// copy or use the software.
//
//
-// Intel License Agreement
+// License Agreement
// For Open Source Computer Vision Library
//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2013, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
-// * The name of Intel Corporation may not be used to endorse or promote products
+// * The name of Itseez Inc. may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
+// In no event shall the copyright holders or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
bool FeatureEvaluator::read(const FileNode&) {return true;}
Ptr<FeatureEvaluator> FeatureEvaluator::clone() const { return Ptr<FeatureEvaluator>(); }
int FeatureEvaluator::getFeatureType() const {return -1;}
-bool FeatureEvaluator::setImage(const Mat&, Size) {return true;}
+bool FeatureEvaluator::setImage(InputArray, Size) {return true;}
bool FeatureEvaluator::setWindow(Point) { return true; }
double FeatureEvaluator::calcOrd(int) const { return 0.; }
int FeatureEvaluator::calcCat(int) const { return 0; }
HaarEvaluator::HaarEvaluator()
{
- features = makePtr<std::vector<Feature> >();
+ optfeaturesPtr = 0;
+ pwin = 0;
+ pqwin = 0;
}
HaarEvaluator::~HaarEvaluator()
{
{
size_t i, n = node.size();
CV_Assert(n > 0);
- features.resize(n);
- featuresPtr = &features[0];
+ features->resize(n);
FileNodeIterator it = node.begin();
hasTiltedFeatures = false;
+ std::vector<Feature> ff = *features;
for(i = 0; i < n; i++, ++it)
{
- if(!features[i].read(*it))
+ if(!ff[i].read(*it))
return false;
- if( features[i].tilted )
+ if( ff[i].tilted )
hasTiltedFeatures = true;
}
return true;
Ptr<HaarEvaluator> ret = makePtr<HaarEvaluator>();
ret->origWinSize = origWinSize;
ret->features = features;
+ ret->optfeatures = optfeatures;
+ ret->optfeaturesPtr = optfeatures->empty() ? 0 : &(*(ret->optfeatures))[0];
ret->hasTiltedFeatures = hasTiltedFeatures;
- ret->sum0 = sum0, ret->sqsum0 = sqsum0, ret->tilted0 = tilted0;
- ret->sum = sum, ret->sqsum = sqsum, ret->tilted = tilted;
+ ret->sum0 = sum0; ret->sqsum0 = sqsum0;
+ ret->sum = sum; ret->sqsum = sqsum; ret->tilted = tilted;
ret->normrect = normrect;
- memcpy( ret->p, p, 4*sizeof(p[0]) );
- memcpy( ret->pq, pq, 4*sizeof(pq[0]) );
- ret->offset = offset;
+ memcpy( ret->nofs, nofs, 4*sizeof(nofs[0]) );
+ memcpy( ret->nqofs, nqofs, 4*sizeof(nqofs[0]) );
+ ret->pwin = pwin; ret->pqwin = pqwin;
ret->varianceNormFactor = varianceNormFactor;
return ret;
}
-bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize )
+bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize )
{
- int rn = image.rows+1, cn = image.cols+1;
+ Size imgsz = _image.size();
+ int rn = imgsz.height+1, cn = imgsz.width+1, rnt = rn;
origWinSize = _origWinSize;
normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2);
- if (image.cols < origWinSize.width || image.rows < origWinSize.height)
+ if (imgsz.width < origWinSize.width || imgsz.height < origWinSize.height)
return false;
- if( sum0.rows < rn || sum0.cols < cn )
+ if( hasTiltedFeatures )
+ rnt = rn*2;
+
+ if( sum0.rows < rnt || sum0.cols < cn )
{
- sum0.create(rn, cn, CV_32S);
+ sum0.create(rnt, cn, CV_32S);
sqsum0.create(rn, cn, CV_64F);
- if (hasTiltedFeatures)
- tilted0.create( rn, cn, CV_32S);
}
sum = Mat(rn, cn, CV_32S, sum0.data);
sqsum = Mat(rn, cn, CV_64F, sqsum0.data);
-
if( hasTiltedFeatures )
{
- tilted = Mat(rn, cn, CV_32S, tilted0.data);
- integral(image, sum, sqsum, tilted);
+ tilted = Mat(rn, cn, CV_32S, sum0.data + rn*sum.step);
+ integral(_image, sum, sqsum, tilted);
}
else
- integral(image, sum, sqsum);
- const int* sdata = (const int*)sum.data;
- const double* sqdata = (const double*)sqsum.data;
- size_t sumStep = sum.step/sizeof(sdata[0]);
- size_t sqsumStep = sqsum.step/sizeof(sqdata[0]);
+ integral(_image, sum, sqsum);
+ int sumStep = (int)(sum.step/sum.elemSize());
+ int sqsumStep = (int)(sqsum.step/sqsum.elemSize());
