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Merge pull request #2021 from vpisarev:ocl_facedetect7
This commit is contained in:
Roman Donchenko
OpenCV Buildbot
commit
b4bd5bab6d
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@ -428,6 +428,61 @@ public:
|
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i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this;
|
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}
|
||||
|
||||
template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
|
||||
typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
|
||||
typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12>
|
||||
Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
|
||||
const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
|
||||
const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
|
||||
const _Tp12& a12)
|
||||
{
|
||||
int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
|
||||
i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
|
||||
set(i, a12); return *this;
|
||||
}
|
||||
|
||||
template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
|
||||
typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
|
||||
typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
|
||||
typename _Tp13>
|
||||
Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
|
||||
const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
|
||||
const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
|
||||
const _Tp12& a12, const _Tp13& a13)
|
||||
{
|
||||
int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
|
||||
i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
|
||||
i = set(i, a12); set(i, a13); return *this;
|
||||
}
|
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|
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template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
|
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typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
|
||||
typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
|
||||
typename _Tp13, typename _Tp14>
|
||||
Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
|
||||
const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
|
||||
const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
|
||||
const _Tp12& a12, const _Tp13& a13, const _Tp14& a14)
|
||||
{
|
||||
int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
|
||||
i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
|
||||
i = set(i, a12); i = set(i, a13); set(i, a14); return *this;
|
||||
}
|
||||
|
||||
template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
|
||||
typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
|
||||
typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
|
||||
typename _Tp13, typename _Tp14, typename _Tp15>
|
||||
Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
|
||||
const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
|
||||
const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
|
||||
const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15)
|
||||
{
|
||||
int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
|
||||
i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
|
||||
i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this;
|
||||
}
|
||||
|
||||
bool run(int dims, size_t globalsize[],
|
||||
size_t localsize[], bool sync, const Queue& q=Queue());
|
||||
bool runTask(bool sync, const Queue& q=Queue());
|
||||
|
||||
@ -1076,6 +1076,11 @@ CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
|
||||
bool normalize = true,
|
||||
int borderType = BORDER_DEFAULT );
|
||||
|
||||
CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
|
||||
Size ksize, Point anchor = Point(-1, -1),
|
||||
bool normalize = true,
|
||||
int borderType = BORDER_DEFAULT );
|
||||
|
||||
//! a synonym for normalized box filter
|
||||
CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
|
||||
Size ksize, Point anchor = Point(-1,-1),
|
||||
|
||||
@ -895,6 +895,114 @@ void cv::blur( InputArray src, OutputArray dst,
|
||||
boxFilter( src, dst, -1, ksize, anchor, true, borderType );
|
||||
}
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
Squared Box Filter
|
||||
\****************************************************************************************/
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
template<typename T, typename ST> struct SqrRowSum : public BaseRowFilter
