pax_global_header00006660000000000000000000000064121111074550014507gustar00rootroot0000000000000052 comment=534c2d544b497f23dfa3ab0c9fd834cab00a6df8 norlab-ulaval-libnabo-534c2d5/000077500000000000000000000000001211110745500162055ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/CMakeLists.txt000066400000000000000000000063571211110745500207600ustar00rootroot00000000000000cmake_minimum_required(VERSION 2.6) set(LIB_NAME nabo) project("lib${LIB_NAME}") # Extract version from header set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}) execute_process( COMMAND grep "NABO_VERSION " nabo/nabo.h WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} OUTPUT_VARIABLE PROJECT_VERSION OUTPUT_STRIP_TRAILING_WHITESPACE ) string(REGEX REPLACE ".*\"(.*)\".*" "\\1" PROJECT_VERSION "${PROJECT_VERSION}" ) # Documentation set(DOXYFILE_LATEX false) include(UseDoxygen) # Compilation add_definitions(-Wall) # Boost required for any and cstdint on MSVC < 2010 find_package(Boost COMPONENTS REQUIRED) if(MSVC AND (MSVC_VERSION LESS 1600)) add_definitions(-DBOOST_STDINT) endif(MSVC AND (MSVC_VERSION LESS 1600)) # openmp # find_package(OpenMP) # if (OPENMP_FOUND) # set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}) # endif(OPENMP_FOUND) # eigen 2 or 3 find_path(EIGEN_INCLUDE_DIR Eigen/Core /usr/local/include/eigen3 /usr/local/include/eigen2 /usr/local/include/eigen /usr/include/eigen3 /usr/include/eigen2 /usr/include/eigen /opt/ros/diamondback/stacks/geometry/eigen/include ) # optionally, opencl # OpenCL disabled as its code is not up-to-date with API # set(USE_OPEN_CL "false" CACHE BOOL "Set to ON to look for OpenCL") # if (USE_OPEN_CL) # find_path(OPENCL_INCLUDE_DIR CL/cl.h # /usr/local/include # /usr/include # ) # if (WIN32) # find_library(OPENCL_LIBRARIES opencl64) # if (!OPENCL_LIBRARIES) # find_library(OPENCL_LIBRARIES opencl32) # endif (!OPENCL_LIBRARIES) # else (WIN32) # find_library(OPENCL_LIBRARIES OpenCL ENV LD_LIBRARY_PATH) # endif (WIN32) # if (OPENCL_INCLUDE_DIR AND OPENCL_LIBRARIES) # add_definitions(-DHAVE_OPENCL) # set(EXTRA_LIBS ${OPENCL_LIBRARIES} ${EXTRA_LIBS}) # include_directories(${OPENCL_INCLUDE_DIR}) # add_definitions(-DOPENCL_SOURCE_DIR=\"${CMAKE_SOURCE_DIR}/nabo/opencl/\") # message("OpenCL enabled and found, enabling CL support") # else (OPENCL_INCLUDE_DIR AND OPENCL_LIBRARIES) # message("OpenCL enabled but not found, disabling CL support") # endif (OPENCL_INCLUDE_DIR AND OPENCL_LIBRARIES) # else(USE_OPEN_CL) # message("OpenCL disabled, not looking for it") # endif(USE_OPEN_CL) # include all libs so far include_directories(${EIGEN_INCLUDE_DIR} ${Boost_INCLUDE_DIRS}) # main nabo lib set(NABO_SRC nabo/nabo.cpp nabo/brute_force_cpu.cpp nabo/kdtree_cpu.cpp nabo/kdtree_opencl.cpp ) set(SHARED_LIBS "false" CACHE BOOL "To build shared (true) or static (false) library") if (SHARED_LIBS) add_library(${LIB_NAME} SHARED ${NABO_SRC}) install(TARGETS ${LIB_NAME} LIBRARY DESTINATION lib) else(SHARED_LIBS) add_library(${LIB_NAME} ${NABO_SRC}) install(TARGETS ${LIB_NAME} ARCHIVE DESTINATION lib) endif(SHARED_LIBS) set_target_properties(${LIB_NAME} PROPERTIES VERSION ${PROJECT_VERSION} SOVERSION 1) # create doc before installing set(DOC_INSTALL_TARGET "share/doc/${PROJECT_NAME}/api" CACHE STRING "Target where to install doxygen documentation") add_dependencies(${LIB_NAME} doc) install(FILES nabo/nabo.h DESTINATION include/nabo) install(FILES README.md DESTINATION share/doc/${PROJECT_NAME}) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/doc/html DESTINATION ${DOC_INSTALL_TARGET}) enable_testing() add_subdirectory(examples) add_subdirectory(tests) add_subdirectory(python)norlab-ulaval-libnabo-534c2d5/Doxyfile.in000066400000000000000000001743451211110745500203360ustar00rootroot00000000000000# Doxyfile 1.5.8 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project # # All text after a hash (#) is considered a comment and will be ignored # The format is: # TAG = value [value, ...] # For lists items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (" ") #--------------------------------------------------------------------------- # Project related configuration options #--------------------------------------------------------------------------- # This tag specifies the encoding used for all characters in the config file # that follow. The default is UTF-8 which is also the encoding used for all # text before the first occurrence of this tag. Doxygen uses libiconv (or the # iconv built into libc) for the transcoding. See # http://www.gnu.org/software/libiconv for the list of possible encodings. DOXYFILE_ENCODING = UTF-8 # The PROJECT_NAME tag is a single word (or a sequence of words surrounded # by quotes) that should identify the project. PROJECT_NAME = "@PROJECT_NAME@" # The PROJECT_NUMBER tag can be used to enter a project or revision number. # This could be handy for archiving the generated documentation or # if some version control system is used. PROJECT_NUMBER = "@PROJECT_VERSION@" # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) # base path where the generated documentation will be put. # If a relative path is entered, it will be relative to the location # where doxygen was started. If left blank the current directory will be used. OUTPUT_DIRECTORY = "@DOXYFILE_OUTPUT_DIR@" # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create # 4096 sub-directories (in 2 levels) under the output directory of each output # format and will distribute the generated files over these directories. # Enabling this option can be useful when feeding doxygen a huge amount of # source files, where putting all generated files in the same directory would # otherwise cause performance problems for the file system. CREATE_SUBDIRS = NO # The OUTPUT_LANGUAGE tag is used to specify the language in which all # documentation generated by doxygen is written. Doxygen will use this # information to generate all constant output in the proper language. # The default language is English, other supported languages are: # Afrikaans, Arabic, Brazilian, Catalan, Chinese, Chinese-Traditional, # Croatian, Czech, Danish, Dutch, Farsi, Finnish, French, German, Greek, # Hungarian, Italian, Japanese, Japanese-en (Japanese with English messages), # Korean, Korean-en, Lithuanian, Norwegian, Macedonian, Persian, Polish, # Portuguese, Romanian, Russian, Serbian, Serbian-Cyrilic, Slovak, Slovene, # Spanish, Swedish, and Ukrainian. OUTPUT_LANGUAGE = English # If the BRIEF_MEMBER_DESC tag is set to YES (the default) Doxygen will # include brief member descriptions after the members that are listed in # the file and class documentation (similar to JavaDoc). # Set to NO to disable this. BRIEF_MEMBER_DESC = YES # If the REPEAT_BRIEF tag is set to YES (the default) Doxygen will prepend # the brief description of a member or function before the detailed description. # Note: if both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the # brief descriptions will be completely suppressed. REPEAT_BRIEF = YES # This tag implements a quasi-intelligent brief description abbreviator # that is used to form the text in various listings. Each string # in this list, if found as the leading text of the brief description, will be # stripped from the text and the result after processing the whole list, is # used as the annotated text. Otherwise, the brief description is used as-is. # If left blank, the following values are used ("$name" is automatically # replaced with the name of the entity): "The $name class" "The $name widget" # "The $name file" "is" "provides" "specifies" "contains" # "represents" "a" "an" "the" ABBREVIATE_BRIEF = # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then # Doxygen will generate a detailed section even if there is only a brief # description. ALWAYS_DETAILED_SEC = NO # If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all # inherited members of a class in the documentation of that class as if those # members were ordinary class members. Constructors, destructors and assignment # operators of the base classes will not be shown. INLINE_INHERITED_MEMB = NO # If the FULL_PATH_NAMES tag is set to YES then Doxygen will prepend the full # path before files name in the file list and in the header files. If set # to NO the shortest path that makes the file name unique will be used. FULL_PATH_NAMES = NO # If the FULL_PATH_NAMES tag is set to YES then the STRIP_FROM_PATH tag # can be used to strip a user-defined part of the path. Stripping is # only done if one of the specified strings matches the left-hand part of # the path. The tag can be used to show relative paths in the file list. # If left blank the directory from which doxygen is run is used as the # path to strip. STRIP_FROM_PATH = # The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of # the path mentioned in the documentation of a class, which tells # the reader which header file to include in order to use a class. # If left blank only the name of the header file containing the class # definition is used. Otherwise one should specify the include paths that # are normally passed to the compiler using the -I flag. STRIP_FROM_INC_PATH = # If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter # (but less readable) file names. This can be useful is your file systems # doesn't support long names like on DOS, Mac, or CD-ROM. SHORT_NAMES = NO # If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen # will interpret the first line (until the first dot) of a JavaDoc-style # comment as the brief description. If set to NO, the JavaDoc # comments will behave just like regular Qt-style comments # (thus requiring an explicit @brief command for a brief description.) JAVADOC_AUTOBRIEF = YES # If the QT_AUTOBRIEF tag is set to YES then Doxygen will # interpret the first line (until the first dot) of a Qt-style # comment as the brief description. If set to NO, the comments # will behave just like regular Qt-style comments (thus requiring # an explicit \brief command for a brief description.) QT_AUTOBRIEF = NO # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen # treat a multi-line C++ special comment block (i.e. a block of //! or /// # comments) as a brief description. This used to be the default behaviour. # The new default is to treat a multi-line C++ comment block as a detailed # description. Set this tag to YES if you prefer the old behaviour instead. MULTILINE_CPP_IS_BRIEF = NO # If the INHERIT_DOCS tag is set to YES (the default) then an undocumented # member inherits the documentation from any documented member that it # re-implements. INHERIT_DOCS = YES # If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce # a new page for each member. If set to NO, the documentation of a member will # be part of the file/class/namespace that contains it. SEPARATE_MEMBER_PAGES = NO # The TAB_SIZE tag can be used to set the number of spaces in a tab. # Doxygen uses this value to replace tabs by spaces in code fragments. TAB_SIZE = 8 # This tag can be used to specify a number of aliases that acts # as commands in the documentation. An alias has the form "name=value". # For example adding "sideeffect=\par Side Effects:\n" will allow you to # put the command \sideeffect (or @sideeffect) in the documentation, which # will result in a user-defined paragraph with heading "Side Effects:". # You can put \n's in the value part of an alias to insert newlines. ALIASES = # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C # sources only. Doxygen will then generate output that is more tailored for C. # For instance, some of the names that are used will be different. The list # of all members will be omitted, etc. OPTIMIZE_OUTPUT_FOR_C = NO # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java # sources only. Doxygen will then generate output that is more tailored for # Java. For instance, namespaces will be presented as packages, qualified # scopes will look different, etc. OPTIMIZE_OUTPUT_JAVA = NO # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran # sources only. Doxygen will then generate output that is more tailored for # Fortran. OPTIMIZE_FOR_FORTRAN = NO # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL # sources. Doxygen will then generate output that is tailored for # VHDL. OPTIMIZE_OUTPUT_VHDL = NO # Doxygen selects the parser to use depending on the extension of the files it parses. # With this tag you can assign which parser to use for a given extension. # Doxygen has a built-in mapping, but you can override or extend it using this tag. # The format is ext=language, where ext is a file extension, and language is one of # the parsers supported by doxygen: IDL, Java, Javascript, C#, C, C++, D, PHP, # Objective-C, Python, Fortran, VHDL, C, C++. For instance to make doxygen treat # .inc files as Fortran files (default is PHP), and .f files as C (default is Fortran), # use: inc=Fortran f=C EXTENSION_MAPPING = # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want # to include (a tag file for) the STL sources as input, then you should # set this tag to YES in order to let doxygen match functions declarations and # definitions whose arguments contain STL classes (e.g. func(std::string); v.s. # func(std::string) {}). This also make the inheritance and collaboration # diagrams that involve STL classes more complete and accurate. BUILTIN_STL_SUPPORT = NO # If you use Microsoft's C++/CLI language, you should set this option to YES to # enable parsing support. CPP_CLI_SUPPORT = NO # Set the SIP_SUPPORT tag to YES if your project consists of sip sources only. # Doxygen will parse them like normal C++ but will assume all classes use public # instead of private inheritance when no explicit protection keyword is present. SIP_SUPPORT = NO # For Microsoft's IDL there are propget and propput attributes to indicate getter # and setter methods for a property. Setting this option to YES (the default) # will make doxygen to replace the get and set methods by a property in the # documentation. This will only work if the methods are indeed getting or # setting a simple type. If this is not the case, or you want to show the # methods anyway, you should set this option to NO. IDL_PROPERTY_SUPPORT = YES # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC # tag is set to YES, then doxygen will reuse the documentation of the first # member in the group (if any) for the other members of the group. By default # all members of a group must be documented explicitly. DISTRIBUTE_GROUP_DOC = NO # Set the SUBGROUPING tag to YES (the default) to allow class member groups of # the same type (for instance a group of public functions) to be put as a # subgroup of that type (e.g. under the Public Functions section). Set it to # NO to prevent subgrouping. Alternatively, this can be done per class using # the \nosubgrouping command. SUBGROUPING = YES # When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum # is documented as struct, union, or enum with the name of the typedef. So # typedef struct TypeS {} TypeT, will appear in the documentation as a struct # with name TypeT. When disabled the typedef will appear as a member of a file, # namespace, or class. And the struct will be named TypeS. This can typically # be useful for C code in case the coding convention dictates that all compound # types are typedef'ed and only the typedef is referenced, never the tag name. TYPEDEF_HIDES_STRUCT = NO # The SYMBOL_CACHE_SIZE determines the size of the internal cache use to # determine which symbols to keep in memory and which to flush to disk. # When the cache is full, less often used symbols will be written to disk. # For small to medium size projects (<1000 input files) the default value is # probably good enough. For larger projects a too small cache size can cause # doxygen to be busy swapping symbols to and from disk most of the time # causing a significant performance penality. # If the system has enough physical memory increasing the cache will improve the # performance by keeping more symbols in memory. Note that the value works on # a logarithmic scale so increasing the size by one will rougly double the # memory usage. The cache size is given by this formula: # 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0, # corresponding to a cache size of 2^16 = 65536 symbols SYMBOL_CACHE_SIZE = 0 #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- # If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in # documentation are documented, even if no documentation was available. # Private class members and static file members will be hidden unless # the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES EXTRACT_ALL = NO # If the EXTRACT_PRIVATE tag is set to YES all private members of a class # will be included in the documentation. EXTRACT_PRIVATE = NO # If the EXTRACT_STATIC tag is set to YES all static members of a file # will be included in the documentation. EXTRACT_STATIC = NO # If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) # defined locally in source files will be included in the documentation. # If set to NO only classes defined in header files are included. EXTRACT_LOCAL_CLASSES = YES # This flag is only useful for Objective-C code. When set to YES local # methods, which are defined in the implementation section but not in # the interface are included in the documentation. # If set to NO (the default) only methods in the interface are included. EXTRACT_LOCAL_METHODS = NO # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called # 'anonymous_namespace{file}', where file will be replaced with the base # name of the file that contains the anonymous namespace. By default # anonymous namespace are hidden. EXTRACT_ANON_NSPACES = NO # If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all # undocumented members of documented classes, files or namespaces. # If set to NO (the default) these members will be included in the # various overviews, but no documentation section is generated. # This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_MEMBERS = NO # If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. # If set to NO (the default) these classes will be included in the various # overviews. This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_CLASSES = NO # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all # friend (class|struct|union) declarations. # If set to NO (the default) these declarations will be included in the # documentation. HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any # documentation blocks found inside the body of a function. # If set to NO (the default) these blocks will be appended to the # function's detailed documentation block. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation # that is typed after a \internal command is included. If the tag is set # to NO (the default) then the documentation will be excluded. # Set it to YES to include the internal documentation. INTERNAL_DOCS = NO # If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate # file names in lower-case letters. If set to YES upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen # will show members with their full class and namespace scopes in the # documentation. If set to YES the scope will be hidden. HIDE_SCOPE_NAMES = NO # If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen # will put a list of the files that are included by a file in the documentation # of that file. SHOW_INCLUDE_FILES = YES # If the INLINE_INFO tag is set to YES (the default) then a tag [inline] # is inserted in the documentation for inline members. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen # will sort the (detailed) documentation of file and class members # alphabetically by member name. If set to NO the members will appear in # declaration order. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the # brief documentation of file, namespace and class members alphabetically # by member name. If set to NO (the default) the members will appear in # declaration order. SORT_BRIEF_DOCS = NO # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the # hierarchy of group names into alphabetical order. If set to NO (the default) # the group names will appear in their defined order. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be # sorted by fully-qualified names, including namespaces. If set to # NO (the default), the class list will be sorted only by class name, # not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the # alphabetical list. SORT_BY_SCOPE_NAME = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or # disable (NO) the todo list. This list is created by putting \todo # commands in the documentation. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or # disable (NO) the test list. This list is created by putting \test # commands in the documentation. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or # disable (NO) the bug list. This list is created by putting \bug # commands in the documentation. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or # disable (NO) the deprecated list. This list is created by putting # \deprecated commands in the documentation. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional # documentation sections, marked by \if sectionname ... \endif. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines # the initial value of a variable or define consists of for it to appear in # the documentation. If the initializer consists of more lines than specified # here it will be hidden. Use a value of 0 to hide initializers completely. # The appearance of the initializer of individual variables and defines in the # documentation can be controlled using \showinitializer or \hideinitializer # command in the documentation regardless of this setting. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated # at the bottom of the documentation of classes and structs. If set to YES the # list will mention the files that were used to generate the documentation. SHOW_USED_FILES = YES # If the sources in your project are distributed over multiple directories # then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy # in the documentation. The default is NO. SHOW_DIRECTORIES = NO # Set the SHOW_FILES tag to NO to disable the generation of the Files page. # This will remove the Files entry from the Quick Index and from the # Folder Tree View (if specified). The default is YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the # Namespaces page. # This will remove the Namespaces entry from the Quick Index # and from the Folder Tree View (if specified). The default is YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command , where is the value of # the FILE_VERSION_FILTER tag, and is the name of an input file # provided by doxygen. Whatever the program writes to standard output # is used as the file version. See the manual for examples. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed by # doxygen. The layout file controls the global structure of the generated output files # in an output format independent way. The create the layout file that represents # doxygen's defaults, run doxygen with the -l option. You can optionally specify a # file name after the option, if omitted DoxygenLayout.xml will be used as the name # of the layout file. LAYOUT_FILE = #--------------------------------------------------------------------------- # configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated # by doxygen. Possible values are YES and NO. If left blank NO is used. QUIET = YES # The WARNINGS tag can be used to turn on/off the warning messages that are # generated by doxygen. Possible values are YES and NO. If left blank # NO is used. WARNINGS = YES # If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings # for undocumented members. If EXTRACT_ALL is set to YES then this flag will # automatically be disabled. WARN_IF_UNDOCUMENTED = YES # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some # parameters in a documented function, or documenting parameters that # don't exist or using markup commands wrongly. WARN_IF_DOC_ERROR = YES # This WARN_NO_PARAMDOC option can be abled to get warnings for # functions that are documented, but have no documentation for their parameters # or return value. If set to NO (the default) doxygen will only warn about # wrong or incomplete parameter documentation, but not about the absence of # documentation. WARN_NO_PARAMDOC = NO # The WARN_FORMAT tag determines the format of the warning messages that # doxygen can produce. The string should contain the $file, $line, and $text # tags, which will be replaced by the file and line number from which the # warning originated and the warning text. Optionally the format may contain # $version, which will be replaced by the version of the file (if it could # be obtained via FILE_VERSION_FILTER) WARN_FORMAT = "$file:$line: $text" # The WARN_LOGFILE tag can be used to specify a file to which warning # and error messages should be written. If left blank the output is written # to stderr. WARN_LOGFILE = #--------------------------------------------------------------------------- # configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag can be used to specify the files and/or directories that contain # documented source files. You may enter file names like "myfile.cpp" or # directories like "/usr/src/myproject". Separate the files or directories # with spaces. INPUT = "@DOXYFILE_SOURCE_DIR@/nabo" # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is # also the default input encoding. Doxygen uses libiconv (or the iconv built # into libc) for the transcoding. See http://www.gnu.org/software/libiconv for # the list of possible encodings. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank the following patterns are tested: # *.c *.cc *.cxx *.cpp *.c++ *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh *.hxx # *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.py *.f90 FILE_PATTERNS = # The RECURSIVE tag can be used to turn specify whether or not subdirectories # should be searched for input files as well. Possible values are YES and NO. # If left blank NO is used. RECURSIVE = YES # The EXCLUDE tag can be used to specify files and/or directories that should # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. EXCLUDE = "_darcs" # The EXCLUDE_SYMLINKS tag can be used select whether or not files or # directories that are symbolic links (a Unix filesystem feature) are excluded # from the input. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. Note that the wildcards are matched # against the file with absolute path, so to exclude all test directories # for example use the pattern */test/* EXCLUDE_PATTERNS = "*/.*" "*/.*/*" # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or # directories that contain example code fragments that are included (see # the \include command). EXAMPLE_PATH = "@CMAKE_CURRENT_SOURCE_DIR@/examples" # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank all files are included. EXAMPLE_PATTERNS = # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude # commands irrespective of the value of the RECURSIVE tag. # Possible values are YES and NO. If left blank NO is used. EXAMPLE_RECURSIVE = NO # The IMAGE_PATH tag can be used to specify one or more files or # directories that contain image that are included in the documentation (see # the \image command). IMAGE_PATH = "@CMAKE_CURRENT_SOURCE_DIR@" # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command , where # is the value of the INPUT_FILTER tag, and is the name of an # input file. Doxygen will then use the output that the filter program writes # to standard output. # If FILTER_PATTERNS is specified, this tag will be # ignored. INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. # Doxygen will compare the file name with each pattern and apply the # filter if there is a match. # The filters are a list of the form: # pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further # info on how filters are used. If FILTER_PATTERNS is empty, INPUT_FILTER # is applied to all files. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will be used to filter the input files when producing source # files to browse (i.e. when SOURCE_BROWSER is set to YES). FILTER_SOURCE_FILES = NO #--------------------------------------------------------------------------- # configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will # be generated. Documented entities will be cross-referenced with these sources. # Note: To get rid of all source code in the generated output, make sure also # VERBATIM_HEADERS is set to NO. SOURCE_BROWSER = NO # Setting the INLINE_SOURCES tag to YES will include the body # of functions and classes directly in the documentation. INLINE_SOURCES = NO # Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct # doxygen to hide any special comment blocks from generated source code # fragments. Normal C and C++ comments will always remain visible. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES # then for each documented function all documented # functions referencing it will be listed. REFERENCED_BY_RELATION = NO # If the REFERENCES_RELATION tag is set to YES # then for each documented function all documented entities # called/used by that function will be listed. REFERENCES_RELATION = NO # If the REFERENCES_LINK_SOURCE tag is set to YES (the default) # and SOURCE_BROWSER tag is set to YES, then the hyperlinks from # functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will # link to the source code. # Otherwise they will link to the documentation. REFERENCES_LINK_SOURCE = YES # If the USE_HTAGS tag is set to YES then the references to source code # will point to the HTML generated by the htags(1) tool instead of doxygen # built-in source browser. The htags tool is part of GNU's global source # tagging system (see http://www.gnu.org/software/global/global.html). You # will need version 4.8.6 or higher. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen # will generate a verbatim copy of the header file for each class for # which an include is specified. Set to NO to disable this. VERBATIM_HEADERS = YES #--------------------------------------------------------------------------- # configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index # of all compounds will be generated. Enable this if the project # contains a lot of classes, structs, unions or interfaces. ALPHABETICAL_INDEX = NO # If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then # the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns # in which this list will be split (can be a number in the range [1..20]) COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all # classes will be put under the same header in the alphabetical index. # The IGNORE_PREFIX tag can be used to specify one or more prefixes that # should be ignored while generating the index headers. IGNORE_PREFIX = #--------------------------------------------------------------------------- # configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES (the default) Doxygen will # generate HTML output. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `html' will be used as the default path. HTML_OUTPUT = "@DOXYFILE_HTML_DIR@" # The HTML_FILE_EXTENSION tag can be used to specify the file extension for # each generated HTML page (for example: .htm,.php,.asp). If it is left blank # doxygen will generate files with .html extension. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a personal HTML header for # each generated HTML page. If it is left blank doxygen will generate a # standard header. HTML_HEADER = # The HTML_FOOTER tag can be used to specify a personal HTML footer for # each generated HTML page. If it is left blank doxygen will generate a # standard footer. HTML_FOOTER = # The HTML_STYLESHEET tag can be used to specify a user-defined cascading # style sheet that is used by each HTML page. It can be used to # fine-tune the look of the HTML output. If the tag is left blank doxygen # will generate a default style sheet. Note that doxygen will try to copy # the style sheet file to the HTML output directory, so don't put your own # stylesheet in the HTML output directory as well, or it will be erased! HTML_STYLESHEET = # If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes, # files or namespaces will be aligned in HTML using tables. If set to # NO a bullet list will be used. HTML_ALIGN_MEMBERS = YES # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. For this to work a browser that supports # JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox # Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari). HTML_DYNAMIC_SECTIONS = NO # If the GENERATE_DOCSET tag is set to YES, additional index files # will be generated that can be used as input for Apple's Xcode 3 # integrated development environment, introduced with OSX 10.5 (Leopard). # To create a documentation set, doxygen will generate a Makefile in the # HTML output directory. Running make will produce the docset in that # directory and running "make install" will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find # it at startup. # See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html for more information. GENERATE_DOCSET = NO # When GENERATE_DOCSET tag is set to YES, this tag determines the name of the # feed. A documentation feed provides an umbrella under which multiple # documentation sets from a single provider (such as a company or product suite) # can be grouped. DOCSET_FEEDNAME = "Doxygen generated docs" # When GENERATE_DOCSET tag is set to YES, this tag specifies a string that # should uniquely identify the documentation set bundle. This should be a # reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen # will append .docset to the name. DOCSET_BUNDLE_ID = org.doxygen.Project # If the GENERATE_HTMLHELP tag is set to YES, additional index files # will be generated that can be used as input for tools like the # Microsoft HTML help workshop to generate a compiled HTML help file (.chm) # of the generated HTML documentation. GENERATE_HTMLHELP = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can # be used to specify the file name of the resulting .chm file. You # can add a path in front of the file if the result should not be # written to the html output directory. CHM_FILE = # If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can # be used to specify the location (absolute path including file name) of # the HTML help compiler (hhc.exe). If non-empty doxygen will try to run # the HTML help compiler on the generated index.hhp. HHC_LOCATION = # If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag # controls if a separate .chi index file is generated (YES) or that # it should be included in the master .chm file (NO). GENERATE_CHI = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING # is used to encode HtmlHelp index (hhk), content (hhc) and project file # content. CHM_INDEX_ENCODING = # If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag # controls whether a binary table of contents is generated (YES) or a # normal table of contents (NO) in the .chm file. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members # to the contents of the HTML help documentation and to the tree view. TOC_EXPAND = NO # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and QHP_VIRTUAL_FOLDER # are set, an additional index file will be generated that can be used as input for # Qt's qhelpgenerator to generate a Qt Compressed Help (.qch) of the generated # HTML documentation. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can # be used to specify the file name of the resulting .qch file. # The path specified is relative to the HTML output folder. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#namespace QHP_NAMESPACE = # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#virtual-folders QHP_VIRTUAL_FOLDER = doc # If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to add. # For more information please see # http://doc.trolltech.com/qthelpproject.html#custom-filters QHP_CUST_FILTER_NAME = # The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the custom filter to add.For more information please see # Qt Help Project / Custom Filters. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this project's # filter section matches. # Qt Help Project / Filter Attributes. QHP_SECT_FILTER_ATTRS = # If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can # be used to specify the location of Qt's qhelpgenerator. # If non-empty doxygen will try to run qhelpgenerator on the generated # .qhp file. QHG_LOCATION = # The DISABLE_INDEX tag can be used to turn on/off the condensed index at # top of each HTML page. The value NO (the default) enables the index and # the value YES disables it. DISABLE_INDEX = NO # This tag can be used to set the number of enum values (range [1..20]) # that doxygen will group on one line in the generated HTML documentation. ENUM_VALUES_PER_LINE = 4 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. # If the tag value is set to FRAME, a side panel will be generated # containing a tree-like index structure (just like the one that # is generated for HTML Help). For this to work a browser that supports # JavaScript, DHTML, CSS and frames is required (for instance Mozilla 1.0+, # Netscape 6.0+, Internet explorer 5.0+, or Konqueror). Windows users are # probably better off using the HTML help feature. Other possible values # for this tag are: HIERARCHIES, which will generate the Groups, Directories, # and Class Hierarchy pages using a tree view instead of an ordered list; # ALL, which combines the behavior of FRAME and HIERARCHIES; and NONE, which # disables this behavior completely. For backwards compatibility with previous # releases of Doxygen, the values YES and NO are equivalent to FRAME and NONE # respectively. GENERATE_TREEVIEW = NONE # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be # used to set the initial width (in pixels) of the frame in which the tree # is shown. TREEVIEW_WIDTH = 250 # Use this tag to change the font size of Latex formulas included # as images in the HTML documentation. The default is 10. Note that # when you change the font size after a successful doxygen run you need # to manually remove any form_*.png images from the HTML output directory # to force them to be regenerated. FORMULA_FONTSIZE = 10 #--------------------------------------------------------------------------- # configuration options related to the LaTeX output #--------------------------------------------------------------------------- # If the GENERATE_LATEX tag is set to YES (the default) Doxygen will # generate Latex output. GENERATE_LATEX = @DOXYFILE_LATEX@ # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `latex' will be used as the default path. LATEX_OUTPUT = "@DOXYFILE_LATEX_DIR@" # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be # invoked. If left blank `latex' will be used as the default command name. LATEX_CMD_NAME = "@LATEX_COMPILER@" # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to # generate index for LaTeX. If left blank `makeindex' will be used as the # default command name. MAKEINDEX_CMD_NAME = "@MAKEINDEX_COMPILER@" # If the COMPACT_LATEX tag is set to YES Doxygen generates more compact # LaTeX documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_LATEX = NO # The PAPER_TYPE tag can be used to set the paper type that is used # by the printer. Possible values are: a4, a4wide, letter, legal and # executive. If left blank a4wide will be used. PAPER_TYPE = a4wide # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX # packages that should be included in the LaTeX output. EXTRA_PACKAGES = # The LATEX_HEADER tag can be used to specify a personal LaTeX header for # the generated latex document. The header should contain everything until # the first chapter. If it is left blank doxygen will generate a # standard header. Notice: only use this tag if you know what you are doing! LATEX_HEADER = # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated # is prepared for conversion to pdf (using ps2pdf). The pdf file will # contain links (just like the HTML output) instead of page references # This makes the output suitable for online browsing using a pdf viewer. PDF_HYPERLINKS = YES # If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of # plain latex in the generated Makefile. Set this option to YES to get a # higher quality PDF documentation. USE_PDFLATEX = @DOXYFILE_PDFLATEX@ # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. # command to the generated LaTeX files. This will instruct LaTeX to keep # running if errors occur, instead of asking the user for help. # This option is also used when generating formulas in HTML. LATEX_BATCHMODE = YES # If LATEX_HIDE_INDICES is set to YES then doxygen will not # include the index chapters (such as File Index, Compound Index, etc.) # in the output. LATEX_HIDE_INDICES = NO #--------------------------------------------------------------------------- # configuration options related to the RTF output #--------------------------------------------------------------------------- # If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output # The RTF output is optimized for Word 97 and may not look very pretty with # other RTF readers or editors. GENERATE_RTF = NO # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `rtf' will be used as the default path. RTF_OUTPUT = rtf # If the COMPACT_RTF tag is set to YES Doxygen generates more compact # RTF documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_RTF = NO # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated # will contain hyperlink fields. The RTF file will # contain links (just like the HTML output) instead of page references. # This makes the output suitable for online browsing using WORD or other # programs which support those fields. # Note: wordpad (write) and others do not support links. RTF_HYPERLINKS = NO # Load stylesheet definitions from file. Syntax is similar to doxygen's # config file, i.e. a series of assignments. You only have to provide # replacements, missing definitions are set to their default value. RTF_STYLESHEET_FILE = # Set optional variables used in the generation of an rtf document. # Syntax is similar to doxygen's config file. RTF_EXTENSIONS_FILE = #--------------------------------------------------------------------------- # configuration options related to the man page output #--------------------------------------------------------------------------- # If the GENERATE_MAN tag is set to YES (the default) Doxygen will # generate man pages GENERATE_MAN = NO # The MAN_OUTPUT tag is used to specify where the man pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `man' will be used as the default path. MAN_OUTPUT = man # The MAN_EXTENSION tag determines the extension that is added to # the generated man pages (default is the subroutine's section .3) MAN_EXTENSION = .3 # If the MAN_LINKS tag is set to YES and Doxygen generates man output, # then it will generate one additional man file for each entity # documented in the real man page(s). These additional files # only source the real man page, but without them the man command # would be unable to find the correct page. The default is NO. MAN_LINKS = NO #--------------------------------------------------------------------------- # configuration options related to the XML output #--------------------------------------------------------------------------- # If the GENERATE_XML tag is set to YES Doxygen will # generate an XML file that captures the structure of # the code including all documentation. GENERATE_XML = NO # The XML_OUTPUT tag is used to specify where the XML pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `xml' will be used as the default path. XML_OUTPUT = xml # The XML_SCHEMA tag can be used to specify an XML schema, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_SCHEMA = # The XML_DTD tag can be used to specify an XML DTD, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_DTD = # If the XML_PROGRAMLISTING tag is set to YES Doxygen will # dump the program listings (including syntax highlighting # and cross-referencing information) to the XML output. Note that # enabling this will significantly increase the size of the XML output. XML_PROGRAMLISTING = YES #--------------------------------------------------------------------------- # configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will # generate an AutoGen Definitions (see autogen.sf.net) file # that captures the structure of the code including all # documentation. Note that this feature is still experimental # and incomplete at the moment. GENERATE_AUTOGEN_DEF = NO #--------------------------------------------------------------------------- # configuration options related to the Perl module output #--------------------------------------------------------------------------- # If the GENERATE_PERLMOD tag is set to YES Doxygen will # generate a Perl module file that captures the structure of # the code including all documentation. Note that this # feature is still experimental and incomplete at the # moment. GENERATE_PERLMOD = NO # If the PERLMOD_LATEX tag is set to YES Doxygen will generate # the necessary Makefile rules, Perl scripts and LaTeX code to be able # to generate PDF and DVI output from the Perl module output. PERLMOD_LATEX = NO # If the PERLMOD_PRETTY tag is set to YES the Perl module output will be # nicely formatted so it can be parsed by a human reader. # This is useful # if you want to understand what is going on. # On the other hand, if this # tag is set to NO the size of the Perl module output will be much smaller # and Perl will parse it just the same. PERLMOD_PRETTY = YES # The names of the make variables in the generated doxyrules.make file # are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. # This is useful so different doxyrules.make files included by the same # Makefile don't overwrite each other's variables. PERLMOD_MAKEVAR_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the preprocessor #--------------------------------------------------------------------------- # If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will # evaluate all C-preprocessor directives found in the sources and include # files. ENABLE_PREPROCESSING = YES # If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro # names in the source code. If set to NO (the default) only conditional # compilation will be performed. Macro expansion can be done in a controlled # way by setting EXPAND_ONLY_PREDEF to YES. MACRO_EXPANSION = NO # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES # then the macro expansion is limited to the macros specified with the # PREDEFINED and EXPAND_AS_DEFINED tags. EXPAND_ONLY_PREDEF = NO # If the SEARCH_INCLUDES tag is set to YES (the default) the includes files # in the INCLUDE_PATH (see below) will be search if a #include is found. SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by # the preprocessor. INCLUDE_PATH = # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard # patterns (like *.h and *.hpp) to filter out the header-files in the # directories. If left blank, the patterns specified with FILE_PATTERNS will # be used. INCLUDE_FILE_PATTERNS = # The PREDEFINED tag can be used to specify one or more macro names that # are defined before the preprocessor is started (similar to the -D option of # gcc). The argument of the tag is a list of macros of the form: name # or name=definition (no spaces). If the definition and the = are # omitted =1 is assumed. To prevent a macro definition from being # undefined via #undef or recursively expanded use the := operator # instead of the = operator. PREDEFINED = HAVE_OPENCL # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then # this tag can be used to specify a list of macro names that should be expanded. # The macro definition that is found in the sources will be used. # Use the PREDEFINED tag if you want to use a different macro definition. EXPAND_AS_DEFINED = # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then # doxygen's preprocessor will remove all function-like macros that are alone # on a line, have an all uppercase name, and do not end with a semicolon. Such # function macros are typically used for boiler-plate code, and will confuse # the parser if not removed. SKIP_FUNCTION_MACROS = YES #--------------------------------------------------------------------------- # Configuration::additions related to external references #--------------------------------------------------------------------------- # The TAGFILES option can be used to specify one or more tagfiles. # Optionally an initial location of the external documentation # can be added for each tagfile. The format of a tag file without # this location is as follows: # # TAGFILES = file1 file2 ... # Adding location for the tag files is done as follows: # # TAGFILES = file1=loc1 "file2 = loc2" ... # where "loc1" and "loc2" can be relative or absolute paths or # URLs. If a location is present for each tag, the installdox tool # does not have to be run to correct the links. # Note that each tag file must have a unique name # (where the name does NOT include the path) # If a tag file is not located in the directory in which doxygen # is run, you must also specify the path to the tagfile here. TAGFILES = # When a file name is specified after GENERATE_TAGFILE, doxygen will create # a tag file that is based on the input files it reads. GENERATE_TAGFILE = # If the ALLEXTERNALS tag is set to YES all external classes will be listed # in the class index. If set to NO only the inherited external classes # will be listed. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed # in the modules index. If set to NO, only the current project's groups will # be listed. EXTERNAL_GROUPS = YES # The PERL_PATH should be the absolute path and name of the perl script # interpreter (i.e. the result of `which perl'). PERL_PATH = /usr/bin/perl #--------------------------------------------------------------------------- # Configuration options related to the dot tool #--------------------------------------------------------------------------- # If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will # generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base # or super classes. Setting the tag to NO turns the diagrams off. Note that # this option is superseded by the HAVE_DOT option below. This is only a # fallback. It is recommended to install and use dot, since it yields more # powerful graphs. CLASS_DIAGRAMS = YES # You can define message sequence charts within doxygen comments using the \msc # command. Doxygen will then run the mscgen tool (see # http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the # documentation. The MSCGEN_PATH tag allows you to specify the directory where # the mscgen tool resides. If left empty the tool is assumed to be found in the # default search path. MSCGEN_PATH = # If set to YES, the inheritance and collaboration graphs will hide # inheritance and usage relations if the target is undocumented # or is not a class. HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz, a graph visualization # toolkit from AT&T and Lucent Bell Labs. The other options in this section # have no effect if this option is set to NO (the default) HAVE_DOT = @DOXYFILE_DOT@ # By default doxygen will write a font called FreeSans.ttf to the output # directory and reference it in all dot files that doxygen generates. This # font does not include all possible unicode characters however, so when you need # these (or just want a differently looking font) you can specify the font name # using DOT_FONTNAME. You need need to make sure dot is able to find the font, # which can be done by putting it in a standard location or by setting the # DOTFONTPATH environment variable or by setting DOT_FONTPATH to the directory # containing the font. DOT_FONTNAME = FreeSans # The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs. # The default size is 10pt. DOT_FONTSIZE = 10 # By default doxygen will tell dot to use the output directory to look for the # FreeSans.ttf font (which doxygen will put there itself). If you specify a # different font using DOT_FONTNAME you can set the path where dot # can find it using this tag. DOT_FONTPATH = # If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect inheritance relations. Setting this tag to YES will force the # the CLASS_DIAGRAMS tag to NO. CLASS_GRAPH = YES # If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect implementation dependencies (inheritance, containment, and # class references variables) of the class with other documented classes. COLLABORATION_GRAPH = YES # If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen # will generate a graph for groups, showing the direct groups dependencies GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling # Language. UML_LOOK = NO # If set to YES, the inheritance and collaboration graphs will show the # relations between templates and their instances. TEMPLATE_RELATIONS = NO # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT # tags are set to YES then doxygen will generate a graph for each documented # file showing the direct and indirect include dependencies of the file with # other documented files. INCLUDE_GRAPH = YES # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and # HAVE_DOT tags are set to YES then doxygen will generate a graph for each # documented header file showing the documented files that directly or # indirectly include this file. INCLUDED_BY_GRAPH = YES # If the CALL_GRAPH and HAVE_DOT options are set to YES then # doxygen will generate a call dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable call graphs # for selected functions only using the \callgraph command. CALL_GRAPH = NO # If the CALLER_GRAPH and HAVE_DOT tags are set to YES then # doxygen will generate a caller dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable caller # graphs for selected functions only using the \callergraph command. CALLER_GRAPH = NO # If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen # will graphical hierarchy of all classes instead of a textual one. GRAPHICAL_HIERARCHY = YES # If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES # then doxygen will show the dependencies a directory has on other directories # in a graphical way. The dependency relations are determined by the #include # relations between the files in the directories. DIRECTORY_GRAPH = YES # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. Possible values are png, jpg, or gif # If left blank png will be used. DOT_IMAGE_FORMAT = png # The tag DOT_PATH can be used to specify the path where the dot tool can be # found. If left blank, it is assumed the dot tool can be found in the path. DOT_PATH = "@DOXYGEN_DOT_PATH@" # The DOTFILE_DIRS tag can be used to specify one or more directories that # contain dot files that are included in the documentation (see the # \dotfile command). DOTFILE_DIRS = # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of # nodes that will be shown in the graph. If the number of nodes in a graph # becomes larger than this value, doxygen will truncate the graph, which is # visualized by representing a node as a red box. Note that doxygen if the # number of direct children of the root node in a graph is already larger than # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note # that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. DOT_GRAPH_MAX_NODES = 50 # The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the # graphs generated by dot. A depth value of 3 means that only nodes reachable # from the root by following a path via at most 3 edges will be shown. Nodes # that lay further from the root node will be omitted. Note that setting this # option to 1 or 2 may greatly reduce the computation time needed for large # code bases. Also note that the size of a graph can be further restricted by # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. MAX_DOT_GRAPH_DEPTH = 0 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent # background. This is disabled by default, because dot on Windows does not # seem to support this out of the box. Warning: Depending on the platform used, # enabling this option may lead to badly anti-aliased labels on the edges of # a graph (i.e. they become hard to read). DOT_TRANSPARENT = YES # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) # support this, this feature is disabled by default. DOT_MULTI_TARGETS = NO # If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will # generate a legend page explaining the meaning of the various boxes and # arrows in the dot generated graphs. GENERATE_LEGEND = YES # If the DOT_CLEANUP tag is set to YES (the default) Doxygen will # remove the intermediate dot files that are used to generate # the various graphs. DOT_CLEANUP = YES #--------------------------------------------------------------------------- # Options related to the search engine #--------------------------------------------------------------------------- # The SEARCHENGINE tag specifies whether or not a search engine should be # used. If set to NO the values of all tags below this one will be ignored. SEARCHENGINE = NO norlab-ulaval-libnabo-534c2d5/README.md000066400000000000000000000132501211110745500174650ustar00rootroot00000000000000libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by [ANN], but with more compact data types. On the average, libnabo is 5% to 20% faster than [ANN]. libnabo depends on [Eigen], a modern C++ matrix and linear-algebra library. libnabo works with either version 2 or 3 of Eigen. libnabo also depends on [Boost], a C++ general library. libnabo is being developed by [Stéphane Magnenat](http://stephane.magnenat.net) as part of his work at [ASL-ETH](http://www.asl.ethz.ch). Download ======== Ubuntu builds are available on my PPA at: https://launchpad.net/~stephane.magnenat They provide a package with the shared library, another with the development headers and a third with the documentation. The source code is available from github, you can clone the git tree by doing: git clone git://github.com/ethz-asl/libnabo.git You can then checkout a specific branch, for instance to checkout version 1.0.2, do: cd libnabo git checkout 1.0.2 Compilation =========== libnabo uses [CMake] as build system. The complete compilation process depends on the system you are using (Linux, Mac OS X or Windows). You will find a nice introductory tutorial in [this video](http://www.youtube.com/watch?v=CLvZTyji_Uw). Prerequisites ------------- If your operating system does not provide it, you must get [Eigen] and [Boost]. [Eigen] only needs to be downloaded and extracted. Compilation options ------------------- libnabo provides the following compilation options, available through [CMake]: * `SHARED_LIBS` (boolean, default: `false`): if `true`, build a shared library, otherwise build a static library You can specify them with a command-line tool, `ccmake`, or with a graphical tool, `cmake-gui`. Please read the [CMake documentation] for more information. Quick compilation and installation under Unix --------------------------------------------- Under Unix, assuming that [Eigen] and [Boost] are installed system-wide, you can compile (with optimisation and debug information) and install libnabo in `/usr/local` with the following commands run in the top-level directory of libnabo's sources: SRC_DIR=`pwd` BUILD_DIR=${SRC_DIR}/build mkdir -p ${BUILD_DIR} && cd ${BUILD_DIR} cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ${SRC_DIR} # if Eigen or Boost are not available system-wide, run at that point: # cmake-gui . # cmake-gui allows you to tell the location of Eigen or Boost make sudo make install These lines will compile libnabo in a `build` sub-directory and therefore keep your source tree clean. Note that you could compile libnabo anywhere you have write access, such as in `/tmp/libnabo`. This out-of-source build is a nice feature of [CMake]. If [Eigen] or [Boost] are not installed system-wide, you might have to tell [CMake] where to find them (using `ccmake` or `cmake-gui`). You can generate the documentation by typing: make doc Usage ===== libnabo is easy to use. For example, assuming that you are working with floats and that you have a point set `M` and a query point `q`, you can find the `K` nearest neighbours of `q` in `M`: #include "nabo/nabo.h" using namespace Nabo; using namespace Eigen; ... NNSearchF* nns = NNSearchF::createKDTreeLinearHeap(M); const int K = 5; VectorXi indices(K); VectorXf dists2(K); nns->knn(q, indices, dists2, K); In this example, `M` is an [Eigen] (refering to the software, not to the math) matrix (column major, float) and `q` is an [Eigen] vector (float). The results `indices` and `dists2` are [Eigen] vectors of indices and squared distances refering to the columns of `M`. See `examples/trivial.cpp` for a compilable version of this example, and `examples/usage.cpp` for a slightly more complex example involving multi-point queries. Running `make doc` in your build directory will generate a browsable documentation in `doc/html`. The main page `doc/html/index.html` contains a detailed overview of the usage of libnabo. Python bindings =============== libnabo includes python bindings that are compiled if python is available. The resulting module is called pynabo, you can see an example in `python/test.py`. You can find more information in the docstring-based documentation: python -c "import pynabo; help(pynabo.NearestNeighbourSearch)" Unit testing ============ The distribution of libnabo integrates a unit test module, based on CTest. Just type: make test ...in the build directory to run the tests. Their outputs are available in the `Testing` directory. These consist of validation and benchmarking tests. If [ANN] or [FLANN] are detected when compiling libnabo, `make test` will also perform comparative benchmarks. Citing libnabo ============== If you use libnabo in the academic context, please cite this paper that evaluates its performances in the contex of ICP: @article{elsebergcomparison, title={Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration}, author={Elseberg, J. and Magnenat, S. and Siegwart, R. and N{\"u}chter, A.