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HЗ…°H…яtиL‹ҐАHЗ…ёHЗ…АM…дt&H‹Ђ;„bA‹D$ЌPяA‰T$ѓш„LЌ¬$РLЌ¤$ђ1ТL‰цHЌњ$аL‰йL‰зHЗ„$ШH‰њ$РЖ„$аиH‹ј$РH9ЯtиL‰зиA‰З„Аu7L‰зиH‹„$dH+%(…HЃД(D‰ш[]A\A]A^A_ГLЌt$L‰жL‰чиL‰цH‰пиA‰З„Аu§LЌ|$PHЌD$`L‰пєHЌL$pL‰юHЌњ$ЂHЗD$xH‰L$H‰\$pЖ„$ЂH‰$H‰D$PЗD$`.ymlHЗD$XЖD$dиH‹|$PH;<$tиH‹|$pH9ЯtиL‰оL‰чиL‰оL‰яиH‹L$єL‰юL‰пH‰\$pHЗD$xЖ„$ЂиH‹|$pH9ЯtиLЌ|$0L‰оL‰яиL‰юL‰чиL‰цH‰пиH‹|$PA‰ЗH;<$tиL‰пий‘юяяI‹$L‰зяPЂ;„•A‹D$ЌPяA‰T$ѓш…ќьяяI‹$L‰зяPйЋьяя„I‹$L‰зяPЂ;tqA‹D$ЌPяA‰T$ѓш…ЗэяяI‹$L‰зяPйёэяя@ёяяяярAБD$йљэяяЂёяяяярAБD$йьяяЂёяяяярAБD$йgяяяЂёяяяярAБD$лЋиуъH‰ЕйуъH‰ЕйуъH‰ЕйуъH‰ГйуъH‰ЕйуъH‰Гйяя$›› µ^і цЏ § Цѓ хї яя8@St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN2cv21BaseCascadeClassifierEN2cv16FeatureEvaluatorEN2cv21CascadeClassifierImplEN2cv13HaarEvaluatorEN2cv12LBPEvaluatorEN2cv24CascadeClassifierInvokerESt23_Sp_counted_ptr_inplaceISt6vectorIN2cv16FeatureEvaluator9ScaleDataESaIS3_EESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt6vectorIN2cv13HaarEvaluator7FeatureESaIS3_EESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt6vectorIN2cv13HaarEvaluator10OptFeatureESaIS3_EESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN2cv13HaarEvaluatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt6vectorIN2cv12LBPEvaluator7FeatureESaIS3_EESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt6vectorIN2cv12LBPEvaluator10OptFeatureESaIS3_EESaIS5_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN2cv12LBPEvaluatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN2cv13HaarEvaluatorELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN2cv12LBPEvaluatorELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN2cv21CascadeClassifierImplESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEint cv::predictCategorical(cv::CascadeClassifierImpl&, cv::Ptr&, double&) [with FEval = cv::LBPEvaluator]int cv::predictOrdered(cv::CascadeClassifierImpl&, cv::Ptr&, double&) [with FEval = cv::HaarEvaluator]int cv::predictCategoricalStump(cv::CascadeClassifierImpl&, cv::Ptr&, double&) [with FEval = cv::LBPEvaluator]int cv::predictOrderedStump(cv::CascadeClassifierImpl&, cv::Ptr&, double&) [with FEval = cv::HaarEvaluator]void cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector >&, std::vector&, std::vector >&, double, int, int, cv::Size, cv::Size, bool)void cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector >&, std::vector&, double, int, int, cv::Size, cv::Size)void cv::CascadeClassifier::detectMultiScale(cv::InputArray, std::vector >&, double, int, int, cv::Size, cv::Size)bool cv::CascadeClassifierImpl::Data::read(const cv::FileNode&)Invalid window size (too large)virtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector >&, std::vector&, double, int, int, cv::Size, cv::Size)virtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector >&, double, int, int, cv::Size, cv::Size)virtual void cv::CascadeClassifierImpl::detectMultiScale(cv::InputArray, std::vector >&, std::vector&, std::vector >&, double, int, int, cv::Size, cv::Size, bool)void cv::CascadeClassifierImpl::detectMultiScaleNoGrouping(cv::InputArray, std::vector >&, std::vector&, std::vector >&, double, cv::Size, cv::Size, bool)virtual void cv::CascadeClassifierInvoker::operator()(const cv::Range&) constint cv::CascadeClassifierImpl::runAt(cv::Ptr&, cv::Point, int, double&)bool cv::LBPEvaluator::Feature::read(const cv::FileNode&, const Size&)virtual void cv::HaarEvaluator::computeOptFeatures()virtual void cv::HaarEvaluator::computeChannels(int, cv::InputArray)bool cv::HaarEvaluator::Feature::read(const cv::FileNode&, const Size&)virtual bool cv::FeatureEvaluator::setImage(cv::InputArray, const std::vector&)void cv::groupRectangles_meanshift(std::vector >&, std::vector >&, std::vector >&, double, cv::Size)void cv::groupRectangles(std::vector >&, std::vector&, std::vector >&, int, double)void cv::groupRectangles(std::vector >&, std::vector&, int, double)void cv::groupRectangles(std::vector >&, int, double)void cv::groupRectangles(std::vector >&, int, double, std::vector*, std::vector >*)зd=¶Ґ—origWinSize.height1000000origWinSize.widthInvalid LBP featurerect.y + rect.heightHrect.x + rect.widthWrect.yrect.x0Invalid HAAR featurerw.r.y + rw.r.heightrw.r.x + rw.r.widthrw.r.yrw.r.xВБђѓkмК¶Љ:98765й†~voBр?љ™™™™™№?а? @0@Ь№б{“КР?сhг€µшд> ?рїљ™™™™™Й?Ђ?H7@¬Е'7sqsum.u == u && sqsum.size() == s.szi && sqsum.tЂnodes[root][PAREяяяGNUАzRxђ0DXlЂ”ЕЁ јР д ш 4H\p„¬АФиь$8L` t € њ ° Д Ш м ( < P d x Њ ґ И Ь р,@TIMЊp CHt3L†b ђoEѓD0j AD8ґУBЌBЊE †A(ѓD@a (A ABBKр$;L†j >4IMЊp CHTIMЊp CHtIMЊp CH”IMЊp CHґIMЊp CHФIMЊp CHфIMЊp CHzPLRx›ђ$$E†J@± DAL@†DhМFЋEЌB ЊA(†D0ѓGАю 0A(A BBBA ° Аѓ†ЊЌЋ,ФE†GѓD`P DAK`ѓ†,0ЬFЊH†A ѓs 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ъяяиуъH‰ЕйЅуъH‰ЕйќуъH‰Гл9уъH‰Гл@уъH‰ГлeуъH‰ЕйЂуъH‰ГлуъH‰Гл+уъH‰ГлHL‰зиL‰пиH‹ј$°H…яtиH‹ј$ђH…яuRH‹|$pH…яtиH‹|$(иL‰чиH‰пH‰ЭиH‹|$PиH‹|$XиH‹|$HиH‰пиил§яяJFb¦ЃЩ‹Ітч“Р– 7о±ЗЄеµѕФе€РьlocconfiouуъAWAVAUATI‰ФUH‰эH‰чSH‰уHЃмxdH‹%(H‰„$h1АиE1А„Аt0H‹„$hdH+%(…YHЃДxD‰А[]A\A]A^A_Г@LЌl$`H‰ЮD‹uD‹}єяяяяL‰пиD;t$`t4H‹EHЌl$@єяяяяH‰ЮH‰пH‰D$`иHЌL‰оH‰пи@D;|$duЕHЗ„$L‰кLЌґ$LЌј$ HЗD$`HѓмH‰ЮH‹L‰яE1ЙE1АL‰сHЗ„$fHnАHЗ„$HЗ„$jиHЌ„$ L‰чH‰„$XZHЌHЌrэиHЌHЌ„$0HЌј$ HЌrьH‰„$ иHЌHЌ„$PHЌј$@HЌrэH‰„$@иHЌ|$ L‰йL‰цєH‰|$иHЌњ$`Hѓл H‹;HЌCH9ЗtиL9уuжHЌEHЌ5HЗD$@HЌњ$ЂH‰D$H‰тHЌ„$ђH‰ЯHЗD$HHЗD$PHЗ„$HЗ„$HЗ„$HЗ„$H‰D$H‰„$ЂиH‹|$L‰сH‰ЪL‰|$hHЗD$`H‹5HЗD$pfHnЖL‰оиH‹ј$ЂH;|$tиH‹T$H‹|$HЌ\$@L‰оHЗD$`HЗD$pH‰\$hиH‰ЪH‰оL‰чиfпЙєL‰жL‰чH‹fHnАиL‰чиH‹\$HH‹l$@H9лtfDH‰пHѓЕ`иH9лuпH‹l$@H…нtH‰пиH‹\$(H‹l$ H9лt&fDH‹}HЌEH9ЗtиHѓЕ H9лuеH‹l$ H…нtH‰пиL‰яиAёйђьяяиуъH‰ГлcуъH‰ГлуъH‰ГлуъH‰ГлXуъH‰ГлeL‰чиL‹d$HH‹l$@I9мusH‹|$@H…яuzL‹d$(H‹l$ I9мuuH‹|$ H…яtиL‰яиH‰ЯиH‹ј$ЂH;|$tЄилЈHЌ¬$`Hѓн H‹}HЌEH9ЗtиL9хt·лгH‰пHѓЕ`ийwяяяий|яяяH‹}HЌEH9ЗtиHѓЕ йkяяяяя.4ЌбUµЛЩрЗ¦РиЗ–ѕ¬St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN2cv14FaceDetectorYNEN2cv18FaceDetectorYNImplESt23_Sp_counted_ptr_inplaceIN2cv18FaceDetectorYNImplESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEvirtual int cv::FaceDetectorYNImpl::detect(cv::InputArray, cv::OutputArray)Size does not match. Call setInputSize(size) if input size does not match the preset sizeinput_image.size()Size(inputW, inputH)`?