generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

converted old haar cascades to the new format; added the conversion function; added OpenCL optimization into CascadeClassfier; optimized the data structures and CPU code for the stump case.

Browse Source
This commit is contained in:
Vadim Pisarevsky
2013-12-19 14:48:42 +04:00
parent 2431c72d1d ec3f22cee2
commit a1784b7320
31 changed files with 492438 additions and 575343 deletions
Download Patch File Download Diff File Expand all files Collapse all files

434
modules/objdetect/src/cascadedetect.cpp
View File

@@ -7,10 +7,10 @@
// 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,
@@ -23,13 +23,13 @@
// 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
@@ -44,6 +44,7 @@
#include "cascadedetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencl_kernels.hpp"
#if defined (LOG_CASCADE_STATISTIC)
struct Logger
@@ -112,6 +113,13 @@ struct Logger
namespace cv
{
template<typename _Tp> void copyVectorToUMat(const std::vector<_Tp>& v, UMat& um)
{
if(v.empty())
um.release();
Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
}
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights)
{
@@ -434,7 +442,7 @@ FeatureEvaluator::~FeatureEvaluator() {}
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, Size) {return true;}
bool FeatureEvaluator::setWindow(Point) { return true; }
double FeatureEvaluator::calcOrd(int) const { return 0.; }
int FeatureEvaluator::calcCat(int) const { return 0; }
@@ -466,7 +474,8 @@ bool HaarEvaluator::Feature :: read( const FileNode& node )
HaarEvaluator::HaarEvaluator()
{
features = makePtr<std::vector<Feature> >();
optfeaturesPtr = 0;
pwin = 0;
}
HaarEvaluator::~HaarEvaluator()
{
@@ -474,16 +483,24 @@ HaarEvaluator::~HaarEvaluator()
bool HaarEvaluator::read(const FileNode& node)
{
features->resize(node.size());
featuresPtr = &(*features)[0];
FileNodeIterator it = node.begin(), it_end = node.end();
size_t i, n = node.size();
CV_Assert(n > 0);
if(features.empty())
features = makePtr<std::vector<Feature> >();
if(optfeatures.empty())
optfeatures = makePtr<std::vector<OptFeature> >();
features->resize(n);
FileNodeIterator it = node.begin();
hasTiltedFeatures = false;
std::vector<Feature>& ff = *features;
sumSize0 = Size();
ufbuf.release();
for(int i = 0; it != it_end; ++it, i++)
for(i = 0; i < n; i++, ++it)
{
if(!featuresPtr[i].read(*it))
if(!ff[i].read(*it))
return false;
if( featuresPtr[i].tilted )
if( ff[i].tilted )
hasTiltedFeatures = true;
}
return true;
@@ -494,59 +511,102 @@ Ptr<FeatureEvaluator> HaarEvaluator::clone() const
Ptr<HaarEvaluator> ret = makePtr<HaarEvaluator>();
ret->origWinSize = origWinSize;
ret->features = features;
ret->featuresPtr = &(*ret->features)[0];
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->usum0 = usum0; ret->usqsum0 = usqsum0; ret->ufbuf = ufbuf;
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]) );
ret->pwin = pwin;
ret->varianceNormFactor = varianceNormFactor;
return ret;
}
bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize )
bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSize )
{
int rn = image.rows+1, cn = image.cols+1;
Size imgsz = _image.size();
int cols = imgsz.width, rows = imgsz.height;
if (imgsz.width < origWinSize.width || imgsz.height < origWinSize.height)
return false;
origWinSize = _origWinSize;
normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2);
if (image.cols < origWinSize.width || image.rows < origWinSize.height)
return false;
if( sum0.rows < rn || sum0.cols < cn )
int rn = _sumSize.height, cn = _sumSize.width, rn_scale = hasTiltedFeatures ? 2 : 1;
int sumStep, tofs = 0;
CV_Assert(rn >= rows+1 && cn >= cols+1);
if( _image.isUMat() )
{
sum0.create(rn, 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);
usum0.create(rn*rn_scale, cn, CV_32S);
usqsum0.create(rn, cn, CV_32S);
usum = UMat(usum0, Rect(0, 0, cols+1, rows+1));
usqsum = UMat(usqsum0, Rect(0, 0, cols, rows));
if( hasTiltedFeatures )
{
UMat utilted(usum0, Rect(0, _sumSize.height, cols+1, rows+1));
integral(_image, usum, noArray(), utilted, CV_32S);
tofs = (int)((utilted.offset - usum.offset)/sizeof(int));
}
else
{
integral(_image, usum, noArray(), noArray(), CV_32S);
}
sqrBoxFilter(_image, usqsum, CV_32S,
Size(normrect.width, normrect.height),
Point(0, 0), false);
/*sqrBoxFilter(_image.getMat(), sqsum, CV_32S,
