generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

added lbp cascade test, fixed race conditions problems

Browse Source
This commit is contained in:
Marina Kolpakova
2012-07-04 12:11:16 +00:00
parent 248f39e13e
commit e496345d33
4 changed files with 73 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
modules/gpu/src/cascadeclassifier.cpp
View File

@@ -290,7 +290,7 @@ namespace cv { namespace gpu { namespace device
DevMem2D_<int4> objects,
unsigned int* classified);
int connectedConmonents(DevMem2D_<int4> candidates, int groupThreshold, float grouping_eps, unsigned int* nclasses);
int connectedConmonents(DevMem2D_<int4> candidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
}
}}}
@@ -308,6 +308,7 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
else
objects.create(1 , defaultObjSearchNum, CV_32SC4);
GpuMat candidates(1 , defaultObjSearchNum, CV_32SC4);
if (maxObjectSize == cv::Size())
maxObjectSize = image.size();
@@ -317,6 +318,7 @@ 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;
for( double factor = 1; ; factor *= scaleFactor )
{
@@ -334,25 +336,22 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
// continue;
cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, CV_INTER_LINEAR);
integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32SC1);
cv::gpu::integral(scaledImageBuffer, integral);
int step = (factor <= 2.) + 1;
step = (factor <= 2.) + 1;
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);
integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified);
}
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;
cv::gpu::device::lbp::connectedConmonents(candidates, groupThreshold, grouping_eps, dclassified);
cv::gpu::device::lbp::connectedConmonents(candidates, objects, groupThreshold, grouping_eps, dclassified);
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);
cudaSafeCall( cudaDeviceSynchronize() );
return *classified;
step = *classified;
delete[] classified;
cudaFree(dclassified);
return step;
}
// ============ old fashioned haar cascade ==============================================//

16
modules/gpu/src/cuda/lbp.cu
View File

@@ -51,8 +51,8 @@ namespace cv { namespace gpu { namespace device
__global__ void lbp_classify_stump(Stage* stages, int nstages, ClNode* nodes, const float* leaves, const int* subsets, const uchar4* features,
const DevMem2Di integral, int workWidth, int workHeight, int clWidth, int clHeight, float scale, int step, int subsetSize, DevMem2D_<int4> objects, unsigned int* n)
{
int y = threadIdx.x * scale;
int x = blockIdx.x * scale;
int x = threadIdx.x * step;
int y = blockIdx.x * step;
int current_node = 0;
int current_leave = 0;
@@ -92,7 +92,7 @@ namespace cv { namespace gpu { namespace device
}
template<typename Pr>
__global__ void disjoin(int4* candidates, unsigned int n, int groupThreshold, float grouping_eps, unsigned int* nclasses)
__global__ void disjoin(int4* candidates, int4* objects, unsigned int n, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
using cv::gpu::device::VecTraits;
unsigned int tid = threadIdx.x;
@@ -119,7 +119,7 @@ namespace cv { namespace gpu { namespace device
__syncthreads();
atomicInc((unsigned int*)labels + cls, n);
labels[n - 1] = 0;
*nclasses = 0;
int active = labels[tid];
if (active)
@@ -152,11 +152,9 @@ namespace cv { namespace gpu { namespace device
(n2 > max(3, n1) || n1 < 3) )
break;
}
if( j == n)
{
// printf("founded gpu %d %d %d %d \n", r1[0], r1[1], r1[2], r1[3]);
candidates[atomicInc((unsigned int*)labels + n -1, n)] = VecTraits<int4>::make(r1[0], r1[1], r1[2], r1[3]);
objects[atomicInc(nclasses, n)] = VecTraits<int4>::make(r1[0], r1[1], r1[2], r1[3]);
}
}
}
@@ -179,11 +177,11 @@ namespace cv { namespace gpu { namespace device
workWidth, workHeight, clWidth, clHeight, scale, step, subsetSize, objects, classified);
}
int connectedConmonents(DevMem2D_<int4> candidates, int groupThreshold, float grouping_eps, unsigned int* nclasses)
int connectedConmonents(DevMem2D_<int4> candidates, DevMem2D_<int4> objects, int groupThreshold, float grouping_eps, unsigned int* nclasses)
{
int threads = candidates.cols;
int smem_amount = threads * sizeof(int) + threads * sizeof(int4);
disjoin<InSameComponint><<<1, threads, smem_amount>>>((int4*)candidates.ptr(), candidates.cols, groupThreshold, grouping_eps, nclasses);
disjoin<InSameComponint><<<1, threads, smem_amount>>>((int4*)candidates.ptr(), (int4*)objects.ptr(), candidates.cols, groupThreshold, grouping_eps, nclasses);
return 0;
}
}

4
modules/gpu/src/opencv2/gpu/device/lbp.hpp
View File

@@ -65,12 +65,12 @@ namespace lbp{
struct InSameComponint
{
public:
__device__ __forceinline__ InSameComponint(float _eps) : eps(_eps * 0.5) {}
__device__ __forceinline__ InSameComponint(float _eps) : eps(_eps) {}
__device__ __forceinline__ InSameComponint(const InSameComponint& other) : eps(other.eps) {}
__device__ __forceinline__ bool operator()(const int4& r1, const int4& r2) const
{
double delta = eps * (min(r1.z, r2.z) + min(r1.w, r2.w));
float delta = eps * (min(r1.z, r2.z) + min(r1.w, r2.w)) * 0.5;
return abs(r1.x - r2.x) <= delta && abs(r1.y - r2.y) <= delta
&& abs(r1.x + r1.z - r2.x - r2.z) <= delta && abs(r1.y + r1.w - r2.y - r2.w) <= delta;