potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

Global CMake reorganization:

Browse Source 
[~] Automatically tracked dependencies between modules
 [+] Support for optional module dependencies
 [+] Options to choose modules to build
 [~] Removed hardcoded modules lists from OpenCVConfig.cmake, opencv.pc and OpenCV.mk
 [+] Added COMPONENTS support for FIND_PACKAGE(OpenCV)
 [~] haartraining and traincascade are moved outside of modules folder since they aren't the modules
This commit is contained in:
Andrey Kamaev
2012-02-03 11:26:49 +00:00
parent ada9158521
commit 984eb99428
96 changed files with 2722 additions and 2103 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/CMakeLists.txt
+3 -8
View File
@@ -1,10 +1,5 @@
file(GLOB ocvmodules RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}/*")
if(ocvmodules)
list(SORT ocvmodules)
if(NOT OPENCV_MODULES_PATH)
set(OPENCV_MODULES_PATH "${CMAKE_CURRENT_SOURCE_DIR}")
endif()
foreach(mod ${ocvmodules})
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/${mod}/CMakeLists.txt")
add_subdirectory("${mod}")
endif()
endforeach()
ocv_glob_modules(${OPENCV_MODULES_PATH})
modules/androidcamera/CMakeLists.txt
+29 -11
View File
@@ -1,11 +1,30 @@
IF(NOT ANDROID OR NOT WITH_ANDROID_CAMERA)
return()
IF(NOT ANDROID OR ANDROID_NATIVE_API_LEVEL LESS 8)
ocv_module_disable(androidcamera)
ENDIF()
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/camera_wrapper")
set(OPENCV_ANDROIDCAMERA_MODULE_TYPE STATIC)
define_opencv_moduleEx(androidcamera INTERNAL log dl)
set(the_description "Auxiliary module for Android native camera support")
set(OPENCV_MODULE_TYPE STATIC)
ocv_define_module(androidcamera INTERNAL log dl)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/camera_wrapper")
# Android source tree for native camera
SET (ANDROID_SOURCE_TREE "ANDROID_SOURCE_TREE-NOTFOUND" CACHE PATH
"Path to Android source tree. Set this variable to path to your Android sources to compile libnative_camera_rx.x.x.so for your Android")
SET(BUILD_ANDROID_CAMERA_WRAPPER OFF)
if(ANDROID_SOURCE_TREE)
FILE(STRINGS "${ANDROID_SOURCE_TREE}/development/sdk/platform_source.properties" ANDROID_VERSION REGEX "Platform\\.Version=[0-9]+\\.[0-9]+(\\.[0-9]+)?" )
string(REGEX REPLACE "Platform\\.Version=([0-9]+\\.[0-9]+(\\.[0-9]+)?)" "\\1" ANDROID_VERSION "${ANDROID_VERSION}")
if(ANDROID_VERSION MATCHES "^[0-9]+\\.[0-9]+$")
SET(ANDROID_VERSION "${ANDROID_VERSION}.0")
endif()
if(NOT "${ANDROID_VERSION}" STREQUAL "")
SET(BUILD_ANDROID_CAMERA_WRAPPER ON)
endif()
endif()
MARK_AS_ADVANCED(ANDROID_SOURCE_TREE)
# process wrapper libs
if (BUILD_ANDROID_CAMERA_WRAPPER)
add_subdirectory(camera_wrapper)
else()
@@ -13,13 +32,12 @@ else()
foreach(wrapper ${camera_wrappers})
ADD_CUSTOM_COMMAND(
TARGET ${the_target}
POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy "${wrapper}" "${LIBRARY_OUTPUT_PATH}"
)
TARGET ${the_module} POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy "${wrapper}" "${LIBRARY_OUTPUT_PATH}"
)
get_filename_component(wrapper_name "${wrapper}" NAME)
install(FILES "${LIBRARY_OUTPUT_PATH}/${wrapper_name}"
DESTINATION ${OPENCV_LIB_INSTALL_PATH}
COMPONENT main)
DESTINATION ${OPENCV_LIB_INSTALL_PATH}
COMPONENT main)
endforeach()
endif()
modules/calib3d/CMakeLists.txt
+2 -1
View File
@@ -1 +1,2 @@
define_opencv_module(calib3d opencv_core opencv_imgproc opencv_features2d opencv_flann)
set(the_description "Camera Calibration and 3D Reconstruction")
ocv_define_module(calib3d opencv_imgproc opencv_features2d)
modules/contrib/CMakeLists.txt
+1 -1
View File
@@ -1 +1 @@
define_opencv_module(contrib opencv_core opencv_imgproc opencv_calib3d opencv_features2d opencv_highgui opencv_ml opencv_video opencv_objdetect opencv_flann)
ocv_define_module(contrib opencv_imgproc opencv_calib3d opencv_features2d opencv_highgui opencv_ml opencv_video opencv_objdetect)
modules/contrib/src/featuretracker.cpp
-1
View File
@@ -42,7 +42,6 @@
#include "precomp.hpp"
#include <stdio.h>
#include <iostream>
#include <highgui.h>
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/contrib/hybridtracker.hpp"
modules/core/CMakeLists.txt
+40 -4
View File
@@ -1,7 +1,43 @@
if(ZLIB_FOUND)
include_directories(${ZLIB_INCLUDE_DIR})
set(the_description "The Core Functionality")
ocv_add_module(core ${ZLIB_LIBRARY})
if(HAVE_CUDA)
file(GLOB lib_cuda "src/cuda/*.cu")
source_group("Cuda" FILES "${lib_cuda}")
include_directories(${CUDA_INCLUDE_DIRS} "${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda")
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -gencode arch=compute_10,code=sm_10
-gencode arch=compute_11,code=sm_11
-gencode arch=compute_12,code=sm_12
-gencode arch=compute_13,code=sm_13
-gencode arch=compute_20,code=sm_20
-gencode arch=compute_20,code=sm_21)
OCV_CUDA_COMPILE(cuda_objs ${lib_cuda})
unset(CUDA_npp_LIBRARY CACHE)
find_cuda_helper_libs(npp)
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
else()
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../../3rdparty/zlib")
set(lib_cuda "")
set(cuda_objs "")
set(cuda_link_libs "")
endif()
define_opencv_module(core ${ZLIB_LIBRARY})
ocv_glob_module_sources(SOURCES ${lib_cuda} ${cuda_objs})
if(NOT ZLIB_FOUND)
set(ZLIB_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../3rdparty/zlib")
endif()
ocv_module_include_directories(${ZLIB_INCLUDE_DIR})
ocv_create_module(${cuda_link_libs})
ocv_add_precompiled_headers(${the_module})
ocv_add_accuracy_tests()
ocv_add_perf_tests()
modules/core/include/opencv2/core/version.hpp
+1 -1
View File
@@ -49,7 +49,7 @@
#define CV_MAJOR_VERSION 2
#define CV_MINOR_VERSION 3
#define CV_SUBMINOR_VERSION 2
#define CV_SUBMINOR_VERSION 3
#define CVAUX_STR_EXP(__A) #__A
#define CVAUX_STR(__A) CVAUX_STR_EXP(__A)
modules/features2d/CMakeLists.txt
+2 -1
View File
@@ -1,2 +1,3 @@
define_opencv_module(features2d opencv_core opencv_imgproc opencv_highgui opencv_flann)
set(the_description "2D Features Framework")
ocv_define_module(features2d opencv_imgproc opencv_highgui opencv_flann)
modules/flann/CMakeLists.txt
+2 -1
View File
@@ -1,2 +1,3 @@
define_opencv_module(flann opencv_core)
set(the_description "Clustering and Search in Multi-Dimensional Spaces")
ocv_define_module(flann opencv_core)
modules/gpu/CMakeLists.txt
+76 -156
View File
@@ -1,181 +1,101 @@
if(ANDROID OR IOS)
return()
ocv_module_disable(gpu)
endif()
option(OCVMODULE_GPU "Include gpu module into the OpenCV build" ON)
if(NOT OCVMODULE_GPU)
return()
endif()
set(the_description "GPU-accelerated Computer Vision")
ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect)
set(name "gpu")
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda")
set(the_target "opencv_${name}")
project(${the_target})
file(GLOB lib_hdrs "include/opencv2/${name}/*.hpp" "include/opencv2/${name}/*.h")
file(GLOB lib_int_hdrs "src/*.hpp" "src/*.h")
file(GLOB lib_cuda_hdrs "src/cuda/*.hpp" "src/cuda/*.h")
file(GLOB lib_device_hdrs "src/opencv2/gpu/device/*.hpp" "src/opencv2/gpu/device/*.h")
file(GLOB lib_device_hdrs_detail "src/opencv2/gpu/device/detail/*.hpp" "src/opencv2/gpu/device/detail/*.h")
file(GLOB lib_srcs "src/*.cpp")
file(GLOB lib_cuda "src/cuda/*.cu*")
set(DEPS "opencv_core" "opencv_imgproc" "opencv_calib3d" "opencv_objdetect")
set(DEPS_HEADER ${DEPS} "opencv_features2d" "opencv_flann")
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} opencv_gpu)
opencv_module_includes(${DEPS_HEADER})
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda")
file(GLOB lib_srcs "src/*.cpp")
file(GLOB lib_int_hdrs "src/*.h*")
file(GLOB lib_cuda "src/cuda/*.cu*")
file(GLOB lib_cuda_hdrs "src/cuda/*.h*")
source_group("Include" FILES ${lib_hdrs})
source_group("Src\\Host" FILES ${lib_srcs} ${lib_int_hdrs})
source_group("Src\\Cuda" FILES ${lib_cuda} ${lib_cuda_hdrs})
file(GLOB lib_hdrs "include/opencv2/${name}/*.h*")
source_group("Include" FILES ${lib_hdrs})
#file(GLOB lib_device_hdrs "include/opencv2/${name}/device/*.h*")
file(GLOB lib_device_hdrs "src/opencv2/gpu/device/*.h*")
file(GLOB lib_device_hdrs_detail "src/opencv2/gpu/device/detail/*.h*")
source_group("Device" FILES ${lib_device_hdrs})
source_group("Device\\Detail" FILES ${lib_device_hdrs_detail})
if (HAVE_CUDA)
file(GLOB_RECURSE ncv_srcs "src/nvidia/*.cpp")
file(GLOB_RECURSE ncv_cuda "src/nvidia/*.cu")
file(GLOB_RECURSE ncv_hdrs "src/nvidia/*.hpp" "src/nvidia/*.h")
file(GLOB_RECURSE ncv_srcs "src/nvidia/*.cpp")
file(GLOB_RECURSE ncv_cuda "src/nvidia/*.cu")
file(GLOB_RECURSE ncv_hdrs "src/nvidia/*.hpp" "src/nvidia/*.h")
set(ncv_files ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda})
source_group("Src\\NVidia" FILES ${ncv_files})
include_directories("src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS})
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep")
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;")
source_group("Src\\NVidia" FILES ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda})
include_directories("src/nvidia/core" "src/nvidia/NPP_staging")
foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
string(REPLACE "/W4" "/W3" ${var} "${${var}}")
endforeach()
if(MSVC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4211 /wd4201 /wd4100 /wd4505 /wd4408 /wd4251")
foreach(var CMAKE_C_FLAGS CMAKE_C_FLAGS_RELEASE CMAKE_C_FLAGS_DEBUG CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
string(REPLACE "/EHsc-" "/EHs" ${var} "${${var}}")
endforeach()
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler /wd4251)
endif()
OCV_CUDA_COMPILE(cuda_objs ${lib_cuda} ${ncv_cuda})
#CUDA_BUILD_CLEAN_TARGET()
unset(CUDA_npp_LIBRARY CACHE)
find_cuda_helper_libs(npp)
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
else()
set(lib_cuda "")
set(cuda_objs "")
set(cuda_link_libs "")
set(ncv_files "")
endif()
if (HAVE_CUDA)
include_directories(${CUDA_INCLUDE_DIRS})
ocv_set_module_sources(
HEADERS ${lib_hdrs}
SOURCES ${lib_int_hdrs} ${lib_cuda_hdrs} ${lib_device_hdrs} ${lib_device_hdrs_detail} ${lib_srcs} ${lib_cuda} ${ncv_files} ${cuda_objs}
)
ocv_create_module(${cuda_link_libs})
if(HAVE_CUDA)
if(HAVE_CUFFT)
CUDA_ADD_CUFFT_TO_TARGET(${the_module})
endif()
if (UNIX OR APPLE)
set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;-fPIC;")
#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}" "-fPIC")
endif()
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep")
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;")
if (APPLE)
set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;-fno-finite-math-only;")
endif()
string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}")
if(MSVC)
#string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
#string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
#string(REPLACE "/W4" "/W3" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4211 /wd4201 /wd4100 /wd4505 /wd4408 /wd4251")
string(REPLACE "/EHsc-" "/EHs" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}")
endif()
if (BUILD_SHARED_LIBS)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;-DCVAPI_EXPORTS")
endif()
if(MSVC)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/wd4251")
endif()
# we remove -ggdb3 flag as it leads to preprocessor errors when compiling CUDA files (CUDA 4.1)
set(tmp ${CMAKE_CXX_FLAGS_DEBUG})
string(REPLACE "-ggdb3" "" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG})
CUDA_COMPILE(cuda_objs ${lib_cuda} ${ncv_cuda})
set(CMAKE_CXX_DEBUG_FLAGS ${tmp})
#CUDA_BUILD_CLEAN_TARGET()
endif()
add_library(${the_target} ${lib_srcs} ${lib_hdrs} ${lib_int_hdrs} ${lib_cuda} ${lib_cuda_hdrs} ${lib_device_hdrs} ${lib_device_hdrs_detail} ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda} ${cuda_objs})
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${IPP_LIBS} ${DEPS} )
opencv_module_register(${the_target})
if (HAVE_CUDA)
target_link_libraries(${the_target} ${CUDA_LIBRARIES})
unset(CUDA_npp_LIBRARY CACHE)
find_cuda_helper_libs(npp)
target_link_libraries(${the_target} ${CUDA_npp_LIBRARY})
if(HAVE_CUFFT)
CUDA_ADD_CUFFT_TO_TARGET(${the_target})
endif()
if(HAVE_CUBLAS)
CUDA_ADD_CUBLAS_TO_TARGET(${the_target})
endif()
endif()
opencv_module_setup(${name})
install(FILES src/nvidia/NPP_staging/NPP_staging.hpp src/nvidia/core/NCV.hpp
if(HAVE_CUBLAS)
CUDA_ADD_CUBLAS_TO_TARGET(${the_module})
endif()
install(FILES src/nvidia/NPP_staging/NPP_staging.hpp src/nvidia/core/NCV.hpp
DESTINATION ${OPENCV_INCLUDE_PREFIX}/opencv2/${name}
COMPONENT main)
endif()
#install(FILES ${lib_device_hdrs}
# DESTINATION ${OPENCV_INCLUDE_PREFIX}/opencv2/${name}/device
# COMPONENT main)
ocv_add_precompiled_headers(${the_module})
################################################################################################################
################################ GPU Module Tests #####################################################
################################################################################################################
# Test files processing is in the separated directory to avoid 'Src' source
# filter creation in Visual Studio
if(BUILD_TESTS AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/test)
set(the_test_target "opencv_test_${name}")
set(test_deps opencv_${name} opencv_ts opencv_highgui opencv_calib3d ${DEPS})
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/test")
opencv_module_includes(${test_deps})
file(GLOB test_srcs "test/*.cpp")
file(GLOB test_hdrs "test/*.h*")
source_group("Src" FILES ${test_hdrs} ${test_srcs})
if(HAVE_CUDA)
include_directories(${CUDA_INCLUDE_DIRS} ${OpenCV_SOURCE_DIR}/modules/gpu/src/nvidia ${OpenCV_SOURCE_DIR}/modules/gpu/src/nvidia/core ${OpenCV_SOURCE_DIR}/modules/gpu/src/nvidia/NPP_staging)
file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.h*")
source_group("Src\\NVidia" FILES ${nvidia})
endif()
add_executable(${the_test_target} ${test_srcs} ${test_hdrs} ${nvidia})
# Additional target properties
set_target_properties(${the_test_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
RUNTIME_OUTPUT_DIRECTORY "${EXECUTABLE_OUTPUT_PATH}"
)
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_test_target} PROPERTIES FOLDER "tests")
endif()
# Add the required libraries for linking:
target_link_libraries(${the_test_target} ${OPENCV_LINKER_LIBS} ${test_deps})
enable_testing()
get_target_property(LOC ${the_test_target} LOCATION)
add_test(${the_test_target} "${LOC}")
#if(WIN32)
# install(TARGETS ${the_test_target} RUNTIME DESTINATION bin COMPONENT main)
#endif()
add_opencv_precompiled_headers(${the_test_target})
file(GLOB test_srcs "test/*.cpp")
file(GLOB test_hdrs "test/*.hpp" "test/*.h")
if(HAVE_CUDA)
file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.hpp" "test/nvidia/*.h")
set(nvidia FILES "Src\\\\\\\\NVidia" ${nvidia}) # 8 ugly backslashes :'(
else()
set(nvidia "")
endif()
define_opencv_perf_test(${name})