+ int tofs = hasTiltedFeatures ? sumStep*rn : 0;
- CV_SUM_PTRS( p[0], p[1], p[2], p[3], sdata, normrect, sumStep );
- CV_SUM_PTRS( pq[0], pq[1], pq[2], pq[3], sqdata, normrect, sqsumStep );
+ CV_SUM_OFS( nofs[0], nofs[1], nofs[2], nofs[3], 0, normrect, sumStep );
+ CV_SUM_OFS( nqofs[0], nqofs[1], nqofs[2], nqofs[3], 0, normrect, sqsumStep );
- size_t fi, nfeatures = features.size();
+ size_t fi, nfeatures = features->size();
+ optfeatures->resize(nfeatures);
+ optfeaturesPtr = &(*optfeatures)[0];
+ const std::vector<Feature>& ff = *features;
for( fi = 0; fi < nfeatures; fi++ )
- optfeaturesPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
+ optfeaturesPtr[fi].setOffsets( ff[fi], sumStep, tofs );
return true;
}
pt.y + origWinSize.height >= sum.rows )
return false;
- size_t pOffset = pt.y * (sum.step/sizeof(int)) + pt.x;
- size_t pqOffset = pt.y * (sqsum.step/sizeof(double)) + pt.x;
- int valsum = CALC_SUM(p, pOffset);
- double valsqsum = CALC_SUM(pq, pqOffset);
+ const int* p = &sum.at<int>(pt);
+ const double* pq = &sqsum.at<double>(pt);
+ int valsum = CALC_SUM_OFS(nofs, p);
+ double valsqsum = CALC_SUM_OFS(nqofs, pq);
double nf = (double)normrect.area() * valsqsum - (double)valsum * valsum;
if( nf > 0. )
else
nf = 1.;
varianceNormFactor = 1./nf;
- offset = (int)pOffset;
+ pwin = p;
return true;
}
return ret;
}
-bool LBPEvaluator::setImage( const Mat& image, Size _origWinSize )
+bool LBPEvaluator::setImage( InputArray _image, Size _origWinSize )
{
+ Mat image = _image.getMat();
int rn = image.rows+1, cn = image.cols+1;
origWinSize = _origWinSize;
return ret;
}
-bool HOGEvaluator::setImage( const Mat& image, Size winSize )
+bool HOGEvaluator::setImage( InputArray _image, Size winSize )
{
+ Mat image = _image.getMat();
int rows = image.rows + 1;
int cols = image.cols + 1;
origWinSize = winSize;
struct getNeighbors { int operator ()(const CvAvgComp& e) const { return e.neighbors; } };
-bool CascadeClassifierImpl::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
- int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
+bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processingRectSize,
+ int yStep, double factor, std::vector<Rect>& candidates,
std::vector<int>& levels, std::vector<double>& weights, bool outputRejectLevels )
{
- if( !featureEvaluator->setImage( image, data.origWinSize ) )
+ if( !featureEvaluator->setImage( _image, data.origWinSize ) )
return false;
#if defined (LOG_CASCADE_STATISTIC)
Mat currentMask;
if (maskGenerator) {
+ Mat image = _image.getMat();
currentMask=maskGenerator->generateMask(image);
}
std::vector<Rect> candidatesVector;
std::vector<int> rejectLevels;
std::vector<double> levelWeights;
- Mutex mtx;
+
+ int stripCount, stripSize;
+
+ const int PTS_PER_THREAD = 1000;
+ stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
+ stripCount = std::min(std::max(stripCount, 1), 100);
+ stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
+
if( outputRejectLevels )
{
parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
return true;
}
+
+bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
+ int yStep, double factor, std::vector<Rect>& candidates,
+ std::vector<int>&, std::vector<double>&, bool )
+{
+ Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
+ if( haar.empty() )
+ return false;
+
+ if( cascadeKernel.empty() )
+ {
+ //cascadeKernel.create(")
+ if( cascadeKernel.empty() )
+ return false;
+ }
+
+ if( ustages.empty() )
+ {
+ #define UPLOAD_CASCADE_PART(NAME) \
+ Mat(1, (int)(data.NAME.size()*sizeof(data.NAME[0])), CV_8U, &data.NAME[0]).copyTo(u##NAME)
+
+ UPLOAD_CASCADE_PART(stages);
+ UPLOAD_CASCADE_PART(classifiers);
+ UPLOAD_CASCADE_PART(nodes);
+ UPLOAD_CASCADE_PART(leaves);
+ ufacepos.create();
+ }
+
+ haar->setUMat(_image, data.origWinSize, ugrayImage.size());
+ std::vector<UMat> bufs;
+ haar->getUMats(bufs);
