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initial support of GPU LBP classifier: added new style xml format loading

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This commit is contained in:
Marina Kolpakova
2012-06-22 15:00:36 +00:00
parent 02170a0a58
commit 1365e28a54
22 changed files with 446 additions and 192 deletions
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2
modules/objdetect/perf/perf_cascadeclassifier.cpp
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@@ -34,7 +34,7 @@ PERF_TEST_P(ImageName_MinSize, CascadeClassifierLBPFrontalFace,
if (cc.empty())
FAIL() << "Can't load cascade file";
Mat img=imread(getDataPath(filename), 0);
Mat img = imread(getDataPath(filename), 0);
if (img.empty())
FAIL() << "Can't load source image";

68
modules/objdetect/src/cascadedetect.hpp
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@@ -56,8 +56,8 @@ namespace cv
+ (step) * ((rect).y + (rect).width + (rect).height)
#define CALC_SUM_(p0, p1, p2, p3, offset) \
((p0)[offset] - (p1)[offset] - (p2)[offset] + (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)
@@ -68,24 +68,24 @@ public:
struct Feature
{
Feature();
float calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read( const FileNode& node );
bool tilted;
enum { RECT_NUM = 3 };
struct
{
Rect r;
float weight;
} rect[RECT_NUM];
const int* p[RECT_NUM][4];
};
HaarEvaluator();
virtual ~HaarEvaluator();
@@ -109,13 +109,13 @@ protected:
Mat sum0, sqsum0, tilted0;
Mat sum, sqsum, tilted;
Rect normrect;
const int *p[4];
const double *pq[4];
int offset;
double varianceNormFactor;
double varianceNormFactor;
};
inline HaarEvaluator::Feature :: Feature()
@@ -123,8 +123,8 @@ 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[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;
}
@@ -134,7 +134,7 @@ inline float HaarEvaluator::Feature :: calc( int offset ) const
if( rect[2].weight != 0.0f )
ret += rect[2].weight * CALC_SUM(p[2], offset);
return ret;
}
@@ -167,27 +167,27 @@ public:
struct Feature
{
Feature();
Feature( int x, int y, int _block_w, int _block_h ) :
Feature( int x, int y, int _block_w, int _block_h ) :
rect(x, y, _block_w, _block_h) {}
int calc( int offset ) const;
void updatePtrs( const Mat& sum );
bool read(const FileNode& node );
Rect rect; // weight and height for block
const int* p[16]; // fast
};
LBPEvaluator();
virtual ~LBPEvaluator();
virtual bool read( const FileNode& node );
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const { return FeatureEvaluator::LBP; }
virtual bool setImage(const Mat& image, Size _origWinSize);
virtual bool setWindow(Point pt);
int operator()(int featureIdx) const
{ return featuresPtr[featureIdx].calc(offset); }
virtual int calcCat(int featureIdx) const
@@ -200,9 +200,9 @@ protected:
Rect normrect;
int offset;
};
};
inline LBPEvaluator::Feature :: Feature()
{
rect = Rect();
@@ -213,7 +213,7 @@ inline LBPEvaluator::Feature :: Feature()
inline int LBPEvaluator::Feature :: calc( int offset ) const
{
int cval = CALC_SUM_( p[5], p[6], p[9], p[10], offset );
return (CALC_SUM_( p[0], p[1], p[4], p[5], offset ) >= cval ? 128 : 0) | // 0
(CALC_SUM_( p[1], p[2], p[5], p[6], offset ) >= cval ? 64 : 0) | // 1
(CALC_SUM_( p[2], p[3], p[6], p[7], offset ) >= cval ? 32 : 0) | // 2
@@ -248,7 +248,7 @@ public:
Feature();
float calc( int offset ) const;
void updatePtrs( const vector<Mat>& _hist, const Mat &_normSum );
bool read( const FileNode& node );
bool read( const FileNode& node );
enum { CELL_NUM = 4, BIN_NUM = 9 };
@@ -331,13 +331,13 @@ inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_f
CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifier::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
for( int si = 0; si < nstages; si++ )
{
CascadeClassifier::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifier::Data::DTree& weak = cascadeWeaks[stage.first + wi];
@@ -355,7 +355,7 @@ inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_f
leafOfs += weak.nodeCount + 1;
}
if( sum < stage.threshold )
return -si;
return -si;
}
return 1;
}
@@ -372,13 +372,13 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifier::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
for(int si = 0; si < nstages; si++ )
{
CascadeClassifier::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifier::Data::DTree& weak = cascadeWeaks[stage.first + wi];
@@ -396,7 +396,7 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
leafOfs += weak.nodeCount + 1;
}
if( sum < stage.threshold )
return -si;
return -si;
}
return 1;
}
@@ -444,7 +444,7 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
#ifdef HAVE_TEGRA_OPTIMIZATION
float tmp; // float accumulator -- float operations are quicker
float tmp; // float accumulator -- float operations are quicker
#endif
for( int si = 0; si < nstages; si++ )
{
@@ -472,11 +472,11 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
#ifdef HAVE_TEGRA_OPTIMIZATION
if( tmp < stage.threshold ) {
sum = (double)tmp;
return -si;
return -si;
}
#else
if( sum < stage.threshold )
return -si;
return -si;
#endif
}

