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C++ ModelPtr::InitialRandomGenerator方法代码示例

本文整理汇总了C++中ModelPtr::InitialRandomGenerator方法的典型用法代码示例。如果您正苦于以下问题：C++ ModelPtr::InitialRandomGenerator方法的具体用法？C++ ModelPtr::InitialRandomGenerator怎么用？C++ ModelPtr::InitialRandomGenerator使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类ModelPtr的用法示例。


在下文中一共展示了ModelPtr::InitialRandomGenerator方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: FitRest

void Optimizer::FitRest(mat& alpha,
	mat& beta,
	mat& rho,
	mat& lamda,
	InputPtr input,
	ModelPtr model,
	MeshPtr mesh,
	ShapePtr shape,
	TexturePtr texture)
{

	mat alpha_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat alpha_hessian_inv = mat(PrincipalNum, PrincipalNum, fill::zeros);

	mat beta_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat beta_hessian_inv = mat(PrincipalNum, PrincipalNum, fill::zeros);


	Rho rho_para(rho, input, model, mesh, shape, texture);
	Lamda lamda_para(lamda, model, mesh, shape, texture);


	// 2500 1000 700 500 300 200

	model->InitialRandomGenerator(ModelImage::REST);

	double weight = 1.0 / 200;


	for (int l = 0; l < 1000; ++l)
	{

		Alpha alpha_para(alpha, input, model, mesh, shape, texture);
		Beta beta_para(beta, model, mesh, shape, texture);

		// Generate random points  
		GenerateRandomPoints(model, GradientRandomNum);

		double function_value = 0;


		for (int i = 0; i < PrincipalNum; ++i)
		{
			double variance = shape->GetVariance(i);
			alpha_gradient[i] = weight * ComputeIntensityGradient(input, &alpha_para, i) + 2 * alpha[i] / variance;
		}

		for (int i = 0; i < PrincipalNum; ++i)
		{
			double variance = texture->GetVariance(i);
			beta_gradient[i] = weight * ComputeIntensityGradient(input, &beta_para, i) + 2 * beta[i] / variance;
		}

		alpha -= alpha_para.Step*alpha_gradient;
		beta -= beta_para.Step*beta_gradient;

	}

}
开发者ID:tpys，项目名称:face-reconstruction-from-one-frontal-image，代码行数:59，代码来源:optimizer.cpp

示例2: ShowPoints

void Optimizer::ShowPoints(mat& alpha, 
	mat& beta,
	mat& rho,
	mat& lamda,
	InputPtr input, 
	ModelPtr model, 
	MeshPtr mesh, 
	ShapePtr shape, 
	TexturePtr texture, 
	int point_type)
{

	mat alpha_gradient(PrincipalNum, 1);
	alpha_gradient.fill(0);

	mat beta_gradient(PrincipalNum, 1);
	beta_gradient.fill(0);


	mat rho_gradient(RhoNum, 1);
	rho_gradient.fill(0);

	mat lamda_gradient(LamdaNum, 1);
	lamda_gradient.fill(0);


	Alpha alpha_para(alpha, input, model, mesh, shape, texture);
	Beta beta_para(beta, model, mesh, shape, texture);
	Rho rho_para(rho, input, model, mesh, shape, texture);
	Lamda lamda_para(lamda, model, mesh, shape, texture);



	switch (point_type)
	{
	case RANDOM:
		model->InitialRandomGenerator();
		GenerateRandomPoints(model, 40);
		ShowRandomPoints(input, model, &alpha_para);
		break;
	case VISIBLE_TRIANGLE:
		model->EnableIterator();
		ShowVisiblePoints(input, model, &alpha_para);
		break;
	case SHADOW_TRIANGLE:
		model->EnableIterator();
		ShowShadowPoints(input, model, &alpha_para);
		break;
	case SEGMENT:
		ShowSegmentPoints(input, model, &alpha_para);
		break;

	default:
		break;
	}

	model->Show();
}
开发者ID:tpys，项目名称:face-reconstruction-from-one-frontal-image，代码行数:58，代码来源:optimizer.cpp

