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generalized_hough.cpp

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//Created by Alexey Latyshev
// Set of functions for Generic Hough Transform (GHT)
#include <cv.h>
#include <highgui.h>
#include "outlet_pose_estimation/detail/generalized_hough.h"
#include "outlet_pose_estimation/detail/features.h"
//using namespace cv;

using namespace std;

CV_IMPL CvSparseMat*
cvCreateOutletSparseMat( int dims, const int* sizes, int type )
{
        type = CV_MAT_TYPE( type );
        int pix_size1 = CV_ELEM_SIZE1(type);
        int pix_size = pix_size1*CV_MAT_CN(type);
        int i, size;
        CvMemStorage* storage;

        if( pix_size == 0 )
                CV_Error( CV_StsUnsupportedFormat, "invalid array data type" );

        if( dims <= 0 || dims > CV_MAX_DIM_HEAP )
                CV_Error( CV_StsOutOfRange, "bad number of dimensions" );

        if( !sizes )
                CV_Error( CV_StsNullPtr, "NULL <sizes> pointer" );

        for( i = 0; i < dims; i++ )
        {
                if( sizes[i] <= 0 )
                        CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
        }

        CvSparseMat* arr = (CvSparseMat*)cvAlloc(sizeof(*arr)+MAX(0,dims-CV_MAX_DIM)*sizeof(arr->size[0]));

        arr->type = CV_SPARSE_MAT_MAGIC_VAL | type;
        arr->dims = dims;
        arr->refcount = 0;
        arr->hdr_refcount = 1;
        memcpy( arr->size, sizes, dims*sizeof(sizes[0]));

        arr->valoffset = (int)cvAlign(sizeof(CvSparseNode), pix_size1);
        arr->idxoffset = (int)cvAlign(arr->valoffset + pix_size, sizeof(int));
        size = (int)cvAlign(arr->idxoffset + dims*sizeof(int), sizeof(CvSetElem));


        storage = cvCreateMemStorage( 10000000 );

        arr->heap = cvCreateSet( 0, sizeof(CvSet), size, storage );

        arr->hashsize = CV_SPARSE_HASH_SIZE0;
        size = arr->hashsize*sizeof(arr->hashtable[0]);

        arr->hashtable = (void**)cvAlloc( size );
        memset( arr->hashtable, 0, size );

        return arr;
}


//Filtering outlets out of frame
void filterOutletOutliers(vector<feature_t>& features, vector<feature_t>& dst_outlet, int accuracy)
{

        //Filtering outlets outside of the window
        bool first = 0;
        bool second = 0;
        bool third = 0;
        bool forth = 0;
        if ((int)dst_outlet.size()/3 < 3)
        {
                third = 1;
                forth = 1;
        }

        int nOutlets = (int)dst_outlet.size()/3;
        for (int i=0;i<(int)dst_outlet.size();i++)
        {
                float distance = -1;
                float min_distance = (float)1e38;
                for (int j=0;j<(int)features.size();j++)
                {
                        if (dst_outlet[i].class_id == features[j].class_id)
                        {
                                distance = (features[j].pt.x - dst_outlet[i].pt.x)*(features[j].pt.x - dst_outlet[i].pt.x)+
                                        (features[j].pt.y - dst_outlet[i].pt.y)*(features[j].pt.y - dst_outlet[i].pt.y);
                                if (distance < min_distance)
                                        min_distance = distance;
                        }
                }

                distance = min_distance;

                if ((distance < 1e38)&&(distance < accuracy*accuracy))
                {
                        if (nOutlets == 4)
                        {
                                if ((i==0)||(i==1)||(i==8))
                                {
                                        first = 1;
                                }
                                if ((i==2)||(i==3)||(i==9))
                                {
                                        second = 1;
                                }
                                if ((i==4)||(i==5)||(i==10))
                                {
                                        third = 1;
                                }
                                if ((i==6)||(i==7)||(i==11))
                                {
                                        forth = 1;
                                }
                        }
                        if (nOutlets == 2)
                        {
                                if ((i==0)||(i==1)||(i==4))
                                {
                                        first = 1;
                                }
                                if ((i==2)||(i==3)||(i==5))
                                {
                                        second = 1;
                                }

                        }

                }

        }
        if (!(first && second && third && forth))
        {
                dst_outlet.clear();
                return;
        }
        //if ((!first && !second)|| (!third && !forth)||(!first && !third)|| (!first && !forth)||(!second && !forth)||(!second && !third))
        //{
        //      dst_outlet.clear();
        //}
        //Filtering by outlet angle
        else
        {
                float alpha = (float)CV_PI;
                float beta = (float)CV_PI;
                float gamma = (float)CV_PI;
                float a,b,c;

                int overSize = (int)dst_outlet.size()%3;
                for (int i=0;i<nOutlets;i++)
                {
                        a = (dst_outlet[2*i].pt.x - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.x)*(dst_outlet[2*i].pt.x - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.x)+
                                (dst_outlet[2*i].pt.y - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.y)*(dst_outlet[2*i].pt.y - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.y);
                        b = (dst_outlet[2*i+1].pt.x - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.x)*(dst_outlet[2*i+1].pt.x - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.x)+
                                (dst_outlet[2*i+1].pt.y - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.y)*(dst_outlet[2*i+1].pt.y - dst_outlet[(int)dst_outlet.size() - overSize - nOutlets+i].pt.y);
                        c = (dst_outlet[2*i].pt.x - dst_outlet[2*i+1].pt.x)*(dst_outlet[2*i].pt.x - dst_outlet[2*i+1].pt.x)+
                                (dst_outlet[2*i].pt.y - dst_outlet[2*i+1].pt.y)*(dst_outlet[2*i].pt.y - dst_outlet[2*i+1].pt.y);

                        if ((a > 0)&&( b> 0))
                                alpha = acos((a + b - c)/2/sqrt(a*b));
                        if ((a > 0)&&( b> 0))
                                beta = acos((a + c - b)/2/sqrt(a*c));
                        gamma = (float)CV_PI - alpha - beta;


                        if ((alpha > CV_PI/2)||(beta > CV_PI/2)||(gamma > CV_PI/2))
                        {
                                dst_outlet.clear();
                                return;
                        }
                }