|
||||
{
|
||||
SqrRowSum( int _ksize, int _anchor )
|
||||
{
|
||||
ksize = _ksize;
|
||||
anchor = _anchor;
|
||||
}
|
||||
|
||||
void operator()(const uchar* src, uchar* dst, int width, int cn)
|
||||
{
|
||||
const T* S = (const T*)src;
|
||||
ST* D = (ST*)dst;
|
||||
int i = 0, k, ksz_cn = ksize*cn;
|
||||
|
||||
width = (width - 1)*cn;
|
||||
for( k = 0; k < cn; k++, S++, D++ )
|
||||
{
|
||||
ST s = 0;
|
||||
for( i = 0; i < ksz_cn; i += cn )
|
||||
{
|
||||
ST val = (ST)S[i];
|
||||
s += val*val;
|
||||
}
|
||||
D[0] = s;
|
||||
for( i = 0; i < width; i += cn )
|
||||
{
|
||||
ST val0 = (ST)S[i], val1 = (ST)S[i + ksz_cn];
|
||||
s += val1*val1 - val0*val0;
|
||||
D[i+cn] = s;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
static Ptr<BaseRowFilter> getSqrRowSumFilter(int srcType, int sumType, int ksize, int anchor)
|
||||
{
|
||||
int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType);
|
||||
CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(srcType) );
|
||||
|
||||
if( anchor < 0 )
|
||||
anchor = ksize/2;
|
||||
|
||||
if( sdepth == CV_8U && ddepth == CV_32S )
|
||||
return makePtr<SqrRowSum<uchar, int> >(ksize, anchor);
|
||||
if( sdepth == CV_8U && ddepth == CV_64F )
|
||||
return makePtr<SqrRowSum<uchar, double> >(ksize, anchor);
|
||||
if( sdepth == CV_16U && ddepth == CV_64F )
|
||||
return makePtr<SqrRowSum<ushort, double> >(ksize, anchor);
|
||||
if( sdepth == CV_16S && ddepth == CV_64F )
|
||||
return makePtr<SqrRowSum<short, double> >(ksize, anchor);
|
||||
if( sdepth == CV_32F && ddepth == CV_64F )
|
||||
return makePtr<SqrRowSum<float, double> >(ksize, anchor);
|
||||
if( sdepth == CV_64F && ddepth == CV_64F )
|
||||
return makePtr<SqrRowSum<double, double> >(ksize, anchor);
|
||||
|
||||
CV_Error_( CV_StsNotImplemented,
|
||||
("Unsupported combination of source format (=%d), and buffer format (=%d)",
|
||||
srcType, sumType));
|
||||
|
||||
return Ptr<BaseRowFilter>();
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
void cv::sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
|
||||
Size ksize, Point anchor,
|
||||
bool normalize, int borderType )
|
||||
{
|
||||
Mat src = _src.getMat();
|
||||
int sdepth = src.depth(), cn = src.channels();
|
||||
if( ddepth < 0 )
|
||||
ddepth = sdepth < CV_32F ? CV_32F : CV_64F;
|
||||
_dst.create( src.size(), CV_MAKETYPE(ddepth, cn) );
|
||||
Mat dst = _dst.getMat();
|
||||
if( borderType != BORDER_CONSTANT && normalize )
|
||||
{
|
||||
if( src.rows == 1 )
|
||||
ksize.height = 1;
|
||||
if( src.cols == 1 )
|
||||
ksize.width = 1;
|
||||
}
|
||||
|
||||
int sumType = CV_64F;
|
||||
if( sdepth == CV_8U )
|
||||
sumType = CV_32S;
|
||||
sumType = CV_MAKETYPE( sumType, cn );
|
||||
int srcType = CV_MAKETYPE(sdepth, cn);
|
||||
int dstType = CV_MAKETYPE(ddepth, cn);
|
||||
|
||||
Ptr<BaseRowFilter> rowFilter = getSqrRowSumFilter(srcType, sumType, ksize.width, anchor.x );
|
||||
Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType,
|
||||
dstType, ksize.height, anchor.y,
|
||||
normalize ? 1./(ksize.width*ksize.height) : 1);
|
||||
|
||||
Ptr<FilterEngine> f = makePtr<FilterEngine>(Ptr<BaseFilter>(), rowFilter, columnFilter,
|
||||
srcType, dstType, sumType, borderType );
|
||||
f->apply( src, dst );
|
||||
}
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
Gaussian Blur
|
||||
\****************************************************************************************/
|
||||
|
||||
@ -86,12 +86,14 @@ FeatureEvaluator::setImage
|
||||
------------------------------
|
||||
Assigns an image to feature evaluator.
|
||||
|
||||
.. ocv:function:: bool FeatureEvaluator::setImage(const Mat& img, Size origWinSize)
|
||||
.. ocv:function:: bool FeatureEvaluator::setImage(InputArray img, Size origWinSize, Size sumSize)
|
||||
|
||||
:param img: Matrix of the type ``CV_8UC1`` containing an image where the features are computed.
|
||||
|
||||
:param origWinSize: Size of training images.
|
||||
|
||||
:param sumSize: The requested size of integral images (so if the integral image is smaller, it resides in the top-left corner of the larger image of requested size). Because the features are represented using offsets from the image origin, using the same sumSize for all scales helps to avoid constant readjustments of the features to different scales.
|
||||
|
||||
The method assigns an image, where the features will be computed, to the feature evaluator.