Ђ? @р?GNUАzRxђ0 D X l Ђ”ЁјРIMЊp CHр*LѓY 04АBЊD†D ѓG0^ AABEh2L†azPLRx›ђL$ыFЏIЋF ЌB(ЊA0†A8ѓD`K 8A0A(B BBBAHфtPЏBЋB ЌB(ЊA0†A8ѓDPг 8A0A(B BBBGhАф FЏBЋB ЌB(ЊA0†D8ѓGђ< 8A0A(B BBBEA 8A0A(B BBBA¬P@ЎFЏBЋB ЌB(ЊD0†E8ѓG°i 8A0A(B BBBA4”µE†C BЏEЋEЌEЊIѓa.e A Мѓ†ЊЌЋЏHpWFЏOЋB ЌB(ЊA0†A8ѓDP‹ 8A0A(B BBBDHј›PЏEЋB ЌB(ЊA0†A8ѓG`я 8A0A(B BBBEP€FЏBЋB ЌE(ЊA0†A8ѓJАР 8A0A(B BBBKdЬўFЏBЋB ЌB(ЊD0†G8ѓJ°} 8D0A(B BBBE‹ёAАYёA°)*+,-./012468;;А=@BDFHILMOPRTpPUWY[]_tuб:ж?лCрzхyъKяKL Ky{\!\'\ -Џ® „")Ї"* Я"+ ", ="- k".ђ"/µ"0"0y"1е"2IbxЎxЈ!cїЖ"4*е!ph -"4*L"6 ®"8 ^e‚“"=2І"@ы!|9?]oЋ$њ"@ы "Dtv "Fф Ј « П и "I! "MЎЊ Ї &N…»ьH"MЎіRµ !r8~ "UWЪ "W›G"YЊџ®їДЯBds…‹"]ўХтЎv0j±—I!`/|!aЇХ!d4 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яяя№L‰яиA‰ЕйЮюяяиуъI‰ДйуъI‰ДйуъI‰ДйуъI‰Дйяя%ЛАъМШЗ®М–ґц1МяяNo element name has been given./modules/core/include/opencv2/core/persistence.hppoperator<<H‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиL‰чиH‰пиH‹|$ H9ЯtиH‰пиL‰чиH‰пиL‰чиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиL‰чиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиL‰чиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиH‹|$ H9ЯtиH‰пиуъAWI‰чAVAUATI‰ьUSHѓмXdH‹%(H‰D$H1АHѓz…u HЌl$ LЌt$1ТHЗD$L‰цH‰пHЌ\$0AЅhoH‰\$ иH‹T$H‰оL‰яfoH‰D$ H№etector-H‰T$0H‰HfD‰hЖ@gH‹D$H‹T$ H‰D$(ЖиH‹|$ I‰ЕH9ЯtиA»izH‰оL‰пH‰\$ ЗD$0winSfD‰\$4ЖD$6eHЗD$(ЖD$7иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„АtqAѓ}„ь IЌUI‰и№L‰оL‰чH‰\$ HЗD$(ЖD$0иH‹|$ H9ЯtиA‹t$L‰пиA‹t$L‰пиL‰чиAцE…\HёblockSizH‰оL‰пH‰\$ H‰D$0ЖD$8eHЗD$( ЖD$9иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„АtqAѓ}„Й IЌUI‰и№L‰оL‰чH‰\$ HЗD$(ЖD$0иH‹|$ H9ЯtиA‹t$L‰пиA‹t$L‰пиL‰чиAцE…©HёblockStrAєidH‰оL‰пH‰\$ H‰D$0fD‰T$8ЖD$:eHЗD$(ЖD$;иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„АtqAѓ}„: IЌUI‰и№L‰оL‰чH‰\$ HЗD$(ЖD$0иH‹|$ H9ЯtиA‹t$L‰пиA‹t$L‰пиL‰чиAцE…кHёcellSizeH‰оL‰пH‰\$ H‰D$0HЗD$(ЖD$8иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„АtqAѓ}„l IЌUI‰и№L‰оL‰чH‰\$ HЗD$(ЖD$0иH‹|$ H9ЯtиA‹t$ L‰пиA‹t$$L‰пиL‰чиAцE…<H‰оL‰пH‰\$ ЗD$0nbinЖD$4sHЗD$(ЖD$5иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„Аt'Aѓ}„ A‹T$(IЌuL‰пиAцE…ЪHёderivApeH‰оL‰пH‰\$ H‰D$0ЗD$8rturЖD$иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„Аt)Aѓ}„тAD$@IЌuL‰пиAцE…НHёgammaCorAёioH‰оL‰пH‰\$ H‰D$0ЗD$8rectfD‰D$<ЖD$>nHЗD$(ЖD$?иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„Аt(Aѓ}„цA¶T$HIЌuL‰пиAцE…OїelH‰оH‰\$ f‰|$4L‰пЗD$0nlevЖD$6sHЗD$(ЖD$7иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„Аt*Aѓ}„9A‹”$јIЌuL‰пиAцE…аѕntL‰пH‰\$ HёsignedGrf‰t$иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„Аt+Aѓ}„pA¶”$АIЌuL‰пиAцE…fI‹D$PI9D$X„N№toH‰оL‰яH‰\$ HёSVMDetecf‰L$8H‰D$0ЖD$:rHЗD$(ЖD$;иH‹|$ I‰ЕH9ЯtиI‹EL‰пяP„А„оAѓ}„eIЌUI‰и№L‰оL‰чH‰\$ HЗD$(ЖD$0иH‹|$ H9ЯtиI‹D$XM‹D$PLЌd$Eє1fL‰зf‰T$EH‰БH‰\$ 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H‰ЗH‹D$H‰D$0лАHЌ=ииуъH‰ЕйуъH‰ЕйуъH‰Ейяя^„ЦњйђЪ%њяя$N2cv13HOGDescriptorEN2cv8HOGCacheEN2cv10HOGInvokerEN2cv14HOGConfInvokerEvoid cv::HOGDescriptor::groupRectangles(std::vector >&, std::vector >&, int, double) constvirtual void cv::HOGDescriptor::detectMultiScaleROI(cv::InputArray, std::vector >&, std::vector&, double, int) constvirtual void cv::HOGDescriptor::detectROI(cv::InputArray, const std::vector >&, std::vector >&, std::vector >&, double, cv::Size, cv::Size) constvirtual void cv::HOGConfInvoker::operator()(const cv::Range&) constvirtual void cv::HOGDescriptor::detectMultiScale(cv::InputArray, std::vector >&, double, cv::Size, cv::Size, double, double, bool) constvirtual void cv::HOGDescriptor::detectMultiScale(cv::InputArray, std::vector >&, std::vector >&, double, cv::Size, cv::Size, double, double, bool) constvirtual void cv::HOGDescriptor::detect(cv::InputArray, std::vector >&, double, cv::Size, cv::Size, const std::vector >&) constvirtual void cv::HOGDescriptor::detect(cv::InputArray, std::vector >&, std::vector >&, double, cv::Size, cv::Size, const std::vector >&) constvirtual void cv::HOGDescriptor::compute(cv::InputArray, std::vector&, cv::Size, cv::Size, const std::vector >&) constvirtual void cv::HOGDescriptor::computeGradient(cv::InputArray, cv::InputOutputArray, cv::InputOutputArray, cv::Size, cv::Size) constw f Щµ–>ЏUКЅjЎѕAIЎ=рiЖ>!з…> q>eь{=|™h>Ъ€ЅФ›±Ѕ:НB=ЯЊЪ=в9=ЄEд=пЗјЇ њ>еd‚>ЛЌЅ]©=SЛ>{h§>v>Мbb={1ґЅ*†>vв>Ч¤Ы<Щнs>.F>ТЕf=ЕЛУј' Ѕаc ѕ"R>ФI¶=ќ*ѕЯНЅЎуЅБsпјСзcЅ/=,D‡јHш>%@}>‘_яЅВMFЅZЩ~ЅЦTF>~б•;Ґa=џї=Іј+>[љ+>°Йљ<l\>mы=Ќb№=Є‰ЅўТHЅуМЅx›w>CWВЅ[z4ѕЃ\в:Jі ѕ°еХ=ЏЁP=Ц9¦=†Ж“=Љ!щ=I.;ђѕЛѕpAvЅЃxЅЅцHЅЃ“Ѕo8ѕЊЅ$µ°=Ю ѕ]ҐЫЅQ>ф>Di>If>’цј,р•=ЌњјVFЈ<(С’=RIќ9етџјЎш±јGи'ЅІЩ±Ѕ,ќЏ=6Лu>хЫ‡>Яъ0=ђБ*>¶K»=ѓy=фoЅБtєЅu”Ј=Ea>~е!>*юѕЊ10Ѕ1я=Хї=·^У=@LВ=®т¤= >jk$>дJ-ѕфe<Хwћ>Г.Љ»ЊJjЅП„Ѕuf<,Цё>®Ђ>jЪҐЅ<щ4ЅQ¤{=ТЅЋZЎЅеэ<ўП‡=}Y=ы>LД»Ѕ—ЖЏ=Ґ_>Ю<=њЅ[оМ; eБЅ~‹>0єјј%°Iѕ[yЙј‡ї6>Ќ^MЅ1#<Ѕш|єvЯ=ѓ==2#ѕщАј‡1)=’лЖ=VjЅ{L„ЅЇћ=e4>H¤ 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иL‰чиH‹„$ёdH+%(…!