Repository for OpenCV's extra modules
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/*
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
(3-clause BSD License)
Copyright (C) 2013, OpenCV Foundation, 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:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions 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.
* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are
disclaimed. In no event shall copyright holders 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,
or tort (including negligence or otherwise) arising in any way out of
the use of this software, even if advised of the possibility of such damage.
*/
#ifndef __OPENCV_ADAS_WALDBOOST_HPP__
#define __OPENCV_ADAS_WALDBOOST_HPP__
class Stump
{
public:
/* Train stump for given data
data — matrix of feature values, size M x N, one feature per row
labels — matrix of sample class labels, size 1 x N. Labels can be from
{-1, +1}
Returns chosen feature index. Feature enumeration starts from 0
*/
int train(const cv::Mat_<int>& data, const cv::Mat_<int>& labels);
/* Predict object class given
value — feature value. Feature must be the same as chose during training
stump
Returns object class from {-1, +1}
*/
int predict(int value);
private:
/* Stump decision threshold */
int threshold_;
/* Stump polarity, can be from {-1, +1} */
int polarity_;
/* Stump decision rule:
h(value) = polarity * sign(value - threshold)
*/
};
/* Save Stump to FileStorage */
cv::FileStorage& operator<< (cv::FileStorage& out, const Stump& classifier);
/* Load Stump from FileStorage */
cv::FileStorage& operator>> (cv::FileStorage& in, Stump& classifier);
class WaldBoost
{
public:
/* Initialize WaldBoost cascade with default of specified parameters */
WaldBoost(const WaldBoostParams& = WaldBoostParams());
/* Train WaldBoost cascade for given data
data — matrix of feature values, size M x N, one feature per row
labels — matrix of sample class labels, size 1 x N. Labels can be from
{-1, +1}
Returns feature indices chosen for cascade.
Feature enumeration starts from 0
*/
std::vector<int> train(const cv::Mat_<int>& data,
const cv::Mat_<int>& labels);
/* Predict object class given object that can compute object features
feature_evaluator — object that can compute features by demand
Returns confidence_value — measure of confidense that object
is from class +1
*/
float predict(const ACFFeatureEvaluator& feature_evaluator);
private:
/* Parameters for cascade training */
WaldBoostParams params_;
/* Stumps in cascade */
std::vector<Stump> stumps_;
/* Weight for stumps in cascade linear combination */
std::vector<float> stump_weights_;
/* Rejection thresholds for linear combination at every stump evaluation */
std::vector<float> thresholds_;
};
/* Save WaldBoost to FileStorage */
cv::FileStorage& operator<< (cv::FileStorage& out, const WaldBoost& classifier);
/* Load WaldBoost from FileStorage */
cv::FileStorage& operator>> (cv::FileStorage& in, WaldBoost& classifier);
#endif /* __OPENCV_ADAS_WALDBOOST_HPP__ */