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, ¶ms), 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)
{
symetricCensusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_MODIFIED_CS_CENSUS);
}
else if(params.kernelType == CV_MODIFIED_CENSUS_TRANSFORM)
{
modifiedCensusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_MODIFIED_CENSUS_TRANSFORM,0);
}
else if(params.kernelType == CV_MEAN_VARIATION)
{
parSumsIntensityImage[0].create(left0.rows, left0.cols,CV_32SC4);
parSumsIntensityImage[1].create(left0.rows, left0.cols,CV_32SC4);
Integral[0].create(left0.rows,left0.cols,CV_32SC4);
Integral[1].create(left0.rows,left0.cols,CV_32SC4);
integral(left, parSumsIntensityImage[0],CV_32S);
integral(right, parSumsIntensityImage[1],CV_32S);
imageMeanKernelSize(parSumsIntensityImage[0], params.kernelSize,Integral[0]);
imageMeanKernelSize(parSumsIntensityImage[1], params.kernelSize, Integral[1]);
modifiedCensusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1],CV_MEAN_VARIATION,0,Integral[0], Integral[1]);
}
else if(params.kernelType == CV_STAR_KERNEL)
{
starCensusTransform(left,right,params.kernelSize,censusImage[0],censusImage[1]);
}
hammingDistanceBlockMatching(censusImage[0], censusImage[1], hammingDistance);
costGathering(hammingDistance, partialSumsLR);
blockAgregation(partialSumsLR, params.agregationWindowSize, agregatedHammingLRCost);
dispartyMapFormation(agregatedHammingLRCost, disp0, 3);
Median1x9Filter<uint8_t>(disp0, aux);
Median9x1Filter<uint8_t>(aux,disp0);
if(params.regionRemoval == CV_SPECKLE_REMOVAL_AVG_ALGORITHM)
{
smallRegionRemoval<uint8_t>(disp0,params.speckleWindowSize,disp0);
}
else if(params.regionRemoval == CV_SPECKLE_REMOVAL_ALGORITHM)
{
if (params.speckleRange >= 0 && params.speckleWindowSize > 0)
filterSpeckles(disp0, FILTERED, params.speckleWindowSize, params.speckleRange, slidingSumBuf);
}
}
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 )
{
std::swap( bl_stack, br_stack );
std::swap( bl_count, br_count );
}
if( stop_idx >= 0 )
{
int check_idx = bl_count > 2 ? bl_stack[1] :
bl_count + br_count > 2 ? br_stack[2-bl_count] : -1;
if( check_idx == stop_idx || (check_idx >= 0 &&
pointer[check_idx]->x == pointer[stop_idx]->x &&
pointer[check_idx]->y == pointer[stop_idx]->y) )
{
// if all the points lie on the same line, then
// the bottom part of the convex hull is the mirrored top part
// (except the exteme points).
bl_count = MIN( bl_count, 2 );
br_count = MIN( br_count, 2 );
}
}
for( i = 0; i < bl_count-1; i++ )
hullbuf[nout++] = int(pointer[bl_stack[i]] - data0);
for( i = br_count-1; i > 0; i-- )
hullbuf[nout++] = int(pointer[br_stack[i]] - data0);
}
if( !returnPoints )
Mat(nout, 1, CV_32S, hullbuf).copyTo(_hull);
else
{
_hull.create(nout, 1, CV_MAKETYPE(depth, 2));
Mat hull = _hull.getMat();
size_t step = !hull.isContinuous() ? hull.step[0] : sizeof(Point);
for( i = 0; i < nout; i++ )
*(Point*)(hull.ptr() + i*step) = data0[hullbuf[i]];
}
}
/* 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);
}
}
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 );
else
cornerMinEigenVal( image, eig, blockSize, 3 );
double maxVal = 0;
minMaxLoc( eig, 0, &maxVal, 0, 0, mask );
threshold( eig, eig, maxVal*qualityLevel, 0, THRESH_TOZERO );
dilate( eig, tmp, Mat());
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]) )
tmpCorners.push_back(eig_data + x);
}
}
sort( tmpCorners, greaterThanPtr<float>() );
vector<Point2f> corners;
size_t i, j, total = tmpCorners.size(), ncorners = 0;
if(minDistance >= 1)
{
// Partition the image into larger grids
int w = image.cols;
int h = image.rows;
const int cell_size = cvRound(minDistance);
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++ )
{
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++ )
{
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;
}
*/
}