¶D$HЃДИ[]A\A]A^A_Г„IѓЕHѓЕL;,$…юяяH‹|$L‰юиH‹ј$ЂH…яtиH‹D$H‰ГI9ДtfђH‹;H…яtиHѓГI9ЬuкHѓ|$„8юяяH‹|$ий)юяяЂ1АйЪьяяђH‹|$ий3яяяђH‰БйэяяHЗD$йFьяяH…ЫyииH‹D$HЗ„$ЂHЗ„$€H‰D$hI‰ДHЗ„$ђLЌј$ЂйяяяH…ЫyиииуъH‰ГйуъH‰ЗйуъH‰ГйуъH‰Гйя›=5I‡ мkџ Ф“ я« •џ љ« …‡ « “ ш џ я›4KH‹|$ H…яtиH‰пиL‰зиуъAWI‰ПAVI‰юAUI‰ХATI‰фUSHЃмшL‰$HЌ¬$ЂH‰пdH‹%(H‰„$и1АиH‰оL‰зиA‰Д„А„bё ‚HЌ\$ HЌT$`L‰чH‰D$`HЌD$@H‰ЖH‰\$hHЗD$ HЗD$(HЗD$0HЗD$pHЗD$@HЗD$PH‰l$HH‰T$H‰D$иA‰Д„А„ЩH‰ЮL‰яиI‹EM‹eH‰D$L9аt/I‰Зf„I‹?IЌGH9ЗtиIѓЗ M9ьuжH‹D$I‰EL‹$H‹T$L‰йL‰чH‹t$ё ЃH‰\$hH‰D$`HЗD$pHЗD$@HЗD$PH‰l$HиA‰ДH‹|$ H…яtиH‰пиH‹„$иdH+%(u2HЃДшD‰а[]A\A]A^A_ГL‰яилґfDL‰яиліиуъI‰ДйуъI‰ДйяяNЫЗсПГЫяяуъUHЌ-H‰пиH‹=H‰о]HЌйSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN2cv13QRDetectMulti14ParallelSearchEN2cv21ParallelDecodeProcessESt15_Sp_counted_ptrIPN2cv14QRCodeDetector4ImplELN9__gnu_cxx12_Lock_policyE2EEbool cv::QRDetectMulti::computeTransformationPoints(size_t)bool cv::QRDetectMulti::localization()void cv::QRDetectMulti::fixationPoints(std::vector >&)void cv::QRDetectMulti::init(const cv::Mat&, double, double)cv::String cv::QRCodeDetector::decodeCurved(cv::InputArray, cv::InputArray, cv::OutputArray)std::string cv::QRCodeDetector::decode(cv::InputArray, cv::InputArray, cv::OutputArray)bool cv::QRDecode::samplingForVersion()bool cv::QRDecode::versionDefinition()bool cv::QRDecode::updatePerspective()bool cv::QRDecode::computeClosestPoints(const std::vector >&)void cv::QRDecode::getPointsInsideQRCode(const std::vector >&)void cv::QRDecode::init(const cv::Mat&, const std::vector >&)std::vector > cv::QRDetect::getQuadrilateral(std::vector >)bool cv::QRDetect::testByPassRoute(std::vector >, int, int)bool cv::QRDetect::computeTransformationPoints()bool cv::QRDetect::localization()void cv::QRDetect::fixationPoints(std::vector >&)std::vector > cv::QRDetect::extractVerticalLines(const std::vector >&, double)std::vector > cv::QRDetect::separateVerticalLines(const std::vector >&)std::vector > 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Ъx@иHo[8yЂ4ЋyЂ4 ‰y@0Ho^аy 4/z 40 *z@xxoa‘zа40 Њz@рxocоz5{5{6`{p6/{ђ6*{@hoi[{6k{6 ѓ{ё6,~{@Ђ°omИbFpш `©мh0Ќ{/122 0 0 0 644 35856 ` ELF>P€@@objdetectobjdetect_hogcascadedetect#define CELL_WIDTH 8 #define CELL_HEIGHT 8 #define CELLS_PER_BLOCK_X 2 #define CELLS_PER_BLOCK_Y 2 #define NTHREADS 256 #define CV_PI_F M_PI_F #ifdef INTEL_DEVICE #define QANGLE_TYPE int #define QANGLE_TYPE2 int2 #else #define QANGLE_TYPE uchar #define QANGLE_TYPE2 uchar2 #endif __kernel void compute_hists_lut_kernel( const int cblock_stride_x, const int cblock_stride_y, const int cnbins, const int cblock_hist_size, const int img_block_width, const int blocks_in_group, const int blocks_total, const int grad_quadstep, const int qangle_step, __global const float* grad, __global const QANGLE_TYPE* qangle, __global const float* gauss_w_lut, __global float* block_hists, __local float* smem) { const int lx = get_local_id(0); const int lp = lx / 24; const int gid = get_group_id(0) * blocks_in_group + lp; const int gidY = gid / img_block_width; const int gidX = gid - gidY * img_block_width; const int lidX = lx - lp * 24; const int lidY = get_local_id(1); const int cell_x = lidX / 12; const int cell_y = lidY; const int cell_thread_x = lidX - cell_x * 12; __local float* hists = smem + lp * cnbins * (CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12 + CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y); __local float* final_hist = hists + cnbins * (CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12); const int offset_x = gidX * cblock_stride_x + (cell_x << 2) + cell_thread_x; const int offset_y = gidY * cblock_stride_y + (cell_y << 2); __global const float* grad_ptr = (gid < blocks_total) ? grad + offset_y * grad_quadstep + (offset_x << 1) : grad; __global const QANGLE_TYPE* qangle_ptr = (gid < blocks_total) ? qangle + offset_y * qangle_step + (offset_x << 1) : qangle; __local float* hist = hists + 12 * (cell_y * CELLS_PER_BLOCK_Y + cell_x) + cell_thread_x; for (int bin_id = 0; bin_id < cnbins; ++bin_id) hist[bin_id * 48] = 0.f; const int dist_x = -4 + cell_thread_x - 4 * cell_x; const int dist_center_x = dist_x - 4 * (1 - 2 * cell_x); const int dist_y_begin = -4 - 4 * lidY; for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y) { float2 vote = (float2) (grad_ptr[0], grad_ptr[1]); QANGLE_TYPE2 bin = (QANGLE_TYPE2) (qangle_ptr[0], qangle_ptr[1]); grad_ptr += grad_quadstep; qangle_ptr += qangle_step; int dist_center_y = dist_y - 4 * (1 - 2 * cell_y); int idx = (dist_center_y + 8) * 16 + (dist_center_x + 8); float gaussian = gauss_w_lut[idx]; idx = (dist_y + 8) * 16 + (dist_x + 8); float interp_weight = gauss_w_lut[256+idx]; hist[bin.x * 48] += gaussian * interp_weight * vote.x; hist[bin.y * 48] += gaussian * interp_weight * vote.y; } barrier(CLK_LOCAL_MEM_FENCE); volatile __local float* hist_ = hist; for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48) { if (cell_thread_x < 6) hist_[0] += hist_[6]; barrier(CLK_LOCAL_MEM_FENCE); if (cell_thread_x < 3) hist_[0] += hist_[3]; barrier(CLK_LOCAL_MEM_FENCE); if (cell_thread_x == 0) final_hist[(cell_x * 2 + cell_y) * cnbins + bin_id] = hist_[0] + hist_[1] + hist_[2]; } barrier(CLK_LOCAL_MEM_FENCE); int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 12 + cell_thread_x; if ((tid < cblock_hist_size) && (gid < blocks_total)) { __global float* block_hist = block_hists + (gidY * img_block_width + gidX) * cblock_hist_size; block_hist[tid] = final_hist[tid]; } } __kernel void normalize_hists_36_kernel(__global float* block_hists, const float threshold, __local float *squares) { const int tid = get_local_id(0); const int gid = get_global_id(0); const int bid = tid / 36; const int boffset = bid * 36; const int hid = tid - boffset; float elem = block_hists[gid]; squares[tid] = elem * elem; barrier(CLK_LOCAL_MEM_FENCE); __local float* smem = squares + boffset; float sum = smem[hid]; if (hid < 18) smem[hid] = sum = sum + smem[hid + 18]; barrier(CLK_LOCAL_MEM_FENCE); if (hid < 9) smem[hid] = sum = sum + smem[hid + 9]; barrier(CLK_LOCAL_MEM_FENCE); if (hid < 4) smem[hid] = sum + smem[hid + 4]; barrier(CLK_LOCAL_MEM_FENCE); sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8]; elem = elem / (sqrt(sum) + 3.6f); elem = min(elem, threshold); barrier(CLK_LOCAL_MEM_FENCE); squares[tid] = elem * elem; barrier(CLK_LOCAL_MEM_FENCE); sum = smem[hid]; if (hid < 18) smem[hid] = sum = sum + smem[hid + 18]; barrier(CLK_LOCAL_MEM_FENCE); if (hid < 9) smem[hid] = sum = sum + smem[hid + 9]; barrier(CLK_LOCAL_MEM_FENCE); if (hid < 4) smem[hid] = sum + smem[hid + 4]; barrier(CLK_LOCAL_MEM_FENCE); sum = smem[0] + smem[1] + smem[2] + smem[3] + smem[8]; block_hists[gid] = elem / (sqrt(sum) + 1e-3f); } inline float reduce_smem(volatile __local float* smem, int size) { unsigned int tid = get_local_id(0); float sum = smem[tid]; if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 64) { if (tid < 32) smem[tid] = sum = sum + smem[tid + 32]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 32) { if (tid < 16) smem[tid] = sum = sum + smem[tid + 16]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 16) { if (tid < 8) smem[tid] = sum = sum + smem[tid + 8]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 8) { if (tid < 4) smem[tid] = sum = sum + smem[tid + 4]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 4) { if (tid < 2) smem[tid] = sum = sum + smem[tid + 2]; barrier(CLK_LOCAL_MEM_FENCE); } if (size >= 2) { if (tid < 1) smem[tid] = sum = sum + smem[tid + 1]; barrier(CLK_LOCAL_MEM_FENCE); } return sum; } __kernel void normalize_hists_kernel( const int nthreads, const int block_hist_size, const int img_block_width, __global float* block_hists, const float threshold, __local float *squares) { const int tid = get_local_id(0); const int gidX = get_group_id(0); const int gidY = get_group_id(1); __global float* hist = block_hists + (gidY * img_block_width + gidX) * block_hist_size + tid; float elem = 0.f; if (tid < block_hist_size) elem = hist[0]; squares[tid] = elem * elem; barrier(CLK_LOCAL_MEM_FENCE); float sum = reduce_smem(squares, nthreads); float scale = 1.0f / (sqrt(sum) + 0.1f * block_hist_size); elem = min(elem * scale, threshold); barrier(CLK_LOCAL_MEM_FENCE); squares[tid] = elem * elem; barrier(CLK_LOCAL_MEM_FENCE); sum = reduce_smem(squares, nthreads); scale = 1.0f / (sqrt(sum) + 1e-3f); if (tid < block_hist_size) hist[0] = elem * scale; } #define reduce_with_sync(target, sharedMemory, localMemory, tid, offset) \ if (tid < target) sharedMemory[tid] = localMemory = localMemory + sharedMemory[tid + offset]; \ barrier(CLK_LOCAL_MEM_FENCE); __kernel void classify_hists_180_kernel( const int cdescr_width, const int cdescr_height, const int cblock_hist_size, const int img_win_width, const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, __global const float * block_hists, __global const float* coefs, float free_coef, float threshold, __global uchar* labels) { const int tid = get_local_id(0); const int gidX = get_group_id(0); const int gidY = get_group_id(1); __global const float* hist = block_hists + (gidY * win_block_stride_y * img_block_width + gidX * win_block_stride_x) * cblock_hist_size; float product = 0.f; for (int i = 0; i < cdescr_height; i++) { product += coefs[i * cdescr_width + tid] * hist[i * img_block_width * cblock_hist_size + tid]; } __local float products[180]; products[tid] = product; barrier(CLK_LOCAL_MEM_FENCE); reduce_with_sync(90, products, product, tid, 90); reduce_with_sync(45, products, product, tid, 45); reduce_with_sync(13, products, product, tid, 32); reduce_with_sync(16, products, product, tid, 16); reduce_with_sync(8, products, product, tid, 8); reduce_with_sync(4, products, product, tid, 4); reduce_with_sync(2, products, product, tid, 2); if (tid == 0){ product = product + products[tid + 1]; labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold); } } __kernel void classify_hists_252_kernel( const int cdescr_width, const int cdescr_height, const int cblock_hist_size, const int img_win_width, const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, __global const float * block_hists, __global const float* coefs, float free_coef, float threshold, __global uchar* labels) { const int tid = get_local_id(0); const int gidX = get_group_id(0); const int gidY = get_group_id(1); __global const float* hist = block_hists + (gidY * win_block_stride_y * img_block_width + gidX * win_block_stride_x) * cblock_hist_size; float product = 0.f; if (tid < cdescr_width) { for (int i = 0; i < cdescr_height; i++) product += coefs[i * cdescr_width + tid] * hist[i * img_block_width * cblock_hist_size + tid]; } __local float products[NTHREADS]; products[tid] = product; barrier(CLK_LOCAL_MEM_FENCE); reduce_with_sync(128, products, product, tid, 128); reduce_with_sync(64, products, product, tid, 64); reduce_with_sync(32, products, product, tid, 32); reduce_with_sync(16, products, product, tid, 16); reduce_with_sync(8, products, product, tid, 8); reduce_with_sync(4, products, product, tid, 4); reduce_with_sync(2, products, product, tid, 2); if (tid == 0){ product = product + products[tid + 1]; labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold); } } __kernel void classify_hists_kernel( const int cdescr_size, const int cdescr_width, const int cblock_hist_size, const int img_win_width, const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, __global const float * block_hists, __global const float* coefs, float free_coef, float threshold, __global uchar* labels) { const int tid = get_local_id(0); const int gidX = get_group_id(0); const int gidY = get_group_id(1); __global const float* hist = block_hists + (gidY * win_block_stride_y * img_block_width + gidX * win_block_stride_x) * cblock_hist_size; float product = 0.f; for (int i = tid; i < cdescr_size; i += NTHREADS) { int offset_y = i / cdescr_width; int offset_x = i - offset_y * cdescr_width; product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x]; } __local float products[NTHREADS]; products[tid] = product; barrier(CLK_LOCAL_MEM_FENCE); reduce_with_sync(128, products, product, tid, 128); reduce_with_sync(64, products, product, tid, 