}, journal={Journal of Software Engineering for Robotics (JOSER)}, pages={2--12}, volume={3}, number={1}, year={2012}, issn={2035-3928} } Bug reporting ============= Please use [github's issue tracker](http://github.com/ethz-asl/libnabo/issues) to report bugs. License ======= libnabo is released under a permissive BSD license. [ANN]: http://www.cs.umd.edu/~mount/ANN [FLANN]: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN [CMake]: http://www.cmake.org [CMake documentation]: http://www.cmake.org/cmake/help/cmake2.6docs.html [Eigen]: http://eigen.tuxfamily.org [Boost]: http://www.boost.org norlab-ulaval-libnabo-534c2d5/UseDoxygen.cmake000066400000000000000000000070321211110745500213030ustar00rootroot00000000000000# - Run Doxygen # # Adds a doxygen target that runs doxygen to generate the html # and optionally the LaTeX API documentation. # The doxygen target is added to the doc target as a dependency. # i.e.: the API documentation is built with: # make doc # # USAGE: GLOBAL INSTALL # # Install it with: # cmake ./ && sudo make install # Add the following to the CMakeLists.txt of your project: # include(UseDoxygen OPTIONAL) # Optionally copy Doxyfile.in in the directory of CMakeLists.txt and edit it. # # USAGE: INCLUDE IN PROJECT # # set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}) # include(UseDoxygen) # Add the Doxyfile.in and UseDoxygen.cmake files to the projects source directory. # # # Variables you may define are: # DOXYFILE_SOURCE_DIR - Path where the Doxygen input files are. # Defaults to the current source and binary directory. # DOXYFILE_OUTPUT_DIR - Path where the Doxygen output is stored. Defaults to "doc". # # DOXYFILE_LATEX - Set to "NO" if you do not want the LaTeX documentation # to be built. # DOXYFILE_LATEX_DIR - Directory relative to DOXYFILE_OUTPUT_DIR where # the Doxygen LaTeX output is stored. Defaults to "latex". # # DOXYFILE_HTML_DIR - Directory relative to DOXYFILE_OUTPUT_DIR where # the Doxygen html output is stored. Defaults to "html". # # # Copyright (c) 2009, 2010 Tobias Rautenkranz # # Redistribution and use is allowed according to the terms of the New # BSD license. # For details see the accompanying COPYING-CMAKE-SCRIPTS file. # macro(usedoxygen_set_default name value) if(NOT DEFINED "${name}") set("${name}" "${value}") endif() endmacro() find_package(Doxygen) if(DOXYGEN_FOUND) find_file(DOXYFILE_IN "Doxyfile.in" PATHS "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_ROOT}/Modules/" NO_DEFAULT_PATH) set(DOXYFILE "${CMAKE_CURRENT_BINARY_DIR}/Doxyfile") include(FindPackageHandleStandardArgs) find_package_handle_standard_args(DOXYFILE_IN DEFAULT_MSG "DOXYFILE_IN") endif() if(DOXYGEN_FOUND AND DOXYFILE_IN_FOUND) usedoxygen_set_default(DOXYFILE_OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/doc") usedoxygen_set_default(DOXYFILE_HTML_DIR "html") usedoxygen_set_default(DOXYFILE_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") set_property(DIRECTORY APPEND PROPERTY ADDITIONAL_MAKE_CLEAN_FILES "${DOXYFILE_OUTPUT_DIR}/${DOXYFILE_HTML_DIR}") add_custom_target(doxygen COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYFILE} COMMENT "Writing documentation to ${DOXYFILE_OUTPUT_DIR}..." WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}) ## LaTeX set(DOXYFILE_PDFLATEX "NO") set(DOXYFILE_DOT "NO") find_package(LATEX) find_program(MAKE_PROGRAM make) if(LATEX_COMPILER AND MAKEINDEX_COMPILER AND MAKE_PROGRAM AND (NOT DEFINED DOXYFILE_LATEX OR DOXYFILE_LATEX STREQUAL "YES")) set(DOXYFILE_LATEX "YES") usedoxygen_set_default(DOXYFILE_LATEX_DIR "latex") set_property(DIRECTORY APPEND PROPERTY ADDITIONAL_MAKE_CLEAN_FILES "${DOXYFILE_OUTPUT_DIR}/${DOXYFILE_LATEX_DIR}") if(PDFLATEX_COMPILER) set(DOXYFILE_PDFLATEX "YES") endif() if(DOXYGEN_DOT_EXECUTABLE) set(DOXYFILE_DOT "YES") endif() add_custom_command(TARGET doxygen POST_BUILD COMMAND ${MAKE_PROGRAM} COMMENT "Running LaTeX for Doxygen documentation in ${DOXYFILE_OUTPUT_DIR}/${DOXYFILE_LATEX_DIR}..." WORKING_DIRECTORY "${DOXYFILE_OUTPUT_DIR}/${DOXYFILE_LATEX_DIR}") else() set(DOXYGEN_LATEX "NO") endif() configure_file(${DOXYFILE_IN} Doxyfile IMMEDIATE @ONLY) get_target_property(DOC_TARGET doc TYPE) if(NOT DOC_TARGET) add_custom_target(doc) endif() add_dependencies(doc doxygen) endif() norlab-ulaval-libnabo-534c2d5/debian/000077500000000000000000000000001211110745500174275ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/debian/changelog000066400000000000000000000002541211110745500213020ustar00rootroot00000000000000libnabo (1.0.1) natty; urgency=low * Latest upstream (including python bindings) -- Stéphane Magnenat Tue, 10 Nov 2011 14:00:00 +0100 norlab-ulaval-libnabo-534c2d5/debian/compat000066400000000000000000000000021211110745500206250ustar00rootroot000000000000007 norlab-ulaval-libnabo-534c2d5/debian/control000066400000000000000000000055751211110745500210460ustar00rootroot00000000000000Source: libnabo Priority: extra Maintainer: Stéphane Magnenat Build-Depends: debhelper (>= 7.0.50~), cmake, doxygen, cdbs, libboost-dev, libboost-python-dev, libeigen3-dev, python-dev, python-numpy Standards-Version: 3.9.1 Section: libs Homepage: https://github.com/ethz-asl/libnabo Vcs-Git: git://github.com/ethz-asl/libnabo.git Vcs-Browser: https://github.com/ethz-asl/libnabo Package: libnabo-dev Section: libdevel Architecture: any Depends: libnabo1 (= ${binary:Version}), libboost-dev, libeigen3-dev Suggests: libnabo-doc Description: Development headers of libnabo, a fast K Nearset Neighbor library. libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN. Package: libnabo1-dbg Section: libdevel Architecture: any Depends: libnabo1 (= ${binary:Version}) Description: Debug symbols for libnabo, a fast K Nearset Neighbor library. libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN. Package: libnabo1 Section: libs Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Description: Version 1 of libnabo, a fast K Nearset Neighbor library. libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN. Package: libnabo-doc Section: doc Architecture: all Description: API documentation of libnabo, a fast K Nearset Neighbor library. libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN. This package contains the API documentation for libnabo Package: python-nabo Section: python Architecture: any Depends: libnabo1 (= ${binary:Version}), python, python-numpy Description: Python bindings for libnabo, a fast K Nearset Neighbor library. libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN.norlab-ulaval-libnabo-534c2d5/debian/copyright000066400000000000000000000034111211110745500213610ustar00rootroot00000000000000Format: http://dep.debian.net/deps/dep5 Upstream-Name: libnabo Source: https://github.com/ethz-asl/libnabo Files: * Copyright: 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland License: BSD-3-Clause Files: debian/* Copyright: 2011 Stéphane Magnenat License: BSD-3-Clause License: BSD-3-Clause Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the University nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. . THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. norlab-ulaval-libnabo-534c2d5/debian/docs000066400000000000000000000000121211110745500202730ustar00rootroot00000000000000README.md norlab-ulaval-libnabo-534c2d5/debian/libnabo-dev.dirs000066400000000000000000000000231211110745500224670ustar00rootroot00000000000000usr/include usr/libnorlab-ulaval-libnabo-534c2d5/debian/libnabo-dev.install000066400000000000000000000000361211110745500232000ustar00rootroot00000000000000usr/include/* usr/lib/lib*.so norlab-ulaval-libnabo-534c2d5/debian/libnabo-doc.dirs000066400000000000000000000000151211110745500224570ustar00rootroot00000000000000usr/share/docnorlab-ulaval-libnabo-534c2d5/debian/libnabo-doc.install000066400000000000000000000000341211110745500231650ustar00rootroot00000000000000usr/share/doc/libnabo-doc/* norlab-ulaval-libnabo-534c2d5/debian/libnabo1.dirs000066400000000000000000000000101211110745500217700ustar00rootroot00000000000000usr/lib norlab-ulaval-libnabo-534c2d5/debian/libnabo1.install000066400000000000000000000000221211110745500225000ustar00rootroot00000000000000usr/lib/lib*.so.* norlab-ulaval-libnabo-534c2d5/debian/python-nabo.dirs000066400000000000000000000000001211110745500225360ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/debian/python-nabo.install000066400000000000000000000000471211110745500232560ustar00rootroot00000000000000usr/lib/python*/dist-packages/pynabo.sonorlab-ulaval-libnabo-534c2d5/debian/rules000077500000000000000000000012171211110745500205100ustar00rootroot00000000000000#!/usr/bin/make -f # -*- makefile -*- # Sample debian/rules that uses debhelper. # This file was originally written by Joey Hess and Craig Small. # As a special exception, when this file is copied by dh-make into a # dh-make output file, you may use that output file without restriction. # This special exception was added by Craig Small in version 0.37 of dh-make. # Uncomment this to turn on verbose mode. #export DH_VERBOSE=1 #%: # dh $@ DEB_CMAKE_EXTRA_FLAGS=-DSHARED_LIBS=true -DDOC_INSTALL_TARGET="share/doc/libnabo-doc/api" -DPYTHON_DEB_INSTALL_TARGET=true include /usr/share/cdbs/1/rules/debhelper.mk include /usr/share/cdbs/1/class/cmake.mk norlab-ulaval-libnabo-534c2d5/debian/source/000077500000000000000000000000001211110745500207275ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/debian/source/format000066400000000000000000000000151211110745500221360ustar00rootroot000000000000003.0 (native) norlab-ulaval-libnabo-534c2d5/examples/000077500000000000000000000000001211110745500200235ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/examples/CMakeLists.txt000066400000000000000000000003631211110745500225650ustar00rootroot00000000000000include_directories(..) add_executable(trivial trivial.cpp) target_link_libraries(trivial ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES}) add_executable(usage usage.cpp) target_link_libraries(usage ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES})norlab-ulaval-libnabo-534c2d5/examples/trivial.cpp000066400000000000000000000011321211110745500221760ustar00rootroot00000000000000// This example is in the public domain #include "nabo/nabo.h" int main() { using namespace Nabo; using namespace Eigen; // 100 points in 3D MatrixXf M = MatrixXf::Random(3, 100); // 1 query points VectorXf q = VectorXf::Random(3); // create a kd-tree for M, note that M must stay valid during the lifetime of the kd-tree NNSearchF* nns = NNSearchF::createKDTreeLinearHeap(M); // look for the 5 nearest neighbour of a the single-point query const int K = 5; VectorXi indices(K); VectorXf dists2(K); nns->knn(q, indices, dists2, K); // cleanup kd-tree delete nns; return 0; }norlab-ulaval-libnabo-534c2d5/examples/usage.cpp000066400000000000000000000041511211110745500216340ustar00rootroot00000000000000// This example is in the public domain #include "nabo/nabo.h" #include int main() { using namespace Nabo; using namespace Eigen; using namespace std; // 100 points in 3D MatrixXf M = MatrixXf::Random(3, 100); // 50 query points MatrixXf q = MatrixXf::Random(3, 50); // Create a kd-tree for M, note that M must stay valid during the lifetime of the kd-tree. NNSearchF* nns = NNSearchF::createKDTreeLinearHeap(M); // The output of the query are a matrix of indices to columns of M and // a matrix of squared distances corresponding to these indices. // These matrix must have the correct size when passed to the search function. MatrixXi indices; MatrixXf dists2; // Look for the 5 nearest neighbours of each query point, // We do not want approximations but we want to sort by the distance, indices.resize(5, q.cols()); dists2.resize(5, q.cols()); nns->knn(q, indices, dists2, 5, 0, NNSearchF::SORT_RESULTS); // Look for the 3 nearest neighbours of each query point, use the data themselves for the query. // We do not want approximations but we want to sort by the distance, // and we want to allow self-match. indices.resize(3, M.cols()); dists2.resize(3, M.cols()); nns->knn(M, indices, dists2, 3, 0, NNSearchF::SORT_RESULTS | NNSearchF::ALLOW_SELF_MATCH); // Make sure that the closest return points correspond to the points from M. for (int i = 0; i < 100; ++i) { // The query is the data itself and we allow self-match. if (indices.coeff(0, i) != i) cerr << "Oups, something went wrong: " << indices.coeff(0, i) << " " << i << endl; } // Now look for the 2 nearset neighbours of each query point. // We do allow 10% approximation but do not want to allow self-match. // We do not care about sorting either. indices.resize(2, M.cols()); dists2.resize(2, M.cols()); nns->knn(M, indices, dists2, 2, 0.1, 0); for (int i = 0; i < 50; ++i) { // The query is the data itself but we forbide self-match. if (indices.coeff(0, i) == i) cerr << "Oups, something went wrong: " << indices.coeff(0, i) << " " << i << endl; } // Cleanup the kd-tree. delete nns; return 0; }norlab-ulaval-libnabo-534c2d5/experimental/000077500000000000000000000000001211110745500207025ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/experimental/CMakeLists.txt000066400000000000000000000001061211110745500234370ustar00rootroot00000000000000# experimental nabo lib add_library(naboexperimental kdtree_cpu.cpp )norlab-ulaval-libnabo-534c2d5/experimental/kdtree_cpu.cpp000066400000000000000000000660741211110745500235500ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo_experimental.h" #include "../nabo/index_heap.h" #include #include #include #include #include #include #include namespace Nabo { using namespace std; template size_t argMax(const typename NearestNeighbourSearch::Vector& v) { T maxVal(0); size_t maxIdx(0); for (int i = 0; i < v.size(); ++i) { if (v[i] > maxVal) { maxVal = v[i]; maxIdx = i; } } return maxIdx; } template size_t KDTreeBalancedPtInNodes::getTreeSize(size_t elCount) const { // FIXME: 64 bits safe stuff, only work for 2^32 elements right now size_t count = 0; int i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } for (int j = 0; j <= i; ++j) count |= (1 << j); //cerr << "tree size " << count << " (" << elCount << " elements)\n"; return count; } template typename KDTreeBalancedPtInNodes::IndexVector KDTreeBalancedPtInNodes::cloudIndexesFromNodesIndexes(const IndexVector& indexes) const { IndexVector cloudIndexes(indexes.size()); for (int i = 0; i < indexes.size(); ++i) cloudIndexes.coeffRef(i) = nodes[indexes[i]].index; return cloudIndexes; } template void KDTreeBalancedPtInNodes::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos) { const size_t count(last - first); //cerr << count << endl; if (count == 1) { nodes[pos] = Node(first->pos, -1, first->index); return; } // estimate variance // get mean Vector mean(Vector::Zero(this->dim)); for (BuildPointsCstIt it(first); it != last; ++it) mean += it->pos; mean /= last - first; // get sum of variance Vector var(Vector::Zero(this->dim)); for (BuildPointsCstIt it(first); it != last; ++it) var += (it->pos - mean).cwise() * (it->pos - mean); // get dimension of maxmial variance const size_t cutDim = argMax(var); // sort sort(first, last, CompareDim(cutDim)); // set node const size_t recurseCount(count-1); const size_t rightCount(recurseCount/2); const size_t leftCount(recurseCount-rightCount); assert(last - rightCount == first + leftCount + 1); nodes[pos] = Node((first+leftCount)->pos, cutDim, (first+leftCount)->index); //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl; // recurse if (count > 2) { buildNodes(first, first + leftCount, childLeft(pos)); buildNodes(first + leftCount + 1, last, childRight(pos)); } else { nodes[childLeft(pos)] = Node(first->pos, -1, first->index); nodes[childRight(pos)] = Node(Vector(), -2, 0); } } template void KDTreeBalancedPtInNodes::dump(const Vector minValues, const Vector maxValues, const size_t pos) const { const Node& node(nodes[pos]); if (node.dim >= -1) { if (this->dim == 2) cout << "" << endl; else cout << "pt at\n" << node.pos << endl; } if (node.dim >= 0) { //cerr << "in bounds:\n" << minValues << "\nto\n" << maxValues << endl; // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[node.dim] = node.pos[node.dim]; // update bounds for right Vector rightMinValues(minValues); rightMinValues[node.dim] = node.pos[node.dim]; // print line if (this->dim == 2) cout << "" << endl; else cout << "cut from\n" << rightMinValues << "\nto\n" << leftMaxValues << endl; // recurs dump(minValues, leftMaxValues, childLeft(pos)); dump(rightMinValues, maxValues, childRight(pos)); } } template KDTreeBalancedPtInNodes::KDTreeBalancedPtInNodes(const Matrix& cloud): NearestNeighbourSearch::NearestNeighbourSearch(cloud) { // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.col(i)); buildPoints.push_back(BuildPoint(v, i)); const_cast(this->minBound) = this->minBound.cwise().min(v); const_cast(this->maxBound) = this->maxBound.cwise().max(v); } // create nodes nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), 0); // dump nodes //dump(minBound, maxBound, 0); } // points in nodes, priority queue template KDTreeBalancedPtInNodesPQ::KDTreeBalancedPtInNodesPQ(const Matrix& cloud): KDTreeBalancedPtInNodes::KDTreeBalancedPtInNodes(cloud) { } template typename KDTreeBalancedPtInNodesPQ::IndexVector KDTreeBalancedPtInNodesPQ::knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags) { typedef priority_queue Queue; const T maxError(1 + epsilon); const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); Queue queue; queue.push(SearchElement(0, 0)); IndexHeapSTL heap(k); statistics.lastQueryVisitCount = 0; while (!queue.empty()) { SearchElement el(queue.top()); queue.pop(); // nothing is closer, we found best if (el.minDist * maxError > heap.headValue()) break; size_t n(el.index); while (1) { const Node& node(nodes[n]); assert (node.dim != -2); // TODO: optimise dist while going down const T dist(dist2(node.pos, query)); if ((dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon()))) heap.replaceHead(n, dist); // if we are at leaf, stop if (node.dim < 0) break; const T offset(query.coeff(node.dim) - node.pos.coeff(node.dim)); const T offset2(offset * offset); const T bestDist(heap.headValue()); if (offset > 0) { // enqueue offside ? if (offset2 < bestDist && nodes[childLeft(n)].dim != -2) queue.push(SearchElement(childLeft(n), offset2)); // continue onside if (nodes[childRight(n)].dim != -2) n = childRight(n); else break; } else { // enqueue offside ? if (offset2 < bestDist && nodes[childRight(n)].dim != -2) queue.push(SearchElement(childRight(n), offset2)); // continue onside if (nodes[childLeft(n)].dim != -2) n = childLeft(n); else break; } ++statistics.lastQueryVisitCount; } } statistics.totalVisitCount += statistics.lastQueryVisitCount; if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); return cloudIndexesFromNodesIndexes(heap.getIndexes()); } template struct KDTreeBalancedPtInNodesPQ; template struct KDTreeBalancedPtInNodesPQ; // points in nodes, stack template KDTreeBalancedPtInNodesStack::KDTreeBalancedPtInNodesStack(const Matrix& cloud): KDTreeBalancedPtInNodes::KDTreeBalancedPtInNodes(cloud) { } template typename KDTreeBalancedPtInNodesStack::IndexVector KDTreeBalancedPtInNodesStack::knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags) { const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); assert(nodes.size() > 0); assert(nodes[0].pos.size() == query.size()); Heap heap(k); Vector off(Vector::Zero(nodes[0].pos.size())); statistics.lastQueryVisitCount = 0; recurseKnn(query, 0, 0, heap, off, 1 + epsilon, allowSelfMatch); if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); statistics.totalVisitCount += statistics.lastQueryVisitCount; return cloudIndexesFromNodesIndexes(heap.getIndexes()); } template void KDTreeBalancedPtInNodesStack::recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch) { const Node& node(nodes[n]); const int cd(node.dim); ++statistics.lastQueryVisitCount; if (cd == -2) return; const T dist(dist2(node.pos, query)); if ((dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon())) ) heap.replaceHead(n, dist); if (cd != -1) { const T old_off(off.coeff(cd)); const T new_off(query.coeff(cd) - node.pos.coeff(cd)); if (new_off > 0) { recurseKnn(query, childRight(n), rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, childLeft(n), rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } else { recurseKnn(query, childLeft(n), rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, childRight(n), rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } } } template struct KDTreeBalancedPtInNodesStack; template struct KDTreeBalancedPtInNodesStack; // NEW: template size_t KDTreeBalancedPtInLeavesStack::getTreeSize(size_t elCount) const { // FIXME: 64 bits safe stuff, only work for 2^32 elements right now assert(elCount > 0); elCount --; size_t count = 0; int i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } for (int j = 0; j <= i; ++j) count |= (1 << j); count <<= 1; count |= 1; return count; } template void KDTreeBalancedPtInLeavesStack::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues, const bool balanceVariance) { const size_t count(last - first); //cerr << count << endl; if (count == 1) { const int dim = -2-(first->index); assert(pos < nodes.size()); nodes[pos] = Node(dim); return; } size_t cutDim; if (balanceVariance) { // estimate variance // get mean Vector mean(Vector::Zero(this->dim)); for (BuildPointsCstIt it(first); it != last; ++it) mean += it->pos; mean /= last - first; // get sum of variance Vector var(Vector::Zero(this->dim)); for (BuildPointsCstIt it(first); it != last; ++it) var += (it->pos - mean).cwise() * (it->pos - mean); // get dimension of maxmial variance cutDim = argMax(var); } else { // find the largest dimension of the box cutDim = argMax(maxValues - minValues); } // compute number of elements const size_t rightCount(count/2); const size_t leftCount(count - rightCount); assert(last - rightCount == first + leftCount); // sort //sort(first, last, CompareDim(cutDim)); nth_element(first, first + leftCount, last, CompareDim(cutDim)); // set node const T cutVal((first+leftCount)->pos.coeff(cutDim)); nodes[pos] = Node(cutDim, cutVal); //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl; // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // recurse buildNodes(first, first + leftCount, childLeft(pos), minValues, leftMaxValues, balanceVariance); buildNodes(first + leftCount, last, childRight(pos), rightMinValues, maxValues, balanceVariance); } template KDTreeBalancedPtInLeavesStack::KDTreeBalancedPtInLeavesStack(const Matrix& cloud, const bool balanceVariance): NearestNeighbourSearch::NearestNeighbourSearch(cloud) { // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.col(i)); buildPoints.push_back(BuildPoint(v, i)); const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); } // create nodes nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), 0, minBound, maxBound, balanceVariance); //for (size_t i = 0; i < nodes.size(); ++i) // cout << i << ": " << nodes[i].dim << " " << nodes[i].cutVal << endl; } template typename KDTreeBalancedPtInLeavesStack::IndexVector KDTreeBalancedPtInLeavesStack::knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags) { const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); assert(nodes.size() > 0); Heap heap(k); Vector off(Vector::Zero(query.size())); statistics.lastQueryVisitCount = 0; recurseKnn(query, 0, 0, heap, off, 1 + epsilon, allowSelfMatch); if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); statistics.totalVisitCount += statistics.lastQueryVisitCount; return heap.getIndexes(); } template void KDTreeBalancedPtInLeavesStack::recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch) { const Node& node(nodes[n]); const int cd(node.dim); ++statistics.lastQueryVisitCount; if (cd < 0) { if (cd == -1) return; const int index(-(cd + 2)); const T dist(dist2(query, cloud.col(index))); if ((dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon())) ) heap.replaceHead(index, dist); } else { const T old_off(off.coeff(cd)); const T new_off(query.coeff(cd) - node.cutVal); if (new_off > 0) { recurseKnn(query, childRight(n), rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, childLeft(n), rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } else { recurseKnn(query, childLeft(n), rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, childRight(n), rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } } } template struct KDTreeBalancedPtInLeavesStack; template struct KDTreeBalancedPtInLeavesStack; template unsigned KDTreeUnbalancedPtInLeavesImplicitBoundsStack::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues) { const size_t count(last - first); const unsigned pos(nodes.size()); //cerr << count << endl; if (count == 1) { nodes.push_back(Node(first->index)); return pos; } // find the largest dimension of the box const unsigned cutDim = argMax(maxValues - minValues); T cutVal((maxValues(cutDim) + minValues(cutDim))/2); // TODO: do only sort once // sort sort(first, last, CompareDim(cutDim)); // TODO: optimise using binary search size_t rightStart(0); while (rightStart < count && (first+rightStart)->pos.coeff(cutDim) < cutVal) ++rightStart; // prevent trivial splits if (rightStart == 0) { cutVal = first->pos.coeff(cutDim); rightStart = 1; } else if (rightStart == count) { rightStart = count - 1; cutVal = (first + rightStart)->pos.coeff(cutDim); } // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // count for recursion const size_t rightCount(count - rightStart); const size_t leftCount(count - rightCount); // add this nodes.push_back(Node(cutDim, cutVal, 0)); // recurse const unsigned __attribute__ ((unused)) leftChild = buildNodes(first, first + leftCount, minValues, leftMaxValues); assert(leftChild == pos + 1); const unsigned rightChild = buildNodes(first + leftCount, last, rightMinValues, maxValues); // write right child index and return nodes[pos].rightChild = rightChild; return pos; } template KDTreeUnbalancedPtInLeavesImplicitBoundsStack::KDTreeUnbalancedPtInLeavesImplicitBoundsStack(const Matrix& cloud): NearestNeighbourSearch::NearestNeighbourSearch(cloud) { // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.col(i)); buildPoints.push_back(BuildPoint(v, i)); const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); } // create nodes //nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), minBound, maxBound); //for (size_t i = 0; i < nodes.size(); ++i) // cout << i << ": " << nodes[i].dim << " " << nodes[i].cutVal << " " << nodes[i].rightChild << endl; } template typename KDTreeUnbalancedPtInLeavesImplicitBoundsStack::IndexVector KDTreeUnbalancedPtInLeavesImplicitBoundsStack::knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags) { const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); assert(nodes.size() > 0); Heap heap(k); Vector off(Vector::Zero(query.size())); statistics.lastQueryVisitCount = 0; recurseKnn(query, 0, 0, heap, off, 1+epsilon, allowSelfMatch); if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); statistics.totalVisitCount += statistics.lastQueryVisitCount; return heap.getIndexes(); } template typename KDTreeUnbalancedPtInLeavesImplicitBoundsStack::IndexMatrix KDTreeUnbalancedPtInLeavesImplicitBoundsStack::knnM(const Matrix& query, const Index k, const T epsilon, const unsigned optionFlags) { const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); assert(nodes.size() > 0); assert(nodes.size() > 0); Heap heap(k); Vector off(query.rows()); IndexMatrix result(k, query.cols()); const int colCount(query.cols()); for (int i = 0; i < colCount; ++i) { const Vector& q(query.col(i)); off.setZero(); heap.reset(); statistics.lastQueryVisitCount = 0; recurseKnn(q, 0, 0, heap, off, 1+epsilon, allowSelfMatch); if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); result.col(i) = heap.getIndexes(); statistics.totalVisitCount += statistics.lastQueryVisitCount; } return result; } template void KDTreeUnbalancedPtInLeavesImplicitBoundsStack::recurseKnn(const Vector& query, const unsigned n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch) { const Node& node(nodes[n]); //++statistics.lastQueryVisitCount; if (node.rightChild == Node::INVALID_CHILD) { const unsigned index(node.ptIndex); //const T dist(dist2(query, cloud.col(index))); //const T dist((query - cloud.col(index)).squaredNorm()); T dist(0); const T* qPtr(&query.coeff(0)); const T* dPtr(&cloud.coeff(0, index)); const int dim(query.size()); for (int i = 0; i < dim; ++i) { const T diff(*qPtr - *dPtr); dist += diff*diff; qPtr++; dPtr++; } if ((dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon())) ) heap.replaceHead(index, dist); } else { const unsigned cd(node.dim); const T old_off(off.coeff(cd)); const T new_off(query.coeff(cd) - node.cutVal); if (new_off > 0) { recurseKnn(query, node.rightChild, rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, n + 1, rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } else { recurseKnn(query, n+1, rd, heap, off, maxError, allowSelfMatch); rd += - old_off*old_off + new_off*new_off; if (rd * maxError < heap.headValue()) { off.coeffRef(cd) = new_off; recurseKnn(query, node.rightChild, rd, heap, off, maxError, allowSelfMatch); off.coeffRef(cd) = old_off; } } } } template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStack>; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStack>; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStack>; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStack>; template unsigned KDTreeUnbalancedPtInLeavesExplicitBoundsStack::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues) { const size_t count(last - first); const unsigned pos(nodes.size()); //cerr << count << endl; if (count == 1) { const int dim = -1-(first->index); nodes.push_back(Node(dim)); return pos; } // find the largest dimension of the box const int cutDim = argMax(maxValues - minValues); T cutVal((maxValues(cutDim) + minValues(cutDim))/2); // TODO: do only sort once // sort sort(first, last, CompareDim(cutDim)); // TODO: optimise using binary search size_t rightStart(0); while (rightStart < count && (first+rightStart)->pos.coeff(cutDim) < cutVal) ++rightStart; // prevent trivial splits if (rightStart == 0) { cutVal = first->pos.coeff(cutDim); rightStart = 1; } else if (rightStart == count) { rightStart = count - 1; cutVal = (first + rightStart)->pos.coeff(cutDim); } // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // count for recursion const size_t rightCount(count - rightStart); const size_t leftCount(count - rightCount); // add this nodes.push_back(Node(cutDim, cutVal, minValues.coeff(cutDim), maxValues.coeff(cutDim))); // recurse const unsigned __attribute__ ((unused)) leftChild = buildNodes(first, first + leftCount, minValues, leftMaxValues); assert(leftChild == pos + 1); const unsigned rightChild = buildNodes(first + leftCount, last, rightMinValues, maxValues); // write right child index and return nodes[pos].rightChild = rightChild; return pos; } template KDTreeUnbalancedPtInLeavesExplicitBoundsStack::KDTreeUnbalancedPtInLeavesExplicitBoundsStack(const Matrix& cloud): NearestNeighbourSearch::NearestNeighbourSearch(cloud) { // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.col(i)); buildPoints.push_back(BuildPoint(v, i)); const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); } // create nodes //nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), minBound, maxBound); //for (size_t i = 0; i < nodes.size(); ++i) // cout << i << ": " << nodes[i].dim << " " << nodes[i].cutVal << " " << nodes[i].rightChild << endl; } template typename KDTreeUnbalancedPtInLeavesExplicitBoundsStack::IndexVector KDTreeUnbalancedPtInLeavesExplicitBoundsStack::knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags) { const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); assert(nodes.size() > 0); Heap heap(k); statistics.lastQueryVisitCount = 0; recurseKnn(query, 0, 0, heap, 1+epsilon, allowSelfMatch); if (optionFlags & NearestNeighbourSearch::SORT_RESULTS) heap.sort(); statistics.totalVisitCount += statistics.lastQueryVisitCount; return heap.getIndexes(); } template void KDTreeUnbalancedPtInLeavesExplicitBoundsStack::recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, const T maxError, const bool allowSelfMatch) { const Node& node(nodes[n]); const int cd(node.dim); ++statistics.lastQueryVisitCount; if (cd < 0) { const int index(-(cd + 1)); const T dist(dist2(query, cloud.col(index))); if ((dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon())) ) heap.replaceHead(index, dist); } else { const T q_val(query.coeff(cd)); const T cut_diff(q_val - node.cutVal); if (cut_diff < 0) { recurseKnn(query, n+1, rd, heap, maxError, allowSelfMatch); T box_diff = node.lowBound - q_val; if (box_diff < 0) box_diff = 0; rd += cut_diff*cut_diff - box_diff*box_diff; if (rd * maxError < heap.headValue()) recurseKnn(query, node.rightChild, rd, heap, maxError, allowSelfMatch); } else { recurseKnn(query, node.rightChild, rd, heap, maxError, allowSelfMatch); T box_diff = q_val - node.highBound; if (box_diff < 0) box_diff = 0; rd += cut_diff*cut_diff - box_diff*box_diff; if (rd * maxError < heap.headValue()) recurseKnn(query, n + 1, rd, heap, maxError, allowSelfMatch); } } } template struct KDTreeUnbalancedPtInLeavesExplicitBoundsStack; template struct KDTreeUnbalancedPtInLeavesExplicitBoundsStack; } norlab-ulaval-libnabo-534c2d5/experimental/nabo_experimental.h000066400000000000000000000277551211110745500245670ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef __NABO_EXPERIMENTAL_H #define __NABO_EXPERIMENTAL_H #include "../nabo/nabo_private.h" #include "../nabo/index_heap.h" namespace Nabo { // KDTree, balanced, points in nodes template struct KDTreeBalancedPtInNodes:public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; protected: struct BuildPoint { Vector pos; size_t index; BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {} }; typedef std::vector BuildPoints; typedef typename BuildPoints::iterator BuildPointsIt; typedef typename BuildPoints::const_iterator BuildPointsCstIt; struct CompareDim { size_t dim; CompareDim(const size_t dim):dim(dim){} bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); } }; struct Node { Vector pos; int dim; // -1 == leaf, -2 == invalid Index index; Node(const Vector& pos = Vector(), const int dim = -2, const Index index = 0):pos(pos), dim(dim), index(index) {} }; typedef std::vector Nodes; Nodes nodes; inline size_t childLeft(size_t pos) const { return 2*pos + 1; } inline size_t childRight(size_t pos) const { return 2*pos + 2; } inline size_t parent(size_t pos) const { return (pos-1)/2; } size_t getTreeSize(size_t size) const; IndexVector cloudIndexesFromNodesIndexes(const IndexVector& indexes) const; void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos); void dump(const Vector minValues, const Vector maxValues, const size_t pos) const; protected: KDTreeBalancedPtInNodes(const Matrix& cloud); }; // KDTree, balanced, points in nodes, priority queue template struct KDTreeBalancedPtInNodesPQ: public KDTreeBalancedPtInNodes { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename KDTreeBalancedPtInNodes::Node Node; typedef typename KDTreeBalancedPtInNodes::Nodes Nodes; using NearestNeighbourSearch::statistics; using KDTreeBalancedPtInNodes::nodes; using KDTreeBalancedPtInNodes::childLeft; using KDTreeBalancedPtInNodes::childRight; protected: struct SearchElement { size_t index; T minDist; SearchElement(const size_t index, const T minDist): index(index), minDist(minDist) {} // invert test as std::priority_queue shows biggest element at top friend bool operator<(const SearchElement& e0, const SearchElement& e1) { return e0.minDist > e1.minDist; } }; public: KDTreeBalancedPtInNodesPQ(const Matrix& cloud); virtual IndexVector knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags); }; // KDTree, balanced, points in nodes, stack template struct KDTreeBalancedPtInNodesStack: public KDTreeBalancedPtInNodes { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename KDTreeBalancedPtInNodes::Node Node; typedef typename KDTreeBalancedPtInNodes::Nodes Nodes; using NearestNeighbourSearch::statistics; using KDTreeBalancedPtInNodes::nodes; using KDTreeBalancedPtInNodes::childLeft; using KDTreeBalancedPtInNodes::childRight; typedef IndexHeapSTL Heap; protected: void recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch); public: KDTreeBalancedPtInNodesStack(const Matrix& cloud); virtual IndexVector knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags); }; // KDTree, balanced, points in leaves, stack template struct KDTreeBalancedPtInLeavesStack: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; using NearestNeighbourSearch::statistics; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; protected: struct BuildPoint { Vector pos; size_t index; BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {} }; typedef std::vector BuildPoints; typedef typename BuildPoints::iterator BuildPointsIt; typedef typename BuildPoints::const_iterator BuildPointsCstIt; struct CompareDim { size_t dim; CompareDim(const size_t dim):dim(dim){} bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); } }; typedef IndexHeapSTL Heap; struct Node { int dim; // -1 == invalid, <= -2 = index of pt T cutVal; Node(const int dim = -1, const T cutVal = 0): dim(dim), cutVal(cutVal) {} }; typedef std::vector Nodes; Nodes nodes; inline size_t childLeft(size_t pos) const { return 2*pos + 1; } inline size_t childRight(size_t pos) const { return 2*pos + 2; } inline size_t parent(size_t pos) const { return (pos-1)/2; } size_t getTreeSize(size_t size) const; void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues, const bool balanceVariance); void recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch); public: KDTreeBalancedPtInLeavesStack(const Matrix& cloud, const bool balanceVariance); virtual IndexVector knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags); }; // KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStack: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::statistics; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; protected: struct BuildPoint { Vector pos; size_t index; BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {} }; typedef std::vector BuildPoints; typedef typename BuildPoints::iterator BuildPointsIt; typedef typename BuildPoints::const_iterator BuildPointsCstIt; struct CompareDim { size_t dim; CompareDim(const size_t dim):dim(dim){} bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); } }; struct Node { enum { INVALID_CHILD = 0xffffffff, INVALID_PT = 0xffffffff }; unsigned dim; unsigned rightChild; union { T cutVal; unsigned ptIndex; }; Node(const int dim, const T cutVal, unsigned rightChild): dim(dim), rightChild(rightChild), cutVal(cutVal) {} Node(const unsigned ptIndex = INVALID_PT): dim(0), rightChild(INVALID_CHILD), ptIndex(ptIndex) {} }; typedef std::vector Nodes; Nodes nodes; unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues); void recurseKnn(const Vector& query, const unsigned n, T rd, Heap& heap, Vector& off, const T maxError, const bool allowSelfMatch); public: KDTreeUnbalancedPtInLeavesImplicitBoundsStack(const Matrix& cloud); virtual IndexVector knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags); virtual IndexMatrix knnM(const Matrix& query, const Index k, const T epsilon, const unsigned optionFlags); }; // KDTree, unbalanced, points in leaves, stack, explicit bounds, ANN_KD_SL_MIDPT template struct KDTreeUnbalancedPtInLeavesExplicitBoundsStack: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; using NearestNeighbourSearch::statistics; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; protected: struct BuildPoint { Vector pos; size_t index; BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {} }; typedef std::vector BuildPoints; typedef typename BuildPoints::iterator BuildPointsIt; typedef typename BuildPoints::const_iterator BuildPointsCstIt; struct CompareDim { size_t dim; CompareDim(const size_t dim):dim(dim){} bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); } }; typedef IndexHeapSTL Heap; struct Node { int dim; // <= -1 = index of pt unsigned rightChild; T cutVal; T lowBound; T highBound; Node(const int dim = -1, const T cutVal = 0, const T lowBound = 0, const T highBound = 0, unsigned rightChild = 0): dim(dim), rightChild(rightChild), cutVal(cutVal), lowBound(lowBound), highBound(highBound) {} }; typedef std::vector Nodes; Nodes nodes; unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues); void recurseKnn(const Vector& query, const size_t n, T rd, Heap& heap, const T maxError, const bool allowSelfMatch); public: KDTreeUnbalancedPtInLeavesExplicitBoundsStack(const Matrix& cloud); virtual IndexVector knn(const Vector& query, const Index k, const T epsilon, const unsigned optionFlags); }; } #endif // __NABO_H norlab-ulaval-libnabo-534c2d5/nabo/000077500000000000000000000000001211110745500171245ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/nabo/brute_force_cpu.cpp000066400000000000000000000101701211110745500227750ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo_private.h" #include "index_heap.h" /*! \file brute_force_cpu.cpp \brief brute force search, cpu implementation \ingroup private */ namespace Nabo { using namespace std; template BruteForceSearch::BruteForceSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags): NearestNeighbourSearch::NearestNeighbourSearch(cloud, dim, creationOptionFlags) { #ifdef EIGEN3_API const_cast(this->minBound) = cloud.topRows(this->dim).rowwise().minCoeff(); const_cast(this->maxBound) = cloud.topRows(this->dim).rowwise().maxCoeff(); #else // EIGEN3_API // compute bounds for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.block(0,i,this->dim,1)); const_cast(this->minBound) = this->minBound.cwise().min(v); const_cast(this->maxBound) = this->maxBound.cwise().max(v); } #endif // EIGEN3_API } template unsigned long BruteForceSearch::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const { const Vector maxRadii(Vector::Constant(query.cols(), maxRadius)); return knn(query, indices, dists2, maxRadii, k, epsilon, optionFlags); } template unsigned long BruteForceSearch::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k, const T epsilon, const unsigned optionFlags) const { checkSizesKnn(query, indices, dists2, k, &maxRadii); const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); const bool sortResults(optionFlags & NearestNeighbourSearch::SORT_RESULTS); const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); IndexHeapSTL heap(k); for (int c = 0; c < query.cols(); ++c) { const T maxRadius(maxRadii[c]); const T maxRadius2(maxRadius * maxRadius); const Vector& q(query.block(0,c,dim,1)); heap.reset(); for (int i = 0; i < this->cloud.cols(); ++i) { const T dist(dist2(this->cloud.block(0,i,dim,1), q)); if ((dist <= maxRadius2) && (dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon()))) heap.replaceHead(i, dist); } if (sortResults) heap.sort(); heap.getData(indices.col(c), dists2.col(c)); } if (collectStatistics) return (unsigned long)query.cols() * (unsigned long)this->cloud.cols(); else return 0; } template struct BruteForceSearch; template struct BruteForceSearch; } norlab-ulaval-libnabo-534c2d5/nabo/index_heap.h000066400000000000000000000261551211110745500214120ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef __INDEX_HEAP_H #define __INDEX_HEAP_H #include "nabo.h" #include #include #include #include /*! \file index_heap.h \brief implementation of index heaps \ingroup private */ namespace Nabo { //! balanced-tree implementation of heap /** It uses a binary heap, which provides replacement in O(log(n)), * however the constant overhead is significative. */ template struct IndexHeapSTL { //! type of an index typedef IT Index; //! type of a value typedef VT Value; //! an entry of the heap tree struct Entry { IT index; //!< index of point VT value; //!< distance for this point //! create a new entry Entry(const IT index, const VT value): index(index), value(value) {} //! return true if e0 is of lower value than e1, false otherwise friend bool operator<(const Entry& e0, const Entry& e1) { return e0.value < e1.value; } }; //! vector of entry, type for the storage of the tree typedef std::vector Entries; //! vector of indices typedef typename Eigen::Matrix IndexVector; //! vector of values typedef typename Eigen::Matrix ValueVector; //! storage for the tree Entries data; //! number of neighbours requested const size_t nbNeighbours; //! Constructor /*! \param size number of elements in the heap */ IndexHeapSTL(const size_t size): data(1, Entry(0, std::numeric_limits::infinity())), nbNeighbours(size) { data.reserve(size); } //! reset to the empty heap inline void reset() { data.clear(); data.push_back(Entry(0, std::numeric_limits::infinity())); } //! get the largest value of the heap /** \return the largest value in the heap */ inline const VT& headValue() const { return data.front().value; } //! put value into heap, replace the largest value if full /** \param index new point index * \param value new distance value */ inline void replaceHead(const Index index, const Value value) { if (data.size() == nbNeighbours) { // we have enough neighbours to discard largest pop_heap(data.begin(), data.end()); data.back() = Entry(index, value); } else { // missing neighbours data.push_back(Entry(index, value)); } // ensure heap push_heap(data.begin(), data.end()); } //! sort the entries, from the smallest to the largest inline void sort() { sort_heap (data.begin(), data.end()); } //! get the data from the heap /** \param indices index vector * \param values value vector */ template inline void getData(const Eigen::MatrixBase& indices, const Eigen::MatrixBase & values) const { // note: we must implement this hack because of problem with reference to temporary // C++0x will solve this with rvalue // see: http://eigen.tuxfamily.org/dox-devel/TopicFunctionTakingEigenTypes.html // for more informations size_t i = 0; for (; i < data.size(); ++i) { const_cast&>(indices).coeffRef(i) = data[i].index; const_cast&>(values).coeffRef(i) = data[i].value; } for (; i < nbNeighbours; ++i) { const_cast&>(indices).coeffRef(i) = 0; const_cast&>(values).coeffRef(i) = std::numeric_limits::infinity(); } } #if 0 //! get the data-point indices from the heap /** \return the indices */ inline IndexVector getIndexes() const { IndexVector indexes(data.capacity()); size_t i = 0; for (; i < data.size(); ++i) indexes.coeffRef(i) = data[i].index; for (; i < data.capacity(); ++i) indexes.coeffRef(i) = 0; return indexes; } #endif }; #if 0 //! brute-force implementation of heap /** It uses a vector and linear search, which provides replacement in O(n), * but with a very low constant overhead. */ template struct IndexHeapBruteForceVector { //! type of an index typedef IT Index; //! type of a value typedef VT Value; //! an entry of the heap vector struct Entry { IT index; //!< index of point VT value; //!< distance for this point //! create a new entry Entry(const IT index, const VT value): index(index), value(value) {} //! return true if e0 is smaller than e1, false otherwise friend bool operator<(const Entry& e0, const Entry& e1) { return e0.value < e1.value; } }; //! vector of entry, type for the storage of the tree typedef std::vector Entries; //! vector of indices typedef typename Eigen::Matrix IndexVector; //! storage for the tree Entries data; //! reference to the largest value in the tree, to optimise access speed const VT& headValueRef; //! pre-competed size minus one, to optimise access speed const size_t sizeMinusOne; //! Constructor /*! \param size number of elements in the heap */ IndexHeapBruteForceVector(const size_t size): data(size, Entry(0, std::numeric_limits::infinity())), headValueRef((data.end() - 1)->value), sizeMinusOne(data.size() - 1) { } //! reset to the empty heap inline void reset() { for (typename Entries::iterator it(data.begin()); it != data.end(); ++it) it->value = std::numeric_limits::infinity(); } //! get the largest value of the heap /** \return the smallest value in the heap */ inline const VT& headValue() const { return data[0].value; } //! replace the largest value of the heap /** \param index new point index * \param value new distance value */ inline void replaceHead(const Index index, const Value value) { register size_t i = 0; for (; i < sizeMinusOne; ++i) { if (data[i + 1].value > value) data[i] = data[i + 1]; else break; } data[i].value = value; data[i].index = index; } //! sort the entries, from the smallest to the largest inline void sort() { // no need to sort as data are already sorted } //! get the data-point indices from the heap /** \return the indices */ inline IndexVector getIndexes() const { IndexVector indexes(data.size()); for (size_t i = 0; i < data.size(); ++i) indexes.coeffRef(i) = data[sizeMinusOne-i].index; return indexes; } }; #endif //! brute-force implementation of heap /** It uses a vector and linear search, which provides replacement in O(n), * but with a very low constant overhead. */ template struct IndexHeapBruteForceVector { //! type of an index typedef IT Index; //! type of a value typedef VT Value; //! an entry of the heap vector struct Entry { IT index; //!< index of point VT value; //!< distance for this point //! create a new entry Entry(const IT index, const VT value): index(index), value(value) {} //! return true if e0 is smaller than e1, false otherwise friend bool operator<(const Entry& e0, const Entry& e1) { return e0.value < e1.value; } }; //! vector of entry, type for the storage of the tree typedef std::vector Entries; //! vector of indices typedef typename Eigen::Matrix IndexVector; //! vector of values typedef typename Eigen::Matrix ValueVector; //! storage for the tree Entries data; //! reference to the largest value in the tree, to optimise access speed const VT& headValueRef; //! pre-competed size minus one, to optimise access speed const size_t sizeMinusOne; //! Constructor /*! \param size number of elements in the heap */ IndexHeapBruteForceVector(const size_t size): data(size, Entry(0, std::numeric_limits::infinity())), headValueRef((data.end() - 1)->value), sizeMinusOne(data.size() - 1) { } //! reset to the empty heap inline void reset() { for (typename Entries::iterator it(data.begin()); it != data.end(); ++it) it->value = std::numeric_limits::infinity(); } //! get the largest value of the heap /** \return the smallest value in the heap */ inline const VT& headValue() const { return headValueRef; } //! replace the largest value of the heap /** \param index new point index * \param value new distance value */ inline void replaceHead(const Index index, const Value value) { register size_t i; for (i = sizeMinusOne; i > 0; --i) { if (data[i-1].value > value) data[i] = data[i-1]; else break; } data[i].value = value; data[i].index = index; } //! sort the entries, from the smallest to the largest inline void sort() { // no need to sort as data are already sorted } //! get the data from the heap /** \param indices index vector * \param values value vector */ template inline void getData(const Eigen::MatrixBase& indices, const Eigen::MatrixBase & values) const { // note: we must implement this hack because of problem with reference to temporary // C++0x will solve this with rvalue // see: http://eigen.tuxfamily.org/dox-devel/TopicFunctionTakingEigenTypes.html // for more informations for (size_t i = 0; i < data.size(); ++i) { const_cast&>(indices).coeffRef(i) = data[i].index; const_cast&>(values).coeffRef(i) = data[i].value; } } #if 0 //! get the data-point indices from the heap /** \return the indices */ inline IndexVector getIndexes() const { IndexVector indexes(data.size()); for (size_t i = 0; i < data.size(); ++i) indexes.coeffRef(i) = data[i].index; return indexes; } #endif }; } #endif // __INDEX_HEAP_H norlab-ulaval-libnabo-534c2d5/nabo/kdtree_cpu.cpp000066400000000000000000000363061211110745500217650ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo_private.h" #include "index_heap.h" #include #include #include #include #include #include #include #include #include /*! \file kdtree_cpu.cpp \brief kd-tree search, cpu implementation \ingroup private */ namespace Nabo { //! \ingroup private //@{ using namespace std; //! Return the number of bit required to store a value /** \param v value to store * \return number of bits required */ template T getStorageBitCount(T v) { for (T i = 0; i < 64; ++i) { if (v == 0) return i; v >>= 1; } return 64; } //! Return the index of the maximum value of a vector /** \param v vector * \return index of maximum value, 0 if the vector is empty */ template size_t argMax(const typename NearestNeighbourSearch::Vector& v) { T maxVal(0); size_t maxIdx(0); for (int i = 0; i < v.size(); ++i) { if (v[i] > maxVal) { maxVal = v[i]; maxIdx = i; } } return maxIdx; } // OPT template pair KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::getBounds(const BuildPointsIt first, const BuildPointsIt last, const unsigned dim) { T minVal(boost::numeric::bounds::highest()); T maxVal(boost::numeric::bounds::lowest()); for (BuildPointsCstIt it(first); it != last; ++it) { const T val(cloud.coeff(dim, *it)); minVal = min(val, minVal); maxVal = max(val, maxVal); } return make_pair(minVal, maxVal); } template unsigned KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues) { const int count(last - first); assert(count >= 1); const unsigned pos(nodes.size()); //cerr << count << endl; if (count <= int(bucketSize)) { const uint32_t initBucketsSize(buckets.size()); //cerr << "creating bucket with " << count << " values" << endl; for (int i = 0; i < count; ++i) { const Index index(*(first+i)); assert(index < cloud.cols()); buckets.push_back(BucketEntry(&cloud.coeff(0, index), index)); //cerr << " " << &cloud.coeff(0, index) << ", " << index << endl; } //cerr << "at address " << bucketStart << endl; nodes.push_back(Node(createDimChildBucketSize(dim, count),initBucketsSize)); return pos; } // find the largest dimension of the box const unsigned cutDim = argMax(maxValues - minValues); const T idealCutVal((maxValues(cutDim) + minValues(cutDim))/2); // get bounds from actual points const pair minMaxVals(getBounds(first, last, cutDim)); // correct cut following bounds T cutVal; if (idealCutVal < minMaxVals.first) cutVal = minMaxVals.first; else if (idealCutVal > minMaxVals.second) cutVal = minMaxVals.second; else cutVal = idealCutVal; int l(0); int r(count-1); // partition points around cutVal while (1) { while (l < count && cloud.coeff(cutDim, *(first+l)) < cutVal) ++l; while (r >= 0 && cloud.coeff(cutDim, *(first+r)) >= cutVal) --r; if (l > r) break; swap(*(first+l), *(first+r)); ++l; --r; } const int br1 = l; // now: points[0..br1-1] < cutVal <= points[br1..count-1] r = count-1; // partition points[br1..count-1] around cutVal while (1) { while (l < count && cloud.coeff(cutDim, *(first+l)) <= cutVal) ++l; while (r >= br1 && cloud.coeff(cutDim, *(first+r)) > cutVal) --r; if (l > r) break; swap(*(first+l), *(first+r)); ++l; --r; } const int br2 = l; // now: points[br1..br2-1] == cutVal < points[br2..count-1] // find best split index int leftCount; if (idealCutVal < minMaxVals.first) leftCount = 1; else if (idealCutVal > minMaxVals.second) leftCount = count-1; else if (br1 > count / 2) leftCount = br1; else if (br2 < count / 2) leftCount = br2; else leftCount = count / 2; assert(leftCount > 0); /*if (leftCount >= count) { cerr << "Error found in kdtree:" << endl; cerr << "cloud size: " << cloud.cols() << endl; cerr << "count:" << count << endl; cerr << "leftCount: " << leftCount << endl; cerr << "br1: " << br1 << endl; cerr << "br2: " << br2 << endl; cerr << "idealCutVal: " << idealCutVal << endl; cerr << "cutVal: " << cutVal << endl; cerr << "minMaxVals.first: " << minMaxVals.first << endl; cerr << "minMaxVals.second: " << minMaxVals.second << endl; }*/ assert(leftCount < count); // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // add this nodes.push_back(Node(0, cutVal)); // recurse const unsigned _UNUSED leftChild = buildNodes(first, first + leftCount, minValues, leftMaxValues); assert(leftChild == pos + 1); const unsigned rightChild = buildNodes(first + leftCount, last, rightMinValues, maxValues); // write right child index and return nodes[pos].dimChildBucketSize = createDimChildBucketSize(cutDim, rightChild); return pos; } template KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters): NearestNeighbourSearch::NearestNeighbourSearch(cloud, dim, creationOptionFlags), bucketSize(additionalParameters.get("bucketSize", 8)), dimBitCount(getStorageBitCount(this->dim)), dimMask((1<dim, cloud.cols()),uint32_t(0))); return; } const uint64_t maxNodeCount((1 << (32-dimBitCount)) - 1); const uint64_t estimatedNodeCount(cloud.cols() / (bucketSize / 2)); if (estimatedNodeCount > maxNodeCount) { throw runtime_error((boost::format("Cloud has a risk to have more nodes (%1%) than the kd-tree allows (%2%). The kd-tree has %3% bits for dimensions and %4% bits for node indices") % estimatedNodeCount % maxNodeCount % dimBitCount % (32-dimBitCount)).str()); } // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.block(0,i,this->dim,1)); buildPoints.push_back(i); #ifdef EIGEN3_API const_cast(minBound) = minBound.array().min(v.array()); const_cast(maxBound) = maxBound.array().max(v.array()); #else // EIGEN3_API const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); #endif // EIGEN3_API } // create nodes buildNodes(buildPoints.begin(), buildPoints.end(), minBound, maxBound); buildPoints.clear(); } template unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const { checkSizesKnn(query, indices, dists2, k); const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); const bool sortResults(optionFlags & NearestNeighbourSearch::SORT_RESULTS); const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); const T maxRadius2(maxRadius * maxRadius); const T maxError2((1+epsilon)*(1+epsilon)); assert(nodes.size() > 0); Heap heap(k); std::vector off(dim, 0); IndexMatrix result(k, query.cols()); const int colCount(query.cols()); unsigned long leafTouchedCount(0); for (int i = 0; i < colCount; ++i) { leafTouchedCount += onePointKnn(query, indices, dists2, i, heap, off, maxError2, maxRadius2, allowSelfMatch, collectStatistics, sortResults); } return leafTouchedCount; } template unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k, const T epsilon, const unsigned optionFlags) const { checkSizesKnn(query, indices, dists2, k, &maxRadii); const bool allowSelfMatch(optionFlags & NearestNeighbourSearch::ALLOW_SELF_MATCH); const bool sortResults(optionFlags & NearestNeighbourSearch::SORT_RESULTS); const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); const T maxError2((1+epsilon)*(1+epsilon)); assert(nodes.size() > 0); Heap heap(k); std::vector off(dim, 0); IndexMatrix result(k, query.cols()); const int colCount(query.cols()); unsigned long leafTouchedCount(0); for (int i = 0; i < colCount; ++i) { const T maxRadius(maxRadii[i]); const T maxRadius2(maxRadius * maxRadius); leafTouchedCount += onePointKnn(query, indices, dists2, i, heap, off, maxError2, maxRadius2, allowSelfMatch, collectStatistics, sortResults); } return leafTouchedCount; } template unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::onePointKnn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, int i, Heap& heap, std::vector& off, const T maxError2, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const { fill(off.begin(), off.end(), 0); heap.reset(); unsigned long leafTouchedCount(0); if (allowSelfMatch) { if (collectStatistics) leafTouchedCount += recurseKnn(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2); else recurseKnn(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2); } else { if (collectStatistics) leafTouchedCount += recurseKnn(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2); else recurseKnn(&query.coeff(0, i), 0, 0, heap, off, maxError2, maxRadius2); } if (sortResults) heap.sort(); heap.getData(indices.col(i), dists2.col(i)); return leafTouchedCount; } template template unsigned long KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::recurseKnn(const T* query, const unsigned n, T rd, Heap& heap, std::vector& off, const T maxError2, const T maxRadius2) const { const Node& node(nodes[n]); const uint32_t cd(getDim(node.dimChildBucketSize)); if (cd == uint32_t(dim)) { //cerr << "entering bucket " << node.bucket << endl; const BucketEntry* bucket(&buckets[node.bucketIndex]); const uint32_t bucketSize(getChildBucketSize(node.dimChildBucketSize)); for (uint32_t i = 0; i < bucketSize; ++i) { //cerr << " " << bucket-> pt << endl; //const T dist(dist2(query, cloud.col(index))); //const T dist((query - cloud.col(index)).squaredNorm()); T dist(0); const T* qPtr(query); const T* dPtr(bucket->pt); for (int i = 0; i < this->dim; ++i) { const T diff(*qPtr - *dPtr); dist += diff*diff; qPtr++; dPtr++; } if ((dist <= maxRadius2) && (dist < heap.headValue()) && (allowSelfMatch || (dist > numeric_limits::epsilon())) ) heap.replaceHead(bucket->index, dist); ++bucket; } return (unsigned long)(bucketSize); } else { const unsigned rightChild(getChildBucketSize(node.dimChildBucketSize)); unsigned long leafVisitedCount(0); T& offcd(off[cd]); //const T old_off(off.coeff(cd)); const T old_off(offcd); const T new_off(query[cd] - node.cutVal); if (new_off > 0) { if (collectStatistics) leafVisitedCount += recurseKnn(query, rightChild, rd, heap, off, maxError2, maxRadius2); else recurseKnn(query, rightChild, rd, heap, off, maxError2, maxRadius2); rd += - old_off*old_off + new_off*new_off; if ((rd <= maxRadius2) && (rd * maxError2 < heap.headValue())) { offcd = new_off; if (collectStatistics) leafVisitedCount += recurseKnn(query, n + 1, rd, heap, off, maxError2, maxRadius2); else recurseKnn(query, n + 1, rd, heap, off, maxError2, maxRadius2); offcd = old_off; } } else { if (collectStatistics) leafVisitedCount += recurseKnn(query, n+1, rd, heap, off, maxError2, maxRadius2); else recurseKnn(query, n+1, rd, heap, off, maxError2, maxRadius2); rd += - old_off*old_off + new_off*new_off; if ((rd <= maxRadius2) && (rd * maxError2 < heap.headValue())) { offcd = new_off; if (collectStatistics) leafVisitedCount += recurseKnn(query, rightChild, rd, heap, off, maxError2, maxRadius2); else recurseKnn(query, rightChild, rd, heap, off, maxError2, maxRadius2); offcd = old_off; } } return leafVisitedCount; } } template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >; template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >; //@} } norlab-ulaval-libnabo-534c2d5/nabo/kdtree_opencl.cpp000066400000000000000000000551431211110745500224560ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifdef HAVE_OPENCL #include "nabo_private.h" #include "index_heap.h" #include #include #include #include #include #include #include // #include #include #include #include #include /*! \file kdtree_opencl.cpp \ *brief kd-tree search, opencl implementation \ingroup private */ namespace cl { //! Vector of device typedef std::vector Devices; } namespace Nabo { // argmax is already defined in kdtree_cpu.cpp, which is always compiled template size_t argMax(const typename NearestNeighbourSearch::Vector& v); //! \ingroup private //@{ //! Maximum number of points acceptable in a query #define MAX_K 32 using namespace std; //! Template to retrieve type-specific code for CL support template struct EnableCLTypeSupport {}; //! CL support code for float template<> struct EnableCLTypeSupport { //! Return CL code to enable float support and set it for use by our CL code static string code(const cl::Device& device) { return "typedef float T;\n"; } }; //! CL support code for double template<> struct EnableCLTypeSupport { //! Return CL code to enable double support and set it for use by our CL code. /** This is more complex than float as double is not supported by default. */ static string code(const cl::Device& device) { string s; const string& exts(device.getInfo()); //cerr << "extensions: " << exts << endl; // first try generic 64-bits fp, otherwise try to fall back on vendor-specific extensions if (exts.find("cl_khr_fp64") != string::npos) s += "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n"; else if (exts.find("cl_amd_fp64") != string::npos) s += "#pragma OPENCL EXTENSION cl_amd_fp64 : enable\n"; else throw runtime_error("The OpenCL platform does not support 64 bits double-precision floating-points scalars."); s += "typedef double T;\n"; return s; } }; //! Cache CL source code (including defines and support code) struct SourceCacher { //! Vector of devices typedef std::vector Devices; //! Map of cached programmes typedef std::map ProgramCache; cl::Context context; //!