Size(normrect.width, normrect.height),
Point(0, 0), false);
sqsum.copyTo(usqsum);*/
sumStep = (int)(usum.step/usum.elemSize());
}
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]);
{
sum0.create(rn*rn_scale, cn, CV_32S);
sqsum0.create(rn, cn, CV_32S);
sum = sum0(Rect(0, 0, cols+1, rows+1));
sqsum = sqsum0(Rect(0, 0, cols, rows));
if( hasTiltedFeatures )
{
Mat tilted = sum0(Rect(0, _sumSize.height, cols+1, rows+1));
integral(_image, sum, noArray(), tilted, CV_32S);
tofs = (int)((tilted.data - sum.data)/sizeof(int));
}
else
integral(_image, sum, noArray(), noArray(), CV_32S);
sqrBoxFilter(_image, sqsum, CV_32S,
Size(normrect.width, normrect.height),
Point(0, 0), false);
sumStep = (int)(sum.step/sum.elemSize());
}
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 );
size_t fi, nfeatures = features->size();
for( fi = 0; fi < nfeatures; fi++ )
featuresPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
const std::vector<Feature>& ff = *features;
if( sumSize0 != _sumSize )
{
optfeatures->resize(nfeatures);
optfeaturesPtr = &(*optfeatures)[0];
for( fi = 0; fi < nfeatures; fi++ )
optfeaturesPtr[fi].setOffsets( ff[fi], sumStep, tofs );
}
if( _image.isUMat() && (sumSize0 != _sumSize || ufbuf.empty()) )
copyVectorToUMat(*optfeatures, ufbuf);
sumSize0 = _sumSize;
return true;
}
bool HaarEvaluator::setWindow( Point pt )
{
if( pt.x < 0 || pt.y < 0 ||
@@ -554,10 +614,9 @@ bool HaarEvaluator::setWindow( Point pt )
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);
int valsum = CALC_SUM_OFS(nofs, p);
double valsqsum = sqsum.at<int>(pt.y + normrect.y, pt.x + normrect.x);
double nf = (double)normrect.area() * valsqsum - (double)valsum * valsum;
if( nf > 0. )
@@ -565,10 +624,23 @@ bool HaarEvaluator::setWindow( Point pt )
else
nf = 1.;
varianceNormFactor = 1./nf;
offset = (int)pOffset;
pwin = p;
return true;
}
Rect HaarEvaluator::getNormRect() const
{
return normrect;
}
void HaarEvaluator::getUMats(std::vector<UMat>& bufs)
{
bufs.clear();
bufs.push_back(usum);
bufs.push_back(usqsum);
bufs.push_back(ufbuf);
}
//---------------------------------------------- LBPEvaluator -------------------------------------
bool LBPEvaluator::Feature :: read(const FileNode& node )
@@ -612,8 +684,9 @@ Ptr<FeatureEvaluator> LBPEvaluator::clone() const
return ret;
}
bool LBPEvaluator::setImage( const Mat& image, Size _origWinSize )
bool LBPEvaluator::setImage( InputArray _image, Size _origWinSize, Size )
{
Mat image = _image.getMat();
int rn = image.rows+1, cn = image.cols+1;
origWinSize = _origWinSize;
@@ -693,8 +766,9 @@ Ptr<FeatureEvaluator> HOGEvaluator::clone() const
return ret;
}
bool HOGEvaluator::setImage( const Mat& image, Size winSize )
bool HOGEvaluator::setImage( InputArray _image, Size winSize, Size )
{
Mat image = _image.getMat();
int rows = image.rows + 1;
int cols = image.cols + 1;
origWinSize = winSize;
@@ -880,7 +954,7 @@ int CascadeClassifierImpl::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, do
if( !evaluator->setWindow(pt) )
return -1;
if( data.isStumpBased )
if( data.isStumpBased() )
{
if( data.featureType == FeatureEvaluator::HAAR )
return predictOrderedStump<HaarEvaluator>( *this, evaluator, weight );
@@ -904,11 +978,6 @@ int CascadeClassifierImpl::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, do
}
}
bool CascadeClassifierImpl::setImage( Ptr<FeatureEvaluator>& evaluator, const Mat& image )
{
return empty() ? false : evaluator->setImage(image, data.origWinSize);
}
void CascadeClassifierImpl::setMaskGenerator(const Ptr<MaskGenerator>& _maskGenerator)
{
maskGenerator=_maskGenerator;
@@ -1010,11 +1079,12 @@ struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } }
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,
std::vector<int>& levels, std::vector<double>& weights, bool outputRejectLevels )
bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processingRectSize,
int yStep, double factor, std::vector<Rect>& candidates,
std::vector<int>& levels, std::vector<double>& weights,
Size sumSize0, bool outputRejectLevels )
{
if( !featureEvaluator->setImage( image, data.origWinSize ) )
if( !featureEvaluator->setImage(_image, data.origWinSize, sumSize0) )
return false;
#if defined (LOG_CASCADE_STATISTIC)
@@ -1023,13 +1093,21 @@ bool CascadeClassifierImpl::detectSingleScale( const Mat& image, int stripCount,
