generic-library/opencv
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feature pool generation:

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- use random from tr1 extension
    - extend cv::Boost
This commit is contained in:
marina.kolpakova
2012-12-06 12:19:35 +04:00
parent 948365b1c8
commit 86973f8ede
5 changed files with 245 additions and 43 deletions
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82
apps/sft/sft.cpp
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@@ -52,63 +52,65 @@ int main(int argc, char** argv)
int npositives = 10;
int nnegatives = 10;
int nsamples = npositives + nnegatives;
cv::Size model(64, 128);
sft::Octave boost;
cv::Mat train_data(nfeatures, nsamples, CV_32FC1);
// cv::RNG rng;
sft::FeaturePool pool(model, nfeatures);
// for (int y = 0; y < nfeatures; ++y)
// for (int x = 0; x < nsamples; ++x)
// train_data.at<float>(y, x) = rng.uniform(0.f, 1.f);
cv::RNG rng;
// int tflag = CV_COL_SAMPLE;
// Mat responses(nsamples, 1, CV_32FC1);
// for (int y = 0; y < nsamples; ++y)
// responses.at<float>(y, 0) = (y < npositives) ? 1.f : 0.f;
for (int y = 0; y < nfeatures; ++y)
for (int x = 0; x < nsamples; ++x)
train_data.at<float>(y, x) = rng.uniform(0.f, 1.f);
int tflag = CV_COL_SAMPLE;
cv::Mat responses(nsamples, 1, CV_32FC1);
for (int y = 0; y < nsamples; ++y)
responses.at<float>(y, 0) = (y < npositives) ? 1.f : 0.f;
// Mat var_idx(1, nfeatures, CV_32SC1);
// for (int x = 0; x < nfeatures; ++x)
// var_idx.at<int>(0, x) = x;
cv::Mat var_idx(1, nfeatures, CV_32SC1);
for (int x = 0; x < nfeatures; ++x)
var_idx.at<int>(0, x) = x;
// // Mat sample_idx;
// Mat sample_idx(1, nsamples, CV_32SC1);
// for (int x = 0; x < nsamples; ++x)
// sample_idx.at<int>(0, x) = x;
// Mat sample_idx;
cv::Mat sample_idx(1, nsamples, CV_32SC1);
for (int x = 0; x < nsamples; ++x)
sample_idx.at<int>(0, x) = x;
// Mat var_type(1, nfeatures + 1, CV_8UC1);
// for (int x = 0; x < nfeatures; ++x)
// var_type.at<uchar>(0, x) = CV_VAR_ORDERED;
cv::Mat var_type(1, nfeatures + 1, CV_8UC1);
for (int x = 0; x < nfeatures; ++x)
var_type.at<uchar>(0, x) = CV_VAR_ORDERED;
// var_type.at<uchar>(0, nfeatures) = CV_VAR_CATEGORICAL;
var_type.at<uchar>(0, nfeatures) = CV_VAR_CATEGORICAL;
// Mat missing_mask;
cv::Mat missing_mask;
// CvBoostParams params;
// {
// params.max_categories = 10;
// params.max_depth = 2;
// params.min_sample_count = 2;
// params.cv_folds = 0;
// params.truncate_pruned_tree = false;
CvBoostParams params;
{
params.max_categories = 10;
params.max_depth = 2;
params.min_sample_count = 2;
params.cv_folds = 0;
params.truncate_pruned_tree = false;
// /// ??????????????????
// params.regression_accuracy = 0.01;
// params.use_surrogates = false;
// params.use_1se_rule = false;
/// ??????????????????
params.regression_accuracy = 0.01;
params.use_surrogates = false;
params.use_1se_rule = false;
// ///////// boost params
// params.boost_type = CvBoost::GENTLE;
// params.weak_count = 1;
// params.split_criteria = CvBoost::SQERR;
// params.weight_trim_rate = 0.95;
// }
///////// boost params
params.boost_type = CvBoost::GENTLE;
params.weak_count = 1;
params.split_criteria = CvBoost::SQERR;
params.weight_trim_rate = 0.95;
}
// bool update = false;
bool update = false;
// boost.train(train_data, tflag, responses,
// var_idx, sample_idx, var_type, missing_mask, params, update);
boost.train(train_data, responses, var_idx, sample_idx, var_type, missing_mask);
// CvFileStorage* fs = cvOpenFileStorage( "/home/kellan/train_res.xml", 0, CV_STORAGE_WRITE );
// boost.write(fs, "test_res");