Update to 2.0.0 tree from current Fremantle build

[opencv] / apps / traincascade / haarfeatures.h

diff --git a/apps/traincascade/haarfeatures.h b/apps/traincascade/haarfeatures.h
new file mode 100755 (executable)
index 0000000..78ad80f
--- /dev/null
+++ b/apps/traincascade/haarfeatures.h
@@ -0,0 +1,88 @@
+#ifndef _OPENCV_HAARFEATURES_H_
+#define _OPENCV_HAARFEATURES_H_
+
+#include "features.h"
+
+#define CV_HAAR_FEATURE_MAX      3
+
+#define HFP_NAME "haarFeatureParams"
+class CvHaarFeatureParams : public CvFeatureParams
+{
+public:
+    enum { BASIC = 0, CORE = 1, ALL = 2 };
+     /* 0 - BASIC = Viola
+     *  1 - CORE  = All upright
+     *  2 - ALL   = All features */
+    
+    CvHaarFeatureParams();
+    CvHaarFeatureParams( int _mode );
+    
+    virtual void init( const CvFeatureParams& fp );
+    virtual void write( FileStorage &fs ) const;
+
+    virtual void printDefaults() const;  
+    virtual void printAttrs() const;
+    virtual bool scanAttr( const String prm, const String val);
+    
+    int mode;
+};
+
+class CvHaarEvaluator : public CvFeatureEvaluator
+{
+public:
+    virtual void init(const CvFeatureParams *_featureParams,
+        int _maxSampleCount, Size _winSize );
+    virtual void setImage(const Mat& img, uchar clsLabel, int idx);
+    virtual float operator()(int featureIdx, int sampleIdx) const;
+    virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
+    void writeFeature( FileStorage &fs, int fi ) const; // for old file fornat
+protected:
+    virtual void generateFeatures();
+
+    class Feature
+    {
+    public:
+        Feature();
+        Feature( int offset, bool _tilted,
+            int x0, int y0, int w0, int h0, float wt0,
+            int x1, int y1, int w1, int h1, float wt1,
+            int x2 = 0, int y2 = 0, int w2 = 0, int h2 = 0, float wt2 = 0.0F ); 
+        float calc( const Mat &sum, const Mat &tilted, size_t y) const;
+        void write( FileStorage &fs ) const;
+
+        bool  tilted;
+        struct
+        {
+            Rect r;
+            float weight;
+        } rect[CV_HAAR_FEATURE_MAX];
+
+        struct                      
+        {
+            int p0, p1, p2, p3;
+        } fastRect[CV_HAAR_FEATURE_MAX];
+    }; 
+
+    vector<Feature> features;
+    Mat  sum;         /* sum images (each row represents image) */
+    Mat  tilted;      /* tilted sum images (each row represents image) */
+    Mat  normfactor;  /* normalization factor */
+};
+
+inline float CvHaarEvaluator::operator()(int featureIdx, int sampleIdx) const
+{
+    float nf = normfactor.at<float>(0, sampleIdx);
+    return !nf ? 0.0f : (features[featureIdx].calc( sum, tilted, sampleIdx)/nf); 
+}
+
+inline float CvHaarEvaluator::Feature::calc( const Mat &_sum, const Mat &_tilted, size_t y) const
+{
+    const int* img = tilted ? _tilted.ptr<int>((int)y) : _sum.ptr<int>((int)y);
+    float ret = rect[0].weight * (img[fastRect[0].p0] - img[fastRect[0].p1] - img[fastRect[0].p2] + img[fastRect[0].p3] ) +
+        rect[1].weight * (img[fastRect[1].p0] - img[fastRect[1].p1] - img[fastRect[1].p2] + img[fastRect[1].p3] );
+    if( rect[2].weight != 0.0f )
+        ret += rect[2].weight * (img[fastRect[2].p0] - img[fastRect[2].p1] - img[fastRect[2].p2] + img[fastRect[2].p3] );
+    return ret;
+}
+
+#endif