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,
@@ -1051,12 +1129,63 @@ bool CascadeClassifierImpl::detectSingleScale( const Mat& image, int stripCount,
return true;
}
bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
int yStep, double factor, Size sumSize0 )
{
const int VECTOR_SIZE = 1;
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
haar->setImage(_image, data.origWinSize, sumSize0);
if( cascadeKernel.empty() )
{
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc,
format("-D VECTOR_SIZE=%d", VECTOR_SIZE));
if( cascadeKernel.empty() )
return false;
}
if( ustages.empty() )
{
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
}
std::vector<UMat> bufs;
haar->getUMats(bufs);
CV_Assert(bufs.size() == 3);
Rect normrect = haar->getNormRect();
//processingRectSize = Size(yStep, yStep);
size_t globalsize[] = { (processingRectSize.width/yStep + VECTOR_SIZE-1)/VECTOR_SIZE, processingRectSize.height/yStep };
cascadeKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::ReadOnlyNoSize(bufs[1]), // sqsum
ocl::KernelArg::PtrReadOnly(bufs[2]), // optfeatures
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(ustumps),
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
normrect, data.origWinSize, MAX_FACES);
bool ok = cascadeKernel.run(2, globalsize, 0, true);
//CV_Assert(ok);
return ok;
}
bool CascadeClassifierImpl::isOldFormatCascade() const
{
return !oldCascade.empty();
}
int CascadeClassifierImpl::getFeatureType() const
{
return featureEvaluator->getFeatureType();
@@ -1067,12 +1196,6 @@ Size CascadeClassifierImpl::getOriginalWindowSize() const
return data.origWinSize;
}
bool CascadeClassifierImpl::setImage(InputArray _image)
{
Mat image = _image.getMat();
return featureEvaluator->setImage(image, data.origWinSize);
}
void* CascadeClassifierImpl::getOldCascade()
{
return oldCascade;
@@ -1096,36 +1219,75 @@ static void detectMultiScaleOldFormat( const Mat& image, Ptr<CvHaarClassifierCas
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();
Mat grayImage = image;
if( grayImage.channels() > 1 )
maxObjectSize = imgsz;
bool use_ocl = ocl::useOpenCL() &&
getFeatureType() == FeatureEvaluator::HAAR &&
!isOldFormatCascade() &&
data.isStumpBased() &&
maskGenerator.empty() &&
!outputRejectLevels &&
tryOpenCL;
if( !use_ocl )
{
Mat temp;
cvtColor(grayImage, temp, COLOR_BGR2GRAY);
grayImage = temp;
}
Mat image = _image.getMat();
if (maskGenerator)
maskGenerator->initializeMask(image);
Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
grayImage = image;
if( CV_MAT_CN(imgtype) > 1 )
{
Mat temp;
cvtColor(grayImage, temp, COLOR_BGR2GRAY);
grayImage = temp;
}
imageBuffer.create(imgsz.height + 1, imgsz.width + 1, CV_8U);
}
else
{
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);
}
Size sumSize0((imgsz.width + SUM_ALIGN) & -SUM_ALIGN, imgsz.height+1);
if( use_ocl )
{
ufacepos.create(1, MAX_FACES*4 + 1, CV_32S);
UMat ufacecount(ufacepos, Rect(0,0,1,1));
ufacecount.setTo(Scalar::all(0));
}
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 scaledImageSize( cvRound( imgsz.width/factor ), cvRound( imgsz.height/factor ) );
Size processingRectSize( scaledImageSize.width - originalWindowSize.width,
scaledImageSize.height - originalWindowSize.height );
if( processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
@@ -1133,10 +1295,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::v
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 )
{
@@ -1147,16 +1306,46 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::v
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, sumSize0 ) )
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, sumSize0, outputRejectLevels ) )
break;
}
}
if( use_ocl && tryOpenCL )
{
Mat facepos = ufacepos.getMat(ACCESS_READ);
const int* fptr = facepos.ptr<int>();
int i, nfaces = fptr[0];
for( i = 0; i < nfaces; i++ )
{
candidates.push_back(Rect(fptr[i*4+1], fptr[i*4+2], fptr[i*4+3], fptr[i*4+4]));
}
}
}
@@ -1167,21 +1356,21 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
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 )
@@ -1235,6 +1424,12 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
}
}
CascadeClassifierImpl::Data::Data()
{
stageType = featureType = ncategories = maxNodesPerTree = 0;
}
bool CascadeClassifierImpl::Data::read(const FileNode &root)
{
static const float THRESHOLD_EPS = 1e-5f;
