[pktools] 01/16: New upstream version 2.6.7.1
Bas Couwenberg
sebastic at debian.org
Thu Jun 29 14:25:04 UTC 2017
This is an automated email from the git hooks/post-receive script.
sebastic pushed a commit to branch master
in repository pktools.
commit 09488e9bb22829378bdb2d7e3ee6bc5920a58329
Author: Bas Couwenberg <sebastic at xs4all.nl>
Date: Thu Jun 29 07:23:13 2017 +0200
New upstream version 2.6.7.1
---
CMakeLists.txt | 91 +-
README.txt | 0
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build/CPackSourceConfig.cmake | 73 +
build/Makefile | 1808 ++++++++++++++
build/base/config.h | 80 +
build/cmake_install.cmake | 724 ++++++
build/pktools-config | 47 +
build/pktools.pc | 11 +
.../src/CMakeFiles/CMakeDirectoryInformation.cmake | 16 +
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build/src/CMakeFiles/progress.marks | 1 +
build/src/Makefile | 712 ++++++
build/src/cmake_install.cmake | 243 ++
doc/apps.dox | 8 +-
doc/createappsdox.sh | 2 +-
doc/doxygen_sqlite3.db | Bin 0 -> 933888 bytes
doc/examples_pkextract.dox | 26 +-
src/CMakeLists.txt | 42 +-
src/{CMakeLists.txt => CMakeLists.txt.orig} | 0
src/CMakeLists.txt.rej | 38 +
src/apps/pkann.cc | 181 +-
src/apps/pkannogr.cc | 916 +++++++
src/apps/pkcomposite.cc | 2 +-
src/apps/pkcomposite_app.cc~ | 271 ---
src/apps/pkcrop.cc | 2 +-
src/apps/pkextract.cc | 2553 --------------------
src/apps/pkextractimg.cc | 23 +-
src/apps/pkextractogr.cc | 14 +-
src/apps/pkfilter.cc | 6 +-
src/apps/pklas2img.cc | 4 +-
src/apps/pkreclass.cc | 62 +-
src/apps/pkreclassogr.cc | 194 ++
src/apps/pkstatprofile.cc | 4 +-
src/apps/pksvm.cc | 206 +-
src/apps/{pksvm.cc => pksvmogr.cc} | 486 +---
355 files changed, 16198 insertions(+), 3841 deletions(-)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 62d1383..79cc307 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -35,7 +35,7 @@ set (PROJECT_SOURCE_DIR src)
# The version number.
set (PKTOOLS_VERSION_MAJOR 2)
set (PKTOOLS_VERSION_MINOR 6)
-set (PKTOOLS_VERSION_PATCH 7)
+set (PKTOOLS_VERSION_PATCH 7.1)
set (PKTOOLS_VERSION "${PKTOOLS_VERSION_MAJOR}.${PKTOOLS_VERSION_MINOR}.${PKTOOLS_VERSION_PATCH}")
set (PKTOOLS_PACKAGE_VERSION "${PKTOOLS_VERSION_MAJOR}.${PKTOOLS_VERSION_MINOR}.${PKTOOLS_VERSION_PATCH}")
set (PKTOOLS_PACKAGE_STRING "PKTOOLS ${PKTOOLS_VERSION_MAJOR}.${PKTOOLS_VERSION_MINOR}.${PKTOOLS_VERSION_PATCH}")
@@ -43,25 +43,35 @@ set (PKTOOLS_PACKAGE_BUGREPORT "kempenep at gmail.com")
set (PKTOOLS_PACKAGE "pktools")
set (PKTOOLS_COPYRIGHT_YEAR 2016)
+set (PKTOOLS_BASE_VERSION_MAJOR 1)
+set (PKTOOLS_BASE_VERSION_MINOR 6)
+set (PKTOOLS_BASE_VERSION_PATCH 1)
+set (PKTOOLS_BASE_VERSION "${PKTOOLS_BASE_VERSION_MAJOR}.${PKTOOLS_BASE_VERSION_MINOR}.${PKTOOLS_BASE_VERSION_PATCH}")
+set (PKTOOLS_BASE_SOVERSION 1)
+
set (PKTOOLS_ALGORITHMS_VERSION_MAJOR 1)
set (PKTOOLS_ALGORITHMS_VERSION_MINOR 6)
-set (PKTOOLS_ALGORITHMS_VERSION_PATCH 0)
+set (PKTOOLS_ALGORITHMS_VERSION_PATCH 1)
set (PKTOOLS_ALGORITHMS_VERSION "${PKTOOLS_ALGORITHMS_VERSION_MAJOR}.${PKTOOLS_ALGORITHMS_VERSION_MINOR}.${PKTOOLS_ALGORITHMS_VERSION_PATCH}")
+set (PKTOOLS_ALGORITHMS_SOVERSION 1)
set (PKTOOLS_FILECLASSES_VERSION_MAJOR 1)
set (PKTOOLS_FILECLASSES_VERSION_MINOR 6)
-set (PKTOOLS_FILECLASSES_VERSION_PATCH 0)
+set (PKTOOLS_FILECLASSES_VERSION_PATCH 1)
set (PKTOOLS_FILECLASSES_VERSION "${PKTOOLS_FILECLASSES_VERSION_MAJOR}.${PKTOOLS_FILECLASSES_VERSION_MINOR}.${PKTOOLS_FILECLASSES_VERSION_PATCH}")
+set (PKTOOLS_FILECLASSES_SOVERSION 1)
set (PKTOOLS_IMAGECLASSES_VERSION_MAJOR 1)
set (PKTOOLS_IMAGECLASSES_VERSION_MINOR 6)
-set (PKTOOLS_IMAGECLASSES_VERSION_PATCH 0)
+set (PKTOOLS_IMAGECLASSES_VERSION_PATCH 1)
set (PKTOOLS_IMAGECLASSES_VERSION "${PKTOOLS_IMAGECLASSES_VERSION_MAJOR}.${PKTOOLS_IMAGECLASSES_VERSION_MINOR}.${PKTOOLS_IMAGECLASSES_VERSION_PATCH}")
+set (PKTOOLS_IMAGECLASSES_SOVERSION 1)
set (PKTOOLS_LASCLASSES_VERSION_MAJOR 1)
set (PKTOOLS_LASCLASSES_VERSION_MINOR 6)
-set (PKTOOLS_LASCLASSES_VERSION_PATCH 0)
+set (PKTOOLS_LASCLASSES_VERSION_PATCH 1)
set (PKTOOLS_LASCLASSES_VERSION "${PKTOOLS_LASCLASSES_VERSION_MAJOR}.${PKTOOLS_LASCLASSES_VERSION_MINOR}.${PKTOOLS_LASCLASSES_VERSION_PATCH}")
+set (PKTOOLS_LASCLASSES_SOVERSION 1)
# Name of C++ library
set(PKTOOLS_BASE_LIB_NAME base)
@@ -253,7 +263,7 @@ configure_file ( "cmake.pc.in" "pktools.pc" @ONLY)
set_property(GLOBAL PROPERTY USE_FOLDERS ON)
-set(PKLIBS ${PKTOOLS_BASE_LIB_NAME} ${PKTOOLS_IMAGECLASSES_LIB_NAME} ${PKTOOLS_FILECLASSES_LIB_NAME} ${PKTOOLS_ALGORITHMS_LIB_NAME} ${GDAL_LIBRARIES} ${GSL_LIBRARIES} ${ARMADILLO_LIBRARIES} ${Boost_LIBRARIES})
+set(PKLIBS ${PKTOOLS_BASE_LIB_NAME} ${PKTOOLS_IMAGECLASSES_LIB_NAME} ${PKTOOLS_FILECLASSES_LIB_NAME} ${PKTOOLS_ALGORITHMS_LIB_NAME} ${GDAL_LIBRARIES} ${GSL_LIBRARIES} ${Boost_LIBRARIES})
set(PKUTILITIES
pkinfo
@@ -261,35 +271,42 @@ set(PKUTILITIES
pkdiff
pkgetmask
pksetmask
- pkcreatect
- pkdumpimg
- pkdumpogr
- pksieve
+ pkcreatect
+ pkdumpimg
+ pkdumpogr
+ pksieve
pkstat
- pkstatascii
- pkstatogr
+ pkstatascii
+ pkstatogr
pkstatprofile
- pkegcs
+ pkegcs
pkextractimg
pkextractogr
- pkfillnodata
- pkfilter
- pkfilterdem
- pkfilterascii
- pkdsm2shadow
+ pkfillnodata
+ pkfilter
+ pkfilterdem
+ pkfilterascii
+ pkdsm2shadow
pkcomposite
- pkpolygonize
- pkascii2img
- pksvm
- pkfssvm
+ pkpolygonize
+ pkascii2img
+ pksvm
+ pksvmogr
+ pkfssvm
pkoptsvm
- pkascii2ogr
- pkreclass
+ pkascii2ogr
+ pkreclass
+ pkreclassogr
pkkalman
)
+set(PKLASUTILITIES
+ pklas2img
+)
+
set(PKFANNUTILITIES
pkann
+ pkannogr
pkfsann
pkregann
)
@@ -300,28 +317,28 @@ set(PKFANNUTILITIES
if (PKTOOLS_WITH_UTILITIES)
foreach(UTILITY ${PKUTILITIES})
- add_executable(${UTILITY} src/apps/${UTILITY}.cc)
- target_link_libraries(${UTILITY} ${PKLIBS})
- set_target_properties(${UTILITY} PROPERTIES FOLDER utilities)
+ add_executable(${UTILITY} src/apps/${UTILITY}.cc)
+ target_link_libraries(${UTILITY} ${PKLIBS})
+ set_target_properties(${UTILITY} PROPERTIES FOLDER utilities)
endforeach()
if (BUILD_WITH_LIBLAS)
set(PKLIBS ${PKLIBS} ${PKTOOLS_LASCLASSES_LIB_NAME})
foreach(PKLASUTILITY ${PKLASUTILITIES})
- add_executable(${PKLASUTILITY} src/apps/${PKLASUTILITY}.cc)
- target_link_libraries (${PKLASUTILITY}
- ${PKLIBS} ${LIBLAS_LIBRARIES})
- set_target_properties(${PKLASUTILITY} PROPERTIES FOLDER utilities)
+ add_executable(${PKLASUTILITY} src/apps/${PKLASUTILITY}.cc)
+ target_link_libraries (${PKLASUTILITY}
+ ${PKLIBS} ${LIBLAS_LIBRARIES})
+ set_target_properties(${PKLASUTILITY} PROPERTIES FOLDER utilities)
endforeach()
endif()
if (BUILD_WITH_FANN)
set(PKLIBS ${PKLIBS} ${FANN_LIBRARIES})
foreach(PKFANNUTILITY ${PKFANNUTILITIES})
- add_executable(${PKFANNUTILITY} src/apps/${PKFANNUTILITY}.cc)
- message(${PKLIBS})
- target_link_libraries (${PKFANNUTILITY} ${PKLIBS})
- set_target_properties(${PKFANNUTILITY} PROPERTIES FOLDER utilities)
- endforeach()
+ add_executable(${PKFANNUTILITY} src/apps/${PKFANNUTILITY}.cc)
+ # message(${PKLIBS})
+ target_link_libraries (${PKFANNUTILITY} ${PKLIBS})
+ set_target_properties(${PKFANNUTILITY} PROPERTIES FOLDER utilities)
+ endforeach()
endif()
# if (BUILD_WITH_NLOPT)
# set(PKLIBS ${PKLIBS} ${NLOPT_LIBRARIES})
@@ -349,7 +366,7 @@ endif()
#install (FILES "${PROJECT_BINARY_DIR}/pktools-config" DESTINATION bin PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)
#test
install (FILES "${CMAKE_CURRENT_BINARY_DIR}/pktools-config" DESTINATION bin PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)
-install (FILES "pktools.pc" DESTINATION lib PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)
+install (FILES "pktools.pc" DESTINATION lib/pkgconfig PERMISSIONS OWNER_READ OWNER_WRITE GROUP_READ WORLD_READ)
if (PKTOOLS_WITH_UTILITIES)
install (TARGETS ${PKUTILITIES} DESTINATION bin PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)
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new file mode 100644
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+
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+endif()
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+ set(CMAKE_LIBRARY_ARCHITECTURE "x86_64-linux-gnu")
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+
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new file mode 100644
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+ set(UNIX 1)
+endif()
+
+set(CMAKE_CXX_COMPILER_ENV_VAR "CXX")
+
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+ set(MINGW 1)
+endif()
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+set(CMAKE_CXX_IGNORE_EXTENSIONS inl;h;hpp;HPP;H;o;O;obj;OBJ;def;DEF;rc;RC)
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+
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+
+if(CMAKE_CXX_SIZEOF_DATA_PTR)
+ set(CMAKE_SIZEOF_VOID_P "${CMAKE_CXX_SIZEOF_DATA_PTR}")
+endif()
+
+if(CMAKE_CXX_COMPILER_ABI)
+ set(CMAKE_INTERNAL_PLATFORM_ABI "${CMAKE_CXX_COMPILER_ABI}")
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+ set(CMAKE_LIBRARY_ARCHITECTURE "x86_64-linux-gnu")
+endif()
+
+
+
+
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+set(CMAKE_CXX_IMPLICIT_LINK_DIRECTORIES "/usr/lib/gcc/x86_64-linux-gnu/4.9;/usr/lib/x86_64-linux-gnu;/usr/lib;/lib/x86_64-linux-gnu;/lib")
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+
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diff --git a/build/CMakeFiles/3.0.2/CMakeDetermineCompilerABI_C.bin b/build/CMakeFiles/3.0.2/CMakeDetermineCompilerABI_C.bin
new file mode 100755
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new file mode 100644
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diff --git a/build/CMakeFiles/3.0.2/CompilerIdC/CMakeCCompilerId.c b/build/CMakeFiles/3.0.2/CompilerIdC/CMakeCCompilerId.c
new file mode 100644
index 0000000..bd1f7d1
--- /dev/null
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@@ -0,0 +1,442 @@
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+# error "A C++ compiler has been selected for C."
+#endif
+
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+ Version date components: YYYY=Year, MM=Month, DD=Day */
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+# define ID_VOID_MAIN
+#endif
+
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+# define COMPILER_ID "Intel"
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+# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE)
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+# define COMPILER_VERSION_MINOR HEX(__SUNPRO_C>>4 & 0xFF)
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+# else
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+#ifdef SIMULATE_ID
+char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]";
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+
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+char const* qnxnto = "INFO" ":" "qnxnto";
+#endif
+
+/* Identify known platforms by name. */
+#if defined(__linux) || defined(__linux__) || defined(linux)
+# define PLATFORM_ID "Linux"
+
+#elif defined(__CYGWIN__)
+# define PLATFORM_ID "Cygwin"
+
+#elif defined(__MINGW32__)
+# define PLATFORM_ID "MinGW"
+
+#elif defined(__APPLE__)
+# define PLATFORM_ID "Darwin"
+
+#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)
+# define PLATFORM_ID "Windows"
+
+#elif defined(__FreeBSD__) || defined(__FreeBSD)
+# define PLATFORM_ID "FreeBSD"
+
+#elif defined(__NetBSD__) || defined(__NetBSD)
+# define PLATFORM_ID "NetBSD"
+
+#elif defined(__OpenBSD__) || defined(__OPENBSD)
+# define PLATFORM_ID "OpenBSD"
+
+#elif defined(__sun) || defined(sun)
+# define PLATFORM_ID "SunOS"
+
+#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__)
+# define PLATFORM_ID "AIX"
+
+#elif defined(__sgi) || defined(__sgi__) || defined(_SGI)
+# define PLATFORM_ID "IRIX"
+
+#elif defined(__hpux) || defined(__hpux__)
+# define PLATFORM_ID "HP-UX"
+
+#elif defined(__HAIKU__)
+# define PLATFORM_ID "Haiku"
+
+#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS)
+# define PLATFORM_ID "BeOS"
+
+#elif defined(__QNX__) || defined(__QNXNTO__)
+# define PLATFORM_ID "QNX"
+
+#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__)
+# define PLATFORM_ID "Tru64"
+
+#elif defined(__riscos) || defined(__riscos__)
+# define PLATFORM_ID "RISCos"
+
+#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__)
+# define PLATFORM_ID "SINIX"
+
+#elif defined(__UNIX_SV__)
+# define PLATFORM_ID "UNIX_SV"
+
+#elif defined(__bsdos__)
+# define PLATFORM_ID "BSDOS"
+
+#elif defined(_MPRAS) || defined(MPRAS)
+# define PLATFORM_ID "MP-RAS"
+
+#elif defined(__osf) || defined(__osf__)
+# define PLATFORM_ID "OSF1"
+
+#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv)
+# define PLATFORM_ID "SCO_SV"
+
+#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX)
+# define PLATFORM_ID "ULTRIX"
+
+#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX)
+# define PLATFORM_ID "Xenix"
+
+#else /* unknown platform */
+# define PLATFORM_ID ""
+
+#endif
+
+/* For windows compilers MSVC and Intel we can determine
+ the architecture of the compiler being used. This is because
+ the compilers do not have flags that can change the architecture,
+ but rather depend on which compiler is being used
+*/
+#if defined(_WIN32) && defined(_MSC_VER)
+# if defined(_M_IA64)
+# define ARCHITECTURE_ID "IA64"
+
+# elif defined(_M_X64) || defined(_M_AMD64)
+# define ARCHITECTURE_ID "x64"
+
+# elif defined(_M_IX86)
+# define ARCHITECTURE_ID "X86"
+
+# elif defined(_M_ARM)
+# define ARCHITECTURE_ID "ARM"
+
+# elif defined(_M_MIPS)
+# define ARCHITECTURE_ID "MIPS"
+
+# elif defined(_M_SH)
+# define ARCHITECTURE_ID "SHx"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#else
+# define ARCHITECTURE_ID ""
+#endif
+
+/* Convert integer to decimal digit literals. */
+#define DEC(n) \
+ ('0' + (((n) / 10000000)%10)), \
+ ('0' + (((n) / 1000000)%10)), \
+ ('0' + (((n) / 100000)%10)), \
+ ('0' + (((n) / 10000)%10)), \
+ ('0' + (((n) / 1000)%10)), \
+ ('0' + (((n) / 100)%10)), \
+ ('0' + (((n) / 10)%10)), \
+ ('0' + ((n) % 10))
+
+/* Convert integer to hex digit literals. */
+#define HEX(n) \
+ ('0' + ((n)>>28 & 0xF)), \
+ ('0' + ((n)>>24 & 0xF)), \
+ ('0' + ((n)>>20 & 0xF)), \
+ ('0' + ((n)>>16 & 0xF)), \
+ ('0' + ((n)>>12 & 0xF)), \
+ ('0' + ((n)>>8 & 0xF)), \
+ ('0' + ((n)>>4 & 0xF)), \
+ ('0' + ((n) & 0xF))
+
+/* Construct a string literal encoding the version number components. */
+#ifdef COMPILER_VERSION_MAJOR
+char const info_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[',
+ COMPILER_VERSION_MAJOR,
+# ifdef COMPILER_VERSION_MINOR
+ '.', COMPILER_VERSION_MINOR,
+# ifdef COMPILER_VERSION_PATCH
+ '.', COMPILER_VERSION_PATCH,
+# ifdef COMPILER_VERSION_TWEAK
+ '.', COMPILER_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct a string literal encoding the version number components. */
+#ifdef SIMULATE_VERSION_MAJOR
+char const info_simulate_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[',
+ SIMULATE_VERSION_MAJOR,
+# ifdef SIMULATE_VERSION_MINOR
+ '.', SIMULATE_VERSION_MINOR,
+# ifdef SIMULATE_VERSION_PATCH
+ '.', SIMULATE_VERSION_PATCH,
+# ifdef SIMULATE_VERSION_TWEAK
+ '.', SIMULATE_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+ getting matched. Store it in a pointer rather than an array
+ because some compilers will just produce instructions to fill the
+ array rather than assigning a pointer to a static array. */
+char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]";
+char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]";
+
+
+
+
+/*--------------------------------------------------------------------------*/
+
+#ifdef ID_VOID_MAIN
+void main() {}
+#else
+int main(int argc, char* argv[])
+{
+ int require = 0;
+ require += info_compiler[argc];
+ require += info_platform[argc];
+ require += info_arch[argc];
+#ifdef COMPILER_VERSION_MAJOR
+ require += info_version[argc];
+#endif
+#ifdef SIMULATE_ID
+ require += info_simulate[argc];
+#endif
+#ifdef SIMULATE_VERSION_MAJOR
+ require += info_simulate_version[argc];
+#endif
+ (void)argv;
+ return require;
+}
+#endif
diff --git a/build/CMakeFiles/3.0.2/CompilerIdC/a.out b/build/CMakeFiles/3.0.2/CompilerIdC/a.out
new file mode 100755
index 0000000..50a51b3
Binary files /dev/null and b/build/CMakeFiles/3.0.2/CompilerIdC/a.out differ
diff --git a/build/CMakeFiles/3.0.2/CompilerIdCXX/CMakeCXXCompilerId.cpp b/build/CMakeFiles/3.0.2/CompilerIdCXX/CMakeCXXCompilerId.cpp
new file mode 100644
index 0000000..a315c6a
--- /dev/null
+++ b/build/CMakeFiles/3.0.2/CompilerIdCXX/CMakeCXXCompilerId.cpp
@@ -0,0 +1,430 @@
+/* This source file must have a .cpp extension so that all C++ compilers
+ recognize the extension without flags. Borland does not know .cxx for
+ example. */
+#ifndef __cplusplus
+# error "A C compiler has been selected for C++."
+#endif
+
+/* Version number components: V=Version, R=Revision, P=Patch
+ Version date components: YYYY=Year, MM=Month, DD=Day */
+
+#if defined(__COMO__)
+# define COMPILER_ID "Comeau"
+ /* __COMO_VERSION__ = VRR */
+# define COMPILER_VERSION_MAJOR DEC(__COMO_VERSION__ / 100)
+# define COMPILER_VERSION_MINOR DEC(__COMO_VERSION__ % 100)
+
+#elif defined(__INTEL_COMPILER) || defined(__ICC)
+# define COMPILER_ID "Intel"
+ /* __INTEL_COMPILER = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100)
+# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10)
+# if defined(__INTEL_COMPILER_UPDATE)
+# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE)
+# else
+# define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER % 10)
+# endif
+# if defined(__INTEL_COMPILER_BUILD_DATE)
+ /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
+# define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE)
+# endif
+# if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+
+#elif defined(__PATHCC__)
+# define COMPILER_ID "PathScale"
+# define COMPILER_VERSION_MAJOR DEC(__PATHCC__)
+# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__)
+# if defined(__PATHCC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__)
+# endif
+
+#elif defined(__clang__)
+# if defined(__apple_build_version__)
+# define COMPILER_ID "AppleClang"
+# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__)
+# else
+# define COMPILER_ID "Clang"
+# endif
+# define COMPILER_VERSION_MAJOR DEC(__clang_major__)
+# define COMPILER_VERSION_MINOR DEC(__clang_minor__)
+# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)
+# if defined(_MSC_VER)
+# define SIMULATE_ID "MSVC"
+ /* _MSC_VER = VVRR */
+# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+
+#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__)
+# define COMPILER_ID "Embarcadero"
+# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF)
+# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF)
+# define COMPILER_VERSION_PATCH HEX(__CODEGEARC_VERSION__ & 0xFFFF)
+
+#elif defined(__BORLANDC__)
+# define COMPILER_ID "Borland"
+ /* __BORLANDC__ = 0xVRR */
+# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8)
+# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF)
+
+#elif defined(__WATCOMC__)
+# define COMPILER_ID "Watcom"
+ /* __WATCOMC__ = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100)
+# define COMPILER_VERSION_MINOR DEC(__WATCOMC__ % 100)
+
+#elif defined(__SUNPRO_CC)
+# define COMPILER_ID "SunPro"
+# if __SUNPRO_CC >= 0x5100
+ /* __SUNPRO_CC = 0xVRRP */
+# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>12)
+# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xFF)
+# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)
+# else
+ /* __SUNPRO_CC = 0xVRP */
+# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_CC>>8)
+# define COMPILER_VERSION_MINOR HEX(__SUNPRO_CC>>4 & 0xF)
+# define COMPILER_VERSION_PATCH HEX(__SUNPRO_CC & 0xF)
+# endif
+
+#elif defined(__HP_aCC)
+# define COMPILER_ID "HP"
+ /* __HP_aCC = VVRRPP */
+# define COMPILER_VERSION_MAJOR DEC(__HP_aCC/10000)
+# define COMPILER_VERSION_MINOR DEC(__HP_aCC/100 % 100)
+# define COMPILER_VERSION_PATCH DEC(__HP_aCC % 100)
+
+#elif defined(__DECCXX)
+# define COMPILER_ID "Compaq"
+ /* __DECCXX_VER = VVRRTPPPP */
+# define COMPILER_VERSION_MAJOR DEC(__DECCXX_VER/10000000)
+# define COMPILER_VERSION_MINOR DEC(__DECCXX_VER/100000 % 100)
+# define COMPILER_VERSION_PATCH DEC(__DECCXX_VER % 10000)
+
+#elif defined(__IBMCPP__)
+# if defined(__COMPILER_VER__)
+# define COMPILER_ID "zOS"
+# else
+# if __IBMCPP__ >= 800
+# define COMPILER_ID "XL"
+# else
+# define COMPILER_ID "VisualAge"
+# endif
+ /* __IBMCPP__ = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__IBMCPP__/100)
+# define COMPILER_VERSION_MINOR DEC(__IBMCPP__/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__IBMCPP__ % 10)
+# endif
+
+#elif defined(__PGI)
+# define COMPILER_ID "PGI"
+# define COMPILER_VERSION_MAJOR DEC(__PGIC__)
+# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__)
+# if defined(__PGIC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__)
+# endif
+
+#elif defined(_CRAYC)
+# define COMPILER_ID "Cray"
+# define COMPILER_VERSION_MAJOR DEC(_RELEASE)
+# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR)
+
+#elif defined(__TI_COMPILER_VERSION__)
+# define COMPILER_ID "TI"
+ /* __TI_COMPILER_VERSION__ = VVVRRRPPP */
+# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000)
+# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000)
+# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000)
+
+#elif defined(__SCO_VERSION__)
+# define COMPILER_ID "SCO"
+
+#elif defined(__GNUC__)
+# define COMPILER_ID "GNU"
+# define COMPILER_VERSION_MAJOR DEC(__GNUC__)
+# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__)
+# if defined(__GNUC_PATCHLEVEL__)
+# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+# endif
+
+#elif defined(_MSC_VER)
+# define COMPILER_ID "MSVC"
+ /* _MSC_VER = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100)
+# if defined(_MSC_FULL_VER)
+# if _MSC_VER >= 1400
+ /* _MSC_FULL_VER = VVRRPPPPP */
+# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000)
+# else
+ /* _MSC_FULL_VER = VVRRPPPP */
+# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000)
+# endif
+# endif
+# if defined(_MSC_BUILD)
+# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD)
+# endif
+
+/* Analog VisualDSP++ >= 4.5.6 */
+#elif defined(__VISUALDSPVERSION__)
+# define COMPILER_ID "ADSP"
+ /* __VISUALDSPVERSION__ = 0xVVRRPP00 */
+# define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24)
+# define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF)
+# define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8 & 0xFF)
+
+/* Analog VisualDSP++ < 4.5.6 */
+#elif defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__)
+# define COMPILER_ID "ADSP"
+
+/* IAR Systems compiler for embedded systems.
+ http://www.iar.com */
+#elif defined(__IAR_SYSTEMS_ICC__ ) || defined(__IAR_SYSTEMS_ICC)
+# define COMPILER_ID "IAR"
+
+#elif defined(_SGI_COMPILER_VERSION) || defined(_COMPILER_VERSION)
+# define COMPILER_ID "MIPSpro"
+# if defined(_SGI_COMPILER_VERSION)
+ /* _SGI_COMPILER_VERSION = VRP */
+# define COMPILER_VERSION_MAJOR DEC(_SGI_COMPILER_VERSION/100)
+# define COMPILER_VERSION_MINOR DEC(_SGI_COMPILER_VERSION/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(_SGI_COMPILER_VERSION % 10)
+# else
+ /* _COMPILER_VERSION = VRP */
+# define COMPILER_VERSION_MAJOR DEC(_COMPILER_VERSION/100)
+# define COMPILER_VERSION_MINOR DEC(_COMPILER_VERSION/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(_COMPILER_VERSION % 10)
+# endif
+
+/* This compiler is either not known or is too old to define an
+ identification macro. Try to identify the platform and guess that
+ it is the native compiler. */
+#elif defined(__sgi)
+# define COMPILER_ID "MIPSpro"
+
+#elif defined(__hpux) || defined(__hpua)
+# define COMPILER_ID "HP"
+
+#else /* unknown compiler */
+# define COMPILER_ID ""
+
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+ getting matched. Store it in a pointer rather than an array
+ because some compilers will just produce instructions to fill the
+ array rather than assigning a pointer to a static array. */
+char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]";
+#ifdef SIMULATE_ID
+char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]";
+#endif
+
+#ifdef __QNXNTO__
+char const* qnxnto = "INFO" ":" "qnxnto";
+#endif
+
+/* Identify known platforms by name. */
+#if defined(__linux) || defined(__linux__) || defined(linux)
+# define PLATFORM_ID "Linux"
+
+#elif defined(__CYGWIN__)
+# define PLATFORM_ID "Cygwin"
+
+#elif defined(__MINGW32__)
+# define PLATFORM_ID "MinGW"
+
+#elif defined(__APPLE__)
+# define PLATFORM_ID "Darwin"
+
+#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)
+# define PLATFORM_ID "Windows"
+
+#elif defined(__FreeBSD__) || defined(__FreeBSD)
+# define PLATFORM_ID "FreeBSD"
+
+#elif defined(__NetBSD__) || defined(__NetBSD)
+# define PLATFORM_ID "NetBSD"
+
+#elif defined(__OpenBSD__) || defined(__OPENBSD)
+# define PLATFORM_ID "OpenBSD"
+
+#elif defined(__sun) || defined(sun)
+# define PLATFORM_ID "SunOS"
+
+#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__)
+# define PLATFORM_ID "AIX"
+
+#elif defined(__sgi) || defined(__sgi__) || defined(_SGI)
+# define PLATFORM_ID "IRIX"
+
+#elif defined(__hpux) || defined(__hpux__)
+# define PLATFORM_ID "HP-UX"
+
+#elif defined(__HAIKU__)
+# define PLATFORM_ID "Haiku"
+
+#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS)
+# define PLATFORM_ID "BeOS"
+
+#elif defined(__QNX__) || defined(__QNXNTO__)
+# define PLATFORM_ID "QNX"
+
+#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__)
+# define PLATFORM_ID "Tru64"
+
+#elif defined(__riscos) || defined(__riscos__)
+# define PLATFORM_ID "RISCos"
+
+#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__)
+# define PLATFORM_ID "SINIX"
+
+#elif defined(__UNIX_SV__)
+# define PLATFORM_ID "UNIX_SV"
+
+#elif defined(__bsdos__)
+# define PLATFORM_ID "BSDOS"
+
+#elif defined(_MPRAS) || defined(MPRAS)
+# define PLATFORM_ID "MP-RAS"
+
+#elif defined(__osf) || defined(__osf__)
+# define PLATFORM_ID "OSF1"
+
+#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv)
+# define PLATFORM_ID "SCO_SV"
+
+#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX)
+# define PLATFORM_ID "ULTRIX"
+
+#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX)
+# define PLATFORM_ID "Xenix"
+
+#else /* unknown platform */
+# define PLATFORM_ID ""
+
+#endif
+
+/* For windows compilers MSVC and Intel we can determine
+ the architecture of the compiler being used. This is because
+ the compilers do not have flags that can change the architecture,
+ but rather depend on which compiler is being used
+*/
+#if defined(_WIN32) && defined(_MSC_VER)
+# if defined(_M_IA64)
+# define ARCHITECTURE_ID "IA64"
+
+# elif defined(_M_X64) || defined(_M_AMD64)
+# define ARCHITECTURE_ID "x64"
+
+# elif defined(_M_IX86)
+# define ARCHITECTURE_ID "X86"
+
+# elif defined(_M_ARM)
+# define ARCHITECTURE_ID "ARM"
+
+# elif defined(_M_MIPS)
+# define ARCHITECTURE_ID "MIPS"
+
+# elif defined(_M_SH)
+# define ARCHITECTURE_ID "SHx"
+
+# else /* unknown architecture */
+# define ARCHITECTURE_ID ""
+# endif
+
+#else
+# define ARCHITECTURE_ID ""
+#endif
+
+/* Convert integer to decimal digit literals. */
+#define DEC(n) \
+ ('0' + (((n) / 10000000)%10)), \
+ ('0' + (((n) / 1000000)%10)), \
+ ('0' + (((n) / 100000)%10)), \
+ ('0' + (((n) / 10000)%10)), \
+ ('0' + (((n) / 1000)%10)), \
+ ('0' + (((n) / 100)%10)), \
+ ('0' + (((n) / 10)%10)), \
+ ('0' + ((n) % 10))
+
+/* Convert integer to hex digit literals. */
+#define HEX(n) \
+ ('0' + ((n)>>28 & 0xF)), \
+ ('0' + ((n)>>24 & 0xF)), \
+ ('0' + ((n)>>20 & 0xF)), \
+ ('0' + ((n)>>16 & 0xF)), \
+ ('0' + ((n)>>12 & 0xF)), \
+ ('0' + ((n)>>8 & 0xF)), \
+ ('0' + ((n)>>4 & 0xF)), \
+ ('0' + ((n) & 0xF))
+
+/* Construct a string literal encoding the version number components. */
+#ifdef COMPILER_VERSION_MAJOR
+char const info_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[',
+ COMPILER_VERSION_MAJOR,
+# ifdef COMPILER_VERSION_MINOR
+ '.', COMPILER_VERSION_MINOR,
+# ifdef COMPILER_VERSION_PATCH
+ '.', COMPILER_VERSION_PATCH,
+# ifdef COMPILER_VERSION_TWEAK
+ '.', COMPILER_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct a string literal encoding the version number components. */
+#ifdef SIMULATE_VERSION_MAJOR
+char const info_simulate_version[] = {
+ 'I', 'N', 'F', 'O', ':',
+ 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[',
+ SIMULATE_VERSION_MAJOR,
+# ifdef SIMULATE_VERSION_MINOR
+ '.', SIMULATE_VERSION_MINOR,
+# ifdef SIMULATE_VERSION_PATCH
+ '.', SIMULATE_VERSION_PATCH,
+# ifdef SIMULATE_VERSION_TWEAK
+ '.', SIMULATE_VERSION_TWEAK,
+# endif
+# endif
+# endif
+ ']','\0'};
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+ getting matched. Store it in a pointer rather than an array
+ because some compilers will just produce instructions to fill the
+ array rather than assigning a pointer to a static array. */
+char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]";
+char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]";
+
+
+
+
+/*--------------------------------------------------------------------------*/
+
+int main(int argc, char* argv[])
+{
+ int require = 0;
+ require += info_compiler[argc];
+ require += info_platform[argc];
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+
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+
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+ if(EXISTS "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/bin/pkcrop" AND
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index 0000000..821193c
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+
+# Install shared libraries without execute permission?