ocv_add_accuracy_tests(FILES "Include" ${test_hdrs}
FILES "Src" ${test_srcs}
${nvidia})
ocv_add_perf_tests()
modules/haartraining/CMakeLists.txt
-98
View File
@@ -1,98 +0,0 @@
if(IOS)
return()
endif()
project(haartraining)
include_directories(
"${CMAKE_CURRENT_SOURCE_DIR}"
"${OpenCV_SOURCE_DIR}/modules/core/include"
"${OpenCV_SOURCE_DIR}/modules/imgproc/include"
"${OpenCV_SOURCE_DIR}/modules/objdetect/include"
"${OpenCV_SOURCE_DIR}/modules/ml/include"
"${OpenCV_SOURCE_DIR}/modules/highgui/include"
"${OpenCV_SOURCE_DIR}/modules/video/include"
"${OpenCV_SOURCE_DIR}/modules/features2d/include"
"${OpenCV_SOURCE_DIR}/modules/flann/include"
"${OpenCV_SOURCE_DIR}/modules/calib3d/include"
"${OpenCV_SOURCE_DIR}/modules/legacy/include"
)
if(WIN32)
link_directories(${OpenCV_SOURCE_DIR}/lib ${OpenCV_SOURCE_DIR}/bin ${CMAKE_CURRENT_BINARY_DIR})
endif()
set(haartraining_libs opencv_core opencv_imgproc opencv_highgui opencv_objdetect opencv_calib3d opencv_haartraining_engine)
link_libraries(${haartraining_libs})
# -----------------------------------------------------------
# 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
)
if(NOT ANDROID)
# -----------------------------------------------------------
# haartraining
# -----------------------------------------------------------
add_executable(opencv_haartraining cvhaartraining.h haartraining.cpp)
add_dependencies(opencv_haartraining ${haartraining_libs})
set_target_properties(opencv_haartraining PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_haartraining")
# -----------------------------------------------------------
# createsamples
# -----------------------------------------------------------
add_executable(opencv_createsamples cvhaartraining.h createsamples.cpp)
add_dependencies(opencv_createsamples ${haartraining_libs})
set_target_properties(opencv_createsamples PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_createsamples")
# -----------------------------------------------------------
# performance
# -----------------------------------------------------------
add_executable(opencv_performance performance.cpp)
add_dependencies(opencv_performance ${haartraining_libs})
set_target_properties(opencv_performance PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_performance")
# -----------------------------------------------------------
# Install part
# -----------------------------------------------------------
install(TARGETS opencv_haartraining RUNTIME DESTINATION bin COMPONENT main)
install(TARGETS opencv_createsamples RUNTIME DESTINATION bin COMPONENT main)
install(TARGETS opencv_performance RUNTIME DESTINATION bin COMPONENT main)
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()
endif()
modules/haartraining/_cvcommon.h
-100
View File
@@ -1,100 +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 "cxcore.h"
#include "cv.h"
#include "cxmisc.h"
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#define CV_DECLARE_QSORT( func_name, T, less_than ) \
void func_name( T* array, size_t length, int aux );
#define less_than( a, b ) ((a) < (b))
CV_DECLARE_QSORT( icvSort_32f, float, less_than )
CV_DECLARE_QSORT( icvSort_32s, int, less_than )
#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_ */
modules/haartraining/_cvhaartraining.h
-414
View File
@@ -1,414 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* _cvhaartraining.h
*
* training of cascade of boosted classifiers based on haar features
*/
#ifndef __CVHAARTRAINING_H_
#define __CVHAARTRAINING_H_
#include "_cvcommon.h"
#include "cvclassifier.h"
#include <cstring>
#include <cstdio>
/* parameters for tree cascade classifier training */
/* max number of clusters */
#define CV_MAX_CLUSTERS 3
/* term criteria for K-Means */
#define CV_TERM_CRITERIA() cvTermCriteria( CV_TERMCRIT_EPS, 1000, 1E-5 )
/* print statistic info */
#define CV_VERBOSE 1
#define CV_STAGE_CART_FILE_NAME "AdaBoostCARTHaarClassifier.txt"
#define CV_HAAR_FEATURE_MAX 3
#define CV_HAAR_FEATURE_DESC_MAX 20
typedef int sum_type;
typedef double sqsum_type;
typedef short idx_type;
#define CV_SUM_MAT_TYPE CV_32SC1
#define CV_SQSUM_MAT_TYPE CV_64FC1
#define CV_IDX_MAT_TYPE CV_16SC1
#define CV_STUMP_TRAIN_PORTION 100
#define CV_THRESHOLD_EPS (0.00001F)
typedef struct CvTHaarFeature
{
char desc[CV_HAAR_FEATURE_DESC_MAX];
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvTHaarFeature;
typedef struct CvFastHaarFeature
{
int tilted;
struct
{
int p0, p1, p2, p3;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvFastHaarFeature;
typedef struct CvIntHaarFeatures
{
CvSize winsize;
int count;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
} CvIntHaarFeatures;
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2 CV_DEFAULT( 0 ), int y2 CV_DEFAULT( 0 ),
int w2 CV_DEFAULT( 0 ), int h2 CV_DEFAULT( 0 ),
float wt2 CV_DEFAULT( 0.0F ) );
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2, int y2, int w2, int h2, float wt2 )
{
CvTHaarFeature hf;
assert( CV_HAAR_FEATURE_MAX >= 3 );
assert( strlen( desc ) < CV_HAAR_FEATURE_DESC_MAX );
strcpy( &(hf.desc[0]), desc );
hf.tilted = ( hf.desc[0] == 't' );
hf.rect[0].r.x = x0;
hf.rect[0].r.y = y0;
hf.rect[0].r.width = w0;
hf.rect[0].r.height = h0;
hf.rect[0].weight = wt0;
hf.rect[1].r.x = x1;
hf.rect[1].r.y = y1;
hf.rect[1].r.width = w1;
hf.rect[1].r.height = h1;
hf.rect[1].weight = wt1;
hf.rect[2].r.x = x2;
hf.rect[2].r.y = y2;
hf.rect[2].r.width = w2;
hf.rect[2].r.height = h2;
hf.rect[2].weight = wt2;
return hf;
}
/* Prepared for training samples */
typedef struct CvHaarTrainingData
{
CvSize winsize; /* training image size */
int maxnum; /* maximum number of samples */
CvMat sum; /* sum images (each row represents image) */
CvMat tilted; /* tilted sum images (each row represents image) */
CvMat normfactor; /* normalization factor */
CvMat cls; /* classes. 1.0 - object, 0.0 - background */
CvMat weights; /* weights */
CvMat* valcache; /* precalculated feature values (CV_32FC1) */
CvMat* idxcache; /* presorted indices (CV_IDX_MAT_TYPE) */
} CvHaarTrainigData;
/* Passed to callback functions */
typedef struct CvUserdata
{
CvHaarTrainingData* trainingData;
CvIntHaarFeatures* haarFeatures;
} CvUserdata;
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures );
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures )
{
CvUserdata userdata;
userdata.trainingData = trainingData;
userdata.haarFeatures = haarFeatures;
return userdata;
}
#define CV_INT_HAAR_CLASSIFIER_FIELDS() \
float (*eval)( CvIntHaarClassifier*, sum_type*, sum_type*, float ); \
void (*save)( CvIntHaarClassifier*, FILE* file ); \
void (*release)( CvIntHaarClassifier** );
/* internal weak classifier*/
typedef struct CvIntHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
} CvIntHaarClassifier;
/*
* CART classifier
*/
typedef struct CvCARTHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
int* compidx;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
float* threshold;
int* left;
int* right;
float* val;
} CvCARTHaarClassifier;
/* internal stage classifier */
typedef struct CvStageHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
float threshold;
CvIntHaarClassifier** classifier;
} CvStageHaarClassifier;
/* internal cascade classifier */
typedef struct CvCascadeHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
CvIntHaarClassifier** classifier;
} CvCascadeHaarClassifier;
/* internal tree cascade classifier node */
typedef struct CvTreeCascadeNode
{
CvStageHaarClassifier* stage;
struct CvTreeCascadeNode* next;
struct CvTreeCascadeNode* child;
struct CvTreeCascadeNode* parent;
struct CvTreeCascadeNode* next_same_level;
struct CvTreeCascadeNode* child_eval;
int idx;
int leaf;
} CvTreeCascadeNode;
/* internal tree cascade classifier */
typedef struct CvTreeCascadeClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
CvTreeCascadeNode* root; /* root of the tree */
CvTreeCascadeNode* root_eval; /* root node for the filtering */
int next_idx;
} CvTreeCascadeClassifier;
CV_INLINE float cvEvalFastHaarFeature( const CvFastHaarFeature* feature,
const sum_type* sum, const sum_type* tilted )
{
const sum_type* img = feature->tilted ? tilted : sum;
float ret = feature->rect[0].weight*
(img[feature->rect[0].p0] - img[feature->rect[0].p1] -
img[feature->rect[0].p2] + img[feature->rect[0].p3]) +
feature->rect[1].weight*
(img[feature->rect[1].p0] - img[feature->rect[1].p1] -
img[feature->rect[1].p2] + img[feature->rect[1].p3]);
if( feature->rect[2].weight != 0.0f )
ret += feature->rect[2].weight *
( img[feature->rect[2].p0] - img[feature->rect[2].p1] -
img[feature->rect[2].p2] + img[feature->rect[2].p3] );
return ret;
}
typedef struct CvSampleDistortionData
{
IplImage* src;
IplImage* erode;
IplImage* dilate;
IplImage* mask;
IplImage* img;
IplImage* maskimg;
int dx;
int dy;
int bgcolor;
} CvSampleDistortionData;
/*
* icvConvertToFastHaarFeature
*
* Convert to fast representation of haar features
*
* haarFeature - input array
* fastHaarFeature - output array
* size - size of arrays
* step - row step for the integral image
*/
void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature,
CvFastHaarFeature* fastHaarFeature,
int size, int step );
void icvWriteVecHeader( FILE* file, int count, int width, int height );
void icvWriteVecSample( FILE* file, CvArr* sample );
void icvPlaceDistortedSample( CvArr* background,
int inverse, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int inscribe, double maxshiftf, double maxscalef,
CvSampleDistortionData* data );
void icvEndSampleDistortion( CvSampleDistortionData* data );
int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
CvSampleDistortionData* data );
typedef int (*CvGetHaarTrainingDataCallback)( CvMat* img, void* userdata );
typedef struct CvVecFile
{
FILE* input;
int count;
int vecsize;
int last;
short* vector;
} CvVecFile;
int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata );
/*
* icvGetHaarTrainingDataFromVec
*
* Fill <data> with samples from .vec file, passed <cascade>
int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
CvIntHaarClassifier* cascade,
const char* filename,
int* consumed );
*/
CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count );
void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier );
void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
CvIntHaarFeatures* intHaarFeatures );
float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold );
void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count );
void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step );
void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step );
/* tree cascade classifier */
float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSetLeafNode( CvTreeCascadeClassifier* tree, CvTreeCascadeNode* leaf );
float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
sum_type* tilted, float normfactor );
CvTreeCascadeNode* icvCreateTreeCascadeNode();
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node );
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier );
/* Prints out current tree structure to <stdout> */
void icvPrintTreeCascade( CvTreeCascadeNode* root );
/* Loads tree cascade classifier */
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
int* splits );
/* Finds leaves belonging to maximal level and connects them via leaf->next_same_level */
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tree );
#endif /* __CVHAARTRAINING_H_ */
modules/haartraining/createsamples.cpp
-245
View File
@@ -1,245 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* createsamples.cpp
*
* Create test/training samples
*/
#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <cmath>
#include <ctime>
using namespace std;
#include "cvhaartraining.h"
int main( int argc, char* argv[] )
{
int i = 0;
char* nullname = (char*)"(NULL)";
char* vecname = NULL; /* .vec file name */
char* infoname = NULL; /* file name with marked up image descriptions */
char* imagename = NULL; /* single sample image */
char* bgfilename = NULL; /* background */
int num = 1000;
int bgcolor = 0;
int bgthreshold = 80;
int invert = 0;
int maxintensitydev = 40;
double maxxangle = 1.1;
double maxyangle = 1.1;
double maxzangle = 0.5;
int showsamples = 0;
/* the samples are adjusted to this scale in the sample preview window */
double scale = 4.0;
int width = 24;
int height = 24;
srand((unsigned int)time(0));
if( argc == 1 )
{
printf( "Usage: %s\n [-info <collection_file_name>]\n"
" [-img <image_file_name>]\n"
" [-vec <vec_file_name>]\n"
" [-bg <background_file_name>]\n [-num <number_of_samples = %d>]\n"
" [-bgcolor <background_color = %d>]\n"
" [-inv] [-randinv] [-bgthresh <background_color_threshold = %d>]\n"
" [-maxidev <max_intensity_deviation = %d>]\n"
" [-maxxangle <max_x_rotation_angle = %f>]\n"
" [-maxyangle <max_y_rotation_angle = %f>]\n"
" [-maxzangle <max_z_rotation_angle = %f>]\n"
" [-show [<scale = %f>]]\n"
" [-w <sample_width = %d>]\n [-h <sample_height = %d>]\n",
argv[0], num, bgcolor, bgthreshold, maxintensitydev,
maxxangle, maxyangle, maxzangle, scale, width, height );
return 0;
}
for( i = 1; i < argc; ++i )
{
if( !strcmp( argv[i], "-info" ) )
{
infoname = argv[++i];
}
else if( !strcmp( argv[i], "-img" ) )
{
imagename = argv[++i];
}
else if( !strcmp( argv[i], "-vec" ) )
{
vecname = argv[++i];
}
else if( !strcmp( argv[i], "-bg" ) )
{
bgfilename = argv[++i];
}
else if( !strcmp( argv[i], "-num" ) )
{
num = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-bgcolor" ) )
{
bgcolor = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-bgthresh" ) )
{
bgthreshold = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-inv" ) )
{
invert = 1;
}
else if( !strcmp( argv[i], "-randinv" ) )
{
invert = CV_RANDOM_INVERT;
}
else if( !strcmp( argv[i], "-maxidev" ) )
{
maxintensitydev = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-maxxangle" ) )
{
maxxangle = atof( argv[++i] );
}
else if( !strcmp( argv[i], "-maxyangle" ) )
{
maxyangle = atof( argv[++i] );
}
else if( !strcmp( argv[i], "-maxzangle" ) )
{
maxzangle = atof( argv[++i] );
}
else if( !strcmp( argv[i], "-show" ) )
{
showsamples = 1;
if( i+1 < argc && strlen( argv[i+1] ) > 0 && argv[i+1][0] != '-' )
{
double d;
d = strtod( argv[i+1], 0 );
if( d != -HUGE_VAL && d != HUGE_VAL && d > 0 ) scale = d;
++i;
}
}
else if( !strcmp( argv[i], "-w" ) )
{
width = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-h" ) )
{
height = atoi( argv[++i] );
}
}
printf( "Info file name: %s\n", ((infoname == NULL) ? nullname : infoname ) );
printf( "Img file name: %s\n", ((imagename == NULL) ? nullname : imagename ) );
printf( "Vec file name: %s\n", ((vecname == NULL) ? nullname : vecname ) );
printf( "BG file name: %s\n", ((bgfilename == NULL) ? nullname : bgfilename ) );
printf( "Num: %d\n", num );
