write features to soft cascade xml

This commit is contained in:
marina.kolpakova 2012-12-14 17:57:55 +04:00
parent 1f01052955
commit 4356d34542
3 changed files with 38 additions and 11 deletions

View File

@ -95,9 +95,11 @@ private:
cv::Rect bb; cv::Rect bb;
int channel; int channel;
friend std::ostream& operator<<(std::ostream& out, const ICF& m); friend void write(cv::FileStorage& fs, const string&, const ICF& f);
friend std::ostream& operator<<(std::ostream& out, const ICF& f);
}; };
void write(cv::FileStorage& fs, const string&, const ICF& f);
std::ostream& operator<<(std::ostream& out, const ICF& m); std::ostream& operator<<(std::ostream& out, const ICF& m);
class FeaturePool class FeaturePool
@ -107,6 +109,7 @@ public:
int size() const { return (int)pool.size(); } int size() const { return (int)pool.size(); }
float apply(int fi, int si, const Mat& integrals) const; float apply(int fi, int si, const Mat& integrals) const;
void write( cv::FileStorage& fs, int index) const;
private: private:
void fill(int desired); void fill(int desired);
@ -140,11 +143,11 @@ public:
virtual ~Octave(); virtual ~Octave();
virtual bool train(const Dataset& dataset, const FeaturePool& pool, int weaks, int treeDepth); virtual bool train(const Dataset& dataset, const FeaturePool& pool, int weaks, int treeDepth);
virtual void write( CvFileStorage* fs, string name) const;
virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const; virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const;
virtual void setRejectThresholds(cv::Mat& thresholds); virtual void setRejectThresholds(cv::Mat& thresholds);
virtual void write( CvFileStorage* fs, string name) const;
virtual void write( cv::FileStorage &fs, const Mat& thresholds = Mat()) const; virtual void write( cv::FileStorage &fs, const FeaturePool& pool, const Mat& thresholds = Mat()) const;
int logScale; int logScale;
@ -157,8 +160,9 @@ protected:
float predict( const Mat& _sample, const cv::Range range) const; float predict( const Mat& _sample, const cv::Range range) const;
private: private:
void traverse(const CvBoostTree* tree, cv::FileStorage& fs, const float* th = 0) const; void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const float* th = 0) const;
virtual void initial_weights(double (&p)[2]); virtual void initial_weights(double (&p)[2]);
cv::Rect boundingBox; cv::Rect boundingBox;
int npositives; int npositives;

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@ -300,7 +300,7 @@ template <typename T> int sgn(T val) {
return (T(0) < val) - (val < T(0)); return (T(0) < val) - (val < T(0));
} }
void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, const float* th) const void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const float* th) const
{ {
std::queue<const CvDTreeNode*> nodes; std::queue<const CvDTreeNode*> nodes;
nodes.push( tree->get_root()); nodes.push( tree->get_root());
@ -336,8 +336,10 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, const f
nodes.push( tempNode->right ); nodes.push( tempNode->right );
fs << internalNodeIdx++; fs << internalNodeIdx++;
} }
int fidx = tempNode->split->var_idx; int fidx = tempNode->split->var_idx;
fs << fidx; fs << nfeatures;
used[nfeatures++] = fidx;
fs << tempNode->split->ord.c; fs << tempNode->split->ord.c;
@ -353,8 +355,11 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, const f
fs << "}"; fs << "}";
} }
void sft::Octave::write( cv::FileStorage &fso, const Mat& thresholds) const void sft::Octave::write( cv::FileStorage &fso, const FeaturePool& pool, const Mat& thresholds) const
{ {
cv::Mat used( 1, weak->total * (pow(2, params.max_depth) - 1), CV_32SC1);
int* usedPtr = used.ptr<int>(0);
int nfeatures = 0;
fso << "{" fso << "{"
<< "scale" << logScale << "scale" << logScale
<< "weaks" << weak->total << "weaks" << weak->total
@ -369,12 +374,19 @@ void sft::Octave::write( cv::FileStorage &fso, const Mat& thresholds) const
CV_READ_SEQ_ELEM( tree, reader ); CV_READ_SEQ_ELEM( tree, reader );
if (!thresholds.empty()) if (!thresholds.empty())
traverse(tree, fso, thresholds.ptr<float>(0)+ i); traverse(tree, fso, nfeatures, usedPtr, thresholds.ptr<float>(0)+ i);
else else
traverse(tree, fso); traverse(tree, fso, nfeatures, usedPtr);
} }
// //
fso << "]";
// features
fso << "features" << "[";
for (int i = 0; i < nfeatures; ++i)
// fso << usedPtr[i];
pool.write(fso, usedPtr[i]);
fso << "]" fso << "]"
<< "}"; << "}";
} }
@ -483,6 +495,17 @@ float sft::FeaturePool::apply(int fi, int si, const Mat& integrals) const
return pool[fi](integrals.row(si), model); return pool[fi](integrals.row(si), model);
} }
void sft::FeaturePool::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 << "}";
}
void sft::FeaturePool::fill(int desired) void sft::FeaturePool::fill(int desired)
{ {

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@ -162,8 +162,8 @@ int main(int argc, char** argv)
cv::Mat thresholds; cv::Mat thresholds;
boost.setRejectThresholds(thresholds); boost.setRejectThresholds(thresholds);
boost.write(fso, thresholds); boost.write(fso, pool, thresholds);
boost.write(fsr); boost.write(fsr, pool);
// std::cout << "thresholds " << thresholds << std::endl; // std::cout << "thresholds " << thresholds << std::endl;
cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE); cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);