64); reduce_with_sync(32, products, product, tid, 32); reduce_with_sync(16, products, product, tid, 16); reduce_with_sync(8, products, product, tid, 8); reduce_with_sync(4, products, product, tid, 4); reduce_with_sync(2, products, product, tid, 2); if (tid == 0){ products[tid] = product = product + products[tid + 1]; labels[gidY * img_win_width + gidX] = (product + free_coef >= threshold); } } __kernel void extract_descrs_by_rows_kernel( const int cblock_hist_size, const int descriptors_quadstep, const int cdescr_size, const int cdescr_width, const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, __global const float* block_hists, __global float* descriptors) { int tid = get_local_id(0); int gidX = get_group_id(0); int gidY = get_group_id(1); __global const float* hist = block_hists + (gidY * win_block_stride_y * img_block_width + gidX * win_block_stride_x) * cblock_hist_size; __global float* descriptor = descriptors + (gidY * get_num_groups(0) + gidX) * descriptors_quadstep; for (int i = tid; i < cdescr_size; i += NTHREADS) { int offset_y = i / cdescr_width; int offset_x = i - offset_y * cdescr_width; descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x]; } } __kernel void extract_descrs_by_cols_kernel( const int cblock_hist_size, const int descriptors_quadstep, const int cdescr_size, const int cnblocks_win_x, const int cnblocks_win_y, const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, __global const float* block_hists, __global float* descriptors) { int tid = get_local_id(0); int gidX = get_group_id(0); int gidY = get_group_id(1); __global const float* hist = block_hists + (gidY * win_block_stride_y * img_block_width + gidX * win_block_stride_x) * cblock_hist_size; __global float* descriptor = descriptors + (gidY * get_num_groups(0) + gidX) * descriptors_quadstep; for (int i = tid; i < cdescr_size; i += NTHREADS) { int block_idx = i / cblock_hist_size; int idx_in_block = i - block_idx * cblock_hist_size; int y = block_idx / cnblocks_win_x; int x = block_idx - y * cnblocks_win_x; descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block] = hist[(y * img_block_width + x) * cblock_hist_size + idx_in_block]; } } __kernel void compute_gradients_8UC4_kernel( const int height, const int width, const int img_step, const int grad_quadstep, const int qangle_step, const __global uchar4 * img, __global float * grad, __global QANGLE_TYPE * qangle, const float angle_scale, const char correct_gamma, const int cnbins) { const int x = get_global_id(0); const int tid = get_local_id(0); const int gSizeX = get_local_size(0); const int gidY = get_group_id(1); __global const uchar4* row = img + gidY * img_step; __local float sh_row[(NTHREADS + 2) * 3]; uchar4 val; if (x < width) val = row[x]; else val = row[width - 2]; sh_row[tid + 1] = val.x; sh_row[tid + 1 + (NTHREADS + 2)] = val.y; sh_row[tid + 1 + 2 * (NTHREADS + 2)] = val.z; if (tid == 0) { val = row[max(x - 1, 1)]; sh_row[0] = val.x; sh_row[(NTHREADS + 2)] = val.y; sh_row[2 * (NTHREADS + 2)] = val.z; } if (tid == gSizeX - 1) { val = row[min(x + 1, width - 2)]; sh_row[gSizeX + 1] = val.x; sh_row[gSizeX + 1 + (NTHREADS + 2)] = val.y; sh_row[gSizeX + 1 + 2 * (NTHREADS + 2)] = val.z; } barrier(CLK_LOCAL_MEM_FENCE); if (x < width) { float4 a = (float4) (sh_row[tid], sh_row[tid + (NTHREADS + 2)], sh_row[tid + 2 * (NTHREADS + 2)], 0); float4 b = (float4) (sh_row[tid + 2], sh_row[tid + 2 + (NTHREADS + 2)], sh_row[tid + 2 + 2 * (NTHREADS + 2)], 0); float4 dx; if (correct_gamma == 1) dx = sqrt(b) - sqrt(a); else dx = b - a; float4 dy = (float4) 0.f; if (gidY > 0 && gidY < height - 1) { a = convert_float4(img[(gidY - 1) * img_step + x].xyzw); b = convert_float4(img[(gidY + 1) * img_step + x].xyzw); if (correct_gamma == 1) dy = sqrt(b) - sqrt(a); else dy = b - a; } float4 mag = hypot(dx, dy); float best_dx = dx.x; float best_dy = dy.x; float mag0 = mag.x; if (mag0 < mag.y) { best_dx = dx.y; best_dy = dy.y; mag0 = mag.y; } if (mag0 < mag.z) { best_dx = dx.z; best_dy = dy.z; mag0 = mag.z; } float ang = (atan2(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f; int hidx = (int)floor(ang); ang -= hidx; hidx = (hidx + cnbins) % cnbins; qangle[(gidY * qangle_step + x) << 1] = hidx; qangle[((gidY * qangle_step + x) << 1) + 1] = (hidx + 1) % cnbins; grad[(gidY * grad_quadstep + x) << 1] = mag0 * (1.f - ang); grad[((gidY * grad_quadstep + x) << 1) + 1] = mag0 * ang; } } __kernel void compute_gradients_8UC1_kernel( const int height, const int width, const int img_step, const int grad_quadstep, const int qangle_step, __global const uchar * img, __global float * grad, __global QANGLE_TYPE * qangle, const float angle_scale, const char correct_gamma, const int cnbins) { const int x = get_global_id(0); const int tid = get_local_id(0); const int gSizeX = get_local_size(0); const int gidY = get_group_id(1); __global const uchar* row = img + gidY * img_step; __local float sh_row[NTHREADS + 2]; if (x < width) sh_row[tid + 1] = row[x]; else sh_row[tid + 1] = row[width - 2]; if (tid == 0) sh_row[0] = row[max(x - 1, 1)]; if (tid == gSizeX - 1) sh_row[gSizeX + 1] = row[min(x + 1, width - 2)]; barrier(CLK_LOCAL_MEM_FENCE); if (x < width) { float dx; if (correct_gamma == 1) dx = sqrt(sh_row[tid + 2]) - sqrt(sh_row[tid]); else dx = sh_row[tid + 2] - sh_row[tid]; float dy = 0.f; if (gidY > 0 && gidY < height - 1) { float a = (float) img[ (gidY + 1) * img_step + x ]; float b = (float) img[ (gidY - 1) * img_step + x ]; if (correct_gamma == 1) dy = sqrt(a) - sqrt(b); else dy = a - b; } float mag = hypot(dx, dy); float ang = (atan2(dy, dx) + CV_PI_F) * angle_scale - 0.5f; int hidx = (int)floor(ang); ang -= hidx; hidx = (hidx + cnbins) % cnbins; qangle[ (gidY * qangle_step + x) << 1 ] = hidx; qangle[ ((gidY * qangle_step + x) << 1) + 1 ] = (hidx + 1) % cnbins; grad[ (gidY * grad_quadstep + x) << 1 ] = mag * (1.f - ang); grad[ ((gidY * grad_quadstep + x) << 1) + 1 ] = mag * ang; } } ceeb73dfe1abfee1e5ddba4466eca44f#ifdef HAAR typedef struct __attribute__((aligned(4))) OptHaarFeature { int4 ofs[3] __attribute__((aligned (4))); float4 weight __attribute__((aligned (4))); } OptHaarFeature; #endif #ifdef LBP typedef struct __attribute__((aligned(4))) OptLBPFeature { int16 ofs __attribute__((aligned (4))); } OptLBPFeature; #endif typedef struct __attribute__((aligned(4))) Stump { float4 st __attribute__((aligned (4))); } Stump; typedef struct __attribute__((aligned(4))) Node { int4 n __attribute__((aligned (4))); } Node; typedef struct __attribute__((aligned (4))) Stage { int first __attribute__((aligned (4))); int ntrees __attribute__((aligned (4))); float threshold __attribute__((aligned (4))); } Stage; typedef struct __attribute__((aligned (4))) ScaleData { float scale __attribute__((aligned (4))); int szi_width __attribute__((aligned (4))); int szi_height __attribute__((aligned (4))); int layer_ofs __attribute__((aligned (4))); int ystep __attribute__((aligned (4))); } ScaleData; #ifndef SUM_BUF_SIZE #define SUM_BUF_SIZE 0 #endif #ifndef NODE_COUNT #define NODE_COUNT 1 #endif #ifdef HAAR __kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1))) void runHaarClassifier( int nscales, __global const ScaleData* scaleData, __global const int* sum, int _sumstep, int sumoffset, __global const OptHaarFeature* optfeatures, __global const Stage* stages, __global const Node* nodes, __global const float* leaves0, volatile __global int* facepos, int4 normrect, int sqofs, int2 windowsize) { int lx = get_local_id(0); int ly = get_local_id(1); int groupIdx = get_group_id(0); int i, ngroups = get_global_size(0)/LOCAL_SIZE_X; int scaleIdx, tileIdx, stageIdx; int sumstep = (int)(_sumstep/sizeof(int)); int4 nofs0 = (int4)(mad24(normrect.y, sumstep, normrect.x), mad24(normrect.y, sumstep, normrect.x + normrect.z), mad24(normrect.y + normrect.w, sumstep, normrect.x), mad24(normrect.y + normrect.w, sumstep, normrect.x + normrect.z)); int normarea = normrect.z * normrect.w; float invarea = 1.f/normarea; int lidx = ly*LOCAL_SIZE_X + lx; #if SUM_BUF_SIZE > 0 int4 nofs = (int4)(mad24(normrect.y, SUM_BUF_STEP, normrect.x), mad24(normrect.y, SUM_BUF_STEP, normrect.x + normrect.z), mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x), mad24(normrect.y + normrect.w, SUM_BUF_STEP, normrect.x + normrect.z)); #else int4 nofs = nofs0; #endif #define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y) __local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*5/2+1]; #if SUM_BUF_SIZE > 0 __local int* ibuf = lstore; __local int* lcount = ibuf + SUM_BUF_SIZE; #else __local int* lcount = lstore; #endif __local float* lnf = (__local float*)(lcount + 1); __local float* lpartsum = lnf + LOCAL_SIZE; __local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE); for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- ) { __global const ScaleData* s = scaleData + scaleIdx; int ystep = s->ystep; int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0)); int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X, (worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y); int totalTiles = ntiles.x*ntiles.y; for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups ) { int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X; int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y; int ix = lx, iy = ly; __global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs; __global const int* psum1 = psum0 + mad24(iy, sumstep, ix); if( ix0 >= worksize.x || iy0 >= worksize.y ) continue; #if SUM_BUF_SIZE > 0 for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 ) { int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP; vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i); } #endif if( lidx == 0 ) lcount[0] = 0; barrier(CLK_LOCAL_MEM_FENCE); if( ix0 + ix < worksize.x && iy0 + iy < worksize.y ) { #if NODE_COUNT==1 __global const Stump* stump = (__global const Stump*)nodes; #else __global const Node* node = nodes; __global const float* leaves = leaves0; #endif #if SUM_BUF_SIZE > 0 __local const int* psum = ibuf + mad24(iy, SUM_BUF_STEP, ix); #else __global const int* psum = psum1; #endif __global const int* psqsum = (__global const int*)(psum1 + sqofs); float sval = (psum[nofs.x] - psum[nofs.y] - psum[nofs.z] + psum[nofs.w])*invarea; float sqval = (psqsum[nofs0.x] - psqsum[nofs0.y] - psqsum[nofs0.z] + psqsum[nofs0.w])*invarea; float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f)); nf = nf > 0 ? nf : 1.f; for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ ) { int ntrees = stages[stageIdx].ntrees; float s = 0.f; #if NODE_COUNT==1 for( i = 0; i < ntrees; i++ ) { float4 st = stump[i].st; __global const OptHaarFeature* f = optfeatures + as_int(st.x); float4 weight = f->weight; int4 ofs = f->ofs[0]; sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x; ofs = f->ofs[1]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval); if( weight.z > 0 ) { ofs = f->ofs[2]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval); } s += (sval < st.y*nf) ? st.z : st.w; } stump += ntrees; #else for( i = 0; i < ntrees; i++, node += NODE_COUNT, leaves += NODE_COUNT+1 ) { int idx = 0; do { int4 n = node[idx].n; __global const OptHaarFeature* f = optfeatures + n.x; float4 weight = f->weight; int4 ofs = f->ofs[0]; sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x; ofs = f->ofs[1]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval); if( weight.z > 0 ) { ofs = f->ofs[2]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval); } idx = (sval < as_float(n.y)*nf) ? n.z : n.w; } while(idx > 0); s += leaves[-idx]; } #endif if( s < stages[stageIdx].threshold ) break; } if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) ) { int count = atomic_inc(lcount); lbuf[count] = (int)(ix | (iy << 8)); lnf[count] = nf; } } for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ ) { barrier(CLK_LOCAL_MEM_FENCE); int nrects = lcount[0]; if( nrects == 0 ) break; barrier(CLK_LOCAL_MEM_FENCE); if( lidx == 0 ) lcount[0] = 0; { #if NODE_COUNT == 1 __global const Stump* stump = (__global const Stump*)nodes + stages[stageIdx].first; #else __global const Node* node = nodes + stages[stageIdx].first*NODE_COUNT; __global const float* leaves = leaves0 + stages[stageIdx].first*(NODE_COUNT+1); #endif int nparts = LOCAL_SIZE / nrects; int ntrees = stages[stageIdx].ntrees; int ntrees_p = (ntrees + nparts - 1)/nparts; int nr = lidx / nparts; int partidx = -1, idxval = 0; float partsum = 0.f, nf = 0.f; if( nr < nrects ) { partidx = lidx % nparts; idxval = lbuf[nr]; nf = lnf[nr]; { int ntrees0 = ntrees_p*partidx; int ntrees1 = min(ntrees0 + ntrees_p, ntrees); int ix1 = idxval & 255, iy1 = idxval >> 8; #if SUM_BUF_SIZE > 0 __local const int* psum = ibuf + mad24(iy1, SUM_BUF_STEP, ix1); #else __global const int* psum = psum0 + mad24(iy1, sumstep, ix1); #endif #if NODE_COUNT == 1 for( i = ntrees0; i < ntrees1; i++ ) { float4 st = stump[i].st; __global const OptHaarFeature* f = optfeatures + as_int(st.x); float4 weight = f->weight; int4 ofs = f->ofs[0]; float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x; ofs = f->ofs[1]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval); if( fabs(weight.z) > 0 ) { ofs = f->ofs[2]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval); } partsum += (sval < st.y*nf) ? st.z : st.w; } #else for( i = ntrees0; i < ntrees1; i++ ) { int idx = 0; do { int4 n = node[i*2 + idx].n; __global const OptHaarFeature* f = optfeatures + n.x; float4 weight = f->weight; int4 ofs = f->ofs[0]; float sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x; ofs = f->ofs[1]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.y, sval); if( weight.z > 0 ) { ofs = f->ofs[2]; sval = mad((float)(psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w]), weight.z, sval); } idx = (sval < as_float(n.y)*nf) ? n.z : n.w; } while(idx > 0); partsum += leaves[i*3-idx]; } #endif } } lpartsum[lidx] = partsum; barrier(CLK_LOCAL_MEM_FENCE); if( partidx == 0 ) { float s = lpartsum[nr*nparts]; for( i = 1; i < nparts; i++ ) s += lpartsum[i + nr*nparts]; if( s >= stages[stageIdx].threshold ) { int count = atomic_inc(lcount); lbuf[count] = idxval; lnf[count] = nf; } } } } barrier(CLK_LOCAL_MEM_FENCE); if( stageIdx == N_STAGES ) { int nrects = lcount[0]; if( lidx < nrects ) { int nfaces = atomic_inc(facepos); if( nfaces < MAX_FACES ) { volatile __global int* face = facepos + 1 + nfaces*3; int val = lbuf[lidx]; face[0] = scaleIdx; face[1] = ix0 + (val & 255); face[2] = iy0 + (val >> 8); } } } } } } #endif #ifdef LBP #undef CALC_SUM_OFS_ #define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \ ((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3]) __kernel void runLBPClassifierStumpSimple( int nscales, __global const ScaleData* scaleData, __global const int* sum, int _sumstep, int sumoffset, __global const OptLBPFeature* optfeatures, __global const Stage* stages, __global const Stump* stumps, __global const int* bitsets, int bitsetSize, volatile __global int* facepos, int2 windowsize) { int lx = get_local_id(0); int ly = get_local_id(1); int local_size_x = get_local_size(0); int local_size_y = get_local_size(1); int groupIdx = get_group_id(1)*get_num_groups(0) + get_group_id(0); int ngroups = get_num_groups(0)*get_num_groups(1); int scaleIdx, tileIdx, stageIdx; int sumstep = (int)(_sumstep/sizeof(int)); for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- ) { __global const ScaleData* s = scaleData + scaleIdx; int ystep = s->ystep; int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0)); int2 ntiles = (int2)((worksize.x/ystep + local_size_x-1)/local_size_x, (worksize.y/ystep + local_size_y-1)/local_size_y); int totalTiles = ntiles.x*ntiles.y; for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups ) { int iy = mad24((tileIdx / ntiles.x), local_size_y, ly) * ystep; int ix = mad24((tileIdx % ntiles.x), local_size_x, lx) * ystep; if( ix < worksize.x && iy < worksize.y ) { __global const int* p = sum + mad24(iy, sumstep, ix) + s->layer_ofs; __global const Stump* stump = stumps; __global const int* bitset = bitsets; for( stageIdx = 0; stageIdx < N_STAGES; stageIdx++ ) { int i, ntrees = stages[stageIdx].ntrees; float s = 0.f; for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize ) { float4 st = stump->st; __global const OptLBPFeature* f = optfeatures + as_int(st.x); int16 ofs = f->ofs; int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p ); int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0); mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0); mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0); mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0); s += (bitset[idx] & (1 << mask)) ? st.z : st.w; } if( s < stages[stageIdx].threshold ) break; } if( stageIdx == N_STAGES ) { int nfaces = atomic_inc(facepos); if( nfaces < MAX_FACES ) { volatile __global int* face = facepos + 1 + nfaces*3; face[0] = scaleIdx; face[1] = ix; face[2] = iy; } } } } } } __kernel __attribute__((reqd_work_group_size(LOCAL_SIZE_X,LOCAL_SIZE_Y,1))) void runLBPClassifierStump( int nscales, __global const ScaleData* scaleData, __global const int* sum, int _sumstep, int sumoffset, __global const OptLBPFeature* optfeatures, __global const