printf( "BG color: %d\n", bgcolor );
printf( "BG threshold: %d\n", bgthreshold );
printf( "Invert: %s\n", (invert == CV_RANDOM_INVERT) ? "RANDOM"
: ( (invert) ? "TRUE" : "FALSE" ) );
printf( "Max intensity deviation: %d\n", maxintensitydev );
printf( "Max x angle: %g\n", maxxangle );
printf( "Max y angle: %g\n", maxyangle );
printf( "Max z angle: %g\n", maxzangle );
printf( "Show samples: %s\n", (showsamples) ? "TRUE" : "FALSE" );
if( showsamples )
{
printf( "Scale: %g\n", scale );
}
printf( "Width: %d\n", width );
printf( "Height: %d\n", height );
/* determine action */
if( imagename && vecname )
{
printf( "Create training samples from single image applying distortions...\n" );
cvCreateTrainingSamples( vecname, imagename, bgcolor, bgthreshold, bgfilename,
num, invert, maxintensitydev,
maxxangle, maxyangle, maxzangle,
showsamples, width, height );
printf( "Done\n" );
}
else if( imagename && bgfilename && infoname )
{
printf( "Create test samples from single image applying distortions...\n" );
cvCreateTestSamples( infoname, imagename, bgcolor, bgthreshold, bgfilename, num,
invert, maxintensitydev,
maxxangle, maxyangle, maxzangle, showsamples, width, height );
printf( "Done\n" );
}
else if( infoname && vecname )
{
int total;
printf( "Create training samples from images collection...\n" );
total = cvCreateTrainingSamplesFromInfo( infoname, vecname, num, showsamples,
width, height );
printf( "Done. Created %d samples\n", total );
}
else if( vecname )
{
printf( "View samples from vec file (press ESC to exit)...\n" );
cvShowVecSamples( vecname, width, height, scale );
printf( "Done\n" );
}
else
{
printf( "Nothing to do\n" );
}
return 0;
}
modules/haartraining/cvboost.cpp
-3779
View File
File diff suppressed because it is too large Load Diff
modules/haartraining/cvclassifier.h
-727
View File
@@ -1,727 +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
#define CV_IS_ROW_SAMPLE( flags ) ( ( flags ) & CV_ROW_SAMPLE )
/* 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_ */
modules/haartraining/cvcommon.cpp
-130
View File
@@ -1,130 +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 */
CV_IMPLEMENT_QSORT( icvSort_32f, float, less_than )
CV_IMPLEMENT_QSORT( icvSort_32s, int, less_than )
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. */
modules/haartraining/cvhaarclassifier.cpp
-826
View File
@@ -1,826 +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;
fscanf( file, "%d", &nrect );
assert( nrect <= CV_HAAR_FEATURE_MAX );
for( j = 0; j < nrect; j++ )
{
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 );
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;
}
fscanf( file, "%s", &(feature->desc[0]) );
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;
fscanf( file, "%d", &count );
if( count > 0 )
{
ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count );
for( i = 0; i < count; i++ )
{
icvLoadHaarFeature( &(ptr->feature[i]), file );
fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]),
&(ptr->right[i]) );
}
for( i = 0; i <= count; i++ )
{
fscanf( file, "%f", &(ptr->val[i]) );
}
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 );
}
CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
{
CvStageHaarClassifier* ptr = NULL;
//CV_FUNCNAME( "icvLoadCARTStageHaarClassifierF" );
__BEGIN__;
if( file != NULL )
{
int count;
int i;
float threshold;
count = 0;
fscanf( file, "%d", &count );
if( count > 0 )
{
ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F );
for( i = 0; i < count; i++ )
{
ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step );
}
fscanf( file, "%f", &threshold );
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. */
modules/haartraining/cvhaartraining.cpp
-3053
View File
File diff suppressed because it is too large Load Diff
modules/haartraining/cvhaartraining.h
-192
View File
@@ -1,192 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* cvhaartraining.h
*
* haar training functions
*/
#ifndef _CVHAARTRAINING_H_
#define _CVHAARTRAINING_H_
/*
* cvCreateTrainingSamples
*
* Create training samples applying random distortions to sample image and
* store them in .vec file
*
* filename - .vec file name
* imgfilename - sample image file name
* bgcolor - background color for sample image
* bgthreshold - background color threshold. Pixels those colors are in range
* [bgcolor-bgthreshold, bgcolor+bgthreshold] are considered as transparent
* bgfilename - background description file name. If not NULL samples
* will be put on arbitrary background
* count - desired number of samples
* invert - if not 0 sample foreground pixels will be inverted
* if invert == CV_RANDOM_INVERT then samples will be inverted randomly
* maxintensitydev - desired max intensity deviation of foreground samples pixels
* maxxangle - max rotation angles
* maxyangle
* maxzangle
* showsamples - if not 0 samples will be shown
* winwidth - desired samples width
* winheight - desired samples height
*/
#define CV_RANDOM_INVERT 0x7FFFFFFF
void cvCreateTrainingSamples( const char* filename,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert = 0, int maxintensitydev = 40,
double maxxangle = 1.1,
double maxyangle = 1.1,
double maxzangle = 0.5,
int showsamples = 0,
int winwidth = 24, int winheight = 24 );
void cvCreateTestSamples( const char* infoname,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int showsamples,
int winwidth, int winheight );
/*
* cvCreateTrainingSamplesFromInfo
*
* Create training samples from a set of marked up images and store them into .vec file
* infoname - file in which marked up image descriptions are stored
* num - desired number of samples
* showsamples - if not 0 samples will be shown
* winwidth - sample width
* winheight - sample height
*
* Return number of successfully created samples
*/
int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilename,
int num,
int showsamples,
int winwidth, int winheight );
/*
* cvShowVecSamples
*
* Shows samples stored in .vec file
*
* filename
* .vec file name
* winwidth
* sample width
* winheight
* sample height
* scale
* the scale each sample is adjusted to
*/
void cvShowVecSamples( const char* filename, int winwidth, int winheight, double scale );
/*
* cvCreateCascadeClassifier
*
* Create cascade classifier
* dirname - directory name in which cascade classifier will be created.
* It must exist and contain subdirectories 0, 1, 2, ... (nstages-1).
* vecfilename - name of .vec file with object's images
* bgfilename - name of background description file
* bg_vecfile - true if bgfilename represents a vec file with discrete negatives
* npos - number of positive samples used in training of each stage
* nneg - number of negative samples used in training of each stage
* nstages - number of stages
* numprecalculated - number of features being precalculated. Each precalculated feature
* requires (number_of_samples*(sizeof( float ) + sizeof( short ))) bytes of memory
* numsplits - number of binary splits in each weak classifier
* 1 - stumps, 2 and more - trees.
* minhitrate - desired min hit rate of each stage
* maxfalsealarm - desired max false alarm of each stage
* weightfraction - weight trimming parameter
* mode - 0 - BASIC = Viola
* 1 - CORE = All upright
* 2 - ALL = All features
* symmetric - if not 0 vertical symmetry is assumed
* equalweights - if not 0 initial weights of all samples will be equal
* winwidth - sample width
* winheight - sample height
* boosttype - type of applied boosting algorithm
* 0 - Discrete AdaBoost
* 1 - Real AdaBoost
* 2 - LogitBoost
* 3 - Gentle AdaBoost
* stumperror - type of used error if Discrete AdaBoost algorithm is applied
* 0 - misclassification error
* 1 - gini error
* 2 - entropy error
*/
void cvCreateCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate = 0.995F, float maxfalsealarm = 0.5F,
float weightfraction = 0.95F,
int mode = 0, int symmetric = 1,
int equalweights = 1,
int winwidth = 24, int winheight = 24,
int boosttype = 3, int stumperror = 0 );
void cvCreateTreeCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate, float maxfalsealarm,
float weightfraction,
int mode, int symmetric,
int equalweights,
int winwidth, int winheight,
int boosttype, int stumperror,
int maxtreesplits, int minpos, bool bg_vecfile = false );
#endif /* _CVHAARTRAINING_H_ */
modules/haartraining/cvsamples.cpp
-955
View File
@@ -1,955 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* cvsamples.cpp
*
* support functions for training and test samples creation.
*/
#include "cvhaartraining.h"
#include "_cvhaartraining.h"
/* if ipl.h file is included then iplWarpPerspectiveQ function
is used for image transformation during samples creation;
otherwise internal cvWarpPerspective function is used */
//#include <ipl.h>
#include "cv.h"
#include "highgui.h"
/* Calculates coefficients of perspective transformation
* which maps <quad> into rectangle ((0,0), (w,0), (w,h), (h,0)):
*
* c00*xi + c01*yi + c02
* ui = ---------------------
* c20*xi + c21*yi + c22
*
* c10*xi + c11*yi + c12
* vi = ---------------------
* c20*xi + c21*yi + c22
*
* Coefficients are calculated by solving linear system:
* / x0 y0 1 0 0 0 -x0*u0 -y0*u0 \ /c00\ /u0\
* | x1 y1 1 0 0 0 -x1*u1 -y1*u1 | |c01| |u1|
* | x2 y2 1 0 0 0 -x2*u2 -y2*u2 | |c02| |u2|
* | x3 y3 1 0 0 0 -x3*u3 -y3*u3 |.|c10|=|u3|,
* | 0 0 0 x0 y0 1 -x0*v0 -y0*v0 | |c11| |v0|
* | 0 0 0 x1 y1 1 -x1*v1 -y1*v1 | |c12| |v1|
* | 0 0 0 x2 y2 1 -x2*v2 -y2*v2 | |c20| |v2|
* \ 0 0 0 x3 y3 1 -x3*v3 -y3*v3 / \c21/ \v3/
*
* where:
* (xi, yi) = (quad[i][0], quad[i][1])
* cij - coeffs[i][j], coeffs[2][2] = 1
* (ui, vi) - rectangle vertices
*/
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 */
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;
double i = 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;
i = i0 + (i1 - i0)*delta_y;
((uchar*)(dst_data + y * dst_step))[x] = (uchar) i;
}
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 )
{
fread( &file.count, sizeof( file.count ), 1, file.input );
fread( &file.vecsize, sizeof( file.vecsize ), 1, file.input );
fread( &tmp, sizeof( tmp ), 1, file.input );
fread( &tmp, sizeof( tmp ), 1, file.input );
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. */
modules/haartraining/haartraining.cpp
-284
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;
}
modules/haartraining/performance.cpp
-375
View File
@@ -1,375 +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 "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, width, height;
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++ )
{
error = (fscanf( info, "%d %d %d %d", &x, &y, &width, &height ) != 4);
if( error ) break;
ref[i].x = 0.5F * width + x;
ref[i].y = 0.5F * height + y;
ref[i].width = sqrtf( 0.5F * (width * width + height * height) );
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;
}
modules/highgui/CMakeLists.txt
+15 -47
View File
@@ -1,21 +1,13 @@
set(the_description "High-level GUI and Media I/O")
ocv_add_module(highgui opencv_imgproc OPTIONAL opencv_androidcamera)
# ----------------------------------------------------------------------------
# CMake file for highgui. See root CMakeLists.txt
# Some parts taken from version of Hartmut Seichter, HIT Lab NZ.
# Jose Luis Blanco, 2008
# ----------------------------------------------------------------------------
option(OCVMODULE_HIGHGUI "Include highgui module into the OpenCV build" ON)
if(NOT OCVMODULE_HIGHGUI)
return()
endif()
set(the_target "opencv_highgui")
project(${the_target})
#so that the headers get installed.
file(GLOB lib_hdrs "include/opencv2/highgui/*.h*")
set(GRFMT_LIBS)
set(GRFMT_LIBS "")
if(WITH_PNG OR WITH_TIFF OR WITH_OPENEXR)
if(ZLIB_FOUND)
@@ -85,6 +77,7 @@ set(grfmt_srcs src/bitstrm.cpp ${grfmt_srcs})
source_group("Src\\grfmts" FILES ${grfmt_hdrs} ${grfmt_srcs})
set(highgui_hdrs src/precomp.hpp src/utils.hpp)
set(highgui_srcs
src/cap.cpp
src/cap_images.cpp
@@ -94,15 +87,7 @@ set(highgui_srcs
src/utils.cpp
src/window.cpp
)
set(highgui_hdrs src/precomp.hpp src/utils.hpp)
#YV
set(lib_hdr_names highgui_c.h highgui.hpp)
set(highgui_ext_hdrs)
foreach(h ${lib_hdr_names})
list(APPEND highgui_ext_hdrs "${CMAKE_CURRENT_SOURCE_DIR}/include/opencv2/highgui/${h}")
endforeach()
file(GLOB highgui_ext_hdrs "include/opencv2/${name}/*.hpp" "include/opencv2/${name}/*.h")
#YV
if (HAVE_QT)
@@ -225,11 +210,9 @@ if(APPLE)
endif()
endif(APPLE)
if(WITH_ANDROID_CAMERA)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../androidcamera/include")
if(WITH_ANDROID_CAMERA AND OPENCV_MODULE_ANDROIDCAMERA)
set(highgui_srcs ${highgui_srcs} src/cap_android.cpp)
add_definitions(-DHAVE_ANDROID_NATIVE_CAMERA)
set(HIGHGUI_LIBRARIES ${HIGHGUI_LIBRARIES} opencv_androidcamera)
add_definitions(-DHAVE_ANDROID_NATIVE_CAMERA)#TODO: remove this line
endif()
if(HAVE_XIMEA AND XIMEA_FOUND)
@@ -265,32 +248,16 @@ if(OPENCV_BUILD_3RDPARTY_LIBS AND WIN32)
link_directories("${CMAKE_CURRENT_SOURCE_DIR}/../../3rdparty/lib")
endif()
if(COMMAND get_module_external_sources)
set( lib_srcs "${highgui_srcs}" )
set( lib_int_hdrs "${highgui_hdrs}" )
get_module_external_sources(highgui)
set( highgui_srcs "${lib_srcs}" )
set( highgui_hdrs "${lib_int_hdrs}" )
endif()
source_group("Src" FILES ${highgui_srcs} ${highgui_hdrs})
source_group("Include" FILES ${highgui_ext_hdrs})
ocv_set_module_sources(HEADERS ${highgui_ext_hdrs} SOURCES ${highgui_srcs} ${highgui_hdrs} ${grfmt_srcs} ${grfmt_hdrs})
#message(STATUS "GRFMT: ${GRFMT_LIBS}")
#message(STATUS "OPENCV_LIBS: ${OPENCV_LINKER_LIBS}")
#message(STATUS "HIGHGUI_LIBS: ${HIGHGUI_LIBRARIES}")
# ----------------------------------------------------------------------------------
# Define the library target:
# ----------------------------------------------------------------------------------
opencv_module_includes(opencv_core opencv_imgproc)
ocv_module_include_directories()
if(WIN32)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../../3rdparty/include")
endif()
add_library(${the_target} ${highgui_srcs} ${grfmt_srcs} ${highgui_hdrs} ${grfmt_hdrs} ${highgui_ext_hdrs})
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} opencv_core opencv_imgproc ${GRFMT_LIBS} ${HIGHGUI_LIBRARIES})
opencv_module_register(${the_target})
ocv_create_module(${GRFMT_LIBS} ${HIGHGUI_LIBRARIES})
if(BUILD_SHARED_LIBS)
add_definitions(-DHIGHGUI_EXPORTS)
@@ -300,6 +267,7 @@ if(MSVC)
set_target_properties(${the_target} PROPERTIES LINK_FLAGS "/NODEFAULTLIB:atlthunk.lib /NODEFAULTLIB:atlsd.lib /NODEFAULTLIB:libcmt.lib /DEBUG")
endif()
opencv_module_setup(highgui)
define_opencv_test(highgui)
define_opencv_perf_test(highgui)
ocv_add_precompiled_headers(${the_module})
ocv_add_accuracy_tests()
ocv_add_perf_tests()
modules/highgui/src/cap_libv4l.cpp
+1 -1
View File
@@ -221,7 +221,6 @@ make & enjoy!
