├── CodeForLineMatching ├── CMakeLists.txt ├── Main(fitPlane).cpp ├── Main.cpp ├── build │ ├── CMakeCache.txt │ ├── CMakeFiles │ │ ├── 3.10.2 │ │ │ ├── CMakeCCompiler.cmake │ │ │ ├── CMakeCXXCompiler.cmake │ │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ │ ├── CMakeSystem.cmake │ │ │ ├── CompilerIdC │ │ │ │ ├── CMakeCCompilerId.c │ │ │ │ └── a.out │ │ │ └── CompilerIdCXX │ │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ │ └── a.out │ │ ├── CMakeDirectoryInformation.cmake │ │ ├── CMakeError.log │ │ ├── CMakeOutput.log │ │ ├── LineMatchingLib.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── linedetection.cpp.o │ │ │ ├── linedetector.cpp.o │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── Makefile.cmake │ │ ├── Makefile2 │ │ ├── TargetDirectories.txt │ │ ├── cmake.check_cache │ │ ├── feature_tests.bin │ │ ├── feature_tests.c │ │ ├── feature_tests.cxx │ │ ├── lineMatching.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── Main.cpp.o │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ └── progress.marks │ ├── Makefile │ ├── Plane1.png │ ├── Plane10.png │ ├── Plane11.png │ ├── Plane2.png │ ├── Plane3.png │ ├── Plane4.png │ ├── Plane5.png │ ├── Plane6.png │ ├── Plane7.png │ ├── Plane8.png │ ├── Plane9.png │ ├── cmake_install.cmake │ ├── eigenVec.txt │ ├── info.txt │ ├── libLineMatchingLib.so │ ├── lineMatching │ ├── outleft.png │ └── outright.png ├── image │ ├── LBDout.png │ ├── LBDout1.png │ ├── LBDout2.png │ ├── left1.png │ ├── left5.png │ ├── out.png │ ├── out1.png │ ├── out2.png │ ├── right1.png │ └── right5.png ├── linedetection.cpp ├── linedetector.cpp ├── linedetector.h ├── readMe.txt ├── removed │ ├── 0.cpp │ ├── Demo_LBD_Multi.cpp │ ├── Demo_LBD_single.cpp │ ├── EDLineDetector.cpp │ ├── EDLineDetector.hh │ ├── LineDescriptor.cpp │ ├── LineDescriptor.hh │ ├── LineMatchingAlgorithm.hpp │ ├── LineStructure.hh │ ├── PairwiseLineMatching.cpp │ ├── PairwiseLineMatching.hh │ ├── Untitled1.cpp │ ├── Untitled1.exe │ ├── m.cpp │ └── test.hpp └── system.h ├── Multiple Homography Estimation via Stereo Line Matching for Textureless Indoor Scenes.pdf └── README.md /CodeForLineMatching/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 2.8) 2 | 3 | find_package(OpenCV PATHS "/usr/local/share/OpenCV") 4 | find_package(OpenGL REQUIRED) 5 | find_package(GLUT REQUIRED) 6 | include_directories(${OpenCV_INCLUDE_DIRS}) 7 | 8 | if(COMMAND cmake_policy) 9 | cmake_policy(SET CMP0003 NEW) 10 | endif(COMMAND cmake_policy) 11 | 12 | SET(BUILD_SHARED_LIBS ON) 13 | 14 | SET(ARPACK_INCLUDE_DIR "/usr/include/arpack++") 15 | SET(ARPACK_LINK_DIR "/usr/lib") 16 | set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} 17 | "${PROJECT_SOURCE_DIR}/cmake_modules/") 18 | 19 | # Default locations to search for on various platforms. 20 | #LIST(APPEND SEARCH_LIBS /usr/lib) 21 | #LIST(APPEND SEARCH_LIBS /usr/local/lib) 22 | #LIST(APPEND SEARCH_LIBS /opt/local/lib) 23 | 24 | #LIST(APPEND SEARCH_HEADERS /usr/include) 25 | #LIST(APPEND SEARCH_HEADERS /usr/local/include) 26 | #LIST(APPEND SEARCH_HEADERS /opt/local/include) 27 | 28 | #message("-- Check for Google Flags") 29 | #find_library(GFLAGS_LIB NAMES gflags PATHS ${SEARCH_LIBS}) 30 | #find_path(GFLAGS_INCLUDE NAMES gflags/gflags.h PATHS ${SEARCH_HEADERS}) 31 | 32 | #set(GOOGLE_LIBRARIES ${GFLAGS_LIB}) 33 | 34 | include_directories(${linedetection_SOURCE_DIR}/include) 35 | include_directories(library ${OPENGL_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS}) 36 | #include_directories(${GFLAGS_INCLUDE}) 37 | #include_directories(.) 38 | INCLUDE_DIRECTORIES(${ARPACK_INCLUDE_DIR} /usr/local/include/opencv) 39 | LINK_DIRECTORIES(${ARPACK_LINK_DIR} /usr/local/lib) 40 | SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -O3") #-fno-omit-frame-pointer") 41 | SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=c++11 -O3") #-fno-omit-frame-pointer") 42 | 43 | # source files of library "LineMatchingLib" to be created 44 | SET(LineMatchingLib_SRCS 45 | linedetection.cpp 46 | linedetector.cpp 47 | ) 48 | # header files to be installed 49 | SET(LineMatchingLib_HEADER 50 | linedetector.h 51 | system.h 52 | ) 53 | 54 | ADD_LIBRARY(LineMatchingLib 55 | ${LineMatchingLib_SRCS} 56 | ${LineMatchingLib_HEADER}) 57 | TARGET_LINK_LIBRARIES(LineMatchingLib ${OpenCV_LIBS} arpack /usr/lib/x86_64-linux-gnu/libsuperlu.so arpack++) 58 | 59 | set(EXECUTABLE_OUTPUT_PATH ${CMAKE_BINARY_DIR}) 60 | 61 | add_executable(lineMatching Main.cpp ) 62 | target_link_libraries(lineMatching LineMatchingLib ${OpenCV_LIBS} ${OPENGL_LIBRARIES} ${GLUT_glut_LIBRARY}) 63 | -------------------------------------------------------------------------------- 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if defined(_MSC_VER) 157 | # define SIMULATE_ID "MSVC" 158 | # endif 159 | # define COMPILER_VERSION_MAJOR DEC(__clang_major__) 160 | # define COMPILER_VERSION_MINOR DEC(__clang_minor__) 161 | # define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) 162 | # if defined(_MSC_VER) 163 | /* _MSC_VER = VVRR */ 164 | # define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) 165 | # define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) 166 | # endif 167 | # define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) 168 | 169 | #elif defined(__clang__) 170 | # define COMPILER_ID "Clang" 171 | # if defined(_MSC_VER) 172 | # define SIMULATE_ID "MSVC" 173 | # endif 174 | # define COMPILER_VERSION_MAJOR DEC(__clang_major__) 175 | # define COMPILER_VERSION_MINOR DEC(__clang_minor__) 176 | # define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) 177 | # if defined(_MSC_VER) 178 | /* _MSC_VER = VVRR */ 179 | # define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) 180 | # define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) 181 | # endif 182 | 183 | #elif defined(__GNUC__) || defined(__GNUG__) 184 | # define COMPILER_ID "GNU" 185 | # if defined(__GNUC__) 186 | # define COMPILER_VERSION_MAJOR DEC(__GNUC__) 187 | # else 188 | # define COMPILER_VERSION_MAJOR DEC(__GNUG__) 189 | # endif 190 | # if defined(__GNUC_MINOR__) 191 | # define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) 192 | # endif 193 | # if defined(__GNUC_PATCHLEVEL__) 194 | # define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) 195 | # endif 196 | 197 | #elif defined(_MSC_VER) 198 | # define COMPILER_ID "MSVC" 199 | /* _MSC_VER = VVRR */ 200 | # define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) 201 | # define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) 202 | # if defined(_MSC_FULL_VER) 203 | # if _MSC_VER >= 1400 204 | /* _MSC_FULL_VER = VVRRPPPPP */ 205 | # define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) 206 | # else 207 | /* _MSC_FULL_VER = VVRRPPPP */ 208 | # define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) 209 | # endif 210 | # endif 211 | # 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define COMPILER_ID "ARMCC" 235 | #if __ARMCC_VERSION >= 1000000 236 | /* __ARMCC_VERSION = VRRPPPP */ 237 | # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) 238 | # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) 239 | # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) 240 | #else 241 | /* __ARMCC_VERSION = VRPPPP */ 242 | # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) 243 | # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) 244 | # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) 245 | #endif 246 | 247 | 248 | #elif defined(_SGI_COMPILER_VERSION) || defined(_COMPILER_VERSION) 249 | # define COMPILER_ID "MIPSpro" 250 | # if defined(_SGI_COMPILER_VERSION) 251 | /* _SGI_COMPILER_VERSION = VRP */ 252 | # define COMPILER_VERSION_MAJOR DEC(_SGI_COMPILER_VERSION/100) 253 | # define COMPILER_VERSION_MINOR DEC(_SGI_COMPILER_VERSION/10 % 10) 254 | # define COMPILER_VERSION_PATCH DEC(_SGI_COMPILER_VERSION % 10) 255 | # else 256 | /* _COMPILER_VERSION = VRP */ 257 | # define COMPILER_VERSION_MAJOR DEC(_COMPILER_VERSION/100) 258 | # define COMPILER_VERSION_MINOR DEC(_COMPILER_VERSION/10 % 10) 259 | # define COMPILER_VERSION_PATCH DEC(_COMPILER_VERSION % 10) 260 | # endif 261 | 262 | 263 | /* These compilers are either not known or too old to define an 264 | identification macro. Try to identify the platform and guess that 265 | it is the native compiler. */ 266 | #elif defined(__sgi) 267 | # define COMPILER_ID "MIPSpro" 268 | 269 | #elif defined(__hpux) || defined(__hpua) 270 | # define COMPILER_ID "HP" 271 | 272 | #else /* unknown compiler */ 273 | # define COMPILER_ID "" 274 | #endif 275 | 276 | /* Construct the string literal in pieces to prevent the source from 277 | getting matched. Store it in a pointer rather than an array 278 | because some compilers will just produce instructions to fill the 279 | array rather than assigning a pointer to a static array. */ 280 | char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; 281 | #ifdef SIMULATE_ID 282 | char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; 283 | #endif 284 | 285 | #ifdef __QNXNTO__ 286 | char const* qnxnto = "INFO" ":" "qnxnto[]"; 287 | #endif 288 | 289 | #if