本文整理汇总了C++中DataSet::HasGtShape方法的典型用法代码示例。如果您正苦于以下问题：C++ DataSet::HasGtShape方法的具体用法？C++ DataSet::HasGtShape怎么用？C++ DataSet::HasGtShape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DataSet的用法示例。
在下文中一共展示了DataSet::HasGtShape方法的1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: Train
//.........这里部分代码省略.........
cart.mean = 0.;
cart.std = 1.;
}
pos.QSort();
neg.QSort();
//JDA_Assert(cart.th == pos.CalcThresholdByNumber(1), "restart error");
}
else {
// recover data scores
pos.ResetScores();
neg.ResetScores();
k--;
continue;
}
}
// update restart parameters
best_drop_rate = 0.;
restarts = 0;
pos.Remove(cart.th);
neg.Remove(cart.th);
// print cart info
cart.PrintSelf();
if ((kk != K) && (kk%c.snapshot_iter == 0)) { // snapshot model and data
DataSet::Snapshot(pos, neg);
c.joincascador->Snapshot();
}
int pos_drop = pos_n - pos.size;
int neg_drop = neg_n - neg.size;
double pos_drop_rate = double(pos_drop) / double(pos_n)* 100.;
double neg_drop_rate = double(neg_drop) / double(neg_n)* 100.;
LOG("Pos drop = %d, Neg drop = %d, drop rate = %.2lf%%", pos_drop, neg_drop, neg_drop_rate);
neg_rejected += neg_n - neg.size;
}
// Global Regression with LBF
// generate lbf
const int pos_n = pos.size;
const int neg_n = neg.size;
LOG("Generate LBF of DataSet");
vector<Mat_<int> > pos_lbf(pos_n);
vector<Mat_<int> > neg_lbf(neg_n);
#pragma omp parallel for
for (int i = 0; i < pos_n; i++) {
pos_lbf[i] = GenLBF(pos.imgs[i], pos.current_shapes[i]);
}
#pragma omp parallel for
for (int i = 0; i < neg_n; i++) {
neg_lbf[i] = GenLBF(neg.imgs[i], neg.current_shapes[i]);
}
// regression, use valid face which has gt_shape
vector<int> valid_pos_idx;
vector<Mat_<int> > valid_pos_lbf;
for (int i = 0; i < pos.size; i++) {
if (pos.HasGtShape(i)) {
valid_pos_idx.push_back(i);
valid_pos_lbf.push_back(pos_lbf[i]);
}
}
Mat_<double> shape_residual_valid = pos.CalcShapeResidual(valid_pos_idx);
LOG("Start Global Regression");
GlobalRegression(valid_pos_lbf, shape_residual_valid);
// update shapes
#pragma omp parallel for
for (int i = 0; i < pos_n; i++) {
pos.current_shapes[i] += GenDeltaShape(pos_lbf[i], pos.stp_mc[i]);
}
#pragma omp parallel for
for (int i = 0; i < neg_n; i++) {
neg.current_shapes[i] += GenDeltaShape(neg_lbf[i], neg.stp_mc[i]);
}
// summary
LOG("====================");
LOG("| Summary |");
LOG("====================");
// regression error
vector<Mat_<double> > valid_gt_shapes;
vector<Mat_<double> > valid_current_shapes;
for (int i = 0; i < pos.size; i++) {
if (pos.HasGtShape(i)) {
valid_gt_shapes.push_back(pos.gt_shapes[i]);
valid_current_shapes.push_back(pos.current_shapes[i]);
}
}
double e = calcMeanError(valid_gt_shapes, valid_current_shapes);
LOG("Regression Mean Error = %.4lf", e);
// accept and reject rate
double accept_rate = 0.;
double reject_rate = 0.;
accept_rate = double(pos_n) / double(pos_original_size) * 100.;
reject_rate = double(neg_rejected) / double(neg_rejected + neg_original_size) * 100.;
LOG("Accept Rate = %.2lf%%", accept_rate);
// Done
}