void cv::goodFeaturesToTrack( InputArray _image, OutputArray _corners,
                              int maxCorners, double qualityLevel, double minDistance,
                              InputArray _mask, int blockSize,
                              bool useHarrisDetector, double harrisK )
{
    Mat image = _image.getMat(), mask = _mask.getMat();

    CV_Assert( qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0 );
    CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );

    Mat eig, tmp;
    if( useHarrisDetector )     
        cornerHarris( image, eig, blockSize, 3, harrisK );  //採用Harris角點檢測
    else
        cornerMinEigenVal( image, eig, blockSize, 3 );  //採用Harris改進演算法,eig儲存矩陣M較小的特徵值。見下面演算法實現

    double maxVal = 0;
    minMaxLoc( eig, 0, &maxVal, 0, 0, mask );//儲存eig中最大的值maxVal
    threshold( eig, eig, maxVal*qualityLevel, 0, THRESH_TOZERO );//閾值處理,小於maxVal*qualityLevel的畫素值歸為0。
    dilate( eig, tmp, Mat());//膨脹，3×3的核，為了取區域性極大值

    Size imgsize = image.size();

    vector<const float*> tmpCorners;

    // collect list of pointers to features - put them into temporary image
    for( int y = 1; y < imgsize.height - 1; y++ )
    {
        const float* eig_data = (const float*)eig.ptr(y);
        const float* tmp_data = (const float*)tmp.ptr(y);
        const uchar* mask_data = mask.data ? mask.ptr(y) : 0;

        for( int x = 1; x < imgsize.width - 1; x++ )
        {
            float val = eig_data[x];
            if( val != 0 && val == tmp_data[x] && (!mask_data || mask_data[x]) )//區域性極大值
                tmpCorners.push_back(eig_data + x);
        }
    }

    sort( tmpCorners, greaterThanPtr<float>() );  //按值從大到小排序
    vector<Point2f> corners;
    size_t i, j, total = tmpCorners.size(), ncorners = 0;
 /*  
  網格處理，即把影象劃分成正方形網格,每個網格邊長為容忍距離minDistance
  以一個角點位置為中心,minDistance為半徑的區域內部不允許出現第二個角點
 */
    if(minDistance >= 1)
    {
         // Partition the image into larger grids
        int w = image.cols;
        int h = image.rows;
        
        const int cell_size = cvRound(minDistance);//劃分成網格，網格邊長為容忍距離
        const int grid_width = (w + cell_size - 1) / cell_size;
        const int grid_height = (h + cell_size - 1) / cell_size;

        std::vector<std::vector<Point2f> > grid(grid_width*grid_height);

        minDistance *= minDistance;

        for( i = 0; i < total; i++ )  //按從大到小的順序,遍歷所有角點
        {
            int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
            int y = (int)(ofs / eig.step);
            int x = (int)((ofs - y*eig.step)/sizeof(float));

            bool good = true;

            int x_cell = x / cell_size;
            int y_cell = y / cell_size;

            int x1 = x_cell - 1;
            int y1 = y_cell - 1;
            int x2 = x_cell + 1;
            int y2 = y_cell + 1;

            // boundary check
            x1 = std::max(0, x1);
            y1 = std::max(0, y1);
            x2 = std::min(grid_width-1, x2);
            y2 = std::min(grid_height-1, y2);

            for( int yy = y1; yy <= y2; yy++ )//檢測角點,minDistance半徑鄰域內,有沒有其他角點出現
            {
                for( int xx = x1; xx <= x2; xx++ )
                {
                    vector <Point2f> &m = grid[yy*grid_width + xx];

                    if( m.size() )
                    {
                        for(j = 0; j < m.size(); j++)
                        {
                            float dx = x - m[j].x;
                            float dy = y - m[j].y;
                            if( dx*dx + dy*dy < minDistance )//有其他角點,丟棄當前角點
                            {
                                good = false;
                                goto break_out;
                            }
                        }
                    }
                }
            }

            break_out:

            if(good)
            {
                // printf("%d: %d %d -> %d %d, %d, %d -- %d %d %d %d, %d %d, c=%d\n",
                //    i,x, y, x_cell, y_cell, (int)minDistance, cell_size,x1,y1,x2,y2, grid_width,grid_height,c);
                grid[y_cell*grid_width + x_cell].push_back(Point2f((float)x, (float)y));

                corners.push_back(Point2f((float)x, (float)y));//滿足條件的存入corners
                ++ncorners;

                if( maxCorners > 0 && (int)ncorners == maxCorners )
                    break;
            }
        }
    }
    else   //不設定容忍距離
    {
        for( i = 0; i < total; i++ )
        {
            int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
            int y = (int)(ofs / eig.step);
            int x = (int)((ofs - y*eig.step)/sizeof(float));

            corners.push_back(Point2f((float)x, (float)y));
            ++ncorners;
            if( maxCorners > 0 && (int)ncorners == maxCorners )
                break;
        }
    }

    Mat(corners).convertTo(_corners, _corners.fixedType() ? _corners.type() : CV_32F);

}
 

求矩陣M最小的特徵值

static void
calcMinEigenVal( const Mat& _cov, Mat& _dst )
{
    int i, j;
    Size size = _cov.size();
    if( _cov.isContinuous() && _dst.isContinuous() )
    {
        size.width *= size.height;
        size.height = 1;
    }

    for( i = 0; i < size.height; i++ )//遍歷所有畫素點
    {
        const float* cov = (const float*)(_cov.data + _cov.step*i);
        float* dst = (float*)(_dst.data + _dst.step*i);
        j = 0;
        for( ; j < size.width; j++ )
        {
            float a = cov[j*3]*0.5f;//cov[j*3]儲存矩陣M左上角元素
            float b = cov[j*3+1];   //cov[j*3+1]儲存左下角和右上角元素
            float c = cov[j*3+2]*0.5f;//cov[j*3+2]右下角元素
            dst[j] = (float)((a + c) - std::sqrt((a - c)*(a - c) + b*b));//求最小特徵值，一元二次方程求根公式
        }
    }
}