< context in which programs are cached Devices devices; //!< devices linked to the context ProgramCache cachedPrograms; //!< cached programs //! Create a source cacher for a given device type, retrieves a list of devices SourceCacher(const cl_device_type deviceType) { // looking for platforms, AMD drivers do not like the default for creating context vector platforms; cl::Platform::get(&platforms); if (platforms.empty()) throw runtime_error("No OpenCL platform found"); //for(vector::iterator i = platforms.begin(); i != platforms.end(); ++i) // cerr << "platform " << i - platforms.begin() << " is " << (*i).getInfo() << endl; cl::Platform platform = platforms[0]; const char *userDefinedPlatform(getenv("NABO_OPENCL_USE_PLATFORM")); if (userDefinedPlatform) { size_t userDefinedPlatformId = atoi(userDefinedPlatform); if (userDefinedPlatformId < platforms.size()) platform = platforms[userDefinedPlatformId]; } // create OpenCL contexts cl_context_properties properties[] = { CL_CONTEXT_PLATFORM, (cl_context_properties)platform(), 0 }; bool deviceFound = false; try { context = cl::Context(deviceType, properties); deviceFound = true; } catch (cl::Error e) { cerr << "Cannot find device type " << deviceType << " for OpenCL, falling back to any device" << endl; } if (!deviceFound) context = cl::Context(CL_DEVICE_TYPE_ALL, properties); devices = context.getInfo(); if (devices.empty()) throw runtime_error("No devices on OpenCL platform"); } //! Destroy the cache, programs will be released automatically ~SourceCacher() { cerr << "Destroying source cacher containing " << cachedPrograms.size() << " cached programs" << endl; } //! Return whether program source is cached bool contains(const std::string& source) { return cachedPrograms.find(source) != cachedPrograms.end(); } }; //! Create and manage CL contexts and corresponding source caches class ContextManager { public: //! A map from device to caches typedef std::map Devices; //! Destroy the manager and all caches ~ContextManager() { cerr << "Destroying CL context manager, used " << devices.size() << " contexts" << endl; for (Devices::iterator it(devices.begin()); it != devices.end(); ++it) delete it->second; } //! Create a new contexc for a given type of device cl::Context& createContext(const cl_device_type deviceType) { boost::mutex::scoped_lock lock(mutex); Devices::iterator it(devices.find(deviceType)); if (it == devices.end()) { it = devices.insert( pair(deviceType, new SourceCacher(deviceType)) ).first; } return it->second->context; } //! Return the cache for a given type of device SourceCacher* getSourceCacher(const cl_device_type deviceType) { boost::mutex::scoped_lock lock(mutex); Devices::iterator it(devices.find(deviceType)); if (it == devices.end()) throw runtime_error("Attempt to get source cacher before creating a context"); return it->second; } protected: Devices devices; //!< devices with caches boost::mutex mutex; //!< mutex to protect concurrent accesses to devices }; //! Static instance of context manager static ContextManager contextManager; template OpenCLSearch::OpenCLSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType): NearestNeighbourSearch::NearestNeighbourSearch(cloud, dim, creationOptionFlags), deviceType(deviceType), context(contextManager.createContext(deviceType)) { } template void OpenCLSearch::initOpenCL(const char* clFileName, const char* kernelName, const std::string& additionalDefines) { const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); SourceCacher* sourceCacher(contextManager.getSourceCacher(deviceType)); SourceCacher::Devices& devices(sourceCacher->devices); // build and load source files cl::Program::Sources sources; // build defines ostringstream oss; oss << EnableCLTypeSupport::code(devices.back()); oss << "#define EPSILON " << numeric_limits::epsilon() << "\n"; oss << "#define DIM_COUNT " << dim << "\n"; //oss << "#define CLOUD_POINT_COUNT " << cloud.cols() << "\n"; oss << "#define POINT_STRIDE " << cloud.stride() << "\n"; oss << "#define MAX_K " << MAX_K << "\n"; if (collectStatistics) oss << "#define TOUCH_STATISTICS\n"; oss << additionalDefines; //cerr << "params:\n" << oss.str() << endl; const std::string& source(oss.str()); if (!sourceCacher->contains(source)) { const size_t defLen(source.length()); char *defContent(new char[defLen+1]); strcpy(defContent, source.c_str()); sources.push_back(std::make_pair(defContent, defLen)); string sourceFileName(OPENCL_SOURCE_DIR); sourceFileName += clFileName; // load files const char* files[] = { OPENCL_SOURCE_DIR "structure.cl", OPENCL_SOURCE_DIR "heap.cl", sourceFileName.c_str(), NULL }; for (const char** file = files; *file != NULL; ++file) { std::ifstream stream(*file); if (!stream.good()) throw runtime_error((string("cannot open file: ") + *file)); stream.seekg(0, std::ios_base::end); size_t size(stream.tellg()); stream.seekg(0, std::ios_base::beg); char* content(new char[size + 1]); std::copy(std::istreambuf_iterator(stream), std::istreambuf_iterator(), content); content[size] = '\0'; sources.push_back(std::make_pair(content, size)); } sourceCacher->cachedPrograms[source] = cl::Program(context, sources); cl::Program& program = sourceCacher->cachedPrograms[source]; // build cl::Error error(CL_SUCCESS); try { program.build(devices); } catch (cl::Error e) { error = e; } // dump for (cl::Devices::const_iterator it = devices.begin(); it != devices.end(); ++it) { cerr << "device : " << it->getInfo() << "\n"; cerr << "compilation log:\n" << program.getBuildInfo(*it) << endl; } // cleanup sources for (cl::Program::Sources::iterator it = sources.begin(); it != sources.end(); ++it) { delete[] it->first; } sources.clear(); // make sure to stop if compilation failed if (error.err() != CL_SUCCESS) throw error; } cl::Program& program = sourceCacher->cachedPrograms[source]; // build kernel and command queue knnKernel = cl::Kernel(program, kernelName); queue = cl::CommandQueue(context, devices.back()); // map cloud if (!(cloud.Flags & Eigen::DirectAccessBit) || (cloud.Flags & Eigen::RowMajorBit)) throw runtime_error("wrong memory mapping in point cloud"); const size_t cloudCLSize(cloud.cols() * cloud.stride() * sizeof(T)); cloudCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, cloudCLSize, const_cast(&cloud.coeff(0,0))); knnKernel.setArg(0, sizeof(cl_mem), &cloudCL); } template unsigned long OpenCLSearch::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const { checkSizesKnn(query, indices, dists2, k); const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); // check K if (k > MAX_K) throw runtime_error("number of neighbors too large for OpenCL"); // check consistency of query wrt cloud if (query.stride() != cloud.stride() || query.rows() != cloud.rows()) throw runtime_error("query is not of the same dimensionality as the point cloud"); // map query if (!(query.Flags & Eigen::DirectAccessBit) || (query.Flags & Eigen::RowMajorBit)) throw runtime_error("wrong memory mapping in query data"); const size_t queryCLSize(query.cols() * query.stride() * sizeof(T)); cl::Buffer queryCL(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, queryCLSize, const_cast(&query.coeff(0,0))); knnKernel.setArg(1, sizeof(cl_mem), &queryCL); // map indices assert((indices.Flags & Eigen::DirectAccessBit) && (!(indices.Flags & Eigen::RowMajorBit))); const int indexStride(indices.stride()); const size_t indicesCLSize(indices.cols() * indexStride * sizeof(int)); cl::Buffer indicesCL(context, CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, indicesCLSize, &indices.coeffRef(0,0)); knnKernel.setArg(2, sizeof(cl_mem), &indicesCL); // map dists2 assert((dists2.Flags & Eigen::DirectAccessBit) && (!(dists2.Flags & Eigen::RowMajorBit))); const int dists2Stride(dists2.stride()); const size_t dists2CLSize(dists2.cols() * dists2Stride * sizeof(T)); cl::Buffer dists2CL(context, CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, dists2CLSize, &dists2.coeffRef(0,0)); knnKernel.setArg(3, sizeof(cl_mem), &dists2CL); // set resulting parameters knnKernel.setArg(4, k); knnKernel.setArg(5, (1 + epsilon)*(1 + epsilon)); knnKernel.setArg(6, maxRadius*maxRadius); knnKernel.setArg(7, optionFlags); knnKernel.setArg(8, indexStride); knnKernel.setArg(9, dists2Stride); knnKernel.setArg(10, cl_uint(cloud.cols())); // if required, map visit count vector visitCounts; const size_t visitCountCLSize(query.cols() * sizeof(cl_uint)); cl::Buffer visitCountCL; if (collectStatistics) { visitCounts.resize(query.cols()); visitCountCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, visitCountCLSize, &visitCounts[0]); knnKernel.setArg(11, sizeof(cl_mem), &visitCountCL); } // execute query queue.enqueueNDRangeKernel(knnKernel, cl::NullRange, cl::NDRange(query.cols()), cl::NullRange); queue.enqueueMapBuffer(indicesCL, true, CL_MAP_READ, 0, indicesCLSize, 0, 0); queue.enqueueMapBuffer(dists2CL, true, CL_MAP_READ, 0, dists2CLSize, 0, 0); if (collectStatistics) queue.enqueueMapBuffer(visitCountCL, true, CL_MAP_READ, 0, visitCountCLSize, 0, 0); queue.finish(); // if required, collect statistics if (collectStatistics) { unsigned long totalVisitCounts(0); for (size_t i = 0; i < visitCounts.size(); ++i) totalVisitCounts += (unsigned long)visitCounts[i]; return totalVisitCounts; } else return 0; } template BruteForceSearchOpenCL::BruteForceSearchOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType): OpenCLSearch::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType) { #ifdef EIGEN3_API const_cast(this->minBound) = cloud.topRows(this->dim).rowwise().minCoeff(); const_cast(this->maxBound) = cloud.topRows(this->dim).rowwise().maxCoeff(); #else // EIGEN3_API // compute bounds for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.block(0,i,this->dim,1)); const_cast(this->minBound) = this->minBound.cwise().min(v); const_cast(this->maxBound) = this->maxBound.cwise().max(v); } #endif // EIGEN3_API // init openCL initOpenCL("knn_bf.cl", "knnBruteForce"); } template struct BruteForceSearchOpenCL; template struct BruteForceSearchOpenCL; template size_t KDTreeBalancedPtInLeavesStackOpenCL::getTreeSize(size_t elCount) const { // FIXME: 64 bits safe stuff, only work for 2^32 elements right now assert(elCount > 0); elCount --; size_t count = 0; int i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } for (int j = 0; j <= i; ++j) count |= (1 << j); count <<= 1; count |= 1; return count; } template size_t KDTreeBalancedPtInLeavesStackOpenCL::getTreeDepth(size_t elCount) const { if (elCount <= 1) return 0; elCount --; size_t i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } return i+1; } template void KDTreeBalancedPtInLeavesStackOpenCL::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues) { const size_t count(last - first); //cerr << count << endl; if (count == 1) { const int d = -2-(first->index); assert(pos < nodes.size()); nodes[pos] = Node(d); return; } // find the largest dimension of the box size_t cutDim = argMax(maxValues - minValues); // compute number of elements const size_t rightCount(count/2); const size_t leftCount(count - rightCount); assert(last - rightCount == first + leftCount); // sort nth_element(first, first + leftCount, last, CompareDim(cutDim)); // set node const T cutVal((first+leftCount)->pos.coeff(cutDim)); nodes[pos] = Node(cutDim, cutVal); //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl; // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // recurse buildNodes(first, first + leftCount, childLeft(pos), minValues, leftMaxValues); buildNodes(first + leftCount, last, childRight(pos), rightMinValues, maxValues); } template KDTreeBalancedPtInLeavesStackOpenCL::KDTreeBalancedPtInLeavesStackOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType): OpenCLSearch::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType) { const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { const Vector& v(cloud.block(0,i,this->dim,1)); buildPoints.push_back(BuildPoint(v, i)); #ifdef EIGEN3_API const_cast(minBound) = minBound.array().min(v.array()); const_cast(maxBound) = maxBound.array().max(v.array()); #else // EIGEN3_API const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); #endif // EIGEN3_API } // create nodes nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), 0, minBound, maxBound); const unsigned maxStackDepth(getTreeDepth(nodes.size()) + 1); // init openCL initOpenCL("knn_kdtree_pt_in_leaves.cl", "knnKDTree", (boost::format("#define MAX_STACK_DEPTH %1%\n") % maxStackDepth).str()); // map nodes, for info about alignment, see sect 6.1.5 const size_t nodesCLSize(nodes.size() * sizeof(Node)); nodesCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, nodesCLSize, &nodes[0]); if (collectStatistics) knnKernel.setArg(12, sizeof(cl_mem), &nodesCL); else knnKernel.setArg(11, sizeof(cl_mem), &nodesCL); } template struct KDTreeBalancedPtInLeavesStackOpenCL; template struct KDTreeBalancedPtInLeavesStackOpenCL; template size_t KDTreeBalancedPtInNodesStackOpenCL::getTreeSize(size_t elCount) const { // FIXME: 64 bits safe stuff, only work for 2^32 elements right now size_t count = 0; int i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } for (int j = 0; j <= i; ++j) count |= (1 << j); //cerr << "tree size " << count << " (" << elCount << " elements)\n"; return count; } template size_t KDTreeBalancedPtInNodesStackOpenCL::getTreeDepth(size_t elCount) const { // FIXME: 64 bits safe stuff, only work for 2^32 elements right now int i = 31; for (; i >= 0; --i) { if (elCount & (1 << i)) break; } return i + 1; } template void KDTreeBalancedPtInNodesStackOpenCL::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues) { const size_t count(last - first); //cerr << count << endl; if (count == 1) { nodes[pos] = Node(-1, *first); return; } // find the largest dimension of the box const size_t cutDim = argMax(maxValues - minValues); // compute number of elements const size_t recurseCount(count-1); const size_t rightCount(recurseCount/2); const size_t leftCount(recurseCount-rightCount); assert(last - rightCount == first + leftCount + 1); // sort nth_element(first, first + leftCount, last, CompareDim(cloud, cutDim)); // set node const Index index(*(first+leftCount)); const T cutVal(cloud.coeff(cutDim, index)); nodes[pos] = Node(cutDim, index); //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl; // update bounds for left Vector leftMaxValues(maxValues); leftMaxValues[cutDim] = cutVal; // update bounds for right Vector rightMinValues(minValues); rightMinValues[cutDim] = cutVal; // recurse if (count > 2) { buildNodes(first, first + leftCount, childLeft(pos), minValues, leftMaxValues); buildNodes(first + leftCount + 1, last, childRight(pos), rightMinValues, maxValues); } else { nodes[childLeft(pos)] = Node(-1, *first); nodes[childRight(pos)] = Node(-2, 0); } } template KDTreeBalancedPtInNodesStackOpenCL::KDTreeBalancedPtInNodesStackOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType): OpenCLSearch::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType) { const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch::TOUCH_STATISTICS); // build point vector and compute bounds BuildPoints buildPoints; buildPoints.reserve(cloud.cols()); for (int i = 0; i < cloud.cols(); ++i) { buildPoints.push_back(i); const Vector& v(cloud.block(0,i,this->dim,1)); #ifdef EIGEN3_API const_cast(minBound) = minBound.array().min(v.array()); const_cast(maxBound) = maxBound.array().max(v.array()); #else // EIGEN3_API const_cast(minBound) = minBound.cwise().min(v); const_cast(maxBound) = maxBound.cwise().max(v); #endif // EIGEN3_API } // create nodes nodes.resize(getTreeSize(cloud.cols())); buildNodes(buildPoints.begin(), buildPoints.end(), 0, minBound, maxBound); const unsigned maxStackDepth(getTreeDepth(nodes.size()) + 1); // init openCL initOpenCL("knn_kdtree_pt_in_nodes.cl", "knnKDTree", (boost::format("#define MAX_STACK_DEPTH %1%\n") % maxStackDepth).str()); // map nodes, for info about alignment, see sect 6.1.5 const size_t nodesCLSize(nodes.size() * sizeof(Node)); nodesCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, nodesCLSize, &nodes[0]); if (collectStatistics) knnKernel.setArg(12, sizeof(cl_mem), &nodesCL); else knnKernel.setArg(11, sizeof(cl_mem), &nodesCL); } template struct KDTreeBalancedPtInNodesStackOpenCL; template struct KDTreeBalancedPtInNodesStackOpenCL; //@} } #endif // HAVE_OPENCL norlab-ulaval-libnabo-534c2d5/nabo/nabo.cpp000066400000000000000000000163061211110745500205550ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo.h" #include "nabo_private.h" #include "index_heap.h" #include #include #include #include /*! \file nabo.cpp \brief implementation of public interface \ingroup private */ namespace Nabo { using namespace std; template NearestNeighbourSearch::NearestNeighbourSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags): cloud(cloud), dim(min(dim, int(cloud.rows()))), creationOptionFlags(creationOptionFlags), minBound(Vector::Constant(this->dim, numeric_limits::max())), maxBound(Vector::Constant(this->dim, numeric_limits::min())) { } template unsigned long NearestNeighbourSearch::knn(const Vector& query, IndexVector& indices, Vector& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const { #ifdef EIGEN3_API const Eigen::Map queryMatrix(&query.coeff(0,0), dim, 1); #else // EIGEN3_API const Eigen::Map queryMatrix(&query.coeff(0,0), dim, 1); #endif // EIGEN3_API // note: this is inefficient, because we copy memory, due to the template- // based abstraction of Eigen. High-performance implementation should // take care of knnM and then implement knn on top of it. // C++0x should solve this with rvalue IndexMatrix indexMatrix(k, 1); Matrix dists2Matrix(k, 1); const unsigned long stats = knn(queryMatrix, indexMatrix, dists2Matrix, k, epsilon, optionFlags, maxRadius); indices = indexMatrix.col(0); dists2 = dists2Matrix.col(0); return stats; } template void NearestNeighbourSearch::checkSizesKnn(const Matrix& query, const IndexMatrix& indices, const Matrix& dists2, const Index k, const Vector* maxRadii) const { if (query.rows() < dim) throw runtime_error((boost::format("Query has less dimensions (%1%) than requested for cloud (%2%)") % query.rows() % dim).str()); if (indices.rows() != k) throw runtime_error((boost::format("Index matrix has a different number of rows (%1%) than k (%2%)") % indices.rows() % k).str()); if (indices.cols() != query.cols()) throw runtime_error((boost::format("Index matrix has a different number of columns (%1%) than query (%2%)") % indices.rows() % query.cols()).str()); if (dists2.rows() != k) throw runtime_error((boost::format("Distance matrix has a different number of rows (%1%) than k (%2%)") % dists2.rows() % k).str()); if (dists2.cols() != query.cols()) throw runtime_error((boost::format("Distance matrix has a different number of columns (%1%) than query (%2%)") % dists2.rows() % query.cols()).str()); if (maxRadii && (maxRadii->size() != query.cols())) throw runtime_error((boost::format("Maximum radii vector has not the same length (%1%) than query has columns (%2%)") % maxRadii->size() % k).str()); } template NearestNeighbourSearch* NearestNeighbourSearch::create(const Matrix& cloud, const Index dim, const SearchType preferedType, const unsigned creationOptionFlags, const Parameters& additionalParameters) { if (dim <= 0) throw runtime_error("Your space must have at least one dimension"); switch (preferedType) { case BRUTE_FORCE: return new BruteForceSearch(cloud, dim, creationOptionFlags); case KDTREE_LINEAR_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >(cloud, dim, creationOptionFlags, additionalParameters); case KDTREE_TREE_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >(cloud, dim, creationOptionFlags, additionalParameters); #ifdef HAVE_OPENCL case KDTREE_CL_PT_IN_NODES: return new KDTreeBalancedPtInNodesStackOpenCL(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU); case KDTREE_CL_PT_IN_LEAVES: return new KDTreeBalancedPtInLeavesStackOpenCL(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU); case BRUTE_FORCE_CL: return new BruteForceSearchOpenCL(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU); #else // HAVE_OPENCL case KDTREE_CL_PT_IN_NODES: throw runtime_error("OpenCL not found during compilation"); case KDTREE_CL_PT_IN_LEAVES: throw runtime_error("OpenCL not found during compilation"); case BRUTE_FORCE_CL: throw runtime_error("OpenCL not found during compilation"); #endif // HAVE_OPENCL default: throw runtime_error("Unknown search type"); } } template NearestNeighbourSearch* NearestNeighbourSearch::createBruteForce(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags) { if (dim <= 0) throw runtime_error("Your space must have at least one dimension"); return new BruteForceSearch(cloud, dim, creationOptionFlags); } template NearestNeighbourSearch* NearestNeighbourSearch::createKDTreeLinearHeap(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters) { if (dim <= 0) throw runtime_error("Your space must have at least one dimension"); return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >(cloud, dim, creationOptionFlags, additionalParameters); } template NearestNeighbourSearch* NearestNeighbourSearch::createKDTreeTreeHeap(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters) { if (dim <= 0) throw runtime_error("Your space must have at least one dimension"); return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt >(cloud, dim, creationOptionFlags, additionalParameters); } template struct NearestNeighbourSearch; template struct NearestNeighbourSearch; } norlab-ulaval-libnabo-534c2d5/nabo/nabo.h000066400000000000000000000445071211110745500202260ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef __NABO_H #define __NABO_H #include "Eigen/Core" #if EIGEN_VERSION_AT_LEAST(2,92,0) #define EIGEN3_API #endif #ifndef EIGEN3_API #include "Eigen/Array" #endif #include #include #include /*! \file nabo.h \brief public interface \ingroup public */ /*! \mainpage libnabo from http://github.com/ethz-asl/libnabo by Stéphane Magnenat (http://stephane.magnenat.net), ASL-ETHZ, Switzerland (http://www.asl.ethz.ch) libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by \ref ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than \ref ANN. libnabo depends on \ref Eigen, a modern C++ matrix and linear-algebra library. libnabo works with either version 2 or 3 of Eigen. libnabo also depends on \ref Boost, a C++ general library. \section Compilation libnabo uses \ref CMake as build system. The complete compilation process depends on the system you are using (Linux, Mac OS X or Windows). You will find a nice introductory tutorial in this you tube video: http://www.youtube.com/watch?v=CLvZTyji_Uw. \subsection Prerequisites If your operating system does not provide it, you must get \ref Eigen and \ref Boost. \ref Eigen only needs to be downloaded and extracted. \subsection CompilationOptions Compilation options libnabo provides the following compilation options, available through \ref CMake : - \c SHARED_LIBS (boolean, default: \c false): if \c true, build a shared library, otherwise build a static library You specify them with a command-line tool, \c ccmake, or with a graphical tool, \c cmake-gui. Please read the CMake documentation for more information. \subsection QuickCompilationUnix Quick compilation and installation under Unix Under Unix, assuming that \ref Eigen and \ref Boost are installed system-wide, you can compile (with optimisation and debug information) and install libnabo in \c /usr/local with the following commands run in the top-level directory of libnabo's sources: \code SRC_DIR=`pwd` BUILD_DIR=${SRC_DIR}/build mkdir -p ${BUILD_DIR} && cd ${BUILD_DIR} cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ${SRC_DIR} # if Eigen or Boost are not available system-wide, run at that point: # cmake-gui . # cmake-gui allows you to tell the location of Eigen or Boost make sudo make install \endcode These lines will compile libnabo in a \c build sub-directory and therefore keep your source tree clean. Note that you could compile libnabo anywhere you have write access, such as in \c /tmp/libnabo. This out-of-source build is a nice feature of \ref CMake. If \ref Eigen or \ref Boost are not installed system-wide, you might have to tell \ref CMake where to find them (using \c ccmake or \c cmake-gui). You can generate the documentation by typing: \code make doc \endcode \section Usage libnabo is easy to use. For example, assuming that you are working with floats and that you have a point set \c M and a query point \c q, you can find the \c K nearest neighbours of \c q in \c M : \include trivial.cpp In this example, \c M is an \ref Eigen (refering to the software, not to the math) matrix (column major, float) and \c q is an \ref Eigen vector (float). The results \c indices and \c dists2 are \ref Eigen vectors of indices and squared distances refering to the columns of \c M. Here is a slightly more complex example: \include usage.cpp Note that the matrix-based interface for query is more efficient than the vector-based one, because some sanity checks can be done only once. Therefore, if you have multiple points to query, we warmly suggest to pass them as a matrix instead of calling \c knn() multiple times. \section ConstructionParameters Construction parameters The following additional construction parameters are available in KDTREE_ algorithms: - \c bucketSize (\c unsigned): bucket size, defaults to 8 \section UnitTesting Unit testing The distribution of libnabo integrates a unit test module, based on CTest. Just type: \code make test \endcode ...in the build directory to run the tests. Their outputs are available in the \c Testing directory. These consist of validation and benchmarking tests. If \ref ANN is detected when compiling libnabo, \c make \c test will also perform comparative benchmarks. \section Citing Citing libnabo If you use libnabo in the academic context, please cite this paper that evaluates its performances in the contex of ICP: \code @article{elsebergcomparison, title={Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration}, author={Elseberg, J. and Magnenat, S. and Siegwart, R. and N{\"u}chter, A.