defined(__CRAYXE) || defined(__CRAYXC) 290 | char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; 291 | #endif 292 | 293 | #define STRINGIFY_HELPER(X) #X 294 | #define STRINGIFY(X) STRINGIFY_HELPER(X) 295 | 296 | /* Identify known platforms by name. */ 297 | #if defined(__linux) || defined(__linux__) || defined(linux) 298 | # define PLATFORM_ID "Linux" 299 | 300 | #elif defined(__CYGWIN__) 301 | # define PLATFORM_ID "Cygwin" 302 | 303 | #elif defined(__MINGW32__) 304 | # define PLATFORM_ID "MinGW" 305 | 306 | #elif defined(__APPLE__) 307 | # define PLATFORM_ID "Darwin" 308 | 309 | #elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) 310 | # define PLATFORM_ID "Windows" 311 | 312 | #elif defined(__FreeBSD__) || defined(__FreeBSD) 313 | # define PLATFORM_ID "FreeBSD" 314 | 315 | #elif defined(__NetBSD__) || defined(__NetBSD) 316 | # define PLATFORM_ID "NetBSD" 317 | 318 | #elif defined(__OpenBSD__) || defined(__OPENBSD) 319 | # define PLATFORM_ID "OpenBSD" 320 | 321 | #elif defined(__sun) || defined(sun) 322 | # define PLATFORM_ID "SunOS" 323 | 324 | #elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) 325 | # define PLATFORM_ID "AIX" 326 | 327 | #elif defined(__sgi) || defined(__sgi__) || defined(_SGI) 328 | # define PLATFORM_ID "IRIX" 329 | 330 | #elif defined(__hpux) || defined(__hpux__) 331 | # define PLATFORM_ID "HP-UX" 332 | 333 | #elif defined(__HAIKU__) 334 | # define PLATFORM_ID "Haiku" 335 | 336 | #elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) 337 | # define PLATFORM_ID "BeOS" 338 | 339 | #elif defined(__QNX__) || defined(__QNXNTO__) 340 | # define PLATFORM_ID "QNX" 341 | 342 | #elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) 343 | # define PLATFORM_ID "Tru64" 344 | 345 | #elif defined(__riscos) || defined(__riscos__) 346 | # define PLATFORM_ID "RISCos" 347 | 348 | #elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) 349 | # define PLATFORM_ID "SINIX" 350 | 351 | #elif defined(__UNIX_SV__) 352 | # define PLATFORM_ID "UNIX_SV" 353 | 354 | #elif defined(__bsdos__) 355 | # define PLATFORM_ID "BSDOS" 356 | 357 | #elif defined(_MPRAS) || defined(MPRAS) 358 | # define PLATFORM_ID "MP-RAS" 359 | 360 | #elif defined(__osf) || defined(__osf__) 361 | # define PLATFORM_ID "OSF1" 362 | 363 | #elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) 364 | # define PLATFORM_ID "SCO_SV" 365 | 366 | #elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) 367 | # define PLATFORM_ID "ULTRIX" 368 | 369 | #elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) 370 | # define PLATFORM_ID "Xenix" 371 | 372 | #elif defined(__WATCOMC__) 373 | # if defined(__LINUX__) 374 | # define PLATFORM_ID "Linux" 375 | 376 | # elif defined(__DOS__) 377 | # define PLATFORM_ID "DOS" 378 | 379 | # elif defined(__OS2__) 380 | # define PLATFORM_ID "OS2" 381 | 382 | # elif defined(__WINDOWS__) 383 | # define PLATFORM_ID "Windows3x" 384 | 385 | # else /* unknown platform */ 386 | # define PLATFORM_ID 387 | # endif 388 | 389 | #else /* unknown platform */ 390 | # define PLATFORM_ID 391 | 392 | #endif 393 | 394 | /* For windows compilers MSVC and Intel we can determine 395 | the architecture of the compiler being used. This is because 396 | the compilers do not have flags that can change the architecture, 397 | but rather depend on which compiler is being used 398 | */ 399 | #if defined(_WIN32) && defined(_MSC_VER) 400 | # if defined(_M_IA64) 401 | # define ARCHITECTURE_ID "IA64" 402 | 403 | # elif defined(_M_X64) || defined(_M_AMD64) 404 | # define ARCHITECTURE_ID "x64" 405 | 406 | # elif defined(_M_IX86) 407 | # define ARCHITECTURE_ID "X86" 408 | 409 | # elif defined(_M_ARM64) 410 | # define ARCHITECTURE_ID "ARM64" 411 | 412 | # elif defined(_M_ARM) 413 | # if _M_ARM == 4 414 | # define ARCHITECTURE_ID "ARMV4I" 415 | # elif _M_ARM == 5 416 | # define ARCHITECTURE_ID "ARMV5I" 417 | # else 418 | # define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) 419 | # endif 420 | 421 | # elif defined(_M_MIPS) 422 | # define ARCHITECTURE_ID "MIPS" 423 | 424 | # elif defined(_M_SH) 425 | # define ARCHITECTURE_ID "SHx" 426 | 427 | # else /* unknown architecture */ 428 | # define ARCHITECTURE_ID "" 429 | # endif 430 | 431 | #elif defined(__WATCOMC__) 432 | # if defined(_M_I86) 433 | # define ARCHITECTURE_ID "I86" 434 | 435 | # elif defined(_M_IX86) 436 | # define ARCHITECTURE_ID "X86" 437 | 438 | # else /* unknown architecture */ 439 | # define ARCHITECTURE_ID "" 440 | # endif 441 | 442 | #elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) 443 | # if defined(__ICCARM__) 444 | # define ARCHITECTURE_ID "ARM" 445 | 446 | # elif defined(__ICCAVR__) 447 | # define ARCHITECTURE_ID "AVR" 448 | 449 | # else /* unknown architecture */ 450 | # define ARCHITECTURE_ID "" 451 | # endif 452 | #else 453 | # define ARCHITECTURE_ID 454 | #endif 455 | 456 | /* Convert integer to decimal digit literals. */ 457 | #define DEC(n) \ 458 | ('0' + (((n) / 10000000)%10)), \ 459 | ('0' + (((n) / 1000000)%10)), \ 460 | ('0' + (((n) / 100000)%10)), \ 461 | ('0' + (((n) / 10000)%10)), \ 462 | ('0' + (((n) / 1000)%10)), \ 463 | ('0' + (((n) / 100)%10)), \ 464 | ('0' + (((n) / 10)%10)), \ 465 | ('0' + ((n) % 10)) 466 | 467 | /* Convert integer to hex digit literals. */ 468 | #define HEX(n) \ 469 | ('0' + ((n)>>28 & 0xF)), \ 470 | ('0' + ((n)>>24 & 0xF)), \ 471 | ('0' + ((n)>>20 & 0xF)), \ 472 | ('0' + ((n)>>16 & 0xF)), \ 473 | ('0' + ((n)>>12 & 0xF)), \ 474 | ('0' + ((n)>>8 & 0xF)), \ 475 | ('0' + ((n)>>4 & 0xF)), \ 476 | ('0' + ((n) & 0xF)) 477 | 478 | /* Construct a string literal encoding the version number components. */ 479 | #ifdef COMPILER_VERSION_MAJOR 480 | char const info_version[] = { 481 | 'I', 'N', 'F', 'O', ':', 482 | 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', 483 | COMPILER_VERSION_MAJOR, 484 | # ifdef COMPILER_VERSION_MINOR 485 | '.', COMPILER_VERSION_MINOR, 486 | # ifdef COMPILER_VERSION_PATCH 487 | '.', COMPILER_VERSION_PATCH, 488 | # ifdef COMPILER_VERSION_TWEAK 489 | '.', COMPILER_VERSION_TWEAK, 490 | # endif 491 | # endif 492 | # endif 493 | ']','\0'}; 494 | #endif 495 | 496 | /* Construct a string literal encoding the internal version number. */ 497 | #ifdef COMPILER_VERSION_INTERNAL 498 | char const info_version_internal[] = { 499 | 'I', 'N', 'F', 'O', ':', 500 | 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', 501 | 'i','n','t','e','r','n','a','l','[', 502 | COMPILER_VERSION_INTERNAL,']','\0'}; 503 | #endif 504 | 505 | /* Construct a string literal encoding the version number components. */ 506 | #ifdef SIMULATE_VERSION_MAJOR 507 | char const info_simulate_version[] = { 508 | 'I', 'N', 'F', 'O', ':', 509 | 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', 510 | SIMULATE_VERSION_MAJOR, 511 | # ifdef SIMULATE_VERSION_MINOR 512 | '.', SIMULATE_VERSION_MINOR, 513 | # ifdef SIMULATE_VERSION_PATCH 514 | '.', SIMULATE_VERSION_PATCH, 515 | # ifdef SIMULATE_VERSION_TWEAK 516 | '.', SIMULATE_VERSION_TWEAK, 517 | # endif 518 | # endif 519 | # endif 520 | ']','\0'}; 521 | #endif 522 | 523 | /* Construct the string literal in pieces to prevent the source from 524 | getting matched. 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bx = 0, cx = 0, dx = 0; 8 | float ay = 0, by = 0, cy = 0, dy = 0; 9 | double li = 0.0, lj = 0.0; 10 | double thi = 0.0, thj = 0.0, thr = 0.0; 11 | double axg = 0.0, bxg = 0.0, cxg = 0.0, dxg = 0.0, delta1xg = 0.0, delta2xg = 0.0; 12 | 13 | ax = seg1->x1; 14 | ay = seg1->y1; 15 | 16 | bx = seg1->x2; 17 | by = seg1->y2; 18 | cx = seg2->x1; 19 | cy = seg2->y1; 20 | 21 | dx = seg2->x2; 22 | dy = seg2->y2; 23 | 24 | float dlix = (bx - ax); 25 | float dliy = (by - ay); 26 | float dljx = (dx - cx); 27 | float dljy = (dy - cy); 28 | 29 | li = sqrt((double) (dlix * dlix) + (double) (dliy * dliy)); 30 | lj = sqrt((double) (dljx * dljx) + (double) (dljy * dljy)); 31 | 32 | xg = (li * (double) (ax + bx) + lj * (double) (cx + dx)) 33 | / (double) (2.0 * (li + lj)); 34 | yg = (li * (double) (ay + by) + lj * (double) (cy + dy)) 35 | / (double) (2.0 * (li + lj)); 36 | 37 | if(dlix == 0.0f) thi = CV_PI / 2.0; 38 | else thi = atan(dliy / dlix); 39 | 40 | if(dljx == 0.0f) thj = CV_PI / 2.0; 41 | else thj = atan(dljy / dljx); 42 | 43 | if (fabs(thi - thj) <= CV_PI / 2.0) 44 | { 45 | thr = (li * thi + lj * thj) / (li + lj); 46 | } 47 | else 48 | { 49 | double tmp = thj - CV_PI * (thj / fabs(thj)); 50 | thr = li * thi + lj * tmp; 51 | thr /= (li + lj); 52 | } 53 | 54 | axg = ((double) ay - yg) * sin(thr) + ((double) ax - xg) * cos(thr); 55 | 56 | bxg = ((double) by - yg) * sin(thr) + ((double) bx - xg) * cos(thr); 57 | 58 | cxg = ((double) cy - yg) * sin(thr) + ((double) cx - xg) * cos(thr); 59 | 60 | dxg = ((double) dy - yg) * sin(thr) + ((double) dx - xg) * cos(thr); 61 | 62 | delta1xg = min(axg,min(bxg,min(cxg,dxg))); 63 | delta2xg = max(axg,max(bxg,max(cxg,dxg))); 64 | 65 | delta1x = delta1xg * cos(thr) + xg; 66 | delta1y = delta1xg * sin(thr) + yg; 67 | delta2x = delta2xg * cos(thr) + xg; 68 | delta2y = delta2xg * sin(thr) + yg; 69 | 70 | seg_merged->x1 = (float) delta1x; 71 | seg_merged->y1 = (float) delta1y; 