        }
        //End of filtering by angles

        // Filtering regions "full-of-features"
#if 1
        CvPoint min, max;
        float coeff = 2.0f;
        min.x = (int)dst_outlet[0].pt.x;
        min.y = (int)dst_outlet[0].pt.y;
        max.x = min.x;
        max.y = min.y;
        for (int i=1; i<(int)dst_outlet.size(); i++)
        {
                if ((int)dst_outlet[i].pt.x > max.x)
                        max.x = (int)dst_outlet[i].pt.x ;
                if ((int)dst_outlet[i].pt.y > max.y)
                        max.y = (int)dst_outlet[i].pt.y ;
                if ((int)dst_outlet[i].pt.x < min.x)
                        min.x = (int)dst_outlet[i].pt.x ;
                if ((int)dst_outlet[i].pt.y < min.y)
                        min.y = (int)dst_outlet[i].pt.y ;
        }
        int width = max.x - min.x;
        int height = max.y - min.y;
        min.x -=(int)(width*0.2);
        max.x +=(int)(width*0.2);
        min.y -=(int)(height*0.2);
        max.y +=(int)(height*0.2);
        //Calculating number of features inside region "min x max"
        int count = 0;
        for (int i=0;i<(int)features.size(); i++)
        {
                if (((int)features[i].pt.x >= min.x)&&((int)features[i].pt.y >= min.y)&&
                        ((int)features[i].pt.x <= max.x)&&((int)features[i].pt.y <= max.y))
                {
                        count++;
                }

        }
        //printf("Features: %d\nCount: %d\n\n",(int)features.size(),count);
        if (count > (int)(coeff*dst_outlet.size()))
        {
                dst_outlet.clear();
                return;
        }
#endif

        //End of filtering

        //End of filtering
}
// Calculates outlet's center from given feature and affine transform
CvPoint* getOutletCenter(feature_t feature, const vector<feature_t>& train_features, int feature_id, float angle1, float x_scale, float y_scale, float angle2)
{
        CvPoint* result;
        int train_length = (int)train_features.size();
        if ((feature_id < 0)||(feature_id > (train_length-1)))
                return NULL;
        CvPoint outlet_center;
        outlet_center.x = 0;
        outlet_center.y = 0;
        for (int i=0;i<train_length;i++)
        {
                outlet_center.x += (int)train_features[i].pt.x;
                outlet_center.y += (int)train_features[i].pt.y;
        }
        outlet_center.x /= train_length;
        outlet_center.y /= train_length;

        float rel_center_x = - outlet_center.x + train_features[feature_id].pt.x;
        float rel_center_y = - outlet_center.y + train_features[feature_id].pt.y;
        float t1 = rel_center_x * cos(angle1) + rel_center_y*sin(angle1);
        float t2 = - rel_center_x * sin(angle1) + rel_center_y * cos(angle1);
        rel_center_x = t1*x_scale;
        rel_center_y = t2*y_scale;
        t1 = rel_center_x * cos(angle2) + rel_center_y*sin(angle2);
        t2 = - rel_center_x * sin(angle2) + rel_center_y * cos(angle2);

        result = new CvPoint();
        result->x = (int)(feature.pt.x-t1);
        result->y = (int)(feature.pt.y-t2);

        return result;

}
// Builds 6-dimension histogram [center x, center y, rotation angle1, x scale, y scale, rotation angle 2]
CvSparseMat* buildHoughHist(vector<feature_t>& input, const vector<feature_t>& train_features, int* hist_size, float** ranges)
{
        CvSparseMat* hist;

        hist = cvCreateOutletSparseMat(6,hist_size,CV_32FC1);

        int* hist_size2 = new int[7];
        for (int i=0;i<6;i++)
                hist_size2[i] = hist_size[i];
        hist_size2[6] = (int)train_features.size();

        CvSparseMat* feature_votes = cvCreateOutletSparseMat(7,hist_size2,CV_8UC1);


        int* idx = new int[6];
        int* votes_idx = new int[7];


        float blur_coeff = 0.17f;

        for (int n = 0; n < (int)input.size();n++)
        {
                for (int feature_id = 0; feature_id < (int)train_features.size(); feature_id++)
                {
                        if (input[n].class_id != train_features[feature_id].class_id)
                                continue;

                        for (float angle1 = ranges[2][0]+(ranges[2][1]-ranges[2][0])/(hist_size[2]*2); angle1 <= ranges[2][1]; angle1 += ((ranges[2][1]-ranges[2][0] > 0) ? (ranges[2][1]-ranges[2][0])/hist_size[2] : 1))
                        {
                                for (float x_scale = ranges[3][0]+(ranges[3][1]-ranges[3][0])/(hist_size[3]*2); x_scale <= ranges[3][1]; x_scale += ((ranges[3][1]-ranges[3][0] > 0) ? (ranges[3][1]-ranges[3][0])/hist_size[3] : 1))
                                {
                                        for (float y_scale = ranges[4][0]+(ranges[4][1]-ranges[4][0])/(hist_size[4]*2); y_scale <= ranges[4][1]; y_scale += ((ranges[4][1]-ranges[4][0] > 0) ? (ranges[4][1]-ranges[4][0])/hist_size[4] : 1))
                                        {
                                                for (float angle2 = ranges[5][0]+(ranges[5][1]-ranges[5][0])/(hist_size[5]*2); angle2 <= ranges[5][1]; angle2 += ((ranges[5][1]-ranges[5][0] > 0) ? (ranges[5][1]-ranges[5][0])/hist_size[5] : 1))
                                                {

                            if(abs(input[n].pt.x - 379) < 5 && abs(input[n].pt.y - 312) < 5 &&
                               fabs(fabs(angle1)) < 0.2 && fabs(fabs(angle2) - 3.1415/3) < 0.2)
                            {
                                int w = 1;
                            }

                                                        CvPoint* center = getOutletCenter(input[n],train_features,feature_id,angle1,x_scale,y_scale,angle2);

                                                        if (center && (center->x >= ranges[0][0]) && (center->x < ranges[0][1]) && (center->y >= ranges[1][0]) && (center->y < ranges[1][1]))
                                                        {
                                                                //Incrementing histogram
                                                                float idx0 = ((center->x - ranges[0][0])/(ranges[0][1]-ranges[0][0]) * hist_size[0]);
                                                                float idx1 = ((center->y - ranges[1][0])/(ranges[1][1]-ranges[1][0]) * hist_size[1]);
                                                                idx[0] = (int)idx0;
                                                                idx[1] = (int)idx1;
                                                                idx[2] = (ranges[2][1] != ranges[2][0]) ? (int)((angle1 - ranges[2][0])/(ranges[2][1]-ranges[2][0]) * hist_size[2]) : 0;
                                                                idx[3] = (ranges[3][1] != ranges[3][0]) ?(int)((x_scale - ranges[3][0])/(ranges[3][1]-ranges[3][0]) * hist_size[3]) : 0;
                                                                idx[4] = (ranges[4][1] != ranges[4][0]) ?(int)((y_scale - ranges[4][0])/(ranges[4][1]-ranges[4][0]) * hist_size[4]) : 0;
                                                                idx[5] = (ranges[5][1] != ranges[5][0]) ?(int)((angle2 - ranges[5][0])/(ranges[5][1]-ranges[5][0]) * hist_size[5]) : 0;