}, journal={Journal of Software Engineering for Robotics (JOSER)}, pages={2--12}, volume={3}, number={1}, year={2012}, issn={2035-3928} } \endcode \section BugReporting Bug reporting Please use github's issue tracker to report bugs. \section License libnabo is released under a permissive BSD license. \section Faq \subsection ANN libnabo differs from \ref ANN on the following points: * API - templates for scalar type - self-match option as execution-time (instead of compile-time) parameter - range search instead of radius search - \ref Eigen library for vector and matrixes - reentrant * limitations - only euclidean distance - only KD-tree, no BD-tree - only ANN_KD_SL_MIDPT splitting rules * implementation - optional O(log(n)) tree heap instead of O(n) vector heap - compact memory representation, one memory allocation for all nodes, 5-fold smaller memory footprint compared than ANN - implicit reference to left child (always next node in array) - do not store bounds in nodes (that is, I do it like in ANN's article instead of like in ANN's source code) * performances - about 5% to 20% faster than ANN (both -O3 -NDEBUG), probably due to the smaller memory footprint - clearly memory-bound, neither OpenMP nor boost::thread improve performances \section References \li \anchor Eigen Eigen: http://eigen.tuxfamily.org \li \anchor ANN ANN: http://www.cs.umd.edu/~mount/ANN \li \anchor CMake CMake: http://www.cmake.org \li \anchor Boost Boost: http://www.boost.org */ //! Namespace for Nabo namespace Nabo { //! \defgroup public public interface //@{ //! version of the Nabo library as string #define NABO_VERSION "1.0.2" //! version of the Nabo library as an int #define NABO_VERSION_INT 10002 //! Parameter vector struct Parameters: public std::map { //! Create an empty parameter vector Parameters(){} //! Create a parameter vector with a single entry /** \param key entry key * \param value entry value */ Parameters(const std::string& key, const boost::any& value){(*this)[key] = value;} //! Get the value of a key, return defaultValue if the key does not exist /** \param key requested key * \param defaultValue value to return if the key does not exist * \return value of the key, or defaultValue if the key does not exist */ template T get(const std::string& key, const T& defaultValue) const { const_iterator it(find(key)); if (it != end()) return boost::any_cast(it->second); else return defaultValue; } }; //! Nearest neighbour search interface, templatized on scalar type template struct NearestNeighbourSearch { //! an Eigen vector of type T, to hold the coordinates of a point typedef typename Eigen::Matrix Vector; //! a column-major Eigen matrix in which each column is a point; this matrix has dim rows typedef typename Eigen::Matrix Matrix; //! an index to a Vector or a Matrix, for refering to data points typedef int Index; //! a vector of indices to data points typedef typename Eigen::Matrix IndexVector; //! a matrix of indices to data points typedef typename Eigen::Matrix IndexMatrix; //! the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeighbourSearch object const Matrix& cloud; //! the dimensionality of the data-point cloud const Index dim; //! creation options const unsigned creationOptionFlags; //! the low bound of the search space (axis-aligned bounding box) const Vector minBound; //! the high bound of the search space (axis-aligned bounding box) const Vector maxBound; //! type of search enum SearchType { BRUTE_FORCE = 0, //!< brute force, check distance to every point in the data KDTREE_LINEAR_HEAP, //!< kd-tree with linear heap, good for small k (~up to 30) KDTREE_TREE_HEAP, //!< kd-tree with tree heap, good for large k (~from 30) KDTREE_CL_PT_IN_NODES, //!< kd-tree using openCL, pt in nodes, only available if OpenCL enabled, UNSTABLE API KDTREE_CL_PT_IN_LEAVES, //!< kd-tree using openCL, pt in leaves, only available if OpenCL enabled, UNSTABLE API BRUTE_FORCE_CL, //!< brute-force using openCL, only available if OpenCL enabled, UNSTABLE API SEARCH_TYPE_COUNT //!< number of search types }; //! creation option enum CreationOptionFlags { TOUCH_STATISTICS = 1, //!< perform statistics on the number of points touched }; //! search option enum SearchOptionFlags { ALLOW_SELF_MATCH = 1, //!< allows the return of the same point as the query, if this point is in the data cloud; forbidden by default SORT_RESULTS = 2 //!< sort points by distances, when k > 1; do not sort by default }; //! Find the k nearest neighbours of query /*! If the search finds less than k points, the empty entries in dists2 will be filled with infinity and the indices with 0. If you must query more than one point at once, use the version of the knn() function taking matrices as input, because it is much faster. * \param query query point * \param indices indices of nearest neighbours, must be of size k * \param dists2 squared distances to nearest neighbours, must be of size k * \param k number of nearest neighbour requested * \param epsilon maximal percentage of error for approximate search, 0 for exact search * \param optionFlags search options, a bitwise OR of elements of SearchOptionFlags * \param maxRadius maximum radius in which to search, can be used to prune search, is not affected by epsilon * \return if creationOptionFlags contains TOUCH_STATISTICS, return the number of point touched, otherwise return 0 */ unsigned long knn(const Vector& query, IndexVector& indices, Vector& dists2, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0, const T maxRadius = std::numeric_limits::infinity()) const; //! Find the k nearest neighbours for each point of query /*! If the search finds less than k points, the empty entries in dists2 will be filled with infinity and the indices with 0. * \param query query points * \param indices indices of nearest neighbours, must be of size k x query.cols() * \param dists2 squared distances to nearest neighbours, must be of size k x query.cols() * \param k number of nearest neighbour requested * \param epsilon maximal percentage of error for approximate search, 0 for exact search * \param optionFlags search options, a bitwise OR of elements of SearchOptionFlags * \param maxRadius maximum radius in which to search, can be used to prune search, is not affected by epsilon * \return if creationOptionFlags contains TOUCH_STATISTICS, return the number of point touched, otherwise return 0 */ virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0, const T maxRadius = std::numeric_limits::infinity()) const = 0; //! Find the k nearest neighbours for each point of query /*! If the search finds less than k points, the empty entries in dists2 will be filled with infinity and the indices with 0. * \param query query points * \param indices indices of nearest neighbours, must be of size k x query.cols() * \param dists2 squared distances to nearest neighbours, must be of size k x query.cols() * \param maxRadii vector of maximum radii in which to search, used to prune search, is not affected by epsilon * \param k number of nearest neighbour requested * \param epsilon maximal percentage of error for approximate search, 0 for exact search * \param optionFlags search options, a bitwise OR of elements of SearchOptionFlags * \return if creationOptionFlags contains TOUCH_STATISTICS, return the number of point touched, otherwise return 0 */ virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const = 0; //! Create a nearest-neighbour search /*! \param cloud data-point cloud in which to search * \param dim number of dimensions to consider, must be lower or equal to cloud.rows() * \param preferedType type of search, one of SearchType * \param creationOptionFlags creation options, a bitwise OR of elements of CreationOptionFlags * \param additionalParameters additional parameters, currently only useful for KDTREE_ * \return an object on which to run nearest neighbour queries */ static NearestNeighbourSearch* create(const Matrix& cloud, const Index dim = std::numeric_limits::max(), const SearchType preferedType = KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags = 0, const Parameters& additionalParameters = Parameters()); //! Create a nearest-neighbour search, using brute-force search, useful for comparison only /*! This is an helper function, you can also use create() with BRUTE_FORCE as preferedType * \param cloud data-point cloud in which to search * \param dim number of dimensions to consider, must be lower or equal to cloud.rows() * \param creationOptionFlags creation options, a bitwise OR of elements of CreationOptionFlags * \return an object on which to run nearest neighbour queries */ static NearestNeighbourSearch* createBruteForce(const Matrix& cloud, const Index dim = std::numeric_limits::max(), const unsigned creationOptionFlags = 0); //! Create a nearest-neighbour search, using a kd-tree with linear heap, good for small k (~up to 30) /*! This is an helper function, you can also use create() with KDTREE_LINEAR_HEAP as preferedType * \param cloud data-point cloud in which to search * \param dim number of dimensions to consider, must be lower or equal to cloud.rows() * \param creationOptionFlags creation options, a bitwise OR of elements of CreationOptionFlags * \param additionalParameters additional parameters * \return an object on which to run nearest neighbour queries */ static NearestNeighbourSearch* createKDTreeLinearHeap(const Matrix& cloud, const Index dim = std::numeric_limits::max(), const unsigned creationOptionFlags = 0, const Parameters& additionalParameters = Parameters()); //! Create a nearest-neighbour search, using a kd-tree with tree heap, good for large k (~from 30) /*! This is an helper function, you can also use create() with KDTREE_TREE_HEAP as preferedType * \param cloud data-point cloud in which to search * \param dim number of dimensions to consider, must be lower or equal to cloud.rows() * \param creationOptionFlags creation options, a bitwise OR of elements of CreationOptionFlags * \param additionalParameters additional parameters * \return an object on which to run nearest neighbour queries */ static NearestNeighbourSearch* createKDTreeTreeHeap(const Matrix& cloud, const Index dim = std::numeric_limits::max(), const unsigned creationOptionFlags = 0, const Parameters& additionalParameters = Parameters()); //! virtual destructor virtual ~NearestNeighbourSearch() {} protected: //! constructor NearestNeighbourSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags); //! Make sure that the output matrices have the right sizes. Throw an exception otherwise. /*! \param query query points * \param k number of nearest neighbour requested * \param indices indices of nearest neighbours, must be of size k x query.cols() * \param dists2 squared distances to nearest neighbours, must be of size k x query.cols() \param maxRadii if non 0, maximum radii, must be of size k */ void checkSizesKnn(const Matrix& query, const IndexMatrix& indices, const Matrix& dists2, const Index k, const Vector* maxRadii = 0) const; }; // Convenience typedefs //! nearest neighbour search with scalars of type float typedef NearestNeighbourSearch NNSearchF; //! nearest neighbour search with scalars of type double typedef NearestNeighbourSearch NNSearchD; //@} } #endif // __NABO_H norlab-ulaval-libnabo-534c2d5/nabo/nabo_private.h000066400000000000000000000441201211110745500217470ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef __NABO_PRIVATE_H #define __NABO_PRIVATE_H #include "nabo.h" #ifdef BOOST_STDINT #include using boost::uint32_t; #else // BOOST_STDINT #include #endif // BOOST_STDINT // OpenCL #ifdef HAVE_OPENCL #define __CL_ENABLE_EXCEPTIONS #include "CL/cl.hpp" #endif // HAVE_OPENCL // Unused macro, add support for your favorite compiler #if defined(__GNUC__) #define _UNUSED __attribute__ ((unused)) #else #define _UNUSED #endif /*! \file nabo_private.h \brief header for implementation \ingroup private */ namespace Nabo { //! \defgroup private private implementation //@{ //! Euclidean distance template inline T dist2(const A& v0, const B& v1) { return (v0 - v1).squaredNorm(); } //! Brute-force nearest neighbour template struct BruteForceSearch: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::dim; using NearestNeighbourSearch::creationOptionFlags; using NearestNeighbourSearch::checkSizesKnn; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; //! constructor, calls NearestNeighbourSearch(cloud) BruteForceSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags); virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const; virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const; }; //! KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation template struct KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::dim; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::creationOptionFlags; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; using NearestNeighbourSearch::checkSizesKnn; protected: //! indices of points during kd-tree construction typedef std::vector BuildPoints; //! iterator to indices of points during kd-tree construction typedef typename BuildPoints::iterator BuildPointsIt; //! const-iterator to indices of points during kd-tree construction typedef typename BuildPoints::const_iterator BuildPointsCstIt; //! size of bucket const unsigned bucketSize; //! number of bits required to store dimension index + number of dimensions const uint32_t dimBitCount; //! mask to access dim const uint32_t dimMask; //! create the compound index containing the dimension and the index of the child or the bucket size inline uint32_t createDimChildBucketSize(const uint32_t dim, const uint32_t childIndex) const { return dim | (childIndex << dimBitCount); } //! get the dimension out of the compound index inline uint32_t getDim(const uint32_t dimChildBucketSize) const { return dimChildBucketSize & dimMask; } //! get the child index or the bucket size out of the coumpount index inline uint32_t getChildBucketSize(const uint32_t dimChildBucketSize) const { return dimChildBucketSize >> dimBitCount; } struct BucketEntry; //! search node struct Node { uint32_t dimChildBucketSize; //!< cut dimension for split nodes (dimBitCount lsb), index of right node or number of bucket(rest). Note that left index is current+1 union { T cutVal; //!< for split node, split value uint32_t bucketIndex; //!< for leaf node, pointer to bucket }; //! construct a split node Node(const uint32_t dimChild, const T cutVal): dimChildBucketSize(dimChild), cutVal(cutVal) {} //! construct a leaf node Node(const uint32_t bucketSize, const uint32_t bucketIndex): dimChildBucketSize(bucketSize), bucketIndex(bucketIndex) {} }; //! dense vector of search nodes, provides better memory performances than many small objects typedef std::vector Nodes; //! entry in a bucket struct BucketEntry { const T* pt; //!< pointer to first value of point data, 0 if end of bucket Index index; //!< index of point //! create a new bucket entry for a point in the data /** \param pt pointer to first component of the point, components must be continuous * \param index index of the point in the data */ BucketEntry(const T* pt = 0, const Index index = 0): pt(pt), index(index) {} }; //! bucket data typedef std::vector Buckets; //! search nodes Nodes nodes; //! buckets Buckets buckets; //! return the bounds of points from [first..last[ on dimension dim std::pair getBounds(const BuildPointsIt first, const BuildPointsIt last, const unsigned dim); //! construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues] unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues); //! search one point, call recurseKnn with the correct template parameters /** \param query pointer to query coordinates * \param indices indices of nearest neighbours, must be of size k x query.cols() * \param dists2 squared distances to nearest neighbours, must be of size k x query.cols() * \param i index of point to search * \param heap reference to heap * \param off reference to array of offsets * \param maxError error factor (1 + epsilon) * \param maxRadius2 square of maximum radius * \param allowSelfMatch whether to allow self match * \param collectStatistics whether to collect statistics * \param sortResults wether to sort results */ unsigned long onePointKnn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, int i, Heap& heap, std::vector& off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const; //! recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993] /** \param query pointer to query coordinates * \param n index of node to visit * \param rd squared dist to this rect * \param heap reference to heap * \param off reference to array of offsets * \param maxError error factor (1 + epsilon) * \param maxRadius2 square of maximum radius */ template unsigned long recurseKnn(const T* query, const unsigned n, T rd, Heap& heap, std::vector& off, const T maxError, const T maxRadius2) const; public: //! constructor, calls NearestNeighbourSearch(cloud) KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters); virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const; virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const; }; #ifdef HAVE_OPENCL //! OpenCL support for nearest neighbour search template struct OpenCLSearch: public NearestNeighbourSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::dim; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::creationOptionFlags; using NearestNeighbourSearch::checkSizesKnn; protected: const cl_device_type deviceType; //!< the type of device to run CL code on (CL_DEVICE_TYPE_CPU or CL_DEVICE_TYPE_GPU) cl::Context& context; //!< the CL context mutable cl::Kernel knnKernel; //!< the kernel to perform knnSearch, mutable because it is stateful, but conceptually const cl::CommandQueue queue; //!< the command queue cl::Buffer cloudCL; //!< the buffer for the input data //! constructor, calls NearestNeighbourSearch(cloud) OpenCLSearch(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType); //! Initialize CL support /** \param clFileName name of file containing CL code * \param kernelName name of the CL kernel function * \param additionalDefines additional CL code to pass to compiler */ void initOpenCL(const char* clFileName, const char* kernelName, const std::string& additionalDefines = ""); public: virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const; }; //! KDTree, balanced, points in leaves, stack, implicit bounds, balance aspect ratio template struct BruteForceSearchOpenCL: public OpenCLSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; using OpenCLSearch::initOpenCL; //! constructor, calls OpenCLSearch(cloud, ...) BruteForceSearchOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType); }; //! KDTree, balanced, points in leaves, stack, implicit bounds, balance aspect ratio template struct KDTreeBalancedPtInLeavesStackOpenCL: public OpenCLSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::dim; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::creationOptionFlags; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; using OpenCLSearch::context; using OpenCLSearch::knnKernel; using OpenCLSearch::initOpenCL; protected: //! Point during kd-tree construction struct BuildPoint { Vector pos; //!< point size_t index; //!< index of point in cloud //! Construct a build point, at a given pos with a specific index BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {} }; //! points during kd-tree construction typedef std::vector BuildPoints; //! iterator to points during kd-tree construction typedef typename BuildPoints::iterator BuildPointsIt; //! const-iterator to points during kd-tree construction typedef typename BuildPoints::const_iterator BuildPointsCstIt; //! Functor to compare point values on a given dimension struct CompareDim { size_t dim; //!< dimension on which to compare //! Build the functor for a specific dimension CompareDim(const size_t dim):dim(dim){} //! Compare the values of p0 and p1 on dim, and return whether p0[dim] < p1[dim] bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); } }; //! Tree node for CL struct Node { int dim; //!< dimension of the cut, or, if negative, index of the point: -1 == invalid, <= -2 = index of pt T cutVal; //!< value of the cut //! Build a tree node, with a given dimension and value to cut on, or, if leaf and dim <= -2, the index of the point as (-dim-2) Node(const int dim = -1, const T cutVal = 0):dim(dim), cutVal(cutVal) {} }; //! dense vector of search nodes typedef std::vector Nodes; Nodes nodes; //!< search nodes cl::Buffer nodesCL; //!< CL buffer for search nodes inline size_t childLeft(size_t pos) const { return 2*pos + 1; } //!< Return the left child of pos inline size_t childRight(size_t pos) const { return 2*pos + 2; } //!< Return the right child of pos inline size_t parent(size_t pos) const { return (pos-1)/2; } //!< Return the parent of pos size_t getTreeDepth(size_t size) const; //!< Return the max depth of a tree of a given size size_t getTreeSize(size_t size) const; //!< Return the storage size of tree of a given size //! Recurse to build nodes void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues); public: //! constructor, calls OpenCLSearch(cloud, ...) KDTreeBalancedPtInLeavesStackOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType); }; //! KDTree, balanced, points in nodes, stack, implicit bounds, balance aspect ratio template struct KDTreeBalancedPtInNodesStackOpenCL: public OpenCLSearch { typedef typename NearestNeighbourSearch::Vector Vector; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::Index Index; typedef typename NearestNeighbourSearch::IndexVector IndexVector; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; using NearestNeighbourSearch::dim; using NearestNeighbourSearch::cloud; using NearestNeighbourSearch::creationOptionFlags; using NearestNeighbourSearch::minBound; using NearestNeighbourSearch::maxBound; using OpenCLSearch::context; using OpenCLSearch::knnKernel; using OpenCLSearch::initOpenCL; protected: //! a point during kd-tree construction is just its index typedef Index BuildPoint; //! points during kd-tree construction typedef std::vector BuildPoints; //! iterator to points during kd-tree construction typedef typename BuildPoints::iterator BuildPointsIt; //! const-iterator to points during kd-tree construction typedef typename BuildPoints::const_iterator BuildPointsCstIt; //! Functor to compare point values on a given dimension struct CompareDim { const Matrix& cloud; //!< reference to data points used to compare size_t dim; //!< dimension on which to compare //! Build the functor for a specific dimension on a specific cloud CompareDim(const Matrix& cloud, const size_t dim): cloud(cloud), dim(dim){} //! Compare the values of p0 and p1 on dim, and return whether p0[dim] < p1[dim] bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return cloud.coeff(dim, p0) < cloud.coeff(dim, p1); } }; //! Tree node for CL struct Node { int dim; //!< dimension of the cut, or, if -1 == leaf, -2 == invalid Index index; //!< index of the point to cut //! Build a tree node, with a given index and a given dimension to cut on Node(const int dim = -2, const Index index = 0):dim(dim), index(index) {} }; //! dense vector of search nodes typedef std::vector Nodes; Nodes nodes; //!< search nodes cl::Buffer nodesCL; //!< CL buffer for search nodes inline size_t childLeft(size_t pos) const { return 2*pos + 1; } //!< Return the left child of pos inline size_t childRight(size_t pos) const { return 2*pos + 2; } //!< Return the right child of pos inline size_t parent(size_t pos) const { return (pos-1)/2; } //!< Return the parent of pos size_t getTreeDepth(size_t size) const; //!< Return the max depth of a tree of a given size size_t getTreeSize(size_t size) const; //!< Return the storage size of tree of a given size //! Recurse to build nodes void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues); public: //! constructor, calls OpenCLSearch(cloud, ...) KDTreeBalancedPtInNodesStackOpenCL(const Matrix& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType); }; #endif // HAVE_OPENCL //@} } #endif // __NABO_H norlab-ulaval-libnabo-534c2d5/nabo/opencl/000077500000000000000000000000001211110745500204045ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/nabo/opencl/heap.cl000066400000000000000000000012351211110745500216420ustar00rootroot00000000000000T heapHeadValue(const HeapEntry* heap) { return heap->value; } void heapHeadReplace(HeapEntry* heap, const int index, const T value, const uint K) { uint i = 0; for (; i < K - 1; ++i) { if (heap[i + 1].value > value) heap[i] = heap[i + 1]; else break; } heap[i].value = value; heap[i].index = index; } void heapInit(HeapEntry* heap, const uint K) { for (uint i = 0; i < K; ++i) heap[i].value = HUGE_VALF; } void heapCopy(global int* indices, global T* dists2, const HeapEntry* heap, const uint K) { for (uint i = 0; i < K; ++i) { *indices++ = heap[K-i-1].index; *dists2++ = heap[K-i-1].value; } } void heapSort(const HeapEntry* heap) { } norlab-ulaval-libnabo-534c2d5/nabo/opencl/knn_bf.cl000066400000000000000000000022771211110745500221710ustar00rootroot00000000000000kernel void knnBruteForce(const global T* cloud, const global T* query, global int* indices, global T* dists2, const uint K, const T maxError, const T maxRadius2, const uint optionFlags, const uint indexStride, const uint dists2Stride, const uint pointCount #ifdef TOUCH_STATISTICS , global uint* touchStatistics #endif ) { HeapEntry heap[MAX_K]; heapInit(heap, K); const size_t queryId = get_global_id(0); const bool allowSelfMatch = optionFlags & ALLOW_SELF_MATCH; const bool doSort = optionFlags & SORT_RESULTS; const global T* q = &query[queryId * POINT_STRIDE]; for (uint index = 0; index < pointCount; ++index) { const global T* p = &cloud[index * POINT_STRIDE]; T dist = 0; for (uint i = 0; i < DIM_COUNT; ++i) { const T diff = q[i] - p[i]; dist += diff * diff; } if ((dist <= maxRadius2) && (dist < heapHeadValue(heap) && (allowSelfMatch || (dist > 0))) heapHeadReplace(heap, index, dist, K); } if (doSort) heapSort(heap); heapCopy(&indices[queryId * indexStride], &dists2[queryId * dists2Stride], heap, K); #ifdef TOUCH_STATISTICS touchStatistics[queryId] = pointCount; #endif } norlab-ulaval-libnabo-534c2d5/nabo/opencl/knn_kdtree_pt_in_leaves.cl000066400000000000000000000062371211110745500256100ustar00rootroot00000000000000#define INVALID_NODE -1 #define OP_BEGIN_FUNCTION 0 #define OP_REC1 1 #define OP_REC2 2 typedef struct { int dim; // -1 == invalid, <= -2 = index of pt T cutVal; //!< for split node, split value } Node; typedef struct { uint op; size_t n; size_t other_n; T rd; T old_off; T new_off; } StackEntry; size_t childLeft(const size_t pos) { return 2*pos + 1; } size_t childRight(const size_t pos) { return 2*pos + 2; } void offInit(T *off) { for (uint i = 0; i < DIM_COUNT; ++i) off[i] = 0; } // for cloud and result, use DirectAccessBit and stride // preconditions: // K < MAX_K // stack_ptr < MAX_STACK_DEPTH kernel void knnKDTree( const global T* cloud, const global T* query, global int* indices, global T* dists2, const uint K, const T maxError, const T maxRadius2, const uint optionFlags, const uint indexStride, const uint dists2Stride, const uint pointCount, #ifdef TOUCH_STATISTICS global uint* touchStatistics, #endif const global Node* nodes ) { StackEntry stack[MAX_STACK_DEPTH]; HeapEntry heap[MAX_K]; T off[DIM_COUNT]; #ifdef TOUCH_STATISTICS uint visitCount = 0; #endif const size_t queryId = get_global_id(0); const bool allowSelfMatch = optionFlags & ALLOW_SELF_MATCH; const bool doSort = optionFlags & SORT_RESULTS; const global T* q = &query[queryId * POINT_STRIDE]; heapInit(heap, K); offInit(off); uint stackPtr = 1; stack[0].op = OP_BEGIN_FUNCTION; stack[0].n = 0; stack[0].rd = 0; while (stackPtr != 0) { --stackPtr; StackEntry* s = stack + stackPtr; const size_t n = s->n; const global Node* node = nodes + n; const int cd = node->dim; switch (stack[stackPtr].op) { case OP_BEGIN_FUNCTION: if (cd < 0) { if (cd != -1) { const int index = -(cd + 2); const global T* p = &cloud[index * POINT_STRIDE]; T dist = 0; for (uint i = 0; i < DIM_COUNT; ++i) { const T diff = q[i] - p[i]; dist += diff * diff; } if ((dist <= maxRadius2) && (dist < heapHeadValue(heap) && (allowSelfMatch || (dist > (T)EPSILON)))) heapHeadReplace(heap, index, dist, K); #ifdef TOUCH_STATISTICS ++visitCount; #endif } } else { s->old_off = off[cd]; s->new_off = q[cd] - node->cutVal; (s+1)->op = OP_BEGIN_FUNCTION; s->op = OP_REC1; if (s->new_off > 0) { (s+1)->n = childRight(n); s->other_n = childLeft(n); } else { (s+1)->n = childLeft(n); s->other_n = childRight(n); } (s+1)->rd = s->rd; stackPtr+=2; } break; case OP_REC1: { T rdE; s->rd += - (s->old_off*s->old_off) + (s->new_off*s->new_off); if ((s->rd <= maxRadius2) && (s->rd * maxError < heapHeadValue(heap))) { off[cd] = s->new_off; (s+1)->op = OP_BEGIN_FUNCTION; (s+1)->n = s->other_n; (s+1)->rd = s->rd; s->op = OP_REC2; stackPtr+=2; } } break; case OP_REC2: off[cd] = s->old_off; break; } } if (doSort) heapSort(heap); heapCopy(&indices[queryId * indexStride], &dists2[queryId * dists2Stride], heap, K); #ifdef TOUCH_STATISTICS touchStatistics[queryId] = visitCount; #endif } norlab-ulaval-libnabo-534c2d5/nabo/opencl/knn_kdtree_pt_in_nodes.cl000066400000000000000000000055041211110745500254350ustar00rootroot00000000000000typedef struct { int dim; // >=0 cut dim, -1 == leaf, -2 == invalid int index; } Node; #define OFFSIDE 0 #define ONSIDE 1 typedef struct { size_t n; uint state; } StackEntry; size_t childLeft(const size_t pos) { return 2*pos + 1; } size_t childRight(const size_t pos) { return 2*pos + 2; } // for cloud and result, use DirectAccessBit and stride // preconditions: // K < MAX_K // stack_ptr < MAX_STACK_DEPTH kernel void knnKDTree( const global T* cloud, const global T* query, global int* indices, global T* dists2, const uint K, const T maxError, const T maxRadius2, const uint optionFlags, const uint indexStride, const uint dists2Stride, const uint pointCount, #ifdef TOUCH_STATISTICS global uint* touchStatistics, #endif const global Node* nodes ) { StackEntry stack[MAX_STACK_DEPTH]; HeapEntry heap[MAX_K]; #ifdef TOUCH_STATISTICS uint visitCount = 0; #endif const size_t queryId = get_global_id(0); const bool allowSelfMatch = optionFlags & ALLOW_SELF_MATCH; const bool doSort = optionFlags & SORT_RESULTS; const global T* q = &query[queryId * POINT_STRIDE]; heapInit(heap, K); uint stackPtr = 1; stack[0].state = ONSIDE; stack[0].n = 0; while (stackPtr != 0) { --stackPtr; StackEntry* s = stack + stackPtr; const size_t n = s->n; const global Node* node = nodes + n; const int cd = node->dim; if (cd == -2) continue; const int index = node->index; const global T* p = &cloud[index * POINT_STRIDE]; // check whether we already have better dist if ((s->state == OFFSIDE) && (cd >= 0)) { const T diff = q[cd] - p[cd]; // TODO: use approximate dist // FIXME: bug in this early out //if (diff * diff > heapHeadValue(heap)) // continue; } // compute new distance and update if lower T dist = 0; for (uint i = 0; i < DIM_COUNT; ++i) { const T diff = q[i] - p[i]; dist += diff * diff; } if ((dist <= maxRadius2) && (dist < heapHeadValue(heap) && (allowSelfMatch || (dist > (T)EPSILON)))) heapHeadReplace(heap, index, dist, K); #ifdef TOUCH_STATISTICS ++visitCount; #endif // look for recursion if (cd >= 0) { const T side = q[cd] - p[cd]; const T side2 = side*side; // should we enqueue off side? if ((side2 <= maxRadius2) && (side2 * maxError < heapHeadValue(heap))) { // yes s->state = OFFSIDE; if (side >= 0) s->n = childLeft(n); else s->n = childRight(n); ++stackPtr; } // always enqueue on side s = stack + stackPtr; s->state = ONSIDE; if (side >= 0) s->n = childRight(n); else s->n = childLeft(n); ++stackPtr; } } if (doSort) heapSort(heap); heapCopy(&indices[queryId * indexStride], &dists2[queryId * dists2Stride], heap, K); #ifdef TOUCH_STATISTICS touchStatistics[queryId] = visitCount; #endif } norlab-ulaval-libnabo-534c2d5/nabo/opencl/structure.cl000066400000000000000000000001461211110745500227650ustar00rootroot00000000000000#define ALLOW_SELF_MATCH 1 #define SORT_RESULTS 2 typedef struct { T value; int index; } HeapEntry;norlab-ulaval-libnabo-534c2d5/python/000077500000000000000000000000001211110745500175265ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/python/CMakeLists.txt000066400000000000000000000060441211110745500222720ustar00rootroot00000000000000# find Python, make sure we use the same version for interpreter and libraries find_package(PythonInterp) message("Python version from interpreter is ${PYTHON_VERSION_STRING}") set(Python_ADDITIONAL_VERSIONS ${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}) find_package(PythonLibs) # helper function to search for a module function(find_python_module module) string(TOUPPER ${module} module_upper) if(NOT PY_${module_upper}) if(ARGC GREATER 1 AND ARGV1 STREQUAL "REQUIRED") set(${module}_FIND_REQUIRED TRUE) endif() # A module's location is usually a directory, but for binary modules # it's a .so file. execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c" "import re, ${module}; print re.compile('/__init__.py.