//
//M*/
#include "highgui.h"
#include "precomp.hpp"
#if !defined WIN32 && defined HAVE_LIBV4L
@@ -393,6 +392,7 @@ static void icvInitCapture_V4L() {
static int try_init_v4l(CvCaptureCAM_V4L* capture, char *deviceName)
{
// if detect = -1 then unable to open device
modules/imgproc/CMakeLists.txt
+2 -1
View File
@@ -1 +1,2 @@
define_opencv_module(imgproc opencv_core)
set(the_description "Image Processing")
ocv_define_module(imgproc opencv_core)
modules/java/CMakeLists.txt
+16 -24
View File
@@ -1,22 +1,18 @@
if(NOT BUILD_JAVA_SUPPORT OR NOT PYTHON_EXECUTABLE)
return()
endif()
# ----------------------------------------------------------------------------
# CMake file for java support
# ----------------------------------------------------------------------------
project(opencv_java)
set(target opencv_java)
if(NOT BUILD_JAVA_SUPPORT OR NOT PYTHON_EXECUTABLE)
ocv_module_disable(java)
endif()
set(the_description "The java bindings")
ocv_add_module(java BINDINGS opencv_objdetect opencv_features2d opencv_imgproc opencv_video opencv_highgui opencv_ml opencv_core opencv_calib3d)
string(REPLACE "opencv_" "" OPENCV_JAVA_MODULES "${OPENCV_MODULE_${the_module}_REQ_DEPS}")
set(target ${the_module})
include_directories("${CMAKE_CURRENT_BINARY_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}/src/cpp")
SET(OPENCV_JAVA_MODULES objdetect features2d imgproc video highgui ml core calib3d)
SET(OPENCV_EXTRA_JAVA_MODULES contrib legacy flann)
IF(WITH_ANDROID_CAMERA AND NOT BUILD_SHARED_LIBS)
LIST(APPEND OPENCV_EXTRA_JAVA_MODULES androidcamera)
ENDIF()
SET(GEN_JAVA "${CMAKE_CURRENT_SOURCE_DIR}/gen_java.py")
SET(HDR_PARSER "${CMAKE_CURRENT_SOURCE_DIR}/../python/src2/hdr_parser.py")
SET(GEN_JAVADOC "${CMAKE_CURRENT_SOURCE_DIR}/gen_javadoc.py")
@@ -81,25 +77,21 @@ foreach(module ${OPENCV_JAVA_MODULES})
endforeach()
FILE(GLOB handwrittren_cpp_sources "${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/*.cpp")
FILE(GLOB handwrittren_h_sources "${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/*.h*")
FILE(GLOB handwrittren_h_sources "${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/*.h??")
FILE(GLOB handwrittren_java_sources "${CMAKE_CURRENT_SOURCE_DIR}/src/java/*.java")
SET (generated_cpp_sources)
SET (generated_java_sources)
SET (documented_java_files)
SET (undocumented_java_files)
SET (dependent_libs)
SET (dependent_extra_libs)
foreach(module ${OPENCV_JAVA_MODULES})
LIST(APPEND generated_cpp_sources "${CMAKE_CURRENT_BINARY_DIR}/${module}.cpp")
LIST(APPEND generated_java_sources ${${module}_generated_java_sources})
LIST(APPEND dependent_libs opencv_${module})
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../${module}/include")
endforeach()
# extra includes and dependencies
foreach(module ${OPENCV_EXTRA_JAVA_MODULES})
LIST(APPEND dependent_extra_libs opencv_${module})
# all needed includes
foreach(module ${OPENCV_MODULE_${the_module}_DEPS})
string(REPLACE "opencv_" "" module "${module}")
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../${module}/include")
endforeach()
@@ -154,8 +146,8 @@ ADD_CUSTOM_TARGET(${api_target} DEPENDS ${java_files})
# add opencv_java library
add_library(${target} SHARED ${handwrittren_h_sources} ${handwrittren_cpp_sources} ${generated_cpp_sources})
target_link_libraries(${target} ${dependent_libs} ${dependent_extra_libs} ${OPENCV_LINKER_LIBS})
add_dependencies(${target} ${dependent_extra_libs} ${dependent_libs} ${api_target})
target_link_libraries(${target} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS})
add_dependencies(${target} ${api_target})
# Additional target properties
set_target_properties(${target} PROPERTIES
modules/legacy/CMakeLists.txt
+1 -1
View File
@@ -1 +1 @@
define_opencv_module(legacy opencv_core opencv_imgproc opencv_calib3d opencv_features2d opencv_highgui opencv_video opencv_flann)
ocv_define_module(legacy opencv_calib3d opencv_highgui opencv_video)
modules/ml/CMakeLists.txt
+2 -1
View File
@@ -1 +1,2 @@
define_opencv_module(ml opencv_core)
set(the_description "Machine Learning")
ocv_define_module(ml opencv_core)
modules/objdetect/CMakeLists.txt
+2 -1
View File
@@ -1 +1,2 @@
define_opencv_module(objdetect opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_calib3d opencv_flann)
set(the_description "Object Detection")
ocv_define_module(objdetect opencv_highgui opencv_calib3d)
modules/python/CMakeLists.txt
+10 -8
View File
@@ -1,14 +1,16 @@
if(WIN32 AND CMAKE_BUILD_TYPE STREQUAL "Debug")
return()
endif()
if(NOT PYTHONLIBS_FOUND OR NOT BUILD_NEW_PYTHON_SUPPORT OR NOT PYTHON_USE_NUMPY)
return()
endif()
# ----------------------------------------------------------------------------
# CMake file for python support
# ----------------------------------------------------------------------------
project(opencv_python)
if(WIN32 AND CMAKE_BUILD_TYPE STREQUAL "Debug")
ocv_module_disable(python)
endif()
if(NOT PYTHONLIBS_FOUND OR NOT BUILD_NEW_PYTHON_SUPPORT OR NOT PYTHON_USE_NUMPY)
ocv_module_disable(python)
endif()
set(the_description "The python bindings")
ocv_add_module(python BINDINGS opencv_core opencv_flann opencv_imgproc opencv_video opencv_ml opencv_features2d opencv_highgui opencv_calib3d opencv_objdetect opencv_legacy opencv_contrib)
include_directories(${PYTHON_INCLUDE_PATH})
include_directories(
modules/stitching/CMakeLists.txt
+2 -10
View File
@@ -1,11 +1,3 @@
if(IOS)
return()
endif()
set(DEPS opencv_core opencv_imgproc opencv_features2d opencv_calib3d opencv_flann opencv_objdetect)
if(NOT ANDROID)
set(DEPS ${DEPS} opencv_gpu)
endif()
define_opencv_module(stitching ${DEPS})
set(the_description "Images stitching")
ocv_define_module(stitching opencv_imgproc opencv_features2d opencv_calib3d opencv_objdetect OPTIONAL opencv_gpu)
modules/stitching/include/opencv2/stitching/detail/matchers.hpp
+3 -2
View File
@@ -46,7 +46,8 @@
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#ifndef ANDROID
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPU
#include "opencv2/gpu/gpu.hpp"
#endif
@@ -102,7 +103,7 @@ private:
};
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder
{
public:
modules/stitching/include/opencv2/stitching/detail/seam_finders.hpp
+2 -1
View File
@@ -44,6 +44,7 @@
#define __OPENCV_STITCHING_SEAM_FINDERS_HPP__
#include "opencv2/core/core.hpp"
#include "opencv2/opencv_modules.hpp"
namespace cv {
namespace detail {
@@ -114,7 +115,7 @@ private:
};
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
class CV_EXPORTS GraphCutSeamFinderGpu : public GraphCutSeamFinderBase, public PairwiseSeamFinder
{
public:
modules/stitching/include/opencv2/stitching/detail/warpers.hpp
+3 -2
View File
@@ -45,7 +45,8 @@
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#ifndef ANDROID
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPU
# include "opencv2/gpu/gpu.hpp"
#endif
@@ -184,7 +185,7 @@ protected:
};
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
class CV_EXPORTS PlaneWarperGpu : public PlaneWarper
{
public:
modules/stitching/include/opencv2/stitching/warpers.hpp
+1 -1
View File
@@ -76,7 +76,7 @@ public:
};
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
class PlaneWarperGpu: public WarperCreator
{
public:
modules/stitching/src/blenders.cpp
+3 -3
View File
@@ -189,7 +189,7 @@ Rect FeatherBlender::createWeightMaps(const vector<Mat> &masks, const vector<Poi
MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands)
{
setNumBands(num_bands);
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
can_use_gpu_ = try_gpu && gpu::getCudaEnabledDeviceCount();
#else
can_use_gpu_ = false;
@@ -383,7 +383,7 @@ void createLaplacePyr(const Mat &img, int num_levels, vector<Mat> &pyr)
void createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
{
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
pyr.resize(num_levels + 1);
vector<gpu::GpuMat> gpu_pyr(num_levels + 1);
@@ -419,7 +419,7 @@ void restoreImageFromLaplacePyr(vector<Mat> &pyr)
void restoreImageFromLaplacePyrGpu(vector<Mat> &pyr)
{
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
if (pyr.empty())
return;
modules/stitching/src/exposure_compensate.cpp
-3
View File
@@ -43,9 +43,6 @@
#include "precomp.hpp"
using namespace std;
#ifndef ANDROID
using namespace cv::gpu;
#endif
namespace cv {
namespace detail {
modules/stitching/src/matchers.cpp
+5 -5
View File
@@ -46,7 +46,7 @@ using namespace std;
using namespace cv;
using namespace cv::detail;
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
using namespace cv::gpu;
#endif
@@ -127,7 +127,7 @@ private:
float match_conf_;
};
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
class GpuMatcher : public FeaturesMatcher
{
public:
@@ -202,7 +202,7 @@ void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &feat
LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);
}
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
{
matches_info.matches.clear();
@@ -398,7 +398,7 @@ void OrbFeaturesFinder::find(const Mat &image, ImageFeatures &features)
}
}
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
SurfFeaturesFinderGpu::SurfFeaturesFinderGpu(double hess_thresh, int num_octaves, int num_layers,
int num_octaves_descr, int num_layers_descr)
{
@@ -499,7 +499,7 @@ void FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<
BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
{
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
impl_ = new GpuMatcher(match_conf);
else
modules/stitching/src/precomp.hpp
+3 -2
View File
@@ -46,6 +46,7 @@
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
#include "opencv2/opencv_modules.hpp"
#include <vector>
#include <algorithm>
@@ -69,11 +70,11 @@
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
# include "opencv2/gpu/gpu.hpp"
#endif
#include "modules/imgproc/src/gcgraph.hpp"
#include "../../imgproc/src/gcgraph.hpp"
#ifdef HAVE_TEGRA_OPTIMIZATION
# include "opencv2/stitching/stitching_tegra.hpp"
modules/stitching/src/seam_finders.cpp
+1 -1
View File
@@ -417,7 +417,7 @@ void GraphCutSeamFinder::find(const vector<Mat> &src, const vector<Point> &corne
}
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
void GraphCutSeamFinderGpu::find(const vector<Mat> &src, const vector<Point> &corners,
vector<Mat> &masks)
{
modules/stitching/src/stitcher.cpp
+1 -1
View File
@@ -58,7 +58,7 @@ Stitcher Stitcher::createDefault(bool try_use_gpu)
stitcher.setFeaturesMatcher(new detail::BestOf2NearestMatcher(try_use_gpu));
stitcher.setBundleAdjuster(new detail::BundleAdjusterRay());
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
if (try_use_gpu && gpu::getCudaEnabledDeviceCount() > 0)
{
stitcher.setFeaturesFinder(new detail::SurfFeaturesFinderGpu());
modules/stitching/src/warpers.cpp
+1 -1
View File
@@ -212,7 +212,7 @@ void SphericalWarper::detectResultRoi(Size src_size, Point &dst_tl, Point &dst_b
}
#ifndef ANDROID
#ifdef HAVE_OPENCV_GPU
Rect PlaneWarperGpu::buildMaps(Size src_size, const Mat &K, const Mat &R, gpu::GpuMat &xmap, gpu::GpuMat &ymap)
{
return buildMaps(src_size, K, R, Mat::zeros(3, 1, CV_32F), xmap, ymap);
modules/traincascade/CMakeLists.txt
-51
View File
@@ -1,51 +0,0 @@
if(IOS)
return()
endif()
project(traincascade)
include_directories(
"${CMAKE_CURRENT_SOURCE_DIR}"
"${OpenCV_SOURCE_DIR}/modules/core/include"
"${OpenCV_SOURCE_DIR}/modules/imgproc/include"
"${OpenCV_SOURCE_DIR}/modules/objdetect/include"
"${OpenCV_SOURCE_DIR}/modules/ml/include"
"${OpenCV_SOURCE_DIR}/modules/highgui/include"
"${OpenCV_SOURCE_DIR}/modules/video/include"
"${OpenCV_SOURCE_DIR}/modules/features2d/include"
"${OpenCV_SOURCE_DIR}/modules/flann/include"
"${OpenCV_SOURCE_DIR}/modules/calib3d/include"
"${OpenCV_SOURCE_DIR}/modules/legacy/include"
)
set(traincascade_libs opencv_core opencv_ml opencv_imgproc
opencv_objdetect opencv_highgui opencv_haartraining_engine)
set(traincascade_files traincascade.cpp
cascadeclassifier.cpp cascadeclassifier.h
boost.cpp boost.h features.cpp traincascade_features.h
haarfeatures.cpp haarfeatures.h
lbpfeatures.cpp lbpfeatures.h
HOGfeatures.cpp HOGfeatures.h
imagestorage.cpp imagestorage.h)
set(the_target opencv_traincascade)
add_executable(${the_target} ${traincascade_files})
add_dependencies(${the_target} ${traincascade_libs})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
OUTPUT_NAME "opencv_traincascade")
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "applications")
endif()
target_link_libraries(${the_target} ${traincascade_libs})
if(NOT ANDROID)
install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)
endif()
modules/traincascade/HOGfeatures.cpp
-245
View File
@@ -1,245 +0,0 @@
#include "HOGfeatures.h"
#include "cascadeclassifier.h"
CvHOGFeatureParams::CvHOGFeatureParams()
{
maxCatCount = 0;
name = HOGF_NAME;
featSize = N_BINS * N_CELLS;
}
void CvHOGEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
{
CV_Assert( _maxSampleCount > 0);
int cols = (_winSize.width + 1) * (_winSize.height + 1);
for (int bin = 0; bin < N_BINS; bin++)
{
hist.push_back(Mat(_maxSampleCount, cols, CV_32FC1));
}
normSum.create( (int)_maxSampleCount, cols, CV_32FC1 );
CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
}
void CvHOGEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_DbgAssert( !hist.empty());
CvFeatureEvaluator::setImage( img, clsLabel, idx );
vector<Mat> integralHist;
for (int bin = 0; bin < N_BINS; bin++)
{
integralHist.push_back( Mat(winSize.height + 1, winSize.width + 1, hist[bin].type(), hist[bin].ptr<float>((int)idx)) );
}
Mat integralNorm(winSize.height + 1, winSize.width + 1, normSum.type(), normSum.ptr<float>((int)idx));
integralHistogram(img, integralHist, integralNorm, (int)N_BINS);
}
//void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
//{
// _writeFeatures( features, fs, featureMap );
//}
void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
int featIdx;
int componentIdx;
const Mat_<int>& featureMap_ = (const Mat_<int>&)featureMap;
