本文整理汇总了C++中ModelPtr::SetSize方法的典型用法代码示例。如果您正苦于以下问题：C++ ModelPtr::SetSize方法的具体用法？C++ ModelPtr::SetSize怎么用？C++ ModelPtr::SetSize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::SetSize方法的2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: 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.........
示例2: 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");
}