+ CV_Assert(bufs.size() == 5);
+
+ size_t globalsize[] = { processingRectSize.width, processingRectSize.height };
+
+ if(!cascadeKernel.args(ocl::KernelArg::PtrReadOnly(bufs[0]), // sum
+ ocl::KernelArg::PtrReadOnly(bufs[1]), // sqsum
+ ocl::KernelArg::PtrReadOnly(bufs[2]), // optfeatures
+
+ // cascade classifier
+ ocl::KernelArg::PtrReadOnly(ustages),
+ ocl::KernelArg::PtrReadOnly(uclassifiers),
+ ocl::KernelArg::PtrReadOnly(unodes),
+ ocl::KernelArg::PtrReadOnly(uleaves),
+
+ ocl::KernelArg::WriteOnly(ufacepos), // positions
+ ocl::KernelArg::ReadWrite(umisc),
+ processingRectSize.width,
+ processingRectSize.height).run(2, globalsize, 0, false))
+ return false;
+
+ Mat facepos = ufacepos.getMat(ACCESS_READ);
+ const int* fptr = facepos.ptr<int>();
+ int nfaces = fptr[0];
+ for( i = 0; i < nfaces; i++ )
+ {
+ int pos = fptr[i+1];
+ int x =
+ candidates.push_back(Rect()
+ return false;
+}
+
+
+
bool CascadeClassifierImpl::isOldFormatCascade() const
{
return !oldCascade.empty();
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
}
-void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
+
+void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
+ Size imgsz = _image.size();
+ int imgtype = _image.type();
+
+ Mat grayImage, imageBuffer;
+
candidates.clear();
-
- if (maskGenerator)
- maskGenerator->initializeMask(image);
-
+ rejectLevels.clear();
+ levelWeights.clear();
+
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
- maxObjectSize = image.size();
+ maxObjectSize = imgsz;
+
+ bool use_ocl = ocl::useOpenCL() &&
+ getFeatureType() == FeatureEvaluator::HAAR &&
+ !isOldFormatCascade() &&
+ maskGenerator.empty() &&
+ !outputRejectLevels &&
+ tryOpenCL;
+
+ if( !use_ocl )
+ {
+ Mat image = _image.getMat();
+ if (maskGenerator)
+ maskGenerator->initializeMask(image);
+
+ grayImage = image;
+ if( CV_MAT_CN(imgtype) > 1 )
+ {
+ Mat temp;
+ cvtColor(grayImage, temp, COLOR_BGR2GRAY);
+ grayImage = temp;
+ }
- Mat grayImage = image;
- if( grayImage.channels() > 1 )
+ imageBuffer.create(imgsz.height + 1, imgsz.width + 1, CV_8U);
+ }
+ else
{
- Mat temp;
- cvtColor(grayImage, temp, COLOR_BGR2GRAY);
- grayImage = temp;
+ UMat uimage = _image.getUMat();
+ if( CV_MAT_CN(imgtype) > 1 )
+ cvtColor(uimage, ugrayImage, COLOR_BGR2GRAY);
+ else
+ uimage.copyTo(ugrayImage);
+ uimageBuffer.create(imgsz.height + 1, imgsz.width + 1, CV_8U);
}
- Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
-
for( double factor = 1; ; factor *= scaleFactor )
{
Size originalWindowSize = getOriginalWindowSize();
Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
Size scaledImageSize( cvRound( grayImage.cols/factor ), cvRound( grayImage.rows/factor ) );
- Size processingRectSize( scaledImageSize.width - originalWindowSize.width, scaledImageSize.height - originalWindowSize.height );
+ Size processingRectSize( scaledImageSize.width - originalWindowSize.width,
+ scaledImageSize.height - originalWindowSize.height );
if( processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
break;
if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height )
continue;
-
- Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data );
- resize( grayImage, scaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
-
+
int yStep;
if( getFeatureType() == cv::FeatureEvaluator::HOG )
{
yStep = factor > 2. ? 1 : 2;
}
- int stripCount, stripSize;
-
- const int PTS_PER_THREAD = 1000;
- stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
- stripCount = std::min(std::max(stripCount, 1), 100);
- stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
-
- if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates,
- rejectLevels, levelWeights, outputRejectLevels ) )
- break;
+ if( use_ocl )
+ {
+ UMat uscaledImage(uimageBuffer, Rect(0, 0, scaledImageSize.width, scaledImageSize.height));
