C++ OutputArray::fixedType方法代码示例

    Vec2d predict2(InputArray _sample, OutputArray _probs) const
    {
        int ptype = CV_64F;
        Mat sample = _sample.getMat();
        CV_Assert(isTrained());

        CV_Assert(!sample.empty());
        if(sample.type() != CV_64FC1)
        {
            Mat tmp;
            sample.convertTo(tmp, CV_64FC1);
            sample = tmp;
        }
        sample = sample.reshape(1, 1);

        Mat probs;
        if( _probs.needed() )
        {
            if( _probs.fixedType() )
                ptype = _probs.type();
            _probs.create(1, nclusters, ptype);
            probs = _probs.getMat();
        }

        return computeProbabilities(sample, !probs.empty() ? &probs : 0, ptype);
    }

void UMat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
    bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
    int stype = type(), cn = CV_MAT_CN(stype);

    if( _type < 0 )
        _type = _dst.fixedType() ? _dst.type() : stype;
    else
        _type = CV_MAKETYPE(CV_MAT_DEPTH(_type), cn);

    int sdepth = CV_MAT_DEPTH(stype), ddepth = CV_MAT_DEPTH(_type);
    if( sdepth == ddepth && noScale )
    {
        copyTo(_dst);
        return;
    }
#ifdef HAVE_OPENCL
    bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
    bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
    if( dims <= 2 && cn && _dst.isUMat() && ocl::useOpenCL() &&
            ((needDouble && doubleSupport) || !needDouble) )
    {
        int wdepth = std::max(CV_32F, sdepth), rowsPerWI = 4;

        char cvt[2][40];
        ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
                      format("-D srcT=%s -D WT=%s -D dstT=%s -D convertToWT=%s -D convertToDT=%s%s%s",
                             ocl::typeToStr(sdepth), ocl::typeToStr(wdepth), ocl::typeToStr(ddepth),
                             ocl::convertTypeStr(sdepth, wdepth, 1, cvt[0]),
                             ocl::convertTypeStr(wdepth, ddepth, 1, cvt[1]),
                             doubleSupport ? " -D DOUBLE_SUPPORT" : "", noScale ? " -D NO_SCALE" : ""));
        if (!k.empty())
        {
            UMat src = *this;
            _dst.create( size(), _type );
            UMat dst = _dst.getUMat();

            float alphaf = (float)alpha, betaf = (float)beta;
            ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
                    dstarg = ocl::KernelArg::WriteOnly(dst, cn);

            if (noScale)
                k.args(srcarg, dstarg, rowsPerWI);
            else if (wdepth == CV_32F)
                k.args(srcarg, dstarg, alphaf, betaf, rowsPerWI);
            else
                k.args(srcarg, dstarg, alpha, beta, rowsPerWI);

            size_t globalsize[2] = { (size_t)dst.cols * cn, ((size_t)dst.rows + rowsPerWI - 1) / rowsPerWI };
            if (k.run(2, globalsize, NULL, false))
            {
                CV_IMPL_ADD(CV_IMPL_OCL);
                return;
            }
        }
    }
#endif
    Mat m = getMat(ACCESS_READ);
    m.convertTo(_dst, _type, alpha, beta);
}

    float predict(InputArray _inputs, OutputArray _outputs, int) const
    {
        bool needprobs = _outputs.needed();
        Mat samples = _inputs.getMat(), probs, probsrow;
        int ptype = CV_64F;
        float firstres = 0.f;
        int i, nsamples = samples.rows;

        if( needprobs )
        {
            if( _outputs.fixedType() )
                ptype = _outputs.type();
            _outputs.create(samples.rows, nclusters, ptype);
        }
        else
            nsamples = std::min(nsamples, 1);

        for( i = 0; i < nsamples; i++ )
        {
            if( needprobs )
                probsrow = probs.row(i);
            Vec2d res = computeProbabilities(samples.row(i), needprobs ? &probsrow : 0, ptype);
            if( i == 0 )
                firstres = (float)res[1];
        }
        return firstres;
    }

int fixedType_handler(OutputArray dst)
{
    int type = CV_32FC2; // return points only {x, y}
    if (dst.fixedType())
    {
        type = dst.type();
        CV_Assert(type == CV_32FC2 || type == CV_32FC3); // allow points + confidence level: {x, y, confidence}
    }
    const int N = 100;
    dst.create(Size(1, N), type);
    Mat m = dst.getMat();
    if (m.type() == CV_32FC2)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec2f>(i) = Vec2f((float)i, (float)(i*2));
    }
    else if (m.type() == CV_32FC3)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec3f>(i) = Vec3f((float)i, (float)(i*2), 1.0f / (i + 1));
    }
    else
    {
        CV_Assert(0 && "Internal error");
    }
    return CV_MAT_CN(type);
}