72 | seg_merged->x2 = (float) delta2x; 73 | seg_merged->y2 = (float) delta2y; 74 | } 75 | 76 | double LineDetector::distPointLine( const Mat & p, Mat & l ) 77 | { 78 | double x, y, w; 79 | 80 | x = l.at(0,0); 81 | y = l.at(1,0); 82 | 83 | w = sqrt(x*x+y*y); 84 | 85 | l.at(0,0) = x / w; 86 | l.at(1,0) = y / w; 87 | l.at(2,0) = l.at(2,0) / w; 88 | 89 | return l.dot(p); 90 | } 91 | 92 | bool LineDetector::mergeSegments( SEGMENT * seg1, SEGMENT * seg2, SEGMENT * seg_merged ) 93 | { 94 | double o[] = { 0.0, 0.0, 1.0 }; 95 | double a[] = { 0.0, 0.0, 1.0 }; 96 | double b[] = { 0.0, 0.0, 1.0 }; 97 | double c[3]; 98 | 99 | o[0] = ( seg2->x1 + seg2->x2 ) / 2.0; 100 | o[1] = ( seg2->y1 + seg2->y2 ) / 2.0; 101 | 102 | a[0] = seg1->x1; 103 | a[1] = seg1->y1; 104 | b[0] = seg1->x2; 105 | b[1] = seg1->y2; 106 | 107 | Mat ori = Mat(3, 1, CV_64FC1, o).clone(); 108 | Mat p1 = Mat(3, 1, CV_64FC1, a).clone(); 109 | Mat p2 = Mat(3, 1, CV_64FC1, b).clone(); 110 | Mat l1 = Mat(3, 1, CV_64FC1, c).clone(); 111 | 112 | l1 = p1.cross(p2); 113 | 114 | Point2f seg1mid, seg2mid; 115 | seg1mid.x = (seg1->x1 + seg1->x2) /2.0f; 116 | seg1mid.y = (seg1->y1 + seg1->y2) /2.0f; 117 | seg2mid.x = (seg2->x1 + seg2->x2) /2.0f; 118 | seg2mid.y = (seg2->y1 + seg2->y2) /2.0f; 119 | 120 | double seg1len, seg2len; 121 | seg1len = sqrt((seg1->x1 - seg1->x2)*(seg1->x1 - seg1->x2)+(seg1->y1 - seg1->y2)*(seg1->y1 - seg1->y2)); 122 | seg2len = sqrt((seg2->x1 - seg2->x2)*(seg2->x1 - seg2->x2)+(seg2->y1 - seg2->y2)*(seg2->y1 - seg2->y2)); 123 | 124 | double middist = sqrt((seg1mid.x - seg2mid.x)*(seg1mid.x - seg2mid.x) + (seg1mid.y - seg2mid.y)*(seg1mid.y - seg2mid.y)); 125 | 126 | float angdiff = seg1->angle - seg2->angle; 127 | angdiff = fabs(angdiff); 128 | 129 | double dist = distPointLine( ori, l1 ); 130 | 131 | if ( fabs( dist ) <= threshold_dist * 2.0 132 | && middist <= seg1len / 2.0 + seg2len / 2.0 + 20.0 133 | && angdiff <= CV_PI / 180.0f * 5.0f) { 134 | mergeLines(seg1, seg2, seg2); 135 | return true; 136 | } else { 137 | return false; 138 | } 139 | } 140 | 141 | template 142 | void LineDetector::incidentPoint( tType * pt, Mat & l ) 143 | { 144 | double a[] = { (double)pt->x, (double)pt->y, 1.0 }; 145 | double b[] = { l.at(0,0), l.at(1,0), 0.0 }; 146 | double c[3]; 147 | 148 | Mat xk = Mat(3, 1, CV_64FC1, a).clone(); 149 | Mat lh = Mat(3, 1, CV_64FC1, b).clone(); 150 | Mat lk = Mat(3, 1, CV_64FC1, c).clone(); 151 | 152 | lk = xk.cross(lh); 153 | xk = lk.cross(l); 154 | 155 | double s = 1.0 / xk.at(2,0); 156 | xk.convertTo(xk, -1, s); 157 | 158 | pt->x = (float)xk.at(0,0) < 0.0f ? 0.0f : (float)xk.at(0,0) 159 | >= (imagewidth - 1.0f) ? (imagewidth - 1.0f) : (float)xk.at(0,0); 160 | pt->y = (float)xk.at(1,0) < 0.0f ? 0.0f : (float)xk.at(1,0) 161 | >= (imageheight - 1.0f) ? (imageheight - 1.0f) : (float)xk.at(1,0); 162 | 163 | } 164 | 165 | void LineDetector::extractSegments( vector * points, vector * segments ) 166 | { 167 | bool is_line; 168 | 169 | int i, j; 170 | SEGMENT seg; 171 | Point2i ps, pe, pt; 172 | 173 | vector l_points; 174 | 175 | int total = points->size(); 176 | 177 | for ( i = 0; i + threshold_length < total; i++ ) { 178 | ps = points->at(i); 179 | pe = points->at(i + threshold_length); 180 | 181 | double a[] = { (double)ps.x, (double)ps.y, 1 }; 182 | double b[] = { (double)pe.x, (double)pe.y, 1 }; 183 | double c[3], d[3]; 184 | 185 | Mat p1 = Mat(3, 1, CV_64FC1, a).clone(); 186 | Mat p2 = Mat(3, 1, CV_64FC1, b).clone(); 187 | Mat p = Mat(3, 1, CV_64FC1, c).clone(); 188 | Mat l = Mat(3, 1, CV_64FC1, d).clone(); 189 | l = p1.cross(p2); 190 | 191 | is_line = true; 192 | 193 | l_points.clear(); 194 | l_points.push_back(ps); 195 | 196 | for ( j = 1; j < threshold_length; j++ ) { 197 | pt.x = points->at(i+j).x; 198 | pt.y = points->at(i+j).y; 199 | 200 | p.at(0,0) = (double)pt.x; 201 | p.at(1,0) = (double)pt.y; 202 | p.at(2,0) = 1.0; 203 | 204 | double dist = distPointLine( p, l ); 205 | 206 | if ( fabs( dist ) > threshold_dist ) { 207 | is_line = false; 208 | break; 209 | } 210 | l_points.push_back(pt); 211 | } 212 | 213 | // Line check fail, test next point 214 | if ( is_line == false ) 215 | continue; 216 | 217 | l_points.push_back(pe); 218 | 219 | Vec4f line; 220 | fitLine( Mat(l_points), line, DIST_L2, 0, 0.01, 0.01); 221 | a[0] = line[2]; 222 | a[1] = line[3]; 223 | b[0] = line[2] + line[0]; 224 | b[1] = line[3] + line[1]; 225 | 226 | p1 = Mat(3, 1, CV_64FC1, a).clone(); 227 | p2 = Mat(3, 1, CV_64FC1, b).clone(); 228 | 229 | l = p1.cross(p2); 230 | 231 | incidentPoint( &ps, l ); 232 | 233 | // Extending line 234 | for ( j = threshold_length + 1; i + j < total; j++ ) { 235 | pt.x = points->at(i+j).x; 236 | pt.y = points->at(i+j).y; 237 | 238 | p.at(0,0) = (double)pt.x; 239 | p.at(1,0) = (double)pt.y; 240 | p.at(2,0) = 1.0; 241 | 242 | double dist = distPointLine( p, l ); 243 | 244 | if ( fabs( dist ) > threshold_dist ) { 245 | j--; 246 | break; 247 | } 248 | 249 | pe = pt; 250 | l_points.push_back(pt); 251 | } 252 | fitLine( Mat(l_points), line, DIST_L2, 0, 0.01, 0.01); 253 | a[0] = line[2]; 254 | a[1] = line[3]; 255 | b[0] = line[2] + line[0]; 256 | b[1] = line[3] + line[1]; 257 | 258 | p1 = Mat(3, 1, CV_64FC1, a).clone(); 259 | p2 = Mat(3, 1, CV_64FC1, b).clone(); 260 | 261 | l = p1.cross(p2); 262 | 263 | Point2f e1, e2; 264 | e1.x = ps.x; 265 | e1.y = ps.y; 266 | e2.x = pe.x; 267 | e2.y = pe.y; 268 | 269 | incidentPoint( &e1, l ); 270 | incidentPoint( &e2, l ); 271 | seg.x1 = e1.x; 272 | seg.y1 = e1.y; 273 | seg.x2 = e2.x; 274 | seg.y2 = e2.y; 275 | 276 | segments->push_back(seg); 277 | i = i + j; 278 | } 279 | } 280 | 281 | void LineDetector::pointInboardTest(Mat & src, Point2i * pt) 282 | { 283 | pt->x = pt->x <= 5.0f ? 5.0f : pt->x >= src.cols - 5.0f ? src.cols - 5.0f : pt->x; 284 | pt->y = pt->y <= 5.0f ? 5.0f : pt->y >= src.rows - 5.0f ? src.rows - 5.0f : pt->y; 285 | } 286 | 287 | bool LineDetector::getPointChain( const Mat & img, const Point pt, Point * chained_pt, 288 | int & direction, int step ) 289 | { 290 | int ri, ci; 291 | int indices[8][2]={ {1,1}, {1,0}, {1,-1}, {0,-1}, {-1,-1},{-1,0}, {-1,1}, {0,1} }; 292 | 293 | for ( int i = 0; i < 8; i++ ) { 294 | ci = pt.x + indices[i][1]; 295 | ri = pt.y + indices[i][0]; 296 | 297 | if ( ri < 0 || ri == img.rows || ci < 0 || ci == img.cols ) 298 | continue; 299 | 300 | if ( img.at(ri, ci) == 0 ) 301 | continue; 302 | 303 | if(step == 0) { 304 | chained_pt->x = ci; 305 | chained_pt->y = ri; 306 | direction = i; 307 | return true; 308 | } else { 309 | if(abs(i-direction) <= 2 || abs(i-direction) >= 6) 310 | { 311 | chained_pt->x = ci; 312 | chained_pt->y = ri; 313 | direction = i; 314 | return true; 315 | } else 316 | continue; 317 | } 318 | } 319 | return false; 320 | } 321 | 322 | void LineDetector::lineDetection( Mat & src, vector & segments_all, bool merge ) 323 | { 324 | int r, c; 325 | imageheight=src.rows; imagewidth=src.cols; 326 | 327 | vector points; 328 | vector segments, segments_tmp, segments_tmp2; 329 | Mat canny = src.clone(); 330 | Canny(src, canny, 50, 50, 3); 331 | 332 | for(int i=0; i src.rows-5 || j < 5 || j > src.cols - 5) 335 | canny.at(i,j) = 0; 336 | } 337 | } 338 | 339 | SEGMENT seg, seg1, seg2; 340 | 341 | for ( r = 0; r < imageheight; r++ ) { 342 | for ( c = 0; c < imagewidth; c++ ) { 343 | // Find seeds - skip for non-seeds 344 | if ( canny.at(r,c) == 0 ) 345 | continue; 346 | 347 | // Found seeds 348 | Point2i pt; 349 | pt.x = c; 350 | pt.y = r; 351 | 352 | points.push_back(pt); 353 | canny.at(pt.y, pt.x) = 0; 354 | 355 | int direction = 0; 356 | int step = 0; 357 | while (getPointChain( canny, pt, &pt, direction, step)) { 358 | points.push_back(pt); 359 | step++; 360 | canny.at(pt.y, pt.x) = 0; 361 | } 362 | 363 | if ( points.size() < (unsigned int)threshold_length + 1 ) { 364 | points.clear(); 365 | continue; 366 | } 367 | 368 | extractSegments( &points, &segments ); 369 | 370 | if ( segments.size() == 0 ) { 371 | points.clear(); 372 | continue; 373 | } 374 | for ( int i = 0; i < (int)segments.size(); i++ ) { 375 | seg = segments.at(i); 376 | float length = sqrt((seg.x1 - seg.x2)*(seg.x1 - seg.x2) + (seg.y1 - seg.y2)*(seg.y1 - seg.y2)); 377 | if(length < threshold_length) continue; 378 | if( (seg.x1 <= 5.0f && seg.x2 <= 5.0f) 379 | || (seg.y1 <= 5.0f && seg.y2 <= 5.0f) 380 | || (seg.x1 >= imagewidth - 5.0f && seg.x2 >= imagewidth - 5.0f) 381 | || (seg.y1 >= imageheight - 5.0f && seg.y2 >= imageheight - 5.0f) ) 382 | continue; 383 | 384 | additionalOperationsOnSegments(src, &seg); 385 | if(!merge) { 386 | segments_all.push_back(seg); 387 | } 388 | segments_tmp.push_back(seg); 389 | } 390 | points.clear(); 391 | segments.clear(); 392 | } 393 | } 394 | if(!merge) 395 | return; 396 | 397 | bool is_merged = false; 398 | int ith = segments_tmp.size() - 1; 399 | int jth = ith - 1; 400 | while(true) 401 | { 402 | seg1 = segments_tmp[ith]; 403 | seg2 = segments_tmp[jth]; 404 | is_merged = mergeSegments(&seg1, &seg2, &seg2); 405 | if(is_merged == true) 406 | { 407 | additionalOperationsOnSegments(src, &seg2); 408 | vector::iterator it = segments_tmp.begin() + ith; 409 | *it = seg2; 410 | segments_tmp.erase(segments_tmp.begin()+jth); 411 | ith--; 412 | jth = ith - 1; 413 | } 414 | else 415 | { 416 | jth--; 417 | } 418 | if(jth < 0) { 419 | ith--; 420 | jth = ith - 1; 421 | } 422 | if(ith == 1 && jth == 0) 423 | break; 424 | } 425 | segments_all = segments_tmp; 426 | } 427 | 428 | void LineDetector::getAngle(SEGMENT *seg) 429 | { 430 | float dx = (float)(seg->x2 - seg->x1); 431 | float dy = (float)(seg->y2 - seg->y1); 432 | double ang=0.0; 433 | 434 | if(dx == 0.0f) { 435 | if(dy > 0) 436 | ang = CV_PI / 2.0; 437 | else 438 | ang = -1.0 * CV_PI / 2.0; 439 | } 440 | else if(dy == 0.0f) { 441 | if(dx > 0) 442 | ang = 0.0; 443 | else 444 | ang = CV_PI; 445 | } 446 | else if(dx < 0.0f && dy > 0.0f) 447 | ang = CV_PI + atan( dy/dx ); 448 | else if(dx > 0.0f && dy < 0.0f) 449 | ang = 2*CV_PI + atan( dy/dx ); 450 | else if(dx < 0.0f && dy < 0.0f) 451 | ang = CV_PI + atan( dy/dx ); 452 | else 453 | ang = atan( dy/dx ); 454 | 455 | if(ang > 2.0 * CV_PI) 456 | ang -= 2.0 * CV_PI; 457 | seg->angle = (float)ang; 458 | } 459 | 460 | void LineDetector::additionalOperationsOnSegments(Mat & src, SEGMENT * seg) 461 | { 462 | if(seg->x1 == 0.0f && seg->x2 == 0.0f && seg->y1 == 0.0f && seg->y2 == 0.0f) 463 | return; 464 | 465 | getAngle(seg); 466 | double ang = (double)seg->angle; 467 | 468 | Point2f start = Point2f(seg->x1, seg->y1); 469 | Point2f end = Point2f(seg->x2, seg->y2); 470 | 471 | double dx = 0.0, dy = 0.0; 472 | dx = (double) end.x - (double) start.x; 473 | dy = (double) end.y - (double) start.y; 474 | 475 | int num_points = 10; 476 | Point2f *points = new Point2f[num_points]; 477 | 478 | points[0] = start; 479 | points[num_points - 1] = end; 480 | for (int i = 0; i < num_points; i++) { 481 | if (i == 0 || i == num_points - 1) 482 | continue; 483 | points[i].x = points[0].x + (dx / double(num_points - 1) * (double) i); 484 | points[i].y = points[0].y + (dy / double(num_points - 1) * (double) i); 485 | } 486 | 487 | Point2i *points_right = new Point2i[num_points]; 488 | Point2i *points_left = new Point2i[num_points]; 489 | double gap = 1.0; 490 | 491 | for(int i = 0; i < num_points; i++) { 492 | points_right[i].x = cvRound(points[i].x + gap*cos(90.0 * CV_PI / 180.0 + ang)); 493 | points_right[i].y = cvRound(points[i].y + gap*sin(90.0 * CV_PI / 180.0 + ang)); 494 | points_left[i].x = cvRound(points[i].x - gap*cos(90.0 * CV_PI / 180.0 + ang)); 495 | points_left[i].y = cvRound(points[i].y - gap*sin(90.0 * CV_PI / 180.0 + ang)); 496 | pointInboardTest(src, &points_right[i]); 497 | pointInboardTest(src, &points_left[i]); 498 | } 499 | 500 | int iR = 0, iL = 0; 501 | for(int i = 0; i < num_points; i++) { 502 | iR += src.at(points_right[i].y, points_right[i].x); 503 | iL += src.at(points_left[i].y, points_left[i].x); 504 | } 505 | 506 | if(iR > iL) 507 | { 508 | std::swap(seg->x1, seg->x2); 509 | std::swap(seg->y1, seg->y2); 510 | ang = ang + CV_PI; 511 | if(ang >= 2.0*CV_PI) 512 | ang = ang - 2.0 * CV_PI; 513 | seg->angle = (float)ang; 514 | } 515 | 516 | delete[] points; delete[] points_right; delete[] points_left; 517 | 518 | seg->label = init_label++; 519 | return; 520 | } 521 | 522 | void LineDetector::drawArrow( Mat & mat, const SEGMENT * seg, Scalar bgr, int thickness, bool directed) 523 | { 524 | Point2i p1; 525 | 526 | double gap = 10.0; 527 | double ang = (double)seg->angle; 528 | double arrow_angle = 30.0; 529 | 530 | p1.x = round(seg->x2 - gap*cos(arrow_angle * CV_PI / 180.0 + ang)); 531 | p1.y = round(seg->y2 - gap*sin(arrow_angle * CV_PI / 180.0 + ang)); 532 | pointInboardTest(mat, &p1); 533 | 534 | line(mat, Point(round(seg->x1), round(seg->y1)), Point(round(seg->x2), 535 | round(seg->y2)), bgr, thickness, 1); 536 | if(directed) 537 | line(mat, Point(round(seg->x2), round(seg->y2)), p1, bgr, thickness, 1); 538 | } 539 | -------------------------------------------------------------------------------- /CodeForLineMatching/linedetector.h: -------------------------------------------------------------------------------- 1 | #ifndef LINEDETECTOR_H_ 2 | #define LINEDETECTOR_H_ 3 | 4 | #include "system.h" 5 | 6 | class LineDetector 7 | { 8 | public: 9 | LineDetector() { 10 | threshold_dist=1.5; 11 | threshold_length=20; 12 | init_label=0; 13 | } 14 | ~LineDetector(){}; 15 | template 16 | void incidentPoint( tType * pt, Mat & l ); 17 | void mergeLines(SEGMENT * Seg1, SEGMENT * Seg2, SEGMENT * SegMerged); 18 | bool getPointChain( const Mat & img, const Point pt, Point * chained_pt, int 19 | & direction, int step ); 20 | // bool getPointChain( const Mat & img, const Mat & scales, const Point pt, Point * chained_pt, int 21 | // & direction, int step ); 22 | double distPointLine( const Mat & p, Mat & l ); 23 | bool mergeSegments( SEGMENT * seg1, SEGMENT * seg2, SEGMENT * seg_merged ); 24 | void extractSegments( vector * points, vector * segments ); 25 | void lineDetection( Mat & src, vector & segments_all, bool merge = true ); 26 | void pointInboardTest(Mat & src, Point2i * pt); 27 | void getAngle(SEGMENT *seg); 28 | void additionalOperationsOnSegments(Mat & src, SEGMENT * seg); 29 | void drawArrow( Mat& mat, const SEGMENT* seg, Scalar bgr=Scalar(0,255,0), 30 | int thickness=1, bool directed=true); 31 | 32 | private: 33 | int init_label, imagewidth, imageheight, threshold_length; 34 | float threshold_dist; 35 | }; 36 | 37 | struct segDesc{ 38 | SEGMENT segment; 39 | float lineLength; 40 | vectordesVec; 41 | }; 42 | struct matchNode{ 43 | unsigned int leftLineID;//the index of line in the left image 44 | unsigned int rightLineID;//the index of line in the right image 45 | }; 46 | typedef std::vector Nodes_list; 47 | 48 | struct CompareL { 49 | bool operator() (const double& lhs, const double& rhs) const 50 | {return lhs>rhs;} 51 | }; 52 | typedef std::multimap EigenMAP; 53 | 54 | 55 | 56 | class descriptor{ 57 | private: 58 | int numOfBand; 59 | int widthOfBand; 60 | vector GaussianCoef_Global,GaussianCoef_Local,desc; 61 | Mat dxImg,dyImg; 62 | void setGlobalGaussianCoef(int n,int w); 63 | void setLocalGaussianCoef(int w); 64 | public: 65 | descriptor(int numberOfBands_,int widthOfBand_); 66 | void getGradientMap(string filename); 67 | void debugShow(); 68 | vector calcLBD(SEGMENT& seg); 69 | }; 70 | 71 | void BuildMat(vector linesInLeft, vector linesInRight); 72 | void MatchingResult(vector linesInLeft, vector linesInRight, vector &matchResult); 73 | #endif 74 | -------------------------------------------------------------------------------- /CodeForLineMatching/readMe.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scottwon/LineMatching/11d9acf514f0b121f5ff6fdd2cbb63720b46ab8a/CodeForLineMatching/readMe.txt -------------------------------------------------------------------------------- /CodeForLineMatching/removed/Demo_LBD_Multi.cpp: -------------------------------------------------------------------------------- 1 | #include "LineMatchingAlgorithm.hpp" 2 | 3 | #include 4 | #include 5 | #include 6 | #include 7 | using namespace std; 8 | 9 | void usage(int argc, char **argv) 10 | { 11 | cout << "Usage: " << argv[0] << " ImgDirPath" 12 | << " OurDirPath" << endl; 13 | } 14 | 15 | int main(int argc, char **argv) 16 | { 17 | 18 | int ret = -1; 19 | if (argc < 3) 20 | { 21 | usage(argc, argv); 22 | return ret; 23 | } 24 | 25 | struct dirent *ptr; 26 | DIR *dir; 27 | string PATH(argv[1]); 28 | string OUT(argv[2]); 29 | string PARAM(argv[3]); 30 | 31 | dir = opendir(PATH.c_str()); 32 | vector files; 33 | fstream _file; 34 | 35 | //读取图片 36 | while ((ptr = readdir(dir)) != NULL) 37 | { 38 | //跳过'.'和'..'两个目录 39 | if (ptr->d_name[0] == '.') 40 | continue; 41 | files.push_back(ptr->d_name); 42 | } 43 | 44 | sort(files.begin(), files.end()); //排序 45 | 46 | for (int i = 0; i < files.size() - 1; i++) 47 | { 48 | string img1path = PATH; 49 | string img2path = PATH; 50 | string outpath = OUT; 51 | string parampath; 52 | if(!PARAM.empty()) 53 | { 54 | parampath = PARAM + to_string(i) + "_" + to_string(i+1); 55 | } 56 | 57 | img1path = img1path + files[i]; 58 | img2path = img2path + files[i + 1]; 59 | outpath = outpath + to_string(i) + "_" + to_string(i+1); 60 | 61 | _file.open(outpath, ios::in); 62 | if(!_file) 63 | { 64 | image_process(img1path, img2path, outpath, parampath,false); 65 | cout << files[i] << "_" << files[i + 1] << endl << endl; 66 | } 67 | else 68 | { 69 | cout< 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include "opencv2/core/utility.hpp" 14 | #include 15 | 16 | 17 | struct Pixel{ 18 | unsigned int x;//X coordinate 19 | unsigned int y;//Y coordinate 20 | }; 21 | struct EdgeChains{ 22 | std::vector xCors;//all the x coordinates of edge points 23 | std::vector yCors;//all the y coordinates of edge points 24 | std::vector sId; //the start index of each edge in the coordinate arrays 25 | unsigned int numOfEdges;//the number of edges whose length are larger than minLineLen; numOfEdges < sId.size; 26 | }; 27 | struct LineChains{ 28 | std::vector xCors;//all the x coordinates of line points 29 | std::vector yCors;//all the y coordinates of line points 30 | std::vector sId; //the start index of each line in the coordinate arrays 31 | unsigned int numOfLines;//the number of lines whose length are larger than minLineLen; numOfLines < sId.size; 32 | }; 33 | 34 | typedef std::list PixelChain;//each edge is a pixel chain 35 | 36 | 37 | struct EDLineParam{ 38 | int ksize; 39 | float sigma; 40 | float gradientThreshold; 41 | float anchorThreshold; 42 | int scanIntervals; 43 | int minLineLen; 44 | double lineFitErrThreshold; 45 | }; 46 | 47 | #define RELATIVE_ERROR_FACTOR 100.0 48 | #define M_LN10 2.30258509299404568402 49 | #define log_gamma(x) ((x)>15.0?log_gamma_windschitl(x):log_gamma_lanczos(x)) 50 | 51 | /* This class is used to detect lines from input image. 