+if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
+ set(CMAKE_INSTALL_SO_NO_EXE "1")
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_CHECK
+ FILE "${file}"
+ RPATH "")
+ endif()
+ endforeach()
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES
+ "/home/kempenep/pktools-2.6.7/build/src/libbase.so.1.6.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libbase.so.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libbase.so"
+ )
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libbase.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ if(CMAKE_INSTALL_DO_STRIP)
+ execute_process(COMMAND "/usr/bin/strip" "${file}")
+ endif()
+ endif()
+ endforeach()
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_CHECK
+ FILE "${file}"
+ RPATH "")
+ endif()
+ endforeach()
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES
+ "/home/kempenep/pktools-2.6.7/build/src/libimageClasses.so.1.6.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libimageClasses.so.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libimageClasses.so"
+ )
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libimageClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ if(CMAKE_INSTALL_DO_STRIP)
+ execute_process(COMMAND "/usr/bin/strip" "${file}")
+ endif()
+ endif()
+ endforeach()
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_CHECK
+ FILE "${file}"
+ RPATH "")
+ endif()
+ endforeach()
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES
+ "/home/kempenep/pktools-2.6.7/build/src/libalgorithms.so.1.6.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libalgorithms.so.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libalgorithms.so"
+ )
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libalgorithms.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_REMOVE
+ FILE "${file}")
+ if(CMAKE_INSTALL_DO_STRIP)
+ execute_process(COMMAND "/usr/bin/strip" "${file}")
+ endif()
+ endif()
+ endforeach()
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_CHECK
+ FILE "${file}"
+ RPATH "")
+ endif()
+ endforeach()
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES
+ "/home/kempenep/pktools-2.6.7/build/src/libfileClasses.so.1.6.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libfileClasses.so.1"
+ "/home/kempenep/pktools-2.6.7/build/src/libfileClasses.so"
+ )
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/libfileClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_REMOVE
+ FILE "${file}")
+ if(CMAKE_INSTALL_DO_STRIP)
+ execute_process(COMMAND "/usr/bin/strip" "${file}")
+ endif()
+ endif()
+ endforeach()
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/pktools/base" TYPE FILE FILES
+ "/home/kempenep/pktools-2.6.7/src/base/IndexValue.h"
+ "/home/kempenep/pktools-2.6.7/src/base/Optionpk.h"
+ "/home/kempenep/pktools-2.6.7/src/base/PosValue.h"
+ "/home/kempenep/pktools-2.6.7/src/base/Vector2d.h"
+ "/home/kempenep/pktools-2.6.7/src/base/Vector2d.cc"
+ )
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/pktools/imageclasses" TYPE FILE FILES
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgRasterGdal.h"
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgReaderGdal.h"
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgReaderOgr.h"
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgWriterGdal.h"
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgWriterOgr.h"
+ "/home/kempenep/pktools-2.6.7/src/imageclasses/ImgUpdaterGdal.h"
+ )
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/pktools/algorithms" TYPE FILE FILES
+ "/home/kempenep/pktools-2.6.7/src/algorithms/ConfusionMatrix.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/CostFactory.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/CostFactorySVM.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/Egcs.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/FeatureSelector.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/Filter.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/Filter2d.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/ImgRegression.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/StatFactory.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/myfann_cpp.h"
+ "/home/kempenep/pktools-2.6.7/src/algorithms/svm.h"
+ )
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/pktools/fileclasses" TYPE FILE FILES "/home/kempenep/pktools-2.6.7/src/fileclasses/FileReaderAscii.h")
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ file(RPATH_CHECK
+ FILE "${file}"
+ RPATH "")
+ endif()
+ endforeach()
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE SHARED_LIBRARY FILES
+ "/home/kempenep/pktools-2.6.7/build/src/liblasClasses.so.1.6.1"
+ "/home/kempenep/pktools-2.6.7/build/src/liblasClasses.so.1"
+ "/home/kempenep/pktools-2.6.7/build/src/liblasClasses.so"
+ )
+ foreach(file
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so.1.6.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so.1"
+ "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/lib/liblasClasses.so"
+ )
+ if(EXISTS "${file}" AND
+ NOT IS_SYMLINK "${file}")
+ if(CMAKE_INSTALL_DO_STRIP)
+ execute_process(COMMAND "/usr/bin/strip" "${file}")
+ endif()
+ endif()
+ endforeach()
+endif()
+
+if(NOT CMAKE_INSTALL_COMPONENT OR "${CMAKE_INSTALL_COMPONENT}" STREQUAL "Unspecified")
+ file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/include/pktools/lasclasses" TYPE FILE FILES "/home/kempenep/pktools-2.6.7/src/lasclasses/FileReaderLas.h")
+endif()
+
diff --git a/doc/apps.dox b/doc/apps.dox
index b620db3..c7221c8 100644
--- a/doc/apps.dox
+++ b/doc/apps.dox
@@ -1,5 +1,6 @@
\section available_tools Available tools
- \ref pkann classify raster image using Artificial Neural Network
+- \ref pkannogr classify vector dataset using Artificial Neural Network
- \ref pkascii2img program to create raster image based on ascii file
- \ref pkascii2ogr program to create vector points or polygons from text file
- \ref pkcomposite program to mosaic and composite geo-referenced images
@@ -10,12 +11,11 @@
- \ref pkdumpimg program to dump image content to ascii or std out
- \ref pkdumpogr dump ogr file to text file or standard output
- \ref pkegcs Utility for raster files in European Grid Coordinate System
-- \ref pkextract extract pixel values from raster image from a (vector or raster) sample
- \ref pkextractimg extract pixel values from raster image using a raster sample
- \ref pkextractogr extract pixel values from raster image from a (vector or raster) sample
- \ref pkfillnodata program to fill holes in raster image
-- \ref pkfilterascii program to filter data in an ASCII file
- \ref pkfilter program to filter raster images
+- \ref pkfilterascii program to filter data in an ASCII file
- \ref pkfilterdem Filter digital elevation model raster datasets
- \ref pkfsann feature selection for artificial neural network classifier pkann
- \ref pkfssvm feature selection for support vector machine classifier pksvm
@@ -26,11 +26,13 @@
- \ref pkoptsvm program to optimize parameters for support vector machine classifier pksvm
- \ref pkpolygonize program to make vector file from raster image
- \ref pkreclass program to replace pixel values in raster image
+- \ref pkreclassogr program to replace field values of attributes in vector dataset
- \ref pkregann regression with artificial neural network (multi-layer perceptron)
- \ref pksetmask program to apply mask image (set invalid values) to raster image
- \ref pksieve program to sieve filter raster image
-- \ref pkstatascii program to calculate basic statistics from text file
- \ref pkstat program to calculate basic statistics from raster dataset
+- \ref pkstatascii program to calculate basic statistics from text file
- \ref pkstatogr program to calculate basic statistics from vector file
- \ref pkstatprofile program to calculate statistics in temporal or spectral profile
- \ref pksvm classify raster image using Support Vector Machine
+- \ref pksvmogr classify vector dataset using Support Vector Machine
diff --git a/doc/createappsdox.sh b/doc/createappsdox.sh
index 6cbd81b..e6fa291 100755
--- a/doc/createappsdox.sh
+++ b/doc/createappsdox.sh
@@ -77,4 +77,4 @@ echo "Savannah repository for homepage can only be maintained via cvs"
#cvs commit -m "update of repository homepage"
#rm -r ~/tmp/pktools
echo "ftp to downloads"
-#sftp kempenep at download.savannah.gnu.org:/releases/pktools
+#scp PKTOOLS-2.6.7.tar.gz kempenep at download.savannah.gnu.org:/releases/pktools
diff --git a/doc/doxygen_sqlite3.db b/doc/doxygen_sqlite3.db
new file mode 100644
index 0000000..608e7bc
Binary files /dev/null and b/doc/doxygen_sqlite3.db differ
diff --git a/doc/examples_pkextract.dox b/doc/examples_pkextract.dox
index 2397f84..9aafed3 100644
--- a/doc/examples_pkextract.dox
+++ b/doc/examples_pkextract.dox
@@ -1,52 +1,52 @@
-\section examples_pkextract Examples of pkextract
+\section examples_pkextract Examples of pkextractogr and pkextractimg
\subsection example_pkextract_vector Using vector samples
Extract all points for all layers read in points.sqlite from input.tif. Create a new point vector dataset named extracted.sqlite, where each point will contain an attribute for the individual input bands in input.tif. Notice that the default vector format is Spatialite (.sqlite).
\code
-pkextract -i input.tif -s points.sqlite -o extracted.sqlite
+pkextractogr -i input.tif -s points.sqlite -o extracted.sqlite
\endcode
Same example as above, but only extract the points for the layer in points.sqlite named "valid"
\code
-pkextract -i input.tif -s points.sqlite -ln valid -o extracted.sqlite
+pkextractogr -i input.tif -s points.sqlite -ln valid -o extracted.sqlite
\endcode
Extract points and write output in ESRI Shapefile format
\code
-pkextract -i input.tif -s points.shp -f "ESRI Shapefile" -o extracted.shp
+pkextractogr -i input.tif -s points.shp -f "ESRI Shapefile" -o extracted.shp
\endcode
Extract the standard deviation for each input band in a 3 by 3 window, centered around the points in the sample vector dataset points.sqlite. The output vector dataset will contain polygon features defined by the buffered points (3x3 window). Use the option -circ to define a circular buffer.
\code
-pkextract -i input.tif -s points.sqlite -o extracted.sqlite -r stdev -buf 3 -polygon
+pkextractogr -i input.tif -s points.sqlite -o extracted.sqlite -r stdev -buf 3 -polygon
\endcode
Extract all pixels from input.tif covered by the polygons in locations.sqlite. Each polygon can thus result in multiple point features with attributes for each input band. Write the extracted points to a point vector dataset training.sqlite.
\code
-pkextract -i input.tif -s polygons.sqlite -o training.sqlite -r point
+pkextractogr -i input.tif -s polygons.sqlite -o training.sqlite -r point
\endcode
Extract the first band from input.tif at the centroids of the polygons in vector dataset polygons.sqlite. Assign the extracted point value to a new attribute of the polygon and write to the vector dataset extracted.sqlite.
\code
-pkextract -i input.tif -b 0 -s polygons.sqlite -r centroid -o extracted.sqlite -polygon
+pkextractogr -i input.tif -b 0 -s polygons.sqlite -r centroid -o extracted.sqlite -polygon
\endcode
Extract the mean values for the second band in input.tif covered by each polygon in polygons.sqlite. The mean values are written to a copy of the polygons in output vector dataset extracted.sqlite
\code
-pkextract -i input.tif -b 1 -s polygons.sqlite -r mean -o extracted.sqlite -polygon
+pkextractogr -i input.tif -b 1 -s polygons.sqlite -r mean -o extracted.sqlite -polygon
\endcode
Extract the majority class in each polygon for the input land cover map. The land cover map contains five valid classes, labeled 1-5. Other class values (e.g., labeled as 0) are not taken into account in the voting.
\code
-pkextract -i landcover.tif -s polygons.sqlite -r maxvote -o majority.sqlite -polygon -c 1 -c 2 -c 3 -c 4 -c 5
+pkextractogr -i landcover.tif -s polygons.sqlite -r maxvote -o majority.sqlite -polygon -c 1 -c 2 -c 3 -c 4 -c 5
\endcode
\subsection example_pkextract_random Using random and grid samples
@@ -54,13 +54,13 @@ pkextract -i landcover.tif -s polygons.sqlite -r maxvote -o majority.sqlite -pol
Extract 100 sample units following a simple random sampling design. For each sample unit, the median value is extracted from the input raster dataset in a window of 3 by 3 pixels and written to an attribute of the output vector dataset. The output vector dataset contains polygon features defined by the windows centered at the random selected sample units.
\code
-pkextract -i input.tif -o random.sqlite -rand 100 -r median -buf 3 -polygon
+pkextractogr -i input.tif -o random.sqlite -rand 100 -r median -buf 3 -polygon
\endcode
Extract points following a systematic grid with grid cell size of 100 m. Discard pixels that have a value 0 in the input raster dataset.
\code
-pkextract -i input.tif -o systematic.sqlite -grid 100 -srcnodata 0
+pkextractogr -i input.tif -o systematic.sqlite -grid 100 -srcnodata 0
\endcode
\subsection example_pkextract_raster Using raster samples
@@ -68,12 +68,12 @@ pkextract -i input.tif -o systematic.sqlite -grid 100 -srcnodata 0
Typical use where pixels are extracted based on a land cover map (sample.tif). Extract all bands for a random sample of 10 percent of the pixels in the land cover map sample.tif where the land cover classes are either 1,2 or 3 (class values). Write output to the point vector dataset extracted.sqlite.
\code
-pkextract -i input.tif -s sample.tif -o extracted.sqlite -t 10 -c 1 -c 2 -c 3
+pkextractimg -i input.tif -s sample.tif -o extracted.sqlite -t 10 -c 1 -c 2 -c 3
\endcode
Extract all bands for the first 5000 pixels encountered in sample.tif where pixels have a value equal to 1. Write output to point vector dataset extracted.sqlite.
\code
-pkextract -i input.tif -s sample.tif -o extracted.sqlite -t -5000 -c 1
+pkextractimg -i input.tif -s sample.tif -o extracted.sqlite -t -5000 -c 1
\endcode
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index d98e6cd..ac6663b 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -40,39 +40,39 @@ set(ALGOR_CC
${ALGOR_SRC_DIR}/ImgRegression.cc
${ALGOR_SRC_DIR}/svm.cpp
)
-
+
set(FILECLASS_H
- ${FILECLASS_SRC_DIR}/FileReaderAscii.h
+ ${FILECLASS_SRC_DIR}/FileReaderAscii.h
)
set(FILECLASS_CC
- ${FILECLASS_SRC_DIR}/FileReaderAscii.cc
+ ${FILECLASS_SRC_DIR}/FileReaderAscii.cc
)
-
+
set(IMGCLASS_H
- ${IMGCLASS_SRC_DIR}/ImgRasterGdal.h
- ${IMGCLASS_SRC_DIR}/ImgReaderGdal.h
- ${IMGCLASS_SRC_DIR}/ImgReaderOgr.h
- ${IMGCLASS_SRC_DIR}/ImgWriterGdal.h
- ${IMGCLASS_SRC_DIR}/ImgWriterOgr.h
- ${IMGCLASS_SRC_DIR}/ImgUpdaterGdal.h
+ ${IMGCLASS_SRC_DIR}/ImgRasterGdal.h
+ ${IMGCLASS_SRC_DIR}/ImgReaderGdal.h
+ ${IMGCLASS_SRC_DIR}/ImgReaderOgr.h
+ ${IMGCLASS_SRC_DIR}/ImgWriterGdal.h
+ ${IMGCLASS_SRC_DIR}/ImgWriterOgr.h
+ ${IMGCLASS_SRC_DIR}/ImgUpdaterGdal.h
)
set(IMGCLASS_CC
${IMGCLASS_SRC_DIR}/ImgRasterGdal.cc
${IMGCLASS_SRC_DIR}/ImgReaderGdal.cc
${IMGCLASS_SRC_DIR}/ImgReaderOgr.cc
- ${IMGCLASS_SRC_DIR}/ImgWriterGdal.cc
+ ${IMGCLASS_SRC_DIR}/ImgWriterGdal.cc
${IMGCLASS_SRC_DIR}/ImgWriterOgr.cc
- ${IMGCLASS_SRC_DIR}/ImgUpdaterGdal.cc
+ ${IMGCLASS_SRC_DIR}/ImgUpdaterGdal.cc
)
-
+
set(LASCLASS_H
- ${LASCLASS_SRC_DIR}/FileReaderLas.h
+ ${LASCLASS_SRC_DIR}/FileReaderLas.h
)
set(LASCLASS_CC
- ${LASCLASS_SRC_DIR}/FileReaderLas.cc
+ ${LASCLASS_SRC_DIR}/FileReaderLas.cc
)
###############################################################################
@@ -115,39 +115,39 @@ endif(BUILD_WITH_LIBLAS)
# Set target properties
SET_TARGET_PROPERTIES(${PKTOOLS_BASE_LIB_NAME}
PROPERTIES
- SOVERSION ${PKTOOLS_BASE_VERSION}
+ SOVERSION ${PKTOOLS_BASE_SOVERSION}
VERSION ${PKTOOLS_BASE_VERSION}
)
SET_TARGET_PROPERTIES(${PKTOOLS_IMAGECLASSES_LIB_NAME}
PROPERTIES
- SOVERSION ${PKTOOLS_IMAGECLASSES_VERSION}
+ SOVERSION ${PKTOOLS_IMAGECLASSES_SOVERSION}
VERSION ${PKTOOLS_IMAGECLASSES_VERSION}
)
SET_TARGET_PROPERTIES(${PKTOOLS_ALGORITHMS_LIB_NAME}
PROPERTIES
- SOVERSION ${PKTOOLS_ALGORITHMS_VERSION}
+ SOVERSION ${PKTOOLS_ALGORITHMS_SOVERSION}
VERSION ${PKTOOLS_ALGORITHMS_VERSION}
)
SET_TARGET_PROPERTIES(${PKTOOLS_FILECLASSES_LIB_NAME}
PROPERTIES
- SOVERSION ${PKTOOLS_FILECLASSES_VERSION}
+ SOVERSION ${PKTOOLS_FILECLASSES_SOVERSION}
VERSION ${PKTOOLS_FILECLASSES_VERSION}
)
if (BUILD_WITH_LIBLAS)
SET_TARGET_PROPERTIES(${PKTOOLS_LASCLASSES_LIB_NAME}
PROPERTIES
- SOVERSION ${PKTOOLS_LASCLASSES_VERSION}
+ SOVERSION ${PKTOOLS_LASCLASSES_SOVERSION}
VERSION ${PKTOOLS_LASCLASSES_VERSION}
)
endif(BUILD_WITH_LIBLAS)
###############################################################################
###############################################################################
-# Installation
+# Installation
install (TARGETS ${PKTOOLS_BASE_LIB_NAME} ${PKTOOLS_IMAGECLASSES_LIB_NAME} ${PKTOOLS_ALGORITHMS_LIB_NAME} ${PKTOOLS_FILECLASSES_LIB_NAME} DESTINATION lib)
install (FILES ${BASE_H} DESTINATION ${PROJECT_INCLUDE_DIR}/base)
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt.orig
similarity index 100%
copy from src/CMakeLists.txt
copy to src/CMakeLists.txt.orig
diff --git a/src/CMakeLists.txt.rej b/src/CMakeLists.txt.rej
new file mode 100644
index 0000000..33f9eee
--- /dev/null
+++ b/src/CMakeLists.txt.rej
@@ -0,0 +1,38 @@
+--- CMakeLists.txt
++++ CMakeLists.txt
+@@ -43,25 +43,35 @@ set (PKTOOLS_PACKAGE_BUGREPORT "kempenep
+ set (PKTOOLS_PACKAGE "pktools")
+ set (PKTOOLS_COPYRIGHT_YEAR 2016)
+
++set (PKTOOLS_BASE_VERSION_MAJOR 1)
++set (PKTOOLS_BASE_VERSION_MINOR 6)
++set (PKTOOLS_BASE_VERSION_PATCH 0)
++set (PKTOOLS_BASE_VERSION "${PKTOOLS_BASE_VERSION_MAJOR}.${PKTOOLS_BASE_VERSION_MINOR}.${PKTOOLS_BASE_VERSION_PATCH}")
++set (PKTOOLS_BASE_SOVERSION 1)
++
+ set (PKTOOLS_ALGORITHMS_VERSION_MAJOR 1)
+ set (PKTOOLS_ALGORITHMS_VERSION_MINOR 6)
+ set (PKTOOLS_ALGORITHMS_VERSION_PATCH 0)
+ set (PKTOOLS_ALGORITHMS_VERSION "${PKTOOLS_ALGORITHMS_VERSION_MAJOR}.${PKTOOLS_ALGORITHMS_VERSION_MINOR}.${PKTOOLS_ALGORITHMS_VERSION_PATCH}")
++set (PKTOOLS_ALGORITHMS_SOVERSION 1)
+
+ set (PKTOOLS_FILECLASSES_VERSION_MAJOR 1)
+ set (PKTOOLS_FILECLASSES_VERSION_MINOR 6)
+ set (PKTOOLS_FILECLASSES_VERSION_PATCH 0)
+ set (PKTOOLS_FILECLASSES_VERSION "${PKTOOLS_FILECLASSES_VERSION_MAJOR}.${PKTOOLS_FILECLASSES_VERSION_MINOR}.${PKTOOLS_FILECLASSES_VERSION_PATCH}")
++set (PKTOOLS_FILECLASSES_SOVERSION 1)
+
+ set (PKTOOLS_IMAGECLASSES_VERSION_MAJOR 1)
+ set (PKTOOLS_IMAGECLASSES_VERSION_MINOR 6)
+ set (PKTOOLS_IMAGECLASSES_VERSION_PATCH 0)
+ set (PKTOOLS_IMAGECLASSES_VERSION "${PKTOOLS_IMAGECLASSES_VERSION_MAJOR}.${PKTOOLS_IMAGECLASSES_VERSION_MINOR}.${PKTOOLS_IMAGECLASSES_VERSION_PATCH}")
++set (PKTOOLS_IMAGECLASSES_SOVERSION 1)
+
+ set (PKTOOLS_LASCLASSES_VERSION_MAJOR 1)
+ set (PKTOOLS_LASCLASSES_VERSION_MINOR 6)
+ set (PKTOOLS_LASCLASSES_VERSION_PATCH 0)
+ set (PKTOOLS_LASCLASSES_VERSION "${PKTOOLS_LASCLASSES_VERSION_MAJOR}.${PKTOOLS_LASCLASSES_VERSION_MINOR}.${PKTOOLS_LASCLASSES_VERSION_PATCH}")
++set (PKTOOLS_LASCLASSES_SOVERSION 1)
+
+ # Name of C++ library
+ set(PKTOOLS_BASE_LIB_NAME base)
diff --git a/src/apps/pkann.cc b/src/apps/pkann.cc
index f84557d..bab0d6f 100644
--- a/src/apps/pkann.cc
+++ b/src/apps/pkann.cc
@@ -736,15 +736,8 @@ int main(int argc, char *argv[])
GDALProgressFunc pfnProgress=GDALTermProgress;
float progress=0;
//-------------------------------- open image file ------------------------------------
- bool inputIsRaster=false;
+ bool inputIsRaster=true;
ImgReaderOgr imgReaderOgr;
- try{
- imgReaderOgr.open(input_opt[0]);
- imgReaderOgr.close();
- }
- catch(string errorString){
- inputIsRaster=true;
- }
if(inputIsRaster){
// if(input_opt[0].find(".shp")==string::npos){
ImgReaderGdal testImage;
@@ -1171,178 +1164,6 @@ int main(int argc, char *argv[])
classImageBag.close();
classImageOut.close();
}
- else{//classify vector file
- cm.clearResults();
- //notice that fields have already been set by readDataImageOgr (taking into account appropriate bands)
- for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
- if(output_opt.size())
- assert(output_opt.size()==input_opt.size());
- if(verbose_opt[0])
- cout << "opening img reader " << input_opt[ivalidation] << endl;
- imgReaderOgr.open(input_opt[ivalidation]);
- ImgWriterOgr imgWriterOgr;
-
- if(output_opt.size()){
- if(verbose_opt[0])
- std::cout << "opening img writer and copying fields from img reader" << output_opt[ivalidation] << std::endl;
- imgWriterOgr.open(output_opt[ivalidation],imgReaderOgr);
- }
- if(verbose_opt[0])
- cout << "number of layers in input ogr file: " << imgReaderOgr.getLayerCount() << endl;
- for(int ilayer=0;ilayer<imgReaderOgr.getLayerCount();++ilayer){
- if(verbose_opt[0])
- cout << "processing input layer " << ilayer << endl;
- if(output_opt.size()){
- if(verbose_opt[0])
- std::cout << "creating field class" << std::endl;
- if(classValueMap.size())
- imgWriterOgr.createField("class",OFTInteger,ilayer);
- else
- imgWriterOgr.createField("class",OFTString,ilayer);
- }
- unsigned int nFeatures=imgReaderOgr.getFeatureCount(ilayer);
- unsigned int ifeature=0;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- OGRFeature *poFeature;
- while( (poFeature = imgReaderOgr.getLayer(ilayer)->GetNextFeature()) != NULL ){
- if(verbose_opt[0]>1)
- cout << "feature " << ifeature << endl;
- if( poFeature == NULL ){
- cout << "Warning: could not read feature " << ifeature << " in layer " << imgReaderOgr.getLayerName(ilayer) << endl;
- continue;
- }
- OGRFeature *poDstFeature = NULL;
- if(output_opt.size()){
- poDstFeature=imgWriterOgr.createFeature(ilayer);
- if( poDstFeature->SetFrom( poFeature, TRUE ) != OGRERR_NONE ){
- CPLError( CE_Failure, CPLE_AppDefined,
- "Unable to translate feature %d from layer %s.\n",
- poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
- OGRFeature::DestroyFeature( poFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }
- }
- vector<float> validationPixel;
- vector<float> validationFeature;
-
- imgReaderOgr.readData(validationPixel,OFTReal,fields,poFeature,ilayer);
- assert(validationPixel.size()==nband);
- vector<float> probOut(nclass);//posterior prob for each class
- for(int iclass=0;iclass<nclass;++iclass)
- probOut[iclass]=0;
- for(int ibag=0;ibag<nbag;++ibag){
- for(int iband=0;iband<nband;++iband){
- validationFeature.push_back((validationPixel[iband]-offset[ibag][iband])/scale[ibag][iband]);
- if(verbose_opt[0]==2)
- std:: cout << " " << validationFeature.back();
- }
- if(verbose_opt[0]==2)
- std::cout << std:: endl;
- vector<float> result(nclass);
- result=net[ibag].run(validationFeature);
-
- if(verbose_opt[0]>1){
- for(int iclass=0;iclass<result.size();++iclass)
- std::cout << result[iclass] << " ";
- std::cout << std::endl;
- }
- //calculate posterior prob of bag
- for(int iclass=0;iclass<nclass;++iclass){
- result[iclass]=(result[iclass]+1.0)/2.0;//bring back to scale [0,1]
- switch(comb_opt[0]){
- default:
- case(0)://sum rule
- probOut[iclass]+=result[iclass]*priors[iclass];//add probabilities for each bag
- break;
- case(1)://product rule
- probOut[iclass]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];//multiply probabilities for each bag
- break;
- case(2)://max rule
- if(priors[iclass]*result[iclass]>probOut[iclass])
- probOut[iclass]=priors[iclass]*result[iclass];
- break;
- }
- }
- }//for ibag
- //search for max class prob
- float maxBag=0;
- float normBag=0;
- string classOut="Unclassified";
- for(int iclass=0;iclass<nclass;++iclass){
- if(verbose_opt[0]>1)
- std::cout << probOut[iclass] << " ";
- if(probOut[iclass]>maxBag){
- maxBag=probOut[iclass];
- classOut=nameVector[iclass];
- }
- }
- //look for class name
- if(verbose_opt[0]>1){
- if(classValueMap.size())
- std::cout << "->" << classValueMap[classOut] << std::endl;
- else
- std::cout << "->" << classOut << std::endl;
- }
- if(output_opt.size()){
- if(classValueMap.size())
- poDstFeature->SetField("class",classValueMap[classOut]);
- else
- poDstFeature->SetField("class",classOut.c_str());
- poDstFeature->SetFID( poFeature->GetFID() );
- }
- int labelIndex=poFeature->GetFieldIndex(label_opt[0].c_str());
- if(labelIndex>=0){
- string classRef=poFeature->GetFieldAsString(labelIndex);
- if(classRef!="0"){
- if(classValueMap.size())
- cm.incrementResult(type2string<short>(classValueMap[classRef]),type2string<short>(classValueMap[classOut]),1);
- else
- cm.incrementResult(classRef,classOut,1);
- }
- }
- CPLErrorReset();
- if(output_opt.size()){
- if(imgWriterOgr.createFeature(poDstFeature,ilayer) != OGRERR_NONE){
- CPLError( CE_Failure, CPLE_AppDefined,
- "Unable to translate feature %d from layer %s.\n",
- poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
- OGRFeature::DestroyFeature( poDstFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }
- }
- ++ifeature;
- if(!verbose_opt[0]){
- progress=static_cast<float>(ifeature+1.0)/nFeatures;
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- OGRFeature::DestroyFeature( poFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }//get next feature
- }//next layer
- imgReaderOgr.close();
- if(output_opt.size())
- imgWriterOgr.close();
- }
- if(cm.nReference()){
- std::cout << cm << std::endl;
- // cout << "class #samples userAcc prodAcc" << endl;
- // double se95_ua=0;
- // double se95_pa=0;
- // double se95_oa=0;
- // double dua=0;
- // double dpa=0;
- // double doa=0;
- // for(short iclass=0;iclass<cm.nClasses();++iclass){
- // dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
- // dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
- // cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
- // }
- // std::cout << "Kappa: " << cm.kappa() << std::endl;
- // doa=cm.oa_pct(&se95_oa);
- // std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" << std::endl;
- }
- }
try{
if(active_opt.size())
activeWriter.close();
diff --git a/src/apps/pkannogr.cc b/src/apps/pkannogr.cc
new file mode 100644
index 0000000..cebf5c1
--- /dev/null
+++ b/src/apps/pkannogr.cc
@@ -0,0 +1,916 @@
+/**********************************************************************
+pkannogr.cc: classify vector dataset using Artificial Neural Network
+Copyright (C) 2008-2017 Pieter Kempeneers
+
+This file is part of pktools
+
+pktools is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+pktools is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with pktools. If not, see <http://www.gnu.org/licenses/>.
+***********************************************************************/
+#include <stdlib.h>
+#include <vector>
+#include <map>
+#include <algorithm>
+#include "imageclasses/ImgReaderGdal.h"
+#include "imageclasses/ImgWriterGdal.h"
+#include "imageclasses/ImgReaderOgr.h"
+#include "imageclasses/ImgWriterOgr.h"
+#include "base/Optionpk.h"
+#include "base/PosValue.h"
+#include "algorithms/ConfusionMatrix.h"
+#include "floatfann.h"
+#include "algorithms/myfann_cpp.h"
+
+/******************************************************************************/
+/*! \page pkannogr pkannogr
+ classify vector dataset using Artificial Neural Network
+ ## SYNOPSIS
+
+ <code>
+ Usage: pkannogr -t training [-i input -o output] [-cv value]
+ </code>
+
+ <code>
+
+
+ Options: [-tln layer]* [-c name -r value]* [-of GDALformat|-f OGRformat] [-co NAME=VALUE]* [-ct filename] [-label attribute] [-prior value]* [-nn number]* [-m filename ] [-nodata value]
+
+ Advanced options:
+ [-b band] [-sband band -eband band]* [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-pim priorimage] [--offset value] [--scale value] [--connection 0|1] [-w weights]* [--learning rate] [--maxit number]
+ </code>
+
+ \section pkannogr_description Description
+
+ The utility pkannogr implements an artificial neural network (ANN) to solve a supervised classification problem. The implementation is based on the open source C++ library <a href="http://leenissen.dk/fann/wp/">fann</a>). Both raster and vector files are supported as input. The output will contain the classification result, either in raster or vector format, corresponding to the format of the input. A training sample must be provided as an OGR vector dataset that contains the class lab [...]