+ resize( ugrayImage, uscaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
+
+ if( ocl_detectSingleScale( uscaledImage, processingRectSize, yStep, factor, candidates,
+ rejectLevels, levelWeights, outputRejectLevels ) )
+ continue;
+
+ /////// if the OpenCL branch has been executed but failed, fall back to CPU: /////
+
+ tryOpenCL = false; // for this cascade do not try OpenCL anymore
+
+ // since we may already have some partial results from OpenCL code (unlikely, but still),
+ // we just recursively call the function again, but with tryOpenCL==false it will
+ // go with CPU route, so there is no infinite recursion
+ detectMultiScaleNoGrouping( _image, candidates, rejectLevels, levelWeights,
+ scaleFactor, minObjectSize, maxObjectSize,
+ outputRejectLevels);
+ return;
+ }
+ else
+ {
+ Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data );
+ resize( grayImage, scaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
+
+ if( !detectSingleScale( scaledImage, processingRectSize, yStep, factor, candidates,
+ rejectLevels, levelWeights, outputRejectLevels ) )
+ break;
+ }
}
}
int flags, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
- Mat image = _image.getMat();
- CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
+ CV_Assert( scaleFactor > 1 && _image.depth() == CV_8U );
if( empty() )
return;
if( isOldFormatCascade() )
{
+ Mat image = _image.getMat();
std::vector<CvAvgComp> fakeVecAvgComp;
detectMultiScaleOldFormat( image, oldCascade, objects, rejectLevels, levelWeights, fakeVecAvgComp, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels );
}
else
{
- detectMultiScaleNoGrouping( image, objects, rejectLevels, levelWeights, scaleFactor, minObjectSize, maxObjectSize,
+ detectMultiScaleNoGrouping( _image, objects, rejectLevels, levelWeights, scaleFactor, minObjectSize, maxObjectSize,
outputRejectLevels );
const double GROUP_EPS = 0.2;
if( outputRejectLevels )
return true;
}
+
bool CascadeClassifierImpl::read_(const FileNode& root)
{
+ tryOpenCL = true;
+ cascadeKernel = ocl::Kernel();
+ ustages.release();
+ uclassifiers.release();
+ unodes.release();
+ uleaves.release();
if( !data.read(root) )
return false;
Ptr<MaskGenerator> getMaskGenerator();
protected:
- bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
- int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
- std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels = false );
-
- void detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
+ bool detectSingleScale( InputArray image, Size processingRectSize,
+ int yStep, double factor, std::vector<Rect>& candidates,
+ std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
+ bool outputRejectLevels = false );
+ bool ocl_detectSingleScale( InputArray image, Size processingRectSize,
+ int yStep, double factor, std::vector<Rect>& candidates,
+ std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
+ bool outputRejectLevels = false );
+
+
+ void detectMultiScaleNoGrouping( InputArray image, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels = false );
Ptr<CvHaarClassifierCascade> oldCascade;
Ptr<MaskGenerator> maskGenerator;
+ UMat ugrayImage, uimageBuffer;
+ UMat ufacepos, ustages, uclassifiers, unodes, uleaves, usubsets;
+ ocl::Kernel cascadeKernel;
+ bool tryOpenCL;
+
+ Mutex mtx;
};
#define CC_CASCADE_PARAMS "cascadeParams"
#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
+#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
+((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])
+
+#define CALC_SUM_OFS(rect, ptr) CALC_SUM_OFS_((rect)[0], (rect)[1], (rect)[2], (rect)[3], ptr)
//---------------------------------------------- HaarEvaluator ---------------------------------------
class HaarEvaluator : public FeatureEvaluator
enum { RECT_NUM = Feature::RECT_NUM };
float calc( const int* pwin ) const;
- void setPtrs( const Mat& sum, const Feature& f );