                                                                bool isOK = true;
                                                                for (int i=0;i<2; i++)
                                                                {
                                                                        if (idx[i] >= hist_size[i])
                                                                        {
                                                                                idx[i] = hist_size[i]-1;
                                                                                isOK = false;
                                                                        }
                                                                        if (idx[i] < 0)
                                                                        {
                                                                                idx[i] = 0;
                                                                                isOK = false;
                                                                        }
                                                                }

                                                                if (isOK)
                                                                {
                                                                        for (int i=0;i<6;i++)
                                                                                votes_idx[i] = idx[i];
                                                                        votes_idx[6] = feature_id;

                                                                        int val = (int)cvGetND(feature_votes,votes_idx).val[0];

                                                                        if (val)
                                                                        {
                                                                                isOK = false;
                                                                        }
                                                                        else
                                                                        {
                                                                                cvSetND(feature_votes,votes_idx,cvScalar(1));
                                                                        }
                                                                }

                                                                if (isOK)
                                                                {

                                                                        float value = (float)cvGetRealND(hist,idx);
                                                                        cvSetRealND(hist,idx,++value);

                                                                        //int idx2[6];
                                                                        //for (int i=0;i<6;i++)
                                                                        //      idx2[i]=idx[i];

                                                                        //int move_x = 0;
                                                                        //int move_y = 0;


                                                                        //if (((idx0 - idx[0]) < blur_coeff)&&(idx[0] > 0))
                                                                        //      move_x=-1;
                                                                        //else
                                                                        //      if (((-idx0 + idx[0]+1) < blur_coeff)&&(idx[0] <(hist_size[0]-1)))
                                                                        //              move_x=1;
                                                                        //if (((idx1 - idx[1]) < blur_coeff)&&(idx[1] > 0))
                                                                        //      move_y=-1;
                                                                        //else
                                                                        //      if (((-idx1 + idx[1]+1) < blur_coeff)&&(idx[1] < (hist_size[1]-1)))
                                                                        //              move_y=1;

                                                                        //idx2[0] = idx[0]+move_x;
                                                                        //idx2[1] = idx[1]+move_y;

                                                                        //if ((move_x != 0) || (move_y !=0))
                                                                        //{
                                                                        //      float value2 = (float)cvGetRealND(hist,idx2);
                                                                        //      //if (value2 > value)
                                                                        //              cvSetRealND(hist,idx2,++value2);
                                                                        //      //else
                                                                        //      //      cvSetRealND(hist,idx,++value);
                                                                        //}


                                                                        //Bins Blur (x & y)
                                                                        //int idx2[6];
                                                                        //for (int i=0;i<6;i++)
                                                                        //      idx2[i]=idx[i];
                                                                        //for (int x = -1;x<=1;x++)
                                                                        //      for (int y= -1;y<=1;y++)
                                                                        //      {
                                                                        //              if (((idx[0]+x) >=0)&&((idx[1]+y) >=0)&&((idx[0]+x) < hist_size[0])&&((idx[1]+y) < hist_size[1]))
                                                                        //              {
                                                                        //                      idx2[0] = idx[0]+x;
                                                                        //                      idx2[1] = idx[1]+y;
                                                                        //                      float value = cvGetRealND(hist,idx2);
                                                                        //                      cvSetRealND(hist,idx2,++value);
                                                                        //              }
                                                                        //      }
                                                                        //End


                                                                }


                                                                delete center;
                                                                center = 0;
                                                        }
                                                        if (center)
                                                                delete center;
                                                }
                                        }
                                }
                        }
                }
        }
        delete[] idx;
        delete[] votes_idx;
        cvReleaseSparseMat(&feature_votes);

        return hist;
}
//---------
// Calculates maximums of histogram.
float** getMaxHistValues(const CvHistogram* hist, int* hist_size)
{
        float** values = new float*[1];
        *values = new float[6];

        float max_val, min_val;
        int* idx = new int[6];
        cvGetMinMaxHistValue(hist,&min_val,&max_val,0,idx);
        printf("\nVotes: %f\n ",max_val);

        (*values)[0] = hist->thresh[0][0]+(hist->thresh[0][1]-hist->thresh[0][0])/hist_size[0]*idx[0];
        (*values)[1] = hist->thresh[1][0]+(hist->thresh[1][1]-hist->thresh[1][0])/hist_size[1]*idx[1];
        (*values)[2] = hist->thresh[2][0]+(hist->thresh[2][1]-hist->thresh[2][0])/hist_size[2]*idx[2];
        (*values)[3] = hist->thresh[3][0]+(hist->thresh[3][1]-hist->thresh[3][0])/hist_size[3]*idx[3];
        (*values)[4] = hist->thresh[4][0]+(hist->thresh[4][1]-hist->thresh[4][0])/hist_size[4]*idx[4];
        (*values)[5] = hist->thresh[5][0]+(hist->thresh[5][1]-hist->thresh[5][0])/hist_size[5]*idx[5];

        delete[] idx;
        return values;
}
//---------
//---------
// Calculates maximums of histogram.
void getMaxHistValues(const CvSparseMat* hist, int* hist_size, float** ranges, float** &maxs, int& count, int MIN_VOTES)
{
        count = 0;
        //int MIN_VOTES = 4;
        CvSparseMatIterator mat_iterator;
        CvSparseNode* node = cvInitSparseMatIterator( hist, &mat_iterator );

        for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
        {
                //  const int* idx = CV_NODE_IDX( (CvSparseMat*)hist->bins, node ); /* get pointer to the element indices */
                float val = *(float*)CV_NODE_VAL( hist, node ); /* get value of the element
                                                                                                                (assume that the type is CV_32FC1) */
                if (val >= MIN_VOTES)
                        (count) ++;
        }
        //Endof

        if (count > 0)
        {
                maxs = new float*[count];
                for (int i=0; i<(count); i++)
                        maxs[i] = new float[6];