@@ -1261,8 +1456,6 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
origWinSize.height = (int)root[CC_HEIGHT];
CV_Assert( origWinSize.height > 0 && origWinSize.width > 0 );
isStumpBased = (int)(root[CC_STAGE_PARAMS][CC_MAX_DEPTH]) == 1 ? true : false;
// load feature params
FileNode fn = root[CC_FEATURE_PARAMS];
if( fn.empty() )
@@ -1280,8 +1473,10 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
stages.reserve(fn.size());
classifiers.clear();
nodes.clear();
stumps.clear();
FileNodeIterator it = fn.begin(), it_end = fn.end();
maxNodesPerTree = 0;
for( int si = 0; it != it_end; si++, ++it )
{
@@ -1307,6 +1502,8 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
DTree tree;
tree.nodeCount = (int)internalNodes.size()/nodeStep;
maxNodesPerTree = std::max(maxNodesPerTree, tree.nodeCount);
classifiers.push_back(tree);
nodes.reserve(nodes.size() + tree.nodeCount);
@@ -1341,12 +1538,35 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
leaves.push_back((float)*internalNodesIter);
}
}
if( isStumpBased() )
{
int nodeOfs = 0, leafOfs = 0;
size_t nstages = stages.size();
for( size_t stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
const Stage& stage = stages[stageIdx];
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
{
const DTreeNode& node = nodes[nodeOfs];
stumps.push_back(Stump(node.featureIdx, node.threshold,
leaves[leafOfs], leaves[leafOfs+1]));
}
}
}
return true;
}
bool CascadeClassifierImpl::read_(const FileNode& root)
{
tryOpenCL = true;
cascadeKernel = ocl::Kernel();
ustages.release();
ustumps.release();
if( !data.read(root) )
return false;

233
modules/objdetect/src/cascadedetect.hpp
View File

@@ -42,24 +42,29 @@ public:
bool isOldFormatCascade() const;
Size getOriginalWindowSize() const;
int getFeatureType() const;
bool setImage( InputArray );
void* getOldCascade();
void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator);
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 );
enum { SUM_ALIGN = 64 };
bool detectSingleScale( InputArray image, Size processingRectSize,
int yStep, double factor, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
Size sumSize0, bool outputRejectLevels = false );
bool ocl_detectSingleScale( InputArray image, Size processingRectSize,
int yStep, double factor, Size sumSize0 );
void detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
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 );
enum { BOOST = 0
};
enum { MAX_FACES = 10000 };
enum { BOOST = 0 };
enum { DO_CANNY_PRUNING = CASCADE_DO_CANNY_PRUNING,
SCALE_IMAGE = CASCADE_SCALE_IMAGE,
FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
@@ -80,7 +85,6 @@ protected:
template<class FEval>
friend int predictCategoricalStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
class Data
@@ -105,14 +109,29 @@ protected:
int ntrees;
float threshold;
};
struct Stump
{
Stump() {};
Stump(int _featureIdx, float _threshold, float _left, float _right)
: featureIdx(_featureIdx), threshold(_threshold), left(_left), right(_right) {}
int featureIdx;
float threshold;
float left;
float right;
};
Data();
bool read(const FileNode &node);
bool isStumpBased;
bool isStumpBased() const { return maxNodesPerTree == 1; }
int stageType;
int featureType;
int ncategories;
int maxNodesPerTree;
Size origWinSize;
std::vector<Stage> stages;
@@ -120,6 +139,7 @@ protected:
std::vector<DTreeNode> nodes;
std::vector<float> leaves;
std::vector<int> subsets;
std::vector<Stump> stumps;
};
Data data;
@@ -127,6 +147,12 @@ protected:
Ptr<CvHaarClassifierCascade> oldCascade;
Ptr<MaskGenerator> maskGenerator;
UMat ugrayImage, uimageBuffer;
UMat ufacepos, ustages, ustumps, usubsets;
ocl::Kernel cascadeKernel;
bool tryOpenCL;
Mutex mtx;
};
#define CC_CASCADE_PARAMS "cascadeParams"
@@ -186,6 +212,36 @@ protected:
#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
#define CV_SUM_OFS( p0, p1, p2, p3, sum, rect, step ) \
/* (x, y) */ \
(p0) = sum + (rect).x + (step) * (rect).y, \
/* (x + w, y) */ \
(p1) = sum + (rect).x + (rect).width + (step) * (rect).y, \
/* (x + w, y) */ \
(p2) = sum + (rect).x + (step) * ((rect).y + (rect).height), \
/* (x + w, y + h) */ \
(p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
#define CV_TILTED_OFS( p0, p1, p2, p3, tilted, rect, step ) \
/* (x, y) */ \
(p0) = tilted + (rect).x + (step) * (rect).y, \
/* (x - h, y + h) */ \