|
||||
|
||||
|
||||
|
||||
@ -111,11 +111,14 @@ public:
|
||||
};
|
||||
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
||||
CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
|
||||
int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
|
||||
double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
||||
std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
|
||||
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
|
||||
std::vector<double>& foundScales,
|
||||
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
|
||||
|
||||
class CV_EXPORTS FeatureEvaluator
|
||||
@ -132,7 +135,7 @@ public:
|
||||
virtual Ptr<FeatureEvaluator> clone() const;
|
||||
virtual int getFeatureType() const;
|
||||
|
||||
virtual bool setImage(const Mat& img, Size origWinSize);
|
||||
virtual bool setImage(InputArray img, Size origWinSize, Size sumSize);
|
||||
virtual bool setWindow(Point p);
|
||||
|
||||
virtual double calcOrd(int featureIdx) const;
|
||||
@ -232,6 +235,8 @@ public:
|
||||
CV_WRAP int getFeatureType() const;
|
||||
void* getOldCascade();
|
||||
|
||||
CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
|
||||
|
||||
void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
|
||||
Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
|
||||
|
||||
|
||||
@ -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
|
||||
@ -113,6 +114,13 @@ 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)
|
||||
{
|
||||
if( groupThreshold <= 0 || rectList.empty() )
|
||||
@ -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;
|
||||
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( sum0.rows < rn || sum0.cols < cn )
|
||||
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);
|
||||
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 )
|
||||
{
|
||||
tilted = Mat(rn, cn, CV_32S, tilted0.data);
|
||||
integral(image, sum, sqsum, tilted);
|
||||
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, 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, usum, noArray(), noArray(), CV_32S);
|
||||
}
|
||||
|
||||
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 );
|
||||
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
|
||||
{
|
||||
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_OFS( nofs[0], nofs[1], nofs[2], nofs[3], 0, normrect, sumStep );
|
||||
|
||||
size_t fi, nfeatures = features->size();
|
||||
const std::vector<Feature>& ff = *features;
|
||||
|
||||
if( sumSize0 != _sumSize )
|
||||
{
|
||||
optfeatures->resize(nfeatures);
|
||||
optfeaturesPtr = &(*optfeatures)[0];
|
||||
for( fi = 0; fi < nfeatures; fi++ )
|
||||
featuresPtr[fi].updatePtrs( !featuresPtr[fi].tilted ? sum : tilted );
|
||||
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. )
|
||||
@ -565,11 +624,24 @@ 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 )
|
||||
{
|
||||
candidates.clear();
|
||||
Size imgsz = _image.size();
|
||||
int imgtype = _image.type();
|
||||
|
||||
Mat grayImage, imageBuffer;
|
||||
|
||||
candidates.clear();
|
||||
rejectLevels.clear();
|
||||
levelWeights.clear();
|
||||
|
||||
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
|
||||
maxObjectSize = imgsz;
|
||||
|
||||
bool use_ocl = ocl::useOpenCL() &&
|
||||
getFeatureType() == FeatureEvaluator::HAAR &&
|
||||
!isOldFormatCascade() &&
|
||||
data.isStumpBased() &&
|
||||
maskGenerator.empty() &&
|
||||
!outputRejectLevels &&
|
||||
tryOpenCL;
|
||||
|
||||
if( !use_ocl )
|
||||
{
|
||||
Mat image = _image.getMat();
|
||||
if (maskGenerator)
|
||||
maskGenerator->initializeMask(image);
|
||||
|
||||
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
|
||||
maxObjectSize = image.size();
|
||||
|
||||
Mat grayImage = image;
|
||||
if( grayImage.channels() > 1 )
|
||||
grayImage = image;
|
||||
if( CV_MAT_CN(imgtype) > 1 )
|
||||
{
|
||||
Mat temp;
|
||||
cvtColor(grayImage, temp, COLOR_BGR2GRAY);
|
||||
grayImage = temp;
|
||||
}
|
||||
|
||||
Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
|
||||
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;
|
||||
@ -1134,9 +1296,6 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::v
|
||||
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 )
|
||||
{
|
||||
@ -1147,19 +1306,49 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::v
|
||||
yStep = factor > 2. ? 1 : 2;
|
||||
}
|
||||
|
||||
int stripCount, stripSize;
|
||||
if( use_ocl )
|
||||
{
|
||||
UMat uscaledImage(uimageBuffer, Rect(0, 0, scaledImageSize.width, scaledImageSize.height));
|
||||
resize( ugrayImage, uscaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
|
||||
|
||||
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( ocl_detectSingleScale( uscaledImage, processingRectSize, yStep, factor, sumSize0 ) )