fs << FEATURES << "[";
for ( int fi = 0; fi < featureMap.cols; fi++ )
if ( featureMap_(0, fi) >= 0 )
{
fs << "{";
featIdx = fi / getFeatureSize();
componentIdx = fi % getFeatureSize();
features[featIdx].write( fs, componentIdx );
fs << "}";
}
fs << "]";
}
void CvHOGEvaluator::generateFeatures()
{
int offset = winSize.width + 1;
Size blockStep;
int x, y, t, w, h;
for (t = 8; t <= winSize.width/2; t+=8) //t = size of a cell. blocksize = 4*cellSize
{
blockStep = Size(4,4);
w = 2*t; //width of a block
h = 2*t; //height of a block
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, t, t));
}
}
w = 2*t;
h = 4*t;
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, t, 2*t));
}
}
w = 4*t;
h = 2*t;
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, 2*t, t));
}
}
}
numFeatures = (int)features.size();
}
CvHOGEvaluator::Feature::Feature()
{
for (int i = 0; i < N_CELLS; i++)
{
rect[i] = Rect(0, 0, 0, 0);
}
}
CvHOGEvaluator::Feature::Feature( int offset, int x, int y, int cellW, int cellH )
{
rect[0] = Rect(x, y, cellW, cellH); //cell0
rect[1] = Rect(x+cellW, y, cellW, cellH); //cell1
rect[2] = Rect(x, y+cellH, cellW, cellH); //cell2
rect[3] = Rect(x+cellW, y+cellH, cellW, cellH); //cell3
for (int i = 0; i < N_CELLS; i++)
{
CV_SUM_OFFSETS(fastRect[i].p0, fastRect[i].p1, fastRect[i].p2, fastRect[i].p3, rect[i], offset);
}
}
void CvHOGEvaluator::Feature::write(FileStorage &fs) const
{
fs << CC_RECTS << "[";
for( int i = 0; i < N_CELLS; i++ )
{
fs << "[:" << rect[i].x << rect[i].y << rect[i].width << rect[i].height << "]";
}
fs << "]";
}
//cell and bin idx writing
//void CvHOGEvaluator::Feature::write(FileStorage &fs, int varIdx) const
//{
// int featComponent = varIdx % (N_CELLS * N_BINS);
// int cellIdx = featComponent / N_BINS;
// int binIdx = featComponent % N_BINS;
//
// fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y <<
// rect[cellIdx].width << rect[cellIdx].height << binIdx << "]";
//}
//cell[0] and featComponent idx writing. By cell[0] it's possible to recover all block
//All block is nessesary for block normalization
void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const
{
fs << CC_RECT << "[:" << rect[0].x << rect[0].y <<
rect[0].width << rect[0].height << featComponentIdx << "]";
}
void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
int x, y, binIdx;
Size gradSize(img.size());
Size histSize(histogram[0].size());
Mat grad(gradSize, CV_32F);
Mat qangle(gradSize, CV_8U);
AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
int* xmap = (int*)mapbuf + 1;
int* ymap = xmap + gradSize.width + 2;
const int borderType = (int)BORDER_REPLICATE;
for( x = -1; x < gradSize.width + 1; x++ )
xmap[x] = borderInterpolate(x, gradSize.width, borderType);
for( y = -1; y < gradSize.height + 1; y++ )
ymap[y] = borderInterpolate(y, gradSize.height, borderType);
int width = gradSize.width;
AutoBuffer<float> _dbuf(width*4);
float* dbuf = _dbuf;
Mat Dx(1, width, CV_32F, dbuf);
Mat Dy(1, width, CV_32F, dbuf + width);
Mat Mag(1, width, CV_32F, dbuf + width*2);
Mat Angle(1, width, CV_32F, dbuf + width*3);
float angleScale = (float)(nbins/CV_PI);
for( y = 0; y < gradSize.height; y++ )
{
const uchar* currPtr = img.data + img.step*ymap[y];
const uchar* prevPtr = img.data + img.step*ymap[y-1];
const uchar* nextPtr = img.data + img.step*ymap[y+1];
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
for( x = 0; x < width; x++ )
{
dbuf[x] = (float)(currPtr[xmap[x+1]] - currPtr[xmap[x-1]]);
dbuf[width + x] = (float)(nextPtr[xmap[x]] - prevPtr[xmap[x]]);
}
cartToPolar( Dx, Dy, Mag, Angle, false );
for( x = 0; x < width; x++ )
{
float mag = dbuf[x+width*2];
float angle = dbuf[x+width*3];
angle = angle*angleScale - 0.5f;
int bidx = cvFloor(angle);
angle -= bidx;
if( bidx < 0 )
bidx += nbins;
else if( bidx >= nbins )
bidx -= nbins;
qanglePtr[x] = (uchar)bidx;
gradPtr[x] = mag;
}
}
integral(grad, norm, grad.depth());
float* histBuf;
const float* magBuf;
const uchar* binsBuf;
int binsStep = (int)( qangle.step / sizeof(uchar) );
int histStep = (int)( histogram[0].step / sizeof(float) );
int magStep = (int)( grad.step / sizeof(float) );
for( binIdx = 0; binIdx < nbins; binIdx++ )
{
histBuf = (float*)histogram[binIdx].data;
magBuf = (const float*)grad.data;
binsBuf = (const uchar*)qangle.data;
memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
histBuf += histStep + 1;
for( y = 0; y < qangle.rows; y++ )
{
histBuf[-1] = 0.f;
float strSum = 0.f;
for( x = 0; x < qangle.cols; x++ )
{
if( binsBuf[x] == binIdx )
strSum += magBuf[x];
histBuf[x] = histBuf[-histStep + x] + strSum;
}
histBuf += histStep;
binsBuf += binsStep;
magBuf += magStep;
}
}
}
modules/traincascade/HOGfeatures.h
-78
View File
@@ -1,78 +0,0 @@
#ifndef _OPENCV_HOGFEATURES_H_
#define _OPENCV_HOGFEATURES_H_
#include "traincascade_features.h"
//#define TEST_INTHIST_BUILD
//#define TEST_FEAT_CALC
#define N_BINS 9
#define N_CELLS 4
#define HOGF_NAME "HOGFeatureParams"
struct CvHOGFeatureParams : public CvFeatureParams
{
CvHOGFeatureParams();
};
class CvHOGEvaluator : public CvFeatureEvaluator
{
public:
virtual ~CvHOGEvaluator() {}
virtual void init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize );
virtual void setImage(const Mat& img, uchar clsLabel, int idx);
virtual float operator()(int varIdx, int sampleIdx) const;
virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
protected:
virtual void generateFeatures();
virtual void integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const;
class Feature
{
public:
Feature();
Feature( int offset, int x, int y, int cellW, int cellH );
float calc( const vector<Mat> &_hists, const Mat &_normSum, size_t y, int featComponent ) const;
void write( FileStorage &fs ) const;
void write( FileStorage &fs, int varIdx ) const;
Rect rect[N_CELLS]; //cells
struct
{
int p0, p1, p2, p3;
} fastRect[N_CELLS];
};
vector<Feature> features;
Mat normSum; //for nomalization calculation (L1 or L2)
vector<Mat> hist;
};
inline float CvHOGEvaluator::operator()(int varIdx, int sampleIdx) const
{
int featureIdx = varIdx / (N_BINS * N_CELLS);
int componentIdx = varIdx % (N_BINS * N_CELLS);
//return features[featureIdx].calc( hist, sampleIdx, componentIdx);
return features[featureIdx].calc( hist, normSum, sampleIdx, componentIdx);
}
inline float CvHOGEvaluator::Feature::calc( const vector<Mat>& _hists, const Mat& _normSum, size_t y, int featComponent ) const
{
float normFactor;
float res;
int binIdx = featComponent % N_BINS;
int cellIdx = featComponent / N_BINS;
const float *hist = _hists[binIdx].ptr<float>(y);
res = hist[fastRect[cellIdx].p0] - hist[fastRect[cellIdx].p1] - hist[fastRect[cellIdx].p2] + hist[fastRect[cellIdx].p3];
const float *normSum = _normSum.ptr<float>(y);
normFactor = (float)(normSum[fastRect[0].p0] - normSum[fastRect[1].p1] - normSum[fastRect[2].p2] + normSum[fastRect[3].p3]);
res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which apper due to floating precision
return res;
}
#endif // _OPENCV_HOGFEATURES_H_
modules/traincascade/boost.cpp
-1648
View File
File diff suppressed because it is too large Load Diff
modules/traincascade/boost.h
-86
View File
@@ -1,86 +0,0 @@
#ifndef _OPENCV_BOOST_H_
#define _OPENCV_BOOST_H_
#include "traincascade_features.h"
#include "ml.h"
struct CvCascadeBoostParams : CvBoostParams
{
float minHitRate;
float maxFalseAlarm;
CvCascadeBoostParams();
CvCascadeBoostParams( int _boostType, float _minHitRate, float _maxFalseAlarm,
double _weightTrimRate, int _maxDepth, int _maxWeakCount );
virtual ~CvCascadeBoostParams() {}
void write( FileStorage &fs ) const;
bool read( const FileNode &node );
virtual void printDefaults() const;
virtual void printAttrs() const;
virtual bool scanAttr( const String prmName, const String val);
};
struct CvCascadeBoostTrainData : CvDTreeTrainData
{
CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
const CvDTreeParams& _params );
CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params = CvDTreeParams() );
virtual void setData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params=CvDTreeParams() );
void precalculate();
virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
virtual const int* get_class_labels( CvDTreeNode* n, int* labelsBuf );
virtual const int* get_cv_labels( CvDTreeNode* n, int* labelsBuf);
virtual const int* get_sample_indices( CvDTreeNode* n, int* indicesBuf );
virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf );
virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf );
virtual float getVarValue( int vi, int si );
virtual void free_train_data();
const CvFeatureEvaluator* featureEvaluator;
Mat valCache; // precalculated feature values (CV_32FC1)
CvMat _resp; // for casting
int numPrecalcVal, numPrecalcIdx;
};
class CvCascadeBoostTree : public CvBoostTree
{
public:
virtual CvDTreeNode* predict( int sampleIdx ) const;
void write( FileStorage &fs, const Mat& featureMap );
void read( const FileNode &node, CvBoost* _ensemble, CvDTreeTrainData* _data );
void markFeaturesInMap( Mat& featureMap );
protected:
virtual void split_node_data( CvDTreeNode* n );
};
class CvCascadeBoost : public CvBoost
{
public:
virtual bool train( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvCascadeBoostParams& _params=CvCascadeBoostParams() );
virtual float predict( int sampleIdx, bool returnSum = false ) const;
float getThreshold() const { return threshold; }
void write( FileStorage &fs, const Mat& featureMap ) const;
bool read( const FileNode &node, const CvFeatureEvaluator* _featureEvaluator,
const CvCascadeBoostParams& _params );
void markUsedFeaturesInMap( Mat& featureMap );
protected:
virtual bool set_params( const CvBoostParams& _params );
virtual void update_weights( CvBoostTree* tree );
virtual bool isErrDesired();
float threshold;
float minHitRate, maxFalseAlarm;
};
#endif
modules/traincascade/cascadeclassifier.cpp
-527
View File
@@ -1,527 +0,0 @@
#include "cascadeclassifier.h"
#include <queue>
using namespace std;
static const char* stageTypes[] = { CC_BOOST };
static const char* featureTypes[] = { CC_HAAR, CC_LBP, CC_HOG };
CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ),
featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
{
name = CC_CASCADE_PARAMS;
}
CvCascadeParams::CvCascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
featureType( _featureType ), winSize( cvSize(24, 24) )
{
name = CC_CASCADE_PARAMS;
}
//---------------------------- CascadeParams --------------------------------------
void CvCascadeParams::write( FileStorage &fs ) const
{
String stageTypeStr = stageType == BOOST ? CC_BOOST : String();
CV_Assert( !stageTypeStr.empty() );
fs << CC_STAGE_TYPE << stageTypeStr;
String featureTypeStr = featureType == CvFeatureParams::HAAR ? CC_HAAR :
featureType == CvFeatureParams::LBP ? CC_LBP :
featureType == CvFeatureParams::HOG ? CC_HOG :
0;
CV_Assert( !stageTypeStr.empty() );
fs << CC_FEATURE_TYPE << featureTypeStr;
fs << CC_HEIGHT << winSize.height;
fs << CC_WIDTH << winSize.width;
}
bool CvCascadeParams::read( const FileNode &node )
{
if ( node.empty() )
return false;
String stageTypeStr, featureTypeStr;
FileNode rnode = node[CC_STAGE_TYPE];
if ( !rnode.isString() )
return false;
rnode >> stageTypeStr;
stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
if (stageType == -1)
return false;
rnode = node[CC_FEATURE_TYPE];
if ( !rnode.isString() )
return false;
rnode >> featureTypeStr;
featureType = !featureTypeStr.compare( CC_HAAR ) ? CvFeatureParams::HAAR :
!featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP :
!featureTypeStr.compare( CC_HOG ) ? CvFeatureParams::HOG :
-1;
if (featureType == -1)
return false;
node[CC_HEIGHT] >> winSize.height;
node[CC_WIDTH] >> winSize.width;
return winSize.height > 0 && winSize.width > 0;
}
void CvCascadeParams::printDefaults() const
{
CvParams::printDefaults();
cout << " [-stageType <";
for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
{
cout << (i ? " | " : "") << stageTypes[i];
if ( i == defaultStageType )
cout << "(default)";
}
cout << ">]" << endl;
cout << " [-featureType <{";
for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
{
cout << (i ? ", " : "") << featureTypes[i];
if ( i == defaultStageType )
cout << "(default)";
}
cout << "}>]" << endl;
cout << " [-w <sampleWidth = " << winSize.width << ">]" << endl;
cout << " [-h <sampleHeight = " << winSize.height << ">]" << endl;
}
void CvCascadeParams::printAttrs() const
{
cout << "stageType: " << stageTypes[stageType] << endl;
cout << "featureType: " << featureTypes[featureType] << endl;
cout << "sampleWidth: " << winSize.width << endl;
cout << "sampleHeight: " << winSize.height << endl;
}
bool CvCascadeParams::scanAttr( const String prmName, const String val )
{
bool res = true;
if( !prmName.compare( "-stageType" ) )
{
for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
if( !val.compare( stageTypes[i] ) )
stageType = i;
}
else if( !prmName.compare( "-featureType" ) )
{
for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
if( !val.compare( featureTypes[i] ) )
featureType = i;
}
else if( !prmName.compare( "-w" ) )
{
winSize.width = atoi( val.c_str() );
}
else if( !prmName.compare( "-h" ) )
{
winSize.height = atoi( val.c_str() );
}
else
res = false;
return res;
}
//---------------------------- CascadeClassifier --------------------------------------
bool CvCascadeClassifier::train( const String _cascadeDirName,
const String _posFilename,
const String _negFilename,
int _numPos, int _numNeg,
int _precalcValBufSize, int _precalcIdxBufSize,
int _numStages,
const CvCascadeParams& _cascadeParams,
const CvFeatureParams& _featureParams,
const CvCascadeBoostParams& _stageParams,
bool baseFormatSave )
{
if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
string dirName;
if ( _cascadeDirName.find('/') != string::npos )
dirName = _cascadeDirName + '/';
else
dirName = _cascadeDirName + '\\';