示例3: FitFirstPrincipalComponents

void Optimizer::FitFirstPrincipalComponents(mat& alpha,
	mat& beta,
	mat& rho,
	mat& lamda,
	InputPtr input,
	ModelPtr model,
	MeshPtr mesh,
	ShapePtr shape,
	TexturePtr texture,
	const cv::Mat& ready)
{


	mat alpha_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat alpha_hessian_inv = mat(PrincipalNum, PrincipalNum, fill::zeros);

	mat beta_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat rho_gradient = mat(RhoNum, 1, fill::zeros);
	mat lamda_gradient = mat(LamdaNum, 1, fill::zeros);

	vec step(RhoNum, 1);
	step.rows(0, 2).fill(0.00000006);
	step.rows(3, 5).fill(0.0002);
	step.rows(6, 6).fill(2.0);
	mat step_mat = diagmat(step);



	// down sampled version
	cv::Mat down_sampled;
	pyrDown(ready, down_sampled, Size(IMAGE_WIDTH / 2, IMAGE_HEIGHT / 2));
	input = make_shared<InputImage>(down_sampled);
	model->SetSize(IMAGE_WIDTH / 2, IMAGE_HEIGHT / 2);
	rho[FOCAL] /= 2;


	//SetStartingValue(rho, lamda);
	double para_num = 10;
	double weight = 1.0 / 900;

	int counter = 0;
	while (counter < 1000)
	{

		Alpha alpha_para(alpha, input, model, mesh, shape, texture);
		Beta beta_para(beta, model, mesh, shape, texture);
		Rho rho_para(rho, input, model, mesh, shape, texture);
		Lamda lamda_para(lamda, model, mesh, shape, texture);


		if (counter == 0 || counter % 1000 == 0)
		{
			Face3dModel face3d_model(shape, texture);
			mesh = face3d_model.Construction(alpha, beta);
			TwoPassZbuffer(rho, lamda, mesh, model);
			model->EnableIterator();
			model->InitialRandomGenerator();

			//GenerateRandomPoints(model, HessianRandomNum);
			//for (int i = 0; i < para_num; ++i)
			//{
			//	double variance = shape->GetVariance(i);

			//	mat alpha1 = alpha;
			//	alpha1[i] -= H;
			//	Alpha alpha_para1(alpha1, input, model, mesh, shape, texture);
			//	double first_derivative1 = weight * ComputeIntensityGradient(input, &alpha_para1, i) + 2 * alpha1[i] / variance;

			//	mat alpha2 = alpha;
			//	alpha2[i] += H;
			//	Alpha alpha_para2(alpha2, input, model, mesh, shape, texture);
			//	double first_derivative2 = weight * ComputeIntensityGradient(input, &alpha_para2, i) + 2 * alpha2[i] / variance;

			//	double second_derivative = (first_derivative2 - first_derivative1) / (2 * H);
			//	alpha_hessian_inv(i, i) = 1 / (second_derivative + 2 / variance);
			//}
		}



		// Generate random points  
		GenerateRandomPoints(model, GradientRandomNum);
		double function_value = 0;
		function_value = ComputeCost(input, model, alpha_para);
		ofstream cost;
		cost.open("first_cost", ios::app);
		cost << function_value << "\n";
		cost.close();



		for (int i = 0; i < para_num; ++i)
		{
			double variance = shape->GetVariance(i);
			alpha_gradient[i] = weight * ComputeIntensityGradient(input, &alpha_para, i) + 2 * alpha[i] / variance;
		}


		//for (int i = 0; i < 10; ++i)
		//{
//.........这里部分代码省略.........
开发者ID:tpys，项目名称:face-reconstruction-from-one-frontal-image，代码行数:101，代码来源:optimizer.cpp

