split public interface and realization for SoftCascadeOctave
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@ -43,6 +43,8 @@
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#include <sft/fpool.hpp>
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#include <sft/fpool.hpp>
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#include <sft/random.hpp>
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#include <sft/random.hpp>
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#include <iostream>
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#include <queue>
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#include <queue>
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// ========= FeaturePool ========= //
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// ========= FeaturePool ========= //
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@ -43,6 +43,7 @@
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// Trating application for Soft Cascades.
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// Trating application for Soft Cascades.
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#include <sft/common.hpp>
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#include <sft/common.hpp>
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#include <iostream>
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#include <sft/fpool.hpp>
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#include <sft/fpool.hpp>
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#include <sft/config.hpp>
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#include <sft/config.hpp>
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@ -127,22 +128,24 @@ int main(int argc, char** argv)
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cv::Rect boundingBox = cfg.bbox(it);
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cv::Rect boundingBox = cfg.bbox(it);
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std::cout << "Object bounding box" << boundingBox << std::endl;
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std::cout << "Object bounding box" << boundingBox << std::endl;
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cv::SoftCascadeOctave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
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typedef cv::SoftCascadeOctave Octave;
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cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage);
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std::string path = cfg.trainPath;
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std::string path = cfg.trainPath;
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sft::ScaledDataset dataset(path, *it);
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sft::ScaledDataset dataset(path, *it);
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if (boost.train(&dataset, &pool, cfg.weaks, cfg.treeDepth))
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if (boost->train(&dataset, &pool, cfg.weaks, cfg.treeDepth))
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{
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{
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CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
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CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
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boost.write(fout, cfg.cascadeName);
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boost->write(fout, cfg.cascadeName);
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cvReleaseFileStorage( &fout);
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cvReleaseFileStorage( &fout);
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cv::Mat thresholds;
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cv::Mat thresholds;
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boost.setRejectThresholds(thresholds);
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boost->setRejectThresholds(thresholds);
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boost.write(fso, &pool, thresholds);
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boost->write(fso, &pool, thresholds);
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cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
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cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
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tfs << "thresholds" << thresholds;
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tfs << "thresholds" << thresholds;
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@ -44,7 +44,6 @@
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#define __OPENCV_SOFTCASCADE_HPP__
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#define __OPENCV_SOFTCASCADE_HPP__
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#include "opencv2/core/core.hpp"
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#include "opencv2/core/core.hpp"
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#include "opencv2/ml/ml.hpp"
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namespace cv {
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namespace cv {
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@ -90,7 +89,7 @@ public:
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// ========================================================================== //
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// ========================================================================== //
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// Implementation of Integral Channel Feature.
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// Public Interface for Integral Channel Feature.
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// ========================================================================== //
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// ========================================================================== //
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class CV_EXPORTS_W ChannelFeatureBuilder : public Algorithm
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class CV_EXPORTS_W ChannelFeatureBuilder : public Algorithm
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@ -155,12 +154,11 @@ private:
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};
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};
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// ========================================================================== //
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// ========================================================================== //
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// Implementation of singe soft (stageless) cascade octave training.
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// Public Interface for singe soft (stageless) cascade octave training.
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// ========================================================================== //
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// ========================================================================== //
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class CV_EXPORTS SoftCascadeOctave : public cv::Boost
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class CV_EXPORTS SoftCascadeOctave : public Algorithm
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{
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{
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public:
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public:
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enum
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enum
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{
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{
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// Direct backward pruning. (Cha Zhang and Paul Viola)
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// Direct backward pruning. (Cha Zhang and Paul Viola)
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@ -171,39 +169,14 @@ public:
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HEURISTIC = 4
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HEURISTIC = 4
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};
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};
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SoftCascadeOctave(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth);
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virtual void setRejectThresholds(OutputArray thresholds);
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virtual void write( CvFileStorage* fs, string name) const;
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virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const;
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virtual float predict( InputArray _sample, InputArray _votes, bool raw_mode, bool return_sum ) const;
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virtual ~SoftCascadeOctave();
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virtual ~SoftCascadeOctave();
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protected:
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static cv::Ptr<SoftCascadeOctave> create(cv::Rect boundingBox, int npositives, int nnegatives,
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virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
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int logScale, int shrinkage);
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const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat());