+ void setOffsets( const Feature& _f, int step, int tofs );
int ofs[RECT_NUM][4];
- float weight[RECT_NUM];
+ float weight[4];
};
HaarEvaluator();
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::HAAR; }
- virtual bool setImage(const Mat&, Size origWinSize);
+ virtual bool setImage(InputArray, Size origWinSize);
virtual bool setWindow(Point pt);
+
+ virtual bool setUMat(InputArray, Size origWinSize, Size origImgSize);
+ virtual void getUMats(std::vector<UMat>& bufs);
double operator()(int featureIdx) const
{ return optfeaturesPtr[featureIdx].calc(pwin) * varianceNormFactor; }
{ return (*this)(featureIdx); }
protected:
- Size origWinSize;
- std::vector<Feature> features;
- std::vector<OptFeature> optfeatures;
+ Size origWinSize, origImgSize;
+ Ptr<std::vector<Feature> > features;
+ Ptr<std::vector<OptFeature> > optfeatures;
OptFeature* optfeaturesPtr; // optimization
bool hasTiltedFeatures;
- Mat sum0, sqsum0, tilted0;
+ Mat sum0, sqsum0;
Mat sum, sqsum, tilted;
+ UMat usum, usqsum, fbuf;
Rect normrect;
- int p[4];
- int pq[4];
+ int nofs[4];
+ int nqofs[4];
const int* pwin;
const double* pqwin;
- int offset;
double varianceNormFactor;
};
ofs[2][0] = ofs[2][1] = ofs[2][2] = ofs[2][3] = 0;
}
-/*inline float HaarEvaluator::Feature :: calc( int _offset ) const
+inline float HaarEvaluator::OptFeature :: calc( const int* ptr ) const
{
- float ret = rect[0].weight * CALC_SUM(p[0], _offset) + rect[1].weight * CALC_SUM(p[1], _offset);
+ float ret = weight[0] * CALC_SUM_OFS(ofs[0], ptr) +
+ weight[1] * CALC_SUM_OFS(ofs[1], ptr);
- if( rect[2].weight != 0.0f )
- ret += rect[2].weight * CALC_SUM(p[2], _offset);
+ if( weight[2] != 0.0f )
+ ret += weight[2] * CALC_SUM_OFS(ofs[2], ptr);
return ret;
-}*/
+}
-inline void HaarEvaluator::OptFeature :: setPtrs( const Mat& _sum, const Feature& _f )
+inline void HaarEvaluator::OptFeature :: setOffsets( const Feature& _f, int step, int tofs )
{
- const int* ptr = (const int*)_sum.data;
- size_t step = _sum.step/sizeof(ptr[0]);
- size_t tiltedofs =
- if (tilted)
+ weight[0] = _f.rect[0].weight;
+ weight[1] = _f.rect[1].weight;
+ weight[2] = _f.rect[2].weight;
+ if (_f.tilted)
{
- CV_TILTED_PTRS( p[0][0], p[0][1], p[0][2], p[0][3], ptr, rect[0].r, step );
- CV_TILTED_PTRS( p[1][0], p[1][1], p[1][2], p[1][3], ptr, rect[1].r, step );
- if (rect[2].weight)
- CV_TILTED_PTRS( p[2][0], p[2][1], p[2][2], p[2][3], ptr, rect[2].r, step );
+ CV_TILTED_OFS( ofs[0][0], ofs[0][1], ofs[0][2], ofs[0][3], tofs, _f.rect[0].r, step );
+ CV_TILTED_OFS( ofs[1][0], ofs[1][1], ofs[1][2], ofs[1][3], tofs, _f.rect[1].r, step );
+ if (weight[2])
+ CV_TILTED_PTRS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], tofs, _f.rect[2].r, step );
}
else
{
- CV_SUM_PTRS( p[0][0], p[0][1], p[0][2], p[0][3], ptr, rect[0].r, step );
- CV_SUM_PTRS( p[1][0], p[1][1], p[1][2], p[1][3], ptr, rect[1].r, step );
- if (rect[2].weight)
- CV_SUM_PTRS( p[2][0], p[2][1], p[2][2], p[2][3], ptr, rect[2].r, step );
+ CV_SUM_OFS( ofs[0][0], ofs[0][1], ofs[0][2], ofs[0][3], 0, _f.rect[0].r, step );
+ CV_SUM_OFS( ofs[1][0], ofs[1][1], ofs[1][2], ofs[1][3], 0, _f.rect[1].r, step );
+ if (weight[2])
+ CV_SUM_OFS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], 0, _f.rect[2].r, step );
}
}
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::LBP; }
- virtual bool setImage(const Mat& image, Size _origWinSize);
+ virtual bool setImage(InputArray image, Size _origWinSize);
virtual bool setWindow(Point pt);
int operator()(int featureIdx) const
virtual bool read( const FileNode& node );
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::HOG; }
- virtual bool setImage( const Mat& image, Size winSize );
+ virtual bool setImage( InputArray image, Size winSize );
virtual bool setWindow( Point pt );
double operator()(int featureIdx) const
{
projects / profile / ivi / opencv.git / commitdiff