Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

move feature pool to softcascade module

Browse Source
pull/322/head
marina.kolpakova 12 years ago
parent 0b039f3c6b
commit fa15fcc53e
6 changed files with 134 additions and 248 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 91
      apps/sft/fpool.cpp
  2. 28
      apps/sft/include/sft/fpool.hpp
  3. 117
      apps/sft/include/sft/random.hpp
  4. 8
      apps/sft/sft.cpp
  5. 24
      modules/softcascade/include/opencv2/softcascade/softcascade.hpp
  6. 114
      modules/softcascade/src/integral_channel_builder.cpp

91
apps/sft/fpool.cpp
Unescape View File

@ -41,101 +41,10 @@
//M*/
#include <sft/fpool.hpp>
#include <sft/random.hpp>
#include <iostream>
#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 ============ //
namespace {
using namespace sft;

28
apps/sft/include/sft/fpool.hpp
Unescape View File

@ -51,36 +51,8 @@
namespace sft
{
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 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
{
public:

117
apps/sft/include/sft/random.hpp
Unescape View File

@ -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
Unescape View File

@ -118,8 +118,8 @@ int main(int argc, char** argv)
int nfeatures = cfg.poolSize;
cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl;
sft::ICFFeaturePool pool(model, nfeatures);
nfeatures = pool.size();
cv::Ptr<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures);
nfeatures = pool->size();
int npositives = cfg.positives;
@ -135,7 +135,7 @@ int main(int argc, char** argv)
std::string path = cfg.trainPath;
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);
boost->write(fout, cfg.cascadeName);
@ -145,7 +145,7 @@ int main(int argc, char** argv)
cv::Mat 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);
tfs << "thresholds" << thresholds;

24
modules/softcascade/include/opencv2/softcascade/softcascade.hpp
Unescape View File

@ -64,15 +64,7 @@ struct CV_EXPORTS Detection
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
{
@ -84,6 +76,22 @@ public:
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.
// ========================================================================== //

114
modules/softcascade/src/integral_channel_builder.cpp
Unescape View File

@ -41,6 +41,7 @@
//M*/
#include "precomp.hpp"
#include "_random.hpp"
namespace {
@ -160,3 +161,116 @@ std::ostream& cv::operator<<(std::ostream& out, const cv::ChannelFeature& m)
}
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;
}

Write Preview
Loading…
Cancel
Save