(p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
/* (x + w, y + w) */ \
(p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width), \
/* (x + w - h, y + w + h) */ \
(p3) = tilted + (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height)
#define CALC_SUM_(p0, p1, p2, p3, offset) \
((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])
#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
@@ -195,8 +251,6 @@ public:
{
Feature();
float calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read( const FileNode& node );
bool tilted;
@@ -208,8 +262,19 @@ public:
Rect r;
float weight;
} rect[RECT_NUM];
};
const int* p[RECT_NUM][4];
struct OptFeature
{
OptFeature();
enum { RECT_NUM = Feature::RECT_NUM };
float calc( const int* pwin ) const;
void setOffsets( const Feature& _f, int step, int tofs );
int ofs[RECT_NUM][4];
float weight[4];
};
HaarEvaluator();
@@ -219,28 +284,30 @@ public:
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, Size sumSize);
virtual bool setWindow(Point pt);
virtual Rect getNormRect() const;
virtual void getUMats(std::vector<UMat>& bufs);
double operator()(int featureIdx) const
{ return featuresPtr[featureIdx].calc(offset) * varianceNormFactor; }
{ return optfeaturesPtr[featureIdx].calc(pwin) * varianceNormFactor; }
virtual double calcOrd(int featureIdx) const
{ return (*this)(featureIdx); }
protected:
Size origWinSize;
Size origWinSize, sumSize0;
Ptr<std::vector<Feature> > features;
Feature* featuresPtr; // optimization
Ptr<std::vector<OptFeature> > optfeatures;
OptFeature* optfeaturesPtr; // optimization
bool hasTiltedFeatures;
Mat sum0, sqsum0, tilted0;
Mat sum, sqsum, tilted;
Mat sum0, sum, sqsum0, sqsum;
UMat usum0, usum, usqsum0, usqsum, ufbuf;
Rect normrect;
const int *p[4];
const double *pq[4];
int nofs[4];
int offset;
const int* pwin;
double varianceNormFactor;
};
@@ -249,38 +316,46 @@ inline HaarEvaluator::Feature :: Feature()
tilted = false;
rect[0].r = rect[1].r = rect[2].r = Rect();
rect[0].weight = rect[1].weight = rect[2].weight = 0;
p[0][0] = p[0][1] = p[0][2] = p[0][3] =
p[1][0] = p[1][1] = p[1][2] = p[1][3] =
p[2][0] = p[2][1] = p[2][2] = p[2][3] = 0;
}
inline float HaarEvaluator::Feature :: calc( int _offset ) const
inline HaarEvaluator::OptFeature :: OptFeature()
{
float ret = rect[0].weight * CALC_SUM(p[0], _offset) + rect[1].weight * CALC_SUM(p[1], _offset);
weight[0] = weight[1] = weight[2] = 0.f;
if( rect[2].weight != 0.0f )
ret += rect[2].weight * CALC_SUM(p[2], _offset);
ofs[0][0] = ofs[0][1] = ofs[0][2] = ofs[0][3] =
ofs[1][0] = ofs[1][1] = ofs[1][2] = ofs[1][3] =
ofs[2][0] = ofs[2][1] = ofs[2][2] = ofs[2][3] = 0;
}
inline float HaarEvaluator::OptFeature :: calc( const int* ptr ) const
{
float ret = weight[0] * CALC_SUM_OFS(ofs[0], ptr) +
weight[1] * CALC_SUM_OFS(ofs[1], ptr);
if( weight[2] != 0.0f )
ret += weight[2] * CALC_SUM_OFS(ofs[2], ptr);
return ret;
}
inline void HaarEvaluator::Feature :: updatePtrs( const Mat& _sum )
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]);
if (tilted)
weight[0] = _f.rect[0].weight;
weight[1] = _f.rect[1].weight;
weight[2] = _f.rect[2].weight;
Rect r2 = weight[2] > 0 ? _f.rect[2].r : Rect(0,0,0,0);
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 );
CV_TILTED_PTRS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], tofs, r2, 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 );
CV_SUM_OFS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], 0, r2, step );
}
}
@@ -311,7 +386,7 @@ public:
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, Size);
virtual bool setWindow(Point pt);
int operator()(int featureIdx) const
@@ -388,7 +463,7 @@ public:
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, Size );
virtual bool setWindow( Point pt );
double operator()(int featureIdx) const
{
@@ -533,30 +608,36 @@ template<class FEval>
inline int predictOrderedStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nodeOfs = 0, leafOfs = 0;
CV_Assert(!cascade.data.stumps.empty());
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
float* cascadeLeaves = &cascade.data.leaves[0];
CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
int nstages = (int)cascade.data.stages.size();
double tmp = 0;
for( int stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
sum = 0.0;
const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
tmp = 0;
