordinate on the circle doesn't correspond to image coordinates, the point get's interpolated. Computer science has a bunch of clever interpolation schemes, the OpenCV implementation does a bilinear interpolation:
.. math::
:nowrap:
\begin{align*}
f(x,y) \approx \begin{bmatrix}
1-x & x \end{bmatrix} \begin{bmatrix}
f(0,0) & f(0,1) \\
f(1,0) & f(1,1) \end{bmatrix} \begin{bmatrix}
1-y \\
y \end{bmatrix}.
\end{align*}
By definition the LBP operator is robust against monotonic gray scale transformations. We can easily verify this by looking at the LBP image of an artificially modified image (so you see what an LBP image looks like!):
.. image:: img/lbp/lbp_yale.jpg
:scale: 60%
:align: center
So what's left to do is how to incorporate the spatial information in the face recognition model. The representation proposed by Ahonen et. al [AHP04]_ is to divide the LBP image into :math:`m` local regions and extract a histogram from each. The spatially enhanced feature vector is then obtained by concatenating the local histograms (**not merging them**). These histograms are called *Local Binary Patterns Histograms*.
Local Binary Patterns Histograms in OpenCV
------------------------------------------
.. literalinclude:: src/facerec_lbph.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_lbph.cpp <src/facerec_lbph.cpp>`
Conclusion
==========
You've learned how to use the new :ocv:class:`FaceRecognizer` in real applications. After reading the document you also know how the algorithms work, so now it's time for you to experiment with the available algorithms. Use them, improve them and let the OpenCV community participate!
Credits
=======
This document wouldn't be possible without the kind permission to use the face images of the *AT&T Database of Faces* and the *Yale Facedatabase A/B*.
The Database of Faces
---------------------
** Important: when using these images, please give credit to "AT&T Laboratories, Cambridge." **
The Database of Faces, formerly *The ORL Database of Faces*, contains a set of face images taken between April 1992 and April 1994. The database was used in the context of a face recognition project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge University Engineering Department.
There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).
The files are in PGM format. The size of each image is 92x112 pixels, with 256 grey levels per pixel. The images are organised in 40 directories (one for each subject), which have names of the form sX, where X indicates the subject number (between 1 and 40). In each of these directories, there are ten different images of that subject, which have names of the form Y.pgm, where Y is the image number for that subject (between 1 and 10).
A copy of the database can be retrieved from: `http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip <http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip>`_.
Yale Facedatabase A
-------------------
*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.*
The Yale Face Database A (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink. (Source: `http://cvc.yale.edu/projects/yalefaces/yalefaces.html <http://cvc.yale.edu/projects/yalefaces/yalefaces.html>`_)
Yale Facedatabase B
--------------------
*With the permission of the authors I am allowed to show a small number of images (say subject 1 and all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase A or the Yale Facedatabase B.*
The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. The data format of this database is the same as the Yale Face Database B. Please refer to the homepage of the Yale Face Database B (or one copy of this page) for more detailed information of the data format.
You are free to use the extended Yale Face Database B for research purposes. All publications which use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference Athinodoros Georghiades, Peter Belhumeur, and David Kriegman's paper, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, `[bibtex] <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/athosref.html>`_.
The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting, PAMI, May, 2005 `[pdf] <http://vision.ucsd.edu/~leekc/papers/9pltsIEEE.pdf>`_." All test image data used in the experiments are manually aligned, cropped, and then re-sized to 168x192 images. If you publish your experimental results with the cropped images, please reference the PAMI2005 paper as well. (Source: `http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html <http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html>`_)
Literature
==========
.. [AHP04] Ahonen, T., Hadid, A., and Pietikainen, M. *Face Recognition with Local Binary Patterns.* Computer Vision - ECCV 2004 (2004), 469–481.
.. [BHK97] Belhumeur, P. N., Hespanha, J., and Kriegman, D. *Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711–720.
.. [Bru92] Brunelli, R., Poggio, T. *Face Recognition through Geometrical Features.* European Conference on Computer Vision (ECCV) 1992, S. 792–800.
.. [Duda01] Duda, Richard O. and Hart, Peter E. and Stork, David G., *Pattern Classification* (2nd Edition) 2001.
.. [Fisher36] Fisher, R. A. *The use of multiple measurements in taxonomic problems.* Annals Eugen. 7 (1936), 179–188.
.. [GBK01] Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.J., *From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose* IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 6 (2001), 643-660.
.. [Kanade73] Kanade, T. *Picture processing system by computer complex and recognition of human faces.* PhD thesis, Kyoto University, November 1973
.. [KM01] Martinez, A and Kak, A. *PCA versus LDA* IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.2, pp. 228-233, 2001.
.. [Lee05] Lee, K., Ho, J., Kriegman, D. *Acquiring Linear Subspaces for Face Recognition under Variable Lighting.* In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27 (2005), Nr. 5
.. [Messer06] Messer, K. et al. *Performance Characterisation of Face Recognition Algorithms and Their Sensitivity to Severe Illumination Changes.* In: In: ICB, 2006, S. 1–11.