/* dst = src */
void Mat::copyTo( OutputArray _dst ) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    if( dims <= 2 )
    {
        _dst.create( rows, cols, type() );
        Mat dst = _dst.getMat();
        if( data == dst.data )
            return;

        if( rows > 0 && cols > 0 )
        {
            const uchar* sptr = data;
            uchar* dptr = dst.data;

            // to handle the copying 1xn matrix => nx1 std vector.
            Size sz = size() == dst.size() ?
                getContinuousSize(*this, dst) :
                getContinuousSize(*this);
            size_t len = sz.width*elemSize();

            for( ; sz.height--; sptr += step, dptr += dst.step )
                memcpy( dptr, sptr, len );
        }
        return;
    }

    _dst.create( dims, size, type() );
    Mat dst = _dst.getMat();
    if( data == dst.data )
        return;

    if( total() != 0 )
    {
        const Mat* arrays[] = { this, &dst };
        uchar* ptrs[2];
        NAryMatIterator it(arrays, ptrs, 2);
        size_t sz = it.size*elemSize();

        for( size_t i = 0; i < it.nplanes; i++, ++it )
            memcpy(ptrs[1], ptrs[0], sz);
    }
}

void cv::convertPointsHomogeneous( InputArray _src, OutputArray _dst )
{
    int stype = _src.type(), dtype = _dst.type();
    CV_Assert( _dst.fixedType() );
    
    if( CV_MAT_CN(stype) > CV_MAT_CN(dtype) )
        convertPointsFromHomogeneous(_src, _dst);
    else
        convertPointsToHomogeneous(_src, _dst);
}

void UMat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
    bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
    int stype = type(), cn = CV_MAT_CN(stype);

    if( _type < 0 )
        _type = _dst.fixedType() ? _dst.type() : stype;
    else
        _type = CV_MAKETYPE(CV_MAT_DEPTH(_type), cn);

    int sdepth = CV_MAT_DEPTH(stype), ddepth = CV_MAT_DEPTH(_type);
    if( sdepth == ddepth && noScale )
    {
        copyTo(_dst);
        return;
    }

    bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
    bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
    if( dims <= 2 && cn && _dst.isUMat() && ocl::useOpenCL() &&
            ((needDouble && doubleSupport) || !needDouble) )
    {
        char cvt[40];
        ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
                      format("-D srcT=%s -D dstT=%s -D convertToDT=%s%s", ocl::typeToStr(sdepth),
                             ocl::typeToStr(ddepth), ocl::convertTypeStr(CV_32F, ddepth, 1, cvt),
                             doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
        if (!k.empty())
        {
            UMat src = *this;
            _dst.create( size(), _type );
            UMat dst = _dst.getUMat();

            float alphaf = (float)alpha, betaf = (float)beta;
            k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn), alphaf, betaf);

            size_t globalsize[2] = { dst.cols * cn, dst.rows };
            if (k.run(2, globalsize, NULL, false))
                return;
        }
    }

    Mat m = getMat(ACCESS_READ);
    m.convertTo(_dst, _type, alpha, beta);
}

void UMat::copyTo(OutputArray _dst) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    size_t i, sz[CV_MAX_DIM], srcofs[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
    for( i = 0; i < (size_t)dims; i++ )
        sz[i] = size.p[i];
    sz[dims-1] *= esz;
    ndoffset(srcofs);
    srcofs[dims-1] *= esz;

    _dst.create( dims, size.p, type() );
    if( _dst.isUMat() )
    {
        UMat dst = _dst.getUMat();
        if( u == dst.u && dst.offset == offset )
            return;

        if (u->currAllocator == dst.u->currAllocator)
        {
            dst.ndoffset(dstofs);
            dstofs[dims-1] *= esz;
            u->currAllocator->copy(u, dst.u, dims, sz, srcofs, step.p, dstofs, dst.step.p, false);
            return;
        }
    }

    Mat dst = _dst.getMat();
    u->currAllocator->download(u, dst.data, dims, sz, srcofs, step.p, dst.step.p);
}

static bool openvx_harris(Mat image, OutputArray _corners,
                          int _maxCorners, double _qualityLevel, double _minDistance,
                          int _blockSize, int _gradientSize, double _harrisK)
{
    using namespace ivx;

    if(image.type() != CV_8UC1) return false;