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
for( int i = 0; i < ntrees; i++ )
{
CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
double value = featureEvaluator(node.featureIdx);
sum += cascadeLeaves[ value < node.threshold ? leafOfs : leafOfs + 1 ];
const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[i];
double value = featureEvaluator(stump.featureIdx);
tmp += value < stump.threshold ? stump.left : stump.right;
}
if( sum < stage.threshold )
if( tmp < stage.threshold )
{
sum = (double)tmp;
return -stageIdx;
}
cascadeStumps += ntrees;
}
sum = (double)tmp;
return 1;
}
@@ -564,56 +645,44 @@ template<class FEval>
inline int predictCategoricalStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_Assert(!cascade.data.stumps.empty());
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
size_t subsetSize = (cascade.data.ncategories + 31)/32;
int* cascadeSubsets = &cascade.data.subsets[0];
float* cascadeLeaves = &cascade.data.leaves[0];
CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
const int* cascadeSubsets = &cascade.data.subsets[0];
const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
#ifdef HAVE_TEGRA_OPTIMIZATION
float tmp = 0; // float accumulator -- float operations are quicker
#else
double tmp = 0;
#endif
for( int si = 0; si < nstages; si++ )
{
CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
#ifdef HAVE_TEGRA_OPTIMIZATION
tmp = 0;
#else
sum = 0;
#endif
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
int c = featureEvaluator(node.featureIdx);
const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
#ifdef HAVE_TEGRA_OPTIMIZATION
tmp += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
#else
sum += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
#endif
nodeOfs++;
leafOfs += 2;
const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[wi];
int c = featureEvaluator(stump.featureIdx);
const int* subset = &cascadeSubsets[wi*subsetSize];
tmp += (subset[c>>5] & (1 << (c & 31))) ? stump.left : stump.right;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if( tmp < stage.threshold ) {
if( tmp < stage.threshold )
{
sum = (double)tmp;
return -si;
}
#else
if( sum < stage.threshold )
return -si;
#endif
cascadeStumps += ntrees;
cascadeSubsets += ntrees*subsetSize;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
sum = (double)tmp;
#endif
return 1;
}
}

276
modules/objdetect/src/cascadedetect_convert.cpp Normal file
View File

@@ -0,0 +1,276 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 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,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// 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
// 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,
// 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
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* Haar features calculation */
#include "precomp.hpp"
#include <stdio.h>
namespace cv
{
/* field names */
#define ICV_HAAR_SIZE_NAME "size"
#define ICV_HAAR_STAGES_NAME "stages"
#define ICV_HAAR_TREES_NAME "trees"
#define ICV_HAAR_FEATURE_NAME "feature"
#define ICV_HAAR_RECTS_NAME "rects"
#define ICV_HAAR_TILTED_NAME "tilted"
#define ICV_HAAR_THRESHOLD_NAME "threshold"
#define ICV_HAAR_LEFT_NODE_NAME "left_node"
#define ICV_HAAR_LEFT_VAL_NAME "left_val"
#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
#define ICV_HAAR_PARENT_NAME "parent"
#define ICV_HAAR_NEXT_NAME "next"
namespace haar_cvt
{
struct HaarFeature
{
enum { RECT_NUM = 3 };
HaarFeature()
{
tilted = false;
for( int i = 0; i < RECT_NUM; i++ )
{
rect[i].r = Rect(0,0,0,0);
rect[i].weight = 0.f;
}
}
bool tilted;
struct
{
Rect r;
float weight;
} rect[RECT_NUM];
};
struct HaarClassifierNode
{
HaarClassifierNode()
{
f = left = right = 0;
threshold = 0.f;
}
int f, left, right;
float threshold;
};
struct HaarClassifier
{
std::vector<HaarClassifierNode> nodes;
std::vector<float> leaves;
};
struct HaarStageClassifier
{
double threshold;
std::vector<HaarClassifier> weaks;
};
static bool convert(const String& oldcascade, const String& newcascade)
{
FileStorage oldfs(oldcascade, FileStorage::READ);
if( !oldfs.isOpened() )
return false;
FileNode oldroot = oldfs.getFirstTopLevelNode();
FileNode sznode = oldroot[ICV_HAAR_SIZE_NAME];
if( sznode.empty() )
return false;
int maxdepth = 0;
Size cascadesize;
cascadesize.width = (int)sznode[0];