+
+ \section pkannogr_options Options
+ - use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
+ - short option `-h` shows basic options only, long option `--help` shows all options
+ |short|long|type|default|description|
+ |-----|----|----|-------|-----------|
+ | i | input | std::string | |input image |
+ | t | training | std::string | |training vector file. A single vector file contains all training features (must be set as: B0, B1, B2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the bag and bsize options, where a random subset is taken from a single training file) |
+ | tln | tln | std::string | |training layer name(s) |
+ | label | label | std::string | label |identifier for class label in training vector file. |
+ | bal | balance | unsigned int | 0 |balance the input data to this number of samples for each class |
+ | min | min | int | 0 |if number of training pixels is less then min, do not take this class into account (0: consider all classes) |
+ | b | band | short | |band index (starting from 0, either use band option or use start to end) |
+ | sband | startband | unsigned short | |Start band sequence number |
+ | eband | endband | unsigned short | |End band sequence number |
+ | | offset | double | 0 |offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band] |
+ | scale | scale | double | 0 |scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0) |
+ | a | aggreg | unsigned short | 1 |how to combine aggregated classifiers, see also rc option (1: sum rule, 2: max rule). |
+ | prior | prior | double | 0 |prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ) |
+ | pim | priorimg | std::string | |prior probability image (multi-band img with band for each class |
+ | cv | cv | unsigned short | 0 |n-fold cross validation mode |
+ | cmf | cmf | std::string | ascii |Format for confusion matrix (ascii or latex) |
+ | nn | nneuron | unsigned int | 5 |number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons) |
+ | | connection | float | 1 |connection reate (default: 1.0 for a fully connected network) |
+ | w | weights | float | 0 |weights for neural network. Apply to fully connected network only, starting from first input neuron to last output neuron, including the bias neurons (last neuron in each but last layer) |
+ | l | learning | float | 0.7 |learning rate (default: 0.7) |
+ | | maxit | unsigned int | 500 |number of maximum iterations (epoch) (default: 500) |
+ | comb | comb | unsigned short | 0 |how to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option. Default is sum rule (0) |
+ | bag | bag | unsigned short | 1 |Number of bootstrap aggregations (default is no bagging: 1) |
+ | bs | bsize | int | 100 |Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively |
+ | cb | classbag | std::string | |output for each individual bootstrap aggregation (default is blank) |
+ | m | mask | std::string | |Only classify within specified mask (vector or raster). |
+ | nodata | nodata | unsigned short | 0 |nodata value to put where image is masked as nodata |
+ | o | output | std::string | |output classification image |
+ | ot | otype | std::string | |Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image |
+ | ct | ct | std::string | |colour table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid) |
+ | co | co | std::string | |Creation option for output file. Multiple options can be specified. |
+ | f | f | std::string | SQLite |Output ogr format for active training sample |
+ | na | nactive | unsigned int | 1 |number of active training points |
+ | c | class | std::string | |list of class names. |
+ | r | reclass | short | |list of class values (use same order as in class opt). |
+
+ Usage: pkannogr -t training [-i input -o output] [-cv value]
+
+
+ Examples
+ ========
+**/
+
+using namespace std;
+
+int main(int argc, char *argv[])
+{
+ vector<double> priors;
+
+ //--------------------------- command line options ------------------------------------
+ Optionpk<string> input_opt("i", "input", "input image");
+ Optionpk<string> training_opt("t", "training", "training vector file. A single vector file contains all training features (must be set as: B0, B1, B2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the bag and bsize options, where a random subset is taken from a single training file)");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name(s)");
+ Optionpk<string> label_opt("label", "label", "identifier for class label in training vector file.","label");
+ Optionpk<unsigned int> balance_opt("bal", "balance", "balance the input data to this number of samples for each class", 0);
+ Optionpk<bool> random_opt("random", "random", "in case of balance, randomize input data", true,2);
+ Optionpk<int> minSize_opt("min", "min", "if number of training pixels is less then min, do not take this class into account (0: consider all classes)", 0);
+ Optionpk<unsigned short> band_opt("b", "band", "band index (starting from 0, either use band option or use start to end)");
+ Optionpk<unsigned short> bstart_opt("sband", "startband", "Start band sequence number");
+ Optionpk<unsigned short> bend_opt("eband", "endband", "End band sequence number");
+ Optionpk<double> offset_opt("offset", "offset", "offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
+ Optionpk<double> scale_opt("scale", "scale", "scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
+ Optionpk<unsigned short> aggreg_opt("a", "aggreg", "how to combine aggregated classifiers, see also rc option (1: sum rule, 2: max rule).",1);
+ Optionpk<double> priors_opt("prior", "prior", "prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 )", 0.0);
+ Optionpk<string> priorimg_opt("pim", "priorimg", "prior probability image (multi-band img with band for each class","",2);
+ Optionpk<unsigned short> cv_opt("cv", "cv", "n-fold cross validation mode",0);
+ Optionpk<string> cmformat_opt("cmf","cmf","Format for confusion matrix (ascii or latex)","ascii");
+ Optionpk<unsigned int> nneuron_opt("nn", "nneuron", "number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons)", 5);
+ Optionpk<float> connection_opt("\0", "connection", "connection reate (default: 1.0 for a fully connected network)", 1.0);
+ Optionpk<float> learning_opt("l", "learning", "learning rate (default: 0.7)", 0.7);
+ Optionpk<float> weights_opt("w", "weights", "weights for neural network. Apply to fully connected network only, starting from first input neuron to last output neuron, including the bias neurons (last neuron in each but last layer)", 0.0);
+ Optionpk<unsigned int> maxit_opt("\0", "maxit", "number of maximum iterations (epoch) (default: 500)", 500);
+ Optionpk<unsigned short> comb_opt("comb", "comb", "how to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option. Default is sum rule (0)",0);
+ Optionpk<unsigned short> bag_opt("bag", "bag", "Number of bootstrap aggregations (default is no bagging: 1)", 1);
+ Optionpk<int> bagSize_opt("bs", "bsize", "Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively", 100);
+ Optionpk<string> classBag_opt("cb", "classbag", "output for each individual bootstrap aggregation (default is blank)");
+ Optionpk<string> mask_opt("m", "mask", "Only classify within specified mask (vector or raster).");
+ Optionpk<unsigned short> nodata_opt("nodata", "nodata", "nodata value to put where image is masked as nodata", 0);
+ Optionpk<string> output_opt("o", "output", "output classification image");
+ Optionpk<string> otype_opt("ot", "otype", "Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image");
+ Optionpk<string> option_opt("co", "co", "Creation option for output file. Multiple options can be specified.");
+ Optionpk<string> colorTable_opt("ct", "ct", "colour table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)");
+ Optionpk<string> entropy_opt("entropy", "entropy", "entropy image (measure for uncertainty of classifier output","",2);
+ Optionpk<string> active_opt("active", "active", "ogr output for active training sample.","",2);
+ Optionpk<string> ogrformat_opt("f", "f", "Output ogr format for active training sample","SQLite");
+ Optionpk<unsigned int> nactive_opt("na", "nactive", "number of active training points",1);
+ Optionpk<string> classname_opt("c", "class", "list of class names.");
+ Optionpk<short> classvalue_opt("r", "reclass", "list of class values (use same order as in class opt).");
+ Optionpk<short> verbose_opt("v", "verbose", "set to: 0 (results only), 1 (confusion matrix), 2 (debug)",0,2);
+
+ option_opt.setHide(1);
+ band_opt.setHide(1);
+ bstart_opt.setHide(1);
+ bend_opt.setHide(1);
+ balance_opt.setHide(1);
+ minSize_opt.setHide(1);
+ bag_opt.setHide(1);
+ bagSize_opt.setHide(1);
+ comb_opt.setHide(1);
+ classBag_opt.setHide(1);
+ minSize_opt.setHide(1);
+ priorimg_opt.setHide(1);
+ minSize_opt.setHide(1);
+ offset_opt.setHide(1);
+ scale_opt.setHide(1);
+ connection_opt.setHide(1);
+ weights_opt.setHide(1);
+ maxit_opt.setHide(1);
+ learning_opt.setHide(1);
+
+ verbose_opt.setHide(2);
+
+ bool doProcess;//stop process when program was invoked with help option (-h --help)
+ try{
+ doProcess=input_opt.retrieveOption(argc,argv);
+ training_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
+ label_opt.retrieveOption(argc,argv);
+ balance_opt.retrieveOption(argc,argv);
+ random_opt.retrieveOption(argc,argv);
+ minSize_opt.retrieveOption(argc,argv);
+ band_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
+ offset_opt.retrieveOption(argc,argv);
+ scale_opt.retrieveOption(argc,argv);
+ aggreg_opt.retrieveOption(argc,argv);
+ priors_opt.retrieveOption(argc,argv);
+ priorimg_opt.retrieveOption(argc,argv);
+ cv_opt.retrieveOption(argc,argv);
+ cmformat_opt.retrieveOption(argc,argv);
+ nneuron_opt.retrieveOption(argc,argv);
+ connection_opt.retrieveOption(argc,argv);
+ weights_opt.retrieveOption(argc,argv);
+ learning_opt.retrieveOption(argc,argv);
+ maxit_opt.retrieveOption(argc,argv);
+ comb_opt.retrieveOption(argc,argv);
+ bag_opt.retrieveOption(argc,argv);
+ bagSize_opt.retrieveOption(argc,argv);
+ classBag_opt.retrieveOption(argc,argv);
+ mask_opt.retrieveOption(argc,argv);
+ nodata_opt.retrieveOption(argc,argv);
+ output_opt.retrieveOption(argc,argv);
+ otype_opt.retrieveOption(argc,argv);
+ colorTable_opt.retrieveOption(argc,argv);
+ option_opt.retrieveOption(argc,argv);
+ entropy_opt.retrieveOption(argc,argv);
+ active_opt.retrieveOption(argc,argv);
+ ogrformat_opt.retrieveOption(argc,argv);
+ nactive_opt.retrieveOption(argc,argv);
+ classname_opt.retrieveOption(argc,argv);
+ classvalue_opt.retrieveOption(argc,argv);
+ verbose_opt.retrieveOption(argc,argv);
+ }
+ catch(string predefinedString){
+ std::cout << predefinedString << std::endl;
+ exit(0);
+ }
+ if(!doProcess){
+ cout << endl;
+ cout << "Usage: pkannogr -t training [-i input -o output] [-cv value]" << endl;
+ cout << endl;
+ cout << "short option -h shows basic options only, use long option --help to show all options" << endl;
+ exit(0);//help was invoked, stop processing
+ }
+
+ if(entropy_opt[0]=="")
+ entropy_opt.clear();
+ if(active_opt[0]=="")
+ active_opt.clear();
+ if(priorimg_opt[0]=="")
+ priorimg_opt.clear();
+
+ if(verbose_opt[0]>=1){
+ if(input_opt.size())
+ cout << "image filename: " << input_opt[0] << endl;
+ if(mask_opt.size())
+ cout << "mask filename: " << mask_opt[0] << endl;
+ if(training_opt.size()){
+ cout << "training vector file: " << endl;
+ for(int ifile=0;ifile<training_opt.size();++ifile)
+ cout << training_opt[ifile] << endl;
+ }
+ else
+ cerr << "no training file set!" << endl;
+ cout << "verbose: " << verbose_opt[0] << endl;
+ }
+ unsigned short nbag=(training_opt.size()>1)?training_opt.size():bag_opt[0];
+ if(verbose_opt[0]>=1)
+ cout << "number of bootstrap aggregations: " << nbag << endl;
+
+ ImgReaderOgr extentReader;
+ OGRLayer *readLayer;
+
+ double ulx=0;
+ double uly=0;
+ double lrx=0;
+ double lry=0;
+
+ bool maskIsVector=false;
+ if(mask_opt.size()){
+ try{
+ extentReader.open(mask_opt[0]);
+ maskIsVector=true;
+ readLayer = extentReader.getDataSource()->GetLayer(0);
+ if(!(extentReader.getExtent(ulx,uly,lrx,lry))){
+ cerr << "Error: could not get extent from " << mask_opt[0] << endl;
+ exit(1);
+ }
+ }
+ catch(string errorString){
+ maskIsVector=false;
+ }
+ }
+
+ ImgWriterOgr activeWriter;
+ if(active_opt.size()){
+ ImgReaderOgr trainingReader(training_opt[0]);
+ activeWriter.open(active_opt[0],ogrformat_opt[0]);
+ activeWriter.createLayer(active_opt[0],trainingReader.getProjection(),wkbPoint,NULL);
+ activeWriter.copyFields(trainingReader);
+ }
+ vector<PosValue> activePoints(nactive_opt[0]);
+ for(int iactive=0;iactive<activePoints.size();++iactive){
+ activePoints[iactive].value=1.0;
+ activePoints[iactive].posx=0.0;
+ activePoints[iactive].posy=0.0;
+ }
+
+ unsigned int totalSamples=0;
+ unsigned int nactive=0;
+ vector<FANN::neural_net> net(nbag);//the neural network
+
+ unsigned int nclass=0;
+ int nband=0;
+ int startBand=2;//first two bands represent X and Y pos
+
+ if(priors_opt.size()>1){//priors from argument list
+ priors.resize(priors_opt.size());
+ double normPrior=0;
+ for(int iclass=0;iclass<priors_opt.size();++iclass){
+ priors[iclass]=priors_opt[iclass];
+ normPrior+=priors[iclass];
+ }
+ //normalize
+ for(int iclass=0;iclass<priors_opt.size();++iclass)
+ priors[iclass]/=normPrior;
+ }
+
+ //convert start and end band options to vector of band indexes
+ try{
+ if(bstart_opt.size()){
+ if(bend_opt.size()!=bstart_opt.size()){
+ string errorstring="Error: options for start and end band indexes must be provided as pairs, missing end band";
+ throw(errorstring);
+ }
+ band_opt.clear();
+ for(int ipair=0;ipair<bstart_opt.size();++ipair){
+ if(bend_opt[ipair]<=bstart_opt[ipair]){
+ string errorstring="Error: index for end band must be smaller then start band";
+ throw(errorstring);
+ }
+ for(int iband=bstart_opt[ipair];iband<=bend_opt[ipair];++iband)
+ band_opt.push_back(iband);
+ }
+ }
+ }
+ catch(string error){
+ cerr << error << std::endl;
+ exit(1);
+ }
+ //sort bands
+ if(band_opt.size())
+ std::sort(band_opt.begin(),band_opt.end());
+
+ map<string,short> classValueMap;
+ vector<std::string> nameVector;
+ if(classname_opt.size()){
+ assert(classname_opt.size()==classvalue_opt.size());
+ for(int iclass=0;iclass<classname_opt.size();++iclass)
+ classValueMap[classname_opt[iclass]]=classvalue_opt[iclass];
+ }
+ //----------------------------------- Training -------------------------------
+ confusionmatrix::ConfusionMatrix cm;
+ vector< vector<double> > offset(nbag);
+ vector< vector<double> > scale(nbag);
+ map<string,Vector2d<float> > trainingMap;
+ vector< Vector2d<float> > trainingPixels;//[class][sample][band]
+ vector<string> fields;
+ for(int ibag=0;ibag<nbag;++ibag){
+ //organize training data
+ if(ibag<training_opt.size()){//if bag contains new training pixels
+ trainingMap.clear();
+ trainingPixels.clear();
+ if(verbose_opt[0]>=1)
+ cout << "reading imageVector file " << training_opt[0] << endl;
+ try{
+ ImgReaderOgr trainingReaderBag(training_opt[ibag]);
+ if(band_opt.size())
+ totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt);
+ else
+ totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,0,0,label_opt[0],tlayer_opt);
+ if(trainingMap.size()<2){
+ string errorstring="Error: could not read at least two classes from training file, did you provide class labels in training sample (see option label)?";
+ throw(errorstring);
+ }
+ trainingReaderBag.close();
+ }
+ catch(string error){
+ cerr << error << std::endl;
+ exit(1);
+ }
+ catch(...){
+ cerr << "error caught" << std::endl;
+ exit(1);
+ }
+ //delete class 0 ?
+ // if(verbose_opt[0]>=1)
+ // std::cout << "erasing class 0 from training set (" << trainingMap[0].size() << " from " << totalSamples << ") samples" << std::endl;
+ // totalSamples-=trainingMap[0].size();
+ // trainingMap.erase(0);
+ //convert map to vector
+ if(verbose_opt[0]>1)
+ std::cout << "training pixels: " << std::endl;
+ map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
+ while(mapit!=trainingMap.end()){
+ //delete small classes
+ if((mapit->second).size()<minSize_opt[0]){
+ trainingMap.erase(mapit);
+ continue;
+ }
+ trainingPixels.push_back(mapit->second);
+ if(verbose_opt[0]>1)
+ std::cout << mapit->first << ": " << (mapit->second).size() << " samples" << std::endl;
+ ++mapit;
+ }
+ if(!ibag){
+ nclass=trainingPixels.size();
+ if(classname_opt.size())
+ assert(nclass==classname_opt.size());
+ nband=(training_opt.size())?trainingPixels[0][0].size()-2:trainingPixels[0][0].size();//X and Y
+ }
+ else{
+ assert(nclass==trainingPixels.size());
+ assert(nband==(training_opt.size())?trainingPixels[0][0].size()-2:trainingPixels[0][0].size());
+ }
+
+ //do not remove outliers here: could easily be obtained through ogr2ogr -where 'B2<110' output.shp input.shp
+ //balance training data
+ if(balance_opt[0]>0){
+ while(balance_opt.size()<nclass)
+ balance_opt.push_back(balance_opt.back());
+ if(random_opt[0])
+ srand(time(NULL));
+ totalSamples=0;
+ for(int iclass=0;iclass<nclass;++iclass){
+ if(trainingPixels[iclass].size()>balance_opt[iclass]){
+ while(trainingPixels[iclass].size()>balance_opt[iclass]){
+ int index=rand()%trainingPixels[iclass].size();
+ trainingPixels[iclass].erase(trainingPixels[iclass].begin()+index);
+ }
+ }
+ else{
+ int oldsize=trainingPixels[iclass].size();
+ for(int isample=trainingPixels[iclass].size();isample<balance_opt[iclass];++isample){
+ int index = rand()%oldsize;
+ trainingPixels[iclass].push_back(trainingPixels[iclass][index]);
+ }
+ }
+ totalSamples+=trainingPixels[iclass].size();
+ }
+ }
+
+ //set scale and offset
+ offset[ibag].resize(nband);
+ scale[ibag].resize(nband);
+ if(offset_opt.size()>1)
+ assert(offset_opt.size()==nband);
+ if(scale_opt.size()>1)
+ assert(scale_opt.size()==nband);
+ for(int iband=0;iband<nband;++iband){
+ if(verbose_opt[0]>=1)
+ cout << "scaling for band" << iband << endl;
+ offset[ibag][iband]=(offset_opt.size()==1)?offset_opt[0]:offset_opt[iband];
+ scale[ibag][iband]=(scale_opt.size()==1)?scale_opt[0]:scale_opt[iband];
+ //search for min and maximum
+ if(scale[ibag][iband]<=0){
+ float theMin=trainingPixels[0][0][iband+startBand];
+ float theMax=trainingPixels[0][0][iband+startBand];
+ for(int iclass=0;iclass<nclass;++iclass){
+ for(int isample=0;isample<trainingPixels[iclass].size();++isample){
+ if(theMin>trainingPixels[iclass][isample][iband+startBand])
+ theMin=trainingPixels[iclass][isample][iband+startBand];
+ if(theMax<trainingPixels[iclass][isample][iband+startBand])
+ theMax=trainingPixels[iclass][isample][iband+startBand];
+ }
+ }
+ offset[ibag][iband]=theMin+(theMax-theMin)/2.0;
+ scale[ibag][iband]=(theMax-theMin)/2.0;
+ if(verbose_opt[0]>=1){
+ std::cout << "Extreme image values for band " << iband << ": [" << theMin << "," << theMax << "]" << std::endl;
+ std::cout << "Using offset, scale: " << offset[ibag][iband] << ", " << scale[ibag][iband] << std::endl;
+ std::cout << "scaled values for band " << iband << ": [" << (theMin-offset[ibag][iband])/scale[ibag][iband] << "," << (theMax-offset[ibag][iband])/scale[ibag][iband] << "]" << std::endl;
+ }
+ }
+ }
+ }
+ else{//use same offset and scale
+ offset[ibag].resize(nband);
+ scale[ibag].resize(nband);
+ for(int iband=0;iband<nband;++iband){
+ offset[ibag][iband]=offset[0][iband];
+ scale[ibag][iband]=scale[0][iband];
+ }
+ }
+
+ if(!ibag){
+ if(priors_opt.size()==1){//default: equal priors for each class
+ priors.resize(nclass);
+ for(int iclass=0;iclass<nclass;++iclass)
+ priors[iclass]=1.0/nclass;
+ }
+ assert(priors_opt.size()==1||priors_opt.size()==nclass);
+
+ //set bagsize for each class if not done already via command line
+ while(bagSize_opt.size()<nclass)
+ bagSize_opt.push_back(bagSize_opt.back());
+
+ if(verbose_opt[0]>=1){
+ std::cout << "number of bands: " << nband << std::endl;
+ std::cout << "number of classes: " << nclass << std::endl;
+ std::cout << "priors:";
+ if(priorimg_opt.empty()){
+ for(int iclass=0;iclass<nclass;++iclass)
+ std::cout << " " << priors[iclass];
+ std::cout << std::endl;
+ }
+ }
+ map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
+ bool doSort=true;
+ while(mapit!=trainingMap.end()){
+ nameVector.push_back(mapit->first);
+ if(classValueMap.size()){
+ //check if name in training is covered by classname_opt (values can not be 0)
+ if(classValueMap[mapit->first]>0){
+ if(cm.getClassIndex(type2string<short>(classValueMap[mapit->first]))<0)
+ cm.pushBackClassName(type2string<short>(classValueMap[mapit->first]),doSort);
+ }
+ else{
+ std::cerr << "Error: names in classname option are not complete, please check names in training vector and make sure classvalue is > 0" << std::endl;
+ exit(1);
+ }
+ }
+ else
+ cm.pushBackClassName(mapit->first,doSort);
+ ++mapit;
+ }
+ if(classname_opt.empty()){
+ //std::cerr << "Warning: no class name and value pair provided for all " << nclass << " classes, using string2type<int> instead!" << std::endl;
+ for(int iclass=0;iclass<nclass;++iclass){
+ if(verbose_opt[0])
+ std::cout << iclass << " " << cm.getClass(iclass) << " -> " << string2type<short>(cm.getClass(iclass)) << std::endl;
+ classValueMap[cm.getClass(iclass)]=string2type<short>(cm.getClass(iclass));
+ }
+ }
+ if(priors_opt.size()==nameVector.size()){
+ std::cerr << "Warning: please check if priors are provided in correct order!!!" << std::endl;
+ for(int iclass=0;iclass<nameVector.size();++iclass)
+ std::cerr << nameVector[iclass] << " " << priors_opt[iclass] << std::endl;
+ }
+ }//if(!ibag)
+
+ //Calculate features of training set
+ vector< Vector2d<float> > trainingFeatures(nclass);
+ for(int iclass=0;iclass<nclass;++iclass){
+ int nctraining=0;
+ if(verbose_opt[0]>=1)
+ cout << "calculating features for class " << iclass << endl;
+ if(random_opt[0])
+ srand(time(NULL));
+ nctraining=(bagSize_opt[iclass]<100)? trainingPixels[iclass].size()/100.0*bagSize_opt[iclass] : trainingPixels[iclass].size();//bagSize_opt[iclass] given in % of training size
+ if(nctraining<=0)
+ nctraining=1;
+ assert(nctraining<=trainingPixels[iclass].size());
+ int index=0;
+ if(bagSize_opt[iclass]<100)
+ random_shuffle(trainingPixels[iclass].begin(),trainingPixels[iclass].end());
+
+ trainingFeatures[iclass].resize(nctraining);
+ for(int isample=0;isample<nctraining;++isample){
+ //scale pixel values according to scale and offset!!!
+ for(int iband=0;iband<nband;++iband){
+ float value=trainingPixels[iclass][isample][iband+startBand];
+ trainingFeatures[iclass][isample].push_back((value-offset[ibag][iband])/scale[ibag][iband]);
+ }
+ }
+ assert(trainingFeatures[iclass].size()==nctraining);
+ }
+
+ unsigned int nFeatures=trainingFeatures[0][0].size();
+ unsigned int ntraining=0;
+ for(int iclass=0;iclass<nclass;++iclass)
+ ntraining+=trainingFeatures[iclass].size();
+
+ const unsigned int num_layers = nneuron_opt.size()+2;
+ const float desired_error = 0.0003;
+ const unsigned int iterations_between_reports = (verbose_opt[0])? maxit_opt[0]+1:0;
+ if(verbose_opt[0]>=1){
+ cout << "number of features: " << nFeatures << endl;
+ cout << "creating artificial neural network with " << nneuron_opt.size() << " hidden layer, having " << endl;
+ for(int ilayer=0;ilayer<nneuron_opt.size();++ilayer)
+ cout << nneuron_opt[ilayer] << " ";
+ cout << "neurons" << endl;
+ cout << "connection_opt[0]: " << connection_opt[0] << std::endl;
+ cout << "num_layers: " << num_layers << std::endl;
+ cout << "nFeatures: " << nFeatures << std::endl;
+ cout << "nneuron_opt[0]: " << nneuron_opt[0] << std::endl;
+ cout << "number of classes (nclass): " << nclass << std::endl;
+ }
+ switch(num_layers){
+ case(3):{
+ // net[ibag].create_sparse(connection_opt[0],num_layers, nFeatures, nneuron_opt[0], nclass);//replace all create_sparse with create_sparse_array due to bug in FANN!