*').sub('',${module}.__file__)" RESULT_VARIABLE _${module}_status OUTPUT_VARIABLE _${module}_location ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) if(NOT _${module}_status) set(PY_${module_upper} ${_${module}_location} CACHE STRING "Location of Python module ${module}") endif(NOT _${module}_status) endif(NOT PY_${module_upper}) endfunction(find_python_module) # make sure we have the right modules if (PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) message("Python libs and executable found, looking for numpy and boost::python") find_package(Boost COMPONENTS python) find_python_module(numpy) find_package_handle_standard_args(numpy DEFAULT_MSG PY_NUMPY) if (Boost_FOUND AND NUMPY_FOUND) message("numpy and boost::python found, generating python bindings") include_directories(${PYTHON_INCLUDE_DIRS}) if (SHARED_LIBS) python_add_module(pynabo SHARED nabo.cpp) target_link_libraries(pynabo ${LIB_NAME} ${Boost_LIBRARIES} ${PYTHON_LIBRARIES}) else (SHARED_LIBS) set(PYTHON_SRC "nabo.cpp") foreach(file ${NABO_SRC}) set(PYTHON_SRC ${PYTHON_SRC} "../${file}") endforeach(file) python_add_module(pynabo ${PYTHON_SRC}) target_link_libraries(pynabo ${Boost_LIBRARIES} ${PYTHON_LIBRARIES}) endif (SHARED_LIBS) # fix for old python_add_module set_target_properties(pynabo PROPERTIES PREFIX "") if (PYTHON_CUSTOM_TARGET) install(TARGETS pynabo LIBRARY DESTINATION ${PYTHON_CUSTOM_TARGET}) else (PYTHON_CUSTOM_TARGET) if (PYTHON_DEB_INSTALL_TARGET) set(PYTHON_COMMAND "import sys; print '/usr/lib/python'+str(sys.version_info[0])+'.'+str(sys.version_info[1])+'/dist-packages'") else (PYTHON_DEB_INSTALL_TARGET) set(PYTHON_COMMAND "from distutils.sysconfig import get_python_lib; print get_python_lib()") endif (PYTHON_DEB_INSTALL_TARGET) execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c" "${PYTHON_COMMAND}" OUTPUT_VARIABLE PYTHON_SITE_MODULES OUTPUT_STRIP_TRAILING_WHITESPACE) install(TARGETS pynabo LIBRARY DESTINATION ${PYTHON_SITE_MODULES}) endif (PYTHON_CUSTOM_TARGET) elseif (Boost_FOUND AND NUMPY_FOUND) message("You need numpy and boost::python to generate python bindings") endif (Boost_FOUND AND NUMPY_FOUND) else (PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) message("Python libs or executable not found, skipping Python bindings") endif (PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) norlab-ulaval-libnabo-534c2d5/python/nabo.cpp000066400000000000000000000157121211110745500211570ustar00rootroot00000000000000#include #include #include #include "../nabo/nabo.h" #include #include using namespace boost::python; typedef Nabo::NNSearchD NNSNabo; typedef NNSNabo::Index Index; typedef NNSNabo::SearchType SearchType; typedef NNSNabo::SearchOptionFlags SearchOptionFlags; typedef Eigen::Map MappedEigenDoubleMatrix; typedef Eigen::Map MappedEigenIndexMatrix; static const double infD = std::numeric_limits::infinity(); static const Index maxI = std::numeric_limits::max(); void matrixSizeFromPythonArray(const PyObject* cloudObj, int& rowCount, int& colCount) { assert(PyArray_CHKFLAGS(cloudObj, NPY_C_CONTIGUOUS) || PyArray_CHKFLAGS(cloudObj, NPY_F_CONTIGUOUS)); assert(PyArray_NDIM(cloudObj) == 2); const npy_intp *shape = PyArray_DIMS(cloudObj); if (PyArray_CHKFLAGS(cloudObj, NPY_F_CONTIGUOUS)) { colCount = shape[1]; rowCount = shape[0]; } else { colCount = shape[0]; rowCount = shape[1]; } } void checkPythonArray(const PyObject* cloudObj, const char *paramName) { std::string startMsg("Argument \""); startMsg += paramName; startMsg += "\" "; if (!PyArray_Check(cloudObj)) throw std::runtime_error(startMsg + "must be a multi-dimensional array"); const int nDim = PyArray_NDIM(cloudObj); if (nDim != 2) throw std::runtime_error(startMsg + "must be a two-dimensional array"); if (PyArray_TYPE(cloudObj) != NPY_FLOAT64) throw std::runtime_error(startMsg + "must hold doubles"); if (!PyArray_CHKFLAGS(cloudObj, NPY_C_CONTIGUOUS) && !PyArray_CHKFLAGS(cloudObj, NPY_F_CONTIGUOUS)) throw std::runtime_error(startMsg + "must be a continuous array"); } MappedEigenDoubleMatrix* eigenFromBoostPython(const object cloudIn, const char *paramName) { int dimCount, pointCount; const PyObject *cloudObj(cloudIn.ptr()); checkPythonArray(cloudObj, paramName); matrixSizeFromPythonArray(cloudObj, dimCount, pointCount); double* cloudData(reinterpret_cast(PyArray_DATA(cloudObj))); return new MappedEigenDoubleMatrix(cloudData, dimCount, pointCount); } void eigenFromBoostPython(NNSNabo::Matrix& cloudOut, const object cloudIn, const char *paramName) { int dimCount, pointCount; const PyObject *cloudObj(cloudIn.ptr()); checkPythonArray(cloudObj, paramName); matrixSizeFromPythonArray(cloudObj, dimCount, pointCount); cloudOut.resize(dimCount, pointCount); memcpy(cloudOut.data(), PyArray_DATA(cloudObj), pointCount*dimCount*sizeof(double)); } class NearestNeighbourSearch { public: NearestNeighbourSearch(const object pycloud, const SearchType searchType = NNSNabo::KDTREE_LINEAR_HEAP, const Index dim = maxI, const dict params = dict()) { // build cloud eigenFromBoostPython(cloud, pycloud, "cloud"); // build params Nabo::Parameters _params; object it = params.iteritems(); for(int i = 0; i < len(params); ++i) { const tuple item(it.attr("next")()); const std::string key = extract(item[0]); const object val(item[1]); const std::string valType(val.ptr()->ob_type->tp_name); if (valType == "int") { const int iVal = extract(val); if (iVal >= 0) _params[key] = (unsigned)iVal; else _params[key] = iVal; } } // create search nns = NNSNabo ::create(cloud, dim, searchType, 0, _params); } ~NearestNeighbourSearch() { delete nns; } tuple knn(const object query, const Index k = 1, const double epsilon = 0, const unsigned optionFlags = 0, const double maxRadius = infD) { // map query and create output matrices MappedEigenDoubleMatrix* mappedQuery(eigenFromBoostPython(query, "query")); NNSNabo::IndexMatrix indexMatrix(k, mappedQuery->cols()); NNSNabo::Matrix dists2Matrix(k, mappedQuery->cols()); // do the search nns->knn(*mappedQuery, indexMatrix, dists2Matrix, k, epsilon, optionFlags, maxRadius); // build resulting python types npy_intp retDims[2] = { mappedQuery->cols(), k }; const int dataCount(k * mappedQuery->cols()); PyObject* dists2 = PyArray_EMPTY(2, retDims, PyArray_DOUBLE, PyArray_ISFORTRAN(query.ptr())); memcpy(PyArray_DATA(dists2), dists2Matrix.data(), dataCount*sizeof(double)); PyObject* indices = PyArray_EMPTY(2, retDims, PyArray_INT, PyArray_ISFORTRAN(query.ptr())); memcpy(PyArray_DATA(indices), indexMatrix.data(), dataCount*sizeof(int)); delete mappedQuery; // return results return make_tuple(object(handle<>(indices)), object(handle<>(dists2))); } protected: NNSNabo *nns; NNSNabo::Matrix cloud; }; BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(knn_overloads, knn, 1, 5) BOOST_PYTHON_MODULE(pynabo) { import_array(); enum_("SearchType", "Type of algorithm used for search.") .value("BRUTE_FORCE", NNSNabo::BRUTE_FORCE) .value("KDTREE_LINEAR_HEAP", NNSNabo::KDTREE_LINEAR_HEAP) .value("KDTREE_TREE_HEAP", NNSNabo::KDTREE_TREE_HEAP) ; enum_("SearchOptionFlags", "Flags you can OR when creating search.") .value("ALLOW_SELF_MATCH", NNSNabo::ALLOW_SELF_MATCH) .value("SORT_RESULTS", NNSNabo::SORT_RESULTS) ; class_( "NearestNeighbourSearch", "Nearest-neighbour search object, containing the data, on which you can do the knn(...) query.\n\n" "The data and query must be continuous numpy arrays.\n" "As numpy proposes both C and Fortran data orders, pynabo\n" "will always consider the contiguous dimension to be coordinates\n" "of points, regardless of order, as this provides the fastest\n" "possible execution. The return values of knn(...) will have\n" "the same order as the query and will have the different results\n" "of each point in the contiguous dimension." , init >( "Create a nearest-neighbour search.\n\n" "Arguments:\n" " data -- data-point cloud in which to search, must be a numpy array\n" " searchType -- type of search, default: KDTREE_LINEAR_HEAP\n" " dim -- number of dimensions to consider, must be lower or equal to cloud.rows(), default: dim of data\n" " creationOptionFlags -- creation options, a bitwise OR of elements of CreationOptionFlags", args("self", "data", "searchType", "dim", "creationOptionFlags, default: 0") ) ) .def("knn", &NearestNeighbourSearch::knn, knn_overloads( args("self", "query", "k", "epsilon", "optionFlags", "maxRadius"), "Find the k nearest neighbours of query in data.\n\n" "Arguments:\n" " query -- query points, must be a numpy array\n" " k -- number of nearest neighbour requested, default: 1\n" " epsilon -- maximal percentage of error for approximate search, 0 for exact search, default: 0.\n" " optionFlags -- search options, a bitwise OR of elements of SearchOptionFlags, default: 0\n" " maxRadius -- maximum radius in which to search, can be used to prune search, is not affected by epsilon, default: inf\n\n" "Returns:\n" " A tuple of two 2D numpy arrays, the first containing indices to points in data, the other containing distances." ) ) ; }norlab-ulaval-libnabo-534c2d5/python/test.py000066400000000000000000000003601211110745500210560ustar00rootroot00000000000000from pynabo import * import numpy as np x = np.array([[0.,3.], [1,2], [4,5]]) print x nns = NearestNeighbourSearch(x) q = np.array([[1.1, 2.]]) print q res = nns.knn(q, 2, 0, SearchOptionFlags.ALLOW_SELF_MATCH) print res[0] print res[1] norlab-ulaval-libnabo-534c2d5/tests/000077500000000000000000000000001211110745500173475ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/tests/CMakeLists.txt000066400000000000000000000105161211110745500221120ustar00rootroot00000000000000include (CheckSymbolExists) include_directories(..) add_executable(knnvalidate knnvalidate.cpp) target_link_libraries(knnvalidate ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES}) # disable as experimental is broken target_link_libraries(knnvalidate naboexperimental nabo ${EXTRA_LIBS} ${Boost_LIBRARIES}) add_test(validation-2D-exhaustive ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.2d.txt 10 -1) add_test(validation-2D-random ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.2d.txt 10 10000) add_test(validation-3D-exhaustive ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.txt 10 -1) add_test(validation-3D-random ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.txt 10 10000) add_test(validation-3D-large-random ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 1000) add_test(validation-3D-large-random-radius ${EXECUTABLE_OUTPUT_PATH}/knnvalidate ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 1000 0.5) find_path(ANN_INCLUDE_DIR ANN.h /usr/local/include/ANN /usr/include/ANN ) find_library(ANN_LIBRARY NAMES ANN ann PATHS /usr/local/lib /usr/lib ) if (ANN_INCLUDE_DIR AND ANN_LIBRARY) include_directories(${ANN_INCLUDE_DIR}) set(EXTRA_LIBS ${ANN_LIBRARY} ${EXTRA_LIBS}) add_definitions(-DHAVE_ANN) message("ANN found, enabling it in benchmarks") else (ANN_INCLUDE_DIR AND ANN_LIBRARY) message("ANN not found, disabling it in benchmarks") endif (ANN_INCLUDE_DIR AND ANN_LIBRARY) find_path(FLANN_INCLUDE_DIR flann/flann.hpp /usr/local/include/ /usr/include ) find_library(FLANN_LIBRARY flann_cpp PATHS /usr/local/lib /usr/lib ) if (FLANN_INCLUDE_DIR AND FLANN_LIBRARY) include_directories(${FLANN_INCLUDE_DIR}) set(EXTRA_LIBS ${FLANN_LIBRARY} ${EXTRA_LIBS}) add_definitions(-DHAVE_FLANN) message("FLANN found, enabling it in benchmarks") else (FLANN_INCLUDE_DIR AND FLANN_LIBRARY) message("FLANN not found, disabling it in benchmarks") endif (FLANN_INCLUDE_DIR AND FLANN_LIBRARY) check_symbol_exists(_POSIX_TIMERS "unistd.h;time.h" POSIX_TIMERS) if (POSIX_TIMERS) set(EXTRA_LIBS ${EXTRA_LIBS} rt) endif (POSIX_TIMERS) include_directories(..) add_executable(knnbench knnbench.cpp) target_link_libraries(knnbench ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES}) # disable as experimental is broken target_link_libraries(knnbench naboexperimental nabo ${EXTRA_LIBS} ${Boost_LIBRARIES}) add_test(bench-3D-large-exhaustive-10000-K1 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 1 -10000 3 5) add_test(bench-3D-large-exhaustive-1000-K1 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 1 -1000 3 5) add_test(bench-3D-large-exhaustive-100-K1 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 1 -100 3 5) add_test(bench-3D-large-random-K1 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 1 40000 3 5) add_test(bench-3D-large-exhaustive-10000-K10 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 -10000 3 5) add_test(bench-3D-large-exhaustive-1000-K10 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 -1000 3 5) add_test(bench-3D-large-exhaustive-100-K10 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 -100 3 5) add_test(bench-3D-large-random-K20 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 10 40000 3 5) add_test(bench-3D-large-exhaustive-10000-K30 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 30 -10000 3 5) add_test(bench-3D-large-exhaustive-1000-K30 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 30 -1000 3 5) add_test(bench-3D-large-exhaustive-100-K30 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 30 -100 3 5) add_test(bench-3D-large-random-K30 ${EXECUTABLE_OUTPUT_PATH}/knnbench ${CMAKE_CURRENT_SOURCE_DIR}/data/scan.3d.large.txt 30 40000 3 5) add_executable(knnepsilon knnepsilon.cpp) target_link_libraries(knnepsilon ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES}) add_executable(knnbucketsize knnbucketsize.cpp) target_link_libraries(knnbucketsize ${LIB_NAME} ${EXTRA_LIBS} ${Boost_LIBRARIES})norlab-ulaval-libnabo-534c2d5/tests/data/000077500000000000000000000000001211110745500202605ustar00rootroot00000000000000norlab-ulaval-libnabo-534c2d5/tests/data/scan.2d.txt000066400000000000000000000272111211110745500222540ustar00rootroot00000000000000 2.8600000e-01 8.8600000e-01 2.9377500e-01 8.9096600e-01 3.0169000e-01 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-591.994 63.8208 510.64 -596.843 63.218 505.817 -600.196 62.458 499.736 -391.214 39.9945 320.002 -386.708 38.836 310.733 -385.826 38.0609 304.531 -383.84 37.1914 297.574 -621.592 59.1518 473.283 -142.716 13.3373 106.714 -142.363 13.0643 104.53 -143.762 12.9535 103.643 -142.714 12.6246 101.011 -141.959 12.3274 98.6334 -141.997 12.1031 96.8384 -145.911 12.2055 97.6582 -148.265 12.1703 97.3768 -385.118 31.0164 248.167 -382.394 30.2121 241.731 -380.689 29.5013 236.045 -383.006 29.1076 232.894 -381.439 28.4237 227.423 -382.65 27.9532 223.658 -381.138 27.29 218.351 -382.608 26.8459 214.798 -380.634 26.1663 209.36 -382.285 25.7415 205.962 -381.875 25.1812 201.479 -715.64 46.2012 369.663 -736.951 46.5679 372.597 -723.017 44.7061 357.7 -402.57 24.3503 194.83 -407.156 24.0846 192.705 -405.482 23.449 187.619 -402.931 22.7728 182.208 -403.604 22.2854 178.309 -405.157 21.8477 174.807 -405.666 21.3551 170.866 -403.181 20.7112 165.714 -405.921 20.3392 162.737 -406.592 19.8627 158.925 -404.614 19.262 154.118 -406.141 18.832 150.678 -403.504 18.2135 145.729 -406.561 17.8549 142.86 -405.344 17.3092 138.494 -406.641 16.8738 135.01 -404.484 16.299 130.41 -387.209 15.1409 121.144 -381.852 14.4783 115.843 -379.98 13.9587 111.686 -381.333 13.5607 108.501 -381.019 13.1044 104.85 -381.348 12.6723 101.393 -380.385 12.2001 97.6149 -381.229 11.788 94.3173 -380.877 11.3402 90.7346 -380.589 10.8969 87.1876 -380.072 10.4497 83.6094 -382.064 10.0715 80.5834 -380.211 9.59332 76.7576 -379.497 9.14835 73.1973 -381.011 8.75763 70.0711 -380.234 8.3148 66.528 -379.917 7.88455 63.0855 -378.383 7.43235 59.4673 -379.288 7.0299 56.2473 -380.263 6.62779 53.0299 -379.327 6.19333 49.5538 -379.351 5.77653 46.2189 -379.246 5.35874 42.8761 -379.708 4.9494 39.6009 -378.649 4.52163 36.1782 -379.351 4.11599 32.9327 -379.925 3.70821 29.67 -379.473 3.29084 26.3305 -378.792 2.87322 22.989 -377.582 2.45408 19.6354 -378.639 2.05021 16.4041 -378.769 1.64036 13.1248 -145.85 0.473649 3.78974 -145.078 0.314054 2.51279 -144.195 0.156059 1.24866 96 0 0 95.9963 0.11139 0.830309 95.9854 0.222771 1.66055 96.9668 0.337616 2.51661 95.9415 0.445475 3.3206 96.9077 0.56258 4.19351 95.8684 0.668043 4.97964 96.8191 0.787373 5.86913 96.7637 0.899683 6.7063 96.8007 1.01297 7.55074 96.7305 1.12525 8.38768 96.653 1.23744 9.22399 96.4686 1.34816 10.0493 96.6745 1.46456 10.9169 96.3762 1.57343 11.7284 96.2693 1.6852 12.5616 96.155 1.79683 13.3937 96.0334 1.90834 14.2249 96.5959 2.03425 15.1635 95.5711 2.1265 15.8511 95.3294 2.235 16.6599 96.064 2.36734 17.6463 95.905 2.47871 18.4765 96.6206 2.61376 19.4832 95.565 2.70089 20.1326 96.2628 2.83757 21.1514 96.0729 2.94916 21.9833 95.9729 3.06363 22.8365 95.7682 3.17487 23.6657 95.5562 3.28587 24.4931 96.1096 3.42415 25.5238 95.8812 3.53553 26.3541 95.6455 3.64665 27.1824 96.2655 3.79147 28.2619 95.8217 3.89525 29.0355 95.5624 4.00629 29.8632 96.1518 4.154 30.9642 95.8755 4.26541 31.7947 96.3483 4.41113 32.8808 96.1494 4.52719 33.746 95.7547 4.63404 34.5424 96.1962 4.78221 35.647 95.8787 4.89365 36.4776 96.5773 5.05831 37.705 97.0761 5.21501 38.8731 96.7302 5.32746 39.7112 96.3769 5.43948 40.5463 96.1079 5.55641 41.4179 96.6531 5.72179 42.6506 96.2739 5.83372 43.4849 96.0686 5.95644 44.3997 96.4864 6.1192 45.613 95.9013 6.21927 46.3589 96.2054 6.37778 47.5404 96.5851 6.54349 48.7756 96.0633 6.64917 49.5634 96.4179 6.81656 50.8111 95.9668 6.92817 51.643 96.2956 7.09727 52.9036 95.7391 7.20219 53.6856 96.0422 7.37284 54.9577 95.5547 7.484 55.7862 95.7456 7.64938 57.019 95.922 7.81576 58.2592 96.0838 7.98313 59.5068 95.5563 8.0943 60.3355 95.6084 8.25556 61.5375 95.8135 8.43224 62.8545 95.2564 8.54309 63.6808 95.3514 8.71354 64.9513 95.4312 8.88486 66.2284 95.5772 9.06474 67.5692 95.6258 9.23782 68.8593 95.659 9.41169 70.1554 95.1175 9.53034 71.0398 95.6784 9.76175 72.7647 95.0341 9.8724 73.5895 95.0869 10.0568 74.9639 95.1227 10.242 76.3449 95.6043 10.4789 78.1103 95.0661 10.6065 79.0616 95.583 10.8546 80.9107 95.0179 10.9825 81.8644 95.5667 11.2421 83.7996 95.4941 11.4327 85.2198 95.2562 11.6059 86.5113 95.2954 11.8157 88.0754 95.024 11.9899 89.3737 95.0248 12.2013 90.9496 94.6483 12.3671 92.1848 95.2473 12.6644 94.4016 95.1835 12.8788 95.9994 94.8209 13.0557 97.3181 94.5799 13.252 98.7815 94.2522 13.439 100.176 94.6502 13.7342 102.376 94.2805 13.9225 103.779 93.9595 14.121 105.259 94.2101 14.4101 107.414 94.2349 14.6706 109.355 93.847 14.8709 110.849 94.0124 15.164 113.033 94.1463 15.4585 115.229 93.6885 15.6609 116.737 93.6421 15.9365 118.792 93.5058 16.2028 120.777 93.4619 16.4912 122.927 93.9225 16.877 125.802 92.2823 16.8885 125.888 97.6742 18.2073 135.718 115.69 21.9687 163.756 115.264 22.2994 166.221 115.138 22.6968 169.183 115.576 23.2176 173.066 115.518 23.6519 176.303 115.466 24.099 179.636 115.204 24.514 182.729 114.95 24.9415 185.915 114.905 25.4272 189.536 115.363 26.0407 194.109 114.95 26.473 197.331 115.129 27.0567 201.682 114.997 27.5847 205.618 113.039 27.682 206.343 110.795 27.7065 206.526 108.557 27.7277 206.684 105.916 27.6394 206.026 103.786 27.678 206.314 101.132 27.5703 205.511 99.3189 27.6865 206.377 96.7796 27.5958 205.701 94.5915 27.5982 205.719 92.3699 27.5855 205.624 90.8749 27.7892 207.142 91.3139 28.6034 213.212 90.9639 29.1996 217.656 90.8796 29.9082 222.938 90.819 30.6557 228.51 90.8104 31.4551 234.468 90.5986 32.2194 240.165 90.4301 33.0355 246.249 89.9609 33.7784 251.786 89.694 34.6357 258.177 89.6703 35.6338 265.616 89.3986 36.5838 272.698 89.039 37.5484 279.889 88.881 38.6548 288.136 88.4992 39.7253 296.115 88.7001 41.1302 306.587 88.0283 42.2054 314.602 88.0244 43.6801 325.594 87.9585 45.2223 337.09 87.2612 46.5355 346.878 86.9817 48.1734 359.088 86.7411 49.9567 372.381 86.4027 51.8209 386.276 85.8883 53.7271 400.485 85.6485 55.9745 417.238 85.0627 58.1861 433.723 84.8853 60.8974 453.933 84.2888 63.5601 473.782 83.8442 66.6192 496.584 83.5829 70.1679 523.036 92.307 82.1237 612.156 86.6909 82.0171 611.361 81.6441 82.4573 614.643 80.5313 87.2075 650.05 79.6045 92.8991 692.476 78.3859 99.1633 739.17 77.2708 106.702 795.363 76.3397 116.02 864.819 72.9199 123.196 918.308 71.9887 136.885 1020.35 62.5259 135.928 1013.21 55.2458 140.164 1044.79 44.4918 135.494 1009.98 36.7945 140.099 1044.3 26.6429 135.284 1008.42 18.3512 139.79 1042.01 8.86529 135.073 1006.84 6.43898e-14 139.825 1042.26 -8.84783 134.807 1004.86 -18.3198 139.551 1040.22 -26.5042 134.579 1003.16 -36.5817 139.288 1038.26 -44.278 134.843 1005.13 -54.8952 139.274 1038.16 -61.9398 134.654 1003.72 -73.0629 138.927 1035.57 -79.6203 134.515 1002.69 -91.2172 138.631 1033.36 -97.3122 134.377 1001.66 -109.295 138.265 1030.64 -115.173 134.408 1001.88 -127.634 138.214 1030.26 -132.915 134.239 1000.63 -123.335 116.686 869.784 -130.723 116.302 866.92 -138.319 116.119 865.56 -150.391 119.495 890.723 -568.993 429.063 3198.27 -597.131 428.387 3193.22 -154.288 105.539 786.696 -157.959 103.232 769.5 -165.186 103.331 770.238 -118.328 70.9682 529.002 -121.699 70.0897 522.454 -126.387 69.9976 521.767 -131.194 69.9643 521.519 -137.308 70.5947 526.218 -848.072 420.837 3136.95 -875.66 419.837 3129.49 -903.181 418.805 3121.8 -930.633 417.74 3113.86 -958.014 416.645 3105.7 -167.162 70.4935 525.464 -149.255 61.0782 455.281 -148.975 59.2005 441.285 -154.515 59.6666 444.759 -1093.78 410.692 3061.32 -1120.7 409.407 3051.75 -1147.52 408.091 3041.94 -434.736 150.585 1122.47 -425.728 143.703 1071.17 -394.611 129.865 968.023 -413.375 132.695 989.114 -427.772 133.997 998.822 -425.465 130.105 969.815 -1332.59 397.967 2966.47 -429.539 125.323 934.169 -138.661 39.5381 294.72 -138.711 38.6676 288.231 -437.144 119.172 888.319 -440.04 117.352 874.747 -441.506 115.214 858.81 -145.636 37.1983 277.279 -145.583 36.4057 271.371 -148.396 36.3406 270.885 -151.842 36.4229 271.499 -111.042 26.0961 194.522 -111.4 25.6554 191.237 -111.405 25.1472 187.449 -112.278 24.8459 185.203 -113.487 24.624 183.549 -115.363 24.5478 182.981 -116.863 24.3906 181.809 -118.296 24.2205 180.542 -120.433 24.1933 180.338 -122.463 24.1408 179.947 -125.799 24.3374 181.413 -491.612 93.3532 695.861 -495.398 92.3463 688.355 -498.324 91.1978 679.795 -502.209 90.242 672.67 -504.622 89.04 663.71 -508.498 88.1135 656.804 -511.245 87.0067 648.554 -514.695 86.0355 641.315 -514.847 84.5361 630.138 -532.843 85.9464 640.651 -525.35 83.247 620.529 -529.82 82.4826 614.831 -164.999 25.2377 188.124 -162.607 24.4379 182.162 -165.476 24.4361 182.148 -544.255 78.974 588.677 -547.645 78.0866 582.063 -551.51 77.2745 576.01 -554.824 76.3926 569.436 -558.905 75.6226 563.696 -561.726 74.6892 556.738 -566.036 73.96 551.303 -569.142 73.0787 544.734 -573.191 72.324 539.108 -573.381 71.094 529.939 -584.292 71.1895 530.651 -584.26 69.9482 521.399 -589.503 69.347 516.917 -592.145 68.4423 510.174 -596.462 67.7353 504.904 -600.196 66.9637 499.152 -605.494 66.3663 494.699 -390.516 42.0475 313.425 -388.956 41.1377 306.643 -386.362 40.1363 299.179 -140.344 14.3189 106.734 -142.876 14.3155 106.709 -142.443 14.0148 104.467 -143.762 13.8879 103.522 -142.796 13.543 100.951 -141.795 13.2015 98.4045 -142.574 13.0289 97.1183 -147.071 13.1901 98.32 -148.015 13.0263 97.0989 -661.124 57.0865 425.527 -381.382 32.3058 240.81 -380.18 31.5873 235.454 -382.921 31.2005 232.57 -383.84 30.666 228.586 -384.2 30.0912 224.302 -382.264 29.3451 218.74 -380.781 28.645 213.522 -382.121 28.1635 209.932 -382.813 27.6365 206.004 -382.669 27.054 201.662 -716.705 49.6078 369.78 -738.288 50.0179 372.837 -723.107 47.9372 357.327 -403.469 26.1653 195.038 -404.268 25.6389 191.114 -405.845 25.1631 187.568 -406.662 24.6416 183.68 -405.797 24.0228 179.068 -404.423 23.3814 174.287 -405.482 22.8853 170.589 -406.045 22.363 166.696 -403.789 21.6918 161.692 -405.011 21.2128 158.121 -404.707 20.6563 153.974 -405.204 20.144 150.155 -405.289 19.6139 146.203 -407.033 19.1651 142.858 -406.195 18.5968 138.622 -406.546 18.0869 134.821 -406.957 17.5815 131.054 -384.062 16.1011 120.019 -381.757 15.5189 115.679 -378.926 14.9243 111.246 -379.891 14.484 107.965 -381.309 14.0604 104.807 -381.734 13.6002 101.377 -380.772 13.0935 97.6001 -380.356 12.6094 93.9914 -380.002 12.1303 90.4204 -381.076 11.6979 87.1972 -380.951 11.2294 83.7048 -380.793 10.7621 80.2213 -380.603 10.296 76.747 -379.497 9.80831 73.1118 -380.323 9.37244 69.8628 -379.84 8.90539 66.3814 -381.002 8.47749 63.1918 -380.062 8.00389 59.6615 -378.892 7.52918 56.123 -379.768 7.09666 52.899 -380.219 6.65574 49.6123 -377.962 6.17056 45.9957 -379.246 5.74532 42.826 -379.708 5.30645 39.5546 -379.545 4.85929 36.2215 -380.347 4.42452 32.9806 -380.224 3.97885 29.6586 -379.074 3.52454 26.2721 -377.894 3.07319 22.9078 -379.379 2.64364 19.7059 -376.841 2.18768 16.3071 -379.769 1.76334 13.144 -144.55 0.503293 3.75158 -142.378 0.330444 2.46315 -145.194 0.168477 1.25584 96 0 0 95.8963 0.118752 0.828407 95.9854 0.237743 1.65848 95.9671 0.356592 2.48756 95.9415 0.475413 3.31645 95.9086 0.594199 4.14509 96.8671 0.720366 5.02522 96.8191 0.840288 5.86179 96.8635 0.961135 6.70481 96.701 1.07993 7.53352 96.7305 1.20087 8.37719 96.5534 1.31925 9.20296 96.5681 1.44024 10.047 96.4758 1.55976 10.8808 96.5747 1.68263 11.7379 96.2693 1.79845 12.5459 96.155 1.91759 13.377 95.9345 2.03448 14.1924 96.5959 2.17096 15.1445 96.4587 2.2905 15.9784 96.2157 2.40738 16.7937 96.064 2.52644 17.6242 95.905 2.64529 18.4534 95.7386 2.76395 19.2811 95.3694 2.8765 20.0663 96.2628 3.02826 21.125 95.878 3.14097 21.9112 95.8757 3.2662 22.7848 95.7682 3.38823 23.6361 95.5562 3.50669 24.4624 96.3028 3.66162 25.5432 95.8812 3.77314 26.3211 95.7417 3.89564 27.1757 96.0737 4.03822 28.1703 95.8217 4.15703 28.9992 95.7532 4.28407 29.8854 96.1518 4.43317 30.9255 95.8755 4.55207 31.7549 96.4429 4.7122 32.872 96.0552 4.8267 33.6707 95.7547 4.94547 34.4992 96.1962 5.1036 35.6024 96.8123 5.27338 36.7868 96.5773 5.39826 37.6578 97.0761 5.56548 38.8244 96.7302 5.68549 39.6616 96.3769 5.80504 40.4955 96.1079 5.92983 41.366 95.8309 6.05439 42.235 96.4559 6.23755 43.5127 95.8874 6.34475 44.2606 96.3058 6.51823 45.4707 96.8001 6.69945 46.7349 96.2054 6.8064 47.4809 95.6941 6.91882 48.2652 96.152 7.10257 49.547 96.5062 7.28132 50.794 95.9668 7.39378 51.5784 96.2082 7.56738 52.7895 95.7391 7.68622 53.6185 96.0422 7.86833 54.8889 95.4685 7.97976 55.6663 95.6599 8.15613 56.8966 96.0073 8.34842 58.238 95.9142 8.50459 59.3274 95.3876 8.62304 60.1537 95.6084 8.81037 61.4605 95.063 8.92843 62.2841 95.2564 9.11723 63.6011 95.269 9.2911 64.814 95.4312 9.48197 66.1455 95.5772 9.67393 67.4846 95.6258 9.85864 68.7732 95.659 10.0442 70.0676 95.8363 10.2477 71.487 95.2024 10.366 72.3122 95.8221 10.6232 74.1069 95.8695 10.821 75.4864 95.1227 10.9304 76.2494 95.6043 11.1831 78.0126 95.0661 11.3193 78.9627 95.7351 11.6025 80.9381 95.5462 11.7858 82.2167 95.5667 11.9976 83.6947 94.8253 12.1155 84.5171 94.5927 12.2996 85.8011 95.2954 12.6098 87.9652 95.1691 12.8152 89.3982 95.0248 13.0213 90.8358 94.8623 13.228 92.2776 94.6816 13.4353 93.7235 94.5527 13.6532 95.2439 94.9598 13.9535 97.3388 94.5799 14.1426 98.6579 95.0024 14.4564 100.847 94.6502 14.6572 102.248 94.2805 14.8582 103.649 94.4896 15.155 105.72 94.7349 15.4642 107.877 94.17 15.6457 109.143 93.9113 15.8813 110.786 93.9488 16.1721 112.816 94.0205 16.4754 114.931 94.1865 16.8022 117.211 93.6421 17.0075 118.643 93.5058 17.2917 120.626 93.5822 17.6222 122.931 93.8036 17.9883 125.485 92.2823 18.0235 125.731 95.9321 19.0844 133.131 115.69 23.4451 163.551 115.717 23.8915 166.665 115.25 24.2457 169.136 115.576 24.7779 172.849 115.518 25.2414 176.082 115.358 25.6946 179.244 115.257 26.1734 182.584 114.897 26.6056 185.599 114.75 27.0995 189.045 114.957 27.693 193.184 115 28.2644 197.17 114.981 28.838 201.172 115.142 29.4757 205.62 113.468 29.6546 206.868 111.218 29.6811 207.053 108.557 29.5912 206.426 106.325 29.6107 206.562 103.696 29.5127 205.878 101.571 29.5507 206.144 99.3189 29.5472 206.119 96.7796 29.4504 205.444 94.5915 29.453 205.462 92.6546 29.5301 206 90.6755 29.5916 206.429 91.3139 30.5257 212.945 91.0787 31.2013 217.658 90.8796 31.9182 222.659 90.8557 32.7293 228.317 90.7746 33.5558 234.083 90.6337 34.398 239.958 90.3275 35.2156 245.662 89.9276 36.0351 251.378 89.7266 36.9769 257.948 89.5434 37.9747 264.909 89.3368 39.0153 272.168 88.9788 40.0448 279.349 88.9102 41.2661 287.869 88.6412 42.4632 296.22 88.5623 43.8261 305.728 88.1887 45.1238 314.78 88.102 46.6567 325.474 87.7332 48.1379 335.806 87.2854 49.6766 346.541 87.2152 51.5488 359.601 86.7186 53.3003 371.819 86.4027 55.3035 385.793 85.9715 57.3933 400.371 85.4092 59.5694 415.552 85.139 62.1523 433.57 84.7577 64.8924 452.684 84.3062 67.8456 473.286 83.9267 71.1663 496.451 83.5516 74.8554 522.186 92.44 87.7693 612.272 86.8162 87.6555 611.478 81.8138 88.1818 615.15 80.5556 93.0965 649.434 79.6045 99.1424 691.61 78.3754 105.813 738.147 77.1846 113.746 793.482 75.9475 123.181 859.3 74.3635 134.078 935.317 70.0774 142.205 992.009 59.6139 138.307 964.816 51.9539 140.671 981.309 42.398 137.795 961.249 34.6203 140.679 981.364 25.4152 137.723 960.746 17.2866 140.53 980.328 8.45863 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62.8018 206.124 95.6238 62.5966 205.451 93.387 62.5966 205.451 90.7668 62.322 204.55 91.27 64.22 210.779 90.6923 65.4225 214.726 90.8923 67.2508 220.726 90.7746 68.9214 226.21 90.5986 70.6238 231.797 90.0881 72.1387 236.769 89.9943 74.0684 243.103 89.7591 75.9754 249.362 89.5116 77.9698 255.908 89.3986 80.1904 263.196 89.0991 82.3602 270.318 88.5302 84.3954 276.998 88.6128 87.1884 286.164 88.3418 89.7917 294.709 85.6232 89.9849 295.343 83.7797 91.1283 299.096 83.9524 94.6111 310.527 84.3824 98.6387 323.746 81.986 99.5297 326.67 82.4221 104.051 341.51 83.3293 109.549 359.556 81.7925 112.152 368.097 85.7083 122.78 402.981 85.0436 127.513 418.517 84.5573 132.969 436.423 84.202 139.178 456.801 83.7947 145.941 478.998 83.2857 153.259 503.016 82.4629 160.815 527.817 80.0941 166.098 545.157 81.1351 179.617 589.526 78.0939 185.37 608.411 71.8501 183.795 603.242 66.846 185.363 608.385 60.4307 182.914 600.35 55.5269 184.978 607.122 49.3822 182.874 600.219 44.3442 184.825 606.62 38.3263 182.632 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547.919 61.5986 185.921 555.8 55.4851 182.8 546.471 51.2824 185.949 555.885 45.3415 182.763 546.361 40.9191 185.635 554.946 