Stage* stages, __global const Stump* stumps, __global const int* bitsets, int bitsetSize, volatile __global int* facepos, int2 windowsize) { int lx = get_local_id(0); int ly = get_local_id(1); int groupIdx = get_group_id(0); int i, ngroups = get_global_size(0)/LOCAL_SIZE_X; int scaleIdx, tileIdx, stageIdx; int sumstep = (int)(_sumstep/sizeof(int)); int lidx = ly*LOCAL_SIZE_X + lx; #define LOCAL_SIZE (LOCAL_SIZE_X*LOCAL_SIZE_Y) __local int lstore[SUM_BUF_SIZE + LOCAL_SIZE*3/2+1]; #if SUM_BUF_SIZE > 0 __local int* ibuf = lstore; __local int* lcount = ibuf + SUM_BUF_SIZE; #else __local int* lcount = lstore; #endif __local float* lpartsum = (__local float*)(lcount + 1); __local short* lbuf = (__local short*)(lpartsum + LOCAL_SIZE); for( scaleIdx = nscales-1; scaleIdx >= 0; scaleIdx-- ) { __global const ScaleData* s = scaleData + scaleIdx; int ystep = s->ystep; int2 worksize = (int2)(max(s->szi_width - windowsize.x, 0), max(s->szi_height - windowsize.y, 0)); int2 ntiles = (int2)((worksize.x + LOCAL_SIZE_X-1)/LOCAL_SIZE_X, (worksize.y + LOCAL_SIZE_Y-1)/LOCAL_SIZE_Y); int totalTiles = ntiles.x*ntiles.y; for( tileIdx = groupIdx; tileIdx < totalTiles; tileIdx += ngroups ) { int ix0 = (tileIdx % ntiles.x)*LOCAL_SIZE_X; int iy0 = (tileIdx / ntiles.x)*LOCAL_SIZE_Y; int ix = lx, iy = ly; __global const int* psum0 = sum + mad24(iy0, sumstep, ix0) + s->layer_ofs; if( ix0 >= worksize.x || iy0 >= worksize.y ) continue; #if SUM_BUF_SIZE > 0 for( i = lidx*4; i < SUM_BUF_SIZE; i += LOCAL_SIZE_X*LOCAL_SIZE_Y*4 ) { int dy = i/SUM_BUF_STEP, dx = i - dy*SUM_BUF_STEP; vstore4(vload4(0, psum0 + mad24(dy, sumstep, dx)), 0, ibuf+i); } barrier(CLK_LOCAL_MEM_FENCE); #endif if( lidx == 0 ) lcount[0] = 0; barrier(CLK_LOCAL_MEM_FENCE); if( ix0 + ix < worksize.x && iy0 + iy < worksize.y ) { __global const Stump* stump = stumps; __global const int* bitset = bitsets; #if SUM_BUF_SIZE > 0 __local const int* p = ibuf + mad24(iy, SUM_BUF_STEP, ix); #else __global const int* p = psum0 + mad24(iy, sumstep, ix); #endif for( stageIdx = 0; stageIdx < SPLIT_STAGE; stageIdx++ ) { int ntrees = stages[stageIdx].ntrees; float s = 0.f; for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize ) { float4 st = stump->st; __global const OptLBPFeature* f = optfeatures + as_int(st.x); int16 ofs = f->ofs; int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p ); int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0); mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0); mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0); mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0); s += (bitset[idx] & (1 << mask)) ? st.z : st.w; } if( s < stages[stageIdx].threshold ) break; } if( stageIdx == SPLIT_STAGE && (ystep == 1 || ((ix | iy) & 1) == 0) ) { int count = atomic_inc(lcount); lbuf[count] = (int)(ix | (iy << 8)); } } for( stageIdx = SPLIT_STAGE; stageIdx < N_STAGES; stageIdx++ ) { int nrects = lcount[0]; barrier(CLK_LOCAL_MEM_FENCE); if( nrects == 0 ) break; if( lidx == 0 ) lcount[0] = 0; { __global const Stump* stump = stumps + stages[stageIdx].first; __global const int* bitset = bitsets + stages[stageIdx].first*bitsetSize; int nparts = LOCAL_SIZE / nrects; int ntrees = stages[stageIdx].ntrees; int ntrees_p = (ntrees + nparts - 1)/nparts; int nr = lidx / nparts; int partidx = -1, idxval = 0; float partsum = 0.f, nf = 0.f; if( nr < nrects ) { partidx = lidx % nparts; idxval = lbuf[nr]; { int ntrees0 = ntrees_p*partidx; int ntrees1 = min(ntrees0 + ntrees_p, ntrees); int ix1 = idxval & 255, iy1 = idxval >> 8; #if SUM_BUF_SIZE > 0 __local const int* p = ibuf + mad24(iy1, SUM_BUF_STEP, ix1); #else __global const int* p = psum0 + mad24(iy1, sumstep, ix1); #endif for( i = ntrees0; i < ntrees1; i++ ) { float4 st = stump[i].st; __global const OptLBPFeature* f = optfeatures + as_int(st.x); int16 ofs = f->ofs; #define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \ ((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3]) int cval = CALC_SUM_OFS_( ofs.s5, ofs.s6, ofs.s9, ofs.sa, p ); int mask, idx = (CALC_SUM_OFS_( ofs.s0, ofs.s1, ofs.s4, ofs.s5, p ) >= cval ? 4 : 0); idx |= (CALC_SUM_OFS_( ofs.s1, ofs.s2, ofs.s5, ofs.s6, p ) >= cval ? 2 : 0); idx |= (CALC_SUM_OFS_( ofs.s2, ofs.s3, ofs.s6, ofs.s7, p ) >= cval ? 1 : 0); mask = (CALC_SUM_OFS_( ofs.s6, ofs.s7, ofs.sa, ofs.sb, p ) >= cval ? 16 : 0); mask |= (CALC_SUM_OFS_( ofs.sa, ofs.sb, ofs.se, ofs.sf, p ) >= cval ? 8 : 0); mask |= (CALC_SUM_OFS_( ofs.s9, ofs.sa, ofs.sd, ofs.se, p ) >= cval ? 4 : 0); mask |= (CALC_SUM_OFS_( ofs.s8, ofs.s9, ofs.sc, ofs.sd, p ) >= cval ? 2 : 0); mask |= (CALC_SUM_OFS_( ofs.s4, ofs.s5, ofs.s8, ofs.s9, p ) >= cval ? 1 : 0); partsum += (bitset[i*bitsetSize + idx] & (1 << mask)) ? st.z : st.w; } } } lpartsum[lidx] = partsum; barrier(CLK_LOCAL_MEM_FENCE); if( partidx == 0 ) { float s = lpartsum[nr*nparts]; for( i = 1; i < nparts; i++ ) s += lpartsum[i + nr*nparts]; if( s >= stages[stageIdx].threshold ) { int count = atomic_inc(lcount); lbuf[count] = idxval; } } } } barrier(CLK_LOCAL_MEM_FENCE); if( stageIdx == N_STAGES ) { int nrects = lcount[0]; if( lidx < nrects ) { int nfaces = atomic_inc(facepos); if( nfaces < MAX_FACES ) { volatile __global int* face = facepos + 1 + nfaces*3; int val = lbuf[lidx]; face[0] = scaleIdx; face[1] = ix0 + (val & 255); face[2] = iy0 + (val >> 8); } } } } } } #endif 91af1bca5e8f46e8426e0f3a1eb8d1f2GNUА(-(_ZN2cv3ocl9objdetect20objdetect_hog_oclsrcE_ZN2cv3ocl9objdetect20cascadedetect_oclsrcE °?Ш?°„.symtab.strtab.shstrtab.text.data.bss.rodata.str1.1.rodata.str1.8.rela.data.rel.local._ZN2cv3ocl9objdetect20objdetect_hog_oclsrcE.rela.data.rel.local._ZN2cv3ocl9objdetect20cascadedetect_oclsrcE.note.GNU-stack.note.gnu.property@!@'@,2@&;2hС„O@…( J@ †`ђЂ…( ‹@‡`МЁ…ЬЁ… И…x @†Y`‡п