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refactored GPU LBP cascade. Added support for big images. Fixed bug in connected components function

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This commit is contained in:
Marina Kolpakova
2012-07-11 12:22:22 +00:00
parent 469ec7c522
commit ed1b293d34
3 changed files with 201 additions and 111 deletions
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80
modules/gpu/src/cascadeclassifier.cpp
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@@ -315,7 +315,24 @@ namespace cv { namespace gpu { namespace device
DevMem2D_<int4> objects,
unsigned int* classified);
int connectedConmonents(DevMem2D_<int4> candidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
void classifyStumpFixed(const DevMem2Db& mstages,
const int nstages,
const DevMem2Di& mnodes,
const DevMem2Df& mleaves,
const DevMem2Di& msubsets,
const DevMem2Db& mfeatures,
const int workWidth,
const int workHeight,
const int clWidth,
const int clHeight,
float scale,
int step,
int subsetSize,
DevMem2D_<int4> objects,
unsigned int* classified,
const int maxX);
int connectedConmonents(DevMem2D_<int4> candidates, int ncandidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
void bindIntegral(DevMem2Di integral);
void unbindIntegral();
}
@@ -337,8 +354,8 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
GpuMat candidates(1 , image.cols >> 1, CV_32SC4);
// GpuMat candidates(1 , defaultObjSearchNum, CV_32SC4);
// used for debug
// candidates.setTo(cv::Scalar::all(0));
// objects.setTo(cv::Scalar::all(0));
candidates.setTo(cv::Scalar::all(0));
objects.setTo(cv::Scalar::all(0));
if (maxObjectSize == cv::Size())
maxObjectSize = image.size();
@@ -349,16 +366,50 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
unsigned int* dclassified;
cudaMalloc(&dclassified, sizeof(int));
cudaMemcpy(dclassified, classified, sizeof(int), cudaMemcpyHostToDevice);
int step;
int step = 2;
cv::gpu::device::lbp::bindIntegral(integral);
for( double factor = 1; ; factor *= scaleFactor )
{
// if (factor > 2.0) break;
cv::Size windowSize(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
cv::Size scaledImageSize(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
cv::Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
cv::Size scaledImageSize(image.cols, image.rows);
cv::Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
cv::Size windowSize(NxM.width, NxM.height);
double factor = 1;
for (; processingRectSize.width / step >= 256;)
{
// std::cout << "IN FIXED: factor " << factor << " size " << processingRectSize.width << " " << processingRectSize.height << std::endl;
// if (factor > 2.0) break;
if (processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height )
break;
// if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height )
// continue;
GpuMat scaledImg(resuzeBuffer, cv::Rect(0, 0, scaledImageSize.width, scaledImageSize.height));
GpuMat scaledIntegral(integral, cv::Rect(0, 0, scaledImageSize.width + 1, scaledImageSize.height + 1));
GpuMat currBuff = integralBuffer;
cv::gpu::resize(image, scaledImg, scaledImageSize, 0, 0, CV_INTER_LINEAR);
cv::gpu::integralBuffered(scaledImg, scaledIntegral, currBuff);
step = (factor <= 2.) + 1;
cv::gpu::device::lbp::classifyStumpFixed(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified, processingRectSize.width);
factor *= scaleFactor;
windowSize = cv::Size(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
scaledImageSize = cv::Size(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
processingRectSize = cv::Size(scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
}
for (; /*processingRectSize.width / step >= 128*/;)
{
// std::cout << "In FLOATING: factor " << factor << " size " << processingRectSize.width << " " << processingRectSize.height << std::endl;
// if (factor > 2.0) break;
if (processingRectSize.width <= 0 || processingRectSize.height <= 0 )
break;
@@ -379,12 +430,19 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
cv::gpu::device::lbp::classifyStump(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified);
factor *= scaleFactor;
windowSize = cv::Size(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
scaledImageSize = cv::Size(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
processingRectSize = cv::Size(scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
}
cv::gpu::device::lbp::unbindIntegral();
if (groupThreshold <= 0 || objects.empty())
return 0;
cv::gpu::device::lbp::connectedConmonents(candidates, objects, groupThreshold, grouping_eps, dclassified);
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
// std::cout << "!!! CLASSIFIED " << *classified << std::endl;
cv::gpu::device::lbp::connectedConmonents(candidates, *classified, objects, groupThreshold, grouping_eps, dclassified);
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
cudaSafeCall( cudaDeviceSynchronize() );
step = *classified;