    //OpenVX implementations don't have to provide other sizes
    if(!(_blockSize == 3 || _blockSize == 5 || _blockSize == 7)) return false;

    try
    {
        Context context = ovx::getOpenVXContext();

        Image ovxImage = Image::createFromHandle(context, Image::matTypeToFormat(image.type()),
                                                 Image::createAddressing(image), image.data);
        //The minimum threshold which to eliminate Harris Corner scores (computed using the normalized Sobel kernel).
        //set to 0, we'll filter it later by threshold
        ivx::Scalar strengthThresh = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, 0);

        //The gradient window size to use on the input.
        vx_int32 gradientSize = _gradientSize;

        //The block window size used to compute the harris corner score
        vx_int32 blockSize = _blockSize;

        //The scalar sensitivity threshold k from the Harris-Stephens equation
        ivx::Scalar sensivity = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, _harrisK);

        //The radial Euclidean distance for non-maximum suppression
        ivx::Scalar minDistance = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, _minDistance);

        vx_size capacity = image.cols * image.rows;
        Array corners = Array::create(context, VX_TYPE_KEYPOINT, capacity);
        ivx::Scalar numCorners = ivx::Scalar::create<VX_TYPE_SIZE>(context, 0);

        IVX_CHECK_STATUS(vxuHarrisCorners(context, ovxImage, strengthThresh, minDistance, sensivity,
                                          gradientSize, blockSize, corners, numCorners));

        std::vector<vx_keypoint_t> vxKeypoints;
        corners.copyTo(vxKeypoints);

        std::sort(vxKeypoints.begin(), vxKeypoints.end(), VxKeypointsComparator());

        vx_float32 maxStrength = 0.0f;
        if(vxKeypoints.size() > 0)
            maxStrength = vxKeypoints[0].strength;
        size_t maxKeypoints = min((size_t)_maxCorners, vxKeypoints.size());
        std::vector<Point2f> keypoints;
        keypoints.reserve(maxKeypoints);
        for(size_t i = 0; i < maxKeypoints; i++)
        {
            vx_keypoint_t kp = vxKeypoints[i];
            if(kp.strength < maxStrength*_qualityLevel) break;
            keypoints.push_back(Point2f((float)kp.x, (float)kp.y));
        }

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

#ifdef VX_VERSION_1_1
        //we should take user memory back before release
        //(it's not done automatically according to standard)
        ovxImage.swapHandle();
#endif
    }
    catch (RuntimeError & e)
    {
        VX_DbgThrow(e.what());
    }
    catch (WrapperError & e)
    {
        VX_DbgThrow(e.what());
    }

    return true;
}

            void compute(InputArray leftarr, InputArray rightarr, OutputArray disparr)
            {
                int dtype = disparr.fixedType() ? disparr.type() : params.dispType;
                Size leftsize = leftarr.size();

                if (leftarr.size() != rightarr.size())
                    CV_Error(Error::StsUnmatchedSizes, "All the images must have the same size");

                if (leftarr.type() != CV_8UC1 || rightarr.type() != CV_8UC1)
                    CV_Error(Error::StsUnsupportedFormat, "Both input images must have CV_8UC1");

                if (dtype != CV_16SC1 && dtype != CV_32FC1)
                    CV_Error(Error::StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format");

                if (params.preFilterType != PREFILTER_NORMALIZED_RESPONSE &&
                    params.preFilterType != PREFILTER_XSOBEL)
                    CV_Error(Error::StsOutOfRange, "preFilterType must be = CV_STEREO_BM_NORMALIZED_RESPONSE");

                if (params.preFilterSize < 5 || params.preFilterSize > 255 || params.preFilterSize % 2 == 0)
                    CV_Error(Error::StsOutOfRange, "preFilterSize must be odd and be within 5..255");

                if (params.preFilterCap < 1 || params.preFilterCap > 63)
                    CV_Error(Error::StsOutOfRange, "preFilterCap must be within 1..63");

                if (params.kernelSize < 5 || params.kernelSize > 255 || params.kernelSize % 2 == 0 ||
                    params.kernelSize >= std::min(leftsize.width, leftsize.height))
                    CV_Error(Error::StsOutOfRange, "kernelSize must be odd, be within 5..255 and be not larger than image width or height");

                if (params.numDisparities <= 0 || params.numDisparities % 16 != 0)
                    CV_Error(Error::StsOutOfRange, "numDisparities must be positive and divisble by 16");

                if (params.textureThreshold < 0)
                    CV_Error(Error::StsOutOfRange, "texture threshold must be non-negative");

                if (params.uniquenessRatio < 0)
                    CV_Error(Error::StsOutOfRange, "uniqueness ratio must be non-negative");

                int FILTERED = (params.minDisparity - 1) << DISPARITY_SHIFT;