示例4: FitAll

void Optimizer::FitAll(mat& alpha,
	mat& beta,
	mat& rho,
	mat& lamda,
	InputPtr input,
	ModelPtr model,
	MeshPtr mesh,
	ShapePtr shape,
	TexturePtr texture)
{

	mat alpha_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat alpha_hessian_inv = mat(PrincipalNum, PrincipalNum, fill::zeros);

	mat beta_gradient = mat(PrincipalNum, 1, fill::zeros);
	mat beta_hessian_inv = mat(PrincipalNum, PrincipalNum, fill::zeros);

	mat rho_gradient = mat(RhoNum, 1, fill::zeros);
	mat rho_hessian_inv = mat(RhoNum, RhoNum, fill::zeros);

	mat lamda_gradient = mat(LamdaNum, 1, fill::zeros);
	mat lamda_hessian_inv = mat(LamdaNum, LamdaNum, fill::zeros);


	vec step(RhoNum, 1);
	step.rows(0, 2).fill(0.00000006);
	step.rows(3, 5).fill(0.0002);
	step.rows(6, 6).fill(2.0);
	mat step_mat = diagmat(step);


	SetStartingValue(rho, lamda);

	// 2500 1000 700 500 300 200
	array<double, 6> weight_intensity = { { 1.0 / 1000, 1.0 / 900, 1.0 / 800, 1.0 / 700, 1.0 / 500, 1.0 / 300 } };
	array<int, 6> para_num = { { 10, 15, 25, 35, 55, 99 } };   // 20 40
	array<int, 6> iterations = { { 1000, 1000, 1000, 1000, 1000, 1000 } };


	//array<double, 4> weight_intensity = { { 1.0 / 900, 1.0 / 700, 1.0 / 500, 1.0 / 400 } };
	//array<int, 4> para_num = { { 10, 20, 40, 99 } };   // 20 40
	//array<int, 4> iterations = { { 1000, 1000, 800, 600 } };


	int counter = 0;
	for (int c = 0; c < 6; ++c)
	{
		for (int l = 0; l < iterations[c]; ++l)
		{

			if (counter == 0 || counter % 1000 == 0)
			{
				Face3dModel face3d_model(shape, texture);
				mesh = face3d_model.Construction(alpha, beta);
				TwoPassZbuffer(rho, lamda, mesh, model);
				model->EnableIterator();
				model->InitialRandomGenerator();

				//GenerateRandomPoints(model, HessianRandomNum);

				//for (int i = 0; i < para_num[c]; ++i)
				//{
				//	double variance = shape->GetVariance(i);

				//	mat alpha1 = alpha;
				//	alpha1[i] -= H;
				//	Alpha alpha_para1(alpha1, input, model, mesh, shape, texture);

				//	double first_derivative1 = weight_feature[c] * ComputeLandmarkGradient(input, &alpha_para1, i) +
				//		weight_intensity[c] * ComputeIntensityGradient(input, &alpha_para1, i);


				//	mat alpha2 = alpha;
				//	alpha2[i] += H;
				//	Alpha alpha_para2(alpha2, input, model, mesh, shape, texture);
				//	double first_derivative2 = weight_feature[c] * ComputeLandmarkGradient(input, &alpha_para2, i) +
				//		weight_intensity[c] * ComputeIntensityGradient(input, &alpha_para2, i);

				//	double second_derivative = (first_derivative2 - first_derivative1) / (2 * H);
				//	alpha_hessian_inv(i, i) = 1 / (second_derivative +2 / variance);

				//}


				/*	for (int i = 0; i < para_num[c]; ++i)
					{
					double variance = texture->GetVariance(i);

					mat beta1 = beta;
					beta1[i] -= H;
					Beta beta_para1(beta1, model, mesh, shape, texture);
					double first_derivative1 = weight_intensity[c] * ComputeIntensityGradient(input, &beta_para1, i);

					mat beta2 = beta;
					beta2[i] += H;
					Beta beta_para2(beta2, model, mesh, shape, texture);
					double first_derivative2 = weight_intensity[c] * ComputeIntensityGradient(input, &beta_para2, i);

					double second_derivative = (first_derivative2 - first_derivative1) / (2 * H);
					beta_hessian_inv(i, i) = 1 / (second_derivative + 2 / variance);
//.........这里部分代码省略.........
开发者ID:tpys，项目名称:face-reconstruction-from-one-frontal-image，代码行数:101，代码来源:optimizer.cpp