cascadesize.height = (int)sznode[1];
std::vector<HaarFeature> features;
size_t i, j, k, n;
FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
size_t nstages = stages_seq.size();
std::vector<HaarStageClassifier> stages(nstages);
for( i = 0; i < nstages; i++ )
{
FileNode stagenode = stages_seq[i];
HaarStageClassifier& stage = stages[i];
stage.threshold = (double)stagenode[ICV_HAAR_STAGE_THRESHOLD_NAME];
FileNode weaks_seq = stagenode[ICV_HAAR_TREES_NAME];
size_t nweaks = weaks_seq.size();
stage.weaks.resize(nweaks);
for( j = 0; j < nweaks; j++ )
{
HaarClassifier& weak = stage.weaks[j];
FileNode weaknode = weaks_seq[j];
size_t nnodes = weaknode.size();
for( n = 0; n < nnodes; n++ )
{
FileNode nnode = weaknode[n];
FileNode fnode = nnode[ICV_HAAR_FEATURE_NAME];
HaarFeature f;
HaarClassifierNode node;
node.f = (int)features.size();
f.tilted = (int)fnode[ICV_HAAR_TILTED_NAME] != 0;
FileNode rects_seq = fnode[ICV_HAAR_RECTS_NAME];
size_t nrects = rects_seq.size();
for( k = 0; k < nrects; k++ )
{
FileNode rnode = rects_seq[k];
f.rect[k].r.x = (int)rnode[0];
f.rect[k].r.y = (int)rnode[1];
f.rect[k].r.width = (int)rnode[2];
f.rect[k].r.height = (int)rnode[3];
f.rect[k].weight = (float)rnode[4];
}
features.push_back(f);
node.threshold = nnode[ICV_HAAR_THRESHOLD_NAME];
FileNode leftValNode = nnode[ICV_HAAR_LEFT_VAL_NAME];
if( !leftValNode.empty() )
{
node.left = -(int)weak.leaves.size();
weak.leaves.push_back((float)leftValNode);
}
else
{
node.left = (int)nnode[ICV_HAAR_LEFT_NODE_NAME];
}
FileNode rightValNode = nnode[ICV_HAAR_RIGHT_VAL_NAME];
if( !rightValNode.empty() )
{
node.right = -(int)weak.leaves.size();
weak.leaves.push_back((float)rightValNode);
}
else
{
node.right = (int)nnode[ICV_HAAR_RIGHT_NODE_NAME];
}
weak.nodes.push_back(node);
}
}
}
FileStorage newfs(newcascade, FileStorage::WRITE);
if( !newfs.isOpened() )
return false;
size_t maxWeakCount = 0, nfeatures = features.size();
for( i = 0; i < nstages; i++ )
maxWeakCount = std::max(maxWeakCount, stages[i].weaks.size());
newfs << "cascade" << "{:opencv-cascade-classifier"
<< "stageType" << "BOOST"
<< "featureType" << "HAAR"
<< "height" << cascadesize.width
<< "width" << cascadesize.height
<< "stageParams" << "{"
<< "maxWeakCount" << (int)maxWeakCount
<< "}"
<< "featureParams" << "{"
<< "maxCatCount" << 0
<< "}"
<< "stageNum" << (int)nstages
<< "stages" << "[";
for( i = 0; i < nstages; i++ )
{
size_t nweaks = stages[i].weaks.size();
newfs << "{" << "maxWeakCount" << (int)nweaks
<< "stageThreshold" << stages[i].threshold
<< "weakClassifiers" << "[";
for( j = 0; j < nweaks; j++ )
{
const HaarClassifier& c = stages[i].weaks[j];
newfs << "{" << "internalNodes" << "[";
size_t nnodes = c.nodes.size(), nleaves = c.leaves.size();
for( k = 0; k < nnodes; k++ )
newfs << c.nodes[k].left << c.nodes[k].right
<< c.nodes[k].f << c.nodes[k].threshold;
newfs << "]" << "leafValues" << "[";
for( k = 0; k < nleaves; k++ )
newfs << c.leaves[k];
newfs << "]" << "}";
}
newfs << "]" << "}";
}
newfs << "]"
<< "features" << "[";
for( i = 0; i < nfeatures; i++ )
{
const HaarFeature& f = features[i];
newfs << "{" << "rects" << "[";
for( j = 0; j < (size_t)HaarFeature::RECT_NUM; j++ )
{
if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
break;
newfs << "[" << f.rect[j].r.x << f.rect[j].r.y <<
f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight << "]";
}
newfs << "]";
if( f.tilted )
newfs << "tilted" << 1;
newfs << "}";
}
newfs << "]" << "}";
return true;
}
}
bool CascadeClassifier::convert(const String& oldcascade, const String& newcascade)
{
bool ok = haar_cvt::convert(oldcascade, newcascade);
if( !ok && newcascade.size() > 0 )
remove(newcascade.c_str());
return ok;
}
}

186
modules/objdetect/src/opencl/cascadedetect.cl Normal file
View File

@@ -0,0 +1,186 @@
///////////////////////////// OpenCL kernels for face detection //////////////////////////////
////////////////////////////// see the opencv/doc/license.txt ///////////////////////////////
typedef struct __attribute__((aligned(4))) OptFeature
{
int4 ofs[3] __attribute__((aligned (4)));
float4 weight __attribute__((aligned (4)));
}
OptFeature;
typedef struct __attribute__((aligned(4))) Stump
{
int featureIdx __attribute__((aligned (4)));
float threshold __attribute__((aligned (4))); // for ordered features only
float left __attribute__((aligned (4)));