+ unsigned int layers[3];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nclass;
+ net[ibag].create_sparse_array(connection_opt[0],num_layers,layers);
+ break;
+ }
+ case(4):{
+ unsigned int layers[4];
+ layers[0]=nFeatures;
+ layers[1]=nneuron_opt[0];
+ layers[2]=nneuron_opt[1];
+ layers[3]=nclass;
+ // layers.push_back(nFeatures);
+ // for(int ihidden=0;ihidden<nneuron_opt.size();++ihidden)
+ // layers.push_back(nneuron_opt[ihidden]);
+ // layers.push_back(nclass);
+ net[ibag].create_sparse_array(connection_opt[0],num_layers,layers);
+ break;
+ }
+ default:
+ cerr << "Only 1 or 2 hidden layers are supported!" << endl;
+ exit(1);
+ break;
+ }
+ if(verbose_opt[0]>=1)
+ cout << "network created" << endl;
+
+ net[ibag].set_learning_rate(learning_opt[0]);
+
+ // net.set_activation_steepness_hidden(1.0);
+ // net.set_activation_steepness_output(1.0);
+
+ net[ibag].set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
+ net[ibag].set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
+
+ // Set additional properties such as the training algorithm
+ // net.set_training_algorithm(FANN::TRAIN_QUICKPROP);
+
+ // Output network type and parameters
+ if(verbose_opt[0]>=1){
+ cout << endl << "Network Type : ";
+ switch (net[ibag].get_network_type())
+ {
+ case FANN::LAYER:
+ cout << "LAYER" << endl;
+ break;
+ case FANN::SHORTCUT:
+ cout << "SHORTCUT" << endl;
+ break;
+ default:
+ cout << "UNKNOWN" << endl;
+ break;
+ }
+ net[ibag].print_parameters();
+ }
+
+ if(cv_opt[0]>1){
+ if(verbose_opt[0])
+ std::cout << "cross validation" << std::endl;
+ vector<unsigned short> referenceVector;
+ vector<unsigned short> outputVector;
+ float rmse=net[ibag].cross_validation(trainingFeatures,
+ ntraining,
+ cv_opt[0],
+ maxit_opt[0],
+ desired_error,
+ referenceVector,
+ outputVector,
+ verbose_opt[0]);
+ map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
+ for(int isample=0;isample<referenceVector.size();++isample){
+ string refClassName=nameVector[referenceVector[isample]];
+ string className=nameVector[outputVector[isample]];
+ if(classValueMap.size())
+ cm.incrementResult(type2string<short>(classValueMap[refClassName]),type2string<short>(classValueMap[className]),1.0/nbag);
+ else
+ cm.incrementResult(cm.getClass(referenceVector[isample]),cm.getClass(outputVector[isample]),1.0/nbag);
+ }
+ }
+
+ if(verbose_opt[0]>=1)
+ cout << endl << "Set training data" << endl;
+
+ if(verbose_opt[0]>=1)
+ cout << endl << "Training network" << endl;
+
+ if(verbose_opt[0]>=1){
+ cout << "Max Epochs " << setw(8) << maxit_opt[0] << ". "
+ << "Desired Error: " << left << desired_error << right << endl;
+ }
+ if(weights_opt.size()==net[ibag].get_total_connections()){//no new training needed (same training sample)
+ vector<fann_connection> convector;
+ net[ibag].get_connection_array(convector);
+ for(int i_connection=0;i_connection<net[ibag].get_total_connections();++i_connection)
+ convector[i_connection].weight=weights_opt[i_connection];
+ net[ibag].set_weight_array(convector);
+ }
+ else{
+ bool initWeights=true;
+ net[ibag].train_on_data(trainingFeatures,ntraining,initWeights, maxit_opt[0],
+ iterations_between_reports, desired_error);
+ }
+
+
+ if(verbose_opt[0]>=2){
+ net[ibag].print_connections();
+ vector<fann_connection> convector;
+ net[ibag].get_connection_array(convector);
+ for(int i_connection=0;i_connection<net[ibag].get_total_connections();++i_connection)
+ cout << "connection " << i_connection << ": " << convector[i_connection].weight << endl;
+
+ }
+ }//for ibag
+ if(cv_opt[0]>1){
+ assert(cm.nReference());
+ cm.setFormat(cmformat_opt[0]);
+ cm.reportSE95(false);
+ std::cout << cm << std::endl;
+ cout << "class #samples userAcc prodAcc" << endl;
+ double se95_ua=0;
+ double se95_pa=0;
+ double se95_oa=0;
+ double dua=0;
+ double dpa=0;
+ double doa=0;
+ for(int iclass=0;iclass<cm.nClasses();++iclass){
+ dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
+ dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
+ cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
+ }
+ std::cout << "Kappa: " << cm.kappa() << std::endl;
+ doa=cm.oa_pct(&se95_oa);
+ std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" << std::endl;
+ }
+ //--------------------------------- end of training -----------------------------------
+ if(input_opt.empty())
+ exit(0);
+
+ const char* pszMessage;
+ void* pProgressArg=NULL;
+ GDALProgressFunc pfnProgress=GDALTermProgress;
+ float progress=0;
+ //-------------------------------- open image file ------------------------------------
+ bool inputIsRaster=false;
+ ImgReaderOgr imgReaderOgr;
+ try{
+ imgReaderOgr.open(input_opt[0]);
+ imgReaderOgr.close();
+ }
+ catch(string errorString){
+ inputIsRaster=true;
+ }
+ if(!inputIsRaster){//classify vector file
+ cm.clearResults();
+ //notice that fields have already been set by readDataImageOgr (taking into account appropriate bands)
+ for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
+ if(output_opt.size())
+ assert(output_opt.size()==input_opt.size());
+ if(verbose_opt[0])
+ cout << "opening img reader " << input_opt[ivalidation] << endl;
+ imgReaderOgr.open(input_opt[ivalidation]);
+ ImgWriterOgr imgWriterOgr;
+
+ if(output_opt.size()){
+ if(verbose_opt[0])
+ std::cout << "opening img writer and copying fields from img reader" << output_opt[ivalidation] << std::endl;
+ imgWriterOgr.open(output_opt[ivalidation],imgReaderOgr);
+ }
+ if(verbose_opt[0])
+ cout << "number of layers in input ogr file: " << imgReaderOgr.getLayerCount() << endl;
+ for(int ilayer=0;ilayer<imgReaderOgr.getLayerCount();++ilayer){
+ if(verbose_opt[0])
+ cout << "processing input layer " << ilayer << endl;
+ if(output_opt.size()){
+ if(verbose_opt[0])
+ std::cout << "creating field class" << std::endl;
+ if(classValueMap.size())
+ imgWriterOgr.createField("class",OFTInteger,ilayer);
+ else
+ imgWriterOgr.createField("class",OFTString,ilayer);
+ }
+ unsigned int nFeatures=imgReaderOgr.getFeatureCount(ilayer);
+ unsigned int ifeature=0;
+ progress=0;
+ pfnProgress(progress,pszMessage,pProgressArg);
+ OGRFeature *poFeature;
+ while( (poFeature = imgReaderOgr.getLayer(ilayer)->GetNextFeature()) != NULL ){
+ if(verbose_opt[0]>1)
+ cout << "feature " << ifeature << endl;
+ if( poFeature == NULL ){
+ cout << "Warning: could not read feature " << ifeature << " in layer " << imgReaderOgr.getLayerName(ilayer) << endl;
+ continue;
+ }
+ OGRFeature *poDstFeature = NULL;
+ if(output_opt.size()){
+ poDstFeature=imgWriterOgr.createFeature(ilayer);
+ if( poDstFeature->SetFrom( poFeature, TRUE ) != OGRERR_NONE ){
+ CPLError( CE_Failure, CPLE_AppDefined,
+ "Unable to translate feature %d from layer %s.\n",
+ poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
+ OGRFeature::DestroyFeature( poFeature );
+ OGRFeature::DestroyFeature( poDstFeature );
+ }
+ }
+ vector<float> validationPixel;
+ vector<float> validationFeature;
+
+ imgReaderOgr.readData(validationPixel,OFTReal,fields,poFeature,ilayer);
+ assert(validationPixel.size()==nband);
+ vector<float> probOut(nclass);//posterior prob for each class
+ for(int iclass=0;iclass<nclass;++iclass)
+ probOut[iclass]=0;
+ for(int ibag=0;ibag<nbag;++ibag){
+ for(int iband=0;iband<nband;++iband){
+ validationFeature.push_back((validationPixel[iband]-offset[ibag][iband])/scale[ibag][iband]);
+ if(verbose_opt[0]==2)
+ std:: cout << " " << validationFeature.back();
+ }
+ if(verbose_opt[0]==2)
+ std::cout << std:: endl;
+ vector<float> result(nclass);
+ result=net[ibag].run(validationFeature);
+
+ if(verbose_opt[0]>1){
+ for(int iclass=0;iclass<result.size();++iclass)
+ std::cout << result[iclass] << " ";
+ std::cout << std::endl;
+ }
+ //calculate posterior prob of bag
+ for(int iclass=0;iclass<nclass;++iclass){
+ result[iclass]=(result[iclass]+1.0)/2.0;//bring back to scale [0,1]
+ switch(comb_opt[0]){
+ default:
+ case(0)://sum rule
+ probOut[iclass]+=result[iclass]*priors[iclass];//add probabilities for each bag
+ break;
+ case(1)://product rule
+ probOut[iclass]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];//multiply probabilities for each bag
+ break;
+ case(2)://max rule
+ if(priors[iclass]*result[iclass]>probOut[iclass])
+ probOut[iclass]=priors[iclass]*result[iclass];
+ break;
+ }
+ }
+ }//for ibag
+ //search for max class prob
+ float maxBag=0;
+ float normBag=0;
+ string classOut="Unclassified";
+ for(int iclass=0;iclass<nclass;++iclass){
+ if(verbose_opt[0]>1)
+ std::cout << probOut[iclass] << " ";
+ if(probOut[iclass]>maxBag){
+ maxBag=probOut[iclass];
+ classOut=nameVector[iclass];
+ }
+ }
+ //look for class name
+ if(verbose_opt[0]>1){
+ if(classValueMap.size())
+ std::cout << "->" << classValueMap[classOut] << std::endl;
+ else
+ std::cout << "->" << classOut << std::endl;
+ }
+ if(output_opt.size()){
+ if(classValueMap.size())
+ poDstFeature->SetField("class",classValueMap[classOut]);
+ else
+ poDstFeature->SetField("class",classOut.c_str());
+ poDstFeature->SetFID( poFeature->GetFID() );
+ }
+ int labelIndex=poFeature->GetFieldIndex(label_opt[0].c_str());
+ if(labelIndex>=0){
+ string classRef=poFeature->GetFieldAsString(labelIndex);
+ if(classRef!="0"){
+ if(classValueMap.size())
+ cm.incrementResult(type2string<short>(classValueMap[classRef]),type2string<short>(classValueMap[classOut]),1);
+ else
+ cm.incrementResult(classRef,classOut,1);
+ }
+ }
+ CPLErrorReset();
+ if(output_opt.size()){
+ if(imgWriterOgr.createFeature(poDstFeature,ilayer) != OGRERR_NONE){
+ CPLError( CE_Failure, CPLE_AppDefined,
+ "Unable to translate feature %d from layer %s.\n",
+ poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
+ OGRFeature::DestroyFeature( poDstFeature );
+ OGRFeature::DestroyFeature( poDstFeature );
+ }
+ }
+ ++ifeature;
+ if(!verbose_opt[0]){
+ progress=static_cast<float>(ifeature+1.0)/nFeatures;
+ pfnProgress(progress,pszMessage,pProgressArg);
+ }
+ OGRFeature::DestroyFeature( poFeature );
+ OGRFeature::DestroyFeature( poDstFeature );
+ }//get next feature
+ }//next layer
+ imgReaderOgr.close();
+ if(output_opt.size())
+ imgWriterOgr.close();
+ }
+ if(cm.nReference()){
+ std::cout << cm << std::endl;
+ // cout << "class #samples userAcc prodAcc" << endl;
+ // double se95_ua=0;
+ // double se95_pa=0;
+ // double se95_oa=0;
+ // double dua=0;
+ // double dpa=0;
+ // double doa=0;
+ // for(short iclass=0;iclass<cm.nClasses();++iclass){
+ // dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
+ // dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
+ // cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
+ // }
+ // std::cout << "Kappa: " << cm.kappa() << std::endl;
+ // doa=cm.oa_pct(&se95_oa);
+ // std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" << std::endl;
+ }
+ }
+ try{
+ if(active_opt.size())
+ activeWriter.close();
+ }
+ catch(string errorString){
+ std::cerr << "Error: errorString" << std::endl;
+ }
+ return 0;
+}
diff --git a/src/apps/pkcomposite.cc b/src/apps/pkcomposite.cc
index aabc105..4dcc156 100644
--- a/src/apps/pkcomposite.cc
+++ b/src/apps/pkcomposite.cc
@@ -97,7 +97,7 @@ pkcomposite -i input1.tif -i input2.tif -o minimum.tif -cr minallbands
| dy | dy | double | |Output resolution in y (in meter) (empty: keep original resolution) |
| e | extent | std::string | |get boundary from extent from polygons in vector file |
| cut | crop_to_cutline | bool | false |Crop the extent of the target dataset to the extent of the cutline |
- | eo | eo | std::string | |special extent options controlling rasterization: ATTRIBUTE|CHUNKYSIZE|ALL_TOUCHED|BURN_VALUE_FROM|MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname |
+ | eo | eo | std::string | |special extent options controlling rasterization: ATTRIBUTE or CHUNKYSIZE or ALL_TOUCHED or BURN_VALUE_FROM or MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname |
| m | mask | std::string | |Use the first band of the specified file as a validity mask (0 is nodata) |
| msknodata | msknodata | float | 0 |Mask value not to consider for composite
| mskband | mskband | short | 0 |Mask band to read (0 indexed) |
diff --git a/src/apps/pkcomposite_app.cc~ b/src/apps/pkcomposite_app.cc~
deleted file mode 100644
index 8f2f7de..0000000
--- a/src/apps/pkcomposite_app.cc~
+++ /dev/null
@@ -1,271 +0,0 @@
-/**********************************************************************
-pkcomposite.cc: program to mosaic and composite geo-referenced images
-Copyright (C) 2008-2014 Pieter Kempeneers
-
-This file is part of pktools
-
-pktools is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-pktools is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with pktools. If not, see <http://www.gnu.org/licenses/>.
-***********************************************************************/
-#include <algorithm>
-#include <vector>
-#include <iostream>
-#include <string>
-#include "imageclasses/ImgReaderGdal.h"
-#include "imageclasses/ImgWriterGdal.h"
-#include "imageclasses/ImgReaderOgr.h"
-#include "base/Vector2d.h"
-#include "base/Optionpk.h"
-#include "algorithms/StatFactory.h"
-#include "AppFactory.h"
-/******************************************************************************/
-/*! \page pkcomposite pkcomposite
-
- program to mosaic and composite geo-referenced images
-## SYNOPSIS
-
-<code>
- Usage: pkcomposite -i input [-i input]* -o output
-</code>
-
-<code>
-
- Options: [-b band]* [-dx xres] [-dy yres] [-e vector] [-ulx ULX -uly ULY -lrx LRX -lry LRY] [-cr rule] [-cb band] [-srcnodata value] [-bndnodata band] [-min value] [-max value] [-dstnodata value] [-r resampling_method] [-ot {Byte / Int16 / UInt16 / UInt32 / Int32 / Float32 / Float64 / CInt16 / CInt32 / CFloat32 / CFloat64}] [-of format] [-co NAME=VALUE]* [-a_srs epsg:number]
-
- Advanced options:
- [-file] [-w weight]* [-c class]* [-ct colortable] [-d description] [-align]
-</code>
-
-\section pkcomposite_description Description
-
-The utility pkcomposite can be used to \em mosaic and \em composite multiple (georeferenced) raster datasets. A mosaic can merge images with different geographical extents into a single larger image. Compositing resolves the overlapping pixels according to some rule (e.g, the median of all overlapping pixels). This utility is complementary to GDAL, which currently does not support a composite step. Input datasets can have different bounding boxes and spatial resolutionsresolution.
-
-\anchor pkcomposite_rules
-composite rule | composite output
-------------- | -------------
-overwrite | Overwrite overlapping pixels: the latter input image on the command line overrules the previous image
-maxndvi | Create a maximum NDVI (normalized difference vegetation index) composite from multi-band input images. Use option -cb to set the indexes of the red and near infrared bands respectively (e.g., -cb 0 -cb 1)
-maxband | Select the pixel with a maximum value in the band specified by option -cb
-minband | Select the pixel with a minimum value in the band specified by option -cb
-mean | Calculate the mean (average) of overlapping pixels
-stdev | Calculate the standard deviation of overlapping pixels
-median | Calculate the median of overlapping pixels
-mode | Select the mode of overlapping pixels (maximum voting): use for Byte images only
-sum | Calculate the arithmetic sum of overlapping pixels
-maxallbands | For each individual band, assign the maximum value found in all overlapping pixels. Unlike maxband, output band values cannot be attributed to a single (date) pixel in the input time series
-minallbands | For each individual band, assign the minimum value found in all overlapping pixels. Unlike minband, output band values cannot be attributed to a single (date) pixel in the input time series
-
-Example: Calculate the maximum NDVI composite of two multispectral input images (e.g., red is band 0 and near infrared is band 1)
-
-\code
-pkcomposite -i input1.tif -i input2.tif -o output.tif -cr maxndvi -cb 0 -cb 1
-\endcode
-
-Example: Calculate the minimum nadir composite of two input images, where the forth band (b=3) contains the view zenith angle
-
-\code
-pkcomposite -i input1.tif -i input2.tif -o minzenith.tif -cr minband -cb 3
-\endcode
-
-Example: Calculate the minimum of two input images in all bands
-
-\code
-pkcomposite -i input1.tif -i input2.tif -o minimum.tif -cr minallbands
-\endcode
-
-
-\section pkcomposite_options Options
- - use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- - short option `-h` shows basic options only, long option `--help` shows all options
-
-|short|long|type|default|description|
-|-----|----|----|-------|-----------|
- | i | input | std::string | |Input image file(s). If input contains multiple images, a multi-band output is created |
- | o | output | std::string | |Output image file |
- | b | band | int | |band index(es) to crop (leave empty if all bands must be retained) |
- | dx | dx | double | |Output resolution in x (in meter) (empty: keep original resolution) |
- | dy | dy | double | |Output resolution in y (in meter) (empty: keep original resolution) |
- | e | extent | std::string | |get boundary from extent from polygons in vector file |
- | cut | crop_to_cutline | bool | false |Crop the extent of the target dataset to the extent of the cutline |
- | eo | eo | std::string | |special extent options controlling rasterization: ATTRIBUTE|CHUNKYSIZE|ALL_TOUCHED|BURN_VALUE_FROM|MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname |
- | m | mask | std::string | |Use the first band of the specified file as a validity mask (0 is nodata) |
- | msknodata | msknodata | float | 0 |Mask value not to consider for composite
- | mskband | mskband | short | 0 |Mask band to read (0 indexed) |
- | ulx | ulx | double | 0 |Upper left x value bounding box |
- | uly | uly | double | 0 |Upper left y value bounding box |
- | lrx | lrx | double | 0 |Lower right x value bounding box |
- | lry | lry | double | 0 |Lower right y value bounding box |
- | cr | crule | std::string | overwrite |Composite rule (overwrite, maxndvi, maxband, minband, mean, mode (only for byte images), median, sum, maxallbands, minallbands, stdev |
- | cb | cband | int | 0 |band index used for the composite rule (e.g., for ndvi, use --cband=0 --cband=1 with 0 and 1 indices for red and nir band respectively |
- | srcnodata | srcnodata | double | |invalid value(s) for input raster dataset |
- | bndnodata | bndnodata | int | 0 |Band(s) in input image to check if pixel is valid (used for srcnodata, min and max options) |
- | min | min | double | |flag values smaller or equal to this value as invalid. |
- | max | max | double | |flag values larger or equal to this value as invalid. |
- | dstnodata | dstnodata | double | 0 |nodata value to put in output raster dataset if not valid or out of bounds. |
- | r | resampling-method | std::string | near |Resampling method (near: nearest neighbor, bilinear: bi-linear interpolation). |
- | ot | otype | std::string | |Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image |
- | of | oformat | std::string | GTiff |Output image format (see also gdal_translate).|
- | co | co | std::string | |Creation option for output file. Multiple options can be specified. |
- | a_srs | a_srs | std::string | |Override the spatial reference for the output file (leave blank to copy from input file, use epsg:3035 to use European projection and force to European grid |
- | file | file | short | 0 |write number of observations (1) or sequence nr of selected file (2) for each pixels as additional layer in composite |
- | w | weight | short | 1 |Weights (type: short) for the composite, use one weight for each input file in same order as input files are provided). Use value 1 for equal weights. |
- | c | class | short | 0 |classes for multi-band output image: each band represents the number of observations for one specific class. Use value 0 for no multi-band output image. |
- | ct | ct | std::string | |color table file with 5 columns: id R G B ALFA (0: transparent, 255: solid) |
- | align | align | bool | |Align output bounding box to first input image |
- | scale | scale | double | |output=scale*input+offset |
- | off | offset | double | |output=scale*input+offset |
- | mem | mem | unsigned long int | 1000 |Buffer size (in MB) to read image data blocks in memory |
- | d | description | std::string | |Set image description |
-
-Examples
-========
-Some examples how to use pkcomposite can be found \ref examples_pkcomposite "here"
-
-FAQ
-===
-Frequently asked questions on pkcomposite can be found \ref faq_pkcomposite "here
-**/
-
-namespace crule{
- enum CRULE_TYPE {overwrite=0, maxndvi=1, maxband=2, minband=3, validband=4, mean=5, mode=6, median=7,sum=8,minallbands=9,maxallbands=10,stdev=11};
-}
-
-using namespace std;
-
-int main(int argc, char *argv[])
-{
- Optionpk<string> input_opt("i", "input", "Input image file(s). If input contains multiple images, a multi-band output is created");
- Optionpk<string> output_opt("o", "output", "Output image file");
- Optionpk<int> band_opt("b", "band", "band index(es) to crop (leave empty if all bands must be retained)");
- Optionpk<double> dx_opt("dx", "dx", "Output resolution in x (in meter) (empty: keep original resolution)");
- Optionpk<double> dy_opt("dy", "dy", "Output resolution in y (in meter) (empty: keep original resolution)");
- Optionpk<string> extent_opt("e", "extent", "get boundary from extent from polygons in vector file");
- Optionpk<bool> cut_opt("cut", "crop_to_cutline", "Crop the extent of the target dataset to the extent of the cutline.",false);
- Optionpk<string> eoption_opt("eo","eo", "special extent options controlling rasterization: ATTRIBUTE|CHUNKYSIZE|ALL_TOUCHED|BURN_VALUE_FROM|MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname");
- Optionpk<string> mask_opt("m", "mask", "Use the first band of the specified file as a validity mask (0 is nodata).");
- Optionpk<float> msknodata_opt("msknodata", "msknodata", "Mask value not to consider for composite.", 0);
- Optionpk<short> mskband_opt("mskband", "mskband", "Mask band to read (0 indexed)", 0);
- Optionpk<double> ulx_opt("ulx", "ulx", "Upper left x value bounding box", 0.0);
- Optionpk<double> uly_opt("uly", "uly", "Upper left y value bounding box", 0.0);
- Optionpk<double> lrx_opt("lrx", "lrx", "Lower right x value bounding box", 0.0);
- Optionpk<double> lry_opt("lry", "lry", "Lower right y value bounding box", 0.0);
- Optionpk<string> crule_opt("cr", "crule", "Composite rule (overwrite, maxndvi, maxband, minband, mean, mode (only for byte images), median, sum, maxallbands, minallbands, stdev", "overwrite");
- Optionpk<int> ruleBand_opt("cb", "cband", "band index used for the composite rule (e.g., for ndvi, use --cband=0 --cband=1 with 0 and 1 indices for red and nir band respectively", 0);
- Optionpk<double> srcnodata_opt("srcnodata", "srcnodata", "invalid value(s) for input raster dataset");
- Optionpk<int> bndnodata_opt("bndnodata", "bndnodata", "Band(s) in input image to check if pixel is valid (used for srcnodata, min and max options)", 0);
- Optionpk<double> minValue_opt("min", "min", "flag values smaller or equal to this value as invalid.");
- Optionpk<double> maxValue_opt("max", "max", "flag values larger or equal to this value as invalid.");
- Optionpk<double> dstnodata_opt("dstnodata", "dstnodata", "nodata value to put in output raster dataset if not valid or out of bounds.", 0);
- Optionpk<string> resample_opt("r", "resampling-method", "Resampling method (near: nearest neighbor, bilinear: bi-linear interpolation).", "near");
- Optionpk<string> otype_opt("ot", "otype", "Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image", "");
- Optionpk<string> oformat_opt("of", "oformat", "Output image format (see also gdal_translate).","GTiff");
- Optionpk<string> option_opt("co", "co", "Creation option for output file. Multiple options can be specified.");
- Optionpk<string> projection_opt("a_srs", "a_srs", "Override the spatial reference for the output file (leave blank to copy from input file, use epsg:3035 to use European projection and force to European grid");
- Optionpk<short> file_opt("file", "file", "write number of observations (1) or sequence nr of selected file (2) for each pixels as additional layer in composite", 0);
- Optionpk<short> weight_opt("w", "weight", "Weights (type: short) for the composite, use one weight for each input file in same order as input files are provided). Use value 1 for equal weights.", 1);
- Optionpk<short> class_opt("c", "class", "classes for multi-band output image: each band represents the number of observations for one specific class. Use value 0 for no multi-band output image.", 0);
- Optionpk<string> colorTable_opt("ct", "ct", "color table file with 5 columns: id R G B ALFA (0: transparent, 255: solid)");
- Optionpk<string> description_opt("d", "description", "Set image description");
- Optionpk<bool> align_opt("align", "align", "Align output bounding box to input image",false);
- Optionpk<double> scale_opt("scale", "scale", "output=scale*input+offset");
- Optionpk<double> offset_opt("offset", "offset", "output=scale*input+offset");
- Optionpk<unsigned long int> memory_opt("mem", "mem", "Buffer size (in MB) to read image data blocks in memory",1000,1);
- Optionpk<short> verbose_opt("v", "verbose", "verbose", 0,2);
-
- extent_opt.setHide(1);
- cut_opt.setHide(1);
- eoption_opt.setHide(1);
- mask_opt.setHide(1);
- msknodata_opt.setHide(1);
- mskband_opt.setHide(1);
- option_opt.setHide(1);
- file_opt.setHide(1);
- weight_opt.setHide(1);
- class_opt.setHide(1);
- colorTable_opt.setHide(1);
- description_opt.setHide(1);
- scale_opt.setHide(1);
- offset_opt.setHide(1);
- memory_opt.setHide(1);
-
- bool doProcess;//stop process when program was invoked with help option (-h --help)
- try{
- doProcess=input_opt.retrieveOption(argc,argv);
- output_opt.retrieveOption(argc,argv);
- band_opt.retrieveOption(argc,argv);
- dx_opt.retrieveOption(argc,argv);
- dy_opt.retrieveOption(argc,argv);
- extent_opt.retrieveOption(argc,argv);
- cut_opt.retrieveOption(argc,argv);
- eoption_opt.retrieveOption(argc,argv);
- mask_opt.retrieveOption(argc,argv);
- msknodata_opt.retrieveOption(argc,argv);
- mskband_opt.retrieveOption(argc,argv);
- ulx_opt.retrieveOption(argc,argv);
- uly_opt.retrieveOption(argc,argv);
- lrx_opt.retrieveOption(argc,argv);
- lry_opt.retrieveOption(argc,argv);
- crule_opt.retrieveOption(argc,argv);
- ruleBand_opt.retrieveOption(argc,argv);
- srcnodata_opt.retrieveOption(argc,argv);
- bndnodata_opt.retrieveOption(argc,argv);
- minValue_opt.retrieveOption(argc,argv);
- maxValue_opt.retrieveOption(argc,argv);
- dstnodata_opt.retrieveOption(argc,argv);
- resample_opt.retrieveOption(argc,argv);
- otype_opt.retrieveOption(argc,argv);
- oformat_opt.retrieveOption(argc,argv);
- option_opt.retrieveOption(argc,argv);
- projection_opt.retrieveOption(argc,argv);
- file_opt.retrieveOption(argc,argv);
- weight_opt.retrieveOption(argc,argv);
- class_opt.retrieveOption(argc,argv);
- colorTable_opt.retrieveOption(argc,argv);
- description_opt.retrieveOption(argc,argv);
- align_opt.retrieveOption(argc,argv);
- scale_opt.retrieveOption(argc,argv);
- offset_opt.retrieveOption(argc,argv);
- memory_opt.retrieveOption(argc,argv);
- verbose_opt.retrieveOption(argc,argv);
- }
- catch(string predefinedString){
- std::cout << predefinedString << std::endl;
- exit(0);
- }
- if(!doProcess){
- cout << endl;
- cout << "Usage: pkcomposite -i input [-i input]* -o output" << endl;
- cout << endl;
- std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
- exit(0);//help was invoked, stop processing
- }
- appfactory::AppFactory app(memory_opt[0]);
- vector<ImgReaderGdal> imgVector(input_opt.size());
- for(int ifile=0;ifile<input_opt.size();++ifile){
- try{
- imgReader[ifile].open(input_opt[ifile],GA_ReadOnly,memory_opt[0]);
- // imgReader[ifile].open(input_opt[ifile],GA_ReadOnly);
- for(int iband=0;iband<scale_opt.size();++iband)
- imgReader[ifile].setScale(scale_opt[iband],iband);
- for(int iband=0;iband<offset_opt.size();++iband)
- imgReader[ifile].setOffset(offset_opt[iband],iband);
- }
- catch(string errorstring){
- cerr << errorstring << " " << input_opt[ifile] << endl;
- }
- }
- ImgWriterGdal imgWriter;//AppFactory has been instantiated with memory_opt so we can set mem in ImgWriter
- app.pkcomposite<(imgVector,imgWriter);
- return 0;
-}
-
diff --git a/src/apps/pkcrop.cc b/src/apps/pkcrop.cc
index 05d82ec..f1d3d35 100644
--- a/src/apps/pkcrop.cc
+++ b/src/apps/pkcrop.cc
@@ -74,7 +74,7 @@ The utility pkcrop can subset and stack raster images. In the spatial domain it
| r | resampling-method | std::string | near |Resampling method (near: nearest neighbor, bilinear: bi-linear interpolation). |
| e | extent | std::string | |get boundary from extent from polygons in vector file |
| cut | crop_to_cutline | bool | false |Crop the extent of the target dataset to the extent of the cutline |
- | eo | eo | std::string | |special extent options controlling rasterization: ATTRIBUTE|CHUNKYSIZE|ALL_TOUCHED|BURN_VALUE_FROM|MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname |
+ | eo | eo | std::string | |special extent options controlling rasterization: ATTRIBUTE or CHUNKYSIZE or ALL_TOUCHED or BURN_VALUE_FROM or MERGE_ALG, e.g., -eo ATTRIBUTE=fieldname |
| m | mask | std::string | |Use the specified file as a validity mask (0 is nodata) |
| msknodata | msknodata | float | 0 |Mask value not to consider for crop
| mskband | mskband | short | 0 |Mask band to read (0 indexed) |
diff --git a/src/apps/pkextract.cc b/src/apps/pkextract.cc
deleted file mode 100644
index a404698..0000000
--- a/src/apps/pkextract.cc
+++ /dev/null
@@ -1,2553 +0,0 @@
-/**********************************************************************
-pkextract.cc: extract pixel values from raster image from a (vector or raster) sample
-Copyright (C) 2008-2014 Pieter Kempeneers
-
-This file is part of pktools
-
-pktools is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-pktools is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with pktools. If not, see <http://www.gnu.org/licenses/>.
-***********************************************************************/
-#include <assert.h>
-#include <math.h>
-#include <stdlib.h>
-#include <sstream>
-#include <string>
-#include <algorithm>
-#include <ctime>
-#include <vector>
-#include "imageclasses/ImgReaderGdal.h"
-#include "imageclasses/ImgWriterOgr.h"
-#include "base/Optionpk.h"
-#include "algorithms/StatFactory.h"
-
-#ifndef PI
-#define PI 3.1415926535897932384626433832795
-#endif
-
-/******************************************************************************/
-/*! \page pkextract pkextract
- extract pixel values from raster image from a (vector or raster) sample
-## SYNOPSIS
-
-<code>
- Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output
-</code>
-
-<code>
-
- Options: [-ln layer]* [-c class]* [-t threshold]* [-f format] [-ft fieldType] [-lt labelType] [-polygon] [-b band]* [-r rule]
-
- Advanced options:
- [-sband band -eband band]* [-bndnodata band -srcnodata value]* [-tp threshold] [-test testSample] [-bn attribute] [-cn attribute] [-geo value] [-down value] [-buf value [-circ]]
-</code>
-
-\section pkextract_description Description
-
-The utility pkextract extracts pixel values from an input raster dataset, based on the locations you provide via a sample file. Alternatively, a random sample or systematic grid of points can also be extracted. The sample can be a vector file with points or polygons. In the case of polygons, you can either extract the values for all raster pixels that are covered by the polygons, or extract a single value for each polygon such as the centroid, mean, median, etc. As output, a new copy of [...]
-
-Instead of a vector dataset, the sample can also be a raster dataset with categorical values. The typical use case is a land cover map that overlaps the input raster dataset. The utility then extracts pixels from the input raster for the respective land cover classes. To select a random subset of the sample raster dataset you can set the threshold option -t with a percentage value.
-
-A typical usage of pkextract is to prepare a training sample for one of the classifiers implemented in pktools.
-
-\anchor pkextract_rules
-
-Overview of the possible extraction rules:
-
-extraction rule | output features
---------------- | ---------------
-point | Extract all pixel values covered by the polygon (option -polygon not set) or extract a pixel on the surface option (-polygon set).
-centroid | Extract pixel value at the centroid of the polygon.
-mean | Extract average of all pixel values within the polygon.
-stdev | Extract standard deviation of all pixel values within the polygon.
-median | Extract median of all pixel values within the polygon.
-min | Extract minimum value of all pixels within the polygon.
-max | Extract maximum value of all pixels within the polygon.
-sum | Extract sum of the values of all pixels within the polygon.
-mode | Extract the mode of classes within the polygon (classes must be set with the option class).
-proportion | Extract proportion of class(es) within the polygon (classes must be set with the option class).
-count | Extract count of class(es) within the polygon (classes must be set with the option class).
-percentile | Extract percentile as defined by option perc (e.g, 95th percentile of values covered by polygon).
