Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
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//M*/
// Trating application for Soft Cascades.
#include <sft/common.hpp>
#include <sft/octave.hpp>
int main(int argc, char** argv)
{
// hard coded now
int nfeatures = 50;
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);
sft::FeaturePool pool(model, nfeatures);
cv::RNG rng;
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;
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;
cv::Mat sample_idx(1, nsamples, CV_32SC1);
for (int x = 0; x < nsamples; ++x)
sample_idx.at<int>(0, x) = x;
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;
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;
/// ??????????????????
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;
}
bool update = false;
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");
// cvReleaseFileStorage( &fs );
}