masmconf.cpp

这是个人脸识别程序

#define P_Mask000_CircleSigmAbsSum		(PROF_WindowCircle|PROF_SigmAbsSum|PROF_FBit|PROF_Mask000)
#define P_Mask202_CircleSigmAbsSum		(PROF_WindowCircle|PROF_SigmAbsSum|PROF_FBit|PROF_Mask202)
#define P_Mask011_CircleSigmAbsSum		(PROF_WindowCircle|PROF_SigmAbsSum|PROF_FBit|PROF_Mask011)
#define P_Mask010_CircleSigmAbsSum		(PROF_WindowCircle|PROF_SigmAbsSum|PROF_FBit|PROF_Mask010)

#define P_GradMagBR_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_GradMagBelowRight)
#define P_Edge_CircleFlat			(PROF_WindowCircle|PROF_Flat|PROF_Edgeness)
#define P_Corn_CircleFlat			(PROF_WindowCircle|PROF_Flat|PROF_Cornerness)
#define P_GradBR_CircleFlat			(PROF_WindowCircle|PROF_Flat|PROF_GradBelowRight)
#define P_Harris_CircleFlat			(PROF_WindowCircle|PROF_Flat|PROF_HarrisStephens)
#define P_MaskGray_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_MaskGray)
#define P_Mask021_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask021)
#define P_Mask141_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask141)
#define P_Mask181_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask181)
#define P_Mask000_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask000)
#define P_Mask202_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask202)
#define P_Mask011_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask011)
#define P_Mask010_CircleFlat		(PROF_WindowCircle|PROF_Flat|PROF_FBit|PROF_Mask010)

#endif CONF_fGen2dProfs

//-----------------------------------------------------------------------------
// This specifies the profile type to use when generating the model.
//
// This function is used during training and not during search
//
// Following apply when GENERATING profile data (that's why there's a "Gen" in the function name).
// When USING profile data from the model file (i.e during search) we
// use the profile type specified in the model file.

unsigned GetGenProfSpec (int iLev, int iPoint)
{
#if CONF_fGen2dProfs
#if CONF_fDebug2dProfs
	if ((iLev == 0) && (iPoint == 7))		// for debugging using m000_11t.bmp and m128_12t.bmp
#else
	if (CONF_Gen2dProf)
#endif
		{
		unsigned Prof0 = P_GradBR_EqSigmAbsSum;
		unsigned Prof1 = 0;
		unsigned Prof2 = 0;

		DASSERT((Prof0 & ~0xff0000ff) == 0);
		DASSERT((Prof1 & ~0xff0000ff) == 0);
		DASSERT((Prof2 & ~0xff0000ff) == 0);

		return PROF_2d | (Prof2 << 16) | (Prof1 << 8) | Prof0;
		}
#endif // CONF_fGen2dProfs

SigmoidScaleGlobal = 1000;	// in my master's thesis: ShapeA=100.0 ShapeB=1000.0

return PROF_Grad|PROF_Flat;	// classic Cootes 1-dimensional profile
}

//-----------------------------------------------------------------------------
// This initializes ASMs for search by reading the ASMs from ASM files on disk
// It also sets parameters of the models not specified in the ASM files
//
// This function is used during search and not during training
//
// On entry Asm[0..nModels].sFile must be initialized
// On exit, all fields of Asm[0..nModels] is initialized

void InitAsmsForSearch (tAsmModel Model[], 					// out, except sFile is in
						int nModels, unsigned ImageBits)	// in
{
for (int iModel = 0; iModel < nModels; iModel++)
	{
	int nWantedLandmarks = 0;		// 0 means any nbr of landmarks in ASM file is ok
	tAsmModel *pModel = &Model[iModel];

	if (pModel->sFile[0] == 0)
		Err("You must specify %d models with the -m flag", nModels);

	pModel->iModel = iModel;

	switch (iModel)
		{
		case 0:
			{
			eStartMethod StartMethod = SM_UseNnFaceAndEyesRealignEyes;
			if (CONF_fDebug1dProfs)
				StartMethod = SM_UseRefShapeAndAlignedMeanShape;
			else if (CONF_fDebug2dProfs)
				StartMethod = SM_UseRefShape;

			InitAsm(*pModel, pModel->sFile,		// get ASM model from file
				CONF_fFancyShapeModel,
				CONF_nShapeModelIters,
				StartMethod);
			break;
			}
		case 1:
			ASSERT(!(ImageBits & IM_DoubleScale)); //TODO notify user of bug: if double scale used, 2nd model search is wrong

			InitAsm(*pModel, pModel->sFile,
				CONF_fFancyShapeModel,
				CONF_nShapeModelIters,
				SM_UsePrevModelResult);

			pModel->nStartLev = 1;	// override file nStartLev: set to 1 which gives almost as good results and
									// is faster than searching all levels
			break;
		default:
			Warn("More than two models");		//TODO I've never done any testing with more than two models

			InitAsm(*pModel, pModel->sFile,
				CONF_fFancyShapeModel,
				CONF_nShapeModelIters,
				SM_UsePrevModelResult);

			pModel->nStartLev = 0;	// override file nStartLev
			break;
		}
	if (nWantedLandmarks && pModel->nPoints != nWantedLandmarks)
		Err("%s doesn't specify %d landmarks", pModel->sFile, nWantedLandmarks);

	if (pModel->StartMethod == SM_UseRefShape && pModel->fExplicitPrevNext)
		{
		// see SysErr in startshape.cpp:GetStartShapeFromGlobalDet TODO
		Warn("Forcing ExplicitPrevNext to false (overrides model setting fgExplicitPrevNext=1)");
		pModel->fExplicitPrevNext = false;
		}
	}
}

//-----------------------------------------------------------------------------
// Accumulate the results of the current search (in Shape) into the
// combined results for all searches (in CombinedShape)
//
// This function is used during search and not during training

void CombineModelSearchResults (SHAPE &CombinedShape,		// io: best shape formed by combining all searches
					int iModel, const SHAPE &Shape)			// in: results of current search
{
switch (iModel)
	{
	case 0:
		CombinedShape = Shape;
		break;
	case 1:
		{
		int nPoints = CombinedShape.nrows();
		ASSERT(Shape.nrows() >= nPoints);
		for (int iPoint = 0; iPoint < nPoints; iPoint++)
			{
#if 1
			if (1)														// use all points
#else
			if (iPoint < MLMouthCorner || iPoint > MMouthBotOfTopLip)	// replace all except mouth
#endif
				{
				CombinedShape(iPoint, VX) = Shape(iPoint, VX);
				CombinedShape(iPoint, VY) = Shape(iPoint, VY);
				}
			}
		break;
		}
	default:
		SysErr("CombineModelSearchResults");
	}
}