                int i=0;
                node = cvInitSparseMatIterator( hist, &mat_iterator );
                for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
                {
                        int* idx = CV_NODE_IDX( hist, node ); /* get pointer to the element indices */
                        float val = *(float*)CV_NODE_VAL( (CvSparseMat*)hist, node ); /* get value of the element
                                                                                                                                                  (assume that the type is CV_32FC1) */
                        if (val >= MIN_VOTES)
                        {
                                maxs[i][0] = (float)(ranges[0][0]+(ranges[0][1]-ranges[0][0])/hist_size[0]*(idx[0]+0.5));
                                maxs[i][1] = (float)(ranges[1][0]+(ranges[1][1]-ranges[1][0])/hist_size[1]*(idx[1]+0.5));
                                maxs[i][2] = (float)(ranges[2][0]+(ranges[2][1]-ranges[2][0])/hist_size[2]*(idx[2]+0.5));
                                maxs[i][3] = (float)(ranges[3][0]+(ranges[3][1]-ranges[3][0])/hist_size[3]*(idx[3]+0.5));
                                maxs[i][4] = (float)(ranges[4][0]+(ranges[4][1]-ranges[4][0])/hist_size[4]*(idx[4]+0.5));
                                maxs[i][5] = (float)(ranges[5][0]+(ranges[5][1]-ranges[5][0])/hist_size[5]*(idx[5]+0.5));
                                i++;
                        }

                        //delete[] idx;
                }

        }
        else
        {
                maxs = NULL;
                count = 0;
        }

}
//---------
int getMaxHistValues(const CvSparseMat* hist, int* hist_size, float** ranges, float** &maxs, int& count)
{
        //IplImage* img = cvCreateImage(cvSize(640,480),8,3);
        count = 0;
        float MIN_VOTES = 0;
        CvSparseMatIterator mat_iterator;
        CvSparseNode* node = cvInitSparseMatIterator( hist, &mat_iterator );

        for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
        {
                //  const int* idx = CV_NODE_IDX( hist, node ); /* get pointer to the element indices */
                float val = *(float*)CV_NODE_VAL( hist, node ); /* get value of the element
                                                                                                                (assume that the type is CV_32FC1) */
                //if (val > 0)
                //{
                //      //CvPoint pt;
                //      int* idx = CV_NODE_IDX( hist, node );
                //      int x = ranges[0][0]+(ranges[0][1]-ranges[0][0])/hist_size[0]*(idx[0]+0.5);
                //      int y = ranges[1][0]+(ranges[1][1]-ranges[1][0])/hist_size[1]*(idx[1]+0.5);
                //      int blue = ((uchar*)(img->imageData + img->widthStep*y))[x*3];
                //      int green = ((uchar*)(img->imageData + img->widthStep*y))[x*3+1];
                //      int red = ((uchar*)(img->imageData + img->widthStep*y))[x*3+2];
                //      int v = blue+green+red;

                //      v+=val;
                //      blue = ((v / 255 > 0) ? 255 : v % 255);

                //      green = ((v / 510 > 0) ? 255 : ((blue == 255) ? v % 510 - 255 : 0));

                //      red = ((green == 255) ? v % 765 - 510 : 0);

                //      //((uchar*)(img->imageData + img->widthStep*y))[x*3] = blue;
                //      //((uchar*)(img->imageData + img->widthStep*y))[x*3+1] = 100;
                //      //((uchar*)(img->imageData + img->widthStep*y))[x*3+2] = red;
                //

                //      //CvScalar color; color.val[0] = img->imageData[pt.y*img->width + pt.x];
                //      //color.val[0] +=5;
                //      // img->imageData[pt.y*img->width + pt.x]+=5;
                //      cvDrawRect(img,cvPoint(x-2,y-2),cvPoint(x+2,y+2),cvScalar(blue,green,red),CV_FILLED);

                //}
                if (val > MIN_VOTES)
                        MIN_VOTES = val-1;
        }
        //cvSaveImage("1.bmp",img);

        node = cvInitSparseMatIterator( hist, &mat_iterator );

        for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
        {
                //  const int* idx = CV_NODE_IDX( hist, node ); /* get pointer to the element indices */
                float val = *(float*)CV_NODE_VAL( hist, node ); /* get value of the element
                                                                                                                (assume that the type is CV_32FC1) */
                if (val >= MIN_VOTES)
                        (count) ++;
        }
        //Endof

        if (count > 0)
        {
                maxs = new float*[count];
                for (int i=0; i<(count); i++)
                        maxs[i] = new float[6];

                int i=0;
                node = cvInitSparseMatIterator( hist, &mat_iterator );
                for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
                {
                        int* idx = CV_NODE_IDX( hist, node ); /* get pointer to the element indices */
                        float val = *(float*)CV_NODE_VAL( hist, node ); /* get value of the element
                                                                                                                        (assume that the type is CV_32FC1) */
                        if (val >= MIN_VOTES)
                        {
                                maxs[i][0] = (float)(ranges[0][0]+(ranges[0][1]-ranges[0][0])/hist_size[0]*(idx[0]+0.5));
                                maxs[i][1] = (float)(ranges[1][0]+(ranges[1][1]-ranges[1][0])/hist_size[1]*(idx[1]+0.5));
                                maxs[i][2] = (float)(ranges[2][0]+(ranges[2][1]-ranges[2][0])/hist_size[2]*(idx[2]+0.5));
                                maxs[i][3] = (float)(ranges[3][0]+(ranges[3][1]-ranges[3][0])/hist_size[3]*(idx[3]+0.5));
                                maxs[i][4] = (float)(ranges[4][0]+(ranges[4][1]-ranges[4][0])/hist_size[4]*(idx[4]+0.5));
                                maxs[i][5] = (float)(ranges[5][0]+(ranges[5][1]-ranges[5][0])/hist_size[5]*(idx[5]+0.5));
                                i++;
                        }

                        //delete[] idx;
                }

        }
        else
        {
                maxs = NULL;
                count = 0;
        }

        int res = (int)MIN_VOTES;
        return res;

}


// Calculates outlet features from given train outlet and affine transform
// Affine transform is array [center x, center y, rotation angle1, x scale, y scale, rotation angle 2]
void calcOutletPosition(const vector<feature_t>& train_features, float* affine_transform, vector<feature_t>& features)
{
        CvPoint center = cvPoint((int)(affine_transform[0]),(int)(affine_transform[1]));
        float angle1 = affine_transform[2];
        float x_scale = affine_transform[3];
        float y_scale = affine_transform[4];
        float angle2 = affine_transform[5];
        int train_length = (int)train_features.size();
        //CvPoint* result = new CvPoint[train_length];