52 | * First, edges are extracted from input image following the method presented in Cihan Topal and 53 | * Cuneyt Akinlar's paper:"Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection", 2010. 54 | * Then, lines are extracted from the edge image following the method presented in Cuneyt Akinlar and 55 | * Cihan Topal's paper:"EDLines: A real-time line segment detector with a false detection control", 2011 56 | * PS: The linking step of edge detection has a little bit difference with the Edge drawing algorithm 57 | * described in the paper. The edge chain doesn't stop when the pixel direction is changed. 58 | */ 59 | class EDLineDetector 60 | { 61 | public: 62 | EDLineDetector(); 63 | EDLineDetector(EDLineParam param); 64 | ~EDLineDetector(); 65 | 66 | /*extract edges from image 67 | *image: In, gray image; 68 | *edges: Out, store the edges, each edge is a pixel chain 69 | *smoothed: In, flag to mark whether the image has already been smoothed by Gaussian filter. 70 | *return -1: error happen 71 | */ 72 | int EdgeDrawing(cv::Mat &image, EdgeChains &edgeChains, bool smoothed=false); 73 | /*extract lines from image 74 | *image: In, gray image; 75 | *lines: Out, store the extracted lines, 76 | *smoothed: In, flag to mark whether the image has already been smoothed by Gaussian filter. 77 | *return -1: error happen 78 | */ 79 | int EDline(cv::Mat &image, LineChains &lines, bool smoothed=false); 80 | /*extract line from image, and store them*/ 81 | int EDline(cv::Mat &image, bool smoothed=false); 82 | 83 | cv::Mat dxImg_;//store the dxImg; 84 | cv::Mat dyImg_;//store the dyImg; 85 | cv::Mat gImgWO_;//store the gradient image without threshold; 86 | LineChains lines_; //store the detected line chains; 87 | //store the line Equation coefficients, vec3=[w1,w2,w3] for line w1*x + w2*y + w3=0; 88 | std::vector > lineEquations_; 89 | //store the line endpoints, [x1,y1,x2,y3] 90 | std::vector > lineEndpoints_; 91 | //store the line direction 92 | std::vector lineDirection_; 93 | //store the line salience, which is the summation of gradients of pixels on line 94 | std::vector lineSalience_; 95 | unsigned int imageWidth; 96 | unsigned int imageHeight; 97 | 98 | private: 99 | void InitEDLine_(); 100 | /*For an input edge chain, find the best fit line, the default chain length is minLineLen_ 101 | *xCors: In, pointer to the X coordinates of pixel chain; 102 | *yCors: In, pointer to the Y coordinates of pixel chain; 103 | *offsetS:In, start index of this chain in array; 104 | *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); 105 | *return: line fit error; -1:error happens; 106 | */ 107 | double LeastSquaresLineFit_(unsigned int *xCors, unsigned int *yCors, 108 | unsigned int offsetS, std::array &lineEquation); 109 | /*For an input pixel chain, find the best fit line. Only do the update based on new points. 110 | *For A*x=v, Least square estimation of x = Inv(A^T * A) * (A^T * v); 111 | *If some new observations are added, i.e, [A; A'] * x = [v; v'], 112 | *then x' = Inv(A^T * A + (A')^T * A') * (A^T * v + (A')^T * v'); 113 | *xCors: In, pointer to the X coordinates of pixel chain; 114 | *yCors: In, pointer to the Y coordinates of pixel chain; 115 | *offsetS:In, start index of this chain in array; 116 | *newOffsetS: In, start index of extended part; 117 | *offsetE:In, end index of this chain in array; 118 | *lineEquation: Out, [a,b] which are the coefficient of lines y=ax+b(horizontal) or x=ay+b(vertical); 119 | *return: line fit error; -1:error happens; 120 | */ 121 | double LeastSquaresLineFit_(unsigned int *xCors, unsigned int *yCors, 122 | unsigned int offsetS, unsigned int newOffsetS, 123 | unsigned int offsetE, std::array &lineEquation); 124 | /* Validate line based on the Helmholtz principle, which basically states that 125 | * for a structure to be perceptually meaningful, the expectation of this structure 126 | * by chance must be very low. 127 | */ 128 | bool LineValidation_(unsigned int *xCors, unsigned int *yCors, 129 | unsigned int offsetS, unsigned int offsetE, 130 | std::array &lineEquation, float &direction); 131 | bool bValidate_;//flag to decide whether line will be validated 132 | int ksize_; //the size of Gaussian kernel: ksize X ksize, default value is 5. 133 | float sigma_;//the sigma of Gaussian kernal, default value is 1.0. 134 | /*the threshold of pixel gradient magnitude. 135 | *Only those pixel whose gradient magnitude are larger than this threshold will be 136 | *taken as possible edge points. Default value is 36*/ 137 | short gradienThreshold_; 138 | /*If the pixel's gradient value is bigger than both of its neighbors by a 139 | *certain threshold (ANCHOR_THRESHOLD), the pixel is marked to be an anchor. 140 | *Default value is 8*/ 141 | unsigned char anchorThreshold_; 142 | /*anchor testing can be performed at different scan intervals, i.e., 143 | *every row/column, every second row/column etc. 144 | *Default value is 2*/ 145 | unsigned int scanIntervals_; 146 | int minLineLen_;//minimal acceptable line length 147 | /*For example, there two edges in the image: 148 | *edge1 = [(7,4), (8,5), (9,6),| (10,7)|, (11, 8), (12,9)] and 149 | *edge2 = [(14,9), (15,10), (16,11), (17,12),| (18, 13)|, (19,14)] ; then we store them as following: 150 | *pFirstPartEdgeX_ = [10, 11, 12, 18, 19];//store the first part of each edge[from middle to end] 151 | *pFirstPartEdgeY_ = [7, 8, 9, 13, 14]; 152 | *pFirstPartEdgeS_ = [0,3,5];// the index of start point of first part of each edge 153 | *pSecondPartEdgeX_ = [10, 9, 8, 7, 18, 17, 16, 15, 14];//store the second part of each edge[from middle to front] 154 | *pSecondPartEdgeY_ = [7, 6, 5, 4, 13, 12, 11, 10, 9];//anchor points(10, 7) and (18, 13) are stored again 155 | *pSecondPartEdgeS_ = [0, 4, 9];// the index of start point of second part of each edge 156 | *This type of storage order is because of the order of edge detection process. 157 | *For each edge, start from one anchor point, first go right, then go left or first go down, then go up*/ 158 | unsigned int *pFirstPartEdgeX_;//store the X coordinates of the first part of the pixels for chains 159 | unsigned int *pFirstPartEdgeY_;//store the Y coordinates of the first part of the pixels for chains 160 | unsigned int *pFirstPartEdgeS_;//store the start index of every edge chain in the first part arrays 161 | unsigned int *pSecondPartEdgeX_;//store the X coordinates of the second part of the pixels for chains 162 | unsigned int *pSecondPartEdgeY_;//store the Y coordinates of the second part of the pixels for chains 163 | unsigned int *pSecondPartEdgeS_;//store the start index of every edge chain in the second part arrays 164 | unsigned int *pAnchorX_;//store the X coordinates of anchors 165 | unsigned int *pAnchorY_;//store the Y coordinates of anchors 166 | cv::Mat edgeImage_; 167 | /*The threshold of line fit error; 168 | *If lineFitErr is large than this threshold, then 169 | *the pixel chain is not accepted as a single line segment.*/ 170 | double lineFitErrThreshold_; 171 | 172 | 173 | cv::Mat gImg_;//store the gradient image; 174 | cv::Mat dirImg_;//store the direction image 175 | double logNT_; 176 | cv::Mat_ ATA; //the previous matrix of A^T * A; 177 | cv::Mat_ ATV; //the previous vector of A^T * V; 178 | cv::Mat_ fitMatT; //the matrix used in line fit function; 179 | cv::Mat_ fitVec; //the vector used in line fit function; 180 | cv::Mat_ tempMatLineFit; //the matrix used in line fit function; 181 | cv::Mat_ tempVecLineFit; //the vector used in line fit function; 182 | 183 | 184 | /** Compare doubles by relative error. 185 | The resulting rounding error after floating point computations 186 | depend on the specific operations done. The same number computed by 187 | different algorithms could present different rounding errors. For a 188 | useful comparison, an estimation of the relative rounding error 189 | should be considered and compared to a factor times EPS. The factor 190 | should be related to the accumulated rounding error in the chain of 191 | computation. Here, as a simplification, a fixed factor is used. 192 | */ 193 | static int double_equal(double a, double b) 194 | { 195 | double abs_diff,aa,bb,abs_max; 196 | /* trivial case */ 197 | if( a == b ) return true; 198 | abs_diff = fabs(a-b); 199 | aa = fabs(a); 200 | bb = fabs(b); 201 | abs_max = aa > bb ? aa : bb; 202 | /* DBL_MIN is the smallest normalized number, thus, the smallest 203 | number whose relative error is bounded by DBL_EPSILON. For 204 | smaller numbers, the same quantization steps as for DBL_MIN 205 | are used. Then, for smaller numbers, a meaningful "relative" 206 | error should be computed by dividing the difference by DBL_MIN. */ 207 | if( abs_max < DBL_MIN ) abs_max = DBL_MIN; 208 | /* equal if relative error <= factor x eps */ 209 | return (abs_diff / abs_max) <= (RELATIVE_ERROR_FACTOR * DBL_EPSILON); 210 | } 211 | /** Computes the natural logarithm of the absolute value of 212 | the gamma function of x using the Lanczos approximation. 