                Mat left0 = leftarr.getMat(), right0 = rightarr.getMat();
                Mat disp0 = disparr.getMat();

                int width = left0.cols;
                int height = left0.rows;
                if(previous_size != width * height)
                {
                    previous_size = width * height;
                    speckleX.create(height,width,CV_32SC4);
                    speckleY.create(height,width,CV_32SC4);
                    puss.create(height,width,CV_32SC4);

                    censusImage[0].create(left0.rows,left0.cols,CV_32SC4);
                    censusImage[1].create(left0.rows,left0.cols,CV_32SC4);

                    partialSumsLR.create(left0.rows + 1,(left0.cols + 1) * (params.numDisparities + 1),CV_16S);
                    agregatedHammingLRCost.create(left0.rows + 1,(left0.cols + 1) * (params.numDisparities + 1),CV_16S);
                    hammingDistance.create(left0.rows, left0.cols * (params.numDisparities + 1),CV_16S);

                    preFilteredImg0.create(left0.size(), CV_8U);
                    preFilteredImg1.create(left0.size(), CV_8U);

                    aux.create(height,width,CV_8UC1);
                }

                Mat left = preFilteredImg0, right = preFilteredImg1;

                int ndisp = params.numDisparities;

                int wsz = params.kernelSize;
                int bufSize0 = (int)((ndisp + 2)*sizeof(int));
                bufSize0 += (int)((height + wsz + 2)*ndisp*sizeof(int));
                bufSize0 += (int)((height + wsz + 2)*sizeof(int));
                bufSize0 += (int)((height + wsz + 2)*ndisp*(wsz + 2)*sizeof(uchar) + 256);

                int bufSize1 = (int)((width + params.preFilterSize + 2) * sizeof(int) + 256);
                if(params.usePrefilter == true)
                {
                    uchar *_buf = slidingSumBuf.ptr();

                    parallel_for_(Range(0, 2), PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, &params), 1);
                }
                else if(params.usePrefilter == false)
                {
                    left = left0;
                    right = right0;
                }
                if(params.kernelType == CV_SPARSE_CENSUS)
                {
                    censusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_SPARSE_CENSUS);
                }
                else if(params.kernelType == CV_DENSE_CENSUS)
                {
                    censusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_SPARSE_CENSUS);
                }
                else if(params.kernelType == CV_CS_CENSUS)
                {
                    symetricCensusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_CS_CENSUS);
                }
                else if(params.kernelType == CV_MODIFIED_CS_CENSUS)
                {
//.........这里部分代码省略.........

void convexHull( InputArray _points, OutputArray _hull, bool clockwise, bool returnPoints )
{
    CV_INSTRUMENT_REGION()

    CV_Assert(_points.getObj() != _hull.getObj());
    Mat points = _points.getMat();
    int i, total = points.checkVector(2), depth = points.depth(), nout = 0;
    int miny_ind = 0, maxy_ind = 0;
    CV_Assert(total >= 0 && (depth == CV_32F || depth == CV_32S));

    if( total == 0 )
    {
        _hull.release();
        return;
    }

    returnPoints = !_hull.fixedType() ? returnPoints : _hull.type() != CV_32S;

    bool is_float = depth == CV_32F;
    AutoBuffer<Point*> _pointer(total);
    AutoBuffer<int> _stack(total + 2), _hullbuf(total);
    Point** pointer = _pointer;
    Point2f** pointerf = (Point2f**)pointer;
    Point* data0 = points.ptr<Point>();
    int* stack = _stack;
    int* hullbuf = _hullbuf;

    CV_Assert(points.isContinuous());

    for( i = 0; i < total; i++ )
        pointer[i] = &data0[i];