numPos = _numPos;
numNeg = _numNeg;
numStages = _numStages;
if ( !imgReader.create( _posFilename, _negFilename, _cascadeParams.winSize ) )
{
cout << "Image reader can not be created from -vec " << _posFilename
<< " and -bg " << _negFilename << "." << endl;
return false;
}
if ( !load( dirName ) )
{
cascadeParams = _cascadeParams;
featureParams = CvFeatureParams::create(cascadeParams.featureType);
featureParams->init(_featureParams);
stageParams = new CvCascadeBoostParams;
*stageParams = _stageParams;
featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
featureEvaluator->init( (CvFeatureParams*)featureParams, numPos + numNeg, cascadeParams.winSize );
stageClassifiers.reserve( numStages );
}
cout << "PARAMETERS:" << endl;
cout << "cascadeDirName: " << _cascadeDirName << endl;
cout << "vecFileName: " << _posFilename << endl;
cout << "bgFileName: " << _negFilename << endl;
cout << "numPos: " << _numPos << endl;
cout << "numNeg: " << _numNeg << endl;
cout << "numStages: " << numStages << endl;
cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
cascadeParams.printAttrs();
stageParams->printAttrs();
featureParams->printAttrs();
int startNumStages = (int)stageClassifiers.size();
if ( startNumStages > 1 )
cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
else if ( startNumStages == 1)
cout << endl << "Stage 0 is loaded" << endl;
double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
(double)stageParams->max_depth;
double tempLeafFARate;
for( int i = startNumStages; i < numStages; i++ )
{
cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
cout << "<BEGIN" << endl;
if ( !updateTrainingSet( tempLeafFARate ) )
{
cout << "Train dataset for temp stage can not be filled. "
"Branch training terminated." << endl;
break;
}
if( tempLeafFARate <= requiredLeafFARate )
{
cout << "Required leaf false alarm rate achieved. "
"Branch training terminated." << endl;
break;
}
CvCascadeBoost* tempStage = new CvCascadeBoost;
tempStage->train( (CvFeatureEvaluator*)featureEvaluator,
curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
*((CvCascadeBoostParams*)stageParams) );
stageClassifiers.push_back( tempStage );
cout << "END>" << endl;
// save params
String filename;
if ( i == 0)
{
filename = dirName + CC_PARAMS_FILENAME;
FileStorage fs( filename, FileStorage::WRITE);
if ( !fs.isOpened() )
{
cout << "Parameters can not be written, because file " << filename
<< " can not be opened." << endl;
return false;
}
fs << FileStorage::getDefaultObjectName(filename) << "{";
writeParams( fs );
fs << "}";
}
// save current stage
char buf[10];
sprintf(buf, "%s%d", "stage", i );
filename = dirName + buf + ".xml";
FileStorage fs( filename, FileStorage::WRITE );
if ( !fs.isOpened() )
{
cout << "Current stage can not be written, because file " << filename
<< " can not be opened." << endl;
return false;
}
fs << FileStorage::getDefaultObjectName(filename) << "{";
tempStage->write( fs, Mat() );
fs << "}";
}
save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
return true;
}
int CvCascadeClassifier::predict( int sampleIdx )
{
CV_DbgAssert( sampleIdx < numPos + numNeg );
for (vector< Ptr<CvCascadeBoost> >::iterator it = stageClassifiers.begin();
it != stageClassifiers.end(); it++ )
{
if ( (*it)->predict( sampleIdx ) == 0.f )
return 0;
}
return 1;
}
bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
{
int64 posConsumed = 0, negConsumed = 0;
imgReader.restart();
int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
if( !posCount )
return false;
cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
if ( !negCount )
return false;
curNumSamples = posCount + negCount;
acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;
return true;
}
int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
{
int getcount = 0;
Mat img(cascadeParams.winSize, CV_8UC1);
for( int i = first; i < first + count; i++ )
{
for( ; ; )
{
bool isGetImg = isPositive ? imgReader.getPos( img ) :
imgReader.getNeg( img );
if( !isGetImg )
return getcount;
consumed++;
featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
if( predict( i ) == 1.0F )
{
getcount++;
break;
}
}
}
return getcount;
}
void CvCascadeClassifier::writeParams( FileStorage &fs ) const
{
cascadeParams.write( fs );
fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
}
void CvCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
((CvFeatureEvaluator*)((Ptr<CvFeatureEvaluator>)featureEvaluator))->writeFeatures( fs, featureMap );
}
void CvCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
{
char cmnt[30];
int i = 0;
fs << CC_STAGES << "[";
for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
it != stageClassifiers.end(); it++, i++ )
{
sprintf( cmnt, "stage %d", i );
cvWriteComment( fs.fs, cmnt, 0 );
fs << "{";
((CvCascadeBoost*)((Ptr<CvCascadeBoost>)*it))->write( fs, featureMap );
fs << "}";
}
fs << "]";
}
bool CvCascadeClassifier::readParams( const FileNode &node )
{
if ( !node.isMap() || !cascadeParams.read( node ) )
return false;
stageParams = new CvCascadeBoostParams;
FileNode rnode = node[CC_STAGE_PARAMS];
if ( !stageParams->read( rnode ) )
return false;
featureParams = CvFeatureParams::create(cascadeParams.featureType);
rnode = node[CC_FEATURE_PARAMS];
if ( !featureParams->read( rnode ) )
return false;
return true;
}
bool CvCascadeClassifier::readStages( const FileNode &node)
{
FileNode rnode = node[CC_STAGES];
if (!rnode.empty() || !rnode.isSeq())
return false;
stageClassifiers.reserve(numStages);
FileNodeIterator it = rnode.begin();
for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
{
CvCascadeBoost* tempStage = new CvCascadeBoost;
if ( !tempStage->read( *it, (CvFeatureEvaluator *)featureEvaluator, *((CvCascadeBoostParams*)stageParams) ) )
{
delete tempStage;
return false;
}
stageClassifiers.push_back(tempStage);
}
return true;
}
// For old Haar Classifier file saving
#define ICV_HAAR_SIZE_NAME "size"
#define ICV_HAAR_STAGES_NAME "stages"
#define ICV_HAAR_TREES_NAME "trees"
#define ICV_HAAR_FEATURE_NAME "feature"
#define ICV_HAAR_RECTS_NAME "rects"
#define ICV_HAAR_TILTED_NAME "tilted"
#define ICV_HAAR_THRESHOLD_NAME "threshold"
#define ICV_HAAR_LEFT_NODE_NAME "left_node"
#define ICV_HAAR_LEFT_VAL_NAME "left_val"
#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
#define ICV_HAAR_PARENT_NAME "parent"
#define ICV_HAAR_NEXT_NAME "next"
void CvCascadeClassifier::save( const String filename, bool baseFormat )
{
FileStorage fs( filename, FileStorage::WRITE );
if ( !fs.isOpened() )
return;
fs << FileStorage::getDefaultObjectName(filename) << "{";
if ( !baseFormat )
{
Mat featureMap;
getUsedFeaturesIdxMap( featureMap );
writeParams( fs );
fs << CC_STAGE_NUM << (int)stageClassifiers.size();
writeStages( fs, featureMap );
writeFeatures( fs, featureMap );
}
else
{
//char buf[256];
CvSeq* weak;
if ( cascadeParams.featureType != CvFeatureParams::HAAR )
CV_Error( CV_StsBadFunc, "old file format is used for Haar-like features only");
fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width <<
cascadeParams.winSize.height << "]";
fs << ICV_HAAR_STAGES_NAME << "[";
for( size_t si = 0; si < stageClassifiers.size(); si++ )
{
fs << "{"; //stage
/*sprintf( buf, "stage %d", si );
CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
weak = stageClassifiers[si]->get_weak_predictors();
fs << ICV_HAAR_TREES_NAME << "[";
for( int wi = 0; wi < weak->total; wi++ )
{
int inner_node_idx = -1, total_inner_node_idx = -1;
queue<const CvDTreeNode*> inner_nodes_queue;
CvCascadeBoostTree* tree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
fs << "[";
/*sprintf( buf, "tree %d", wi );
CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
const CvDTreeNode* tempNode;
inner_nodes_queue.push( tree->get_root() );
total_inner_node_idx++;
while (!inner_nodes_queue.empty())
{
tempNode = inner_nodes_queue.front();
inner_node_idx++;
fs << "{";
fs << ICV_HAAR_FEATURE_NAME << "{";
((CvHaarEvaluator*)((CvFeatureEvaluator*)featureEvaluator))->writeFeature( fs, tempNode->split->var_idx );
fs << "}";
fs << ICV_HAAR_THRESHOLD_NAME << tempNode->split->ord.c;
if( tempNode->left->left || tempNode->left->right )
{
inner_nodes_queue.push( tempNode->left );
total_inner_node_idx++;
fs << ICV_HAAR_LEFT_NODE_NAME << total_inner_node_idx;
}
else
fs << ICV_HAAR_LEFT_VAL_NAME << tempNode->left->value;
if( tempNode->right->left || tempNode->right->right )
{
inner_nodes_queue.push( tempNode->right );
total_inner_node_idx++;
fs << ICV_HAAR_RIGHT_NODE_NAME << total_inner_node_idx;
}
else
fs << ICV_HAAR_RIGHT_VAL_NAME << tempNode->right->value;
fs << "}"; // ICV_HAAR_FEATURE_NAME
inner_nodes_queue.pop();
}
fs << "]";
}
fs << "]"; //ICV_HAAR_TREES_NAME
fs << ICV_HAAR_STAGE_THRESHOLD_NAME << stageClassifiers[si]->getThreshold();
fs << ICV_HAAR_PARENT_NAME << (int)si-1 << ICV_HAAR_NEXT_NAME << -1;
fs << "}"; //stage
} /* for each stage */
fs << "]"; //ICV_HAAR_STAGES_NAME
}
fs << "}";
}
bool CvCascadeClassifier::load( const String cascadeDirName )
{
FileStorage fs( cascadeDirName + CC_PARAMS_FILENAME, FileStorage::READ );
if ( !fs.isOpened() )
return false;
FileNode node = fs.getFirstTopLevelNode();
if ( !readParams( node ) )
return false;
featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
featureEvaluator->init( ((CvFeatureParams*)featureParams), numPos + numNeg, cascadeParams.winSize );
fs.release();
char buf[10];
for ( int si = 0; si < numStages; si++ )
{
sprintf( buf, "%s%d", "stage", si);
fs.open( cascadeDirName + buf + ".xml", FileStorage::READ );
node = fs.getFirstTopLevelNode();
if ( !fs.isOpened() )
break;
CvCascadeBoost *tempStage = new CvCascadeBoost;
if ( !tempStage->read( node, (CvFeatureEvaluator*)featureEvaluator, *((CvCascadeBoostParams*)stageParams )) )
{
delete tempStage;
fs.release();
break;
}
stageClassifiers.push_back(tempStage);
}
return true;
}
void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
{
int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
featureMap.create( 1, varCount, CV_32SC1 );
featureMap.setTo(Scalar(-1));
for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
it != stageClassifiers.end(); it++ )
((CvCascadeBoost*)((Ptr<CvCascadeBoost>)(*it)))->markUsedFeaturesInMap( featureMap );
for( int fi = 0, idx = 0; fi < varCount; fi++ )
if ( featureMap.at<int>(0, fi) >= 0 )
featureMap.ptr<int>(0)[fi] = idx++;
}
modules/traincascade/cascadeclassifier.h
-126
View File
@@ -1,126 +0,0 @@
#ifndef _OPENCV_CASCADECLASSIFIER_H_
#define _OPENCV_CASCADECLASSIFIER_H_
#include <ctime>
#include "traincascade_features.h"
#include "haarfeatures.h"
#include "lbpfeatures.h"
#include "HOGfeatures.h" //new
#include "boost.h"
#include "cv.h"
#include "cxcore.h"
#define CC_CASCADE_FILENAME "cascade.xml"
#define CC_PARAMS_FILENAME "params.xml"
#define CC_CASCADE_PARAMS "cascadeParams"
#define CC_STAGE_TYPE "stageType"
#define CC_FEATURE_TYPE "featureType"
#define CC_HEIGHT "height"
#define CC_WIDTH "width"
#define CC_STAGE_NUM "stageNum"
#define CC_STAGES "stages"
#define CC_STAGE_PARAMS "stageParams"
#define CC_BOOST "BOOST"
#define CC_BOOST_TYPE "boostType"
#define CC_DISCRETE_BOOST "DAB"
#define CC_REAL_BOOST "RAB"
#define CC_LOGIT_BOOST "LB"
#define CC_GENTLE_BOOST "GAB"
#define CC_MINHITRATE "minHitRate"
#define CC_MAXFALSEALARM "maxFalseAlarm"
#define CC_TRIM_RATE "weightTrimRate"
#define CC_MAX_DEPTH "maxDepth"
#define CC_WEAK_COUNT "maxWeakCount"
#define CC_STAGE_THRESHOLD "stageThreshold"
#define CC_WEAK_CLASSIFIERS "weakClassifiers"
#define CC_INTERNAL_NODES "internalNodes"
#define CC_LEAF_VALUES "leafValues"
#define CC_FEATURES FEATURES
#define CC_FEATURE_PARAMS "featureParams"
#define CC_MAX_CAT_COUNT "maxCatCount"
#define CC_FEATURE_SIZE "featSize"
#define CC_HAAR "HAAR"
#define CC_MODE "mode"
#define CC_MODE_BASIC "BASIC"
#define CC_MODE_CORE "CORE"
#define CC_MODE_ALL "ALL"
#define CC_RECTS "rects"
#define CC_TILTED "tilted"
#define CC_LBP "LBP"
#define CC_RECT "rect"
#define CC_HOG "HOG"
#ifdef _WIN32
#define TIME( arg ) (((double) clock()) / CLOCKS_PER_SEC)
#else
#define TIME( arg ) (time( arg ))
#endif
class CvCascadeParams : public CvParams
{
public:
enum { BOOST = 0 };
static const int defaultStageType = BOOST;
static const int defaultFeatureType = CvFeatureParams::HAAR;
CvCascadeParams();
CvCascadeParams( int _stageType, int _featureType );
void write( FileStorage &fs ) const;
bool read( const FileNode &node );
void printDefaults() const;
void printAttrs() const;
bool scanAttr( const String prmName, const String val );
int stageType;
int featureType;
Size winSize;
};
class CvCascadeClassifier
{
public:
bool train( const String _cascadeDirName,
const String _posFilename,
const String _negFilename,
int _numPos, int _numNeg,
int _precalcValBufSize, int _precalcIdxBufSize,
int _numStages,
const CvCascadeParams& _cascadeParams,
const CvFeatureParams& _featureParams,
const CvCascadeBoostParams& _stageParams,
bool baseFormatSave = false );
private:
int predict( int sampleIdx );
void save( const String cascadeDirName, bool baseFormat = false );
bool load( const String cascadeDirName );
bool updateTrainingSet( double& acceptanceRatio );
int fillPassedSamples( int first, int count, bool isPositive, int64& consumed );
void writeParams( FileStorage &fs ) const;
void writeStages( FileStorage &fs, const Mat& featureMap ) const;
void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
bool readParams( const FileNode &node );
bool readStages( const FileNode &node );
void getUsedFeaturesIdxMap( Mat& featureMap );
CvCascadeParams cascadeParams;
Ptr<CvFeatureParams> featureParams;
Ptr<CvCascadeBoostParams> stageParams;
Ptr<CvFeatureEvaluator> featureEvaluator;
vector< Ptr<CvCascadeBoost> > stageClassifiers;
CvCascadeImageReader imgReader;
int numStages, curNumSamples;
int numPos, numNeg;
};
#endif
modules/traincascade/features.cpp
-90
View File
@@ -1,90 +0,0 @@
#include "traincascade_features.h"
