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move feature pool to softcascade module

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This commit is contained in:
marina.kolpakova
2013-01-30 10:43:18 +04:00
parent 0b039f3c6b
commit fa15fcc53e
6 changed files with 134 additions and 248 deletions
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91
apps/sft/fpool.cpp
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@@ -41,101 +41,10 @@
//M*/ //M*/
#include <sft/fpool.hpp> #include <sft/fpool.hpp>
#include <sft/random.hpp>
#include <iostream> #include <iostream>
#include <queue> #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);
}
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];
}
sft::ICFFeaturePool::~ICFFeaturePool(){}
#if defined _WIN32 && (_WIN32 || _WIN64)
# if _WIN64
# define USE_LONG_SEEDS
# endif
#endif
#if defined (__GNUC__) &&__GNUC__
# if defined(__x86_64__) || defined(__ppc64__)
# define USE_LONG_SEEDS
# endif
#endif
#if defined USE_LONG_SEEDS
# define FEATURE_RECT_SEED 8854342234LU
#else
# define FEATURE_RECT_SEED 88543422LU
#endif
# define DCHANNELS_SEED 314152314LU
#undef USE_LONG_SEEDS
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(FEATURE_RECT_SEED);
sft::Random::engine eng_ch(DCHANNELS_SEED);
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);
cv::ChannelFeature f(x, y, w, h, ch);
if (std::find(pool.begin(), pool.end(),f) == pool.end())
{
pool.push_back(f);
std::cout << f << std::endl;
}
}
}
// ============ Dataset ============ // // ============ Dataset ============ //
namespace { namespace {
using namespace sft; using namespace sft;

28
apps/sft/include/sft/fpool.hpp
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@@ -51,36 +51,8 @@
namespace sft namespace sft
{ {
using cv::FeaturePool;
using cv::Dataset; 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 write( cv::FileStorage& fs, int index) const;
virtual ~ICFFeaturePool();
private:
void fill(int desired);
cv::Size model;
int nfeatures;
std::vector<cv::ChannelFeature> pool;
static const unsigned int seed = 0;
enum { N_CHANNELS = 10 };
};
class ScaledDataset : public Dataset class ScaledDataset : public Dataset
{ {
public: public:

117
apps/sft/include/sft/random.hpp
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@@ -1,117 +0,0 @@
/*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.
//
// * The name of the copyright holders may not 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 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,
// 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.
//
//M*/
#ifndef __SFT_RANDOM_HPP__
#define __SFT_RANDOM_HPP__
#if defined(_MSC_VER) && _MSC_VER >= 1600
# include <random>
namespace sft {
struct Random
{
typedef std::mt19937 engine;
typedef std::uniform_int<int> uniform;
};
}
#elif (__GNUC__) && __GNUC__ > 3 && __GNUC_MINOR__ > 1 && !defined(__ANDROID__)
# if defined (__cplusplus) && __cplusplus > 201100L
# include <random>
namespace sft {
struct Random
{
typedef std::mt19937 engine;
typedef std::uniform_int<int> uniform;
};
}
# else
# include <tr1/random>
namespace sft {
struct Random
{
typedef std::tr1::mt19937 engine;
typedef std::tr1::uniform_int<int> uniform;
};
}
# endif
#else
#include <opencv2/core/core.hpp>
namespace rnd {
typedef cv::RNG engine;
template<typename T>
struct uniform_int
{
uniform_int(const int _min, const int _max) : min(_min), max(_max) {}
T operator() (engine& eng, const int bound) const
{
return (T)eng.uniform(min, bound);
}
T operator() (engine& eng) const
{
return (T)eng.uniform(min, max);
}
private:
int min;
int max;
};
}
namespace sft {
struct Random
{
typedef rnd::engine engine;
typedef rnd::uniform_int<int> uniform;
};
}
#endif
#endif

8
apps/sft/sft.cpp
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@@ -118,8 +118,8 @@ int main(int argc, char** argv)
int nfeatures = cfg.poolSize; int nfeatures = cfg.poolSize;
cv::Size model = cfg.model(it); cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl; std::cout << "Model " << model << std::endl;
sft::ICFFeaturePool pool(model, nfeatures); cv::Ptr<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures);
nfeatures = pool.size(); nfeatures = pool->size();
int npositives = cfg.positives; int npositives = cfg.positives;
@@ -135,7 +135,7 @@ int main(int argc, char** argv)
std::string path = cfg.trainPath; std::string path = cfg.trainPath;
sft::ScaledDataset dataset(path, *it); sft::ScaledDataset dataset(path, *it);
if (boost->train(&dataset, &pool, cfg.weaks, cfg.treeDepth)) if (boost->train(&dataset, pool, cfg.weaks, cfg.treeDepth))
{ {
CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE); CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost->write(fout, cfg.cascadeName); boost->write(fout, cfg.cascadeName);
@@ -145,7 +145,7 @@ int main(int argc, char** argv)
cv::Mat thresholds; cv::Mat thresholds;
boost->setRejectThresholds(thresholds); boost->setRejectThresholds(thresholds);
boost->write(fso, &pool, thresholds); boost->write(fso, pool, thresholds);
cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE); cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
tfs << "thresholds" << thresholds; tfs << "thresholds" << thresholds;