float right __attribute__((aligned (4)));
}
Stump;
typedef struct __attribute__((aligned (4))) Stage
{
int first __attribute__((aligned (4)));
int ntrees __attribute__((aligned (4)));
float threshold __attribute__((aligned (4)));
}
Stage;
__kernel void runHaarClassifierStump(
__global const int* sum,
int sumstep, int sumoffset,
__global const int* sqsum,
int sqsumstep, int sqsumoffset,
__global const OptFeature* optfeatures,
int nstages,
__global const Stage* stages,
__global const Stump* stumps,
volatile __global int* facepos,
int2 imgsize, int xyscale, float factor,
int4 normrect, int2 windowsize, int maxFaces)
{
int ix = get_global_id(0)*xyscale;
int iy = get_global_id(1)*xyscale;
sumstep /= sizeof(int);
sqsumstep /= sizeof(int);
if( ix < imgsize.x && iy < imgsize.y )
{
int ntrees;
int stageIdx, i;
float s = 0.f;
__global const Stump* stump = stumps;
__global const OptFeature* f;
__global const int* psum = sum + mad24(iy, sumstep, ix);
__global const int* pnsum = psum + mad24(normrect.y, sumstep, normrect.x);
int normarea = normrect.z * normrect.w;
float invarea = 1.f/normarea;
float sval = (pnsum[0] - pnsum[normrect.z] - pnsum[mul24(normrect.w, sumstep)] +
pnsum[mad24(normrect.w, sumstep, normrect.z)])*invarea;
float sqval = (sqsum[mad24(iy + normrect.y, sqsumstep, ix + normrect.x)])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
float4 weight, vsval;
int4 ofs, ofs0, ofs1, ofs2;
nf = nf > 0 ? nf : 1.f;
for( stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
ntrees = stages[stageIdx].ntrees;
s = 0.f;
for( i = 0; i < ntrees; i++, stump++ )
{
f = optfeatures + stump->featureIdx;
weight = f->weight;
ofs = f->ofs[0];
sval = (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.x;
ofs = f->ofs[1];
sval += (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.y;
if( weight.z > 0 )
{
ofs = f->ofs[2];
sval += (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.z;
}
s += (sval < stump->threshold*nf) ? stump->left : stump->right;
}
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == nstages )
{
int nfaces = atomic_inc(facepos);
if( nfaces < maxFaces )
{
volatile __global int* face = facepos + 1 + nfaces*4;
face[0] = convert_int_rte(ix*factor);
face[1] = convert_int_rte(iy*factor);
face[2] = convert_int_rte(windowsize.x*factor);
face[3] = convert_int_rte(windowsize.y*factor);
}
}
}
}
#if 0
__kernel void runLBPClassifierStump(
__global const int* sum,
int sumstep, int sumoffset,
__global const int* sqsum,
int sqsumstep, int sqsumoffset,
__global const OptFeature* optfeatures,
int nstages,
__global const Stage* stages,
__global const Stump* stumps,
__global const int* bitsets,
int bitsetSize,
volatile __global int* facepos,
int2 imgsize, int xyscale, float factor,
int4 normrect, int2 windowsize, int maxFaces)
{
int ix = get_global_id(0)*xyscale*VECTOR_SIZE;
int iy = get_global_id(1)*xyscale;
sumstep /= sizeof(int);
sqsumstep /= sizeof(int);
if( ix < imgsize.x && iy < imgsize.y )
{
int ntrees;
int stageIdx, i;
float s = 0.f;
__global const Stump* stump = stumps;
__global const int* bitset = bitsets;
__global const OptFeature* f;
__global const int* psum = sum + mad24(iy, sumstep, ix);
__global const int* pnsum = psum + mad24(normrect.y, sumstep, normrect.x);
int normarea = normrect.z * normrect.w;
float invarea = 1.f/normarea;
float sval = (pnsum[0] - pnsum[normrect.z] - pnsum[mul24(normrect.w, sumstep)] +
pnsum[mad24(normrect.w, sumstep, normrect.z)])*invarea;
float sqval = (sqsum[mad24(iy + normrect.y, sqsumstep, ix + normrect.x)])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
float4 weight;
int4 ofs;
nf = nf > 0 ? nf : 1.f;
for( stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
ntrees = stages[stageIdx].ntrees;
s = 0.f;
for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
{
f = optfeatures + stump->featureIdx;
weight = f->weight;
// compute LBP feature to val
s += (bitset[val >> 5] & (1 << (val & 31))) ? stump->left : stump->right;
}
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == nstages )
{
int nfaces = atomic_inc(facepos);
if( nfaces < maxFaces )
{
volatile __global int* face = facepos + 1 + nfaces*4;
face[0] = convert_int_rte(ix*factor);
face[1] = convert_int_rte(iy*factor);
face[2] = convert_int_rte(windowsize.x*factor);
face[3] = convert_int_rte(windowsize.y*factor);
}
}
}
}
#endif