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.
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//M*/
#ifndef __SFT_OCTAVE_HPP__
#define __SFT_OCTAVE_HPP__
#include <sft/common.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/softcascade/softcascade.hpp>
namespace sft
{
struct ICF
{
ICF(int x, int y, int w, int h, int ch) : bb(cv::Rect(x, y, w, h)), channel(ch) {}
bool operator ==(ICF b)
{
return bb == b.bb && channel == b.channel;
}
bool operator !=(ICF b)
{
return bb != b.bb || channel != b.channel;
}
float operator() (const cv::Mat& integrals, const cv::Size& model) const
{
int step = model.width + 1;
const int* ptr = integrals.ptr<int>(0) + (model.height * channel + bb.y) * step + bb.x;
int a = ptr[0];
int b = ptr[bb.width];
ptr += bb.height * step;
int c = ptr[bb.width];
int d = ptr[0];
return (float)(a - b + c - d);
}
private:
cv::Rect bb;
int channel;
friend void write(cv::FileStorage& fs, const std::string&, const ICF& f);
friend std::ostream& operator<<(std::ostream& out, const ICF& f);
};
void write(cv::FileStorage& fs, const std::string&, const ICF& f);
std::ostream& operator<<(std::ostream& out, const ICF& m);
using cv::FeaturePool;
using cv::Dataset;
class ICFFeaturePool : public cv::FeaturePool
{
public:
ICFFeaturePool(cv::Size model, int nfeatures);
virtual int size() const { return (int)pool.size(); }
virtual float apply(int fi, int si, const cv::Mat& integrals) const;
virtual void preprocess(cv::InputArray _frame, cv::OutputArray _integrals) const;
virtual void write( cv::FileStorage& fs, int index) const;
virtual ~ICFFeaturePool();
private:
void fill(int desired);
cv::Size model;
int nfeatures;
std::vector<ICF> pool;
static const unsigned int seed = 0;
cv::ICFPreprocessor preprocessor;
enum { N_CHANNELS = 10 };
};
class ScaledDataset : public Dataset
{
public:
ScaledDataset(const sft::string& path, const int octave);
virtual cv::Mat get(SampleType type, int idx) const;
virtual int available(SampleType type) const;
virtual ~ScaledDataset();
private:
svector pos;
svector neg;
};
}
#endif