24
modules/softcascade/include/opencv2/softcascade/softcascade.hpp
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@@ -64,15 +64,7 @@ struct CV_EXPORTS Detection
int kind; int kind;
}; };
class CV_EXPORTS FeaturePool
{
public:
virtual int size() const = 0;
virtual float apply(int fi, int si, const Mat& integrals) const = 0;
virtual void write( cv::FileStorage& fs, int index) const = 0;
virtual ~FeaturePool();
};
class CV_EXPORTS Dataset class CV_EXPORTS Dataset
{ {
@@ -84,6 +76,22 @@ public:
virtual ~Dataset(); virtual ~Dataset();
}; };
// ========================================================================== //
// Public interface feature pool.
// ========================================================================== //
class CV_EXPORTS FeaturePool
{
public:
virtual int size() const = 0;
virtual float apply(int fi, int si, const Mat& channels) const = 0;
virtual void write( cv::FileStorage& fs, int index) const = 0;
virtual ~FeaturePool();
static cv::Ptr<FeaturePool> create(const cv::Size& model, int nfeatures);
};
// ========================================================================== // // ========================================================================== //
// First order channel feature. // First order channel feature.
// ========================================================================== // // ========================================================================== //

114
modules/softcascade/src/integral_channel_builder.cpp
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@@ -41,6 +41,7 @@
//M*/ //M*/
#include "precomp.hpp" #include "precomp.hpp"
#include "_random.hpp"
namespace { namespace {
@@ -160,3 +161,116 @@ std::ostream& cv::operator<<(std::ostream& out, const cv::ChannelFeature& m)
} }
cv::ChannelFeature::~ChannelFeature(){} cv::ChannelFeature::~ChannelFeature(){}
namespace {
class ChannelFeaturePool : public cv::FeaturePool
{
public:
ChannelFeaturePool(cv::Size m, int n) : FeaturePool(), model(m)
{
CV_Assert(m != cv::Size() && n > 0);
fill(n);
}
virtual int size() const { return (int)pool.size(); }
virtual float apply(int fi, int si, const cv::Mat& integrals) const;
virtual void write( cv::FileStorage& fs, int index) const;
virtual ~ChannelFeaturePool() {}
private:
void fill(int desired);
cv::Size model;
std::vector<cv::ChannelFeature> pool;
enum { N_CHANNELS = 10 };
};
float ChannelFeaturePool::apply(int fi, int si, const cv::Mat& integrals) const
{
return pool[fi](integrals.row(si), model);
}
void ChannelFeaturePool::write( cv::FileStorage& fs, int index) const
{
CV_Assert((index > 0) && (index < (int)pool.size()));
fs << pool[index];
}
#if defined _WIN32 && (_WIN32 || _WIN64)
# if _WIN64
# define USE_LONG_SEEDS
# endif
#endif
#if defined (__GNUC__) &&__GNUC__
# if defined(__x86_64__) || defined(__ppc64__)
# define USE_LONG_SEEDS
# endif
#endif
#if defined USE_LONG_SEEDS
# define FEATURE_RECT_SEED 8854342234LU
#else
# define FEATURE_RECT_SEED 88543422LU
#endif
# define DCHANNELS_SEED 314152314LU
#undef USE_LONG_SEEDS
void ChannelFeaturePool::fill(int desired)
{
int mw = model.width;
int mh = model.height;
int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS;
int 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(FEATURE_RECT_SEED);
sft::Random::engine eng_ch(DCHANNELS_SEED);
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);
cv::ChannelFeature f(x, y, w, h, ch);
if (std::find(pool.begin(), pool.end(),f) == pool.end())
{
pool.push_back(f);
std::cout << f << std::endl;
}
}
}
}
cv::Ptr<cv::FeaturePool> cv::FeaturePool::create(const cv::Size& model, int nfeatures)
{
cv::Ptr<cv::FeaturePool> pool(new ChannelFeaturePool(model, nfeatures));
return pool;
}