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LBP: implemented image data preparing

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This commit is contained in:
Marina Kolpakova 2012-06-25 16:39:45 +00:00
parent 91ba48c5f9
commit 71f94e12fb
2 changed files with 31 additions and 5 deletions
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4
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1436,6 +1436,7 @@ public:
void release(); void release();
int detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4/*, Size minSize = Size()*/); int detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4/*, Size minSize = Size()*/);
void preallocateIntegralBuffer(cv::Size desired);
bool findLargestObject; bool findLargestObject;
bool visualizeInPlace; bool visualizeInPlace;
@ -1450,6 +1451,8 @@ private:
cv::Size NxM; cv::Size NxM;
bool isStumps; bool isStumps;
int ncategories; int ncategories;
int subsetSize;
int nodeStep;
// located on gpu // located on gpu
GpuMat stage_mat; GpuMat stage_mat;
@ -1457,6 +1460,7 @@ private:
GpuMat nodes_mat; GpuMat nodes_mat;
GpuMat leaves_mat; GpuMat leaves_mat;
GpuMat subsets_mat; GpuMat subsets_mat;
GpuMat integral;
}; };
////////////////////////////////// SURF ////////////////////////////////////////// ////////////////////////////////// SURF //////////////////////////////////////////

26
modules/gpu/src/cascadeclassifier.cpp
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@ -84,6 +84,7 @@ cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP() {
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; } bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&) { throw_nogpu(); return true; } bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); } Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { throw_nogpu(); return Size(); }
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired) { throw_nogpu();}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; } int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size) { throw_nogpu(); return 0; }
@ -97,6 +98,12 @@ cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
{ {
} }
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired)
{
integral.create(desired.width + 1, desired.height + 1, CV_32FC1);
}
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
{ {
return stage_mat.empty(); return stage_mat.empty();
@ -140,6 +147,7 @@ bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
CV_Assert( NxM.height > 0 && NxM.width > 0 ); CV_Assert( NxM.height > 0 && NxM.width > 0 );
isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false; isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false;
CV_Assert(isStumps);
// features // features
FileNode fn = root[GPU_CC_FEATURE_PARAMS]; FileNode fn = root[GPU_CC_FEATURE_PARAMS];
@ -148,7 +156,7 @@ bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
ncategories = fn[GPU_CC_MAX_CAT_COUNT]; ncategories = fn[GPU_CC_MAX_CAT_COUNT];
int subsetSize = (ncategories + 31) / 32, nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 ); subsetSize = (ncategories + 31) / 32, nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );
fn = root[GPU_CC_STAGES]; fn = root[GPU_CC_STAGES];
if (fn.empty()) if (fn.empty())
@ -293,10 +301,24 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
break; break;
// TODO: min max object sizes cheching // TODO: min max object sizes cheching
cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, INTER_NEAREST); cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, INTER_NEAREST);
//prepare image for evaluation
integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32FC1);
cv::gpu::integral(scaledImageBuffer, integral);
int yStep = (factor > 2.) + 1; int step = (factor <= 2.) + 1;
int stripCount = 1, stripSize = processingRectSize.height; int stripCount = 1, stripSize = processingRectSize.height;
int y1 = 0;
int y2 = processingRectSize.height;
for (int y = y1; y < y2; y += step)
for (int x = 0; x < processingRectSize.width; x+=step)
{
//ToDO: classify
int result = 0;
}
} }
// TODO: reject levels // TODO: reject levels