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void processPositives(const Dataset* dataset, const FeaturePool* pool);
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
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void generateNegatives(const Dataset* dataset, const FeaturePool* pool);
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virtual void setRejectThresholds(OutputArray thresholds) = 0;
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virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const = 0;
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float predict( const Mat& _sample, const cv::Range range) const;
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virtual void write( CvFileStorage* fs, string name) const = 0;
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private:
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void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
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virtual void initial_weights(double (&p)[2]);
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int logScale;
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cv::Rect boundingBox;
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int npositives;
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int nnegatives;
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int shrinkage;
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Mat integrals;
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Mat responses;
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CvBoostParams params;
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Mat trainData;
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};
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};
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CV_EXPORTS bool initModule_softcascade(void);
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CV_EXPORTS bool initModule_softcascade(void);
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@ -52,6 +52,7 @@
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/internal.hpp"
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#include "opencv2/core/internal.hpp"
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#include "opencv2/ml/ml.hpp"
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#include "opencv2/opencv_modules.hpp"
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#include "opencv2/opencv_modules.hpp"
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@ -42,6 +42,7 @@
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#include "precomp.hpp"
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#include "precomp.hpp"
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#include <queue>
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#include <queue>
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#include <string>
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#define WITH_DEBUG_OUT
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#define WITH_DEBUG_OUT
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@ -122,10 +123,56 @@ struct Random
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}
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}
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#endif
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#endif
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using cv::Dataset;
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using cv::FeaturePool;
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using cv::InputArray;
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using cv::OutputArray;
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using cv::Mat;
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cv::FeaturePool::~FeaturePool(){}
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cv::FeaturePool::~FeaturePool(){}
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cv::Dataset::~Dataset(){}
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cv::Dataset::~Dataset(){}
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cv::SoftCascadeOctave::SoftCascadeOctave(cv::Rect bb, int np, int nn, int ls, int shr)
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class BoostedSoftCascadeOctave : public cv::Boost, public cv::SoftCascadeOctave
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{
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public:
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BoostedSoftCascadeOctave(cv::Rect boundingBox = cv::Rect(), int npositives = 0, int nnegatives = 0, int logScale = 0, int shrinkage = 1);
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virtual ~BoostedSoftCascadeOctave();
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virtual cv::AlgorithmInfo* info() const;
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth);
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virtual void setRejectThresholds(OutputArray thresholds);
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virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const;
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virtual void write( CvFileStorage* fs, std::string name) const;
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protected:
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virtual float predict( InputArray _sample, InputArray _votes, bool raw_mode, bool return_sum ) const;
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virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
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const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat());
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void processPositives(const Dataset* dataset, const FeaturePool* pool);
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void generateNegatives(const Dataset* dataset, const FeaturePool* pool);
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float predict( const Mat& _sample, const cv::Range range) const;
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private:
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void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
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virtual void initial_weights(double (&p)[2]);
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int logScale;
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cv::Rect boundingBox;
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int npositives;
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int nnegatives;
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int shrinkage;
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Mat integrals;
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Mat responses;
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CvBoostParams params;
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Mat trainData;
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};
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BoostedSoftCascadeOctave::BoostedSoftCascadeOctave(cv::Rect bb, int np, int nn, int ls, int shr)
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: logScale(ls), boundingBox(bb), npositives(np), nnegatives(nn), shrinkage(shr)
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: logScale(ls), boundingBox(bb), npositives(np), nnegatives(nn), shrinkage(shr)
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{
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{
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int maxSample = npositives + nnegatives;
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int maxSample = npositives + nnegatives;
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@ -155,9 +202,9 @@ cv::SoftCascadeOctave::SoftCascadeOctave(cv::Rect bb, int np, int nn, int ls, in
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params = _params;
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params = _params;
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}
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}
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cv::SoftCascadeOctave::~SoftCascadeOctave(){}
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BoostedSoftCascadeOctave::~BoostedSoftCascadeOctave(){}
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bool cv::SoftCascadeOctave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
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bool BoostedSoftCascadeOctave::train( const cv::Mat& _trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
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const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
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const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
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{
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{
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bool update = false;
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bool update = false;
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update);
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update);
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}
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}
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void cv::SoftCascadeOctave::setRejectThresholds(cv::OutputArray _thresholds)
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void BoostedSoftCascadeOctave::setRejectThresholds(cv::OutputArray _thresholds)
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{
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{
