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added hipotesis filtration

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This commit is contained in:
Marina Kolpakova
2012-07-04 04:51:09 +00:00
parent a53f0f397e
commit 4128d5782f
3 changed files with 139 additions and 10 deletions
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modules/gpu/src/cascadeclassifier.cpp
+12 -6
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@@ -273,7 +273,7 @@ namespace cv { namespace gpu { namespace device
{
namespace lbp
{
classifyStump(const DevMem2Db mstages,
void classifyStump(const DevMem2Db mstages,
const int nstages,
const DevMem2Di mnodes,
const DevMem2Df mleaves,
@@ -289,16 +289,19 @@ namespace cv { namespace gpu { namespace device
int subsetSize,
DevMem2D_<int4> objects,
unsigned int* classified);
int connectedConmonents(DevMem2D_<int4> candidates, int groupThreshold, float grouping_eps, unsigned int* nclasses);
}
}}}
int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objects,
double scaleFactor, int minNeighbors, cv::Size maxObjectSize /*, Size minSize=Size()*/)
double scaleFactor, int groupThreshold, cv::Size maxObjectSize /*, Size minSize=Size()*/)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
CV_Assert(!empty());
const int defaultObjSearchNum = 100;
const float grouping_eps = 0.2;
if( !objects.empty() && objects.depth() == CV_32S)
objects.reshape(4, 1);
@@ -340,11 +343,14 @@ 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,
integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects, dclassified);
}
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
std::cout << *classified << "Results: " << cv::Mat(objects).row(0).colRange(0, *classified) << std::endl;
// TODO: reject levels
return 0;
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
GpuMat candidates(1, *classified, objects.type(), objects.ptr());
// std::cout << *classified << " Results: " << cv::Mat(candidates) << std::endl;
if (groupThreshold <= 0 || objects.empty())
return 0;
return cv::gpu::device::lbp::connectedConmonents(candidates, groupThreshold, grouping_eps, dclassified);
}
// ============ old fashioned haar cascade ==============================================//