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Bm]v}a um '._d IAJJ iYa6==hE.%& YIU`t &&0\% IEG'Tw M {!tl j\ h!f^ (w wb F Z R  LiրNmmQl )\b+4Q')'')'O??;:\;:o:<:<:!_descriptors.empty()add/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/bagofwords.cppdescriptors[0].cols == _descriptors.colsdescriptors[0].type() == _descriptors.type()virtual cv::Mat cv::BOWKMeansTrainer::cluster() const!descriptors.empty()clustervirtual cv::Mat cv::BOWKMeansTrainer::cluster(const cv::Mat &) constvoid cv::BOWImgDescriptorExtractor::compute(cv::InputArray, std::vector &, cv::OutputArray, std::vector > *, cv::Mat *)void cv::BOWImgDescriptorExtractor::compute(cv::InputArray, cv::OutputArray, std::vector > *)!vocabulary.empty()computequeryIdx == (int)iallocator::allocate(size_t n) 'n' exceeds maximum supported sizeN2cv10BOWTrainerEN2cv16BOWKMeansTrainerEN2cv25BOWImgDescriptorExtractorE/0P`pXA3` p Ax4`HH XA4 0 P * XA|5pA5  A$6 XAp6j XA47'' ( Al8) A8 , @-<aA8. 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M>JL BUf! N3,A $Jf! U/= Fj! \6D Mf!} uU2@ Ik!\ TH&4 =k!; 3K+ 4k! Gl d'f! J K C f~ !  _ * " k !  R    f !  K   ft !u m U   ku !T L R  fn!3 +K }f`! Yd \ke! KC ;fW!x N" fL!m v<    #6 DW ex      +> L_ m     % 3F Tg u      - ;N \o }     / :M Xk |t33t\$M0m]ge(  uIY Q m  R  ^pug TkAlltl]l3KmCu; +#0 [Mo%e ]\ nP0  w[  kS `Kh yw+m#  IA%X})[..6I  )'P)'VÀA ###.-DT!)@?{Gz??o@?thresholdStepminThresholdmaxThresholdminRepeatabilityminDistBetweenBlobsfilterByColorblobColorfilterByAreaminAreamaxAreafilterByCircularityminCircularitymaxCircularityfilterByInertiaminInertiaRatiomaxInertiaRatiofilterByConvexityminConvexitymaxConvexityvirtual void cv::SimpleBlobDetectorImpl::findBlobs(cv::InputArray, cv::InputArray, std::vector
&) const/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/blobdetector.cppvirtual void cv::SimpleBlobDetectorImpl::detect(cv::InputArray, std::vector &, cv::InputArray)Blob detector only supports 8-bit images!detect.SimpleBlobDetectorNo element name has been givenoperator<</Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/persistence.hppallocator::allocate(size_t n) 'n' exceeds maximum supported sizePPPPPPcctdc5PPPPN2cv18SimpleBlobDetectorEN2cv22SimpleBlobDetectorImplEN2cv6detail12PtrOwnerImplINS_22SimpleBlobDetectorImplENS_14DefaultDeleterIS2_EEEEN2cv6detail8PtrOwnerEwwaXAU Mp3!/!?aPSXAx]53XA4_GaA`H!A`pIII#I+J#0J+pJcXAdaKXAaN PQXAaSlXPU`U pU'zRx $8wAC $DwAC $lAC LzPLRx 4$0'AC M$MAC $3AC C$<ȫAC $dAC $/AC C$?AC F4LȫSAC P43cAC P,AC F,xAC G$AC B$AC $#AC B$$+AC B$L#AC B$t+AC B4 (c AC M4D`AC J, AC G4QAC M,tlAC J$AC $ AC $'AC 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M    D \ ? . { {  2  z |   zE k  }uii !!?  * ;3IAee 9  1)րNyy!Z R] nf %0 }&8Vlz;CDp3OG4! X |+  ! ^q{  YY0"   >m@   (^ rjրN##  +# '+~g^'y^4K  U~]B[!!AGn.6v\iրNnnaC|Y)'67)'7}uRR!ls d|N!N FQ \o x)'{??A@?HB????4:M/h'static void EllipticKeyPoint::convert(const std::vector &, std::vector &)/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/evaluation.cppradconvertstatic void EllipticKeyPoint::convert(const std::vector &, std::vector &)!H.empty() && H.cols == 3 && H.rows == 3calcProjectionvoid cv::evaluateFeatureDetector(const cv::Mat &, const cv::Mat &, const cv::Mat &, std::vector *, std::vector *, float &, int &, const Ptr &)fdetector must not be empty when keypoints1 or keypoints2 is emptyevaluateFeatureDetectorvoid cv::computeRecallPrecisionCurve(const std::vector > &, const std::vector > &, std::vector &)matches1to2.size() == correctMatches1to2Mask.size()computeRecallPrecisionCurvefloat cv::getRecall(const std::vector &, float)int cv::getNearestPoint(const std::vector &, float)allocator::allocate(size_t n) 'n' exceeds maximum supported sizeminy < maxyoperator()dr > 1.19209290e-7F(0N(N!! 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N ZI %-  % u i  u  u  20 A Y j    - q  u I u     % ; G*q %  ~ ? !  y %]  U i ) u  u . 2` q    7 ] q  u y u  IA+,~$p~ ~b )'Q'+~g^5[kS5[kS!n!!5?!`hN     -+!4  AAL7ktDnkpzr)'OÀA29*22.)'^^fN((qvN9 7 5 3 1"L[ ;Ze=U]Uۂ]] %  % r  HA t9 "l1 _)  ! y, > J{   _ k  qj ~  @@>'R"   !"lC dT..,< E  F o } AA  5 Mj v ..x ! | qJwBiu:o2q *}F tk kt  n  4 ,  ( 4 Z, ( &{ .0y  R [.t l I d  ~\  uT   9 ~{ usGLFF4    ) A ^ j . . .   ! 2   dw Mo ug y u_  oW  O  J  k ( k  m* @ T L րN ; )'0W ~    {}7}qXi0IAFFj z )/ - O) 4 4     Y ~  u  Y ~  B)~i~@րN[[ Fl qzb+'y^[!!AGn.6v\iրNnnaC|Y??L>?@???>?? @??5< @?????5<?5<??0><ÇAÇA0FbA0FbA?>p???