24
modules/objdetect/src/resizeimg.cpp
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@@ -53,13 +53,13 @@ IplImage* resize_opencv(IplImage* img, float scale)
//}
//// resize along each column
//// result is transposed, so we can apply it twice for a complete resize
//void resize1dtran(float *src, int sheight, float *dst, int dheight,
//void resize1dtran(float *src, int sheight, float *dst, int dheight,
// int width, int chan) {
// alphainfo *ofs;
// float scale = (float)dheight/(float)sheight;
// float invscale = (float)sheight/(float)dheight;
//
// // we cache the interpolation values since they can be
//
// // we cache the interpolation values since they can be
// // shared among different columns
// int len = (int)ceilf(dheight*invscale) + 2*dheight;
// int k = 0;
@@ -126,7 +126,7 @@ IplImage* resize_opencv(IplImage* img, float scale)
// int index;
// int widthStep;
// int tW, tH;
//
//
// W = (float)img->width;
// H = (float)img->height;
// channels = img->nChannels;
@@ -149,16 +149,16 @@ IplImage* resize_opencv(IplImage* img, float scale)
// }
// }
// }
//
//
// imgTmp = cvCreateImage(cvSize(tW , tH), IPL_DEPTH_32F, channels);
//
// dst = (float *)malloc(sizeof(float) * (int)(tH * tW) * channels);
// tmp = (float *)malloc(sizeof(float) * (int)(tH * W) * channels);
//
// resize1dtran(src, (int)H, tmp, (int)tH, (int)W , 3);
//
//
// resize1dtran(tmp, (int)W, dst, (int)tW, (int)tH, 3);
//
//
// index = 0;
// //dataf = (float*)imgTmp->imageData;
// for (kk = 0; kk < channels; kk++)
@@ -188,7 +188,7 @@ IplImage* resize_opencv(IplImage* img, float scale)
// int index;
// int widthStep;
// int tW, tH;
//
//
// W = (float)img->width;
// H = (float)img->height;
// channels = img->nChannels;
@@ -210,16 +210,16 @@ IplImage* resize_opencv(IplImage* img, float scale)
// }
// }
// }
//
//
// imgTmp = cvCreateImage(cvSize(tW , tH), IPL_DEPTH_32F, channels);
//
// dst = (float *)malloc(sizeof(float) * (int)(tH * tW) * channels);
// tmp = (float *)malloc(sizeof(float) * (int)(tH * W) * channels);
//
// resize1dtran(src, (int)H, tmp, (int)tH, (int)W , 3);
//
//
// resize1dtran(tmp, (int)W, dst, (int)tW, (int)tH, 3);
//
//
// index = 0;
// for (kk = 0; kk < channels; kk++)
// {
@@ -232,7 +232,7 @@ IplImage* resize_opencv(IplImage* img, float scale)
// }
// }
// }
//
//
// free(src);
// free(dst);
// free(tmp);

12
modules/objdetect/src/routine.cpp
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@@ -2,7 +2,7 @@
#include "_lsvm_routine.h"
int allocFilterObject(CvLSVMFilterObject **obj, const int sizeX,
const int sizeY, const int numFeatures)
const int sizeY, const int numFeatures)
{
int i;
(*obj) = (CvLSVMFilterObject *)malloc(sizeof(CvLSVMFilterObject));
@@ -16,7 +16,7 @@ int allocFilterObject(CvLSVMFilterObject **obj, const int sizeX,
(*obj)->V.x = 0;
(*obj)->V.y = 0;
(*obj)->V.l = 0;
(*obj)->H = (float *) malloc(sizeof (float) *
(*obj)->H = (float *) malloc(sizeof (float) *
(sizeX * sizeY * numFeatures));
for(i = 0; i < sizeX * sizeY * numFeatures; i++)
{
@@ -33,7 +33,7 @@ int freeFilterObject (CvLSVMFilterObject **obj)
return LATENT_SVM_OK;
}
int allocFeatureMapObject(CvLSVMFeatureMap **obj, const int sizeX,
int allocFeatureMapObject(CvLSVMFeatureMap **obj, const int sizeX,
const int sizeY, const int numFeatures)
{
int i;
@@ -41,7 +41,7 @@ int allocFeatureMapObject(CvLSVMFeatureMap **obj, const int sizeX,
(*obj)->sizeX = sizeX;
(*obj)->sizeY = sizeY;
(*obj)->numFeatures = numFeatures;
(*obj)->map = (float *) malloc(sizeof (float) *
(*obj)->map = (float *) malloc(sizeof (float) *
(sizeX * sizeY * numFeatures));
for(i = 0; i < sizeX * sizeY * numFeatures; i++)
{
@@ -59,7 +59,7 @@ int freeFeatureMapObject (CvLSVMFeatureMap **obj)
}
int allocFeaturePyramidObject(CvLSVMFeaturePyramid **obj,
const int numLevels)
const int numLevels)
{
(*obj) = (CvLSVMFeaturePyramid *)malloc(sizeof(CvLSVMFeaturePyramid));
(*obj)->numLevels = numLevels;
@@ -70,7 +70,7 @@ int allocFeaturePyramidObject(CvLSVMFeaturePyramid **obj,
int freeFeaturePyramidObject (CvLSVMFeaturePyramid **obj)
{
int i;
int i;
if(*obj == NULL) return LATENT_SVM_MEM_NULL;
for(i = 0; i < (*obj)->numLevels; i++)
{