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
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// * The name of the copyright holders may not be used to endorse or promote products
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// 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*/
#include <sft/fpool.hpp>
#include <sft/random.hpp>
#include <glob.h>
#include <queue>
// ========= FeaturePool ========= //
sft::ICFFeaturePool::ICFFeaturePool(cv::Size m, int n) : FeaturePool(), model(m), nfeatures(n)
{
CV_Assert(m != cv::Size() && n > 0);
fill(nfeatures);
}
void sft::ICFFeaturePool::preprocess(cv::InputArray frame, cv::OutputArray integrals) const
{
preprocessor.apply(frame, integrals);
}
float sft::ICFFeaturePool::apply(int fi, int si, const Mat& integrals) const
{
return pool[fi](integrals.row(si), model);
}
void sft::ICFFeaturePool::write( cv::FileStorage& fs, int index) const
{
CV_Assert((index > 0) && (index < (int)pool.size()));
fs << pool[index];
}
void sft::write(cv::FileStorage& fs, const string&, const ICF& f)
{
fs << "{" << "channel" << f.channel << "rect" << f.bb << "}";
}
sft::ICFFeaturePool::~ICFFeaturePool(){}
void sft::ICFFeaturePool::fill(int desired)
{
int mw = model.width;
int mh = model.height;
int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS;
nfeatures = std::min(desired, maxPoolSize);
dprintf("Requeste feature pool %d max %d suggested %d\n", desired, maxPoolSize, nfeatures);
pool.reserve(nfeatures);
sft::Random::engine eng(8854342234L);
sft::Random::engine eng_ch(314152314L);
sft::Random::uniform chRand(0, N_CHANNELS - 1);
sft::Random::uniform xRand(0, model.width - 2);
sft::Random::uniform yRand(0, model.height - 2);
sft::Random::uniform wRand(1, model.width - 1);
sft::Random::uniform hRand(1, model.height - 1);
while (pool.size() < size_t(nfeatures))
{
int x = xRand(eng);
int y = yRand(eng);
int w = 1 + wRand(eng, model.width - x - 1);
int h = 1 + hRand(eng, model.height - y - 1);
CV_Assert(w > 0);
CV_Assert(h > 0);
CV_Assert(w + x < model.width);
CV_Assert(h + y < model.height);
int ch = chRand(eng_ch);
sft::ICF f(x, y, w, h, ch);
if (std::find(pool.begin(), pool.end(),f) == pool.end())
{
pool.push_back(f);
}
}
}
std::ostream& sft::operator<<(std::ostream& out, const sft::ICF& m)
{
out << m.channel << " " << m.bb;
return out;
}
// ============ Dataset ============ //
namespace {
using namespace sft;
string itoa(long i)
{
char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
void glob(const string& path, svector& ret)
{
glob_t glob_result;
glob(path.c_str(), GLOB_TILDE, 0, &glob_result);
ret.clear();
ret.reserve(glob_result.gl_pathc);
for(uint i = 0; i < glob_result.gl_pathc; ++i)
{
ret.push_back(std::string(glob_result.gl_pathv[i]));
dprintf("%s\n", ret[i].c_str());
}
globfree(&glob_result);
}
}
// in the default case data folders should be alligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
ScaledDataset::ScaledDataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
dprintf("%s\n", "Positives globbing...");
glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);
dprintf("%s\n", "Negatives globbing...");
glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}
cv::Mat ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
ScaledDataset::~ScaledDataset(){}