#include "cascadeclassifier.h"
using namespace std;
float calcNormFactor( const Mat& sum, const Mat& sqSum )
{
CV_DbgAssert( sum.cols > 3 && sqSum.rows > 3 );
Rect normrect( 1, 1, sum.cols - 3, sum.rows - 3 );
size_t p0, p1, p2, p3;
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
double area = normrect.width * normrect.height;
const int *sp = (const int*)sum.data;
int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
const double *sqp = (const double *)sqSum.data;
double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
}
CvParams::CvParams() : name( "params" ) {}
void CvParams::printDefaults() const
{ cout << "--" << name << "--" << endl; }
void CvParams::printAttrs() const {}
bool CvParams::scanAttr( const String prmName, const String val ) { return false; }
//---------------------------- FeatureParams --------------------------------------
CvFeatureParams::CvFeatureParams() : maxCatCount( 0 ), featSize( 1 )
{
name = CC_FEATURE_PARAMS;
}
void CvFeatureParams::init( const CvFeatureParams& fp )
{
maxCatCount = fp.maxCatCount;
featSize = fp.featSize;
}
void CvFeatureParams::write( FileStorage &fs ) const
{
fs << CC_MAX_CAT_COUNT << maxCatCount;
fs << CC_FEATURE_SIZE << featSize;
}
bool CvFeatureParams::read( const FileNode &node )
{
if ( node.empty() )
return false;
maxCatCount = node[CC_MAX_CAT_COUNT];
featSize = node[CC_FEATURE_SIZE];
return ( maxCatCount >= 0 && featSize >= 1 );
}
Ptr<CvFeatureParams> CvFeatureParams::create( int featureType )
{
return featureType == HAAR ? Ptr<CvFeatureParams>(new CvHaarFeatureParams) :
featureType == LBP ? Ptr<CvFeatureParams>(new CvLBPFeatureParams) :
featureType == HOG ? Ptr<CvFeatureParams>(new CvHOGFeatureParams) :
Ptr<CvFeatureParams>();
}
//------------------------------------- FeatureEvaluator ---------------------------------------
void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize )
{
CV_Assert(_maxSampleCount > 0);
featureParams = (CvFeatureParams *)_featureParams;
winSize = _winSize;
numFeatures = 0;
cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
generateFeatures();
}
void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_Assert(img.cols == winSize.width);
CV_Assert(img.rows == winSize.height);
CV_Assert(idx < cls.rows);
cls.ptr<float>(idx)[0] = clsLabel;
}
Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create(int type)
{
return type == CvFeatureParams::HAAR ? Ptr<CvFeatureEvaluator>(new CvHaarEvaluator) :
type == CvFeatureParams::LBP ? Ptr<CvFeatureEvaluator>(new CvLBPEvaluator) :
type == CvFeatureParams::HOG ? Ptr<CvFeatureEvaluator>(new CvHOGEvaluator) :
Ptr<CvFeatureEvaluator>();
}
modules/traincascade/haarfeatures.cpp
-303
View File
@@ -1,303 +0,0 @@
#include "haarfeatures.h"
#include "cascadeclassifier.h"
using namespace std;
CvHaarFeatureParams::CvHaarFeatureParams() : mode(BASIC)
{
name = HFP_NAME;
}
CvHaarFeatureParams::CvHaarFeatureParams( int _mode ) : mode( _mode )
{
name = HFP_NAME;
}
void CvHaarFeatureParams::init( const CvFeatureParams& fp )
{
CvFeatureParams::init( fp );
mode = ((const CvHaarFeatureParams&)fp).mode;
}
void CvHaarFeatureParams::write( FileStorage &fs ) const
{
CvFeatureParams::write( fs );
String modeStr = mode == BASIC ? CC_MODE_BASIC :
mode == CORE ? CC_MODE_CORE :
mode == ALL ? CC_MODE_ALL : String();
CV_Assert( !modeStr.empty() );
fs << CC_MODE << modeStr;
}
bool CvHaarFeatureParams::read( const FileNode &node )
{
if( !CvFeatureParams::read( node ) )
return false;
FileNode rnode = node[CC_MODE];
if( !rnode.isString() )
return false;
String modeStr;
rnode >> modeStr;
mode = !modeStr.compare( CC_MODE_BASIC ) ? BASIC :
!modeStr.compare( CC_MODE_CORE ) ? CORE :
!modeStr.compare( CC_MODE_ALL ) ? ALL : -1;
return (mode >= 0);
}
void CvHaarFeatureParams::printDefaults() const
{
CvFeatureParams::printDefaults();
cout << " [-mode <" CC_MODE_BASIC << "(default) | "
<< CC_MODE_CORE <<" | " << CC_MODE_ALL << endl;
}
void CvHaarFeatureParams::printAttrs() const
{
CvFeatureParams::printAttrs();
String mode_str = mode == BASIC ? CC_MODE_BASIC :
mode == CORE ? CC_MODE_CORE :
mode == ALL ? CC_MODE_ALL : 0;
cout << "mode: " << mode_str << endl;
}
bool CvHaarFeatureParams::scanAttr( const String prmName, const String val)
{
if ( !CvFeatureParams::scanAttr( prmName, val ) )
{
if( !prmName.compare("-mode") )
{
mode = !val.compare( CC_MODE_CORE ) ? CORE :
!val.compare( CC_MODE_ALL ) ? ALL :
!val.compare( CC_MODE_BASIC ) ? BASIC : -1;
if (mode == -1)
return false;
}
return false;
}
return true;
}
//--------------------- HaarFeatureEvaluator ----------------
void CvHaarEvaluator::init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize )
{
CV_Assert(_maxSampleCount > 0);
int cols = (_winSize.width + 1) * (_winSize.height + 1);
sum.create((int)_maxSampleCount, cols, CV_32SC1);
tilted.create((int)_maxSampleCount, cols, CV_32SC1);
normfactor.create(1, (int)_maxSampleCount, CV_32FC1);
CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
}
void CvHaarEvaluator::setImage(const Mat& img, uchar clsLabel, int idx)
{
CV_DbgAssert( !sum.empty() && !tilted.empty() && !normfactor.empty() );
CvFeatureEvaluator::setImage( img, clsLabel, idx);
Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
Mat innTilted(winSize.height + 1, winSize.width + 1, tilted.type(), tilted.ptr<int>((int)idx));
Mat innSqSum;
integral(img, innSum, innSqSum, innTilted);
normfactor.ptr<float>(0)[idx] = calcNormFactor( innSum, innSqSum );
}
void CvHaarEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
_writeFeatures( features, fs, featureMap );
}
void CvHaarEvaluator::writeFeature(FileStorage &fs, int fi) const
{
CV_DbgAssert( fi < (int)features.size() );
features[fi].write(fs);
}
void CvHaarEvaluator::generateFeatures()
{
int mode = ((const CvHaarFeatureParams*)((CvFeatureParams*)featureParams))->mode;
int offset = winSize.width + 1;
for( int x = 0; x < winSize.width; x++ )
{
for( int y = 0; y < winSize.height; y++ )
{
for( int dx = 1; dx <= winSize.width; dx++ )
{
for( int dy = 1; dy <= winSize.height; dy++ )
{
// haar_x2
if ( (x+dx*2 <= winSize.width) && (y+dy <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx*2, dy, -1,
x+dx, y, dx , dy, +2 ) );
}
// haar_y2
if ( (x+dx <= winSize.width) && (y+dy*2 <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx, dy*2, -1,
x, y+dy, dx, dy, +2 ) );
}
// haar_x3
if ( (x+dx*3 <= winSize.width) && (y+dy <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx*3, dy, -1,
x+dx, y, dx , dy, +3 ) );
}
// haar_y3
if ( (x+dx <= winSize.width) && (y+dy*3 <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx, dy*3, -1,
x, y+dy, dx, dy, +3 ) );
}
if( mode != CvHaarFeatureParams::BASIC )
{
// haar_x4
if ( (x+dx*4 <= winSize.width) && (y+dy <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx*4, dy, -1,
x+dx, y, dx*2, dy, +2 ) );
}
// haar_y4
if ( (x+dx <= winSize.width ) && (y+dy*4 <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx, dy*4, -1,
x, y+dy, dx, dy*2, +2 ) );
}
}
// x2_y2
if ( (x+dx*2 <= winSize.width) && (y+dy*2 <= winSize.height) )
{
features.push_back( Feature( offset, false,
x, y, dx*2, dy*2, -1,
x, y, dx, dy, +2,
x+dx, y+dy, dx, dy, +2 ) );
}
if (mode != CvHaarFeatureParams::BASIC)
{
if ( (x+dx*3 <= winSize.width) && (y+dy*3 <= winSize.height) )
{
features.push_back( Feature( offset, false,
x , y , dx*3, dy*3, -1,
x+dx, y+dy, dx , dy , +9) );
}
}
if (mode == CvHaarFeatureParams::ALL)
{
// tilted haar_x2
if ( (x+2*dx <= winSize.width) && (y+2*dx+dy <= winSize.height) && (x-dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx*2, dy, -1,
x, y, dx, dy, +2 ) );
}
// tilted haar_y2
if ( (x+dx <= winSize.width) && (y+dx+2*dy <= winSize.height) && (x-2*dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx, 2*dy, -1,
x, y, dx, dy, +2 ) );
}
// tilted haar_x3
if ( (x+3*dx <= winSize.width) && (y+3*dx+dy <= winSize.height) && (x-dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx*3, dy, -1,
x+dx, y+dx, dx, dy, +3 ) );
}
// tilted haar_y3
if ( (x+dx <= winSize.width) && (y+dx+3*dy <= winSize.height) && (x-3*dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx, 3*dy, -1,
x-dy, y+dy, dx, dy, +3 ) );
}
// tilted haar_x4
if ( (x+4*dx <= winSize.width) && (y+4*dx+dy <= winSize.height) && (x-dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx*4, dy, -1,
x+dx, y+dx, dx*2, dy, +2 ) );
}
// tilted haar_y4
if ( (x+dx <= winSize.width) && (y+dx+4*dy <= winSize.height) && (x-4*dy>= 0) )
{
features.push_back( Feature( offset, true,
x, y, dx, 4*dy, -1,
x-dy, y+dy, dx, 2*dy, +2 ) );
}
}
}
}
}
}
numFeatures = (int)features.size();
}
CvHaarEvaluator::Feature::Feature()
{
tilted = false;
rect[0].r = rect[1].r = rect[2].r = Rect(0,0,0,0);
rect[0].weight = rect[1].weight = rect[2].weight = 0;
}
CvHaarEvaluator::Feature::Feature( int offset, bool _tilted,
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 )
{
tilted = _tilted;
rect[0].r.x = x0;
rect[0].r.y = y0;
rect[0].r.width = w0;
rect[0].r.height = h0;
rect[0].weight = wt0;
rect[1].r.x = x1;
rect[1].r.y = y1;
rect[1].r.width = w1;
rect[1].r.height = h1;
rect[1].weight = wt1;
rect[2].r.x = x2;
rect[2].r.y = y2;
rect[2].r.width = w2;
rect[2].r.height = h2;
rect[2].weight = wt2;
if( !tilted )
{
for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
{
if( rect[j].weight == 0.0F )
break;
CV_SUM_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
}
}
else
{
for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
{
if( rect[j].weight == 0.0F )
break;
CV_TILTED_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
}
}
}
void CvHaarEvaluator::Feature::write( FileStorage &fs ) const
{
fs << CC_RECTS << "[";
for( int ri = 0; ri < CV_HAAR_FEATURE_MAX && rect[ri].r.width != 0; ++ri )
{
fs << "[:" << rect[ri].r.x << rect[ri].r.y <<
rect[ri].r.width << rect[ri].r.height << rect[ri].weight << "]";
}
fs << "]" << CC_TILTED << tilted;
}
modules/traincascade/haarfeatures.h
-89
View File
@@ -1,89 +0,0 @@
#ifndef _OPENCV_HAARFEATURES_H_
#define _OPENCV_HAARFEATURES_H_
#include "traincascade_features.h"
#define CV_HAAR_FEATURE_MAX 3
#define HFP_NAME "haarFeatureParams"
class CvHaarFeatureParams : public CvFeatureParams
{
public:
enum { BASIC = 0, CORE = 1, ALL = 2 };
/* 0 - BASIC = Viola
* 1 - CORE = All upright
* 2 - ALL = All features */
CvHaarFeatureParams();
CvHaarFeatureParams( int _mode );
virtual void init( const CvFeatureParams& fp );
virtual void write( FileStorage &fs ) const;
virtual bool read( const FileNode &node );
virtual void printDefaults() const;
virtual void printAttrs() const;
virtual bool scanAttr( const String prm, const String val);
int mode;
};
class CvHaarEvaluator : public CvFeatureEvaluator
{
public:
virtual void init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize );
virtual void setImage(const Mat& img, uchar clsLabel, int idx);
virtual float operator()(int featureIdx, int sampleIdx) const;
virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
void writeFeature( FileStorage &fs, int fi ) const; // for old file fornat
protected:
virtual void generateFeatures();
class Feature
{
public:
Feature();
Feature( int offset, bool _tilted,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2 = 0, int y2 = 0, int w2 = 0, int h2 = 0, float wt2 = 0.0F );
float calc( const Mat &sum, const Mat &tilted, size_t y) const;
void write( FileStorage &fs ) const;
bool tilted;
struct
{
Rect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
struct
{
int p0, p1, p2, p3;
} fastRect[CV_HAAR_FEATURE_MAX];
};
vector<Feature> features;
Mat sum; /* sum images (each row represents image) */
Mat tilted; /* tilted sum images (each row represents image) */
Mat normfactor; /* normalization factor */
};
inline float CvHaarEvaluator::operator()(int featureIdx, int sampleIdx) const
{
float nf = normfactor.at<float>(0, sampleIdx);
return !nf ? 0.0f : (features[featureIdx].calc( sum, tilted, sampleIdx)/nf);
}
inline float CvHaarEvaluator::Feature::calc( const Mat &_sum, const Mat &_tilted, size_t y) const
{
const int* img = tilted ? _tilted.ptr<int>((int)y) : _sum.ptr<int>((int)y);
float ret = rect[0].weight * (img[fastRect[0].p0] - img[fastRect[0].p1] - img[fastRect[0].p2] + img[fastRect[0].p3] ) +
rect[1].weight * (img[fastRect[1].p0] - img[fastRect[1].p1] - img[fastRect[1].p2] + img[fastRect[1].p3] );
if( rect[2].weight != 0.0f )
ret += rect[2].weight * (img[fastRect[2].p0] - img[fastRect[2].p1] - img[fastRect[2].p2] + img[fastRect[2].p3] );
return ret;
}
#endif
modules/traincascade/imagestorage.cpp
-179
View File
@@ -1,179 +0,0 @@
#include "cv.h"
#include "imagestorage.h"
#include <stdio.h>
#include <iostream>
#include <fstream>
bool CvCascadeImageReader::create( const String _posFilename, const String _negFilename, Size _winSize )
{
return posReader.create(_posFilename) && negReader.create(_negFilename, _winSize);
}
CvCascadeImageReader::NegReader::NegReader()
{
src.create( 0, 0 , CV_8UC1 );
img.create( 0, 0, CV_8UC1 );
point = offset = Point( 0, 0 );
scale = 1.0F;
scaleFactor = 1.4142135623730950488016887242097F;
stepFactor = 0.5F;
}
bool CvCascadeImageReader::NegReader::create( const String _filename, Size _winSize )
{
String dirname, str;
std::ifstream file(_filename.c_str());
if ( !file.is_open() )
return false;
size_t pos = _filename.rfind('\\');
char dlmrt = '\\';
if (pos == String::npos)
{
pos = _filename.rfind('/');
dlmrt = '/';
}
dirname = pos == String::npos ? "" : _filename.substr(0, pos) + dlmrt;
while( !file.eof() )
{
std::getline(file, str);
if (str.empty()) break;
if (str.at(0) == '#' ) continue; /* comment */
imgFilenames.push_back(dirname + str);
}
file.close();
winSize = _winSize;
last = round = 0;
return true;
}
bool CvCascadeImageReader::NegReader::nextImg()
{
Point _offset = Point(0,0);
size_t count = imgFilenames.size();
for( size_t i = 0; i < count; i++ )
{
src = imread( imgFilenames[last++], 0 );
if( src.empty() )
continue;