213 | See http://www.rskey.org/gamma.htm 214 | The formula used is 215 | @f[ 216 | \Gamma(x) = \frac{ \sum_{n=0}^{N} q_n x^n }{ \Pi_{n=0}^{N} (x+n) } 217 | (x+5.5)^{x+0.5} e^{-(x+5.5)} 218 | @f] 219 | so 220 | @f[ 221 | \log\Gamma(x) = \log\left( \sum_{n=0}^{N} q_n x^n \right) 222 | + (x+0.5) \log(x+5.5) - (x+5.5) - \sum_{n=0}^{N} \log(x+n) 223 | @f] 224 | and 225 | q0 = 75122.6331530, 226 | q1 = 80916.6278952, 227 | q2 = 36308.2951477, 228 | q3 = 8687.24529705, 229 | q4 = 1168.92649479, 230 | q5 = 83.8676043424, 231 | q6 = 2.50662827511. 232 | */ 233 | static double log_gamma_lanczos(double x) 234 | { 235 | static double q[7] = { 75122.6331530, 80916.6278952, 36308.2951477, 236 | 8687.24529705, 1168.92649479, 83.8676043424, 237 | 2.50662827511 }; 238 | double a = (x+0.5) * log(x+5.5) - (x+5.5); 239 | double b = 0.0; 240 | int n; 241 | for(n=0;n<7;n++){ 242 | a -= log( x + (double) n ); 243 | b += q[n] * pow( x, (double) n ); 244 | } 245 | return a + log(b); 246 | } 247 | /** Computes the natural logarithm of the absolute value of 248 | the gamma function of x using Windschitl method. 249 | See http://www.rskey.org/gamma.htm 250 | The formula used is 251 | @f[ 252 | \Gamma(x) = \sqrt{\frac{2\pi}{x}} \left( \frac{x}{e} 253 | \sqrt{ x\sinh(1/x) + \frac{1}{810x^6} } \right)^x 254 | @f] 255 | so 256 | @f[ 257 | \log\Gamma(x) = 0.5\log(2\pi) + (x-0.5)\log(x) - x 258 | + 0.5x\log\left( x\sinh(1/x) + \frac{1}{810x^6} \right). 259 | @f] 260 | This formula is a good approximation when x > 15. 261 | */ 262 | static double log_gamma_windschitl(double x) 263 | { 264 | return 0.918938533204673 + (x-0.5)*log(x) - x 265 | + 0.5*x*log( x*sinh(1/x) + 1/(810.0*pow(x,6.0)) ); 266 | } 267 | /** Computes -log10(NFA). 268 | NFA stands for Number of False Alarms: 269 | @f[ 270 | \mathrm{NFA} = NT \cdot B(n,k,p) 271 | @f] 272 | - NT - number of tests 273 | - B(n,k,p) - tail of binomial distribution with parameters n,k and p: 274 | @f[ 275 | B(n,k,p) = \sum_{j=k}^n 276 | \left(\begin{array}{c}n\\j\end{array}\right) 277 | p^{j} (1-p)^{n-j} 278 | @f] 279 | The value -log10(NFA) is equivalent but more intuitive than NFA: 280 | - -1 corresponds to 10 mean false alarms 281 | - 0 corresponds to 1 mean false alarm 282 | - 1 corresponds to 0.1 mean false alarms 283 | - 2 corresponds to 0.01 mean false alarms 284 | - ... 285 | Used this way, the bigger the value, better the detection, 286 | and a logarithmic scale is used. 287 | @param n,k,p binomial parameters. 288 | @param logNT logarithm of Number of Tests 289 | The computation is based in the gamma function by the following 290 | relation: 291 | @f[ 292 | \left(\begin{array}{c}n\\k\end{array}\right) 293 | = \frac{ \Gamma(n+1) }{ \Gamma(k+1) \cdot \Gamma(n-k+1) }. 294 | @f] 295 | We use efficient algorithms to compute the logarithm of 296 | the gamma function. 297 | To make the computation faster, not all the sum is computed, part 298 | of the terms are neglected based on a bound to the error obtained 299 | (an error of 10% in the result is accepted). 300 | */ 301 | static double nfa(int n, int k, double p, double logNT) 302 | { 303 | double tolerance = 0.1; /* an error of 10% in the result is accepted */ 304 | double log1term,term,bin_term,mult_term,bin_tail,err,p_term; 305 | int i; 306 | 307 | /* check parameters */ 308 | if( n<0 || k<0 || k>n || p<=0.0 || p>=1.0 ){ 309 | std::cout<<"nfa: wrong n, k or p values."< (double) n * p ) /* at begin or end of the tail? */ 335 | return -log1term / M_LN10 - logNT; /* end: use just the first term */ 336 | else 337 | return -logNT; /* begin: the tail is roughly 1 */ 338 | } 339 | 340 | /* compute more terms if needed */ 341 | bin_tail = term; 342 | for(i=k+1;i<=n;i++){ 343 | /* As 344 | term_i = bincoef(n,i) * p^i * (1-p)^(n-i) 345 | and 346 | bincoef(n,i)/bincoef(n,i-1) = n-i+1 / i, 347 | then, 348 | term_i / term_i-1 = (n-i+1)/i * p/(1-p) 349 | and 350 | term_i = term_i-1 * (n-i+1)/i * p/(1-p). 351 | p/(1-p) is computed only once and stored in 'p_term'. 352 | */ 353 | bin_term = (double) (n-i+1) / (double) i; 354 | mult_term = bin_term * p_term; 355 | term *= mult_term; 356 | bin_tail += term; 357 | if(bin_term<1.0){ 358 | /* When bin_term<1 then mult_term_ji. 359 | Then, the error on the binomial tail when truncated at 360 | the i term can be bounded by a geometric series of form 361 | term_i * sum mult_term_i^j. */ 362 | err = term * ( ( 1.0 - pow( mult_term, (double) (n-i+1) ) ) / 363 | (1.0-mult_term) - 1.0 ); 364 | /* One wants an error at most of tolerance*final_result, or: 365 | tolerance * abs(-log10(bin_tail)-logNT). 366 | Now, the error that can be accepted on bin_tail is 367 | given by tolerance*final_result divided by the derivative 368 | of -log10(x) when x=bin_tail. that is: 369 | tolerance * abs(-log10(bin_tail)-logNT) / (1/bin_tail) 370 | Finally, we truncate the tail if the error is less than: 371 | tolerance * abs(-log10(bin_tail)-logNT) * bin_tail */ 372 | if( err < tolerance * fabs(-log10(bin_tail)-logNT) * bin_tail ) break; 373 | } 374 | } 375 | return -log10(bin_tail) - logNT; 376 | } 377 | }; 378 | 379 | #endif /* EDLINEDETECTOR_HH_ */ 380 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/LineDescriptor.hh: -------------------------------------------------------------------------------- 1 | 2 | #ifndef LINEDESCRIPTOR_HH_ 3 | #define LINEDESCRIPTOR_HH_ 4 | 5 | 6 | #include "EDLineDetector.hh" 7 | #include "LineStructure.hh" 8 | 9 | 10 | #include 11 | struct OctaveLine{ 12 | unsigned int octaveCount;//the octave which this line is detected 13 | unsigned int lineIDInOctave;//the line ID in that octave image 14 | unsigned int lineIDInScaleLineVec;//the line ID in Scale line vector 15 | float lineLength; //the length of line in original image scale 16 | }; 17 | 18 | 19 | /* This class is used to generate the line descriptors from multi-scale images */ 20 | class LineDescriptor 21 | { 22 | public: 23 | LineDescriptor(); 24 | LineDescriptor(unsigned int numOfBand, unsigned int widthOfBand); 25 | ~LineDescriptor(); 26 | enum{ 27 | NearestNeighbor=0, //the nearest neighbor is taken as matching 28 | NNDR=1//nearest/next ratio 29 | }; 30 | /*This function is used to detect lines from multi-scale images.*/ 31 | int OctaveKeyLines(cv::Mat & image, ScaleLines &keyLines); 32 | int GetLineDescriptor(cv::Mat & image, 33 | ScaleLines &keyLines); 34 | int MatchLineByDescriptor(ScaleLines &keyLinesLeft, ScaleLines &keyLinesRight, 35 | std::vector &matchLeft, std::vector &matchRight, 36 | int criteria=NNDR); 37 | float LowestThreshold;//global threshold for line descriptor distance, default is 0.35 38 | float NNDRThreshold;//the NNDR threshold for line descriptor distance, default is 0.6 39 | private: 40 | 41 | void sample(float *igray,float *ogray, float factor, int width, int height) 42 | { 43 | 44 | int swidth = (int)((float) width / factor); 45 | int sheight = (int)((float) height / factor); 46 | 47 | for(int j=0; j < sheight; j++) 48 | for(int i=0; i < swidth; i++) 49 | ogray[j*swidth + i] = igray[(int)((float) j * factor) * width + (int) ((float) i*factor)]; 50 | 51 | } 52 | void sampleUchar(uchar *igray,uchar *ogray, float factor, int width, int height) 53 | { 54 | 55 | int swidth = (int)((float) width / factor); 56 | int sheight = (int)((float) height / factor); 57 | 58 | for(int j=0; j < sheight; j++) 59 | for(int i=0; i < swidth; i++) 60 | ogray[j*swidth + i] = igray[(int)((float) j * factor) * width + (int) ((float) i*factor)]; 61 | 62 | } 63 | /*Compute the line descriptor of input line set. This function should be called 64 | *after OctaveKeyLines() function; */ 65 | int ComputeLBD_(ScaleLines &keyLines); 66 | /*For each octave of image, we define an EDLineDetector, because we can get gradient images (dxImg, dyImg, gImg) 67 | *from the EDLineDetector class without extra computation cost. Another reason is that, if we use 68 | *a single EDLineDetector to detect lines in different octave of images, then we need to allocate and release 69 | *memory for gradient images (dxImg, dyImg, gImg) repeatedly for their varying size*/ 70 | std::vector edLineVec_; 71 | 72 | int ksize_; //the size of Gaussian kernel: ksize X ksize, default value is 5. 