35.2067 182.606 545.892 30.6636 185.611 554.876 25.1553 182.773 546.392 20.4127 185.436 554.351 15.0491 182.314 545.018 10.1992 185.362 554.13 5.01252 182.211 544.71 3.57646e-14 185.295 553.93 -5.01165 182.179 544.615 -10.194 185.267 553.846 -15.0203 181.965 543.975 -20.3639 184.992 553.024 -25.055 182.044 544.212 -30.538 184.851 552.602 -35.0297 181.688 543.147 -40.7238 184.749 552.297 -45.1297 181.909 543.808 -50.9338 184.685 552.105 -55.1305 181.632 542.979 -61.1178 184.469 551.462 -65.1484 181.393 542.266 -71.3667 184.386 551.213 -75.1961 181.194 541.669 -81.5833 184.151 550.511 -85.4191 181.314 542.03 -91.9522 184.173 550.576 -95.6781 181.377 542.218 -102.331 184.105 550.371 -105.824 181.132 541.484 -112.539 183.666 549.059 -116.311 181.358 542.159 -119.778 178.764 534.405 -122.894 175.855 525.708 -127.165 174.735 522.361 -132.527 175.116 523.501 -144.863 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4.564182 -5.978697 -0.690689 5.981566 -2.786044 -0.349820 4.469724 -4.038494 -0.482951 6.323491 -1.128066 -0.172789 3.285720 -0.735078 -0.130281 4.134250 -0.680899 -0.123719 4.977933 -0.013302 -0.051888 6.245642 -3.477068 -0.308743 -0.952327 -7.256730 -0.602042 -0.952574 -6.416618 -0.534315 5.487653 -2.785033 -0.253080 4.520314 -4.201212 -0.361793 6.404025 -0.948628 -0.110997 3.717803 -0.835843 -0.102114 3.730886 -0.651700 -0.087436 3.705096 -0.252531 -0.056441 4.511102 -3.969234 -0.213833 -0.952669 -6.477083 -0.333977 -0.948583 -8.265545 -0.419102 -0.959977 -10.966218 -0.547876 -0.940058 -5.656957 -0.292643 5.380330 -2.298129 -0.132345 5.058929 -1.001842 -0.070478 3.540015norlab-ulaval-libnabo-534c2d5/tests/data/sources.txt000066400000000000000000000003421211110745500225030ustar00rootroot00000000000000scan.2d.txt scan.3d.txt François Pomerleau, ETHZ, Switzerland scan.3d.large.txt Andreas Nüchter and Kai Lingemann, University of Osnabrück, Germany http://kos.informatik.uni-osnabrueck.de/3Dscans/dagstuhl/scan000.3d norlab-ulaval-libnabo-534c2d5/tests/helpers.h000066400000000000000000000074701211110745500211720ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #ifndef __NABE_TEST_HELPERS_H #define __NABE_TEST_HELPERS_H #include "nabo/nabo.h" #include #include #include using namespace std; using namespace Nabo; template typename NearestNeighbourSearch::Matrix load(const char *fileName) { typedef typename NearestNeighbourSearch::Matrix Matrix; ifstream ifs(fileName); if (!ifs.good()) { cerr << "Cannot open file "<< fileName << endl; exit(1); } vector data; int dim(0); bool firstLine(true); while (!ifs.eof()) { char line[1024]; ifs.getline(line, sizeof(line)); line[sizeof(line)-1] = 0; char *token = strtok(line, " \t,;"); while (token) { if (firstLine) ++dim; data.push_back(atof(token)); token = strtok(NULL, " \t,;"); // FIXME: non reentrant, use strtok_r } firstLine = false; } return Matrix::Map(&data[0], dim, data.size() / dim); } template typename NearestNeighbourSearch::Vector createQuery(const typename NearestNeighbourSearch::Matrix& d, const NearestNeighbourSearch& kdt, const int i, const int method) { typedef typename NearestNeighbourSearch::Vector VectorT; if (method < 0) { VectorT q = d.col(i % d.cols()); T absBound = 0; for (int j = 0; j < q.size(); ++j) absBound += kdt.maxBound(j) - kdt.minBound(j); absBound /= (-method); // divided by -method #ifdef EIGEN3_API if (i < d.cols()) q.array() += absBound; else q.array() -= absBound; #else // EIGEN3_API if (i < d.cols()) q.cwise() += absBound; else q.cwise() -= absBound; #endif // EIGEN3_API return q; } else { VectorT q(kdt.minBound.size()); for (int j = 0; j < q.size(); ++j) q(j) = kdt.minBound(j) + T(rand()) * (kdt.maxBound(j) - kdt.minBound(j)) / T(RAND_MAX); return q; } } template typename NearestNeighbourSearch::Matrix createQuery(const typename NearestNeighbourSearch::Matrix& d, const int itCount, const int method) { typedef typename NearestNeighbourSearch::Matrix MatrixT; typedef Nabo::NearestNeighbourSearch NNS; NNS* nns = NNS::create(d, d.rows(), typename NNS::SearchType(0)); MatrixT q(d.rows(), itCount); for (int i = 0; i < itCount; ++i) q.col(i) = createQuery(d, *nns, i, method); delete nns; return q; } #endif // __NABE_TEST_HELPERS_H norlab-ulaval-libnabo-534c2d5/tests/knnbench.cpp000066400000000000000000000355771211110745500216620ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ // currently disable FLANN #undef HAVE_FLANN #include "nabo/nabo.h" #include "experimental/nabo_experimental.h" #include "helpers.h" #ifdef HAVE_ANN #include "ANN.h" #endif // HAVE_ANN #ifdef HAVE_FLANN #include "flann/flann.hpp" #endif // HAVE_FLANN #include #include #include #ifdef _POSIX_TIMERS namespace boost { /* High-precision timer class, using gettimeofday(). The interface is a subset of the one boost::timer provides, but the implementation is much more precise on systems where clock() has low precision, such as glibc. */ struct timer { typedef unsigned long long Time; timer():_start_time(curTime()){ } void restart() { _start_time = curTime(); } double elapsed() const // return elapsed time in seconds { return double(curTime() - _start_time) / double(1000000000); } private: Time curTime() const { struct timespec ts; clock_gettime(CLOCK_REALTIME, &ts); return Time(ts.tv_sec) * Time(1000000000) + Time(ts.tv_nsec); } Time _start_time; }; } #else // _POSIX_TIMERS #include #endif // _POSIX_TIMERS using namespace std; using namespace Nabo; typedef Nabo::NearestNeighbourSearch::Matrix MatrixD; typedef Nabo::NearestNeighbourSearch::Vector VectorD; typedef Nabo::NearestNeighbourSearch::Index IndexD; typedef Nabo::NearestNeighbourSearch::IndexVector IndexVectorD; typedef Nabo::NearestNeighbourSearch::Matrix MatrixF; typedef Nabo::NearestNeighbourSearch::Vector VectorF; typedef Nabo::NearestNeighbourSearch::Index IndexF; typedef Nabo::NearestNeighbourSearch::IndexVector IndexVectorF; typedef Nabo::BruteForceSearch BFSD; typedef Nabo::BruteForceSearch BFSF; // typedef Nabo::KDTreeBalancedPtInNodesPQ KDTD1; // typedef Nabo::KDTreeBalancedPtInNodesStack KDTD2; // struct KDTD3: public Nabo::KDTreeBalancedPtInLeavesStack // { // KDTD3(const Matrix& cloud): // Nabo::KDTreeBalancedPtInLeavesStack(cloud, true) // {} // }; // struct KDTD4: public Nabo::KDTreeBalancedPtInLeavesStack // { // KDTD4(const Matrix& cloud): // Nabo::KDTreeBalancedPtInLeavesStack(cloud, false) // {} // }; // typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStack> KDTD5A; // typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStack> KDTD5B; // typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt> KDTD5OB; // typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt> KDTD5OA; // typedef Nabo::KDTreeUnbalancedPtInLeavesExplicitBoundsStack KDTD6; struct BenchResult { double creationDuration; double executionDuration; double visitCount; double totalCount; BenchResult(): creationDuration(0), executionDuration(0), visitCount(0), totalCount(0) {} void operator +=(const BenchResult& that) { creationDuration += that.creationDuration; executionDuration += that.executionDuration; visitCount += that.visitCount; totalCount += that.totalCount; } void operator /=(const double factor) { creationDuration /= factor; executionDuration /= factor; visitCount /= factor; totalCount /= factor; } }; typedef vector BenchResults; // template // BenchResult doBench(const Matrix& d, const Matrix& q, const Index K, const int itCount) // { // BenchResult result; // boost::timer t; // T nns(d); // result.creationDuration = t.elapsed(); // // t.restart(); // nns.knnM(q, K, 0, 0); // result.executionDuration = t.elapsed(); // // result.visitCount = double(nns.getStatistics().totalVisitCount); // result.totalCount = double(itCount) * double(d.cols()); // // return result; // } template BenchResult doBenchType(const typename NearestNeighbourSearch::SearchType type, const unsigned creationOptionFlags, const typename NearestNeighbourSearch::Matrix& d, const typename NearestNeighbourSearch::Matrix& q, const int K, const int itCount, const int searchCount) { typedef NearestNeighbourSearch nnsT; typedef typename NearestNeighbourSearch::Matrix Matrix; typedef typename NearestNeighbourSearch::IndexMatrix IndexMatrix; BenchResult result; boost::timer t; nnsT* nns(nnsT::create(d, d.rows(), type, creationOptionFlags)); result.creationDuration = t.elapsed(); for (int s = 0; s < searchCount; ++s) { t.restart(); IndexMatrix indices(K, q.cols()); Matrix dists2(K, q.cols()); const unsigned long visitCount = nns->knn(q, indices, dists2, K, 0, 0); result.executionDuration += t.elapsed(); result.visitCount += double(visitCount); } result.executionDuration /= double(searchCount); result.visitCount /= double(searchCount); delete nns; result.totalCount = double(q.cols()) * double(d.cols()); return result; } #ifdef HAVE_ANN BenchResult doBenchANNStack(const MatrixD& d, const MatrixD& q, const int K, const int itCount, const int searchCount) { BenchResult result; boost::timer t; const int ptCount(d.cols()); const double **pa = new const double *[d.cols()]; for (int i = 0; i < ptCount; ++i) pa[i] = &d.coeff(0, i); ANNkd_tree* ann_kdt = new ANNkd_tree(const_cast(pa), ptCount, d.rows(), 8); result.creationDuration = t.elapsed(); for (int s = 0; s < searchCount; ++s) { t.restart(); ANNidx nnIdx[K]; ANNdist dists[K]; for (int i = 0; i < itCount; ++i) { const VectorD& tq(q.col(i)); ANNpoint queryPt(const_cast(&tq.coeff(0))); ann_kdt->annkSearch( // search queryPt, // query point K, // number of near neighbours nnIdx, // nearest neighbours (returned) dists, // distance (returned) 0); // error bound } result.executionDuration += t.elapsed(); } result.executionDuration /= double(searchCount); return result; } BenchResult doBenchANNPriority(const MatrixD& d, const MatrixD& q, const int K, const int itCount, const int searchCount) { BenchResult result; boost::timer t; const int ptCount(d.cols()); const double **pa = new const double *[d.cols()]; for (int i = 0; i < ptCount; ++i) pa[i] = &d.coeff(0, i); ANNkd_tree* ann_kdt = new ANNkd_tree(const_cast(pa), ptCount, d.rows(), 8); result.creationDuration = t.elapsed(); for (int s = 0; s < searchCount; ++s) { t.restart(); ANNidx nnIdx[K]; ANNdist dists[K]; for (int i = 0; i < itCount; ++i) { const VectorD& tq(q.col(i)); ANNpoint queryPt(const_cast(&tq.coeff(0))); ann_kdt->annkPriSearch( // search queryPt, // query point K, // number of near neighbours nnIdx, // nearest neighbours (returned) dists, // distance (returned) 0); // error bound } result.executionDuration += t.elapsed(); } result.executionDuration /= double(searchCount); return result; } #endif // HAVE_ANN #ifdef HAVE_FLANN template BenchResult doBenchFLANN(const Matrix& d, const Matrix& q, const Index K, const int itCount) { BenchResult result; const int dimCount(d.rows()); const int dPtCount(d.cols()); const int qPtCount(itCount); flann::Matrix dataset(new T[dPtCount*dimCount], dPtCount, dimCount); for (int point = 0; point < dPtCount; ++point) for (int dim = 0; dim < dimCount; ++dim) dataset[point][dim] = d(dim, point); flann::Matrix query(new T[qPtCount*dimCount], qPtCount, dimCount); for (int point = 0; point < qPtCount; ++point) for (int dim = 0; dim < dimCount; ++dim) query[point][dim] = q(dim, point); flann::Matrix indices(new int[query.rows*K], query.rows, K); flann::Matrix dists(new float[query.rows*K], query.rows, K); // construct an randomized kd-tree index using 4 kd-trees boost::timer t; flann::Index index(dataset, flann::KDTreeIndexParams(4) /*flann::AutotunedIndexParams(0.9)*/); // exact search index.buildIndex(); result.creationDuration = t.elapsed(); t.restart(); // do a knn search, using 128 checks index.knnSearch(query, indices, dists, int(K), flann::SearchParams(128)); // last parameter ignored because of autotuned result.executionDuration = t.elapsed(); dataset.free(); query.free(); indices.free(); dists.free(); return result; } #endif // HAVE_FLANN int main(int argc, char* argv[]) { if (argc != 6) { cerr << "Usage " << argv[0] << " DATA K METHOD RUN_COUNT SEARCH_COUNT" << endl; return 1; } const MatrixD dD(load(argv[1])); const MatrixF dF(load(argv[1])); const int K(atoi(argv[2])); const int method(atoi(argv[3])); const int itCount(method >= 0 ? method : dD.cols() * 2); const int runCount(atoi(argv[4])); const int searchCount(atoi(argv[5])); // compare KDTree with brute force search if (K >= dD.cols()) { cerr << "Requested more nearest neighbour than points in the data set" << endl; return 2; } // create queries MatrixD qD(createQuery(dD, itCount, method)); MatrixF qF(createQuery(dF, itCount, method)); const char* benchLabels[] = { //doBench("Nabo, pt in nodes, priority, balance variance", //doBench("Nabo, pt in nodes, stack, balance variance", //doBench("Nabo, balanced, stack, pt in leaves only, balance variance", //"Nabo, balanced, stack, pt in leaves only, balance cell aspect ratio", //"Nabo, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap", //"Nabo, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap", "Nabo, double, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap, opt", "Nabo, double, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt", "Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap, opt", "Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt", "Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt, stats", #ifdef HAVE_OPENCL "Nabo, float, OpenCL, GPU, balanced, points in nodes, stack, implicit bounds, balance aspect ratio, stats", "Nabo, float, OpenCL, GPU, balanced, points in leaves, stack, implicit bounds, balance aspect ratio, stats", //"Nabo, float, OpenCL, GPU, brute force", #endif // HAVE_OPENCL //"Nabo, unbalanced, points in leaves, stack, explicit bounds, ANN_KD_SL_MIDPT", #ifdef HAVE_ANN "ANN stack, double", //"ANN priority", #endif // HAVE_ANN #ifdef HAVE_FLANN "FLANN, double", "FLANN, float", #endif // HAVE_FLANN }; // do bench themselves, accumulate over several times size_t benchCount(sizeof(benchLabels) / sizeof(const char *)); cout << "Doing " << benchCount << " different benches " << runCount << " times, with " << searchCount << " query per run" << endl; BenchResults results(benchCount); for (int run = 0; run < runCount; ++run) { size_t i = 0; //results.at(i++) += doBench(d, q, K, itCount, searchCount); //results.at(i++) += doBench(d, q, K, itCount, searchCount); //results.at(i++) += doBench(d, q, K, itCount, searchCount); //results.at(i++) += doBench(d, q, K, itCount, searchCount); //results.at(i++) += doBench(d, q, K, itCount, searchCount); //results.at(i++) += doBench(d, q, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchD::KDTREE_LINEAR_HEAP, 0, dD, qD, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchD::KDTREE_TREE_HEAP, 0, dD, qD, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchF::KDTREE_LINEAR_HEAP, 0, dF, qF, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchF::KDTREE_TREE_HEAP, 0, dF, qF, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchF::KDTREE_TREE_HEAP, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount); #ifdef HAVE_OPENCL results.at(i++) += doBenchType(NNSearchF::KDTREE_CL_PT_IN_NODES, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount); results.at(i++) += doBenchType(NNSearchF::KDTREE_CL_PT_IN_LEAVES, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount); //results.at(i++) += doBenchType(NNSearchF::BRUTE_FORCE_CL, dF, qF, K, itCount, searchCount); #endif // HAVE_OPENCL #ifdef HAVE_ANN results.at(i++) += doBenchANNStack(dD, qD, K, itCount, searchCount); //results.at(i++) += doBenchANNPriority(d, q, K, itCount); #endif // HAVE_ANN #ifdef HAVE_FLANN results.at(i++) += doBenchFLANN(dD, qD, K, itCount, searchCount); results.at(i++) += doBenchFLANN(dF, qF, K, itCount, searchCount); #endif // HAVE_FLANN } // print results cout << "Showing average over " << runCount << " runs\n\n"; for (size_t i = 0; i < benchCount; ++i) { results[i] /= double(runCount); cout << "Method " << benchLabels[i] << ":\n"; cout << " creation duration: " << results[i].creationDuration << "\n"; cout << " execution duration: " << results[i].executionDuration << "\n"; if (results[i].totalCount != 0) { cout << " visit count: " << results[i].visitCount << "\n"; cout << " total count: " << results[i].totalCount << "\n"; cout << " precentage visit: " << (results[i].visitCount * 100.) / results[i].totalCount << "\n"; } else cout << " no stats for visits\n"; cout << endl; } return 0; }norlab-ulaval-libnabo-534c2d5/tests/knnbucketsize.cpp000066400000000000000000000102371211110745500227350ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo/nabo.h" #include "helpers.h" #include #include #include #ifdef _POSIX_TIMERS namespace boost { /* High-precision timer class, using gettimeofday(). The interface is a subset of the one boost::timer provides, but the implementation is much more precise on systems where clock() has low precision, such as glibc. */ struct timer { typedef unsigned long long Time; timer():_start_time(curTime()){ } void restart() { _start_time = curTime(); } double elapsed() const // return elapsed time in seconds { return double(curTime() - _start_time) / double(1000000000); } private: Time curTime() const { struct timespec ts; clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &ts); return Time(ts.tv_sec) * Time(1000000000) + Time(ts.tv_nsec); } Time _start_time; }; } #else // _POSIX_TIMERS #include #endif // _POSIX_TIMERS using namespace std; using namespace Nabo; template void doTestEpsilon(const char *fileName, const int K, const int method, const int searchCount) { typedef Nabo::NearestNeighbourSearch NNS; typedef typename NNS::Matrix Matrix; typedef typename NNS::Vector Vector; typedef typename NNS::Index Index; typedef typename NNS::IndexVector IndexVector; typedef typename NNS::IndexMatrix IndexMatrix; // check if file is ok const Matrix d(load(fileName)); if (K >= d.cols()) { cerr << "Requested more nearest neighbour than points in the data set" << endl; exit(2); } // check methods together const int itCount(method >= 0 ? method : d.cols() * 2); // create big query const Matrix q(createQuery(d, itCount, method)); IndexMatrix indexes_bf(K, q.cols()); Matrix dists2_bf(K, q.cols()); for (unsigned bucketSize = 2; bucketSize < 40; ++bucketSize) { Parameters additionalParameters("bucketSize", unsigned(bucketSize)); NNS* nns = NNS::createKDTreeLinearHeap(d, std::numeric_limits::max(), 0, additionalParameters); double duration(0); for (int s = 0; s < searchCount; ++s) { boost::timer t; nns->knn(q, indexes_bf, dists2_bf, K, 0, NNS::ALLOW_SELF_MATCH); duration += t.elapsed(); } cout << bucketSize << " " << duration/double(searchCount) << endl; delete nns; } } int main(int argc, char* argv[]) { if (argc != 5) { cerr << "Usage " << argv[0] << " DATA K METHOD SEARCH_COUNT" << endl; return 1; } const int K(atoi(argv[2])); const int method(atoi(argv[3])); const int searchCount(atoi(argv[4])); cout << "bucketSize average_duration\n"; doTestEpsilon(argv[1], K, method, searchCount); return 0; }norlab-ulaval-libnabo-534c2d5/tests/knnepsilon.cpp000066400000000000000000000104151211110745500222340ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo/nabo.h" #include "helpers.h" #include #include #include #ifdef _POSIX_TIMERS namespace boost { /* High-precision timer class, using gettimeofday(). The interface is a subset of the one boost::timer provides, but the implementation is much more precise on systems where clock() has low precision, such as glibc. */ struct timer { typedef unsigned long long Time; timer():_start_time(curTime()){ } void restart() { _start_time = curTime(); } double elapsed() const // return elapsed time in seconds { return double(curTime() - _start_time) / double(1000000000); } private: Time curTime() const { struct timespec ts; clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &ts); return Time(ts.tv_sec) * Time(1000000000) + Time(ts.tv_nsec); } Time _start_time; }; } #else // _POSIX_TIMERS #include #endif // _POSIX_TIMERS using namespace std; using namespace Nabo; template void doTestEpsilon(const char *fileName, const int K, const int method, const int searchCount) { typedef Nabo::NearestNeighbourSearch NNS; typedef typename NNS::Matrix Matrix; typedef typename NNS::Vector Vector; typedef typename NNS::Index Index; typedef typename NNS::IndexVector IndexVector; typedef typename NNS::IndexMatrix IndexMatrix; // check if file is ok const Matrix d(load(fileName)); if (K >= d.cols()) { cerr << "Requested more nearest neighbour than points in the data set" << endl; exit(2); } // check methods together const int itCount(method >= 0 ? method : d.cols() * 2); // create big query const Matrix q(createQuery(d, itCount, method)); IndexMatrix indexes_bf(K, q.cols()); Matrix dists2_bf(K, q.cols()); NNS* nns = NNS::createKDTreeLinearHeap(d, std::numeric_limits::max(), NNS::TOUCH_STATISTICS); for (T epsilon = 0; epsilon < 2; epsilon += 0.01) { double duration(0); double touchStats(0); for (int s = 0; s < searchCount; ++s) { boost::timer t; touchStats += nns->knn(q, indexes_bf, dists2_bf, K, epsilon, NNS::ALLOW_SELF_MATCH); duration += t.elapsed(); } cout << epsilon << " " << duration/double(searchCount) << " " << touchStats/double(searchCount) << endl; } delete nns; } int main(int argc, char* argv[]) { if (argc != 5) { cerr << "Usage " << argv[0] << " DATA K METHOD SEARCH_COUNT" << endl; return 1; } const int K(atoi(argv[2])); const int method(atoi(argv[3])); const int searchCount(atoi(argv[4])); //cout << "Float: (epsilon, average duration)\n"; //doTestEpsilon(argv[1], K, method, searchCount); cout << "epsilon average_duration search_count\n"; doTestEpsilon(argv[1], K, method, searchCount); return 0; }norlab-ulaval-libnabo-534c2d5/tests/knnshow.cpp000066400000000000000000000134431211110745500215470ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo/nabo.h" #include #include #include using namespace std; using namespace Nabo; template typename NearestNeighbourSearch::Matrix load(const char *fileName) { typedef typename NearestNeighbourSearch::Matrix Matrix; ifstream ifs(fileName); if (!ifs.good()) throw runtime_error(string("Cannot open file ") + fileName); vector data; int dim(0); bool firstLine(true); while (!ifs.eof()) { char line[1024]; ifs.getline(line, sizeof(line)); line[sizeof(line)-1] = 0; char *token = strtok(line, " \t,;"); while (token) { if (firstLine) ++dim; data.push_back(atof(token)); //cout << atof(token) << " "; token = strtok(NULL, " \t,;"); // FIXME: non reentrant, use strtok_r } firstLine = false; } return Matrix::Map(&data[0], dim, data.size() / dim); } template void dumpCoordinateForSVG(const typename NearestNeighbourSearch::Vector coord, const float zoom = 1, const float ptSize = 1, const char* style = "stroke=\"black\" fill=\"red\"") { if (coord.size() == 2) cout << "" << endl; else assert(false); } int main(int argc, char* argv[]) { typedef Nabo::NearestNeighbourSearch::Matrix Matrix; typedef Nabo::NearestNeighbourSearch::Vector Vector; typedef Nabo::NearestNeighbourSearch::Index Index; typedef Nabo::NearestNeighbourSearch::Indexes Indexes; typedef Nabo::BruteForceSearch BFSF; typedef Nabo::KDTree KDTF; if (argc != 2) { cerr << "Usage " << argv[0] << " DATA" << endl; return 1; } Matrix d(load(argv[1])); BFSF bfs(d); KDTF kdt(d); const Index K(10); // uncomment to compare KDTree with brute force search if (K >= d.size()) return 2; const int itCount(100); for (int i = 0; i < itCount; ++i) { //Vector q(bfs.minBound.size()); //for (int i = 0; i < q.size(); ++i) // q(i) = bfs.minBound(i) + float(rand()) * (bfs.maxBound(i) - bfs.minBound(i)) / float(RAND_MAX); Vector q(d.col(rand() % d.cols())); q.cwise() += 0.01; //cerr << "bfs:\n"; Indexes indexes_bf(bfs.knn(q, K, false)); //cerr << "kdt:\n"; Indexes indexes_kdtree(kdt.knn(q, K, false)); //cout << indexes_bf.size() << " " << indexes_kdtree.size() << " " << K << endl; if (ndexes_bf.size() != indexes_kdtree.size()) return 2; assert(indexes_bf.size() == indexes_kdtree.size()); assert(indexes_bf.size() == K); //cerr << "\nquery:\n" << q << "\n\n"; for (size_t j = 0; j < K; ++j) { Vector pbf(d.col(indexes_bf[j])); Vector pkdtree(d.col(indexes_kdtree[j])); //cerr << "index " << j << ": " << indexes_bf[j] << ", " << indexes_kdtree[j] << "\n"; //cerr << "point " << j << ": " << "\nbf:\n" << pbf << "\nkdtree:\n" << pkdtree << "\n\n"; assert(indexes_bf[j] == indexes_kdtree[j]); assert(dist2(pbf, pkdtree) < numeric_limits::epsilon()); } } cerr << "stats kdtree: " << kdt.getStatistics().totalVisitCount << " on " << itCount * d.cols() << " (" << double(100 * kdt.getStatistics().totalVisitCount) / double(itCount * d.cols()) << " %" << ")" << endl; /* // uncomment to randomly get a point and find its minimum cout << "" << endl; cout << "" << endl; cout << "" << endl; srand(time(0)); for (int i = 0; i < d.cols(); ++i) dumpCoordinateForSVG(d.col(i), 100, 1); Vector q(bfs.minBound.size()); for (int i = 0; i < q.size(); ++i) q(i) = bfs.minBound(i) + float(rand()) * (bfs.maxBound(i) - bfs.minBound(i)) / float(RAND_MAX); Indexes indexes_bf(bfs.knn(q, K, false)); for (size_t i = 0; i < K; ++i) dumpCoordinateForSVG(d.col(indexes_bf[i]), 100, 1, "stroke=\"black\" fill=\"green\""); dumpCoordinateForSVG(q, 100, 1, "stroke=\"black\" fill=\"blue\""); cout << "" << endl; */ //cout << "Average KDTree visit count: " << double(totKDTreeVisitCount) * 100. / double(itCount * d.cols()) << " %" << endl; return 0; }norlab-ulaval-libnabo-534c2d5/tests/knnvalidate.cpp000066400000000000000000000164611211110745500223630ustar00rootroot00000000000000/* Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland You can contact the author at All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "nabo/nabo.h" #include "helpers.h" //#include "experimental/nabo_experimental.h" #include #include #include using namespace std; using namespace Nabo; template void validate(const char *fileName, const int K, const int method, const T maxRadius) { typedef Nabo::NearestNeighbourSearch NNS; typedef vector NNSV; typedef typename NNS::Matrix Matrix; typedef typename NNS::Vector Vector; typedef typename NNS::Index Index; typedef typename NNS::IndexVector IndexVector; typedef typename NNS::IndexMatrix IndexMatrix; // check if file is ok const Matrix d(load(fileName)); if (K >= d.cols()) { cerr << "Requested more nearest neighbour than points in the data set" << endl; exit(2); } // create different methods NNSV nnss; unsigned searchTypeCount(NNS::SEARCH_TYPE_COUNT); #ifndef HAVE_OPENCL searchTypeCount -= 3; #endif // HAVE_OPENCL for (unsigned i = 0; i < searchTypeCount; ++i) nnss.push_back(NNS::create(d, d.rows(), typename NNS::SearchType(i))); //nnss.push_back(new KDTreeBalancedPtInLeavesStack(d, false)); // check methods together const int itCount(method != -1 ? method : d.cols() * 2); /* // element-by-element search for (int i = 0; i < itCount; ++i) { const Vector q(createQuery(d, *nnss[0], i, method)); const IndexVector indexes_bf(nnss[0]->knn(q, K, 0, NNS::SORT_RESULTS)); for (size_t j = 1; j < nnss.size(); ++j) { const IndexVector indexes_kdtree(nnss[j]->knn(q, K, 0, NNS::SORT_RESULTS)); if (indexes_bf.size() != K) { cerr << "Different number of points found between brute force and request" << endl; exit(3); } if (indexes_bf.size() != indexes_kdtree.size()) { cerr << "Different number of points found between brute force and NNS type "<< j << endl; exit(3); } for (size_t k = 0; k < size_t(K); ++k) { Vector pbf(d.col(indexes_bf[k])); //cerr << indexes_kdtree[k] << endl; Vector pkdtree(d.col(indexes_kdtree[k])); if (fabsf((pbf-q).squaredNorm() - (pkdtree-q).squaredNorm()) >= numeric_limits::epsilon()) { cerr << "Method " << j << ", cloud point " << i << ", neighbour " << k << " of " << K << " is different between bf and kdtree (dist " << (pbf-pkdtree).norm() << ")\n"; cerr << "* query:\n"; cerr << q << "\n"; cerr << "* indexes " << indexes_bf[k] << " (bf) vs " << indexes_kdtree[k] << " (kdtree)\n"; cerr << "* coordinates:\n"; cerr << "bf: (dist " << (pbf-q).norm() << ")\n"; cerr << pbf << "\n"; cerr << "kdtree (dist " << (pkdtree-q).norm() << ")\n"; cerr << pkdtree << endl; exit(4); } } } } */ // create big query // check all-in-one query Matrix q(createQuery(d, itCount, method)); IndexMatrix indexes_bf(K, q.cols()); Matrix dists2_bf(K, q.cols()); nnss[0]->knn(q, indexes_bf, dists2_bf, K, 0, NNS::SORT_RESULTS, maxRadius); assert(indexes_bf.cols() == q.cols()); for (size_t j = 1; j < nnss.size(); ++j) { IndexMatrix indexes_kdtree(K, q.cols()); Matrix dists2_kdtree(K, q.cols()); nnss[j]->knn(q, indexes_kdtree, dists2_kdtree, K, 0, NNS::SORT_RESULTS, maxRadius); if (indexes_bf.rows() != K) { cerr << "Different number of points found between brute force and request" << endl; exit(3); } if (indexes_bf.cols() != indexes_kdtree.cols()) { cerr << "Different number of points found between brute force and NNS type "<< j << endl; exit(3); } for (int i = 0; i < q.cols(); ++i) { for (size_t k = 0; k < size_t(K); ++k) { if (dists2_bf(k,i) == numeric_limits::infinity()) continue; const int pbfi(indexes_bf(k,i)); const Vector pbf(d.col(pbfi)); const int pkdt(indexes_kdtree(k,i)); if (pkdt < 0 || pkdt >= d.cols()) { cerr << "Method " << j << ", query point " << i << ", neighbour " << k << " of " << K << " has invalid index " << pkdt << " out of range [0:" << d.cols() << "[" << endl; exit(4); } const Vector pkdtree(d.col(pkdt)); const Vector pq(q.col(i)); const float distDiff(fabsf((pbf-pq).squaredNorm() - (pkdtree-pq).squaredNorm())); if (distDiff > numeric_limits::epsilon()) { cerr << "Method " << j << ", query point " << i << ", neighbour " << k << " of " << K << " is different between bf and kdtree (dist2 " << distDiff << ")\n"; cerr << "* query point:\n"; cerr << pq << "\n"; cerr << "* indexes " << pbfi << " (bf) vs " << pkdt << " (kdtree)\n"; cerr << "* coordinates:\n"; cerr << "bf: (dist " << (pbf-pq).norm() << ")\n"; cerr << pbf << "\n"; cerr << "kdtree (dist " << (pkdtree-pq).norm() << ")\n"; cerr << pkdtree << endl; cerr << "* bf neighbours:\n"; for (int l = 0; l < K; ++l) cerr << indexes_bf(l,i) << " (dist " << (d.col(indexes_bf(l,i))-pq).norm() << ")\n"; cerr << "* kdtree neighbours:\n"; for (int l = 0; l < K; ++l) cerr << indexes_kdtree(l,i) << " (dist " << (d.col(indexes_kdtree(l,i))-pq).norm() << ")\n"; exit(4); } } } } // cout << "\tstats kdtree: " // << kdt.getStatistics().totalVisitCount << " on " // << (long long)(itCount) * (long long)(d.cols()) << " (" // << (100. * double(kdt.getStatistics().totalVisitCount)) / (double(itCount) * double(d.cols())) << " %" // << ")\n" << endl; // delete searches for (typename NNSV::iterator it(nnss.begin()); it != nnss.end(); ++it) delete (*it); } int main(int argc, char* argv[]) { if (argc < 4) { cerr << "Usage " << argv[0] << " DATA K METHOD [MAX_RADIUS]" << endl; return 1; } const int K(atoi(argv[2])); const int method(atoi(argv[3])); const float maxRadius(argc >= 5 ? float(atof(argv[4])) : numeric_limits::infinity()); validate(argv[1], K, method, maxRadius); //validate(argv[1], K, method); return 0; }