-
-\section pkextract_options Options
- - use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- - short option `-h` shows basic options only, long option `--help` shows all options
-|short|long|type|default|description|
-|-----|----|----|-------|-----------|
- | i | input | std::string | |Raster input dataset containing band information |
- | s | sample | std::string | |OGR vector dataset with features to be extracted from input data. Output will contain features with input band information included. Sample image can also be GDAL raster dataset. |
- | ln | ln | std::string | |Layer name(s) in sample (leave empty to select all) |
- | rand | random | unsigned int | |Create simple random sample of points. Provide number of points to generate |
- | grid | grid | double | |Create systematic grid of points. Provide cell grid size (in projected units, e.g,. m) |
- | o | output | std::string | |Output sample dataset |
- | c | class | int | |Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset. Make sure to set classes if rule is set to mode, proportion or count |
- | t | threshold | float | 100 |Probability threshold for selecting samples (randomly). Provide probability in percentage (>0) or absolute (<0). Use a single threshold per vector sample layer. If using raster land cover maps as a sample dataset, you can provide a threshold value for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for selected class(es) |
- | perc | perc | double | 95 |Percentile value used for rule percentile |
- | f | f | std::string | SQLite |Output sample dataset format |
- | ft | ftype | std::string | Real |Field type (only Real or Integer) |
- | lt | ltype | std::string | Integer |Label type: In16 or String |
- | polygon | polygon | bool | false |Create OGRPolygon as geometry instead of OGRPoint. |
- | b | band | int | |Band index(es) to extract (0 based). Leave empty to use all bands |
- | sband | startband | unsigned short | |Start band sequence number |
- | eband | endband | unsigned short | |End band sequence number |
- | r | rule | std::string | centroid |Rule how to report image information per feature (only for vector sample). point (value at each point or at centroid if polygon), centroid, mean, stdev, median, proportion, count, min, max, mode, sum, percentile. |
- | bndnodata | bndnodata | int | 0 |Band(s) in input image to check if pixel is valid (used for srcnodata) |
- | srcnodata | srcnodata | double | |Invalid value(s) for input image |
- | tp | thresholdPolygon | float | |(absolute) threshold for selecting samples in each polygon |
- | test | test | std::string | |Test sample dataset (use this option in combination with threshold<100 to create a training (output) and test set |
- | bn | bname | std::string | b |For single band input data, this extra attribute name will correspond to the raster values. For multi-band input data, multiple attributes with this prefix will be added (e.g. b0, b1, b2, etc.) |
- | cn | cname | std::string | label |Name of the class label in the output vector dataset |
- | geo | geo | short | 1 |Use geo coordinates (set to 0 to use image coordinates) |
- | down | down | short | 1 |Down sampling factor (for raster sample datasets only). Can be used to create grid points |
- | buf | buffer | short | |Buffer for calculating statistics for point features |
- | circ | circular | bool | false |Use a circular disc kernel buffer (for vector point sample datasets only, use in combination with buffer option) |
-
-Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output
-
-
-Examples
-========
-Some examples how to use pkextract can be found \ref examples_pkextract "here"
-**/
-
-namespace rule{
- enum RULE_TYPE {point=0, mean=1, proportion=2, custom=3, min=4, max=5, mode=6, centroid=7, sum=8, median=9, stdev=10, percentile=11, count=12};
-}
-
-using namespace std;
-
-int main(int argc, char *argv[])
-{
- Optionpk<string> image_opt("i", "input", "Raster input dataset containing band information");
- Optionpk<string> sample_opt("s", "sample", "OGR vector dataset with features to be extracted from input data. Output will contain features with input band information included. Sample image can also be GDAL raster dataset.");
- Optionpk<string> layer_opt("ln", "ln", "Layer name(s) in sample (leave empty to select all)");
- Optionpk<unsigned int> random_opt("rand", "random", "Create simple random sample of points. Provide number of points to generate");
- Optionpk<double> grid_opt("grid", "grid", "Create systematic grid of points. Provide cell grid size (in projected units, e.g,. m)");
- Optionpk<string> output_opt("o", "output", "Output sample dataset");
- Optionpk<int> class_opt("c", "class", "Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset. Make sure to set classes if rule is set to mode, proportion or count");
- Optionpk<float> threshold_opt("t", "threshold", "Probability threshold for selecting samples (randomly). Provide probability in percentage (>0) or absolute (<0). Use a single threshold per vector sample layer. If using raster land cover maps as a sample dataset, you can provide a threshold value for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for selected class(es)", 100);
- Optionpk<double> percentile_opt("perc","perc","Percentile value used for rule percentile",95);
- Optionpk<string> ogrformat_opt("f", "f", "Output sample dataset format","SQLite");
- Optionpk<string> ftype_opt("ft", "ftype", "Field type (only Real or Integer)", "Real");
- Optionpk<string> ltype_opt("lt", "ltype", "Label type: In16 or String", "Integer");
- Optionpk<bool> polygon_opt("polygon", "polygon", "Create OGRPolygon as geometry instead of OGRPoint.", false);
- Optionpk<int> band_opt("b", "band", "Band index(es) to extract (0 based). Leave empty to use all bands");
- Optionpk<unsigned short> bstart_opt("sband", "startband", "Start band sequence number");
- Optionpk<unsigned short> bend_opt("eband", "endband", "End band sequence number");
- Optionpk<string> rule_opt("r", "rule", "Rule how to report image information per feature (only for vector sample). point (value at each point or at centroid if polygon), centroid, mean, stdev, median, proportion, count, min, max, mode, sum, percentile.", "centroid");
- Optionpk<double> srcnodata_opt("srcnodata", "srcnodata", "Invalid value(s) for input image");
- Optionpk<int> bndnodata_opt("bndnodata", "bndnodata", "Band(s) in input image to check if pixel is valid (used for srcnodata)", 0);
- Optionpk<float> polythreshold_opt("tp", "thresholdPolygon", "(absolute) threshold for selecting samples in each polygon");
- Optionpk<string> test_opt("test", "test", "Test sample dataset (use this option in combination with threshold<100 to create a training (output) and test set");
- Optionpk<string> fieldname_opt("bn", "bname", "For single band input data, this extra attribute name will correspond to the raster values. For multi-band input data, multiple attributes with this prefix will be added (e.g. b0, b1, b2, etc.)", "b");
- Optionpk<string> label_opt("cn", "cname", "Name of the class label in the output vector dataset", "label");
- Optionpk<short> geo_opt("geo", "geo", "Use geo coordinates (set to 0 to use image coordinates)", 1);
- Optionpk<short> down_opt("down", "down", "Down sampling factor (for raster sample datasets only).", 1);
- Optionpk<short> buffer_opt("buf", "buffer", "Buffer for calculating statistics for point features ");
- Optionpk<bool> disc_opt("circ", "circular", "Use a circular disc kernel buffer (for vector point sample datasets only, use in combination with buffer option)", false);
- Optionpk<short> verbose_opt("v", "verbose", "Verbose mode if > 0", 0,2);
-
- bstart_opt.setHide(1);
- bend_opt.setHide(1);
- bndnodata_opt.setHide(1);
- srcnodata_opt.setHide(1);
- polythreshold_opt.setHide(1);
- percentile_opt.setHide(1);
- test_opt.setHide(1);
- fieldname_opt.setHide(1);
- label_opt.setHide(1);
- geo_opt.setHide(1);
- down_opt.setHide(1);
- buffer_opt.setHide(1);
- disc_opt.setHide(1);
-
- bool doProcess;//stop process when program was invoked with help option (-h --help)
- try{
- doProcess=image_opt.retrieveOption(argc,argv);
- sample_opt.retrieveOption(argc,argv);
- layer_opt.retrieveOption(argc,argv);
- random_opt.retrieveOption(argc,argv);
- grid_opt.retrieveOption(argc,argv);
- output_opt.retrieveOption(argc,argv);
- class_opt.retrieveOption(argc,argv);
- threshold_opt.retrieveOption(argc,argv);
- percentile_opt.retrieveOption(argc,argv);
- ogrformat_opt.retrieveOption(argc,argv);
- ftype_opt.retrieveOption(argc,argv);
- ltype_opt.retrieveOption(argc,argv);
- polygon_opt.retrieveOption(argc,argv);
- band_opt.retrieveOption(argc,argv);
- bstart_opt.retrieveOption(argc,argv);
- bend_opt.retrieveOption(argc,argv);
- rule_opt.retrieveOption(argc,argv);
- bndnodata_opt.retrieveOption(argc,argv);
- srcnodata_opt.retrieveOption(argc,argv);
- polythreshold_opt.retrieveOption(argc,argv);
- test_opt.retrieveOption(argc,argv);
- fieldname_opt.retrieveOption(argc,argv);
- label_opt.retrieveOption(argc,argv);
- geo_opt.retrieveOption(argc,argv);
- down_opt.retrieveOption(argc,argv);
- buffer_opt.retrieveOption(argc,argv);
- disc_opt.retrieveOption(argc,argv);
- // rbox_opt.retrieveOption(argc,argv);
- // cbox_opt.retrieveOption(argc,argv);
- verbose_opt.retrieveOption(argc,argv);
- }
- catch(string predefinedString){
- std::cout << predefinedString << std::endl;
- exit(0);
- }
- if(!doProcess){
- cout << endl;
- cout << "Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output" << endl;
- cout << endl;
- std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
- exit(0);//help was invoked, stop processing
- }
-
- std::map<std::string, rule::RULE_TYPE> ruleMap;
- //initialize ruleMap
- ruleMap["point"]=rule::point;
- ruleMap["centroid"]=rule::centroid;
- ruleMap["mean"]=rule::mean;
- ruleMap["stdev"]=rule::stdev;
- ruleMap["median"]=rule::median;
- ruleMap["proportion"]=rule::proportion;
- ruleMap["count"]=rule::count;
- ruleMap["min"]=rule::min;
- ruleMap["max"]=rule::max;
- ruleMap["custom"]=rule::custom;
- ruleMap["mode"]=rule::mode;
- ruleMap["sum"]=rule::sum;
- ruleMap["percentile"]=rule::percentile;
-
- if(srcnodata_opt.size()){
- while(srcnodata_opt.size()<bndnodata_opt.size())
- srcnodata_opt.push_back(srcnodata_opt[0]);
- }
-
- if(verbose_opt[0])
- std::cout << class_opt << std::endl;
- statfactory::StatFactory stat;
- stat.setNoDataValues(srcnodata_opt);
- Vector2d<unsigned int> posdata;
- unsigned long int nsample=0;
- unsigned long int ntotalvalid=0;
- unsigned long int ntotalinvalid=0;
- vector<unsigned long int> nvalid(class_opt.size());
- vector<unsigned long int> ninvalid(class_opt.size());
- for(int it=0;it<nvalid.size();++it){
- nvalid[it]=0;
- ninvalid[it]=0;
- }
-
- ImgReaderGdal imgReader;
- if(image_opt.empty()){
- std::cerr << "No image dataset provided (use option -i). Use --help for help information";
- exit(0);
- }
- if(output_opt.empty()){
- std::cerr << "No output dataset provided (use option -o). Use --help for help information";
- exit(0);
- }
- try{
- imgReader.open(image_opt[0]);
- }
- catch(std::string errorstring){
- std::cout << errorstring << std::endl;
- exit(0);
- }
-
- //convert start and end band options to vector of band indexes
- try{
- if(bstart_opt.size()){
- if(bend_opt.size()!=bstart_opt.size()){
- string errorstring="Error: options for start and end band indexes must be provided as pairs, missing end band";
- throw(errorstring);
- }
- band_opt.clear();
- for(int ipair=0;ipair<bstart_opt.size();++ipair){
- if(bend_opt[ipair]<=bstart_opt[ipair]){
- string errorstring="Error: index for end band must be smaller then start band";
- throw(errorstring);
- }
- for(int iband=bstart_opt[ipair];iband<=bend_opt[ipair];++iband)
- band_opt.push_back(iband);
- }
- }
- }
- catch(string error){
- cerr << error << std::endl;
- exit(1);
- }
-
- int nband=(band_opt.size()) ? band_opt.size() : imgReader.nrOfBand();
-
- if(fieldname_opt.size()<nband){
- std::string bandString=fieldname_opt[0];
- fieldname_opt.clear();
- fieldname_opt.resize(nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- ostringstream fs;
- fs << bandString << theBand;
- fieldname_opt[iband]=fs.str();
- }
- }
-
- if(verbose_opt[0])
- std::cout << fieldname_opt << std::endl;
-
- if(verbose_opt[0]>1)
- std::cout << "Number of bands in input image: " << imgReader.nrOfBand() << std::endl;
-
- OGRFieldType fieldType;
- OGRFieldType labelType;
- int ogr_typecount=11;//hard coded for now!
- if(verbose_opt[0]>1)
- std::cout << "field and label types can be: ";
- for(int iType = 0; iType < ogr_typecount; ++iType){
- if(verbose_opt[0]>1)
- std::cout << " " << OGRFieldDefn::GetFieldTypeName((OGRFieldType)iType);
- if( OGRFieldDefn::GetFieldTypeName((OGRFieldType)iType) != NULL
- && EQUAL(OGRFieldDefn::GetFieldTypeName((OGRFieldType)iType),
- ftype_opt[0].c_str()))
- fieldType=(OGRFieldType) iType;
- if( OGRFieldDefn::GetFieldTypeName((OGRFieldType)iType) != NULL
- && EQUAL(OGRFieldDefn::GetFieldTypeName((OGRFieldType)iType),
- ltype_opt[0].c_str()))
- labelType=(OGRFieldType) iType;
- }
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- case OFTRealList:
- case OFTString:
- if(verbose_opt[0]>1)
- std::cout << std::endl << "field type is: " << OGRFieldDefn::GetFieldTypeName(fieldType) << std::endl;
- break;
- default:
- cerr << "field type " << OGRFieldDefn::GetFieldTypeName(fieldType) << " not supported" << std::endl;
- exit(0);
- break;
- }
- switch( labelType ){
- case OFTInteger:
- case OFTReal:
- case OFTRealList:
- case OFTString:
- if(verbose_opt[0]>1)
- std::cout << std::endl << "label type is: " << OGRFieldDefn::GetFieldTypeName(labelType) << std::endl;
- break;
- default:
- cerr << "label type " << OGRFieldDefn::GetFieldTypeName(labelType) << " not supported" << std::endl;
- exit(0);
- break;
- }
-
- const char* pszMessage;
- void* pProgressArg=NULL;
- GDALProgressFunc pfnProgress=GDALTermProgress;
- double progress=0;
- srand(time(NULL));
-
- bool sampleIsRaster=false;
- bool sampleIsVirtual=false;
-
- ImgReaderOgr sampleReaderOgr;
- ImgWriterOgr sampleWriterOgr;
-
- if(sample_opt.size()){
- try{
- sampleReaderOgr.open(sample_opt[0]);
- }
- catch(string errorString){
- sampleIsRaster=true;
- }
- }
- else{
- try{
- sampleWriterOgr.open("/vsimem/virtual",ogrformat_opt[0]);
- }
- catch(string errorString){
- cerr << errorString << endl;
- }
- char **papszOptions=NULL;
- sampleWriterOgr.createLayer("points", imgReader.getProjection(), wkbPoint, papszOptions);
- sampleIsVirtual=true;
-
- // string fieldName="label";
- // string fieldValue="class";
- // sampleWriterOgr.createField(fieldName,OFTString);
- if(random_opt.size()){
- //create simple random sampling within boundary
- OGRPoint pt;
- double ulx,uly,lrx,lry;
- imgReader.getBoundingBox(ulx,uly,lrx,lry);
- for(unsigned int ipoint=1;ipoint<=random_opt[0];++ipoint){
- OGRFeature *pointFeature;
- pointFeature=sampleWriterOgr.createFeature();
- // pointFeature->SetField(fieldName.c_str(),fieldValue.c_str());
- double theX=ulx+static_cast<double>(rand())/(RAND_MAX)*(lrx-ulx);
- double theY=uly-static_cast<double>(rand())/(RAND_MAX)*(uly-lry);
- pt.setX(theX);
- pt.setY(theY);
- pointFeature->SetGeometry( &pt );
- if(sampleWriterOgr.createFeature(pointFeature) != OGRERR_NONE ){
- cerr << "Failed to create feature in vector dataset" << endl;
- exit( 1 );
- }
- OGRFeature::DestroyFeature(pointFeature);
- }
- }
- else if(grid_opt.size()){
- //create systematic grid of points
- OGRPoint pt;
- double ulx,uly,lrx,lry;
- imgReader.getBoundingBox(ulx,uly,lrx,lry);
- unsigned int ipoint=0;
- for(double theY=uly-grid_opt[0]/2;theY>lry;theY-=grid_opt[0]){
- for(double theX=ulx+grid_opt[0]/2;theX<lrx;theX+=grid_opt[0]){
- if(verbose_opt[0]>1)
- cout << "position: " << theX << " " << theY << endl;
- OGRFeature *pointFeature;
- pointFeature=sampleWriterOgr.createFeature();
- // pointFeature->SetField(fieldName.c_str(),fieldValue.c_str());
- pt.setX(theX);
- pt.setY(theY);
- pointFeature->SetGeometry( &pt );
- if(sampleWriterOgr.createFeature(pointFeature) != OGRERR_NONE ){
- cerr << "Failed to create feature in vector dataset" << endl;
- exit( 1 );
- }
- OGRFeature::DestroyFeature(pointFeature);
- }
- }
- }
- else{
- std::cerr << "No sample dataset provided (use option -s). Use --help for help information";
- exit(0);
- }
- try{
- sampleWriterOgr.close();
- sampleReaderOgr.open("/vsimem/virtual");
- }
- catch(string errorString){
- cerr << errorString << endl;
- }
- }
-
- if(sampleIsRaster){
- if(class_opt.empty()){
- // std::cout << "Warning: no classes selected, if a classes must be extracted, set to -1 for all classes using option -c -1" << std::endl;
- ImgReaderGdal classReader;
- ImgWriterOgr ogrWriter;
- assert(sample_opt.size());
- classReader.open(sample_opt[0]);
- // vector<int> classBuffer(classReader.nrOfCol());
- vector<double> classBuffer(classReader.nrOfCol());
- Vector2d<double> imgBuffer(nband);//[band][col]
- vector<double> sample(2+nband);//x,y,band values
- Vector2d<double> writeBuffer;
- // vector<int> writeBufferClass;
- vector<double> writeBufferClass;
- vector<int> selectedClass;
- Vector2d<double> selectedBuffer;
- double oldimgrow=-1;
- int irow=0;
- int icol=0;
- if(verbose_opt[0]>1)
- std::cout << "extracting sample from image..." << std::endl;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- for(irow=0;irow<classReader.nrOfRow();++irow){
- if(irow%down_opt[0])
- continue;
- // classReader.readData(classBuffer,GDT_Int32,irow);
- classReader.readData(classBuffer,GDT_Float64,irow);
- double x,y;//geo coordinates
- double iimg,jimg;//image coordinates in img image
- for(icol=0;icol<classReader.nrOfCol();++icol){
- if(icol%down_opt[0])
- continue;
- // int theClass=0;
- double theClass=classBuffer[icol];
- // int processClass=-1;
- int processClass=0;
- // if(class_opt[0]<0){//process every class except 0
- // if(classBuffer[icol]){
- // processClass=0;
- // theClass=classBuffer[icol];
- // }
- // }
- // else{
- // for(int iclass=0;iclass<class_opt.size();++iclass){
- // if(classBuffer[icol]==class_opt[iclass]){
- // processClass=iclass;
- // theClass=class_opt[iclass];
- // }
- // }
- // }
- // if(processClass>=0){
- bool valid=true;
- if(valid){
- if(geo_opt[0]){
- classReader.image2geo(icol,irow,x,y);
- sample[0]=x;
- sample[1]=y;
- if(verbose_opt[0]>1){
- std::cout.precision(12);
- std::cout << theClass << " " << x << " " << y << std::endl;
- }
- //find col in img
- imgReader.geo2image(x,y,iimg,jimg);
- //nearest neighbour
- jimg=static_cast<int>(jimg);
- iimg=static_cast<int>(iimg);
- if(static_cast<int>(iimg)<0||static_cast<int>(iimg)>=imgReader.nrOfCol())
- continue;
- }
- else{
- iimg=icol;
- jimg=irow;
- sample[0]=iimg;
- sample[1]=jimg;
- }
- if(static_cast<int>(jimg)<0||static_cast<int>(jimg)>=imgReader.nrOfRow())
- continue;
-
- bool valid=true;
-
- if(static_cast<int>(jimg)!=static_cast<int>(oldimgrow)){
- assert(imgBuffer.size()==nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- imgReader.readData(imgBuffer[iband],GDT_Float64,static_cast<int>(jimg),theBand);
- assert(imgBuffer[iband].size()==imgReader.nrOfCol());
- if(srcnodata_opt.size()){
- vector<int>::const_iterator bndit=bndnodata_opt.begin();
- vector<double>::const_iterator srcit=srcnodata_opt.begin();
- while(bndit!=bndnodata_opt.end()&&srcit!=srcnodata_opt.end()){
- if((*bndit==theBand)&&(*srcit==imgBuffer[iband][static_cast<int>(iimg)])){
- valid=false;
- break;
- }
- else{
- ++bndit;
- ++srcit;
- }
- }
- }
- }
- oldimgrow=jimg;
- }
-
- if(valid){
- for(int iband=0;iband<imgBuffer.size();++iband){
- if(imgBuffer[iband].size()!=imgReader.nrOfCol()){
- std::cout << "Error in band " << iband << ": " << imgBuffer[iband].size() << "!=" << imgReader.nrOfCol() << std::endl;
- assert(imgBuffer[iband].size()==imgReader.nrOfCol());
- }
- sample[iband+2]=imgBuffer[iband][static_cast<int>(iimg)];
- }
- float theThreshold=(threshold_opt.size()>1)?threshold_opt[processClass]:threshold_opt[0];
- if(theThreshold>0){//percentual value
- double p=static_cast<double>(rand())/(RAND_MAX);
- p*=100.0;
- if(p>theThreshold)
- continue;//do not select for now, go to next column
- }
- else if(nvalid.size()>processClass){//absolute value
- if(nvalid[processClass]>=-theThreshold)
- continue;//do not select any more pixels for this class, go to next column to search for other classes
- }
- writeBuffer.push_back(sample);
- writeBufferClass.push_back(theClass);
- ++ntotalvalid;
- if(nvalid.size()>processClass)
- ++(nvalid[processClass]);
- }
- else{
- ++ntotalinvalid;
- if(ninvalid.size()>processClass)
- ++(ninvalid[processClass]);
- }
- }//processClass
- }//icol
- progress=static_cast<float>(irow+1.0)/classReader.nrOfRow();
- pfnProgress(progress,pszMessage,pProgressArg);
- }//irow
- progress=100;
- pfnProgress(progress,pszMessage,pProgressArg);
- if(writeBuffer.size()>0){
- assert(ntotalvalid==writeBuffer.size());
- if(verbose_opt[0]>0)
- std::cout << "creating image sample writer " << output_opt[0] << " with " << writeBuffer.size() << " samples (" << ntotalinvalid << " invalid)" << std::endl;
- ogrWriter.open(output_opt[0],ogrformat_opt[0]);
- char **papszOptions=NULL;
- ostringstream slayer;
- slayer << "training data";
- std::string layername=slayer.str();
- ogrWriter.createLayer(layername, imgReader.getProjection(), wkbPoint, papszOptions);
- std::string fieldname="fid";//number of the point
- ogrWriter.createField(fieldname,OFTInteger);
- map<std::string,double> pointAttributes;
- ogrWriter.createField(label_opt[0],labelType);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- ogrWriter.createField(fieldname_opt[iband],fieldType);
- }
- std::cout << "writing sample to " << output_opt[0] << "..." << std::endl;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- for(int isample=0;isample<writeBuffer.size();++isample){
- if(verbose_opt[0]>1)
- std::cout << "writing sample " << isample << std::endl;
- pointAttributes[label_opt[0]]=writeBufferClass[isample];
- for(int iband=0;iband<writeBuffer[0].size()-2;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- // ostringstream fs;
- // if(nband==1)
- // fs << fieldname_opt[0];
- // else
- // fs << fieldname_opt[0] << theBand;
- // pointAttributes[fs.str()]=writeBuffer[isample][iband+2];
- pointAttributes[fieldname_opt[iband]]=writeBuffer[isample][iband+2];
- }
- if(verbose_opt[0]>1)
- std::cout << "all bands written" << std::endl;
- ogrWriter.addPoint(writeBuffer[isample][0],writeBuffer[isample][1],pointAttributes,fieldname,isample);
- progress=static_cast<float>(isample+1.0)/writeBuffer.size();
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- ogrWriter.close();
- }
- else{
- std::cout << "No data found for any class " << std::endl;
- }
- classReader.close();
- nsample=writeBuffer.size();
- if(verbose_opt[0])
- std::cout << "total number of samples written: " << nsample << std::endl;
- }
- else{//class_opt.size()!=0
- assert(class_opt[0]);
- // if(class_opt[0]){
- assert(threshold_opt.size()==1||threshold_opt.size()==class_opt.size());
- ImgReaderGdal classReader;
- ImgWriterOgr ogrWriter;
- if(verbose_opt[0]>1){
- std::cout << "reading position from sample dataset " << std::endl;
- std::cout << "class thresholds: " << std::endl;
- for(int iclass=0;iclass<class_opt.size();++iclass){
- if(threshold_opt.size()>1)
- std::cout << class_opt[iclass] << ": " << threshold_opt[iclass] << std::endl;
- else
- std::cout << class_opt[iclass] << ": " << threshold_opt[0] << std::endl;
- }
- }
- classReader.open(sample_opt[0]);
- vector<int> classBuffer(classReader.nrOfCol());
- // vector<double> classBuffer(classReader.nrOfCol());
- Vector2d<double> imgBuffer(nband);//[band][col]
- vector<double> sample(2+nband);//x,y,band values
- Vector2d<double> writeBuffer;
- vector<int> writeBufferClass;
- // vector<double> writeBufferClass;
- vector<int> selectedClass;
- Vector2d<double> selectedBuffer;
- double oldimgrow=-1;
- int irow=0;
- int icol=0;
- if(verbose_opt[0]>1)
- std::cout << "extracting sample from image..." << std::endl;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- for(irow=0;irow<classReader.nrOfRow();++irow){
- if(irow%down_opt[0])
- continue;
- classReader.readData(classBuffer,GDT_Int32,irow);
- // classReader.readData(classBuffer,GDT_Float64,irow);
- double x,y;//geo coordinates
- double iimg,jimg;//image coordinates in img image
- for(icol=0;icol<classReader.nrOfCol();++icol){
- if(icol%down_opt[0])
- continue;
- int theClass=0;
- // double theClass=0;
- int processClass=-1;
- if(class_opt.empty()){//process every class
- if(classBuffer[icol]){
- processClass=0;
- theClass=classBuffer[icol];
- }
- }
- else{
- for(int iclass=0;iclass<class_opt.size();++iclass){
- if(classBuffer[icol]==class_opt[iclass]){
- processClass=iclass;
- theClass=class_opt[iclass];
- }
- }
- }
- if(processClass>=0){
- // if(classBuffer[icol]==class_opt[0]){
- if(geo_opt[0]){
- classReader.image2geo(icol,irow,x,y);
- sample[0]=x;
- sample[1]=y;
- if(verbose_opt[0]>1){
- std::cout.precision(12);
- std::cout << theClass << " " << x << " " << y << std::endl;
- }
- //find col in img
- imgReader.geo2image(x,y,iimg,jimg);
- //nearest neighbour
- jimg=static_cast<int>(jimg);
- iimg=static_cast<int>(iimg);
- if(static_cast<int>(iimg)<0||static_cast<int>(iimg)>=imgReader.nrOfCol())
- continue;
- }
- else{
- iimg=icol;
- jimg=irow;
- sample[0]=iimg;
- sample[1]=jimg;
- }
- if(static_cast<int>(jimg)<0||static_cast<int>(jimg)>=imgReader.nrOfRow())
- continue;
-
- bool valid=true;
-
- if(static_cast<int>(jimg)!=static_cast<int>(oldimgrow)){
- assert(imgBuffer.size()==nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- imgReader.readData(imgBuffer[iband],GDT_Float64,static_cast<int>(jimg),theBand);
- assert(imgBuffer[iband].size()==imgReader.nrOfCol());
-
- if(srcnodata_opt.size()){
- vector<int>::const_iterator bndit=bndnodata_opt.begin();
- vector<double>::const_iterator srcit=srcnodata_opt.begin();
- while(bndit!=bndnodata_opt.end()&&srcit!=srcnodata_opt.end()){
- if((*bndit==theBand)&&(*srcit==imgBuffer[iband][static_cast<int>(iimg)])){
- valid=false;
- break;
- }
- else{
- ++bndit;
- ++srcit;
- }
- }
- }
- }
- oldimgrow=jimg;
- }
- if(valid){
- for(int iband=0;iband<imgBuffer.size();++iband){
- if(imgBuffer[iband].size()!=imgReader.nrOfCol()){
- std::cout << "Error in band " << iband << ": " << imgBuffer[iband].size() << "!=" << imgReader.nrOfCol() << std::endl;
- assert(imgBuffer[iband].size()==imgReader.nrOfCol());
- }
- sample[iband+2]=imgBuffer[iband][static_cast<int>(iimg)];
- }
- float theThreshold=(threshold_opt.size()>1)?threshold_opt[processClass]:threshold_opt[0];
- if(theThreshold>0){//percentual value
- double p=static_cast<double>(rand())/(RAND_MAX);
- p*=100.0;
- if(p>theThreshold)
- continue;//do not select for now, go to next column
- }
- else if(nvalid.size()>processClass){//absolute value
- if(nvalid[processClass]>=-theThreshold)
- continue;//do not select any more pixels for this class, go to next column to search for other classes
- }
- writeBuffer.push_back(sample);
- // writeBufferClass.push_back(class_opt[processClass]);
- writeBufferClass.push_back(theClass);
- ++ntotalvalid;
- if(nvalid.size()>processClass)
- ++(nvalid[processClass]);
- }
- else{
- ++ntotalinvalid;
- if(ninvalid.size()>processClass)
- ++(ninvalid[processClass]);
- }
- }//processClass
- }//icol
- progress=static_cast<float>(irow+1.0)/classReader.nrOfRow();
- pfnProgress(progress,pszMessage,pProgressArg);
- }//irow
- if(writeBuffer.size()>0){
- assert(ntotalvalid==writeBuffer.size());
- if(verbose_opt[0]>0)
- std::cout << "creating image sample writer " << output_opt[0] << " with " << writeBuffer.size() << " samples (" << ntotalinvalid << " invalid)" << std::endl;
- ogrWriter.open(output_opt[0],ogrformat_opt[0]);
- char **papszOptions=NULL;
- ostringstream slayer;
- slayer << "training data";
- std::string layername=slayer.str();
- ogrWriter.createLayer(layername, imgReader.getProjection(), wkbPoint, papszOptions);
- std::string fieldname="fid";//number of the point
- ogrWriter.createField(fieldname,OFTInteger);
- map<std::string,double> pointAttributes;
- // ogrWriter.createField(label_opt[0],OFTInteger);
- ogrWriter.createField(label_opt[0],labelType);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- // ostringstream fs;
- // if(nband==1)
- // fs << fieldname_opt[0];
- // else
- // fs << fieldname_opt[0] << theBand;
- // ogrWriter.createField(fs.str(),fieldType);
- ogrWriter.createField(fieldname_opt[iband],fieldType);
- }
- pfnProgress(progress,pszMessage,pProgressArg);
- std::cout << "writing sample to " << output_opt[0] << "..." << std::endl;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- for(int isample=0;isample<writeBuffer.size();++isample){
- pointAttributes[label_opt[0]]=writeBufferClass[isample];
- for(int iband=0;iband<writeBuffer[0].size()-2;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- // ostringstream fs;
- // if(nband==1)
- // fs << fieldname_opt[0];
- // else
- // fs << fieldname_opt[0] << theBand;
- // pointAttributes[fs.str()]=writeBuffer[isample][iband+2];
- pointAttributes[fieldname_opt[iband]]=writeBuffer[isample][iband+2];
- }
- ogrWriter.addPoint(writeBuffer[isample][0],writeBuffer[isample][1],pointAttributes,fieldname,isample);
- progress=static_cast<float>(isample+1.0)/writeBuffer.size();
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- ogrWriter.close();
- }
- else{
- std::cout << "No data found for any class " << std::endl;
- }
- classReader.close();
- nsample=writeBuffer.size();
- if(verbose_opt[0]){
- std::cout << "total number of samples written: " << nsample << std::endl;
- if(nvalid.size()==class_opt.size()){
- for(int iclass=0;iclass<class_opt.size();++iclass)
- std::cout << "class " << class_opt[iclass] << " has " << nvalid[iclass] << " samples" << std::endl;
- }
- }
- }
- }
- else{//vector dataset
- if(verbose_opt[0]>1)
- std::cout << "creating image sample writer " << output_opt[0] << std::endl;
- ImgWriterOgr ogrWriter;
- ImgWriterOgr ogrTestWriter;
- double e_ulx;
- double e_uly;
- double e_lrx;
- double e_lry;
- try{
- sampleReaderOgr.getExtent(e_ulx,e_uly,e_lrx,e_lry);
- bool hasCoverage=((e_ulx < imgReader.getLrx())&&(e_lrx > imgReader.getUlx())&&(e_lry < imgReader.getUly())&&(e_uly > imgReader.getLry()));
- if(!hasCoverage){
- ostringstream ess;
- ess << "No coverage in " << image_opt[0] << " for any layer in " << sample_opt[0] << endl;
- throw(ess.str());
- }
- ogrWriter.open(output_opt[0],ogrformat_opt[0]);
- if(test_opt.size()){
- if(verbose_opt[0]>1)
- std::cout << "creating image test writer " << test_opt[0] << std::endl;
- ogrTestWriter.open(test_opt[0],ogrformat_opt[0]);
- }
-
- //if class_opt not set, get number of classes from input image for these rules
- switch(ruleMap[rule_opt[0]]){
- case(rule::proportion):
- case(rule::count):
- case(rule::custom):
- case(rule::mode):{
- if(class_opt.empty()){
- int theBand=0;
- double minValue=0;
- double maxValue=0;
- if(band_opt.size())
- theBand=band_opt[0];
- imgReader.getMinMax(minValue,maxValue,theBand);
- int nclass=maxValue-minValue+1;
- if(nclass<0&&nclass<256){
- string errorString="Could not automatically define classes, please set class option";
- throw(errorString);
- }
- for(int iclass=minValue;iclass<=maxValue;++iclass)
- class_opt.push_back(iclass);
- }
- break;
- }
- }
- }
- catch(string errorString){
- cerr << errorString << endl;
- exit(1);
- }
-
- //support multiple layers
- int nlayerRead=sampleReaderOgr.getDataSource()->GetLayerCount();
- int ilayerWrite=0;
- unsigned long int ntotalvalid=0;
-
- if(verbose_opt[0])
- std::cout << "number of layers: " << nlayerRead << endl;
-
- for(int ilayer=0;ilayer<nlayerRead;++ilayer){
- OGRLayer *readLayer=sampleReaderOgr.getLayer(ilayer);
- string currentLayername=readLayer->GetName();
- int layerIndex=ilayer;
- if(layer_opt.size()){
- vector<string>::const_iterator it=find(layer_opt.begin(),layer_opt.end(),currentLayername);
- if(it==layer_opt.end())
- continue;
- else
- layerIndex=it-layer_opt.begin();
- }
- sampleReaderOgr.getExtent(e_ulx,e_uly,e_lrx,e_lry,ilayer);
- bool hasCoverage=((e_ulx < imgReader.getLrx())&&(e_lrx > imgReader.getUlx())&&(e_lry < imgReader.getUly())&&(e_uly > imgReader.getLry()));
- if(!hasCoverage)
- continue;
- float theThreshold=(threshold_opt.size()==layer_opt.size())? threshold_opt[layerIndex]: threshold_opt[0];
- cout << "processing layer " << currentLayername << endl;