`/YkBk1~sDvoid cv::AKAZEFeatures::Allocate_Memory_Evolution()/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/kaze/AKAZEFeatures.cppvoid cv::AKAZEFeatures::Feature_Detection(std::vector &)void cv::AKAZEFeatures::Find_Scale_Space_Extrema(std::vector &)void cv::AKAZEFeatures::Do_Subpixel_Refinement(std::vector &, std::vector &)void cv::AKAZEFeatures::Compute_Descriptors(std::vector &, cv::OutputArray)0 <= kpts[i].class_id && kpts[i].class_id < static_cast(evolution_.size())Compute_Descriptorsvoid cv::AKAZEFeatures::Compute_Keypoints_Orientation(std::vector &) constdesc_size == dsizeGet_MSURF_Upright_Descriptor_64dcount == desc_sizeGet_MSURF_Descriptor_64options.descriptor_channels <= max_channelsGet_Upright_MLDB_Full_Descriptor(pattern_size & 1) == 0dcount1 <= desc_size*8divUp(dcount1, 8) == desc_sizesize == Lx.size()MLDB_Fill_Valuessize == Ly.size()options_->descriptor_channels <= max_channelsGet_MLDB_Full_Descriptordpos == 486divUp(dpos, 8) == desc_sizechannels <= max_channelsGet_MLDB_Descriptor_Subsetcoords[0] >= 0 && coords[0] < 3divUp(descriptorBits_.rows, 8) == desc_sizeGet_Upright_MLDB_Descriptor_SubsetgenerateDescriptorSubsamplenbits <= ssz && "Descriptor size can't be bigger than full descriptor (486 = 162*3 - 3 channels)"x0 - 6 * scale >= 0 && x0 + 6 * scale < Lx.colsSample_Derivative_Response_Radius6y0 - 6 * scale >= 0 && y0 + 6 * scale < Lx.rowsk < 109gtablecum[nkeys] == nquantized_counting_sortallocator::allocate(size_t n) 'n' exceeds maximum supported sizevoid cv::create_nonlinear_scale_space(cv::InputArray, const cv::AKAZEOptions &, const std::vector > &, std::vector > &) [MatType = cv::UMat]evolution.size() > 0create_nonlinear_scale_spacevoid cv::Compute_Determinant_Hessian_Response(cv::UMatPyramid &)void cv::compute_determinant(cv::InputArray, cv::InputArray, cv::InputArray, cv::OutputArray, float)AKAZE_compute_determinantfloat cv::compute_kcontrast(cv::InputArray, cv::InputArray, float, int)nbins > 2compute_kcontrast!Lx_.empty()void cv::compute_diffusivity(cv::InputArray, cv::InputArray, cv::OutputArray, float, int)Diffusivity is not supportedcompute_diffusivityAKAZE_pm_g2void cv::non_linear_diffusion_step(cv::InputArray, cv::InputArray, cv::OutputArray, float)AKAZE_nld_step_scalarvoid cv::nld_step_scalar_one_lane(const cv::Mat &, const cv::Mat &, cv::Mat &, float, int, int)void cv::create_nonlinear_scale_space(cv::InputArray, const cv::AKAZEOptions &, const std::vector > &, std::vector > &) [MatType = cv::Mat]void cv::Compute_Determinant_Hessian_Response(cv::Pyramid &)N2cv22FindKeypointsSameScaleEN2cv35MSURF_Upright_Descriptor_64_InvokerEN2cv27MSURF_Descriptor_64_InvokerEN2cv36Upright_MLDB_Full_Descriptor_InvokerEN2cv38Upright_MLDB_Descriptor_Subset_InvokerEN2cv28MLDB_Full_Descriptor_InvokerEN2cv30MLDB_Descriptor_Subset_InvokerEN2cv26ComputeKeypointOrientationE<-}-%}-}-}- }-}-|-|-||-|-{-|{-X{-z-z-Mz-z-y-y-y-|y-6v-.v-v|-u-u-u|-rrz-o-zn-rn-jn-Rn-Jn-*n=#n--_-^-^:^=^-z^-f^-X^-P^-H^-.^-$^-]-]-]-0]-]-\-\-s\-k\-c\-[\-S\-9\--\-!\-\- \-[-[-[-[-[-[=[-t[-[-Z-Z-ZZ-Y-Y-UY-@W-W-hV-`V-HV-U-U-U-T-T-T- T-T-T-S-S-S-S-S-S-S-S-{S-iS-WS-ES-3S-S-R-R-oR-fR-ZR-DR1R-,R= R-Q-Q-Q-Q-Q-Q-Q-cQ-VQ-BQ-6Q8(Q=P-P-P-P-P-P-P-P-P-P-P-sP- P-O-qO-JO-N-N-3N-M-M-M-M-lM-`M-TM-#M-L-L-!L-K-K-ZK-J-J6-I-I-H-OH-G-G-hF-F-(B- B-B-A-A-A-hA-^A-@-@-@-l@-E@-6@-.@-&@-@-@-?-?-?-?-?-?-?-?-p?=i?-T?->->-a>-:>-=-=-=-=C=&=-=-e<-]<-E<-;-;-|;-:-:-:-:-:-:-:-~:-u:-a:-O:-=:-+:-9-9-9-9-9-x9-k9-b9-V9-@9-9-(9= 9-8-8-8-8-8-8-o8-j8-N8-A8--8-!848=7-7-7-7-7-7-7-x7-l7-`7-T7-97-/7- 7-7-7-6=6-6-6-6-6-6-6-6-a6-X6-D6-06-6- 6-5-5-5-k5-_5-K5D5 5-4-4-4-44-4=4-n4-Z4-14-4- 4-3-3-3-3-3-z3-m3-\3-S32E3=3-3-3-2-2-2-P2-F2-1-1-1-1-1-1-1-|1-o1-b1-S1-K1-A1-0-0-0-|0-[0-/`/-;/- /-.=.-.-1.- .-------,+-+-+-X*-P*-8*-)-)-)-w)p)U)-4)-,)- )- ))(-(-(0(=y(-q(-i(-a(-(-'-''-''-P'-/'- '-&-&-%&- &-&-%-%-%-%-%-%-u%-N%-.%-"-"-"-z"-m"-"-B!- -- --{-a-Y-P-D-8--------}-0-k-c-M-A-5-------u-R--]--^-V -K----- -===-----v-O-E----C----%-U-- - 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B%  *O% I _ n % u  2 : O ] X +      ( 3 > O G NN\ e  DQ    -)!+ <+!#+J!kv +!* "4 [+!  .D<<Rm+!\ +|!u +D!ep +!C " W g r s I 6" 0 9 ,e s |  r j eT A Y  { Y Z R Y 9* 1< )Y !  +Y  K + !   +{Y!z {+] !C,;= ]a+q!#q Y1?UYp+l!u l+b!+ bY+vG!h s v+g!\3"gW Yg r92 L) DO ]u     KF T_ mx     - ;F T_ mx      & 4G U    )< J] k     # 1T/66D8|%| t r Cu bRdZrj RkJg فAApO,*"Yu dGthp ( ΂JJ03W $ n  R  &rgI~lW c*  |f   B 3  +  u#    ( Li)'_)'f4ÀA29*22.4Y'X)'4R E4Q')''րN[[ Fl qzb+րNmmQl )\b+)'_րN l:Y\b2+4??:,EPGYNyܯ(ywimageCount > 0set/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/matchers.cppdim > 0descriptors[i].cols == dim && descriptors[i].type() == typeimgIdx < (int)startIdxs.size()getDescriptorglobalIdx < (int)size()globalDescIdx < size()(globalDescIdx>=0) && (globalDescIdx < size())getLocalIdx_descriptors.isUMat() || _descriptors.isUMatVector() || _descriptors.isMat() || _descriptors.isMatVector()addvoid cv::DescriptorMatcher::match(cv::InputArray, cv::InputArray, std::vector &, cv::InputArray) constvoid cv::DescriptorMatcher::knnMatch(cv::InputArray, cv::InputArray, std::vector > &, int, cv::InputArray, bool) constvoid cv::DescriptorMatcher::radiusMatch(cv::InputArray, cv::InputArray, std::vector > &, float, cv::InputArray, bool) constvoid cv::DescriptorMatcher::match(cv::InputArray, std::vector &, cv::InputArrayOfArrays)masks.size() == imageCountcheckMasksmasks[i].rows == queryDescriptorsCount && masks[i].cols == rows && masks[i].type() == (((0) & ((1 << 3) - 1)) + (((1)-1) << 3))void cv::DescriptorMatcher::knnMatch(cv::InputArray, std::vector > &, int, cv::InputArrayOfArrays, bool)knn > 0knnMatchvoid cv::DescriptorMatcher::radiusMatch(cv::InputArray, std::vector > &, float, cv::InputArrayOfArrays, bool)maxDistance > std::numeric_limits::epsilon()radiusMatch_queryDescriptors.type() == trainDescTypeknnMatchImpl(int64)imgCount*IMGIDX_ONE < 2147483647trainDescCollection[iIdx].rows < IMGIDX_ONEradiusMatchImplFlannBasedBruteForceBruteForce-SL2BruteForce-L1BruteForce-HammingBruteForce-HammingLUTBruteForce-Hamming(2)Unknown matcher namecreateSpecified descriptor matcher type is not supported._indexParamsFlannBasedMatcher_searchParamsvirtual void cv::FlannBasedMatcher::train()trainDescCollection.size() == 0trainindexParamsip.type() == FileNode::SEQreadip[i].type() == FileNode::MAPnametypevaluesearchParamssp.type() == FileNode::SEQsp[i].type() == FileNode::MAPdeep clone functionality is not implemented, because Flann::Index has not copy constructor or clone method clonevirtual void cv::FlannBasedMatcher::knnMatchImpl(cv::InputArray, std::vector > &, int, cv::InputArrayOfArrays, bool)virtual void cv::FlannBasedMatcher::radiusMatchImpl(cv::InputArray, std::vector > &, float, cv::InputArrayOfArrays, bool)knnMatches[i].size() <= 1convertMatchesallocator::allocate(size_t n) 'n' exceeds maximum supported size-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d-D T_FLOATBruteForceMatch_MatchBruteForceMatch_knnMatch-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%dBruteForceMatch_RadiusMatchNo element name has been givenoperator<</Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/persistence.hppN2cv9BFMatcherEN2cv17DescriptorMatcherEN2cv17DescriptorMatcher20DescriptorCollectionEN2cv17FlannBasedMatcherEN2cv6detail12PtrOwnerImplINS_9BFMatcherENS_14DefaultDeleterIS2_EEEEN2cv6detail8PtrOwnerEN2cv6detail12PtrOwnerImplINS_5flann17KDTreeIndexParamsENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_17FlannBasedMatcherENS_14DefaultDeleterIS2_EEEEN2cv6detail12PtrOwnerImplINS_5flann5IndexENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_5flann11IndexParamsENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_5flann12SearchParamsENS_14DefaultDeleterIS3_EEEE XAw@Z  AxpA x0 A8x XAPxXAHy   Ay  OXA4zpnaQ Az` XA{%XA|* + Al|, ,0,XA|1XAT}3XA}8VXA~9XAP>XA @XAHHHrXAt KXA@PeaPea Q AQcXA<@Y APZ@ AD[! 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LIMIA?<0A|0A|0IIOHL0H|0HD0L9tEH}ffff.KJKJKJHKHsH[HrHJHI9uHH}HHGLGHtHH[A\A]A^A_]H[A\A]A^A_]E11(@UHAWAVAUATSPIL'H_IM)IIEHH=ILM)LHHH9w]II9LBMMH}M9vGHH5HHHHH5HHH}MJ<H}NNAB ANBLNLL9HKL)Ht(HLҐCBCH[BHRHuHs KLHrBHfCBCCBCBCBCBCBCH[BHI9uHHILLOLGMtLH[A\A]A^A_]H[A\A]A^A_]E11 IHLUH]f.UH]fDUHSPHH{HtHPHHH[]`;f?o@? ڹ<@z?@??? #%5< aar%r r"r rg1rr{prmcr ;z2!zSz prS 1r^HKrH3r hr; /rryFy$wYv4vw<YvuF9w2s'we's$s 9w2sSziuxxu x) x!r>! r^!q! u!!u"u"u"q# q#m% qx%9'qA'*k' r'Av(s)I Tg xp)'{րN:: ]h<dme)'PjjM)'J8PPPPPPPPPPvirtual void cv::ORB_Impl::detectAndCompute(cv::InputArray, cv::InputArray, std::vector &, cv::OutputArray, bool)/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/features2d/src/orb.cpppatchSize >= 2detectAndComputelevel >= 00 <= levelwta_k == 2 || wta_k == 3 || wta_k == 4allocator::allocate(size_t n) 'n' exceeds maximum supported sizetotal() == 0 || data != __nullMat/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppStep must be a multiple of esz1ORB_HarrisResponses-D ORB_RESPONSES -D blockSize=%d -D scale_sq_sq=%.12ef -D HARRIS_K=%.12ffimg.type() == (((0) & ((1 << 3) - 1)) + (((1)-1) << 3)) && blockSize*blockSize <= 2048HarrisResponsesORB_ICAngle-D ORB_ANGLESORB_computeDescriptor-D ORB_DESCRIPTORS -D WTA_K=%dWrong wta_k. It can be only 2, 3 or 4.computeOrbDescriptors                                                                                                             N2cv3ORBEN2cv8ORB_ImplEN2cv6detail12PtrOwnerImplINS_8ORB_ImplENS_14DefaultDeleterIS2_EEEEN2cv6detail8PtrOwnerE  0XA0@u!ATu!A@XA 0!AX 0#`+    І      0 @ P ` p    :XAu!A PXAHЏX$XAȫu!A` AP A|lX0XA 0'zRx $X2 AC $D@2AC $l(2 AC zPLRx 4$1AC P,\ȲuAC G,uAC G4h#AC M, 7AC G$AC B$AC $AC $,hAC $TP#AC B$|X+AC B$` AC $H AC $0 AC $ AC $D AC $l AC $е AC $ AC $ AC $  AC $4p AC $\X AC $@ AC $( AC $ AC $ AC $$ AC $Lȴ AC 4:AC J,ȵuAC G,AC G4|AC M,DAC J4$AC J,uAC G4LAC G4`AC G,LlAC J4(AC J$AC $ AC $'AC B&- O---؞---۝=ԝ=Ɲ=--(- -K-O---f-Q-O--ݙ-™--'-== 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1548786701 501 20 100644 25356 ` opencl_kernels_features2d.cpp.o 8____text__TEXT__cstring__TEXT___data__DATA_`a$ b@b Pfeatures2dakaze__kernel void AKAZE_pm_g2(__global const float* lx, __global const float* ly, __global float* dst, float k, int size) { int i = get_global_id(0); if (!(i < size)) { return; } const float k2inv = 1.0f / (k * k); dst[i] = 1.0f / (1.0f + ((lx[i] * lx[i] + ly[i] * ly[i]) * k2inv)); } __kernel void AKAZE_nld_step_scalar(__global const float* lt, int lt_step, int lt_offset, int rows, int cols, __global const float* lf, __global float* dst, float step_size) { int i = get_global_id(1); int j = get_global_id(0); if (!