All modules (except ocl and gpu) compiles and pass tests
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@@ -109,7 +109,7 @@ public:
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float operator() (const cv::Mat& integrals, const cv::Size& model) const;
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friend void write(cv::FileStorage& fs, const std::string&, const ChannelFeature& f);
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friend void write(cv::FileStorage& fs, const cv::String&, const ChannelFeature& f);
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friend std::ostream& operator<<(std::ostream& out, const ChannelFeature& f);
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private:
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@@ -117,7 +117,7 @@ private:
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int channel;
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};
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void write(cv::FileStorage& fs, const std::string&, const ChannelFeature& f);
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void write(cv::FileStorage& fs, const cv::String&, const ChannelFeature& f);
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std::ostream& operator<<(std::ostream& out, const ChannelFeature& m);
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// ========================================================================== //
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@@ -135,7 +135,7 @@ public:
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CV_WRAP virtual int totalChannels() const = 0;
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virtual cv::AlgorithmInfo* info() const = 0;
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CV_WRAP static cv::Ptr<ChannelFeatureBuilder> create(const std::string& featureType);
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CV_WRAP static cv::Ptr<ChannelFeatureBuilder> create(const cv::String& featureType);
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};
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// ========================================================================== //
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@@ -211,7 +211,7 @@ public:
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virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
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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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virtual void write( CvFileStorage* fs, std::string name) const = 0;
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virtual void write( CvFileStorage* fs, cv::String name) const = 0;
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};
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CV_EXPORTS bool initModule_softcascade(void);
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@@ -190,7 +190,7 @@ struct ChannelStorage
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enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
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ChannelStorage(const cv::Mat& colored, int shr, std::string featureTypeStr) : shrinkage(shr)
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ChannelStorage(const cv::Mat& colored, int shr, cv::String featureTypeStr) : shrinkage(shr)
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{
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model_height = cvRound(colored.rows / (float)shrinkage);
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if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
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@@ -240,7 +240,7 @@ struct cv::softcascade::Detector::Fields
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typedef std::vector<SOctave>::iterator octIt_t;
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typedef std::vector<Detection> dvector;
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std::string featureTypeStr;
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cv::String featureTypeStr;
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void detectAt(const int dx, const int dy, const Level& level, const ChannelStorage& storage, dvector& detections) const
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{
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@@ -364,14 +364,14 @@ struct cv::softcascade::Detector::Fields
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static const char *const FEATURE_FORMAT = "featureFormat";
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// only Ada Boost supported
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std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
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cv::String stageTypeStr = (cv::String)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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std::string fformat = (std::string)root[FEATURE_FORMAT];
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cv::String fformat = (cv::String)root[FEATURE_FORMAT];
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bool useBoxes = (fformat == "BOX");
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// only HOG-like integral channel features supported
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featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
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featureTypeStr = (cv::String)root[SC_FEATURE_TYPE];
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CV_Assert(featureTypeStr == SC_ICF || featureTypeStr == SC_HOG6_MAG_LUV);
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origObjWidth = (int)root[SC_ORIG_W];
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@@ -136,17 +136,17 @@ struct cv::softcascade::SCascade::Fields
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static const char *const SC_F_RECT = "rect";
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// only Ada Boost supported
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std::string stageTypeStr = (std::string)root[SC_STAGE_TYPE];
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cv::String stageTypeStr = (cv::String)root[SC_STAGE_TYPE];
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CV_Assert(stageTypeStr == SC_BOOST);
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// only HOG-like integral channel features supported
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std::string featureTypeStr = (std::string)root[SC_FEATURE_TYPE];
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cv::String featureTypeStr = (cv::String)root[SC_FEATURE_TYPE];
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CV_Assert(featureTypeStr == SC_ICF);
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int origWidth = (int)root[SC_ORIG_W];
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int origHeight = (int)root[SC_ORIG_H];
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std::string fformat = (std::string)root[SC_FEATURE_FORMAT];
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cv::String fformat = (cv::String)root[SC_FEATURE_FORMAT];
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bool useBoxes = (fformat == "BOX");
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ushort shrinkage = cv::saturate_cast<ushort>((int)root[SC_SHRINKAGE]);
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@@ -122,7 +122,7 @@ CV_INIT_ALGORITHM(HOG6MagLuv, "ChannelFeatureBuilder.HOG6MagLuv", );
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ChannelFeatureBuilder::~ChannelFeatureBuilder() {}
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cv::Ptr<ChannelFeatureBuilder> ChannelFeatureBuilder::create(const std::string& featureType)
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cv::Ptr<ChannelFeatureBuilder> ChannelFeatureBuilder::create(const cv::String& featureType)
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{
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return Algorithm::create<ChannelFeatureBuilder>("ChannelFeatureBuilder." + featureType);
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}
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@@ -158,7 +158,7 @@ float ChannelFeature::operator() (const cv::Mat& integrals, const cv::Size& mode
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return (float)(a - b + c - d);
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}
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void cv::softcascade::write(cv::FileStorage& fs, const std::string&, const ChannelFeature& f)
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void cv::softcascade::write(cv::FileStorage& fs, const cv::String&, const ChannelFeature& f)
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{
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fs << "{" << "channel" << f.channel << "rect" << f.bb << "}";
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}
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@@ -69,7 +69,7 @@ public:
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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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virtual void write( CvFileStorage* fs, cv::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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@@ -436,7 +436,7 @@ float BoostedSoftCascadeOctave::predict( const Mat& _sample, const cv::Range ran
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return CvBoost::predict(&sample, 0, 0, range, false, true);
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}
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void BoostedSoftCascadeOctave::write( CvFileStorage* fs, std::string _name) const
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void BoostedSoftCascadeOctave::write( CvFileStorage* fs, cv::String _name) const
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{
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CvBoost::write(fs, _name.c_str());
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}
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@@ -53,7 +53,7 @@ namespace {
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using namespace cv::softcascade;
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typedef vector<string> svector;
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typedef vector<cv::String> svector;
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class ScaledDataset : public Dataset
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{
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public:
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