-
- readLayer->ResetReading();
- OGRLayer *writeLayer;
- OGRLayer *writeTestLayer;
-
- if(polygon_opt[0]){
- if(verbose_opt[0])
- std::cout << "create polygons" << std::endl;
- char **papszOptions=NULL;
- writeLayer=ogrWriter.createLayer(readLayer->GetName(), imgReader.getProjection(), wkbPolygon, papszOptions);
- if(test_opt.size())
- writeTestLayer=ogrTestWriter.createLayer(readLayer->GetName(), imgReader.getProjection(), wkbPolygon, papszOptions);
- }
- else{
- if(verbose_opt[0])
- std::cout << "create points in layer " << readLayer->GetName() << std::endl;
- char **papszOptions=NULL;
-
- writeLayer=ogrWriter.createLayer(readLayer->GetName(), imgReader.getProjection(), wkbPoint, papszOptions);
- if(test_opt.size()){
- char **papszOptions=NULL;
- writeTestLayer=ogrTestWriter.createLayer(readLayer->GetName(), imgReader.getProjection(), wkbPoint, papszOptions);
- }
- }
- if(verbose_opt[0])
- std::cout << "copy fields from layer " << ilayer << std::flush << std::endl;
- ogrWriter.copyFields(sampleReaderOgr,ilayer,ilayerWrite);
-
- if(test_opt.size()){
- if(verbose_opt[0])
- std::cout << "copy fields test writer" << std::flush << std::endl;
- ogrTestWriter.copyFields(sampleReaderOgr,ilayer,ilayerWrite);
- }
- // vector<std::string> fieldnames;
- // if(verbose_opt[0])
- // std::cout << "get fields" << std::flush << std::endl;
- // sampleReaderOgr.getFields(fieldnames);
- // assert(fieldnames.size()==ogrWriter.getFieldCount(ilayerWrite));
- // map<std::string,double> pointAttributes;
-
- if(class_opt.size()){
- switch(ruleMap[rule_opt[0]]){
- case(rule::proportion)://proportion for each class
- case(rule::count):{//count for each class
- for(int iclass=0;iclass<class_opt.size();++iclass){
- ostringstream cs;
- cs << class_opt[iclass];
- ogrWriter.createField(cs.str(),fieldType,ilayerWrite);
- }
- break;
- }
- case(rule::custom):
- case(rule::mode):
- ogrWriter.createField(label_opt[0],fieldType,ilayerWrite);
- if(test_opt.size())
- ogrTestWriter.createField(label_opt[0],fieldType,ilayerWrite);
- break;
- }
- }
- else{
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- ostringstream fs;
- fs << fieldname_opt[iband];
- if(verbose_opt[0]>1)
- std::cout << "creating field " << fs.str() << std::endl;
-
- ogrWriter.createField(fs.str(),fieldType,ilayerWrite);
- if(test_opt.size())
- ogrTestWriter.createField(fs.str(),fieldType,ilayerWrite);
- }
- }
- OGRFeature *readFeature;
- unsigned long int ifeature=0;
- unsigned long int nfeatureLayer=sampleReaderOgr.getFeatureCount(ilayer);
- unsigned long int ntotalvalidLayer=0;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- while( (readFeature = readLayer->GetNextFeature()) != NULL ){
- bool validFeature=false;
- bool writeTest=false;//write this feature to test_opt[0] instead of output_opt
- if(verbose_opt[0]>0)
- std::cout << "reading feature " << readFeature->GetFID() << std::endl;
- if(theThreshold>0){//percentual value
- // if(!test_opt.size()&&ntotalvalid>threshold_opt[0]/100.0*nfeature)
- // break;
- double p=static_cast<double>(rand())/(RAND_MAX);
- p*=100.0;
- if(p>theThreshold){
- if(test_opt.size())
- writeTest=true;
- else
- continue;//do not select for now, go to next feature
- }
- }
- else{//absolute value
- if(threshold_opt.size()==layer_opt.size()){
- if(ntotalvalidLayer>=-theThreshold){
- if(test_opt.size())
- writeTest=true;
- else
- continue;//do not select any more pixels, go to next column feature
- }
- }
- else{
- if(ntotalvalid>=-theThreshold){
- if(test_opt.size())
- writeTest=true;
- else
- continue;//do not select any more pixels, go to next column feature
- }
- }
- }
- if(verbose_opt[0]>0)
- std::cout << "processing feature " << readFeature->GetFID() << std::endl;
- //get x and y from readFeature
- double x,y;
- OGRGeometry *poGeometry;
- poGeometry = readFeature->GetGeometryRef();
- assert(poGeometry!=NULL);
- try{
- if(wkbFlatten(poGeometry->getGeometryType()) == wkbPoint ){
-
- if(!buffer_opt.size()){
- switch(ruleMap[rule_opt[0]]){
- case(rule::point):
- case(rule::centroid):
- buffer_opt.push_back(1);//default
- break;
- default:
- buffer_opt.push_back(3);//default
- }
- }
-
- if(verbose_opt[0]>1)
- std::cout << "boundary: " << buffer_opt[0] << std::endl;
-
- OGRPolygon writePolygon;
- OGRLinearRing writeRing;
- OGRPoint writeCentroidPoint;
- OGRFeature *writePolygonFeature;
- OGRFeature *writeCentroidFeature;
-
- OGRPoint *poPoint = (OGRPoint *) poGeometry;
- writeCentroidPoint=*poPoint;
-
- x=poPoint->getX();
- y=poPoint->getY();
-
- double i_centre,j_centre;
- if(geo_opt[0])
- imgReader.geo2image(x,y,i_centre,j_centre);
- else{
- i_centre=x;
- j_centre=y;
- }
- //nearest neighbour
- j_centre=static_cast<int>(j_centre);
- i_centre=static_cast<int>(i_centre);
-
- double uli=i_centre-buffer_opt[0]/2;
- double ulj=j_centre-buffer_opt[0]/2;
- double lri=i_centre+buffer_opt[0]/2;
- double lrj=j_centre+buffer_opt[0]/2;
-
- //nearest neighbour
- ulj=static_cast<int>(ulj);
- uli=static_cast<int>(uli);
- lrj=static_cast<int>(lrj);
- lri=static_cast<int>(lri);
-
- // if((polygon_opt[0]&&ruleMap[rule_opt[0]]==rule::point)||(ruleMap[rule_opt[0]]==rule::centroid)){
- // uli=i_centre;
- // ulj=j_centre;
- // lri=i_centre;
- // lrj=j_centre;
- // }
-
- //check if j is out of bounds
- if(static_cast<int>(ulj)<0||static_cast<int>(ulj)>=imgReader.nrOfRow())
- continue;
- //check if j is out of bounds
- if(static_cast<int>(uli)<0||static_cast<int>(lri)>=imgReader.nrOfCol())
- continue;
-
- OGRPoint ulPoint,urPoint,llPoint,lrPoint;
- double ulx,uly;
- double urx,ury;
-
- if(polygon_opt[0]){
- if(disc_opt[0]){
- double gt[6];// { 444720, 30, 0, 3751320, 0, -30 };
- double radius=buffer_opt[0]/2.0*sqrt(imgReader.getDeltaX()*imgReader.getDeltaY());
- unsigned short nstep = 25;
- for(int i=0;i<nstep;++i){
- OGRPoint aPoint;
- aPoint.setX(x+imgReader.getDeltaX()/2.0+radius*cos(2*PI*i/nstep));
- aPoint.setY(y-imgReader.getDeltaY()/2.0+radius*sin(2*PI*i/nstep));
- writeRing.addPoint(&aPoint);
- }
- writePolygon.addRing(&writeRing);
- writePolygon.closeRings();
- }
- else{
- double llx,lly;
- double lrx,lry;
- imgReader.image2geo(uli,ulj,ulx,uly);
- imgReader.image2geo(lri,lrj,lrx,lry);
- ulPoint.setX(ulx-imgReader.getDeltaX()/2.0);
- ulPoint.setY(uly+imgReader.getDeltaY()/2.0);
- lrPoint.setX(lrx+imgReader.getDeltaX()/2.0);
- lrPoint.setY(lry-imgReader.getDeltaY()/2.0);
- urPoint.setX(lrx+imgReader.getDeltaX()/2.0);
- urPoint.setY(uly+imgReader.getDeltaY()/2.0);
- llPoint.setX(ulx-imgReader.getDeltaX()/2.0);
- llPoint.setY(lry-imgReader.getDeltaY()/2.0);
-
- writeRing.addPoint(&ulPoint);
- writeRing.addPoint(&urPoint);
- writeRing.addPoint(&lrPoint);
- writeRing.addPoint(&llPoint);
- writePolygon.addRing(&writeRing);
- writePolygon.closeRings();
- }
- }
-
- if((polygon_opt[0]&&ruleMap[rule_opt[0]]==rule::point)||(ruleMap[rule_opt[0]]==rule::centroid)){
- uli=i_centre;
- ulj=j_centre;
- lri=i_centre;
- lrj=j_centre;
- }
-
- int nPointWindow=0;//similar to nPointPolygon for polygons
- if(polygon_opt[0]){
- if(writeTest)
- writePolygonFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePolygonFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- else if(ruleMap[rule_opt[0]]!=rule::point){
- if(writeTest)
- writeCentroidFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writeCentroidFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- Vector2d<double> windowValues;
- vector<double> windowClassValues;
-
- if(class_opt.size()){
- windowClassValues.resize(class_opt.size());
- //initialize
- for(int iclass=0;iclass<class_opt.size();++iclass)
- windowClassValues[iclass]=0;
- }
- else
- windowValues.resize(nband);
- vector< Vector2d<double> > readValues(nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- imgReader.readDataBlock(readValues[iband],GDT_Float64,uli,lri,ulj,lrj,theBand);
- }
-
- OGRPoint thePoint;
- for(int j=ulj;j<=lrj;++j){
- for(int i=uli;i<=lri;++i){
- //check if within raster image
- if(i<0||i>=imgReader.nrOfCol())
- continue;
- if(j<0||j>=imgReader.nrOfRow())
- continue;
- //no need to check if point is on surface
- double theX=0;
- double theY=0;
- imgReader.image2geo(i,j,theX,theY);
- thePoint.setX(theX);
- thePoint.setY(theY);
-
- if(disc_opt[0]&&buffer_opt[0]>1){
- double radius=buffer_opt[0]/2.0*sqrt(imgReader.getDeltaX()*imgReader.getDeltaY());
- if((theX-x)*(theX-x)+(theY-y)*(theY-y)>radius*radius)
- continue;
- }
- bool valid=true;
-
- if(srcnodata_opt.size()){
- for(int vband=0;vband<bndnodata_opt.size();++vband){
- double value=((readValues[bndnodata_opt[vband]])[j-ulj])[i-uli];
- if(value==srcnodata_opt[vband]){
- valid=false;
- break;
- }
- }
- }
-
- if(!valid)
- continue;
- else
- validFeature=true;
-
- // writeRing.addPoint(&thePoint);//already done
-
- ++nPointWindow;
- OGRFeature *writePointFeature;
- if(!polygon_opt[0]){
- //create feature
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean, stdev, median, sum or centroid (only create point at centroid)
- if(writeTest)
- writePointFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePointFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- //Geometry of readFeature and writePointFeature are both wkbPoint
- //attributes AND geometry are copied with SetFrom
- //test
- // writePointFeature->SetGeometry(&thePoint);
- if(writePointFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
-
- assert(wkbFlatten(writePointFeature->GetGeometryRef()->getGeometryType()) == wkbPoint);
- // OGRGeometry *updateGeometry;
- // updateGeometry = writePointFeature->GetGeometryRef();
- // OGRPoint *poPoint = (OGRPoint *) updateGeometry;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePointFeature->GetFieldCount() << " fields" << std::endl;
- }
- }
- if(class_opt.size()){
- short value=((readValues[0])[j-ulj])[i-uli];
- for(int iclass=0;iclass<class_opt.size();++iclass){
- if(value==class_opt[iclass])
- windowClassValues[iclass]+=1;
- }
- }
- else{
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- assert(j-ulj>=0);
- assert(j-ulj<readValues[iband].size());
- assert(i-uli>=0);
- assert(i-uli<((readValues[iband])[j-ulj]).size());
- double value=((readValues[iband])[j-ulj])[i-uli];
- // imgReader.readData(value,GDT_Float64,i,j,theBand);
- if(verbose_opt[0]>1)
- std::cout << ": " << value << std::endl;
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point){
- windowValues[iband].push_back(value);
- }
- else{
- try{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[iband] << " to " << value << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),value);
- break;
- case OFTString:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),type2string<double>(value).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }//else
- }//iband
- }//else (class_opt.size())
- if(!polygon_opt[0]){
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean or median value (only at centroid)
- //write feature
- if(verbose_opt[0]>1)
- std::cout << "creating point feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in test ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in ogr vector dataset";
- throw(errorString);
- }
- }
- //destroy feature
- OGRFeature::DestroyFeature( writePointFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- }
- }
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point){
- //do not create if no points found within polygon
- if(!nPointWindow){
- if(verbose_opt[0])
- cout << "no points found in window, continuing" << endl;
- continue;
- }
- //add ring to polygon
- if(polygon_opt[0]){
- // writePolygon.addRing(&writeRing);//already done
- // writePolygon.closeRings();//already done
- //write geometry of writePolygon
- //test
- // writePolygonFeature->SetGeometry(&writePolygon);
- if(writePolygonFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- //test
- writePolygonFeature->SetGeometry(&writePolygon);
- assert(wkbFlatten(writePolygonFeature->GetGeometryRef()->getGeometryType()) == wkbPolygon);
-
- if(verbose_opt[0]>1)
- std::cout << "copying new fields write polygon " << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePolygonFeature->GetFieldCount() << " fields" << std::endl;
- //write polygon feature
- }
- else{//write value of polygon to centroid point
- //create feature
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- //test
- //Geometry of readFeature and writeCentroidFeature are both wkbPoint
- //attributes AND geometry are copied with SetFrom
- // writeCentroidFeature->SetGeometry(&writeCentroidPoint);
- if(writeCentroidFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- assert(wkbFlatten(writeCentroidFeature->GetGeometryRef()->getGeometryType()) == wkbPoint);
- //test
- // OGRGeometry *updateGeometry;
- // updateGeometry = writeCentroidFeature->GetGeometryRef();
- // assert(wkbFlatten(updateGeometry->getGeometryType()) == wkbPoint );
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writeCentroidFeature->GetFieldCount() << " fields" << std::endl;
- }
- if(class_opt.empty()){
- if(ruleMap[rule_opt[0]]==rule::point){//value at centroid of polygon
- if(verbose_opt[0])
- std::cout << "number of points in window: " << nPointWindow << std::endl;
- for(int index=0;index<windowValues.size();++index){
- //test
- if(windowValues[index].size()!=1){
- cerr << "Error: windowValues[index].size()=" << windowValues[index].size() << endl;
- assert(windowValues[index].size()==1);
- }
- double theValue=windowValues[index].back();
-
- if(verbose_opt[0])
- std::cout << "number of points in window: " << nPointWindow << std::endl;
- int theBand=(band_opt.size()) ? band_opt[index] : index;
-
- try{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- else{//ruleMap[rule_opt[0]] is not rule::point
- double theValue=0;
- for(int index=0;index<windowValues.size();++index){
- try{
- if(ruleMap[rule_opt[0]]==rule::mean)
- theValue=stat.mean(windowValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::stdev)
- theValue=sqrt(stat.var(windowValues[index]));
- else if(ruleMap[rule_opt[0]]==rule::median)
- theValue=stat.median(windowValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::percentile)
- theValue=stat.percentile(windowValues[index],windowValues[index].begin(),windowValues[index].end(),percentile_opt[0]);
- else if(ruleMap[rule_opt[0]]==rule::sum)
- theValue=stat.sum(windowValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::max)
- theValue=stat.mymax(windowValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::min)
- theValue=stat.mymin(windowValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::centroid){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointWindow << std::endl;
- assert(nPointWindow<=1);
- assert(nPointWindow==windowValues[index].size());
- theValue=windowValues[index].back();
- }
- else{
- std::string errorString="rule not supported";
- throw(errorString);
- }
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- }
- else{//class_opt is set
- if(ruleMap[rule_opt[0]]==rule::proportion||ruleMap[rule_opt[0]]==rule::count){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointWindow << std::endl;
- if(ruleMap[rule_opt[0]]==rule::proportion)
- stat.normalize_pct(windowClassValues);
- for(int index=0;index<windowClassValues.size();++index){
- double theValue=windowClassValues[index];
- ostringstream fs;
- fs << class_opt[index];
- if(polygon_opt[0])
- writePolygonFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- else
- writeCentroidFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::custom){
- assert(polygon_opt[0]);//not implemented for points
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointWindow << std::endl;
- stat.normalize_pct(windowClassValues);
- assert(windowClassValues.size()==2);//11:broadleaved, 12:coniferous
- if(windowClassValues[0]>=75)//broadleaved
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(11));
- else if(windowClassValues[1]>=75)//coniferous
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(12));
- else if(windowClassValues[0]>25&&windowClassValues[1]>25)//mixed
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(13));
- else{
- if(verbose_opt[0]){
- std::cout << "No valid value in windowClassValues..." << std::endl;
- for(int index=0;index<windowClassValues.size();++index){
- double theValue=windowClassValues[index];
- std::cout << theValue << " ";
- }
- std::cout << std::endl;
- }
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::mode){
- //maximum votes in polygon
- if(verbose_opt[0])
- std::cout << "number of points in window: " << nPointWindow << std::endl;
- //search for class with maximum votes
- int maxClass=stat.mymin(class_opt);
- vector<double>::iterator maxit;
- maxit=stat.mymax(windowClassValues,windowClassValues.begin(),windowClassValues.end());
- int maxIndex=distance(windowClassValues.begin(),maxit);
- maxClass=class_opt[maxIndex];
- if(verbose_opt[0]>0)
- std::cout << "maxClass: " << maxClass << std::endl;
- if(polygon_opt[0])
- writePolygonFeature->SetField(label_opt[0].c_str(),maxClass);
- else
- writeCentroidFeature->SetField(label_opt[0].c_str(),maxClass);
- }
- }
- if(polygon_opt[0]){
- if(verbose_opt[0]>1)
- std::cout << "creating polygon feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writePolygonFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- else{
- if(verbose_opt[0]>1)
- std::cout << "creating point feature in centroid" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- //test
- assert(validFeature);
- if(writeLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writeCentroidFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- }//if wkbPoint
- else if(wkbFlatten(poGeometry->getGeometryType()) == wkbPolygon){
-
- OGRPolygon readPolygon = *((OGRPolygon *) poGeometry);
- OGRPolygon writePolygon;
- OGRLinearRing writeRing;
- OGRPoint writeCentroidPoint;
- OGRFeature *writePolygonFeature;
- OGRFeature *writeCentroidFeature;
-
- readPolygon.closeRings();
-
- if(verbose_opt[0]>1)
- std::cout << "get point on polygon" << std::endl;
- if(ruleMap[rule_opt[0]]==rule::centroid)
- readPolygon.Centroid(&writeCentroidPoint);
- else if(readPolygon.PointOnSurface(&writeCentroidPoint)!=OGRERR_NONE){
- // cerr << "function PointOnSurface failed, trying centroid instead" << endl;
- readPolygon.Centroid(&writeCentroidPoint);
- }
- double ulx,uly,lrx,lry;
- double uli,ulj,lri,lrj;
- if((polygon_opt[0]&&ruleMap[rule_opt[0]]==rule::point)||(ruleMap[rule_opt[0]]==rule::centroid)){
- ulx=writeCentroidPoint.getX();
- uly=writeCentroidPoint.getY();
- lrx=ulx;
- lry=uly;
- }
- else{
- //get envelope
- if(verbose_opt[0])
- std::cout << "reading envelope for polygon " << ifeature << std::endl;
- OGREnvelope* psEnvelope=new OGREnvelope();
- readPolygon.getEnvelope(psEnvelope);
- ulx=psEnvelope->MinX;
- uly=psEnvelope->MaxY;
- lrx=psEnvelope->MaxX;
- lry=psEnvelope->MinY;
- delete psEnvelope;
- }
- if(geo_opt[0]){
- imgReader.geo2image(ulx,uly,uli,ulj);
- imgReader.geo2image(lrx,lry,lri,lrj);
- }
- else{
- uli=ulx;
- ulj=uly;
- lri=lrx;
- lrj=lry;
- }
- //nearest neighbour
- ulj=static_cast<int>(ulj);
- uli=static_cast<int>(uli);
- lrj=static_cast<int>(lrj);
- lri=static_cast<int>(lri);
- //iterate through all pixels
- if(verbose_opt[0]>1)
- std::cout << "bounding box for polygon feature " << ifeature << ": " << uli << " " << ulj << " " << lri << " " << lrj << std::endl;
-
- if(uli<0)
- uli=0;
- if(lri<0)
- lri=0;
- if(uli>=imgReader.nrOfCol())
- uli=imgReader.nrOfCol()-1;
- if(lri>=imgReader.nrOfCol())
- lri=imgReader.nrOfCol()-1;
- if(ulj<0)
- ulj=0;
- if(lrj<0)
- lrj=0;
- if(ulj>=imgReader.nrOfRow())
- ulj=imgReader.nrOfRow()-1;
- if(lrj>=imgReader.nrOfRow())
- lrj=imgReader.nrOfRow()-1;
- // if(uli<0||lri>=imgReader.nrOfCol()||ulj<0||lrj>=imgReader.nrOfRow())
- // continue;
-
- int nPointPolygon=0;
-
- if(polygon_opt[0]){
- if(writeTest)
- writePolygonFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePolygonFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- else if(ruleMap[rule_opt[0]]!=rule::point){
- if(writeTest)
- writeCentroidFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writeCentroidFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- // vector<double> polyValues;
- Vector2d<double> polyValues;
- vector<double> polyClassValues;
-
- if(class_opt.size()){
-
- polyClassValues.resize(class_opt.size());
- //initialize
- for(int iclass=0;iclass<class_opt.size();++iclass)
- polyClassValues[iclass]=0;
- }
- else
- polyValues.resize(nband);
- vector< Vector2d<double> > readValues(nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- imgReader.readDataBlock(readValues[iband],GDT_Float64,uli,lri,ulj,lrj,theBand);
- }
-
- OGRPoint thePoint;
- for(int j=ulj;j<=lrj;++j){
- for(int i=uli;i<=lri;++i){
- //check if within raster image
- if(i<0||i>=imgReader.nrOfCol())
- continue;
- if(j<0||j>=imgReader.nrOfRow())
- continue;
- //check if point is on surface
- double theX=0;
- double theY=0;
- imgReader.image2geo(i,j,theX,theY);
- thePoint.setX(theX);
- thePoint.setY(theY);
- if(ruleMap[rule_opt[0]]!=rule::centroid&&!readPolygon.Contains(&thePoint))
- continue;
-
- bool valid=true;
-
- if(srcnodata_opt.size()){
- for(int vband=0;vband<bndnodata_opt.size();++vband){
- double value=((readValues[bndnodata_opt[vband]])[j-ulj])[i-uli];
- if(value==srcnodata_opt[vband]){
- valid=false;
- break;
- }
- }
- }
-
- if(!valid)
- continue;
- else
- validFeature=true;
-
- writeRing.addPoint(&thePoint);//todo: check if I need to add all interior points to ring or do I need to check if point is on ring first?
- // if(writeRing.isPointOnRingBoundary(&thePoint))
- // writeRing.addPoint(&thePoint);
- if(verbose_opt[0]>1)
- std::cout << "point is on surface:" << thePoint.getX() << "," << thePoint.getY() << std::endl;
- ++nPointPolygon;
-
- if(polythreshold_opt.size())
- if(nPointPolygon>polythreshold_opt[0])
- continue;
- // throw(nPointPolygon);
- OGRFeature *writePointFeature;
- if(!polygon_opt[0]){
- //create feature
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean, stdev, median, sum or centroid (only create point at centroid)
- if(writeTest)
- writePointFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePointFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- if(writePointFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "set geometry as point " << std::endl;
- //test
- writePointFeature->SetGeometry(&thePoint);
- assert(wkbFlatten(writePointFeature->GetGeometryRef()->getGeometryType()) == wkbPoint);
- // OGRGeometry *updateGeometry;
- // updateGeometry = writePointFeature->GetGeometryRef();
- // OGRPoint *poPoint = (OGRPoint *) updateGeometry;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePointFeature->GetFieldCount() << " fields" << std::endl;
- }
- }
- if(class_opt.size()){
- short value=((readValues[0])[j-ulj])[i-uli];
- for(int iclass=0;iclass<class_opt.size();++iclass){
- if(value==class_opt[iclass])
- polyClassValues[iclass]+=1;
- }
- }
- else{
- for(int iband=0;iband<nband;++iband){
- double value=((readValues[iband])[j-ulj])[i-uli];
- if(verbose_opt[0]>1)
- std::cout << ": " << value << std::endl;
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point)
- polyValues[iband].push_back(value);
- else{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[iband] << " to " << value << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),value);
- break;
- case OFTString:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),type2string<double>(value).c_str());
- break;
- default://not supported
- assert(0);
- break;
- }
- }//else
- }//iband
- }//else (class_opt.size())
- if(!polygon_opt[0]){
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean or median value (only at centroid)
- //write feature
- if(verbose_opt[0]>1)
- std::cout << "creating point feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in test ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in ogr vector dataset";
- throw(errorString);
- }
- }
- //destroy feature
- OGRFeature::DestroyFeature( writePointFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- }
- }
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point){
- //do not create if no points found within polygon
- if(!nPointPolygon){
- if(verbose_opt[0])
- cout << "no points found in polygon, continuing" << endl;
- continue;
- }
- //add ring to polygon
- if(polygon_opt[0]){
- writePolygon.addRing(&writeRing);
- writePolygon.closeRings();
- //write geometry of writePolygon
- //test
- //writePolygonFeature and readFeature are both of type wkbPolygon
- // writePolygonFeature->SetGeometry(&writePolygon);
- if(writePolygonFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "copying new fields write polygon " << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePolygonFeature->GetFieldCount() << " fields" << std::endl;
- //write polygon feature
- }
- else{//write value of polygon to centroid point
- //create feature
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- if(writeCentroidFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- writeCentroidFeature->SetGeometry(&writeCentroidPoint);
- assert(wkbFlatten(writeCentroidFeature->GetGeometryRef()->getGeometryType()) == wkbPoint );
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writeCentroidFeature->GetFieldCount() << " fields" << std::endl;
- }
- if(class_opt.empty()){
- if(ruleMap[rule_opt[0]]==rule::point){//value at centroid of polygon
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- for(int index=0;index<polyValues.size();++index){
- assert(polyValues[index].size()==1);
- double theValue=polyValues[index].back();
-
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- int theBand=(band_opt.size()) ? band_opt[index] : index;
- try{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- else{//ruleMap[rule_opt[0]] is not rule::point
- double theValue=0;
- for(int index=0;index<polyValues.size();++index){
- try{
- if(ruleMap[rule_opt[0]]==rule::mean)
- theValue=stat.mean(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::stdev)
- theValue=sqrt(stat.var(polyValues[index]));
- else if(ruleMap[rule_opt[0]]==rule::median)
- theValue=stat.median(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::percentile)
- theValue=stat.percentile(polyValues[index],polyValues[index].begin(),polyValues[index].end(),percentile_opt[0]);
- else if(ruleMap[rule_opt[0]]==rule::sum)
- theValue=stat.sum(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::max)
- theValue=stat.mymax(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::min)
- theValue=stat.mymin(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::centroid){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- assert(nPointPolygon<=1);
- assert(nPointPolygon==polyValues[index].size());
- theValue=polyValues[index].back();
- }
- else{
- std::string errorString="rule not supported";
- throw(errorString);
- }
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default:
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- }
- else{//class_opt is set
- if(ruleMap[rule_opt[0]]==rule::proportion||ruleMap[rule_opt[0]]==rule::count){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- if(ruleMap[rule_opt[0]]==rule::proportion)
- stat.normalize_pct(polyClassValues);
- for(int index=0;index<polyClassValues.size();++index){
- double theValue=polyClassValues[index];
- ostringstream fs;
- fs << class_opt[index];
- if(polygon_opt[0])
- writePolygonFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- else
- writeCentroidFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::custom){
- assert(polygon_opt[0]);//not implemented for points
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- stat.normalize_pct(polyClassValues);
- assert(polyClassValues.size()==2);//11:broadleaved, 12:coniferous
- if(polyClassValues[0]>=75)//broadleaved
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(11));
- else if(polyClassValues[1]>=75)//coniferous
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(12));
- else if(polyClassValues[0]>25&&polyClassValues[1]>25)//mixed
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(13));
- else{
- if(verbose_opt[0]){
- std::cout << "No valid value in polyClassValues..." << std::endl;
- for(int index=0;index<polyClassValues.size();++index){
- double theValue=polyClassValues[index];
- std::cout << theValue << " ";
- }
- std::cout << std::endl;
- }
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::mode){
- //maximum votes in polygon
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- //search for class with maximum votes
- int maxClass=stat.mymin(class_opt);
- vector<double>::iterator maxit;
- maxit=stat.mymax(polyClassValues,polyClassValues.begin(),polyClassValues.end());
- int maxIndex=distance(polyClassValues.begin(),maxit);
- maxClass=class_opt[maxIndex];
- if(verbose_opt[0]>0)
- std::cout << "maxClass: " << maxClass << std::endl;
- if(polygon_opt[0])
- writePolygonFeature->SetField(label_opt[0].c_str(),maxClass);
- else
- writeCentroidFeature->SetField(label_opt[0].c_str(),maxClass);
- }
- }
- if(polygon_opt[0]){
- if(verbose_opt[0]>1)
- std::cout << "creating polygon feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writePolygonFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- else{
- if(verbose_opt[0]>1)
- std::cout << "creating point feature in centroid" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- //test
- assert(validFeature);
- if(writeLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writeCentroidFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- }
- else if(wkbFlatten(poGeometry->getGeometryType()) == wkbMultiPolygon){//todo: try to use virtual OGRGeometry instead of OGRMultiPolygon and OGRPolygon
- OGRMultiPolygon readPolygon = *((OGRMultiPolygon *) poGeometry);
- OGRPolygon writePolygon;
- OGRLinearRing writeRing;
- OGRPoint writeCentroidPoint;
- OGRFeature *writePolygonFeature;
- OGRFeature *writeCentroidFeature;
-
- readPolygon.closeRings();
-
- if(verbose_opt[0]>1)
- std::cout << "get centroid point from polygon" << std::endl;
- readPolygon.Centroid(&writeCentroidPoint);
-
- double ulx,uly,lrx,lry;
- double uli,ulj,lri,lrj;
- if((polygon_opt[0]&&ruleMap[rule_opt[0]]==rule::point)||(ruleMap[rule_opt[0]]==rule::centroid)){
- ulx=writeCentroidPoint.getX();
- uly=writeCentroidPoint.getY();
- lrx=ulx;
- lry=uly;
- }
- else{
- //get envelope
- if(verbose_opt[0])
- std::cout << "reading envelope for polygon " << ifeature << std::endl;
- OGREnvelope* psEnvelope=new OGREnvelope();
- readPolygon.getEnvelope(psEnvelope);
- ulx=psEnvelope->MinX;
- uly=psEnvelope->MaxY;
- lrx=psEnvelope->MaxX;
- lry=psEnvelope->MinY;
- delete psEnvelope;
- }
- // if(geo_opt[0]){
- imgReader.geo2image(ulx,uly,uli,ulj);
- imgReader.geo2image(lrx,lry,lri,lrj);
- // }
- // else{
- // uli=ulx;
- // ulj=uly;
- // lri=lrx;
- // lrj=lry;
- // }
- //nearest neighbour
- ulj=static_cast<int>(ulj);
- uli=static_cast<int>(uli);
- lrj=static_cast<int>(lrj);
- lri=static_cast<int>(lri);
- //iterate through all pixels
- if(verbose_opt[0]>1)
- std::cout << "bounding box for multipologon feature " << ifeature << ": " << uli << " " << ulj << " " << lri << " " << lrj << std::endl;
-
- if(uli<0)
- uli=0;
- if(lri<0)
- lri=0;
- if(uli>=imgReader.nrOfCol())
- uli=imgReader.nrOfCol()-1;
- if(lri>=imgReader.nrOfCol())
- lri=imgReader.nrOfCol()-1;
- if(ulj<0)
- ulj=0;
- if(lrj<0)
- lrj=0;
- if(ulj>=imgReader.nrOfRow())
- ulj=imgReader.nrOfRow()-1;
- if(lrj>=imgReader.nrOfRow())
- lrj=imgReader.nrOfRow()-1;
- // if(uli<0||lri>=imgReader.nrOfCol()||ulj<0||lrj>=imgReader.nrOfRow())
- // continue;
-
- int nPointPolygon=0;
- if(polygon_opt[0]){
- if(writeTest)
- writePolygonFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePolygonFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- else if(ruleMap[rule_opt[0]]!=rule::point){
- if(writeTest)
- writeCentroidFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writeCentroidFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- }
- // vector<double> polyValues;
- Vector2d<double> polyValues;
- vector<double> polyClassValues;
-
- if(class_opt.size()){
- polyClassValues.resize(class_opt.size());
- //initialize
- for(int iclass=0;iclass<class_opt.size();++iclass)
- polyClassValues[iclass]=0;
- }
- else
- polyValues.resize(nband);
- vector< Vector2d<double> > readValues(nband);
- for(int iband=0;iband<nband;++iband){
- int theBand=(band_opt.size()) ? band_opt[iband] : iband;
- imgReader.readDataBlock(readValues[iband],GDT_Float64,uli,lri,ulj,lrj,theBand);
- }
-
- OGRPoint thePoint;
- for(int j=ulj;j<=lrj;++j){
- for(int i=uli;i<=lri;++i){
