Opencv级联分类器训练指南

Opencvsrapidobjectdetection EE:\Temp,Opency-_objectMarkerexe 四日3-6-1453-18.bmp Lrectx 29y=233W1dth=13B height=98 eCtx=26区 y=186width=112 height=74 IaddsaveandloadneTexit |口x Fig1-1:Rawimageandaboundingrectangleonthebowlinthemiddle Ifyouwanttotrytotrainbyoneexampleimageanyway,youmusthave"iplPXllibavailable Alsoyouhavetouncomment#definehaveIplinfilecvhaartrainingh'sothat iplWarpPerspectiveg0isrunningcorrectlyfor"createsamples,exe?.Otherwisewhencompiled thestepofpatterngenerationfromyouronesampleisnotincludedintotheprogramandonlythe negativesareputintoavecfile Thesepositivesampleswillbestoredinafolder"bowls""C:\Temp\positives\"where theinfofiles“train.txt”and‘testing.txt'arelocated[Fig.1-2 鬥-0b-17聊t量1211161 如一日6隐1351151061414道04314655 us20-06017份0p39114035517511179 22006-0p32341的1047101+1的3761 -聊的71m 』m---即2印5222202:20 四s一唧p。1舒巒11日 u0--05柳卿5i6237m10711171571291552181 m-00蝉58540型1智的1题191妇 452 1200-06-08-0-0聊00135856m06153067412075份1512071171559565610856 m隐—-聊最卧蘸11節3011经 IQ-6-08180-1柳p295181卵1605 u00--16bp23量111imi0m -槨論18即加2115論1⑩a7111 u000含娜63611员惚1356175m723135511”55120513 cu2001-06081-0-34B4135 r47251112 38196396156 lu20-81-柳p227714431174911的1912劉5m1根3977711m u/0-b0--tp1票6“1的115昌 b0-017-46即p3417念锁20113152415010 0017娜313爿常8167 us/:m-7-bp:15]2:7 Fig.1-2:Examplecontentoffiletrain.txt(fromlefttoright):BMPfilelocation,numberofrectangles,eachrectangles X/ycoordinateoftheupperleftcornerandwidth/heightfromthispointx/y Florianadolf Page3of6 2003-09-02 Opencvsrapidobjectdetection Step2-Sample/TestCreation Assumingthatasamplesizeof20x20isagoodchoiceformostobjects,samplesarereducedto thissize Basicallythereshouldbefoursetsofimagesthatyouworkon apositivesamplesetrepresentingyourobjectthatyouwanttotrain anotherpositivesamplesetfortestingpurpose anegativesampleset(orso-calledbackgroundsfortraining andanothernegativesamplesetfortesting,too Note:Thetestingsetsshouldnotcontainanyimagesthatwereusedfortraining Ofcourse,definingthenumberofimagesincachsetdependsonhowmanyimagesyouhavein total.Weuse5500negativesandsplittheminto5350samplesfortrainingand150samplesfor testing.Aspositivesampleswehave1350imagesfromourbowl[Fig2-1where50aretakenfor testing Fig.2-1:Examplesofhowdifferentasimplebowlcanappearinarealvideoimage Accordingtothisamountofsamplesineachsetyoumustspecifythenumberparamtersforthe trainingutilities,too Onceyouhaveallyoursetsarrangedtheobjectimageshavetobepacked"intoavec-fileinthe folder"data?.Thiscanonlybedonebythecreatesamplestool,evenifyoualreadyhaveasetof objectimagesanddontwanttogenerateartificialobjectimages.Thecallinourcasewouldbe createsamples.exe-infopositives/train.txt-vecdata/positivesvec-num1300-w20-h20 Itshouldbecheckedifthevecfilereallycontainsthedesiredimages.Forexamplewhenyou tookthenon-IPLversionofcreatesamplestocreateartificialobjectimages,youwillseenowthat itcontainspartsofyournegativesetwithnoobjectonit.Inourcasecallfollowingandpress toscrollthroughtheimagesinthis"highGUIwindow createsamples.exe-vecdata/positivesvec-W20-h20 Florianadolf Page4of6 2003-09-02 Opencvsrapidobjectdetection Step3-Training Assumingthedefaultvaluesofhitrate(0.995),maxfalsealarm(0.5),weighttrimming(0.95)and boostingtype(GAB,GentleAdaBoost)aregoodinmostcases,onlysomeparameterswillbe