    // sort the point set by x-coordinate, find min and max y
    if( !is_float )
    {
        std::sort(pointer, pointer + total, CHullCmpPoints<int>());
        for( i = 1; i < total; i++ )
        {
            int y = pointer[i]->y;
            if( pointer[miny_ind]->y > y )
                miny_ind = i;
            if( pointer[maxy_ind]->y < y )
                maxy_ind = i;
        }
    }
    else
    {
        std::sort(pointerf, pointerf + total, CHullCmpPoints<float>());
        for( i = 1; i < total; i++ )
        {
            float y = pointerf[i]->y;
            if( pointerf[miny_ind]->y > y )
                miny_ind = i;
            if( pointerf[maxy_ind]->y < y )
                maxy_ind = i;
        }
    }

    if( pointer[0]->x == pointer[total-1]->x &&
        pointer[0]->y == pointer[total-1]->y )
    {
        hullbuf[nout++] = 0;
    }
    else
    {
        // upper half
        int *tl_stack = stack;
        int tl_count = !is_float ?
            Sklansky_( pointer, 0, maxy_ind, tl_stack, -1, 1) :
            Sklansky_( pointerf, 0, maxy_ind, tl_stack, -1, 1);
        int *tr_stack = stack + tl_count;
        int tr_count = !is_float ?
            Sklansky_( pointer, total-1, maxy_ind, tr_stack, -1, -1) :
            Sklansky_( pointerf, total-1, maxy_ind, tr_stack, -1, -1);

        // gather upper part of convex hull to output
        if( !clockwise )
        {
            std::swap( tl_stack, tr_stack );
            std::swap( tl_count, tr_count );
        }

        for( i = 0; i < tl_count-1; i++ )
            hullbuf[nout++] = int(pointer[tl_stack[i]] - data0);
        for( i = tr_count - 1; i > 0; i-- )
            hullbuf[nout++] = int(pointer[tr_stack[i]] - data0);
        int stop_idx = tr_count > 2 ? tr_stack[1] : tl_count > 2 ? tl_stack[tl_count - 2] : -1;

        // lower half
        int *bl_stack = stack;
        int bl_count = !is_float ?
            Sklansky_( pointer, 0, miny_ind, bl_stack, 1, -1) :
            Sklansky_( pointerf, 0, miny_ind, bl_stack, 1, -1);
        int *br_stack = stack + bl_count;
        int br_count = !is_float ?
            Sklansky_( pointer, total-1, miny_ind, br_stack, 1, 1) :
            Sklansky_( pointerf, total-1, miny_ind, br_stack, 1, 1);

        if( clockwise )
        {
//.........这里部分代码省略.........

/* dst = src */
void Mat::copyTo( OutputArray _dst ) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    if( _dst.isUMat() )
    {
        _dst.create( dims, size.p, type() );
        UMat dst = _dst.getUMat();

        size_t i, sz[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
        for( i = 0; i < (size_t)dims; i++ )
            sz[i] = size.p[i];
        sz[dims-1] *= esz;
        dst.ndoffset(dstofs);
        dstofs[dims-1] *= esz;
        dst.u->currAllocator->upload(dst.u, data, dims, sz, dstofs, dst.step.p, step.p);
        return;
    }

    if( dims <= 2 )
    {
        _dst.create( rows, cols, type() );
        Mat dst = _dst.getMat();
        if( data == dst.data )
            return;

        if( rows > 0 && cols > 0 )
        {
            const uchar* sptr = data;
            uchar* dptr = dst.data;

            Size sz = getContinuousSize(*this, dst);
            size_t len = sz.width*elemSize();

            for( ; sz.height--; sptr += step, dptr += dst.step )
                memcpy( dptr, sptr, len );
        }
        return;
    }

    _dst.create( dims, size, type() );
    Mat dst = _dst.getMat();
    if( data == dst.data )
        return;

    if( total() != 0 )
    {
        const Mat* arrays[] = { this, &dst };
        uchar* ptrs[2];
        NAryMatIterator it(arrays, ptrs, 2);
        size_t sz = it.size*elemSize();

        for( size_t i = 0; i < it.nplanes; i++, ++it )
            memcpy(ptrs[1], ptrs[0], sz);
    }
}

//.........这里部分代码省略.........

	        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++ )
            {
                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 )
                            {
                                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));
                ++ncorners;

                if( maxCorners > 0 && (int)ncorners == maxCorners )
                    break;
            }
        }
    }
    else
    {
        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);

    /*
    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));

        if( minDistance > 0 )
        {
            for( j = 0; j < ncorners; j++ )
            {
                float dx = x - corners[j].x;
                float dy = y - corners[j].y;
                if( dx*dx + dy*dy < minDistance )
                    break;
            }
            if( j < ncorners )
                continue;
        }

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