        CvPoint outlet_center;
        outlet_center.x = 0;
        outlet_center.y = 0;
        for (int i=0;i<train_length;i++)
        {
                outlet_center.x += (int)train_features[i].pt.x;
                outlet_center.y += (int)train_features[i].pt.y;
        }
        outlet_center.x /= train_length;
        outlet_center.y /= train_length;

        for (int i=0; i< train_length; i++)
        {
                float rel_center_x = - outlet_center.x + train_features[i].pt.x;
                float rel_center_y = - outlet_center.y + train_features[i].pt.y;
                float t1 = rel_center_x * cos(angle1) + rel_center_y*sin(angle1);
                float t2 = - rel_center_x * sin(angle1) + rel_center_y * cos(angle1);
                rel_center_x = t1*x_scale;
                rel_center_y = t2*y_scale;
                t1 = rel_center_x * cos(angle2) + rel_center_y*sin(angle2);
                t2 = - rel_center_x * sin(angle2) + rel_center_y * cos(angle2);

                CvPoint result_point;
                result_point.x = (int)(center.x+t1);
                result_point.y = (int)(center.y+t2);
                feature_t feature;
                feature.size = train_features[i].size;
                feature.pt = result_point;
                feature.class_id = train_features[i].class_id;

                features.push_back(feature);
        }


}
//------
//Use false movements filter
// dst_outlet is outlet after moving some outlet holes to the new places
// projected_outlet is outlet after affine transform applying to the template outlet
//The algorithm checks should we apply the movement or return to the original position
void filterFalseMovements(const vector<feature_t>& projected_outlet, vector<feature_t>& dst_outlet)
{
        // Filtering false movements
        vector <float> orig_right;
        vector <float> orig_left;
        vector <float> new_right;
        vector <float> new_left;
        float dist = 0.0f;
        float diff_coeff = 1.6f;
        float diff_coeff_1 = 1/diff_coeff;
        int nPower = (int)dst_outlet.size()/3*2;
        int nGround = nPower/2;
        orig_right.resize(nPower+nGround);
        orig_left.resize(nPower+nGround);
        new_right.resize(nPower+nGround);
        new_left.resize(nPower+nGround);
        for (int i=0;i<nGround;i++)
        {
                dist = (dst_outlet[nPower+i].pt.x-dst_outlet[2*i].pt.x)*(dst_outlet[nPower+i].pt.x-dst_outlet[2*i].pt.x)+
                        (dst_outlet[nPower+i].pt.y-dst_outlet[2*i].pt.y)*(dst_outlet[nPower+i].pt.y-dst_outlet[2*i].pt.y);
                new_right[nPower+i] = dist;
                new_left[2*i] = dist;

                dist = (dst_outlet[nPower+i].pt.x-dst_outlet[2*i+1].pt.x)*(dst_outlet[nPower+i].pt.x-dst_outlet[2*i+1].pt.x)+
                        (dst_outlet[nPower+i].pt.y-dst_outlet[2*i+1].pt.y)*(dst_outlet[nPower+i].pt.y-dst_outlet[2*i+1].pt.y);
                new_left[nPower+i] = dist;
                new_right[2*i+1] = dist;

                dist = (dst_outlet[2*i+1].pt.x-dst_outlet[2*i].pt.x)*(dst_outlet[2*i+1].pt.x-dst_outlet[2*i].pt.x)+
                        (dst_outlet[2*i+1].pt.y-dst_outlet[2*i].pt.y)*(dst_outlet[2*i+1].pt.y-dst_outlet[2*i].pt.y);
                new_right[2*i] = dist;
                new_left[2*i+1] = dist;

                dist = (projected_outlet[nPower+i].pt.x-projected_outlet[2*i].pt.x)*(projected_outlet[nPower+i].pt.x-projected_outlet[2*i].pt.x)+
                        (projected_outlet[nPower+i].pt.y-projected_outlet[2*i].pt.y)*(projected_outlet[nPower+i].pt.y-projected_outlet[2*i].pt.y);
                orig_right[nPower+i] = dist;
                orig_left[2*i] = dist;

                dist = (projected_outlet[nPower+i].pt.x-projected_outlet[2*i+1].pt.x)*(projected_outlet[nPower+i].pt.x-projected_outlet[2*i+1].pt.x)+
                        (projected_outlet[nPower+i].pt.y-projected_outlet[2*i+1].pt.y)*(projected_outlet[nPower+i].pt.y-projected_outlet[2*i+1].pt.y);
                orig_left[nPower+i] = dist;
                orig_right[2*i+1] = dist;

                dist = (projected_outlet[2*i+1].pt.x-projected_outlet[2*i].pt.x)*(projected_outlet[2*i+1].pt.x-projected_outlet[2*i].pt.x)+
                        (projected_outlet[2*i+1].pt.y-projected_outlet[2*i].pt.y)*(projected_outlet[2*i+1].pt.y-projected_outlet[2*i].pt.y);
                orig_right[2*i] = dist;
                orig_left[2*i+1] = dist;
        }

        for (int i=0;i<nPower+nGround;i++)
        {
                if (new_left[i] > 0)
                {
                        float rel = orig_left[i]/new_left[i];
                        if ((rel > diff_coeff)||(rel  <diff_coeff_1))
                        {
                                dst_outlet[i].pt = projected_outlet[i].pt;
                                continue;

                        }
                }

                if (new_right[i] > 0)
                {
                        float rel = orig_right[i]/new_right[i];
                        if ((rel > diff_coeff)||(rel  <diff_coeff_1))
                        {
                                dst_outlet[i].pt = projected_outlet[i].pt;
                                continue;
                        }
                }
        }

        //{
        //      bool useProjected = false;
        //      if (new_left[i] > 0)
        //      {
        //              float rel = orig_left[i]/new_left[i];
        //              if ((rel > diff_coeff)||(rel  <diff_coeff_1))
        //              {
        //                      useProjected = true;
        //              }
        //      }
        //      if (new_right[i] > 0)
        //      {
        //              float rel = orig_right[i]/new_right[i];
        //              if ((rel > diff_coeff)||(rel  <diff_coeff_1))
        //              {
        //                      useProjected = true;
        //              }
        //      }
        //      if (new_right[2*(i-nPower)] > 0)
        //      {
        //              float rel = orig_right[2*(i-nPower)]/new_right[2*(i-nPower)];
        //              if ((rel > diff_coeff)||(rel  <diff_coeff_1))
        //              {
        //                      useProjected = true;
        //              }
        //      }