(i < rows && j < cols)) { return; } int a = (i - 1) * cols; int c = (i ) * cols; int b = (i + 1) * cols; float res = 0.0f; if (i == 0) { if (j == 0 || j == (cols - 1)) { res = 0.0f; } else { res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) + (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) + (lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]); } } else if (i == (rows - 1)) { if (j == 0 || j == (cols - 1)) { res = 0.0f; } else { res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) + (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) + (lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]); } } else { if (j == 0) { res = (lf[c + 0] + lf[c + 1])*(lt[c + 1] - lt[c + 0]) + (lf[c + 0] + lf[b + 0])*(lt[b + 0] - lt[c + 0]) + (lf[c + 0] + lf[a + 0])*(lt[a + 0] - lt[c + 0]); } else if (j == (cols - 1)) { res = (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) + (lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]) + (lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]); } else { res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) + (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) + (lf[c + j] + lf[b + j ])*(lt[b + j ] - lt[c + j]) + (lf[c + j] + lf[a + j ])*(lt[a + j ] - lt[c + j]); } } dst[c + j] = res * step_size; } __kernel void AKAZE_compute_determinant(__global const float* lxx, __global const float* lxy, __global const float* lyy, __global float* dst, float sigma, int size) { int i = get_global_id(0); if (!(i < size)) { return; } dst[i] = (lxx[i] * lyy[i] - lxy[i] * lxy[i]) * sigma; } 80f6cd2f334b70062ed64a0a1a866593brute_force_match#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable #define MAX_FLOAT 3.40282e+038f #ifndef T #define T float #endif #ifndef BLOCK_SIZE #define BLOCK_SIZE 16 #endif #ifndef MAX_DESC_LEN #define MAX_DESC_LEN 64 #endif #define BLOCK_SIZE_ODD (BLOCK_SIZE + 1) #ifndef SHARED_MEM_SZ # if (BLOCK_SIZE < MAX_DESC_LEN) # define SHARED_MEM_SZ (kercn * (BLOCK_SIZE * MAX_DESC_LEN + BLOCK_SIZE * BLOCK_SIZE)) # else # define SHARED_MEM_SZ (kercn * 2 * BLOCK_SIZE_ODD * BLOCK_SIZE) # endif #endif #ifndef DIST_TYPE #define DIST_TYPE 2 #endif #if (DIST_TYPE == 2) # ifdef T_FLOAT typedef float result_type; # if (8 == kercn) typedef float8 value_type; # define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;} # elif (4 == kercn) typedef float4 value_type; # define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;} # else typedef float value_type; # define DIST(x, y) result += fabs((x) - (y)) # endif # else typedef int result_type; # if (8 == kercn) typedef int8 value_type; # define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;} # elif (4 == kercn) typedef int4 value_type; # define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;} # else typedef int value_type; # define DIST(x, y) result += abs((x) - (y)) # endif # endif # define DIST_RES(x) (x) #elif (DIST_TYPE == 4) typedef float result_type; # if (8 == kercn) typedef float8 value_type; # define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d.s0123, d.s0123) + dot(d.s4567, d.s4567);} # elif (4 == kercn) typedef float4 value_type; # define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d, d);} # else typedef float value_type; # define DIST(x, y) {value_type d = ((x) - (y)); result = mad(d, d, result);} # endif # define DIST_RES(x) sqrt(x) #elif (DIST_TYPE == 6) # if (8 == kercn) typedef int8 value_type; # elif (4 == kercn) typedef int4 value_type; # else typedef int value_type; # endif typedef int result_type; # define DIST(x, y) result += popcount( (x) ^ (y) ) # define DIST_RES(x) (x) #endif inline result_type reduce_block( __local value_type *s_query, __local value_type *s_train, int lidx, int lidy ) { result_type result = 0; #pragma unroll for (int j = 0 ; j < BLOCK_SIZE ; j++) { DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]); } return DIST_RES(result); } inline result_type reduce_block_match( __local value_type *s_query, __local value_type *s_train, int lidx, int lidy ) { result_type result = 0; #pragma unroll for (int j = 0 ; j < BLOCK_SIZE ; j++) { DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]); } return result; } inline result_type reduce_multi_block( __local value_type *s_query, __local value_type *s_train, int block_index, int lidx, int lidy ) { result_type result = 0; #pragma unroll for (int j = 0 ; j < BLOCK_SIZE ; j++) { DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]); } return result; } __kernel void BruteForceMatch_Match( __global T *query, __global T *train, __global int *bestTrainIdx, __global float *bestDistance, int query_rows, int query_cols, int train_rows, int train_cols, int step ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy); const int queryOffset = min(queryIdx, query_rows - 1) * step; __global TN *query_vec = (__global TN *)(query + queryOffset); query_cols /= kercn; __local float sharebuffer[SHARED_MEM_SZ]; __local value_type *s_query = (__local value_type *)sharebuffer; #if 0 < MAX_DESC_LEN __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN; #pragma unroll for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++) { const int loadx = mad24(BLOCK_SIZE, i, lidx); s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0; } #else __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE; const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx); const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy); #endif float myBestDistance = MAX_FLOAT; int myBestTrainIdx = -1; for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++) { result_type result = 0; const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step; __global TN *train_vec = (__global TN *)(train + trainOffset); #if 0 < MAX_DESC_LEN #pragma unroll for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++) { const int loadx = mad24(BLOCK_SIZE, i, lidx); s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0; barrier(CLK_LOCAL_MEM_FENCE); result += reduce_multi_block(s_query, s_train, i, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } #else for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++) { const int loadx = mad24(i, BLOCK_SIZE, lidx); if (loadx < query_cols) { s_query[s_query_i] = query_vec[loadx]; s_train[s_train_i] = train_vec[loadx]; } else { s_query[s_query_i] = 0; s_train[s_train_i] = 0; } barrier(CLK_LOCAL_MEM_FENCE); result += reduce_block_match(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } #endif result = DIST_RES(result); const int trainIdx = mad24(BLOCK_SIZE, t, lidx); if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance ) { myBestDistance = result; myBestTrainIdx = trainIdx; } } barrier(CLK_LOCAL_MEM_FENCE); __local float *s_distance = (__local float *)sharebuffer; __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE); s_distance += lidy * BLOCK_SIZE_ODD; s_trainIdx += lidy * BLOCK_SIZE_ODD; s_distance[lidx] = myBestDistance; s_trainIdx[lidx] = myBestTrainIdx; barrier(CLK_LOCAL_MEM_FENCE); #pragma unroll for (int k = 0 ; k < BLOCK_SIZE; k++) { if (myBestDistance > s_distance[k]) { myBestDistance = s_distance[k]; myBestTrainIdx = s_trainIdx[k]; } } if (queryIdx < query_rows && lidx == 0) { bestTrainIdx[queryIdx] = myBestTrainIdx; bestDistance[queryIdx] = myBestDistance; } } __kernel void BruteForceMatch_RadiusMatch( __global T *query, __global T *train, float maxDistance, __global int *bestTrainIdx, __global float *bestDistance, __global int *nMatches, int query_rows, int query_cols, int train_rows, int train_cols, int bestTrainIdx_cols, int step, int ostep ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); const int groupidy = get_group_id(1); const int queryIdx = mad24(BLOCK_SIZE, groupidy, lidy); const int queryOffset = min(queryIdx, query_rows - 1) * step; __global TN *query_vec = (__global TN *)(query + queryOffset); const int trainIdx = mad24(BLOCK_SIZE, groupidx, lidx); const int trainOffset = min(mad24(BLOCK_SIZE, groupidx, lidy), train_rows - 1) * step; __global TN *train_vec = (__global TN *)(train + trainOffset); query_cols /= kercn; __local float sharebuffer[SHARED_MEM_SZ]; __local value_type *s_query = (__local value_type *)sharebuffer; __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE; result_type result = 0; const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx); const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy); for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i) { const int loadx = mad24(BLOCK_SIZE, i, lidx); if (loadx < query_cols) { s_query[s_query_i] = query_vec[loadx]; s_train[s_train_i] = train_vec[loadx]; } else { s_query[s_query_i] = 0; s_train[s_train_i] = 0; } barrier(CLK_LOCAL_MEM_FENCE); result += reduce_block(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } if (queryIdx < query_rows && trainIdx < train_rows && convert_float(result) < maxDistance) { int ind = atom_inc(nMatches + queryIdx); if(ind < bestTrainIdx_cols) { bestTrainIdx[mad24(queryIdx, ostep, ind)] = trainIdx; bestDistance[mad24(queryIdx, ostep, ind)] = result; } } } __kernel void BruteForceMatch_knnMatch( __global T *query, __global T *train, __global int2 *bestTrainIdx, __global float2 *bestDistance, int query_rows, int query_cols, int train_rows, int train_cols, int step ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy); const int queryOffset = min(queryIdx, query_rows - 1) * step; __global TN *query_vec = (__global TN *)(query + queryOffset); query_cols /= kercn; __local float sharebuffer[SHARED_MEM_SZ]; __local value_type *s_query = (__local value_type *)sharebuffer; #if 0 < MAX_DESC_LEN __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN; #pragma unroll for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++) { int loadx = mad24(BLOCK_SIZE, i, lidx); s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0; } #else __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE; const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx); const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy); #endif float myBestDistance1 = MAX_FLOAT; float myBestDistance2 = MAX_FLOAT; int myBestTrainIdx1 = -1; int myBestTrainIdx2 = -1; for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt ; t++) { result_type result = 0; int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step; __global TN *train_vec = (__global TN *)(train + trainOffset); #if 0 < MAX_DESC_LEN #pragma unroll