LBP: switched to PImpl pattern
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marina.kolpakova
parent
6c3eeb7d7c
commit
2dc93574e1
@ -1426,8 +1426,6 @@ private:
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class CV_EXPORTS CascadeClassifier_GPU_LBP
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{
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public:
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enum stage { BOOST = 0 };
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enum feature { LBP = 0 };
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CascadeClassifier_GPU_LBP(cv::Size detectionFrameSize = cv::Size());
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~CascadeClassifier_GPU_LBP();
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@ -1438,33 +1436,10 @@ public:
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.1, int minNeighbors = 4,
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cv::Size maxObjectSize = cv::Size()/*, Size minSize = Size()*/);
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Size getClassifierSize() const;
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private:
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bool read(const FileNode &root);
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void allocateBuffers(cv::Size frame = cv::Size());
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static const stage stageType = BOOST;
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static const feature featureType = LBP;
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cv::Size NxM;
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bool isStumps;
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int ncategories;
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int subsetSize;
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int nodeStep;
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// gpu representation of classifier
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GpuMat stage_mat;
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GpuMat trees_mat;
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GpuMat nodes_mat;
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GpuMat leaves_mat;
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GpuMat subsets_mat;
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GpuMat features_mat;
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GpuMat integral;
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GpuMat integralBuffer;
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GpuMat resuzeBuffer;
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GpuMat candidates;
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static const int integralFactor = 4;
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struct CascadeClassifierImpl;
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CascadeClassifierImpl* impl;
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};
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////////////////////////////////// SURF //////////////////////////////////////////
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@ -74,10 +74,131 @@ double /*scaleFactor*/, int /*minNeighbors*/, cv::Size /*maxObjectSize*/){ throw
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#else
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cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP(cv::Size detectionFrameSize) { allocateBuffers(detectionFrameSize); }
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cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP(){}
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cv::Size operator -(const cv::Size& a, const cv::Size& b)
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{
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return cv::Size(a.width - b.width, a.height - b.height);
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}
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void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
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cv::Size operator +(const cv::Size& a, const int& i)
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{
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return cv::Size(a.width + i, a.height + i);
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}
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cv::Size operator *(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
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}
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cv::Size operator /(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
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}
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bool operator <=(const cv::Size& a, const cv::Size& b)
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{
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return a.width <= b.width && a.height <= b.width;
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}
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struct PyrLavel
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{
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PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window) : order(_order)
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{
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scale = pow(_scale, order);
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sFrame = frame / scale;
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workArea = sFrame - window + 1;
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sWindow = window * scale;
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}
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bool isFeasible(cv::Size maxObj)
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{
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return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
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}
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PyrLavel next(float factor, cv::Size frame, cv::Size window)
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{
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return PyrLavel(order + 1, factor, frame, window);
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}
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int order;
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float scale;
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cv::Size sFrame;
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cv::Size workArea;
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cv::Size sWindow;
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};
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namespace cv { namespace gpu { namespace device
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{
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namespace lbp
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{
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void classifyPyramid(int frameW,
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int frameH,
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int windowW,
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int windowH,
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float initalScale,
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float factor,
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int total,
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const DevMem2Db& mstages,
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const int nstages,
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const DevMem2Di& mnodes,
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const DevMem2Df& mleaves,
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const DevMem2Di& msubsets,
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const DevMem2Db& mfeatures,
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const int subsetSize,
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DevMem2D_<int4> objects,
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unsigned int* classified,
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DevMem2Di integral);
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void connectedConmonents(DevMem2D_<int4> candidates, int ncandidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
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}
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}}}
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struct cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifierImpl
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{
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public:
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struct Stage
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{
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int first;
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int ntrees;
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float threshold;
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};
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bool read(const FileNode &root);
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void allocateBuffers(cv::Size frame = cv::Size());
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bool empty() const {return stage_mat.empty();}
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int process(const GpuMat& image, GpuMat& objects, double scaleFactor, int groupThreshold, cv::Size maxObjectSize);
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private:
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enum stage { BOOST = 0 };
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enum feature { LBP = 0 };
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static const stage stageType = BOOST;
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static const feature featureType = LBP;
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cv::Size NxM;
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bool isStumps;
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int ncategories;
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int subsetSize;
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int nodeStep;
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// gpu representation of classifier
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GpuMat stage_mat;
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GpuMat trees_mat;
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GpuMat nodes_mat;
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GpuMat leaves_mat;