        //      if (useProjected)
        //      {
        //              dst_outlet[i] = projected_outlet[i];
        //              dst_outlet[2*(i-nPower)] = projected_outlet[2*(i-nPower)];
        //              dst_outlet[2*(i-nPower)+1] = projected_outlet[2*(i-nPower)+1];
        //      }
        //}*/

}
//------
//for each point of src_outlet the closest point of features vector founds (not far than accuracy distznce)
//index of the closest features puts into index array (its size MUST BE equal to src_outlet.size())
//index[i] == -1 means unable to choose the closest feature
//max_diff_coeff - coefficient is used for two distances comparision. If relation betweeen distances to the two nearest features less than sqrt(max_diff_coeff) we unable to choose one of them
void getNearestFeaturesIndexes(const vector<feature_t>& src_outlet, const vector<feature_t>& features, int* indexes, int accuracy, float max_diff_coeff)
{
                //float max_diff_coeff = 2.0f;
        // Trying to find the nearest features for src_outlet holes
        for (int i=0;i<(int)src_outlet.size();i++)
        {
                int min_index = -1;
                float min_distance = (float)1e30;
                float last_min_distance;
                for (int j=0;j<(int)features.size();j++)
                {
                        if (features[j].class_id == src_outlet[i].class_id)
                        {
                                float distance = (features[j].pt.x - src_outlet[i].pt.x)*(features[j].pt.x - src_outlet[i].pt.x)+
                                        (features[j].pt.y - src_outlet[i].pt.y)*(features[j].pt.y - src_outlet[i].pt.y);
                                if ((distance < min_distance)/* && (distance < accuracy*accuracy)*/)
                                {
                                        last_min_distance = min_distance;
                                        min_distance = distance;
                                        min_index = j;
                                }
                                else
                                {
                                        //Add points comparing
                                        if ((distance < last_min_distance)&&((features[j].pt.x != features[min_index].pt.x)||(features[j].pt.y != features[min_index].pt.y)))
                                        {
                                                last_min_distance = distance;
                                        }
                                }
                        }

                }
                if (min_distance < accuracy*accuracy)
                        indexes[i] = min_index;
                else
                        min_index = -1;
                if (min_index > -1)
                {
                        if ((min_distance > 0) && (last_min_distance/min_distance <= max_diff_coeff))
                        {
                                indexes[i] = -1;
                                //printf("---->Unable to choose feature\n");
                        }
                }
        }
        // Removing the same indexes
        for (int i=0;i< (int)(src_outlet.size());i++)
        {
                if (indexes[i] >=0)
                {
                        bool wasRepeat = false;
                        for (int j=i+1;j< (int)(src_outlet.size());j++)
                        {
                                if (indexes[i]==indexes[j])
                                {
                                        indexes[j] = -1;
                                        wasRepeat = true;
                                }
                        }
                        if (wasRepeat)
                                indexes[i] = -1;
                }
        }
}
//------
//for each point of dst_outlet the closest point of features vector founds (not far than distance between two power holes / 3)
//index of the closest feature is in the index array (its size MUST BE equal to dst_outlet.size())
//index[i] == -1 means unable to choose the closest feature
//max_diff_coeff - coefficient is used for two distances comparision. If relation betweeen distances to the two nearest features less than sqrt(max_diff_coeff) we unable to choose one of them
// if we are able to choose the closest feature we believe it is real outlet feature
void attractOutletToFeatures(const vector<feature_t>& train_features, const vector<feature_t>& features,vector<feature_t>& dst_outlet, const int* indexes, float max_diff_coeff)
{
                for (int i=0;i< (int)(dst_outlet.size());i++)
                {
                        // The second attraction
                        //Temp
                        int min_index = -1;
                        float min_distance = (float)1e38;
                        float last_min_distance;
                        for (int j=0;j<(int)features.size();j++)
                        {
                                if (features[j].class_id == dst_outlet[i].class_id)
                                {
                                        float distance = (features[j].pt.x - dst_outlet[i].pt.x)*(features[j].pt.x - dst_outlet[i].pt.x)+
                                                (features[j].pt.y - dst_outlet[i].pt.y)*(features[j].pt.y - dst_outlet[i].pt.y);
                                        if (distance < min_distance)
                                        {
                                                last_min_distance = distance;
                                                //setting min distance to power holes distance / 3
                                                float acc = (float)((train_features[1].pt.x - train_features[0].pt.x)*(train_features[1].pt.x - train_features[0].pt.x)+
                                                        (train_features[1].pt.y - train_features[0].pt.y)*(train_features[1].pt.y - train_features[0].pt.y))/9;
                                                if (distance < acc)
                                                {
                                                        min_distance = distance;
                                                        min_index = j;
                                                }
                                        }
                                        else
                                        {
                                                if (distance < last_min_distance)
                                                {
                                                        last_min_distance = distance;
                                                }
                                        }
                                }
                        }
                        if (min_index >= 0)
                        {
                                if (((min_distance > 0) && (last_min_distance/min_distance <= max_diff_coeff))||(min_distance == 0))
                                        dst_outlet[i] = features[min_index];

                        }
                        else

                                //End

                                if (indexes[i] >=0)
                                {
                                        dst_outlet[i] = features[indexes[i]];
                                }
                }
}

//------
void calcExactLocation(vector<feature_t>& features,const vector<feature_t>& train_features, vector<feature_t>& src_outlet,
                       vector<feature_t>& dst_outlet, float& reprojectionError, int accuracy, bool useSecondAttraction)
{
        float max_diff_coeff = 2.0f;
        if (((int)train_features.size()) == ((int)src_outlet.size()))
        {
                vector<CvPoint> train_points;
                train_points.clear();

                //vector<CvPoint> src_outlet_points;
                vector<CvPoint> features_points;
                features_points.clear();
                int* indexes = new int[(int)train_features.size()];


                for (int i=0;i<(int)train_features.size();i++)
                {
                        indexes[i] = -1;
                }
                //for (int i=0;i<(int)src_outlet.size();i++)
                //{
                //      src_outlet_points.push_back(src_outlet[i].center);
                //}


                getNearestFeaturesIndexes(src_outlet,features, indexes, accuracy, max_diff_coeff);


                for (int i=0;i< (int)(src_outlet.size());i++)
                {
                        if (indexes[i] >=0)
                        {
                                train_points.push_back(train_features[i].pt);
                                features_points.push_back(features[indexes[i]].pt);
                        }
                }
                int nPoints = (int)train_points.size();
                //The nearest features were found

                if (((int)train_points.size() > 3) /*&& ((int)train_points.size() > ((int)train_features.size()/2))*/)
                {
                        //Projecting the template outlet to the image
                        CvMat* homography = cvCreateMat(2, 3, CV_32FC1);
                        FindAffineTransform(train_points, features_points, homography);
                        reprojectionError =     CalcAffineReprojectionError(train_points, features_points, homography) + 1000000 - 10000*(int)train_points.size();
                        dst_outlet.clear();
                        MapFeaturesAffine(train_features, dst_outlet, homography);