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dprintf("set thresholds according to DBP strategy\n");
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dprintf("set thresholds according to DBP strategy\n");
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}
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}
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}
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}
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void cv::SoftCascadeOctave::processPositives(const Dataset* dataset, const FeaturePool* pool)
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void BoostedSoftCascadeOctave::processPositives(const Dataset* dataset, const FeaturePool* pool)
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{
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{
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int w = boundingBox.width;
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int w = boundingBox.width;
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int h = boundingBox.height;
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int h = boundingBox.height;
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@ -259,7 +306,7 @@ void cv::SoftCascadeOctave::processPositives(const Dataset* dataset, const Featu
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#undef USE_LONG_SEEDS
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#undef USE_LONG_SEEDS
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void cv::SoftCascadeOctave::generateNegatives(const Dataset* dataset, const FeaturePool* pool)
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void BoostedSoftCascadeOctave::generateNegatives(const Dataset* dataset, const FeaturePool* pool)
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{
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{
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// ToDo: set seed, use offsets
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// ToDo: set seed, use offsets
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sft::Random::engine eng(DX_DY_SEED);
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sft::Random::engine eng(DX_DY_SEED);
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return (T(0) < val) - (val < T(0));
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return (T(0) < val) - (val < T(0));
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}
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}
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void cv::SoftCascadeOctave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const
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void BoostedSoftCascadeOctave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const
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{
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{
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std::queue<const CvDTreeNode*> nodes;
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std::queue<const CvDTreeNode*> nodes;
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nodes.push( tree->get_root());
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nodes.push( tree->get_root());
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fs << "}";
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fs << "}";
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}
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}
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void cv::SoftCascadeOctave::write( cv::FileStorage &fso, const FeaturePool* pool, InputArray _thresholds) const
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void BoostedSoftCascadeOctave::write( cv::FileStorage &fso, const FeaturePool* pool, InputArray _thresholds) const
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{
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{
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CV_Assert(!_thresholds.empty());
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CV_Assert(!_thresholds.empty());
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cv::Mat used( 1, weak->total * ( (int)pow(2.f, params.max_depth) - 1), CV_32SC1);
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cv::Mat used( 1, weak->total * ( (int)pow(2.f, params.max_depth) - 1), CV_32SC1);
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<< "}";
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<< "}";
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}
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}
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void cv::SoftCascadeOctave::initial_weights(double (&p)[2])
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void BoostedSoftCascadeOctave::initial_weights(double (&p)[2])
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{
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{
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double n = data->sample_count;
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double n = data->sample_count;
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p[0] = n / (2. * (double)(nnegatives));
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p[0] = n / (2. * (double)(nnegatives));
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p[1] = n / (2. * (double)(npositives));
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p[1] = n / (2. * (double)(npositives));
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}
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}
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bool cv::SoftCascadeOctave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
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bool BoostedSoftCascadeOctave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)
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{
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{
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CV_Assert(treeDepth == 2);
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CV_Assert(treeDepth == 2);
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CV_Assert(weaks > 0);
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CV_Assert(weaks > 0);
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}
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}
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float cv::SoftCascadeOctave::predict( cv::InputArray _sample, cv::InputArray _votes, bool raw_mode, bool return_sum ) const
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float BoostedSoftCascadeOctave::predict( cv::InputArray _sample, cv::InputArray _votes, bool raw_mode, bool return_sum ) const
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{
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{
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cv::Mat sample = _sample.getMat();
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cv::Mat sample = _sample.getMat();
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CvMat csample = sample;
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CvMat csample = sample;
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}
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}
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}
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}
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float cv::SoftCascadeOctave::predict( const Mat& _sample, const cv::Range range) const
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float BoostedSoftCascadeOctave::predict( const Mat& _sample, const cv::Range range) const
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{
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{
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CvMat sample = _sample;
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CvMat sample = _sample;
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return CvBoost::predict(&sample, 0, 0, range, false, true);
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return CvBoost::predict(&sample, 0, 0, range, false, true);
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}
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}
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void cv::SoftCascadeOctave::write( CvFileStorage* fs, string name) const
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void BoostedSoftCascadeOctave::write( CvFileStorage* fs, std::string _name) const
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{
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{
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CvBoost::write(fs, name.c_str());
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CvBoost::write(fs, _name.c_str());
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}
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CV_INIT_ALGORITHM(BoostedSoftCascadeOctave, "SoftCascadeOctave.BoostedSoftCascadeOctave", );
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cv::SoftCascadeOctave::~SoftCascadeOctave(){}
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cv::Ptr<cv::SoftCascadeOctave> cv::SoftCascadeOctave::create(cv::Rect boundingBox, int npositives, int nnegatives,
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int logScale, int shrinkage)
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{
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cv::Ptr<cv::SoftCascadeOctave> octave(new BoostedSoftCascadeOctave(boundingBox, npositives, nnegatives, logScale, shrinkage));
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return octave;
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}
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}
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