- //check if within raster image
- if(i<0||i>=imgReader.nrOfCol())
- continue;
- if(j<0||j>=imgReader.nrOfRow())
- continue;
- //check if point is on surface
- double theX=0;
- double theY=0;
- imgReader.image2geo(i,j,theX,theY);
- thePoint.setX(theX);
- thePoint.setY(theY);
-
- if(ruleMap[rule_opt[0]]!=rule::centroid&&!readPolygon.Contains(&thePoint))
- continue;
-
- bool valid=true;
-
- if(srcnodata_opt.size()){
- for(int vband=0;vband<bndnodata_opt.size();++vband){
- double value=((readValues[bndnodata_opt[vband]])[j-ulj])[i-uli];
- if(value==srcnodata_opt[vband]){
- valid=false;
- break;
- }
- }
- }
-
- if(!valid)
- continue;
- else
- validFeature=true;
-
- writeRing.addPoint(&thePoint);
- // if(writeRing.isPointOnRingBoundary(&thePoint))
- // writeRing.addPoint(&thePoint);
- if(verbose_opt[0]>1)
- std::cout << "point is on surface:" << thePoint.getX() << "," << thePoint.getY() << std::endl;
- ++nPointPolygon;
-
- if(polythreshold_opt.size())
- if(nPointPolygon>polythreshold_opt[0])
- continue;
- // throw(nPointPolygon);
- OGRFeature *writePointFeature;
- if(!polygon_opt[0]){
- //create feature
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean, stdev, median or sum (only create point at centroid)
- if(writeTest)
- writePointFeature = OGRFeature::CreateFeature(writeTestLayer->GetLayerDefn());
- else
- writePointFeature = OGRFeature::CreateFeature(writeLayer->GetLayerDefn());
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- if(writePointFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "set geometry as point " << std::endl;
- //test
- writePointFeature->SetGeometry(&thePoint);
- assert(wkbFlatten(writePointFeature->GetGeometryRef()->getGeometryType()) == wkbPoint);
- // OGRGeometry *updateGeometry;
- // updateGeometry = writePointFeature->GetGeometryRef();
- // OGRPoint *poPoint = (OGRPoint *) updateGeometry;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePointFeature->GetFieldCount() << " fields" << std::endl;
- }
- }
- if(class_opt.size()){
- short value=((readValues[0])[j-ulj])[i-uli];
- for(int iclass=0;iclass<class_opt.size();++iclass){
- if(value==class_opt[iclass])
- polyClassValues[iclass]+=1;
- }
- }
- else{
- for(int iband=0;iband<nband;++iband){
- double value=((readValues[iband])[j-ulj])[i-uli];
- if(verbose_opt[0]>1)
- std::cout << ": " << value << std::endl;
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point)
- polyValues[iband].push_back(value);
- else{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[iband] << " to " << value << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),value);
- break;
- case OFTString:
- writePointFeature->SetField(fieldname_opt[iband].c_str(),type2string<double>(value).c_str());
- break;
- default://not supported
- assert(0);
- break;
- }
- }//else
- }//iband
- }//else (class_opt.size())
- if(!polygon_opt[0]){
- if(ruleMap[rule_opt[0]]==rule::point){//do not create in case of mean, stdev or median value (only at centroid)
- //write feature
- if(verbose_opt[0]>1)
- std::cout << "creating point feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePointFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create feature in ogr vector dataset";
- throw(errorString);
- }
- }
- //destroy feature
- OGRFeature::DestroyFeature( writePointFeature );
- }
- }
- // ++isample;
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- if(!validFeature)
- continue;
- if(polygon_opt[0]||ruleMap[rule_opt[0]]!=rule::point){
- //do not create if no points found within polygon
- if(!nPointPolygon)
- continue;
- //add ring to polygon
- if(polygon_opt[0]){
- writePolygon.addRing(&writeRing);
- writePolygon.closeRings();
- //write geometry of writePolygon
- //test
- //writePolygon and readFeature are from geometry type wkbMultiPolygon
- // writePolygonFeature->SetGeometry(&writePolygon);
- if(writePolygonFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- assert(writePolygonFeature->GetGeometryRef()->getGeometryType()==wkbMultiPolygon);
- if(verbose_opt[0]>1)
- std::cout << "copying new fields write polygon " << std::endl;
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writePolygonFeature->GetFieldCount() << " fields" << std::endl;
- //write polygon feature
- }
- else{//write band information of polygon to centroid point
- //create feature
- if(verbose_opt[0]>1)
- std::cout << "copying fields from polygons " << std::endl;
- if(writeCentroidFeature->SetFrom(readFeature)!= OGRERR_NONE)
- cerr << "writing feature failed" << std::endl;
- writeCentroidFeature->SetGeometry(&writeCentroidPoint);
- assert(wkbFlatten(writeCentroidFeature->GetGeometryRef()->getGeometryType()) == wkbPoint);
- if(verbose_opt[0]>1)
- std::cout << "write feature has " << writeCentroidFeature->GetFieldCount() << " fields" << std::endl;
- }
- if(class_opt.empty()){
- if(ruleMap[rule_opt[0]]==rule::point){//value at centroid of polygon
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- for(int index=0;index<polyValues.size();++index){
- //test
- assert(polyValues[index].size()==1);
- double theValue=polyValues[index].back();
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- int theBand=(band_opt.size()) ? band_opt[index] : index;
- try{
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- else{//ruleMap[rule_opt[0]] is not rule::point
- double theValue=0;
- for(int index=0;index<polyValues.size();++index){
- try{
- if(ruleMap[rule_opt[0]]==rule::mean)
- theValue=stat.mean(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::stdev)
- theValue=sqrt(stat.var(polyValues[index]));
- else if(ruleMap[rule_opt[0]]==rule::median)
- theValue=stat.median(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::percentile)
- theValue=stat.percentile(polyValues[index],polyValues[index].begin(),polyValues[index].end(),percentile_opt[0]);
- else if(ruleMap[rule_opt[0]]==rule::sum)
- theValue=stat.sum(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::max)
- theValue=stat.mymax(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::min)
- theValue=stat.mymin(polyValues[index]);
- else if(ruleMap[rule_opt[0]]==rule::centroid){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- assert(nPointPolygon<=1);
- assert(nPointPolygon==polyValues[index].size());
- theValue=polyValues[index].back();
- }
- else{
- std::string errorString="rule not supported";
- throw(errorString);
- }
- if(verbose_opt[0]>1)
- std::cout << "set field " << fieldname_opt[index] << " to " << theValue << std::endl;
- switch( fieldType ){
- case OFTInteger:
- case OFTReal:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),theValue);
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),theValue);
- break;
- case OFTString:
- if(polygon_opt[0])
- writePolygonFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- else
- writeCentroidFeature->SetField(fieldname_opt[index].c_str(),type2string<double>(theValue).c_str());
- break;
- default://not supported
- std::string errorString="field type not supported";
- throw(errorString);
- break;
- }
- }
- catch(std::string e){
- std::cout << e << std::endl;
- exit(1);
- }
- }
- }
- }
- else{//class_opt is set
- if(ruleMap[rule_opt[0]]==rule::proportion||ruleMap[rule_opt[0]]==rule::count){
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- if(ruleMap[rule_opt[0]]==rule::proportion)
- stat.normalize_pct(polyClassValues);
- for(int index=0;index<polyClassValues.size();++index){
- double theValue=polyClassValues[index];
- ostringstream fs;
- fs << class_opt[index];
- if(polygon_opt[0])
- writePolygonFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- else
- writeCentroidFeature->SetField(fs.str().c_str(),static_cast<int>(theValue));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::custom){
- assert(polygon_opt[0]);//not implemented for points
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- stat.normalize_pct(polyClassValues);
- assert(polyClassValues.size()==2);//11:broadleaved, 12:coniferous
- if(polyClassValues[0]>=75)//broadleaved
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(11));
- else if(polyClassValues[1]>=75)//coniferous
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(12));
- else if(polyClassValues[0]>25&&polyClassValues[1]>25)//mixed
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(13));
- else{
- if(verbose_opt[0]){
- std::cout << "No valid value in polyClassValues..." << std::endl;
- for(int index=0;index<polyClassValues.size();++index){
- double theValue=polyClassValues[index];
- std::cout << theValue << " ";
- }
- std::cout << std::endl;
- }
- writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
- }
- }
- else if(ruleMap[rule_opt[0]]==rule::mode){
- //maximum votes in polygon
- if(verbose_opt[0])
- std::cout << "number of points in polygon: " << nPointPolygon << std::endl;
- //search for class with maximum votes
- int maxClass=stat.mymin(class_opt);
- vector<double>::iterator maxit;
- maxit=stat.mymax(polyClassValues,polyClassValues.begin(),polyClassValues.end());
- int maxIndex=distance(polyClassValues.begin(),maxit);
- maxClass=class_opt[maxIndex];
- if(verbose_opt[0]>0)
- std::cout << "maxClass: " << maxClass << std::endl;
- if(polygon_opt[0])
- writePolygonFeature->SetField(label_opt[0].c_str(),maxClass);
- else
- writeCentroidFeature->SetField(label_opt[0].c_str(),maxClass);
- }
- }
-
- if(polygon_opt[0]){
- if(verbose_opt[0]>1)
- std::cout << "creating polygon feature" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- if(writeLayer->CreateFeature( writePolygonFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create polygon feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writePolygonFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- else{
- if(verbose_opt[0]>1)
- std::cout << "creating point feature in centroid" << std::endl;
- if(writeTest){
- if(writeTestLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- else{
- //test
- assert(validFeature);
- if(writeLayer->CreateFeature( writeCentroidFeature ) != OGRERR_NONE ){
- std::string errorString="Failed to create point feature in ogr vector dataset";
- throw(errorString);
- }
- }
- OGRFeature::DestroyFeature( writeCentroidFeature );
- ++ntotalvalid;
- ++ntotalvalidLayer;
- }
- }
- }
- else{
- std::string test;
- test=poGeometry->getGeometryName();
- ostringstream oss;
- oss << "geometry " << test << " not supported";
- throw(oss.str());
- }
- ++ifeature;
- if(theThreshold>0){
- if(threshold_opt.size()==layer_opt.size())
- progress=(100.0/theThreshold)*static_cast<float>(ntotalvalidLayer)/nfeatureLayer;
- else
- progress=static_cast<float>(ntotalvalidLayer)/nfeatureLayer;
- }
- else
- progress=static_cast<float>(ifeature+1)/(-theThreshold);
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- catch(std::string e){
- std::cout << e << std::endl;
- continue;
- }
- catch(int npoint){
- if(verbose_opt[0])
- std::cout << "number of points read in polygon: " << npoint << std::endl;
- continue;
- }
- }//end of getNextFeature
- // if(rbox_opt[0]>0||cbox_opt[0]>0)
- // boxWriter.close();
- progress=1.0;
- pfnProgress(progress,pszMessage,pProgressArg);
- ++ilayerWrite;
- }//for ilayer
- sampleReaderOgr.close();
- ogrWriter.close();
- if(test_opt.size())
- ogrTestWriter.close();
- }//else (vector)
- progress=1.0;
- pfnProgress(progress,pszMessage,pProgressArg);
- imgReader.close();
-}
-
diff --git a/src/apps/pkextractimg.cc b/src/apps/pkextractimg.cc
index 7265474..16ce535 100644
--- a/src/apps/pkextractimg.cc
+++ b/src/apps/pkextractimg.cc
@@ -45,7 +45,7 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
<code>
- Options: [-c class]* [-t threshold]* [-f format] [-ft fieldType] [-lt labelType] [-b band]* [-r rule]
+ Options: [-c class]* [-t threshold]* [-f format] [-ft fieldType] [-lt labelType] [-b band]*
Advanced options:
[-sband band -eband band]* [-bndnodata band [-srcnodata value]*] [-bn attribute] [-cn attribute] [-down value]
@@ -55,10 +55,6 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
The utility pkextractimg extracts pixel values from an input raster dataset, based on the locations you provide via a sample file. The sample should be a raster dataset with categorical (integer) values. The typical use case is a land cover map that overlaps the input raster dataset. The utility then extracts pixels from the input raster for the respective land cover classes. To select a random subset of the sample raster dataset you can set the threshold option -t with a percentage valu [...]
-\anchor pkextractimg_rules
-
-Overview of the possible extraction rules:
-
\section pkextractimg_options Options
- use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- short option `-h` shows basic options only, long option `--help` shows all options
@@ -66,11 +62,8 @@ Overview of the possible extraction rules:
|-----|----|----|-------|-----------|
| i | input | std::string | |Raster input dataset containing band information |
| s | sample | std::string | |Raster dataset with categorical values to sample the input raster dataset. Output will contain features with input band information included |
- | ln | ln | std::string | |Layer name(s) in sample (leave empty to select all) |
- | rand | random | unsigned int | |Create simple random sample of points. Provide number of points to generate |
- | grid | grid | double | |Create systematic grid of points. Provide cell grid size (in projected units, e.g,. m) |
| o | output | std::string | |Output sample dataset |
- | c | class | int | |Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset. Make sure to set classes if rule is set to mode, proportion or count |
+ | c | class | int | |Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset |
| t | threshold | float | 100 |Probability threshold for selecting samples (randomly). Provide probability in percentage (>0) or absolute (<0). You can provide a threshold value for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for selected class(es) |
| f | f | std::string | SQLite |Output sample dataset format |
| ft | ftype | std::string | Real |Field type (only Real or Integer) |
@@ -84,12 +77,12 @@ Overview of the possible extraction rules:
| cn | cname | std::string | label |Name of the class label in the output vector dataset |
| down | down | short | 1 |Down sampling factor |
-Usage: pkextractimg -i input [-s sample | -rand number | -grid size] -o output
+Usage: pkextractimg -i input [-s sample] -o output
Examples
========
-Some examples how to use pkextractimg can be found \ref examples_pkextractimg "here"
+Some examples how to use pkextractimg can be found \ref examples_pkextract "here"
**/
using namespace std;
@@ -98,10 +91,8 @@ int main(int argc, char *argv[])
{
Optionpk<string> image_opt("i", "input", "Raster input dataset containing band information");
Optionpk<string> sample_opt("s", "sample", "OGR vector dataset with features to be extracted from input data. Output will contain features with input band information included. Sample image can also be GDAL raster dataset.");
- Optionpk<unsigned int> random_opt("rand", "random", "Create simple random sample of points. Provide number of points to generate");
- Optionpk<double> grid_opt("grid", "grid", "Create systematic grid of points. Provide cell grid size (in projected units, e.g,. m)");
Optionpk<string> output_opt("o", "output", "Output sample dataset");
- Optionpk<int> class_opt("c", "class", "Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset. Make sure to set classes if rule is set to mode, proportion or count");
+ Optionpk<int> class_opt("c", "class", "Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset");
Optionpk<float> threshold_opt("t", "threshold", "Probability threshold for selecting samples (randomly). Provide probability in percentage (>0) or absolute (<0). Use a single threshold per vector sample layer. If using raster land cover maps as a sample dataset, you can provide a threshold value for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for selected class(es)", 100);
Optionpk<string> ogrformat_opt("f", "f", "Output sample dataset format","SQLite");
Optionpk<string> ftype_opt("ft", "ftype", "Field type (only Real or Integer)", "Real");
@@ -128,8 +119,6 @@ int main(int argc, char *argv[])
try{
doProcess=image_opt.retrieveOption(argc,argv);
sample_opt.retrieveOption(argc,argv);
- random_opt.retrieveOption(argc,argv);
- grid_opt.retrieveOption(argc,argv);
output_opt.retrieveOption(argc,argv);
class_opt.retrieveOption(argc,argv);
threshold_opt.retrieveOption(argc,argv);
@@ -152,7 +141,7 @@ int main(int argc, char *argv[])
}
if(!doProcess){
cout << endl;
- cout << "Usage: pkextractimg -i input [-s sample | -rand number | -grid size] -o output" << endl;
+ cout << "Usage: pkextractimg -i input -s sample -o output" << endl;
cout << endl;
std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
exit(0);//help was invoked, stop processing
diff --git a/src/apps/pkextractogr.cc b/src/apps/pkextractogr.cc
index 0ad1fa4..19af55a 100644
--- a/src/apps/pkextractogr.cc
+++ b/src/apps/pkextractogr.cc
@@ -1,6 +1,6 @@
/**********************************************************************
pkextractogr.cc: extract pixel values from raster image from a (vector or raster) sample
-Copyright (C) 2008-2016 Pieter Kempeneers
+Copyright (C) 2008-2017 Pieter Kempeneers
This file is part of pktools
@@ -55,12 +55,14 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
The utility pkextractogr extracts pixel values from an input raster dataset, based on the locations you provide via a sample file. Alternatively, a random sample or systematic grid of points can also be extracted. The sample can be a vector file with points or polygons. In the case of polygons, you can either extract the values for all raster pixels that are covered by the polygons, or extract a single value for each polygon such as the centroid, mean, median, etc. As output, a new copy [...]
-A typical usage of pkextract is to prepare a training sample for one of the classifiers implemented in pktools.
+A typical usage of pkextractogr is to prepare a training sample for one of the classifiers implemented in pktools.
\anchor pkextractogr_rules
Overview of the possible extraction rules:
+\section pkextractogr_rules Extraction rules:
+
extraction rule | output features
--------------- | ---------------
point | extract a single pixel within the polygon or on each point feature
@@ -77,7 +79,7 @@ proportion | Extract proportion of class(es) within the polygon (classes must be
count | Extract count of class(es) within the polygon (classes must be set with the option class).
percentile | Extract percentile as defined by option perc (e.g, 95th percentile of values covered by polygon)
-\section pkextract_options Options
+\section pkextractogr_options Options
- use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- short option `-h` shows basic options only, long option `--help` shows all options
|short|long|type|default|description|
@@ -103,12 +105,12 @@ percentile | Extract percentile as defined by option perc (e.g, 95th percentile
| buf | buffer | short | 0 |Buffer for calculating statistics for point features (in number of pixels) |
| circ | circular | bool | false |Use a circular disc kernel buffer (for vector point sample datasets only, use in combination with buffer option) |
-Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output -r rule
+Usage: pkextractogr -i input [-s sample | -rand number | -grid size] -o output -r rule
Examples
========
-Some examples how to use pkextract can be found \ref examples_pkextract "here"
+Some examples how to use pkextractogr can be found \ref examples_pkextract "here"
**/
namespace rule{
@@ -180,7 +182,7 @@ int main(int argc, char *argv[])
}
if(!doProcess){
cout << endl;
- cout << "Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output" << endl;
+ cout << "Usage: pkextractogr -i input [-s sample | -rand number | -grid size] -o output" << endl;
cout << endl;
std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
exit(0);//help was invoked, stop processing
diff --git a/src/apps/pkfilter.cc b/src/apps/pkfilter.cc
index 9134bfb..2b74415 100644
--- a/src/apps/pkfilter.cc
+++ b/src/apps/pkfilter.cc
@@ -39,7 +39,7 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
## SYNOPSIS
<code>
- Usage: pkfilter -i input -o ouptut [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]
+ Usage: pkfilter -i input -o output [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]
</code>
<code>
@@ -214,7 +214,7 @@ pkfilter -i lena.tif -o sobelx.tif -f sobelx -dx 5 -dy 5
| ct | ct | std::string | |color table (file with 5 columns: id R G B ALFA (0: transparent, 255: solid). Use none to ommit color table |
| circ | circular | bool | false |circular disc kernel for dilation and erosion |
-Usage: pkfilter -i input -o ouptut [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]
+Usage: pkfilter -i input -o output [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]
Examples
@@ -343,7 +343,7 @@ int main(int argc,char **argv) {
}
if(!doProcess){
cout << endl;
- cout << "Usage: pkfilter -i input -o ouptut [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]" << endl;
+ cout << "Usage: pkfilter -i input -o output [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]" << endl;
cout << endl;
std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
exit(0);//help was invoked, stop processing
diff --git a/src/apps/pklas2img.cc b/src/apps/pklas2img.cc
index 4ec7875..4968342 100644
--- a/src/apps/pklas2img.cc
+++ b/src/apps/pklas2img.cc
@@ -287,9 +287,7 @@ int main(int argc,char **argv) {
gt[5]=-dy_opt[0];
outputWriter.setGeoTransform(gt);
if(projection_opt.size()){
- string projectionString=outputWriter.setProjectionProj4(projection_opt[0]);
- if(verbose_opt[0])
- cout << "projection: " << projectionString << endl;
+ outputWriter.setProjectionProj4(projection_opt[0]);
}
if(!outputWriter.isGeoRef())
cout << "Warning: output image " << output_opt[0] << " is not georeferenced!" << endl;
diff --git a/src/apps/pkreclass.cc b/src/apps/pkreclass.cc
index 8a268d4..35d65a4 100644
--- a/src/apps/pkreclass.cc
+++ b/src/apps/pkreclass.cc
@@ -47,7 +47,7 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
| c | class | std::string | |list of classes to reclass (in combination with reclass option) |
| r | reclass | std::string | |list of recoded classes (in combination with class option) |
| ct | ct | std::string | |color table (file with 5 columns: id R G B ALFA (0: transparent, 255: solid) |
- | o | output | std::string | |Output mask file |
+ | o | output | std::string | |Output file |
| ot | otype | std::string | |Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image |
| of | oformat | std::string | GTiff |Output image format (see also gdal_translate)|
| b | band | unsigned short | 0 |band index(es) to replace (other bands are copied to output) |
@@ -160,65 +160,9 @@ int main(int argc, char *argv[])
for(mit=codemapString.begin();mit!=codemapString.end();++mit)
cout << (*mit).first << " " << (*mit).second << endl;
}
- bool refIsRaster=false;
+ bool refIsRaster=true;
ImgReaderOgr ogrReader;
- try{
- ogrReader.open(input_opt[0]);
- }
- catch(string errorString){
- refIsRaster=true;
- }
- // if(input_opt[0].find(".shp")!=string::npos){//shape file
- if(!refIsRaster){
- if(verbose_opt[0])
- cout << "opening " << input_opt[0] << " for reading " << endl;
- // ImgReaderOgr ogrReader(input_opt[0]);
- if(verbose_opt[0])
- cout << "opening " << output_opt[0] << " for writing " << endl;
- ImgWriterOgr ogrWriter(output_opt[0],ogrReader);
- if(verbose_opt[0])
- cout << "copied layer from " << input_opt[0] << endl << flush;
- OGRFeatureDefn *poFDefn = ogrWriter.getLayer()->GetLayerDefn();
- //start reading features from the layer
- if(verbose_opt[0])
- cout << "reset reading" << endl;
- ogrReader.getLayer()->ResetReading();
- unsigned long int ifeature=0;
- if(verbose_opt[0])
- cout << "going through features" << endl << flush;
- while(true){
-// while( (poFeature = ogrWriter.getLayer()->GetNextFeature()) != NULL ){
- OGRFeature *poFeature;
- poFeature=ogrReader.getLayer()->GetNextFeature();
- if(poFeature== NULL)
- break;
- OGRFeatureDefn *poFDefn = ogrWriter.getLayer()->GetLayerDefn();
- string featurename;
- for(int iField=0;iField<poFDefn->GetFieldCount();++iField){
- OGRFieldDefn *poFieldDefn = poFDefn->GetFieldDefn(iField);
- string fieldname=poFieldDefn->GetNameRef();
- if(fieldname==fieldname_opt[0]){
- string fromClass=poFeature->GetFieldAsString(iField);
- string toClass=fromClass;
- if(codemapString.find(fromClass)!=codemapString.end())
- toClass=codemapString[fromClass];
- poFeature->SetField(iField,toClass.c_str());
- if(verbose_opt[0])
- cout << "feature " << ifeature << ": " << fromClass << "->" << poFeature->GetFieldAsInteger(iField) << endl << flush;
-// cout << "feature " << ifeature << ": " << fromClass << "->" << toClass << endl << flush;
- }
- }
- //do not forget to actually write feature to file!!!
- ogrWriter.createFeature(poFeature);
- OGRFeature::DestroyFeature( poFeature );
- ++ifeature;
- }
- if(verbose_opt[0])
- cout << "replaced " << ifeature << " features" << endl;
- ogrReader.close();
- ogrWriter.close();
- }
- else{//image file
+ if(refIsRaster){//image file
ImgReaderGdal inputReader;
vector<ImgReaderGdal> maskReader(mask_opt.size());
ImgWriterGdal outputWriter;
diff --git a/src/apps/pkreclassogr.cc b/src/apps/pkreclassogr.cc
new file mode 100644
index 0000000..39f8bc7
--- /dev/null
+++ b/src/apps/pkreclassogr.cc
@@ -0,0 +1,194 @@
+/**********************************************************************
+pkreclassogr.cc: program to replace field values of attributes in vector dataset
+Copyright (C) 2008-2017 Pieter Kempeneers
+
+This file is part of pktools
+
+pktools is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+pktools is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with pktools. If not, see <http://www.gnu.org/licenses/>.
+***********************************************************************/
+#include <assert.h>
+#include <map>
+#include "base/Optionpk.h"
+#include "imageclasses/ImgReaderOgr.h"
+#include "imageclasses/ImgWriterOgr.h"
+#include "imageclasses/ImgReaderGdal.h"
+#include "imageclasses/ImgWriterGdal.h"
+
+/******************************************************************************/
+/*! \page pkreclassogr pkreclassogr
+ program to replace field values of attributes in vector dataset
+ ## SYNOPSIS
+
+ <code>
+ Usage: pkreclassogr -i input [-c from -r to]* -o output
+ </code>
+
+ \section pkreclassogr_options Options
+ - use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
+ - short option `-h` shows basic options only, long option `--help` shows all options
+ |short|long|type|default|description|
+ |-----|----|----|-------|-----------|
+ | i | input | std::string | |Input vector dataset |
+ | nodata | nodata | int | 0 |nodata value to put in vector dataset if not valid (0) |
+ | code | code | std::string | |Recode text file (2 colums: from to) |
+ | c | class | std::string | |list of classes to reclass (in combination with reclass option) |
+ | r | reclass | std::string | |list of recoded classes (in combination with class option) |
+ | o | output | std::string | |Output vector dataset |
+ | n | fname | std::string | label |field name of the shape file to be replaced |
+ | v | verbose | short | 0 |verbose |
+
+ Usage: pkreclassogr -i input [-c from -r to]* -o output
+
+
+ Examples
+ ========
+ Some examples how to use pkreclassogr can be found \ref examples_pkreclassogr "here"
+**/
+
+using namespace std;
+
+int main(int argc, char *argv[])
+{
+ Optionpk<string> input_opt("i", "input", "Input vector dataset");
+ Optionpk<string> output_opt("o", "output", "Output file");
+ Optionpk<int> nodata_opt("nodata", "nodata", "nodata value to put in vector dataset if not valid (0)", 0);
+ Optionpk<string> code_opt("code", "code", "Recode text file (2 colums: from to)");
+ Optionpk<string> class_opt("c", "class", "list of classes to reclass (in combination with reclass option)");
+ Optionpk<string> reclass_opt("r", "reclass", "list of recoded classes (in combination with class option)");
+ Optionpk<string> fieldname_opt("n", "fname", "field name of the shape file to be replaced", "label");
+ Optionpk<short> verbose_opt("v", "verbose", "verbose", 0);
+
+ bool doProcess;//stop process when program was invoked with help option (-h --help)
+ try{
+ doProcess=input_opt.retrieveOption(argc,argv);
+ nodata_opt.retrieveOption(argc,argv);
+ code_opt.retrieveOption(argc,argv);
+ class_opt.retrieveOption(argc,argv);
+ reclass_opt.retrieveOption(argc,argv);
+ output_opt.retrieveOption(argc,argv);
+ fieldname_opt.retrieveOption(argc,argv);
+ verbose_opt.retrieveOption(argc,argv);
+ }
+ catch(string predefinedString){
+ std::cout << predefinedString << std::endl;
+ exit(0);
+ }
+ if(!doProcess){
+ cout << endl;
+ cout << "Usage: pkreclassogr -i input [-c from -r to]* -o output" << endl;
+ cout << endl;
+ std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
+ exit(0);//help was invoked, stop processing
+ }
+
+ if(input_opt.empty()){
+ std::cerr << "No input file provided (use option -i). Use --help for help information" << std::endl;
+ exit(0);
+ }
+ if(output_opt.empty()){
+ std::cerr << "No output file provided (use option -o). Use --help for help information" << std::endl;
+ exit(0);
+ }
+
+ // vector<short> bandVector;
+ map<string,string> codemapString;//map with codes: codemapString[theKey(from)]=theValue(to)
+ map<double,double> codemap;//map with codes: codemap[theKey(from)]=theValue(to)
+ if(code_opt.size()){
+ if(verbose_opt[0])
+ cout << "opening code text file " << code_opt[0] << endl;
+ ifstream codefile;
+ codefile.open(code_opt[0].c_str());
+ string theKey;
+ string theValue;
+ while(codefile>>theKey){
+ codefile >> theValue;
+ codemapString[theKey]=theValue;
+ codemap[string2type<double>(theKey)]=string2type<double>(theValue);
+ }
+ codefile.close();
+ }
+ else{//use combination of class_opt and reclass_opt
+ assert(class_opt.size()==reclass_opt.size());
+ for(int iclass=0;iclass<class_opt.size();++iclass){
+ codemapString[class_opt[iclass]]=reclass_opt[iclass];
+ codemap[string2type<double>(class_opt[iclass])]=string2type<double>(reclass_opt[iclass]);
+ }
+ }
+ assert(codemapString.size());
+ assert(codemap.size());
+ //if verbose true, print the codes to screen
+ if(verbose_opt[0]){
+ map<string,string>::iterator mit;
+ cout << codemapString.size() << " codes used: " << endl;
+ for(mit=codemapString.begin();mit!=codemapString.end();++mit)
+ cout << (*mit).first << " " << (*mit).second << endl;
+ }
+ bool refIsRaster=false;
+ ImgReaderOgr ogrReader;
+ try{
+ ogrReader.open(input_opt[0]);
+ }
+ catch(string errorString){
+ refIsRaster=true;
+ }
+ if(!refIsRaster){
+ if(verbose_opt[0])
+ cout << "opening " << input_opt[0] << " for reading " << endl;
+ // ImgReaderOgr ogrReader(input_opt[0]);
+ if(verbose_opt[0])
+ cout << "opening " << output_opt[0] << " for writing " << endl;
+ ImgWriterOgr ogrWriter(output_opt[0],ogrReader);
+ if(verbose_opt[0])
+ cout << "copied layer from " << input_opt[0] << endl << flush;
+ OGRFeatureDefn *poFDefn = ogrWriter.getLayer()->GetLayerDefn();
+ //start reading features from the layer
+ if(verbose_opt[0])
+ cout << "reset reading" << endl;
+ ogrReader.getLayer()->ResetReading();
+ unsigned long int ifeature=0;
+ if(verbose_opt[0])
+ cout << "going through features" << endl << flush;
+ while(true){
+ // while( (poFeature = ogrWriter.getLayer()->GetNextFeature()) != NULL ){
+ OGRFeature *poFeature;
+ poFeature=ogrReader.getLayer()->GetNextFeature();
+ if(poFeature== NULL)
+ break;
+ OGRFeatureDefn *poFDefn = ogrWriter.getLayer()->GetLayerDefn();
+ string featurename;
+ for(int iField=0;iField<poFDefn->GetFieldCount();++iField){
+ OGRFieldDefn *poFieldDefn = poFDefn->GetFieldDefn(iField);
+ string fieldname=poFieldDefn->GetNameRef();
+ if(fieldname==fieldname_opt[0]){
+ string fromClass=poFeature->GetFieldAsString(iField);
+ string toClass=fromClass;
+ if(codemapString.find(fromClass)!=codemapString.end())
+ toClass=codemapString[fromClass];
+ poFeature->SetField(iField,toClass.c_str());
+ if(verbose_opt[0])
+ cout << "feature " << ifeature << ": " << fromClass << "->" << poFeature->GetFieldAsInteger(iField) << endl << flush;
+ // cout << "feature " << ifeature << ": " << fromClass << "->" << toClass << endl << flush;
+ }
+ }
+ //do not forget to actually write feature to file!!!