                        vector<feature_t> projected_outlet = dst_outlet;

                        // Trying to find the nearest feaatures for dst_outlet holes and change holes position to features positions
                        if (useSecondAttraction)
                        {
                                attractOutletToFeatures(train_features,features,dst_outlet,indexes,max_diff_coeff);
                        }


                        //train_points.clear();
                        //features_points.clear();
                        //for (int i=0;i< (int)(src_outlet.size());i++)
                        //{
                        //      if (indexes[i] >=0)
                        //      {
                        //              train_points.push_back(temp[i].pt);
                        //              features_points.push_back(dst_outlet[i].pt);
                        //      }

                        //}
                        //float reprojectionError_new = CalcAffineReprojectionError(train_points, features_points, homography) + 1000000 - 10000*(int)train_points.size();
                        //if (reprojectionError_new > reprojectionError)
                        //{
                        //      dst_outlet = temp;
                        //      printf("old\n");
                        //}
                        //else
                        //{
                        //      reprojectionError = reprojectionError_new;
                        //      printf("new\n");
                        //}


#if 1
                        filterFalseMovements(projected_outlet, dst_outlet);
#endif

                        cvReleaseMat(&homography);

                }
                else
                {
                        dst_outlet.clear();
                        reprojectionError = (float)1e38;
                }


                delete[] indexes;
        }
        else
        {
                dst_outlet.clear();
                reprojectionError = (float)1e38;
        }


}
//----------

void convertFeaturesToOutlet(const vector<feature_t>& res_features, vector<outlet_t>& holes, IplImage* resImage)
{
        holes.clear();
        outlet_t outlet;

        for (int i=0;i<(int)res_features.size()/3;i++)
        {
                outlet.hole1 = res_features[2*i].pt;
                outlet.hole2 = res_features[2*i+1].pt;
                outlet.ground_hole = res_features[(int)res_features.size()/3*2+i].pt;
                holes.push_back(outlet);
                if (resImage)
                {
                        CvScalar pointColor = cvScalar(255,0,50);
                        cvLine(resImage, cvPoint((int)(outlet.ground_hole.x+7), (int)(outlet.ground_hole.y)), cvPoint((int)(outlet.ground_hole.x-7),(int)(outlet.ground_hole.y)),pointColor,2);
                        cvLine(resImage, cvPoint((int)(outlet.ground_hole.x), (int)(outlet.ground_hole.y+7)), cvPoint((int)(outlet.ground_hole.x), (int)(outlet.ground_hole.y-7)),pointColor,2);
                        pointColor = cvScalar(0,255,50);
                        cvLine(resImage, cvPoint((int)(outlet.hole1.x+7), (int)(outlet.hole1.y)), cvPoint((int)(outlet.hole1.x-7),(int)(outlet.hole1.y)),pointColor,2);
                        cvLine(resImage, cvPoint((int)(outlet.hole1.x), (int)(outlet.hole1.y+7)), cvPoint((int)(outlet.hole1.x), (int)(outlet.hole1.y-7)),pointColor,2);
                        cvLine(resImage, cvPoint((int)(outlet.hole2.x+7), (int)(outlet.hole2.y)), cvPoint((int)(outlet.hole2.x-7),(int)(outlet.hole2.y)),pointColor,2);
                        cvLine(resImage, cvPoint((int)(outlet.hole2.x), (int)(outlet.hole2.y+7)), cvPoint((int)(outlet.hole2.x), (int)(outlet.hole2.y-7)),pointColor,2);
                }
        }
}

void convertFeaturesToOutlet(const vector<feature_t>& res_features, const vector<bool>& is_detected, vector<outlet_t>& holes)
{
        holes.clear();
        outlet_t outlet;

        for (int i=0;i<(int)res_features.size()/3;i++)
        {
                outlet.hole1 = res_features[2*i].pt;
                outlet.hole1_detected = is_detected[2*i];

                outlet.hole2 = res_features[2*i+1].pt;
                outlet.hole2_detected = is_detected[2*i+1];

                outlet.ground_hole = res_features[(size_t)res_features.size()/3*2+i].pt;
                outlet.ground_hole_detected = is_detected[(size_t)res_features.size()/3*2+i];

                holes.push_back(outlet);
        }
}
//----------
//repainting features (hole candidates)
//features are closed to the ground hole will be ground hole candidates
//the same is for power holes
void repaintFeatures(const vector<feature_t> hole_candidates, const vector<feature_t>& hole_features, vector<feature_t>& hole_candidates_repainted, int accuracy = 10)
{
        float dist_coeff = (float)0.015;
        for(int i = 0; i < (int)hole_candidates.size(); i++)
        {
                float min_dist = 1e10;
                int min_idx = -1;
                for (int j=0; j<(int)hole_features.size();j++)
                {
                        float d = (hole_features[j].pt.x - hole_candidates[i].pt.x)*(hole_features[j].pt.x - hole_candidates[i].pt.x) +
                                (hole_features[j].pt.y - hole_candidates[i].pt.y)*(hole_features[j].pt.y - hole_candidates[i].pt.y);
                        if ((d < accuracy*accuracy*dist_coeff)&&(d<min_dist))
                        {
                                min_idx = j;
                                min_dist = d;
                        }
                }
                hole_candidates_repainted.push_back(hole_candidates[i]);
                if (min_idx>=0)
                        hole_candidates_repainted[i].class_id = hole_features[min_idx].class_id;
        }
}
//----------
float generalizedHoughTransform(vector<feature_t>& hole_candidates, const vector<feature_t>& train_features, int* hist_size, float** ranges,vector<outlet_t>& holes, IplImage* ghtImage, IplImage* resImage, char* output_path, char* base_filename)
{
    const float max_error = (float)1e38;
    const int min_votes = 3;