73 | unsigned int numOfOctave_;//the number of image octave 74 | unsigned int numOfBand_;//the number of band used to compute line descriptor 75 | unsigned int widthOfBand_;//the width of band; 76 | std::vector gaussCoefL_;//the local gaussian coefficient apply to the orthogonal line direction within each band; 77 | std::vector gaussCoefG_;//the global gaussian coefficient apply to each Row within line support region 78 | }; 79 | 80 | 81 | 82 | 83 | 84 | 85 | #endif /* LINEDESCRIPTOR_HH_ */ 86 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/LineMatchingAlgorithm.hpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "LineDescriptor.hh" 6 | #include 7 | #include "PairwiseLineMatching.hh" 8 | #include "test.hpp" 9 | 10 | using namespace std; 11 | int image_process(string img1path, string img2path, string outpath, string paramfilepath, bool saveLR = false) 12 | { 13 | //load first image from file 14 | std::string imageName1 = img1path; 15 | std::string imageName2 = img2path; 16 | cout << "imgProcess: " << imageName1 << " + " << imageName2 << " = " << outpath << endl; 17 | cv::Mat leftImage; 18 | cv::Mat rightImage; 19 | 20 | leftImage = imread(imageName1, cv::IMREAD_GRAYSCALE); // Read the file 21 | rightImage = imread(imageName2, cv::IMREAD_GRAYSCALE); // Read the file 22 | if (!leftImage.data || !rightImage.data) // Check for invalid input 23 | { 24 | cout << "Could not open or find the image" << std::endl; 25 | return -1; 26 | } 27 | 28 | unsigned int imageWidth = leftImage.cols; //图片的宽与高 29 | unsigned int imageHeight = leftImage.rows; 30 | 31 | srand((unsigned)time(0)); 32 | int lowest = 100, highest = 255; 33 | int range = (highest - lowest) + 1; 34 | unsigned int r, g, b; //the color of lines 35 | 36 | //initial variables 37 | cv::Mat leftColorImage(leftImage.size(), CV_8UC3); 38 | cv::Mat rightColorImage(rightImage.size(), CV_8UC3); 39 | 40 | cvtColor(leftImage, leftColorImage, cv::COLOR_GRAY2RGB); 41 | cvtColor(rightImage, rightColorImage, cv::COLOR_GRAY2RGB); 42 | 43 | ///////////#################################################################### 44 | ///////////#################################################################### 45 | //extract lines, compute their descriptors and match lines 46 | //提取线段，计算descriptor并进行线段匹配 47 | LineDescriptor lineDesc; 48 | PairwiseLineMatching lineMatch; 49 | 50 | ScaleLines linesInLeft; 51 | ScaleLines linesInRight; 52 | std::vector matchResult; 53 | 54 | lineDesc.GetLineDescriptor(leftImage, linesInLeft); 55 | lineDesc.GetLineDescriptor(rightImage, linesInRight); 56 | //TODO remove BIAS dependecies in PairwiseMatching 57 | lineMatch.LineMatching(linesInLeft, linesInRight, matchResult); 58 | //save the param of matching 59 | if(!paramfilepath.empty()) 60 | { 61 | svScaleLines(linesInLeft,paramfilepath,"Llines"); 62 | svScaleLines(linesInRight,paramfilepath,"Rlines"); 63 | svMatchResult(matchResult,paramfilepath,"MatchRes"); 64 | } 65 | ///////////#################################################################### 66 | ///////////#################################################################### 67 | //draw extracted lines into images 68 | cv::Point startPoint; 69 | cv::Point endPoint; 70 | cv::Point point; 71 | 72 | for (unsigned int i = 0; i < linesInLeft.size(); i++) 73 | { 74 | r = lowest + int(rand() % range); 75 | g = lowest + int(rand() % range); 76 | b = lowest + int(rand() % range); 77 | startPoint = cv::Point(int(linesInLeft[i][0].startPointX), int(linesInLeft[i][0].startPointY)); 78 | endPoint = cv::Point(int(linesInLeft[i][0].endPointX), int(linesInLeft[i][0].endPointY)); 79 | cv::line(leftColorImage, startPoint, endPoint, cv::Scalar(r, g, b)); 80 | } 81 | for (unsigned int i = 0; i < linesInRight.size(); i++) 82 | { 83 | r = lowest + int(rand() % range); 84 | g = lowest + int(rand() % range); 85 | b = lowest + int(rand() % range); 86 | startPoint = cv::Point(int(linesInRight[i][0].startPointX), int(linesInRight[i][0].startPointY)); 87 | endPoint = cv::Point(int(linesInRight[i][0].endPointX), int(linesInRight[i][0].endPointY)); 88 | cv::line(rightColorImage, startPoint, endPoint, cv::Scalar(r, g, b)); 89 | } 90 | if (saveLR) 91 | { 92 | imwrite("LinesInImage1.png", leftColorImage); 93 | imwrite("LinesInImage2.png", rightColorImage); 94 | } 95 | //TODO enable after BIAS dependecies in PairwiseMatching have been removed 96 | // ///////////#################################################################### 97 | // ///////////#################################################################### 98 | // //store the matching results of the first and second images into a single image 99 | double ww1, ww2; 100 | int lineIDLeft; 101 | int lineIDRight; 102 | int lowest1 = 0, highest1 = 255; 103 | int range1 = (highest1 - lowest1) + 1; 104 | std::vector r1(matchResult.size() / 2), g1(matchResult.size() / 2), b1(matchResult.size() / 2); //the color of lines 105 | 106 | for (unsigned int pair = 0; pair < matchResult.size() / 2; pair++) 107 | { 108 | r1[pair] = lowest1 + int(rand() % range1); 109 | g1[pair] = lowest1 + int(rand() % range1); 110 | b1[pair] = 255 - r1[pair]; 111 | ww1 = 0.2 * (rand() % 5); 112 | ww2 = 1 - ww1; 113 | char buf[10]; 114 | sprintf(buf, "%d ", pair); 115 | lineIDLeft = matchResult[2 * pair]; 116 | lineIDRight = matchResult[2 * pair + 1]; 117 | startPoint = cv::Point(int(linesInLeft[lineIDLeft][0].startPointX), int(linesInLeft[lineIDLeft][0].startPointY)); 118 | endPoint = cv::Point(int(linesInLeft[lineIDLeft][0].endPointX), int(linesInLeft[lineIDLeft][0].endPointY)); 119 | cv::line(leftColorImage, startPoint, endPoint, CV_RGB(r1[pair], g1[pair], b1[pair]), 4, cv::LINE_AA, 0); 120 | startPoint = cv::Point(int(linesInRight[lineIDRight][0].startPointX), int(linesInRight[lineIDRight][0].startPointY)); 121 | endPoint = cv::Point(int(linesInRight[lineIDRight][0].endPointX), int(linesInRight[lineIDRight][0].endPointY)); 122 | cv::line(rightColorImage, startPoint, endPoint, CV_RGB(r1[pair], g1[pair], b1[pair]), 4, cv::LINE_AA, 0); 123 | } 124 | 125 | cv::Mat cvResultColorImage1 = cv::Mat(cv::Size(imageWidth * 2, imageHeight), leftColorImage.type(), 3); 126 | cv::Mat cvResultColorImage2 = cv::Mat(cv::Size(imageWidth * 2, imageHeight), leftColorImage.type(), 3); 127 | cv::Mat cvResultColorImage = cv::Mat(cv::Size(imageWidth * 2, imageHeight), leftColorImage.type(), 3); 128 | cv::Mat roi = cvResultColorImage1(cv::Rect(0, 0, imageWidth, imageHeight)); 129 | cv::resize(leftColorImage, roi, roi.size(), 0, 0, 0); 130 | 131 | cv::Mat roi2 = cvResultColorImage1(cv::Rect(imageWidth, 0, imageWidth, imageHeight)); 132 | cv::resize(rightColorImage, roi2, roi2.size(), 0, 0, 0); 133 | cvResultColorImage1.copyTo(cvResultColorImage2); 134 | 135 | for (unsigned int pair = 0; pair < matchResult.size() / 2; pair++) 136 | { 137 | lineIDLeft = matchResult[2 * pair]; 138 | lineIDRight = matchResult[2 * pair + 1]; 139 | startPoint = cv::Point(int(linesInLeft[lineIDLeft][0].startPointX), int(linesInLeft[lineIDLeft][0].startPointY)); 140 | endPoint = cv::Point(int(linesInRight[lineIDRight][0].startPointX + imageWidth), int(linesInRight[lineIDRight][0].startPointY)); 141 | cv::line(cvResultColorImage2, startPoint, endPoint, CV_RGB(r1[pair], g1[pair], b1[pair]), 1, cv::LINE_AA, 0); 142 | } 143 | cv::addWeighted(cvResultColorImage1, 0.5, cvResultColorImage2, 0.5, 0.0, cvResultColorImage, -1); 144 | 145 | cv::imwrite(outpath+".png", cvResultColorImage); 146 | cvResultColorImage.release(); 147 | cvResultColorImage1.release(); 148 | cvResultColorImage2.release(); 149 | leftImage.release(); 150 | rightImage.release(); 151 | leftColorImage.release(); 152 | rightColorImage.release(); 153 | roi.release(); 154 | roi2.release(); 155 | 156 | cout << "number of total matches = " << matchResult.size() / 2 << endl; 157 | matchResult.clear(); 158 | linesInLeft.clear(); 159 | linesInRight.clear(); 160 | 161 | } 162 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/LineStructure.hh: -------------------------------------------------------------------------------- 1 | 2 | 3 | #ifndef LINESTRUCTURE_HH_ 4 | #define LINESTRUCTURE_HH_ 5 | 6 | #include 7 | // A 2D line (normal equation parameters). 8 | struct SingleLine 9 | { 10 | //note: rho and theta are based on coordinate origin, i.e. the top-left corner of image 11 | double rho;//unit: pixel length 12 | double theta;//unit: rad 13 | double linePointX;// = rho * cos(theta); 14 | double linePointY;// = rho * sin(theta); 15 | //for EndPoints, the coordinate origin is the top-left corner of image. 16 | double startPointX; 17 | double startPointY; 18 | double endPointX; 19 | double endPointY; 20 | //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. 21 | double direction; 22 | //mean gradient magnitude 23 | double gradientMagnitude; 24 | //mean gray value of pixels in dark side of line 25 | double darkSideGrayValue; 26 | //mean gray value of pixels in light side of line 27 | double lightSideGrayValue; 28 | //the length of line 29 | double lineLength; 30 | //the width of line; 31 | double width; 32 | //number of pixels 33 | int numOfPixels; 34 | //the decriptor of line 35 | std::vector descriptor; 36 | }; 37 | 38 | // Specifies a vector of lines. 39 | typedef std::vector Lines_list; 40 | 41 | struct OctaveSingleLine 42 | { 43 | /*endPoints, the coordinate origin is the top-left corner of the original image. 