+ ogrWriter.createFeature(poFeature);
+ OGRFeature::DestroyFeature( poFeature );
+ ++ifeature;
+ }
+ if(verbose_opt[0])
+ cout << "replaced " << ifeature << " features" << endl;
+ ogrReader.close();
+ ogrWriter.close();
+ }
+}
diff --git a/src/apps/pkstatprofile.cc b/src/apps/pkstatprofile.cc
index b9e59e4..841dc3b 100644
--- a/src/apps/pkstatprofile.cc
+++ b/src/apps/pkstatprofile.cc
@@ -87,7 +87,7 @@ pkstatprofile -i modis_ndvi_2010.tif -o modis_stats_2010.tif -f min -f max
|class | class | std::string | | class value(s) to use for mode, proportion |
|nodata | nodata | double | | nodata value(s) |
-Usage: pkstatprofile -i input -o ouptut [-f function]*
+Usage: pkstatprofile -i input -o output [-f function]*
**/
@@ -133,7 +133,7 @@ int main(int argc,char **argv) {
}
if(!doProcess){
cout << endl;
- cout << "Usage: pkstatprofile -i input -o ouptut [-function]*" << endl;
+ cout << "Usage: pkstatprofile -i input -o output [-function]*" << endl;
cout << endl;
std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
exit(0);//help was invoked, stop processing
diff --git a/src/apps/pksvm.cc b/src/apps/pksvm.cc
index eb91aed..b632f46 100644
--- a/src/apps/pksvm.cc
+++ b/src/apps/pksvm.cc
@@ -1,6 +1,6 @@
/**********************************************************************
pksvm.cc: classify raster image using Support Vector Machine
-Copyright (C) 2008-2014 Pieter Kempeneers
+Copyright (C) 2008-2017 Pieter Kempeneers
This file is part of pktools
@@ -751,15 +751,8 @@ int main(int argc, char *argv[])
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
//-------------------------------- open image file ------------------------------------
- bool inputIsRaster=false;
+ bool inputIsRaster=true;
ImgReaderOgr imgReaderOgr;
- try{
- imgReaderOgr.open(input_opt[0]);
- imgReaderOgr.close();
- }
- catch(string errorString){
- inputIsRaster=true;
- }
if(inputIsRaster){
ImgReaderGdal testImage;
try{
@@ -1194,201 +1187,6 @@ int main(int argc, char *argv[])
classImageBag.close();
classImageOut.close();
}
- else{//classify vector file
- cm.clearResults();
- //notice that fields have already been set by readDataImageOgr (taking into account appropriate bands)
- for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
- if(output_opt.size())
- assert(output_opt.size()==input_opt.size());
- if(verbose_opt[0])
- std::cout << "opening img reader " << input_opt[ivalidation] << std::endl;
- imgReaderOgr.open(input_opt[ivalidation]);
- ImgWriterOgr imgWriterOgr;
-
- if(output_opt.size()){
- if(verbose_opt[0])
- std::cout << "opening img writer and copying fields from img reader" << output_opt[ivalidation] << std::endl;
- imgWriterOgr.open(output_opt[ivalidation],imgReaderOgr);
- }
- if(verbose_opt[0])
- cout << "number of layers in input ogr file: " << imgReaderOgr.getLayerCount() << endl;
- for(int ilayer=0;ilayer<imgReaderOgr.getLayerCount();++ilayer){
- if(verbose_opt[0])
- cout << "processing input layer " << ilayer << endl;
- if(output_opt.size()){
- if(verbose_opt[0])
- std::cout << "creating field class" << std::endl;
- if(classValueMap.size())
- imgWriterOgr.createField("class",OFTInteger,ilayer);
- else
- imgWriterOgr.createField("class",OFTString,ilayer);
- }
- unsigned int nFeatures=imgReaderOgr.getFeatureCount(ilayer);
- unsigned int ifeature=0;
- progress=0;
- pfnProgress(progress,pszMessage,pProgressArg);
- OGRFeature *poFeature;
- while( (poFeature = imgReaderOgr.getLayer(ilayer)->GetNextFeature()) != NULL ){
- if(verbose_opt[0]>1)
- std::cout << "feature " << ifeature << std::endl;
- if( poFeature == NULL ){
- cout << "Warning: could not read feature " << ifeature << " in layer " << imgReaderOgr.getLayerName(ilayer) << endl;
- continue;
- }
- OGRFeature *poDstFeature = NULL;
- if(output_opt.size()){
- poDstFeature=imgWriterOgr.createFeature(ilayer);
- if( poDstFeature->SetFrom( poFeature, TRUE ) != OGRERR_NONE ){
- CPLError( CE_Failure, CPLE_AppDefined,
- "Unable to translate feature %d from layer %s.\n",
- poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
- OGRFeature::DestroyFeature( poFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }
- }
- vector<float> validationPixel;
- vector<float> validationFeature;
-
- imgReaderOgr.readData(validationPixel,OFTReal,fields,poFeature,ilayer);
- assert(validationPixel.size()==nband);
- vector<float> probOut(nclass);//posterior prob for each class
- for(short iclass=0;iclass<nclass;++iclass)
- probOut[iclass]=0;
- for(int ibag=0;ibag<nbag;++ibag){
- for(int iband=0;iband<nband;++iband){
- validationFeature.push_back((validationPixel[iband]-offset[ibag][iband])/scale[ibag][iband]);
- if(verbose_opt[0]==2)
- std::cout << " " << validationFeature.back();
- }
- if(verbose_opt[0]==2)
- std::cout << std::endl;
- vector<double> result(nclass);
- struct svm_node *x;
- x = (struct svm_node *) malloc((validationFeature.size()+1)*sizeof(struct svm_node));
- for(int i=0;i<validationFeature.size();++i){
- x[i].index=i+1;
- x[i].value=validationFeature[i];
- }
-
- x[validationFeature.size()].index=-1;//to end svm feature vector
- double predict_label=0;
- if(!prob_est_opt[0]){
- predict_label = svm_predict(svm[ibag],x);
- for(short iclass=0;iclass<nclass;++iclass){
- if(iclass==static_cast<short>(predict_label))
- result[iclass]=1;
- else
- result[iclass]=0;
- }
- }
- else{
- assert(svm_check_probability_model(svm[ibag]));
- predict_label = svm_predict_probability(svm[ibag],x,&(result[0]));
- }
- if(verbose_opt[0]>1){
- std::cout << "predict_label: " << predict_label << std::endl;
- for(int iclass=0;iclass<result.size();++iclass)
- std::cout << result[iclass] << " ";
- std::cout << std::endl;
- }
-
- //calculate posterior prob of bag
- for(short iclass=0;iclass<nclass;++iclass){
- switch(comb_opt[0]){
- default:
- case(0)://sum rule
- probOut[iclass]+=result[iclass]*priors[iclass];//add probabilities for each bag
- break;
- case(1)://product rule
- probOut[iclass]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];//multiply probabilities for each bag
- break;
- case(2)://max rule
- if(priors[iclass]*result[iclass]>probOut[iclass])
- probOut[iclass]=priors[iclass]*result[iclass];
- break;
- }
- }
- free(x);
- }//for ibag
-
- //search for max class prob
- float maxBag=0;
- float normBag=0;
- string classOut="Unclassified";
- for(short iclass=0;iclass<nclass;++iclass){
- if(verbose_opt[0]>1)
- std::cout << probOut[iclass] << " ";
- if(probOut[iclass]>maxBag){
- maxBag=probOut[iclass];
- classOut=nameVector[iclass];
- }
- }
- //look for class name
- if(verbose_opt[0]>1){
- if(classValueMap.size())
- std::cout << "->" << classValueMap[classOut] << std::endl;
- else
- std::cout << "->" << classOut << std::endl;
- }
- if(output_opt.size()){
- if(classValueMap.size())
- poDstFeature->SetField("class",classValueMap[classOut]);
- else
- poDstFeature->SetField("class",classOut.c_str());
- poDstFeature->SetFID( poFeature->GetFID() );
- }
- int labelIndex=poFeature->GetFieldIndex(label_opt[0].c_str());
- if(labelIndex>=0){
- string classRef=poFeature->GetFieldAsString(labelIndex);
- if(classRef!="0"){
- if(classValueMap.size())
- cm.incrementResult(type2string<short>(classValueMap[classRef]),type2string<short>(classValueMap[classOut]),1);
- else
- cm.incrementResult(classRef,classOut,1);
- }
- }
- CPLErrorReset();
- if(output_opt.size()){
- if(imgWriterOgr.createFeature(poDstFeature,ilayer) != OGRERR_NONE){
- CPLError( CE_Failure, CPLE_AppDefined,
- "Unable to translate feature %d from layer %s.\n",
- poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
- OGRFeature::DestroyFeature( poDstFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }
- }
- ++ifeature;
- if(!verbose_opt[0]){
- progress=static_cast<float>(ifeature+1.0)/nFeatures;
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- OGRFeature::DestroyFeature( poFeature );
- OGRFeature::DestroyFeature( poDstFeature );
- }//get next feature
- }//next layer
- imgReaderOgr.close();
- if(output_opt.size())
- imgWriterOgr.close();
- }
- if(cm.nReference()){
- std::cout << cm << std::endl;
- cout << "class #samples userAcc prodAcc" << endl;
- double se95_ua=0;
- double se95_pa=0;
- double se95_oa=0;
- double dua=0;
- double dpa=0;
- double doa=0;
- for(short iclass=0;iclass<cm.nClasses();++iclass){
- dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
- dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
- cout << cm.getClass(iclass) << " " << cm.nReference(cm.getClass(iclass)) << " " << dua << " (" << se95_ua << ")" << " " << dpa << " (" << se95_pa << ")" << endl;
- }
- std::cout << "Kappa: " << cm.kappa() << std::endl;
- doa=cm.oa(&se95_oa);
- std::cout << "Overall Accuracy: " << 100*doa << " (" << 100*se95_oa << ")" << std::endl;
- }
- }
try{
if(active_opt.size())
activeWriter.close();
diff --git a/src/apps/pksvm.cc b/src/apps/pksvmogr.cc
similarity index 70%
copy from src/apps/pksvm.cc
copy to src/apps/pksvmogr.cc
index eb91aed..845ac98 100644
--- a/src/apps/pksvm.cc
+++ b/src/apps/pksvmogr.cc
@@ -1,6 +1,6 @@
/**********************************************************************
-pksvm.cc: classify raster image using Support Vector Machine
-Copyright (C) 2008-2014 Pieter Kempeneers
+pksvmogr.cc: classify vector dataset using Support Vector Machine
+Copyright (C) 2008-2017 Pieter Kempeneers
This file is part of pktools
@@ -34,41 +34,40 @@ along with pktools. If not, see <http://www.gnu.org/licenses/>.
#include <config.h>
#endif
/******************************************************************************/
-/*! \page pksvm pksvm
- classify raster image using Support Vector Machine
+/*! \page pksvmogr pksvmogr
+ classify vector dataset using Support Vector Machine
## SYNOPSIS
<code>
- Usage: pksvm -t training [-i input -o output] [-cv value]
+ Usage: pksvmogr -t training [-i input -o output] [-cv value]
</code>
<code>
- Options: [-tln layer]* [-c name -r value]* [-of GDALformat|-f OGRformat] [-co NAME=VALUE]* [-ct filename] [-label attribute] [-prior value]* [-g gamma] [-cc cost] [-m filename [-msknodata value]*] [-nodata value]
+ Options: [-tln layer]* [-c name -r value]* [-of GDALformat|-f OGRformat] [-co NAME=VALUE]* [-ct filename] [-label attribute] [-prior value]* [-g gamma] [-cc cost] [-m filename ] [-nodata value]
Advanced options:
- [-b band] [-sband band -eband band]* [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-prob filename] [-pim priorimage] [--offset value] [--scale value] [-svmt type] [-kt type] [-kd value] [-c0 value] [-nu value] [-eloss value] [-cache value] [-etol value] [-shrink]
+ [-b band] [-sband band -eband band]* [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-pim priorimage] [--offset value] [--scale value] [-svmt type] [-kt type] [-kd value] [-c0 value] [-nu value] [-eloss value] [-cache value] [-etol value] [-shrink]
</code>
-\section pksvm_description Description
+\section pksvmogr_description Description
-The utility pksvm implements a support vector machine (SVM) to solve a supervised classification problem. The implementation is based on the open source C++ library libSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm).
+The utility pksvmogr implements a support vector machine (SVM) to solve a supervised classification problem. The implementation is based on the open source C++ library libSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm).
Both raster and vector files are supported as input. The output will contain the classification result, either in raster or vector format, corresponding to the format of the input. A training sample must be provided as an OGR vector dataset that contains the class labels and the features for each training point. The point locations are not considered in the training step. You can use the same training sample for classifying different images, provided the number of bands of the images are [...]
-\section pksvm_options Options
+\section pksvmogr_options Options
- use either `-short` or `--long` options (both `--long=value` and `--long value` are supported)
- short option `-h` shows basic options only, long option `--help` shows all options
|short|long|type|default|description|
|-----|----|----|-------|-----------|
| t | training | std::string | |Training vector file. A single vector file contains all training features (must be set as: b0, b1, b2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the bag and bsize options, where a random subset is taken from a single training file) |
- | i | input | std::string | |input image |
- | o | output | std::string | |Output classification image |
+ | i | input | std::string | |input vector dataset |
+ | o | output | std::string | |Output classification vector dataset |
| cv | cv | unsigned short | 0 |N-fold cross validation mode |
| cmf | cmf | std::string | ascii |Format for confusion matrix (ascii or latex) |
| tln | tln | std::string | |Training layer name(s) |
| c | class | std::string | |List of class names. |
| r | reclass | short | |List of class values (use same order as in class opt). |
- | of | oformat | std::string | GTiff |Output image format (see also gdal_translate).|
| f | f | std::string | SQLite |Output ogr format for active training sample |
| co | co | std::string | |Creation option for output file. Multiple options can be specified. |
| ct | ct | std::string | |Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid) |
@@ -76,9 +75,8 @@ Both raster and vector files are supported as input. The output will contain the
| prior | prior | double | 0 |Prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ). Used for input only (ignored for cross validation) |
| g | gamma | float | 1 |Gamma in kernel function |
| cc | ccost | float | 1000 |The parameter C of C_SVC, epsilon_SVR, and nu_SVR |
- | m | mask | std::string | |Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option msknodata. |
- | msknodata | msknodata | short | 0 |Mask value(s) not to consider for classification. Values will be taken over in classification image. |
- | nodata | nodata | unsigned short | 0 |Nodata value to put where image is masked as nodata |
+ | m | mask | std::string | |Only classify within specified mask (vector or raster). |
+ | nodata | nodata | unsigned short | 0 |Nodata value to put where vector dataset is masked as nodata |
| b | band | short | |Band index (starting from 0, either use band option or use start to end) |
| sband | startband | unsigned short | |Start band sequence number |
| eband | endband | unsigned short | |End band sequence number |
@@ -88,7 +86,6 @@ Both raster and vector files are supported as input. The output will contain the
| bagsize | bagsize | int | 100 |Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively |
| comb | comb | unsigned short | 0 |How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option. |
| cb | classbag | std::string | |Output for each individual bootstrap aggregation |
- | prob | prob | std::string | |Probability image. |
| pim | priorimg | std::string | |Prior probability image (multi-band img with band for each class |
| offset | offset | double | 0 |Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band] |
| scale | scale | double | 0 |Scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0) |
@@ -107,12 +104,11 @@ Both raster and vector files are supported as input. The output will contain the
| na | nactive | unsigned int | 1 |Number of active training points |
| random | random | bool | true |Randomize training data for balancing and bagging |
-Usage: pksvm -t training [-i input -o output] [-cv value]
+Usage: pksvmogr -t training [-i input -o output] [-cv value]
Examples
========
-Some examples how to use pksvm can be found \ref examples_pksvm "here"
**/
namespace svm{
@@ -162,14 +158,11 @@ int main(int argc, char *argv[])
Optionpk<unsigned short> bag_opt("bag", "bag", "Number of bootstrap aggregations", 1);
Optionpk<int> bagSize_opt("bagsize", "bagsize", "Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively", 100);
Optionpk<string> classBag_opt("cb", "classbag", "Output for each individual bootstrap aggregation");
- Optionpk<string> mask_opt("m", "mask", "Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option msknodata.");
- Optionpk<short> msknodata_opt("msknodata", "msknodata", "Mask value(s) not to consider for classification. Values will be taken over in classification image.", 0);
- Optionpk<unsigned short> nodata_opt("nodata", "nodata", "Nodata value to put where image is masked as nodata", 0);
- Optionpk<string> output_opt("o", "output", "Output classification image");
- Optionpk<string> oformat_opt("of", "oformat", "Output image format (see also gdal_translate).","GTiff");
+ Optionpk<string> mask_opt("m", "mask", "Only classify within specified mask (vector or raster).");
+ Optionpk<unsigned short> nodata_opt("nodata", "nodata", "Nodata value to put where vector dataset is masked as nodata", 0);
+ Optionpk<string> output_opt("o", "output", "Output vector dataset");
Optionpk<string> option_opt("co", "co", "Creation option for output file. Multiple options can be specified.");
Optionpk<string> colorTable_opt("ct", "ct", "Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)");
- Optionpk<string> prob_opt("prob", "prob", "Probability image.");
Optionpk<string> entropy_opt("entropy", "entropy", "Entropy image (measure for uncertainty of classifier output","",2);
Optionpk<string> active_opt("active", "active", "Ogr output for active training sample.","",2);
Optionpk<string> ogrformat_opt("f", "f", "Output ogr format for active training sample","SQLite");
@@ -178,7 +171,6 @@ int main(int argc, char *argv[])
Optionpk<short> classvalue_opt("r", "reclass", "List of class values (use same order as in class opt).");
Optionpk<short> verbose_opt("v", "verbose", "Verbose level",0,2);
- oformat_opt.setHide(1);
option_opt.setHide(1);
band_opt.setHide(1);
bstart_opt.setHide(1);
@@ -189,7 +181,6 @@ int main(int argc, char *argv[])
bagSize_opt.setHide(1);
comb_opt.setHide(1);
classBag_opt.setHide(1);
- prob_opt.setHide(1);
priorimg_opt.setHide(1);
offset_opt.setHide(1);
scale_opt.setHide(1);
@@ -220,7 +211,6 @@ int main(int argc, char *argv[])
tlayer_opt.retrieveOption(argc,argv);
classname_opt.retrieveOption(argc,argv);
classvalue_opt.retrieveOption(argc,argv);
- oformat_opt.retrieveOption(argc,argv);
ogrformat_opt.retrieveOption(argc,argv);
option_opt.retrieveOption(argc,argv);
colorTable_opt.retrieveOption(argc,argv);
@@ -229,7 +219,6 @@ int main(int argc, char *argv[])
gamma_opt.retrieveOption(argc,argv);
ccost_opt.retrieveOption(argc,argv);
mask_opt.retrieveOption(argc,argv);
- msknodata_opt.retrieveOption(argc,argv);
nodata_opt.retrieveOption(argc,argv);
// Advanced options
band_opt.retrieveOption(argc,argv);
@@ -241,7 +230,6 @@ int main(int argc, char *argv[])
bagSize_opt.retrieveOption(argc,argv);
comb_opt.retrieveOption(argc,argv);
classBag_opt.retrieveOption(argc,argv);
- prob_opt.retrieveOption(argc,argv);
priorimg_opt.retrieveOption(argc,argv);
offset_opt.retrieveOption(argc,argv);
scale_opt.retrieveOption(argc,argv);
@@ -267,7 +255,7 @@ int main(int argc, char *argv[])
}
if(!doProcess){
cout << endl;
- cout << "Usage: pksvm -t training [-i input -o output] [-cv value]" << endl;
+ cout << "Usage: pksvmogr -t training [-i input -o output] [-cv value]" << endl;
cout << endl;
std::cout << "short option -h shows basic options only, use long option --help to show all options" << std::endl;
exit(0);//help was invoked, stop processing
@@ -760,441 +748,7 @@ int main(int argc, char *argv[])
catch(string errorString){
inputIsRaster=true;
}
- if(inputIsRaster){
- ImgReaderGdal testImage;
- try{
- if(verbose_opt[0]>=1)
- std::cout << "opening image " << input_opt[0] << std::endl;
- testImage.open(input_opt[0]);
- }
- catch(string error){
- cerr << error << std::endl;
- exit(2);
- }
- ImgReaderGdal priorReader;
- if(priorimg_opt.size()){
- try{
- if(verbose_opt[0]>=1)
- std::cout << "opening prior image " << priorimg_opt[0] << std::endl;
- priorReader.open(priorimg_opt[0]);
- assert(priorReader.nrOfCol()==testImage.nrOfCol());
- assert(priorReader.nrOfRow()==testImage.nrOfRow());
- }
- catch(string error){
- cerr << error << std::endl;
- exit(2);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(1);
- }
- }
-
- int nrow=testImage.nrOfRow();
- int ncol=testImage.nrOfCol();
- if(option_opt.findSubstring("INTERLEAVE=")==option_opt.end()){
- string theInterleave="INTERLEAVE=";
- theInterleave+=testImage.getInterleave();
- option_opt.push_back(theInterleave);
- }
- vector<char> classOut(ncol);//classified line for writing to image file
-
- // assert(nband==testImage.nrOfBand());
- ImgWriterGdal classImageBag;
- ImgWriterGdal classImageOut;
- ImgWriterGdal probImage;
- ImgWriterGdal entropyImage;
-
- string imageType=testImage.getImageType();
- if(oformat_opt.size())//default
- imageType=oformat_opt[0];
- try{
- assert(output_opt.size());
- if(verbose_opt[0]>=1)
- std::cout << "opening class image for writing output " << output_opt[0] << std::endl;
- if(classBag_opt.size()){
- classImageBag.open(classBag_opt[0],ncol,nrow,nbag,GDT_Byte,imageType,option_opt);
- classImageBag.GDALSetNoDataValue(nodata_opt[0]);
- classImageBag.copyGeoTransform(testImage);
- classImageBag.setProjection(testImage.getProjection());
- }
- classImageOut.open(output_opt[0],ncol,nrow,1,GDT_Byte,imageType,option_opt);
- classImageOut.GDALSetNoDataValue(nodata_opt[0]);
- classImageOut.copyGeoTransform(testImage);
- classImageOut.setProjection(testImage.getProjection());
- if(colorTable_opt.size())
- classImageOut.setColorTable(colorTable_opt[0],0);
- if(prob_opt.size()){
- probImage.open(prob_opt[0],ncol,nrow,nclass,GDT_Byte,imageType,option_opt);
- probImage.GDALSetNoDataValue(nodata_opt[0]);
- probImage.copyGeoTransform(testImage);
- probImage.setProjection(testImage.getProjection());
- }
- if(entropy_opt.size()){
- entropyImage.open(entropy_opt[0],ncol,nrow,1,GDT_Byte,imageType,option_opt);
- entropyImage.GDALSetNoDataValue(nodata_opt[0]);
- entropyImage.copyGeoTransform(testImage);
- entropyImage.setProjection(testImage.getProjection());
- }
- }
- catch(string error){
- cerr << error << std::endl;
- }
-
- ImgWriterGdal maskWriter;
-
- if(maskIsVector){
- try{
- maskWriter.open("/vsimem/mask.tif",ncol,nrow,1,GDT_Float32,imageType,option_opt);
- maskWriter.GDALSetNoDataValue(nodata_opt[0]);
- maskWriter.copyGeoTransform(testImage);
- maskWriter.setProjection(testImage.getProjection());
- vector<double> burnValues(1,1);//burn value is 1 (single band)
- maskWriter.rasterizeOgr(extentReader,burnValues);
- extentReader.close();
- maskWriter.close();
- }
- catch(string error){
- cerr << error << std::endl;
- exit(2);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(1);
- }
- mask_opt.clear();
- mask_opt.push_back("/vsimem/mask.tif");
- }
- ImgReaderGdal maskReader;
- if(mask_opt.size()){
- try{
- if(verbose_opt[0]>=1)
- std::cout << "opening mask image file " << mask_opt[0] << std::endl;
- maskReader.open(mask_opt[0]);
- }
- catch(string error){
- cerr << error << std::endl;
- exit(2);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(1);
- }
- }
-
- for(int iline=0;iline<nrow;++iline){
- vector<float> buffer(ncol);
- vector<short> lineMask;
- Vector2d<float> linePrior;
- if(priorimg_opt.size())
- linePrior.resize(nclass,ncol);//prior prob for each class
- Vector2d<float> hpixel(ncol);
- Vector2d<float> probOut(nclass,ncol);//posterior prob for each (internal) class
- vector<float> entropy(ncol);
- Vector2d<char> classBag;//classified line for writing to image file
- if(classBag_opt.size())
- classBag.resize(nbag,ncol);
- try{
- if(band_opt.size()){
- for(int iband=0;iband<band_opt.size();++iband){
- if(verbose_opt[0]==2)
- std::cout << "reading band " << band_opt[iband] << std::endl;
- assert(band_opt[iband]>=0);
- assert(band_opt[iband]<testImage.nrOfBand());
- testImage.readData(buffer,iline,band_opt[iband]);
- for(int icol=0;icol<ncol;++icol)
- hpixel[icol].push_back(buffer[icol]);
- }
- }
- else{
- for(int iband=0;iband<nband;++iband){
- if(verbose_opt[0]==2)
- std::cout << "reading band " << iband << std::endl;
- assert(iband>=0);
- assert(iband<testImage.nrOfBand());
- testImage.readData(buffer,iline,iband);
- for(int icol=0;icol<ncol;++icol)
- hpixel[icol].push_back(buffer[icol]);
- }
- }
- }
- catch(string theError){
- cerr << "Error reading " << input_opt[0] << ": " << theError << std::endl;
- exit(3);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(3);
- }
- assert(nband==hpixel[0].size());
- if(verbose_opt[0]>1)
- std::cout << "used bands: " << nband << std::endl;
- //read prior
- if(priorimg_opt.size()){
- try{
- for(short iclass=0;iclass<nclass;++iclass){
- if(verbose_opt.size()>1)
- std::cout << "Reading " << priorimg_opt[0] << " band " << iclass << " line " << iline << std::endl;
- priorReader.readData(linePrior[iclass],iline,iclass);
- }
- }
- catch(string theError){
- std::cerr << "Error reading " << priorimg_opt[0] << ": " << theError << std::endl;
- exit(3);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(3);
- }
- }
- double oldRowMask=-1;//keep track of row mask to optimize number of line readings
- //process per pixel
- for(int icol=0;icol<ncol;++icol){
- assert(hpixel[icol].size()==nband);
- bool doClassify=true;
- bool masked=false;
- double geox=0;
- double geoy=0;
- if(maskIsVector){
- doClassify=false;
- testImage.image2geo(icol,iline,geox,geoy);
- //check enveloppe first
- if(uly>=geoy&&lry<=geoy&&ulx<=geox&&lrx>=geox){
- doClassify=true;
- }
- }
- if(mask_opt.size()){
- //read mask
- double colMask=0;
- double rowMask=0;
-
- testImage.image2geo(icol,iline,geox,geoy);
- maskReader.geo2image(geox,geoy,colMask,rowMask);
- colMask=static_cast<int>(colMask);
- rowMask=static_cast<int>(rowMask);
- if(rowMask>=0&&rowMask<maskReader.nrOfRow()&&colMask>=0&&colMask<maskReader.nrOfCol()){
- if(static_cast<int>(rowMask)!=static_cast<int>(oldRowMask)){
- assert(rowMask>=0&&rowMask<maskReader.nrOfRow());
- try{
- // maskReader.readData(lineMask[imask],GDT_Int32,static_cast<int>(rowMask));
- maskReader.readData(lineMask,static_cast<int>(rowMask));
- }
- catch(string errorstring){
- cerr << errorstring << endl;
- exit(1);
- }
- catch(...){
- cerr << "error caught" << std::endl;
- exit(3);
- }
- oldRowMask=rowMask;
- }
- short theMask=0;
- for(short ivalue=0;ivalue<msknodata_opt.size();++ivalue){
- // if(msknodata_opt[ivalue]>=0){//values set in msknodata_opt are invalid
- if(lineMask[colMask]==msknodata_opt[ivalue]){
- theMask=lineMask[colMask];
- masked=true;
- break;
- }
- // }
- // else{//only values set in msknodata_opt are valid
- // if(lineMask[colMask]!=-msknodata_opt[ivalue]){
- // theMask=lineMask[colMask];
- // masked=true;
- // }
- // else{
- // masked=false;
- // break;
- // }
- // }
- }
- if(masked){
- if(classBag_opt.size())
- for(int ibag=0;ibag<nbag;++ibag)
- classBag[ibag][icol]=theMask;
- classOut[icol]=theMask;
- continue;
- }
- }
- bool valid=false;
- for(int iband=0;iband<hpixel[icol].size();++iband){
- if(hpixel[icol][iband]){
- valid=true;
- break;
- }
- }
- if(!valid)
- doClassify=false;
- }
- for(short iclass=0;iclass<nclass;++iclass)
- probOut[iclass][icol]=0;
- if(!doClassify){
- if(classBag_opt.size())
- for(int ibag=0;ibag<nbag;++ibag)
- classBag[ibag][icol]=nodata_opt[0];
- classOut[icol]=nodata_opt[0];
- continue;//next column
- }
- if(verbose_opt[0]>1)
- std::cout << "begin classification " << std::endl;
- //----------------------------------- classification -------------------
- for(int ibag=0;ibag<nbag;++ibag){
- vector<double> result(nclass);
- struct svm_node *x;
- x = (struct svm_node *) malloc((nband+1)*sizeof(struct svm_node));
- for(int iband=0;iband<nband;++iband){
- x[iband].index=iband+1;
- x[iband].value=(hpixel[icol][iband]-offset[ibag][iband])/scale[ibag][iband];
- }
- x[nband].index=-1;//to end svm feature vector
- double predict_label=0;
- vector<float> prValues(nclass);
- float maxP=0;
- if(!prob_est_opt[0]){
- predict_label = svm_predict(svm[ibag],x);
- for(short iclass=0;iclass<nclass;++iclass){
- if(iclass==static_cast<short>(predict_label))
- result[iclass]=1;
- else
- result[iclass]=0;
- }
- }
- else{
- assert(svm_check_probability_model(svm[ibag]));
- predict_label = svm_predict_probability(svm[ibag],x,&(result[0]));
- }
- //calculate posterior prob of bag
- if(classBag_opt.size()){
- //search for max prob within bag
- maxP=0;
- classBag[ibag][icol]=0;
- }
- double normPrior=0;
- if(priorimg_opt.size()){
- for(short iclass=0;iclass<nclass;++iclass)
- normPrior+=linePrior[iclass][icol];
- }
- for(short iclass=0;iclass<nclass;++iclass){
- if(priorimg_opt.size())
- priors[iclass]=linePrior[iclass][icol]/normPrior;//todo: check if correct for all cases... (automatic classValueMap and manual input for names and values)
- switch(comb_opt[0]){
- default:
- case(0)://sum rule
- probOut[iclass][icol]+=result[iclass]*priors[iclass];//add probabilities for each bag
- break;
- case(1)://product rule
- probOut[iclass][icol]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];//multiply probabilities for each bag
- break;
- case(2)://max rule
- if(priors[iclass]*result[iclass]>probOut[iclass][icol])
- probOut[iclass][icol]=priors[iclass]*result[iclass];
- break;
- }
- if(classBag_opt.size()){
- //search for max prob within bag
- // if(prValues[iclass]>maxP){
- // maxP=prValues[iclass];
- // classBag[ibag][icol]=iclass;
- // }
- if(result[iclass]>maxP){
- maxP=result[iclass];
- classBag[ibag][icol]=iclass;
- }
- }
- }
- free(x);
- }//ibag
-
- //search for max class prob
- float maxBag1=0;//max probability
- float maxBag2=0;//second max probability
- float normBag=0;
- for(short iclass=0;iclass<nclass;++iclass){
- if(probOut[iclass][icol]>maxBag1){
- maxBag1=probOut[iclass][icol];
- classOut[icol]=classValueMap[nameVector[iclass]];
- }
- else if(probOut[iclass][icol]>maxBag2)
- maxBag2=probOut[iclass][icol];
- normBag+=probOut[iclass][icol];
- }
- //normalize probOut and convert to percentage
- entropy[icol]=0;
- for(short iclass=0;iclass<nclass;++iclass){
- float prv=probOut[iclass][icol];
- prv/=normBag;
- entropy[icol]-=prv*log(prv)/log(2.0);
- prv*=100.0;
-
- probOut[iclass][icol]=static_cast<short>(prv+0.5);
- // assert(classValueMap[nameVector[iclass]]<probOut.size());
- // assert(classValueMap[nameVector[iclass]]>=0);
- // probOut[classValueMap[nameVector[iclass]]][icol]=static_cast<short>(prv+0.5);
- }
- entropy[icol]/=log(static_cast<double>(nclass))/log(2.0);
- entropy[icol]=static_cast<short>(100*entropy[icol]+0.5);
- if(active_opt.size()){
- if(entropy[icol]>activePoints.back().value){
- activePoints.back().value=entropy[icol];//replace largest value (last)
- activePoints.back().posx=icol;
- activePoints.back().posy=iline;
- std::sort(activePoints.begin(),activePoints.end(),Decrease_PosValue());//sort in descending order (largest first, smallest last)
- if(verbose_opt[0])
- std::cout << activePoints.back().posx << " " << activePoints.back().posy << " " << activePoints.back().value << std::endl;
- }
- }
- }//icol
- //----------------------------------- write output ------------------------------------------
- if(classBag_opt.size())
- for(int ibag=0;ibag<nbag;++ibag)
- classImageBag.writeData(classBag[ibag],iline,ibag);
- if(prob_opt.size()){
- for(short iclass=0;iclass<nclass;++iclass)
- probImage.writeData(probOut[iclass],iline,iclass);
- }
- if(entropy_opt.size()){
- entropyImage.writeData(entropy,iline);
- }
- classImageOut.writeData(classOut,iline);
- if(!verbose_opt[0]){
- progress=static_cast<float>(iline+1.0)/classImageOut.nrOfRow();
- pfnProgress(progress,pszMessage,pProgressArg);
- }
- }
- //write active learning points
- if(active_opt.size()){
- for(int iactive=0;iactive<activePoints.size();++iactive){
- std::map<string,double> pointMap;
- for(int iband=0;iband<testImage.nrOfBand();++iband){
- double value;
- testImage.readData(value,static_cast<int>(activePoints[iactive].posx),static_cast<int>(activePoints[iactive].posy),iband);
- ostringstream fs;
- fs << "B" << iband;
- pointMap[fs.str()]=value;
- }
- pointMap[label_opt[0]]=0;
- double x, y;
- testImage.image2geo(activePoints[iactive].posx,activePoints[iactive].posy,x,y);
- std::string fieldname="id";//number of the point
- activeWriter.addPoint(x,y,pointMap,fieldname,++nactive);
- }
- }
-
- testImage.close();
- if(mask_opt.size())
- maskReader.close();
- if(priorimg_opt.size())
- priorReader.close();
- if(prob_opt.size())
- probImage.close();
- if(entropy_opt.size())
- entropyImage.close();
- if(classBag_opt.size())
- classImageBag.close();
- classImageOut.close();
- }
- else{//classify vector file
+ if(!inputIsRaster){//classify vector file
cm.clearResults();
//notice that fields have already been set by readDataImageOgr (taking into account appropriate bands)
for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
--
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