        IplImage* ght = 0;
        if (ghtImage)
        {
                ght = cvCloneImage(ghtImage);
        }
        CvSparseMat* hist = buildHoughHist(hole_candidates,train_features,hist_size,ranges);
        float** values = new float*[1];// = getMaxHistValues(hist,hist_size);
        int count = 1;
        int votes = 0;
        votes = getMaxHistValues(hist,hist_size,ranges,values,count);
#if defined(_VERBOSE)
        printf("Votes: %d\n",votes);
#endif
        cvReleaseSparseMat(&hist);

    if(votes < min_votes)
    {
        cvReleaseImage(&ght);
        return max_error;
    }

        //      x_size = test_image->width/11;
        //      y_size = test_image->height/11;
        //      int hist_size2[] = {x_size, y_size, angle1_size, x_scale_size, y_scale_size, angle2_size};
        //      hist = buildHoughHist(hole_candidates,train_features,hist_size2,ranges);
        //      float** values2 = new float*[1];// = getMaxHistValues(hist,hist_size);
        //      int count2 = 1;
        //      int votes2 = 0;
        //      votes2 = getMaxHistValues(hist,hist_size2,ranges,values2,count2);
        //#if defined(_VERBOSE)
        //              printf("Votes2: %d\n",votes2);
        //#endif
        //      cvReleaseSparseMat(&hist);
        //
        //      if (votes2 > votes)
        //      {
        //              for (int i=0;i<count;i++)
        //                      delete[] values[i];
        //              delete[] values;
        //              values = new float*[count2];
        //              for (int i=0;i<count2;i++)
        //              {
        //                      values[i] = new float[6];
        //                      for (int j=0;j<6;j++)
        //                      {
        //                              values[i][j] = values2[i][j];
        //                      }
        //              }
        //              count = count2;
        //              votes = votes2;
        //      }
        //      for (int i=0;i<count2;i++)
        //              delete[] values2[i];
        //      delete[] values2;
        //

        vector<feature_t> hole_features;
        vector<feature_t> hole_features_corrected;
        vector<feature_t> res_features;

        float accuracy = sqrt((float)((train_features[1].pt.x -train_features[0].pt.x)*(train_features[1].pt.x -train_features[0].pt.x)+
                (train_features[1].pt.y -train_features[0].pt.y)*(train_features[1].pt.y -train_features[0].pt.y)));
        float error = (float)1e38;
        int index = -1;
        //IplImage* t = cvCloneImage(resImage);

        //We have count candidates to the outlet position
        //Chooseng the best one
        for (int j=0;j<count;j++)
        {
                float currError;
                hole_features.clear();
                calcOutletPosition(train_features, values[j],hole_features);

                //drawing current candidate if we have to do it
                if (ghtImage && (hole_features.size() > 0) && output_path && base_filename)
                {
                        char path[1024];
                        sprintf(path,"%s/%s_%d.jpg",output_path,base_filename,j);
                        IplImage* tmp = cvCloneImage(ght);
                        //cvNamedWindow(path);
                        //cvShowImage(path,tmp);
                        //cvWaitKey();
                        for(int l = 0; l < (int)hole_features.size(); l++)
                        {

                                CvScalar pointColor = hole_features[l].class_id == 0 ? cvScalar(0,255,50) : cvScalar(255,0,50);
                                cvLine(tmp, cvPoint((int)(hole_features[l].pt.x+7), (int)(hole_features[l].pt.y)), cvPoint((int)(hole_features[l].pt.x-7),(int)(hole_features[l].pt.y)),pointColor,2);
                                cvLine(tmp, cvPoint((int)(hole_features[l].pt.x), (int)(hole_features[l].pt.y+7)), cvPoint((int)(hole_features[l].pt.x), (int)(hole_features[l].pt.y-7)),pointColor,2);
                        }
                        //cvShowImage(path,tmp);
                        //cvWaitKey();
                        cvSaveImage(path,tmp);
                        cvReleaseImage(&tmp);
                }

                if (ghtImage)
                {
                        for(int i = 0; i < (int)hole_features.size(); i++)
                        {
                                CvScalar pointColor = hole_features[i].class_id == 0 ? cvScalar(0,255,50) : cvScalar(255,0,50);
                                cvLine(ghtImage, cvPoint((int)(hole_features[i].pt.x+7), (int)(hole_features[i].pt.y)), cvPoint((int)(hole_features[i].pt.x-7),(int)(hole_features[i].pt.y)),pointColor,2);
                                cvLine(ghtImage, cvPoint((int)(hole_features[i].pt.x), (int)(hole_features[i].pt.y+7)), cvPoint((int)(hole_features[i].pt.x), (int)(hole_features[i].pt.y-7)),pointColor,2);

                        }
                }
                //Repaint features
                vector<feature_t> hole_candidates2;
                if (hole_features.size() >0)
                {
                        repaintFeatures(hole_candidates, hole_features, hole_candidates2,(int)accuracy);
                }
                else
                {
                        hole_candidates2 = hole_candidates;
                }
                //  CvScalar color_parts[] = {CV_RGB(255, 255, 0), CV_RGB(0, 255, 255)};
                //  for(int i = 0; i < (int)hole_candidates.size(); i++)
                //  {
                //      cvCircle(ght, hole_candidates2[i].pt, hole_candidates2[i].size, color_parts[hole_candidates2[i].class_id], 2);

                //  }
                //cvNamedWindow("1");
                //cvShowImage("1",ght);
                //cvWaitKey();
                //End of
#if 1
                calcExactLocation(hole_candidates2,train_features,hole_features,hole_features_corrected,currError,(int)accuracy);
#else
                calcExactLocation(hole_candidates2,train_features,hole_features,hole_features_corrected,currError,(int)accuracy,false);
#endif

                if (currError < error)
                {
                        index = j;
                        error = currError;
                        res_features.clear();
                        for (int i =0; i< (int)hole_features_corrected.size(); i++)
                                res_features.push_back(hole_features_corrected[i]);
                }

//        printf("candidate %d, error %f\n", j, currError);

        }

#if 1
        if (res_features.size() > 0)
        {
                filterOutletOutliers(hole_candidates,res_features,(int)accuracy);
        }
#endif

        // int outlet_holes_count = 3;
        if ((int)(res_features.size()) > 0)
        {
                convertFeaturesToOutlet(res_features, holes, resImage);
        }
        else
        {
                error = max_error;
        }


        for (int i=0;i<count;i++)
                delete[] values[i];
        delete[] values;
        if (ghtImage)
                cvReleaseImage(&ght);

        return error;
}

---

outlet_pose_estimation
Author(s): Patrick Mihelich
autogenerated on Fri Mar 1 17:34:08 2013