示例5: FitIllumination

void Optimizer::FitIllumination(mat& alpha, 
	mat& beta, 
	mat& rho, 
	mat& lamda, 
	InputPtr input, 
	ModelPtr model,
	MeshPtr mesh,
	ShapePtr shape, 
	TexturePtr texture, 
	const cv::Mat& ready)
{


	mat lamda_gradient=mat(LamdaNum, 1,fill::zeros);
	mat lamda_hessian_inv = mat(LamdaNum, LamdaNum, fill::zeros);


	Face3dModel face3d_model(shape, texture);
	mesh = face3d_model.Construction(alpha, beta);
	//VisualizeResult(rho, lamda, input, mesh, 0);
	TwoPassZbuffer(rho, lamda, mesh, model);
	model->EnableIterator();
	model->InitialRandomGenerator();

	// down sampled version
	cv::Mat down_sampled;
	pyrDown(ready, down_sampled, Size(IMAGE_WIDTH / 2, IMAGE_HEIGHT / 2));
	input = make_shared<InputImage>(down_sampled);
	model->SetSize(IMAGE_WIDTH / 2, IMAGE_HEIGHT / 2);
	rho[FOCAL] /= 2;

	double weight = 1.0 / 1000;
	int counter = 0;

	while (counter < 500)
	{

		Alpha alpha_para(alpha, input, model, mesh, shape, texture);
		Beta beta_para(beta, model, mesh, shape, texture);
		Rho rho_para(rho, input, model, mesh, shape, texture);
		Lamda lamda_para(lamda, model, mesh, shape, texture);

		// Generate random points  
		//if (counter == 0 || counter % 1000 == 0)
		//{
		//	GenerateRandomPoints(model, HessianRandomNum);

		//	for (int i = 0; i < LamdaNum - 7; ++i)
		//	{
		//		mat lamda1 = lamda;
		//		lamda1[i] -= H;
		//		Lamda lamda_para1(lamda1, model, mesh, shape, texture);
		//		double first_derivative1 = weight*ComputeIntensityGradient(input, &lamda_para1, i);


		//		mat lamda2 = lamda;
		//		lamda2[i] += H;
		//		Lamda lamda_para2(lamda2, model, mesh, shape, texture);
		//		double first_derivative2 = weight* ComputeIntensityGradient(input, &lamda_para2, i);
		//		double second_derivative = (first_derivative2 - first_derivative1) / (2 * H);

		//		lamda_hessian_inv(i, i) = 1 / second_derivative;

		//	}


		//}
	
		GenerateRandomPoints(model, GradientRandomNum);

		//double function_value = 0;
		//function_value = ComputeCost(input, model, alpha_para);
		//ofstream cost;
		//cost.open("illumination_cost", ios::app);
		//cost << function_value << "\n";
		//cost.close();


		for (int i = 0; i < LamdaNum; ++i)
		{		 
			lamda_gradient[i] = weight*ComputeIntensityGradient(input, &lamda_para, i);
		}

		lamda -= lamda_para.Step*lamda_gradient;
		//lamda -= 0.5*lamda_hessian_inv*lamda_gradient;
		++counter;
	}

	// restore original version
	input= make_shared<InputImage>(ready);
	model->SetSize(IMAGE_WIDTH, IMAGE_HEIGHT);
	rho[FOCAL] *= 2;

	VisualizeResult(rho, lamda, input, mesh, "illumination");

}
开发者ID:tpys，项目名称:face-reconstruction-from-one-frontal-image，代码行数:96，代码来源:optimizer.cpp


注：本文中的ModelPtr::InitialRandomGenerator方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。