.. [RJ91] S. Raudys and A.K. Jain. *Small sample size effects in statistical pattern recognition: Recommendations for practitioneers.* - IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 3 (1991), 252-264.
.. [Tan10] Tan, X., and Triggs, B. *Enhanced local texture feature sets for face recognition under difficult lighting conditions.* IEEE Transactions on Image Processing 19 (2010), 1635–650.
.. [TP91] Turk, M., and Pentland, A. *Eigenfaces for recognition.* Journal of Cognitive Neuroscience 3 (1991), 71–86.
.. [Tu06] Chiara Turati, Viola Macchi Cassia, F. S., and Leo, I. *Newborns face recognition: Role of inner and outer facial features. Child Development* 77, 2 (2006), 297–311.
.. [Wiskott97] Wiskott, L., Fellous, J., Krüger, N., Malsburg, C. *Face Recognition By Elastic Bunch Graph Matching.* IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997), S. 775–779
.. [Zhao03] Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A. Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35, 4 (2003), 399–458.
.. _appendixft:
Appendix
========
Creating the CSV File
---------------------
You don't really want to create the CSV file by hand. I have prepared you a little Python script ``create_csv.py`` (you find it at ``/src/create_csv.py`` coming with this tutorial) that automatically creates you a CSV file. If you have your images in hierarchie like this (``/basepath/<subject>/<image.ext>``):
.. code-block:: none
philipp@mango:~/facerec/data/at$ tree
.
|-- s1
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
|-- s2
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
...
|-- s40
| |-- 1.pgm
| |-- ...
| |-- 10.pgm
Then simply call ``create_csv.py`` with the path to the folder, just like this and you could save the output:
.. code-block:: none
philipp@mango:~/facerec/data$ python create_csv.py
at/s13/2.pgm;0
at/s13/7.pgm;0
at/s13/6.pgm;0
at/s13/9.pgm;0
at/s13/5.pgm;0
at/s13/3.pgm;0
at/s13/4.pgm;0
at/s13/10.pgm;0
at/s13/8.pgm;0
at/s13/1.pgm;0
at/s17/2.pgm;1
at/s17/7.pgm;1
at/s17/6.pgm;1
at/s17/9.pgm;1
at/s17/5.pgm;1
at/s17/3.pgm;1
[...]
Here is the script, if you can't find it:
.. literalinclude:: ./src/create_csv.py
:language: python
:linenos:
Aligning Face Images
---------------------
An accurate alignment of your image data is especially important in tasks like emotion detection, were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*, where:
* *eye_left* is the position of the left eye
* *eye_right* is the position of the right eye
* *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal, vertical direction)
* *dest_sz* is the size of the output image
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the eyes.
.. literalinclude:: ./src/crop_face.py
:language: python
:linenos:
Imagine we are given `this photo of Arnold Schwarzenegger <http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg>`_, which is under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical offset and the size your scaled, rotated & cropped face should have.
Here are some examples:
+---------------------------------+----------------------------------------------------------------------------+
| Configuration | Cropped, Scaled, Rotated Face |
+=================================+============================================================================+
| 0.1 (10%), 0.1 (10%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_10_10_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.3 (30%), 0.3 (30%), (200,200) | .. image:: ./img/tutorial/gender_classification/arnie_30_30_200_200.jpg |
+---------------------------------+----------------------------------------------------------------------------+
| 0.2 (20%), 0.2 (20%), (70,70) | .. image:: ./img/tutorial/gender_classification/arnie_20_20_70_70.jpg |
+---------------------------------+----------------------------------------------------------------------------+
CSV for the AT&T Facedatabase
------------------------------
.. literalinclude:: etc/at.txt
:language: none
:linenos:

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@ -1,32 +0,0 @@
FaceRecognizer - Face Recognition with OpenCV
##############################################
OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition. This documentation is going to explain you :doc:`the API <facerec_api>` in detail and it will give you a lot of help to get started (full source code examples). :doc:`Face Recognition with OpenCV <facerec_tutorial>` is the definite guide to the new :ocv:class:`FaceRecognizer`. There's also a :doc:`tutorial on gender classification <tutorial/facerec_gender_classification>`, a :doc:`tutorial for face recognition in videos <tutorial/facerec_video_recognition>` and it's shown :doc:`how to load & save your results <tutorial/facerec_save_load>`.
These documents are the help I have wished for, when I was working myself into face recognition. I hope you also think the new :ocv:class:`FaceRecognizer` is a useful addition to OpenCV.
Please issue any feature requests and/or bugs on the official OpenCV bug tracker at:
* http://code.opencv.org/projects/opencv/issues
Contents
========
.. toctree::
:maxdepth: 1
FaceRecognizer API <facerec_api>
Guide to Face Recognition with OpenCV <facerec_tutorial>
Tutorial on Gender Classification <tutorial/facerec_gender_classification>
Tutorial on Face Recognition in Videos <tutorial/facerec_video_recognition>
Tutorial On Saving & Loading a FaceRecognizer <tutorial/facerec_save_load>
How to use Colormaps in OpenCV <colormaps>
Changelog <facerec_changelog>
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

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