round += last / count;
round = round % (winSize.width * winSize.height);
last %= count;
_offset.x = min( (int)round % winSize.width, src.cols - winSize.width );
_offset.y = min( (int)round / winSize.width, src.rows - winSize.height );
if( !src.empty() && src.type() == CV_8UC1
&& offset.x >= 0 && offset.y >= 0 )
break;
}
if( src.empty() )
return false; // no appropriate image
point = offset = _offset;
scale = max( ((float)winSize.width + point.x) / ((float)src.cols),
((float)winSize.height + point.y) / ((float)src.rows) );
Size sz( (int)(scale*src.cols + 0.5F), (int)(scale*src.rows + 0.5F) );
resize( src, img, sz );
return true;
}
bool CvCascadeImageReader::NegReader::get( Mat& _img )
{
CV_Assert( !_img.empty() );
CV_Assert( _img.type() == CV_8UC1 );
CV_Assert( _img.cols == winSize.width );
CV_Assert( _img.rows == winSize.height );
if( img.empty() )
if ( !nextImg() )
return false;
Mat mat( winSize.height, winSize.width, CV_8UC1,
(void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
mat.copyTo(_img);
if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )
point.x += (int)(stepFactor * winSize.width);
else
{
point.x = offset.x;
if( (int)( point.y + (1.0F + stepFactor ) * winSize.height ) < img.rows )
point.y += (int)(stepFactor * winSize.height);
else
{
point.y = offset.y;
scale *= scaleFactor;
if( scale <= 1.0F )
resize( src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows) ) );
else
{
if ( !nextImg() )
return false;
}
}
}
return true;
}
CvCascadeImageReader::PosReader::PosReader()
{
file = 0;
vec = 0;
}
bool CvCascadeImageReader::PosReader::create( const String _filename )
{
if ( file )
fclose( file );
file = fopen( _filename.c_str(), "rb" );
if( !file )
return false;
short tmp = 0;
if( fread( &count, sizeof( count ), 1, file ) != 1 ||
fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
CV_Error_( CV_StsParseError, ("wrong file format for %s\n", _filename.c_str()) );
base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
if( feof( file ) )
return false;
last = 0;
vec = (short*) cvAlloc( sizeof( *vec ) * vecSize );
CV_Assert( vec );
return true;
}
bool CvCascadeImageReader::PosReader::get( Mat &_img )
{
CV_Assert( _img.rows * _img.cols == vecSize );
uchar tmp = 0;
fread( &tmp, sizeof( tmp ), 1, file );
fread( vec, sizeof( vec[0] ), vecSize, file );
if( feof( file ) || last++ >= count )
return false;
for( int r = 0; r < _img.rows; r++ )
{
for( int c = 0; c < _img.cols; c++ )
_img.ptr(r)[c] = (uchar)vec[r * _img.cols + c];
}
return true;
}
void CvCascadeImageReader::PosReader::restart()
{
CV_Assert( file );
last = 0;
fseek( file, base, SEEK_SET );
}
CvCascadeImageReader::PosReader::~PosReader()
{
if (file)
fclose( file );
cvFree( &vec );
}
modules/traincascade/imagestorage.h
-53
View File
@@ -1,53 +0,0 @@
#ifndef _OPENCV_IMAGESTORAGE_H_
#define _OPENCV_IMAGESTORAGE_H_
#include "highgui.h"
using namespace cv;
class CvCascadeImageReader
{
public:
bool create( const String _posFilename, const String _negFilename, Size _winSize );
void restart() { posReader.restart(); }
bool getNeg(Mat &_img) { return negReader.get( _img ); }
bool getPos(Mat &_img) { return posReader.get( _img ); }
private:
class PosReader
{
public:
PosReader();
virtual ~PosReader();
bool create( const String _filename );
bool get( Mat &_img );
void restart();
short* vec;
FILE* file;
int count;
int vecSize;
int last;
int base;
} posReader;
class NegReader
{
public:
NegReader();
bool create( const String _filename, Size _winSize );
bool get( Mat& _img );
bool nextImg();
Mat src, img;
vector<String> imgFilenames;
Point offset, point;
float scale;
float scaleFactor;
float stepFactor;
size_t last, round;
Size winSize;
} negReader;
};
#endif
modules/traincascade/lbpfeatures.cpp
-62
View File
@@ -1,62 +0,0 @@
#include "lbpfeatures.h"
#include "cascadeclassifier.h"
CvLBPFeatureParams::CvLBPFeatureParams()
{
maxCatCount = 256;
name = LBPF_NAME;
}
void CvLBPEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
{
CV_Assert( _maxSampleCount > 0);
sum.create((int)_maxSampleCount, (_winSize.width + 1) * (_winSize.height + 1), CV_32SC1);
CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
}
void CvLBPEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_DbgAssert( !sum.empty() );
CvFeatureEvaluator::setImage( img, clsLabel, idx );
Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
integral( img, innSum );
}
void CvLBPEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
_writeFeatures( features, fs, featureMap );
}
void CvLBPEvaluator::generateFeatures()
{
int offset = winSize.width + 1;
for( int x = 0; x < winSize.width; x++ )
for( int y = 0; y < winSize.height; y++ )
for( int w = 1; w <= winSize.width / 3; w++ )
for( int h = 1; h <= winSize.height / 3; h++ )
if ( (x+3*w <= winSize.width) && (y+3*h <= winSize.height) )
features.push_back( Feature(offset, x, y, w, h ) );
numFeatures = (int)features.size();
}
CvLBPEvaluator::Feature::Feature()
{
rect = cvRect(0, 0, 0, 0);
}
CvLBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
{
Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
tr.x += 2*rect.width;
CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )
tr.y +=2*rect.height;
CV_SUM_OFFSETS( p[10], p[11], p[14], p[15], tr, offset )
tr.x -= 2*rect.width;
CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
}
void CvLBPEvaluator::Feature::write(FileStorage &fs) const
{
fs << CC_RECT << "[:" << rect.x << rect.y << rect.width << rect.height << "]";
}
modules/traincascade/lbpfeatures.h
-57
View File
@@ -1,57 +0,0 @@
#ifndef _OPENCV_LBPFEATURES_H_
#define _OPENCV_LBPFEATURES_H_
#include "traincascade_features.h"
#define LBPF_NAME "lbpFeatureParams"
struct CvLBPFeatureParams : CvFeatureParams
{
CvLBPFeatureParams();
};
class CvLBPEvaluator : public CvFeatureEvaluator
{
public:
virtual ~CvLBPEvaluator() {}
virtual void init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize );
virtual void setImage(const Mat& img, uchar clsLabel, int idx);
virtual float operator()(int featureIdx, int sampleIdx) const
{ return (float)features[featureIdx].calc( sum, sampleIdx); }
virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
protected:
virtual void generateFeatures();
class Feature
{
public:
Feature();
Feature( int offset, int x, int y, int _block_w, int _block_h );
uchar calc( const Mat& _sum, size_t y ) const;
void write( FileStorage &fs ) const;
Rect rect;
int p[16];
};
vector<Feature> features;
Mat sum;
};
inline uchar CvLBPEvaluator::Feature::calc(const Mat &_sum, size_t y) const
{
const int* sum = _sum.ptr<int>((int)y);
int cval = sum[p[5]] - sum[p[6]] - sum[p[9]] + sum[p[10]];
return (uchar)((sum[p[0]] - sum[p[1]] - sum[p[4]] + sum[p[5]] >= cval ? 128 : 0) | // 0
(sum[p[1]] - sum[p[2]] - sum[p[5]] + sum[p[6]] >= cval ? 64 : 0) | // 1
(sum[p[2]] - sum[p[3]] - sum[p[6]] + sum[p[7]] >= cval ? 32 : 0) | // 2
(sum[p[6]] - sum[p[7]] - sum[p[10]] + sum[p[11]] >= cval ? 16 : 0) | // 5
(sum[p[10]] - sum[p[11]] - sum[p[14]] + sum[p[15]] >= cval ? 8 : 0) | // 8
(sum[p[9]] - sum[p[10]] - sum[p[13]] + sum[p[14]] >= cval ? 4 : 0) | // 7
(sum[p[8]] - sum[p[9]] - sum[p[12]] + sum[p[13]] >= cval ? 2 : 0) | // 6
(sum[p[4]] - sum[p[5]] - sum[p[8]] + sum[p[9]] >= cval ? 1 : 0)); // 3
}
#endif
modules/traincascade/traincascade.cpp
-109
View File
@@ -1,109 +0,0 @@
#include "cv.h"
#include "cascadeclassifier.h"
using namespace std;
int main( int argc, char* argv[] )
{
CvCascadeClassifier classifier;
String cascadeDirName, vecName, bgName;
int numPos = 2000;
int numNeg = 1000;
int numStages = 20;
int precalcValBufSize = 256,
precalcIdxBufSize = 256;
bool baseFormatSave = false;
CvCascadeParams cascadeParams;
CvCascadeBoostParams stageParams;
Ptr<CvFeatureParams> featureParams[] = { Ptr<CvFeatureParams>(new CvHaarFeatureParams),
Ptr<CvFeatureParams>(new CvLBPFeatureParams),
Ptr<CvFeatureParams>(new CvHOGFeatureParams)
};
int fc = sizeof(featureParams)/sizeof(featureParams[0]);
if( argc == 1 )
{
cout << "Usage: " << argv[0] << endl;
cout << " -data <cascade_dir_name>" << endl;
cout << " -vec <vec_file_name>" << endl;
cout << " -bg <background_file_name>" << endl;
cout << " [-numPos <number_of_positive_samples = " << numPos << ">]" << endl;
cout << " [-numNeg <number_of_negative_samples = " << numNeg << ">]" << endl;
cout << " [-numStages <number_of_stages = " << numStages << ">]" << endl;
cout << " [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;
cout << " [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;
cout << " [-baseFormatSave]" << endl;
cascadeParams.printDefaults();
stageParams.printDefaults();
for( int fi = 0; fi < fc; fi++ )
featureParams[fi]->printDefaults();
return 0;
}
for( int i = 1; i < argc; i++ )
{
bool set = false;
if( !strcmp( argv[i], "-data" ) )
{
cascadeDirName = 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], "-numPos" ) )
{
numPos = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-numNeg" ) )
{
numNeg = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-numStages" ) )
{
numStages = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-precalcValBufSize" ) )
{
precalcValBufSize = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-precalcIdxBufSize" ) )
{
precalcIdxBufSize = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-baseFormatSave" ) )
{
baseFormatSave = true;
}
else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( !set )
{
for( int fi = 0; fi < fc; fi++ )
{
set = featureParams[fi]->scanAttr(argv[i], argv[i+1]);
if ( !set )
{
i++;
break;
}
}
}
}
classifier.train( cascadeDirName,
vecName,
bgName,
numPos, numNeg,
precalcValBufSize, precalcIdxBufSize,
numStages,
cascadeParams,
*featureParams[cascadeParams.featureType],
stageParams,
baseFormatSave );
return 0;
}
modules/traincascade/traincascade_features.h
-104
View File
@@ -1,104 +0,0 @@
#ifndef _OPENCV_FEATURES_H_
#define _OPENCV_FEATURES_H_
#include "imagestorage.h"
#include "cxcore.h"
#include "cv.h"
#include "ml.h"
#include <stdio.h>
#define FEATURES "features"
#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \
/* (x, y) */ \
(p0) = (rect).x + (step) * (rect).y; \
/* (x + w, y) */ \
(p1) = (rect).x + (rect).width + (step) * (rect).y; \
/* (x + w, y) */ \
(p2) = (rect).x + (step) * ((rect).y + (rect).height); \
/* (x + w, y + h) */ \
(p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);
#define CV_TILTED_OFFSETS( p0, p1, p2, p3, rect, step ) \
/* (x, y) */ \
(p0) = (rect).x + (step) * (rect).y; \
/* (x - h, y + h) */ \
(p1) = (rect).x - (rect).height + (step) * ((rect).y + (rect).height);\
/* (x + w, y + w) */ \
(p2) = (rect).x + (rect).width + (step) * ((rect).y + (rect).width); \
/* (x + w - h, y + w + h) */ \
(p3) = (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height);
float calcNormFactor( const Mat& sum, const Mat& sqSum );
template<class Feature>
void _writeFeatures( const vector<Feature> features, FileStorage &fs, const Mat& featureMap )
{
fs << FEATURES << "[";
const Mat_<int>& featureMap_ = (const Mat_<int>&)featureMap;
for ( int fi = 0; fi < featureMap.cols; fi++ )
if ( featureMap_(0, fi) >= 0 )
{
fs << "{";
features[fi].write( fs );
fs << "}";
}
fs << "]";
}
class CvParams
{
public:
CvParams();
virtual ~CvParams() {}
// from|to file
virtual void write( FileStorage &fs ) const = 0;
virtual bool read( const FileNode &node ) = 0;
// from|to screen
virtual void printDefaults() const;
virtual void printAttrs() const;
virtual bool scanAttr( const String prmName, const String val );
String name;
};
class CvFeatureParams : public CvParams
{
public:
enum { HAAR = 0, LBP = 1, HOG = 2 };
CvFeatureParams();
virtual void init( const CvFeatureParams& fp );
virtual void write( FileStorage &fs ) const;
virtual bool read( const FileNode &node );
static Ptr<CvFeatureParams> create( int featureType );
int maxCatCount; // 0 in case of numerical features
int featSize; // 1 in case of simple features (HAAR, LBP) and N_BINS(9)*N_CELLS(4) in case of Dalal's HOG features
};
class CvFeatureEvaluator
{
public:
virtual ~CvFeatureEvaluator() {}
virtual void init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize );
virtual void setImage(const Mat& img, uchar clsLabel, int idx);
virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const = 0;
virtual float operator()(int featureIdx, int sampleIdx) const = 0;
static Ptr<CvFeatureEvaluator> create(int type);
int getNumFeatures() const { return numFeatures; }
int getMaxCatCount() const { return featureParams->maxCatCount; }
int getFeatureSize() const { return featureParams->featSize; }
const Mat& getCls() const { return cls; }
float getCls(int si) const { return cls.at<float>(si, 0); }
protected:
virtual void generateFeatures() = 0;
int npos, nneg;
int numFeatures;
Size winSize;
CvFeatureParams *featureParams;
Mat cls;
};
#endif
modules/ts/CMakeLists.txt
+23 -15
View File
@@ -1,16 +1,24 @@
if(BUILD_TESTS OR BUILD_PERF_TESTS)
if(BUILD_SHARED_LIBS AND NOT MINGW)
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=1)
if (MSVC)
add_definitions( "/wd4251 /wd4275")
endif()
else()
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=0)
endif()
if(MINGW)
set(OPENCV_TS_MODULE_TYPE STATIC)
endif()
define_opencv_module(ts opencv_core)
if(IOS)
ocv_module_disable(ts)
endif()
if(MINGW)
set(OPENCV_MODULE_TYPE STATIC)
endif()
set(the_description "The ts module")
ocv_add_module(ts opencv_core)
ocv_glob_module_sources()
ocv_module_include_directories()
ocv_create_module()
if(BUILD_SHARED_LIBS AND NOT MINGW)
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=1)
if (MSVC)
add_definitions( "/wd4251 /wd4275")
endif()
else()
add_definitions(-DGTEST_CREATE_SHARED_LIBRARY=0)
endif()
ocv_add_precompiled_headers(${the_module})
modules/video/CMakeLists.txt
+2 -1
View File
@@ -1 +1,2 @@
define_opencv_module(video opencv_core opencv_imgproc)
set(the_description "Video Analysis")
ocv_define_module(video opencv_imgproc)