for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++) { const int loadx = mad24(BLOCK_SIZE, i, lidx); s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0; barrier(CLK_LOCAL_MEM_FENCE); result += reduce_multi_block(s_query, s_train, i, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } #else for (int i = 0, endq = (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE; i < endq ; i++) { const int loadx = mad24(BLOCK_SIZE, i, lidx); if (loadx < query_cols) { s_query[s_query_i] = query_vec[loadx]; s_train[s_train_i] = train_vec[loadx]; } else { s_query[s_query_i] = 0; s_train[s_train_i] = 0; } barrier(CLK_LOCAL_MEM_FENCE); result += reduce_block_match(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } #endif result = DIST_RES(result); const int trainIdx = mad24(BLOCK_SIZE, t, lidx); if (queryIdx < query_rows && trainIdx < train_rows) { if (result < myBestDistance1) { myBestDistance2 = myBestDistance1; myBestTrainIdx2 = myBestTrainIdx1; myBestDistance1 = result; myBestTrainIdx1 = trainIdx; } else if (result < myBestDistance2) { myBestDistance2 = result; myBestTrainIdx2 = trainIdx; } } } barrier(CLK_LOCAL_MEM_FENCE); __local float *s_distance = (__local float *)sharebuffer; __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE); s_distance += lidy * BLOCK_SIZE_ODD; s_trainIdx += lidy * BLOCK_SIZE_ODD; s_distance[lidx] = myBestDistance1; s_trainIdx[lidx] = myBestTrainIdx1; float bestDistance1 = MAX_FLOAT; float bestDistance2 = MAX_FLOAT; int bestTrainIdx1 = -1; int bestTrainIdx2 = -1; barrier(CLK_LOCAL_MEM_FENCE); if (lidx == 0) { for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; if (val < bestDistance1) { bestDistance2 = bestDistance1; bestTrainIdx2 = bestTrainIdx1; bestDistance1 = val; bestTrainIdx1 = s_trainIdx[i]; } else if (val < bestDistance2) { bestDistance2 = val; bestTrainIdx2 = s_trainIdx[i]; } } } barrier(CLK_LOCAL_MEM_FENCE); s_distance[lidx] = myBestDistance2; s_trainIdx[lidx] = myBestTrainIdx2; barrier(CLK_LOCAL_MEM_FENCE); if (lidx == 0) { for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; if (val < bestDistance2) { bestDistance2 = val; bestTrainIdx2 = s_trainIdx[i]; } } } myBestDistance1 = bestDistance1; myBestDistance2 = bestDistance2; myBestTrainIdx1 = bestTrainIdx1; myBestTrainIdx2 = bestTrainIdx2; if (queryIdx < query_rows && lidx == 0) { bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2); bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2); } } 35c3a1e231d446e4088561e3604fb94ffastinline int cornerScore(__global const uchar* img, int step) { int k, tofs, v = img[0], a0 = 0, b0; int d[16]; #define LOAD2(idx, ofs) \ tofs = ofs; d[idx] = (short)(v - img[tofs]); d[idx+8] = (short)(v - img[-tofs]) LOAD2(0, 3); LOAD2(1, -step+3); LOAD2(2, -step*2+2); LOAD2(3, -step*3+1); LOAD2(4, -step*3); LOAD2(5, -step*3-1); LOAD2(6, -step*2-2); LOAD2(7, -step-3); #pragma unroll for( k = 0; k < 16; k += 2 ) { int a = min((int)d[(k+1)&15], (int)d[(k+2)&15]); a = min(a, (int)d[(k+3)&15]); a = min(a, (int)d[(k+4)&15]); a = min(a, (int)d[(k+5)&15]); a = min(a, (int)d[(k+6)&15]); a = min(a, (int)d[(k+7)&15]); a = min(a, (int)d[(k+8)&15]); a0 = max(a0, min(a, (int)d[k&15])); a0 = max(a0, min(a, (int)d[(k+9)&15])); } b0 = -a0; #pragma unroll for( k = 0; k < 16; k += 2 ) { int b = max((int)d[(k+1)&15], (int)d[(k+2)&15]); b = max(b, (int)d[(k+3)&15]); b = max(b, (int)d[(k+4)&15]); b = max(b, (int)d[(k+5)&15]); b = max(b, (int)d[(k+6)&15]); b = max(b, (int)d[(k+7)&15]); b = max(b, (int)d[(k+8)&15]); b0 = min(b0, max(b, (int)d[k])); b0 = min(b0, max(b, (int)d[(k+9)&15])); } return -b0-1; } __kernel void FAST_findKeypoints( __global const uchar * _img, int step, int img_offset, int img_rows, int img_cols, volatile __global int* kp_loc, int max_keypoints, int threshold ) { int j = get_global_id(0) + 3; int i = get_global_id(1) + 3; if (i < img_rows - 3 && j < img_cols - 3) { __global const uchar* img = _img + mad24(i, step, j + img_offset); int v = img[0], t0 = v - threshold, t1 = v + threshold; int k, tofs, v0, v1; int m0 = 0, m1 = 0; #define UPDATE_MASK(idx, ofs) \ tofs = ofs; v0 = img[tofs]; v1 = img[-tofs]; \ m0 |= ((v0 < t0) << idx) | ((v1 < t0) << (8 + idx)); \ m1 |= ((v0 > t1) << idx) | ((v1 > t1) << (8 + idx)) UPDATE_MASK(0, 3); if( (m0 | m1) == 0 ) return; UPDATE_MASK(2, -step*2+2); UPDATE_MASK(4, -step*3); UPDATE_MASK(6, -step*2-2); #define EVEN_MASK (1+4+16+64) if( ((m0 | (m0 >> 8)) & EVEN_MASK) != EVEN_MASK && ((m1 | (m1 >> 8)) & EVEN_MASK) != EVEN_MASK ) return; UPDATE_MASK(1, -step+3); UPDATE_MASK(3, -step*3+1); UPDATE_MASK(5, -step*3-1); UPDATE_MASK(7, -step-3); if( ((m0 | (m0 >> 8)) & 255) != 255 && ((m1 | (m1 >> 8)) & 255) != 255 ) return; m0 |= m0 << 16; m1 |= m1 << 16; #define CHECK0(i) ((m0 & (511 << i)) == (511 << i)) #define CHECK1(i) ((m1 & (511 << i)) == (511 << i)) if( CHECK0(0) + CHECK0(1) + CHECK0(2) + CHECK0(3) + CHECK0(4) + CHECK0(5) + CHECK0(6) + CHECK0(7) + CHECK0(8) + CHECK0(9) + CHECK0(10) + CHECK0(11) + CHECK0(12) + CHECK0(13) + CHECK0(14) + CHECK0(15) + CHECK1(0) + CHECK1(1) + CHECK1(2) + CHECK1(3) + CHECK1(4) + CHECK1(5) + CHECK1(6) + CHECK1(7) + CHECK1(8) + CHECK1(9) + CHECK1(10) + CHECK1(11) + CHECK1(12) + CHECK1(13) + CHECK1(14) + CHECK1(15) == 