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GpuMat subsets_mat;
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GpuMat features_mat;
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GpuMat integral;
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GpuMat integralBuffer;
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GpuMat resuzeBuffer;
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GpuMat candidates;
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static const int integralFactor = 4;
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};
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void cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifierImpl::allocateBuffers(cv::Size frame)
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{
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if (frame == cv::Size())
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return;
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@ -102,26 +223,8 @@ void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
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}
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}
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bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
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{
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return stage_mat.empty();
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}
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bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
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{
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FileStorage fs(classifierAsXml, FileStorage::READ);
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return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
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}
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struct Stage
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{
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int first;
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int ntrees;
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float threshold;
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};
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// currently only stump based boost classifiers are supported
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bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
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bool CascadeClassifier_GPU_LBP::CascadeClassifierImpl::read(const FileNode &root)
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{
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const char *GPU_CC_STAGE_TYPE = "stageType";
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const char *GPU_CC_FEATURE_TYPE = "featureType";
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@ -262,85 +365,7 @@ bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
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return true;
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}
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namespace cv { namespace gpu { namespace device
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{
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namespace lbp
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{
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void classifyPyramid(int frameW,
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int frameH,
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int windowW,
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int windowH,
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float initalScale,
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float factor,
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int total,
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const DevMem2Db& mstages,
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const int nstages,
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const DevMem2Di& mnodes,
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const DevMem2Df& mleaves,
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const DevMem2Di& msubsets,
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const DevMem2Db& mfeatures,
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const int subsetSize,
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DevMem2D_<int4> objects,
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unsigned int* classified,
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DevMem2Di integral);
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void connectedConmonents(DevMem2D_<int4> candidates, int ncandidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
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}
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}}}
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cv::Size operator -(const cv::Size& a, const cv::Size& b)
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{
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return cv::Size(a.width - b.width, a.height - b.height);
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}
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cv::Size operator +(const cv::Size& a, const int& i)
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{
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return cv::Size(a.width + i, a.height + i);
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}
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cv::Size operator *(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
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}
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cv::Size operator /(const cv::Size& a, const float& f)
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{
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return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
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}
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bool operator <=(const cv::Size& a, const cv::Size& b)
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{
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return a.width <= b.width && a.height <= b.width;
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}
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struct PyrLavel
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{
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PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window) : order(_order)
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{
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scale = pow(_scale, order);
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sFrame = frame / scale;
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workArea = sFrame - window + 1;
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sWindow = window * scale;
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}
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bool isFeasible(cv::Size maxObj)
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{
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return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
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}
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PyrLavel next(float factor, cv::Size frame, cv::Size window)
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{
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return PyrLavel(order + 1, factor, frame, window);
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}
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int order;
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float scale;
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cv::Size sFrame;
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cv::Size workArea;
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cv::Size sWindow;
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};
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int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& objects, double scaleFactor, int groupThreshold, cv::Size maxObjectSize)
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int cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifierImpl::process(const GpuMat& image, GpuMat& objects, double scaleFactor, int groupThreshold, cv::Size maxObjectSize)
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{
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CV_Assert(!empty() && scaleFactor > 1 && image.depth() == CV_8U);
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@ -419,6 +444,26 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
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return classified;
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}
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cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP(cv::Size detectionFrameSize) : impl(new CascadeClassifierImpl()) { (*impl).allocateBuffers(detectionFrameSize); }
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cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP(){ delete impl; }
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bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
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{
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return (*impl).empty();
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}
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bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
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{
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FileStorage fs(classifierAsXml, FileStorage::READ);
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return fs.isOpened() ? (*impl).read(fs.getFirstTopLevelNode()) : false;
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}
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int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& objects, double scaleFactor, int groupThreshold, cv::Size maxObjectSize)
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{
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return (*impl).process(image, objects, scaleFactor, groupThreshold, maxObjectSize);
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}
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// ============ old fashioned haar cascade ==============================================//
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struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
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{
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