|
||||
continue;
|
||||
|
||||
if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates,
|
||||
rejectLevels, levelWeights, outputRejectLevels ) )
|
||||
/////// 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]));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
|
||||
std::vector<int>& rejectLevels,
|
||||
std::vector<double>& levelWeights,
|
||||
@ -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);
|
||||
@ -1342,11 +1539,34 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
|
||||
|
||||
@ -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 };
|
||||
|
||||
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,
|
||||
Size sumSize0, bool outputRejectLevels = false );
|
||||
bool ocl_detectSingleScale( InputArray image, Size processingRectSize,
|
||||
int yStep, double factor, Size sumSize0 );
|
||||
|
||||
|
||||
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
|
||||
@ -106,13 +110,28 @@ protected:
|
||||
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;
|
||||
}
|
||||
}
|
||||
|
||||
275
modules/objdetect/src/cascadedetect_convert.cpp
Normal file
275
modules/objdetect/src/cascadedetect_convert.cpp
Normal file
@ -0,0 +1,275 @@
|
||||
/*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;
|
||||
Size cascadesize;
|
||||
cascadesize.width = (int)sznode[0];
|
||||
cascadesize.height = (int)sznode[1];
|
||||
std::vector<HaarFeature> features;
|
||||
|
||||
int i, j, k, n;
|
||||
|
||||
FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
|
||||
int nstages = (int)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];
|
||||
int nweaks = (int)weaks_seq.size();
|
||||
stage.weaks.resize(nweaks);
|
||||
|
||||
for( j = 0; j < nweaks; j++ )
|
||||
{
|
||||
HaarClassifier& weak = stage.weaks[j];
|
||||
FileNode weaknode = weaks_seq[j];
|
||||
int nnodes = (int)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];
|
||||
int nrects = (int)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;
|
||||
|
||||
int maxWeakCount = 0, nfeatures = (int)features.size();
|
||||
for( i = 0; i < nstages; i++ )
|
||||
maxWeakCount = std::max(maxWeakCount, (int)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++ )
|
||||
{
|
||||
int nweaks = (int)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" << "[";
|
||||
int nnodes = (int)c.nodes.size(), nleaves = (int)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 < 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;
|
||||
}
|
||||
|
||||
}
|
||||
185
modules/objdetect/src/opencl/cascadedetect.cl
Normal file
185
modules/objdetect/src/opencl/cascadedetect.cl
Normal file
@ -0,0 +1,185 @@
|
||||
///////////////////////////// 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
|
||||
@ -98,6 +98,8 @@ int main( int argc, const char** argv )
|
||||
return -1;
|
||||
}
|
||||
|
||||
cout << "old cascade: " << (cascade.isOldFormatCascade() ? "TRUE" : "FALSE") << endl;
|
||||
|
||||
if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') )
|
||||
{
|
||||
int c = inputName.empty() ? 0 : inputName.c_str()[0] - '0';
|
||||
@ -199,13 +201,12 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
|
||||
t = (double)getTickCount();
|
||||
|
||||
cvtColor( img, gray, COLOR_BGR2GRAY );
|
||||
resize( gray, smallImg, Size(), scale0, scale0, INTER_LINEAR );
|
||||
cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
|
||||
equalizeHist( smallImg, smallImg );
|
||||
resize( img, smallImg, Size(), scale0, scale0, INTER_LINEAR );
|
||||
cvtColor( smallImg, gray, COLOR_BGR2GRAY );
|
||||
equalizeHist( gray, gray );
|
||||
|
||||
cascade.detectMultiScale( smallImg, faces,
|
||||
1.1, 2, 0
|
||||
cascade.detectMultiScale( gray, faces,
|
||||
1.1, 3, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
|CASCADE_SCALE_IMAGE
|
||||
@ -213,8 +214,8 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
Size(30, 30) );
|
||||
if( tryflip )
|
||||
{
|
||||
flip(smallImg, smallImg, 1);
|
||||
cascade.detectMultiScale( smallImg, faces2,
|
||||
flip(gray, gray, 1);
|
||||
cascade.detectMultiScale( gray, faces2,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
//|CASCADE_DO_ROUGH_SEARCH
|
||||
@ -227,7 +228,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
}
|
||||
}
|
||||
t = (double)getTickCount() - t;
|
||||
cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
|
||||
smallImg.copyTo(canvas);
|
||||
|
||||
double fps = getTickFrequency()/t;
|
||||
|
||||
@ -255,7 +256,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
color, 3, 8, 0);
|
||||
if( nestedCascade.empty() )
|
||||
continue;
|
||||
UMat smallImgROI = smallImg(*r);
|
||||
UMat smallImgROI = gray(*r);
|
||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
|
||||
Loading…
Reference in New Issue
Block a user