0 ) return; { int idx = atomic_inc(kp_loc); if( idx < max_keypoints ) { kp_loc[1 + 2*idx] = j; kp_loc[2 + 2*idx] = i; } } } } __kernel void FAST_nonmaxSupression( __global const int* kp_in, volatile __global int* kp_out, __global const uchar * _img, int step, int img_offset, int rows, int cols, int counter, int max_keypoints) { const int idx = get_global_id(0); if (idx < counter) { int x = kp_in[1 + 2*idx]; int y = kp_in[2 + 2*idx]; __global const uchar* img = _img + mad24(y, step, x + img_offset); int s = cornerScore(img, step); if( (x < 4 || s > cornerScore(img-1, step)) + (y < 4 || s > cornerScore(img-step, step)) != 2 ) return; if( (x >= cols - 4 || s > cornerScore(img+1, step)) + (y >= rows - 4 || s > cornerScore(img+step, step)) + (x < 4 || y < 4 || s > cornerScore(img-step-1, step)) + (x >= cols - 4 || y < 4 || s > cornerScore(img-step+1, step)) + (x < 4 || y >= rows - 4 || s > cornerScore(img+step-1, step)) + (x >= cols - 4 || y >= rows - 4 || s > cornerScore(img+step+1, step)) == 6) { int new_idx = atomic_inc(kp_out); if( new_idx < max_keypoints ) { kp_out[1 + 3*new_idx] = x; kp_out[2 + 3*new_idx] = y; kp_out[3 + 3*new_idx] = s; } } } } f5e6f463f21a7ed77bd4d2c753478305orb#define LAYERINFO_SIZE 1 #define LAYERINFO_OFS 0 #define KEYPOINT_SIZE 3 #define ORIENTED_KEYPOINT_SIZE 4 #define KEYPOINT_X 0 #define KEYPOINT_Y 1 #define KEYPOINT_Z 2 #define KEYPOINT_ANGLE 3 #ifdef ORB_RESPONSES __kernel void ORB_HarrisResponses(__global const uchar* imgbuf, int imgstep, int imgoffset0, __global const int* layerinfo, __global const int* keypoints, __global float* responses, int nkeypoints ) { int idx = get_global_id(0); if( idx < nkeypoints ) { __global const int* kpt = keypoints + idx*KEYPOINT_SIZE; __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE; __global const uchar* img = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] + (kpt[KEYPOINT_Y] - blockSize/2)*imgstep + (kpt[KEYPOINT_X] - blockSize/2); int i, j; int a = 0, b = 0, c = 0; for( i = 0; i < blockSize; i++, img += imgstep-blockSize ) { for( j = 0; j < blockSize; j++, img++ ) { int Ix = (img[1] - img[-1])*2 + img[-imgstep+1] - img[-imgstep-1] + img[imgstep+1] - img[imgstep-1]; int Iy = (img[imgstep] - img[-imgstep])*2 + img[imgstep-1] - img[-imgstep-1] + img[imgstep+1] - img[-imgstep+1]; a += Ix*Ix; b += Iy*Iy; c += Ix*Iy; } } responses[idx] = ((float)a * b - (float)c * c - HARRIS_K * (float)(a + b) * (a + b))*scale_sq_sq; } } #endif #ifdef ORB_ANGLES #define _DBL_EPSILON 2.2204460492503131e-16f #define atan2_p1 (0.9997878412794807f*57.29577951308232f) #define atan2_p3 (-0.3258083974640975f*57.29577951308232f) #define atan2_p5 (0.1555786518463281f*57.29577951308232f) #define atan2_p7 (-0.04432655554792128f*57.29577951308232f) inline float fastAtan2( float y, float x ) { float ax = fabs(x), ay = fabs(y); float a, c, c2; if( ax >= ay ) { c = ay/(ax + _DBL_EPSILON); c2 = c*c; a = (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c; } else { c = ax/(ay + _DBL_EPSILON); c2 = c*c; a = 90.f - (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c; } if( x < 0 ) a = 180.f - a; if( y < 0 ) a = 360.f - a; return a; } __kernel void ORB_ICAngle(__global const uchar* imgbuf, int imgstep, int imgoffset0, __global const int* layerinfo, __global const int* keypoints, __global float* responses, const __global int* u_max, int nkeypoints, int half_k ) { int idx = get_global_id(0); if( idx < nkeypoints ) { __global const int* kpt = keypoints + idx*KEYPOINT_SIZE; __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE; __global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] + kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X]; int u, v, m_01 = 0, m_10 = 0; for( u = -half_k; u <= half_k; u++ ) m_10 += u * center[u]; for( v = 1; v <= half_k; v++ ) { int v_sum = 0; int d = u_max[v]; for( u = -d; u <= d; u++ ) { int val_plus = center[u + v*imgstep], val_minus = center[u - v*imgstep]; v_sum += (val_plus - val_minus); m_10 += u * (val_plus + val_minus); } m_01 += v * v_sum; } responses[idx] = fastAtan2((float)m_01, (float)m_10); } } #endif #ifdef ORB_DESCRIPTORS __kernel void ORB_computeDescriptor(__global const uchar* imgbuf, int imgstep, int imgoffset0, __global const int* layerinfo, __global const int* keypoints, __global uchar* _desc, const __global int* pattern, int nkeypoints, int dsize ) { int idx = get_global_id(0); if( idx < nkeypoints ) { int i; __global const int* kpt = keypoints + idx*ORIENTED_KEYPOINT_SIZE; __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE; __global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] + kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X]; float angle = as_float(kpt[KEYPOINT_ANGLE]); angle *= 0.01745329251994329547f; float cosa; float sina = sincos(angle, &cosa); __global uchar* desc = _desc + idx*dsize; #define GET_VALUE(idx) \ center[mad24(convert_int_rte(pattern[(idx)*2] * sina + pattern[(idx)*2+1] * cosa), imgstep, \ convert_int_rte(pattern[(idx)*2] * cosa - pattern[(idx)*2+1] * sina))] for( i = 0; i < dsize; i++ ) { int val; #if WTA_K == 2 int t0, t1; t0 = GET_VALUE(0); t1 = GET_VALUE(1); val = t0 < t1; t0 = GET_VALUE(2); t1 = GET_VALUE(3); val |= (t0 < t1) << 1; t0 = GET_VALUE(4); t1 = GET_VALUE(5); val |= (t0 < t1) << 2; t0 = GET_VALUE(6); t1 = GET_VALUE(7); val |= (t0 < t1) << 3; t0 = GET_VALUE(8); t1 = GET_VALUE(9); val |= (t0 < t1) << 4; t0 = GET_VALUE(10); t1 = GET_VALUE(11); val |= (t0 < t1) << 5; t0 = GET_VALUE(12); t1 = GET_VALUE(13); val |= (t0 < t1) << 6; t0 = GET_VALUE(14); t1 = GET_VALUE(15); val |= (t0 < t1) << 7; pattern += 16*2; #elif WTA_K == 3 int t0, t1, t2; t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2); val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0); t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5); val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2; t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8); val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4; t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11); val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6; pattern += 12*2; #elif WTA_K == 4 int t0, t1, t2, t3, k; int a, b; t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2); t3 = GET_VALUE(3); a = 0, b = 2; if( t1 > t0 ) t0 = t1, a = 1; if( t3 > t2 ) t2 = t3, b = 3; k = t0 > t2 ? a : b; val = k; t0 = GET_VALUE(4); t1 = GET_VALUE(5); t2 = GET_VALUE(6); t3 = GET_VALUE(7); a = 0, b = 2; if( t1 > t0 ) t0 = t1, a = 1; if( t3 > t2 ) t2 = t3, b = 3; k = t0 > t2 ? a : b; val |= k << 2; t0 = GET_VALUE(8); t1 = GET_VALUE(9); t2 = GET_VALUE(10); t3 = GET_VALUE(11); a = 0, b = 2; if( t1 > t0 ) t0 = t1, a = 1; if( t3 > t2 ) t2 = t3, b = 3; k = t0 > t2 ? a : b; val |= k << 4; t0 = GET_VALUE(12); t1 = GET_VALUE(13); t2 = GET_VALUE(14); t3 = GET_VALUE(15); a = 0, b = 2; if( t1 > t0 ) t0 = t1, a = 1; if( t3 > t2 ) t2 = t3, b = 3; k = t0 > t2 ? a : b; val |= k << 6; pattern += 16*2; #else #error "unknown/undefined WTA_K value; should be 2, 3 or 4" #endif desc[i] = (uchar)val; } } } #endif a7c2cfaeda19907b637211b1cc91d253 Ghz888H5H9H^xh`XP@80(_`_Z_'8___ZN2cv3ocl10features2d11fast_oclsrcE__ZN2cv3ocl10features2d24brute_force_match_oclsrcE__ZN2cv3ocl10features2d12akaze_oclsrcE__ZN2cv3ocl10features2d10orb_oclsrcE#1/20 1548786701 501 20 100644 6572 ` fast.avx2.cpp.o xx x __text__TEXT__literal8__TEXT __const__TEXT& __literal16__TEXT __data__DATA@ P h__compact_unwind__LDX h __eh_frame__TEXT8 @H h$ 0 PUHAWAVAUATSPLEAωAI IHIEnE~HEIFA^ 1I=LAN @H HLpI$Mt$LH[A\A]A^A_]fDUHAWAVAUATSHHMLD$HL$IIIHHH$u0H=t }YH=wu0H=t }YH=wAAG9yxG }8D$ I$o-o@o8oL$ Eo5IWHc2~o 05Hcz~o8-Hcz ~o8%Hcz0~o$85d-d=A=dA=d%duA=dA%A udA-A=dAA-AA%A5uuq dfxA5ɿAA-A%ffff.4HHc 0udA5AŽdAuA-A%Hu-d }EA91fDAAIcu~A}HD$AM$E MOAHcAD)fT$@AQHcAD)fD$BAAHAD)fT$DAA HAD)fT$FAAHAD)fT$HAAHAD)fT$JAAHAD)fT$LAAHAD)fT$NAA HAD)fT$PAA$HAD)fT$RAA(HAD)fT$TAA,HAD)fT$VAA0HAD)fT$XAA4HAD)fT$ZAA8HAD)fT$\n}XuI<~HcAD)f|$^H AD)f|$`HcAD)f|$bH AD)fT$d}9~HcA<:D)ft$fH AD)ft$hHcAD)ft$jH AD)fT$lAA\HAD)fT$nAA`HAA)fD\$poL$Bot$Dot$Fot$Hot$Jot$Lot$Not$PoT$@od$RoT$Tod$Vod$Xod$Zod$\od$^od$`od$bŹsss~AHcHt$Hw HAA@AHI$ AI$H I$AW9qwHHH;$uHe[A\A]A^A_]fDUH]f.UH]fDUH]f.UH]fDUHSPHH{HtHPHHH[]`N2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplEN2cv8opt_AVX225FAST_t_patternSize16_AVX2EN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEEEN2cv6detail8PtrOwnerEXXP` p 'zRx ,AC J,LAC T$|AC $ AC $hAC $P AC $8'AC B-f-F-%=WP-HA9,-'--=tf--#-   P H@0(  `@ @  OpI`|P@ 0 h  PK  @  I ! 6__ZdlPv__ZN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEE10deleteSelfEv__ZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplD1Ev__ZN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEED1Ev__ZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplD0Ev__ZN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEED0Ev__Znwm___stack_chk_fail__ZN2cv8opt_AVX225FAST_t_patternSize16_AVX27getImplEiibPKi___cxa_guard_release___cxa_guard_acquire___stack_chk_guard__ZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_Impl7processERiRPKhPhPiS2___ZTSN2cv6detail8PtrOwnerE__ZTIN2cv6detail8PtrOwnerE__ZTVN10__cxxabiv120__si_class_type_infoE__ZTVN10__cxxabiv117__class_type_infoE__ZTVN2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplE__ZTSN2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplE__ZTIN2cv8opt_AVX230FAST_t_patternSize16_AVX2_ImplE__ZTVN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEEE__ZTSN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEEE__ZTIN2cv6detail12PtrOwnerImplINS_8opt_AVX230FAST_t_patternSize16_AVX2_ImplENS_14DefaultDeleterIS3_EEEE__ZTSN2cv8opt_AVX225FAST_t_patternSize16_AVX2E__ZTIN2cv8opt_AVX225FAST_t_patternSize16_AVX2E__ZZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_Impl7processERiRPKhPhPiS2_E8delta256__ZGVZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_Impl7processERiRPKhPhPiS2_E8delta256__ZZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_Impl7processERiRPKhPhPiS2_E7K16_256__ZGVZN2cv8opt_AVX230FAST_t_patternSize16_AVX2_Impl7processERiRPKhPhPiS2_E7K16_256