changed.Theextendedfeaturesetshouldbeusedandthenumberofstagesshouldbeatleast20 Ifthesearetoomanystagesyoucanaborttrainingatanytime.Ifthesearetoolessstagesyoucan restartthetrainingtoolandstageswillbeaddedtoanexistingcascade(startingpointisthelast completedstage).Iftheobjectissymmetric(likethebowlinourexample)theparameter nonsym"isnotneeded.Thissavesfeaturecalculationtimeandmemoryusageineachstage ThesystemyoushoulduseforhaartrainingshouldhaveafastprocessorandenoughRAM installed.Themachineusedfortrainingherehas1.5GBofRaMandap42.4GHzwithout HyperThreadingUsingWindows2000AdvancedServerforbettermemorymanagementand pagingfilebehaviour,wecanuse1,300MBofRAMfor"haartraining.exe"It'simportantnotto useallsystemRAMbecauseotherwiseitwillresultinaconsiderabletrainingslowdown Thetrainingofourbowlwillbestartedbythefollowingcall haartraining.exe-datadata/cascade-vecdata/positives.vec-bgnegatives/train.txt -npos1300-nneg5350-stages30-mem1300-modeall-w20-h20 Whiletrainingisrunning,youalreadycangeta"feeling"whetheritwillbesuitableclassifieror somethinghastobeimprovedinyourtrainingsetand/ortrainingparameters Thelinestartingwith"POS:showsthehitrateinthesetoftrainingsamples.Thenextline startingwith"NEGindicatesthefalsealarmrate.Therateofthepositivesshouldbeequalor ncar1.0(asitisin"stage0).Thefalscalarmrateshouldreachatleast5*10(fivezeros)untilit isausableclassifier[Fig3-1].Otherwisethefalsalarmisbetoohighforrealworldapplication Ifoneofthesevaluesgetsbelowzero["Stage18,Fig3-1]there'sjustanoverflow.Thismeans thatthefalsealarmrateissolowthatiscanbestopped,nofurthertrainingwouldmakesense STAGETRAININGTIME:5037.31 STAGE:17 POS:129312931.000000 NEG:500013087771430.000004 BACKGROUNDPROCESSINGTIME:26671.78 PRECALCULATIONTIME:108.59 STAGETRAININGTIME:5389.59 sTAGE:18 POS:129312931.000000 NEG:5000-14651568600.000003 BACKGROUNDPROCESSINGTIME:58371.50 PRECALCULATIONTIME:108.56 Fig3-1:Exampleofbowltraining:In"STAGE17"fivezeros(redcolourednumber)indicatetopossiblybecomea suitableclassifier.In1.3billionbackgroundsmightbe5000backgroundsinwhichanobjectisdetectedfalsly Florianadolf Page5of6 2003-09-02 Opencvsrapidobjectdetection step4·Testing Aclassifiercanbetestedwiththeperformancetoolmentionedunder"utiliesordirectlyviaa livetestifadetailedreportisnotnecessary Ifyouwantareporttestyoumusthaveadifferentsetofpositivesandnegativesasmentionedin pl-Preparation” Theinfofileforthisperformanceutilitymustnotcontainapathtotheimage.Onlythefilename itselfisallowed.Otherwisethecvsavelmageofunctionthrowsanerrorbecauseitcannotsavethe imagewheretherectanglesaredrawninto Toavoidthiserroryoucanalsousetheoption"andnodetectionresultissavedtoanimage Inourexamplethetestofhitrateandfalsealarmwillbedonebycalling performance.exe-datadata/cascade-infopositives/testing/testing.txt-W20-h20-rs30 Itwillgothroughallimagesandtriesdetecttheobject.Ifoneobjectisfoundandoption"-ni"is notspecified,itwillsavethecurrentimage Theresultsofthisperformanceutilityshouldonlybeseenasonepossibleresultanddontreflect thepossibledetectionbehaviourofyourapplication[Fig4-1] Fig.4-1:Differentdetectionresultsforthesameclassifierbase:performancetool(leftcolumn)andexample applicationfromOpencvdocumention(rightcolumn) Florianadolf Page6of6 2003-09-02