44 | *startPointX = sPointInOctaveX * (factor)^octaveCount; */ 45 | float startPointX; 46 | float startPointY; 47 | float endPointX; 48 | float endPointY; 49 | //endPoints, the coordinate origin is the top-left corner of the octave image. 50 | float sPointInOctaveX; 51 | float sPointInOctaveY; 52 | float ePointInOctaveX; 53 | float ePointInOctaveY; 54 | //direction of a line, the angle between positive line direction (dark side is in the left) and positive X axis. 55 | float direction; 56 | //the summation of gradient magnitudes of pixels on lines 57 | float salience; 58 | //the length of line 59 | float lineLength; 60 | //number of pixels 61 | unsigned int numOfPixels; 62 | //the octave which this line is detected 63 | unsigned int octaveCount; 64 | //the decriptor of line 65 | std::vector descriptor; 66 | }; 67 | 68 | // Specifies a vector of lines. 69 | typedef std::vector LinesVec; 70 | 71 | typedef std::vector ScaleLines;//each element in ScaleLines is a vector of lines which corresponds the same line detected in different octave images. 72 | 73 | #endif /* LINESTRUCTURE_HH_ */ 74 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/PairwiseLineMatching.hh: -------------------------------------------------------------------------------- 1 | /* 2 | * PairwiseLineMatching.hh 3 | * 4 | * Created on: 2011-8-19 5 | * Author: lz 6 | */ 7 | 8 | #ifndef PAIRWISELINEMATCHING_HH_ 9 | #define PAIRWISELINEMATCHING_HH_ 10 | #include 11 | #include 12 | #include "LineDescriptor.hh" 13 | #include 14 | 15 | 16 | //each node in the graph is a possible line matching pair in the left and right image 17 | struct Node{ 18 | unsigned int leftLineID;//the index of line in the left image 19 | unsigned int rightLineID;//the index of line in the right image 20 | }; 21 | 22 | // Specifies a vector of nodes. 23 | typedef std::vector Nodes_list; 24 | 25 | struct CompareL { 26 | bool operator() (const double& lhs, const double& rhs) const 27 | {return lhs>rhs;} 28 | }; 29 | typedef std::multimap EigenMAP; 30 | struct CompareS { 31 | bool operator() (const double& lhs, const double& rhs) const 32 | {return lhs DISMAP; 35 | 36 | class PairwiseLineMatching 37 | { 38 | public: 39 | PairwiseLineMatching(){}; 40 | void LineMatching(ScaleLines &linesInLeft,ScaleLines &linesInRight, std::vector &matchResult); 41 | ~PairwiseLineMatching(){}; 42 | 43 | private: 44 | /* Compute the approximate global rotation angle between image pair(i.e. the left and right images). 45 | * As shown in Bin Fan's work "Robust line matching through line-point invariants", this approximate 46 | * global rotation angle can greatly prune the spurious line correspondences. This is the idea of their 47 | * fast matching version. Nevertheless, the approaches to estimate the approximate global rotation angle 48 | * are different. Their is based on the rotation information included in the matched point feature(such as SIFT) 49 | * while ours is computed from angle histograms of lines in images. Our approach also detect whether there is an 50 | * appropriate global rotation angle between image pair. 51 | * step 1: Get the angle histograms of detected lines in the left and right images, respectively; 52 | * step 2: Search the shift angle between two histograms to minimize their difference. Take this shift angle as 53 | * approximate global rotation angle between image pair. 54 | * input: detected lines in the left and right images 55 | * return: the global rotation angle 56 | */ 57 | double GlobalRotationOfImagePair_(ScaleLines &linesInLeft, ScaleLines &linesInRight); 58 | /* Build the symmetric non-negative adjacency matrix M, whose nodes are the potential assignments a = (i_l, j_r) 59 | * and whose weights on edges measure the agreements between pairs of potential assignments. That is where the pairwise 60 | * constraints are applied(c.f. A spectral technique for correspondence problems using pairwise constraints, M.Leordeanu). 61 | */ 62 | void BuildAdjacencyMatrix_(ScaleLines &linesInLeft,ScaleLines &linesInRight); 63 | /* Get the final matching from the principal eigenvector. 64 | */ 65 | void MatchingResultFromPrincipalEigenvector_(ScaleLines &linesInLeft,ScaleLines &linesInRight, 66 | std::vector &matchResult); 67 | double globalRotationAngle_;//the approximate global rotation angle between image pairs 68 | 69 | /*construct a map to store the principal eigenvector and its index. 70 | *each pair in the map is in this form (eigenvalue, index); 71 | *Note that, we use eigenvalue as key in the map and index as their value. 72 | *This is because the map need be sorted by the eigenvalue rather than index 73 | *for our purpose. 74 | */ 75 | EigenMAP eigenMap_; 76 | Nodes_list nodesList_;//save all the possible matched line pairs 77 | double minOfEigenVec_;//the acceptable minimal value in the principal eigen vector; 78 | }; 79 | 80 | 81 | 82 | 83 | #endif /* PAIRWISELINEMATCHING_HH_ */ 84 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/Untitled1.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | using namespace std; 3 | int main(){ 4 | return 0; 5 | } 6 | -------------------------------------------------------------------------------- /CodeForLineMatching/removed/Untitled1.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scottwon/LineMatching/11d9acf514f0b121f5ff6fdd2cbb63720b46ab8a/CodeForLineMatching/removed/Untitled1.exe -------------------------------------------------------------------------------- /CodeForLineMatching/removed/test.hpp: -------------------------------------------------------------------------------- 1 | #ifndef TEST_HPP 2 | #define TEST_HPP 3 | #include "LineDescriptor.hh" 4 | #include 5 | using namespace std; 6 | void svScaleLines(ScaleLines &sl,string filename,string subname) 7 | { 8 | int j; 9 | int i; 10 | int k; 11 | ofstream savefile(filename + subname + ".txt"); 12 | for(i = 0;i matchresult, string filename, string subname) 40 | { 41 | int i; 42 | ofstream savefile(filename + subname + ".txt"); 43 | for(i = 0;i 13 | #include 14 | #include 15 | #include 16 | #include 17 | #include 18 | #include 19 | 20 | #include 21 | #include 22 | 23 | #include 24 | // #include 25 | // #include 26 | // #include 27 | // #include 28 | // #include 29 | // #include 30 | // #include 31 | // #include 32 | // #include 33 | 34 | 35 | using namespace std; 36 | using namespace cv; 37 | 38 | struct SEGMENT { 39 | float x1, y1, x2, y2, angle; 40 | int label; 41 | }; 42 | 43 | struct Point4f { 44 | float x1,y1,x2,y2; 45 | }; 46 | 47 | #endif 48 | -------------------------------------------------------------------------------- /Multiple Homography Estimation via Stereo Line Matching for Textureless Indoor Scenes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scottwon/LineMatching/11d9acf514f0b121f5ff6fdd2cbb63720b46ab8a/Multiple Homography Estimation via Stereo Line Matching for Textureless Indoor Scenes.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Multiple Homography Estimation via Stereo Line Matching for Textureless Indoor Scenes 2 | 3 | I have done the oral presentation on this topic on the International Conference on Control, Automation and Robotics(ICCAR2019). This paper can be found online at: https://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=iccar&tdsourcetag=s_pctim_aiomsg. 4 | 5 | The major contributions of this paper are: 6 | 7 | 1. Developed a unified approach for line detection and stereo matching that can produce appealing results in indoor textureless scenes. 8 | 9 | 2. Proposed a co-planar line classification algorithm. 10 | 11 | 3. Pointed out that the compatibility of multiple homographies can be improved by enforcing the epipolar line constraints. 12 | 13 | The code here is our hybrid algorithm for line detection and stereo matching. 14 | 15 | In https://github.com/slslam/slslam, J. Lee, S. Lee, G. Zhang, J. Lim and I. Suh proposed a Canny-based line extractor and integrated it with the MSLD descriptor for line detection and matching. It is shown that this line detection algorithm is capable of detecting weak textures as well as generating non-fragmented line segments. 16 | 17 | In https://github.com/mtamburrano/LBD_Descriptor, L. Zhang and R. Koch proposed the LBD descriptor for line matching. According to them, LBD descriptor is more efficient to compute and it is faster to generate the matching results than the state-of-the-art methods. 18 | 19 | However, there are a couple of drawbacks of the originally proposed LBD-based approach. First, in the originally proposed LBD-based approach, LBD descriptor is incorporated with EDLine detector and the latter is proved to be inefficient to detect low-contrast edges in our experiments. Second, in the originally proposed LBD-based approach, efforts are made to overcome the scale changes and the global rotation, which won't produce any improvement in our cases given that we apply the algorithm to calibrated, stereo cameras. 20 | 21 | Our code has the advantages of both the Canny+MSLD approach and the EDLine+LBD approach and it can produce superior results in the indoor textureless scenes. 22 | --------------------------------------------------------------------------------