Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

added delobal memory version

Browse Source
pull/2/head
Marina Kolpakova 13 years ago
parent 422f650b20
commit 965109228d
2 changed files with 127 additions and 47 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 70
      modules/gpu/src/cascadeclassifier.cpp
  2. 104
      modules/gpu/src/cuda/lbp.cu

70
modules/gpu/src/cascadeclassifier.cpp
Unescape View File

@ -298,37 +298,39 @@ namespace cv { namespace gpu { namespace device
{
namespace lbp
{
void classifyStump(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);
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);
// void classifyStump(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);
void classifyStumpFixed(const DevMem2Di& integral,
const int integralPitch,
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);
int connectedConmonents(DevMem2D_<int4> candidates, int ncandidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
void bindIntegral(DevMem2Di integral);
@ -365,7 +367,7 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
cudaMalloc(&dclassified, sizeof(int));
cudaMemcpy(dclassified, classified, sizeof(int), cudaMemcpyHostToDevice);
int step = 2;
cv::gpu::device::lbp::bindIntegral(integral);
// cv::gpu::device::lbp::bindIntegral(integral);
cv::Size scaledImageSize(image.cols, image.rows);
cv::Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
@ -393,7 +395,7 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
step = (factor <= 2.) + 1;
cv::gpu::device::lbp::classifyStumpFixed(stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
cv::gpu::device::lbp::classifyStumpFixed(integral, integral.step1(), 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;
@ -402,7 +404,7 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
processingRectSize = cv::Size(scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
}
cv::gpu::device::lbp::unbindIntegral();
// cv::gpu::device::lbp::unbindIntegral();
if (groupThreshold <= 0 || objects.empty())
return 0;
cudaMemcpy(classified, dclassified, sizeof(int), cudaMemcpyDeviceToHost);

104
modules/gpu/src/cuda/lbp.cu
Unescape View File

@ -56,6 +56,80 @@ namespace cv { namespace gpu { namespace device
__host__ __device__ __forceinline__ LBP(const LBP& other) {(void)other;}
__host__ __device__ __forceinline__ LBP() {}
// for integral matrix stored in the global memory
__device__ __forceinline__ int operator() (const int* integral, const int pitch, int ty, int tx, int fh, int fw, int& shift) const
{
int anchors[9];
anchors[0] = integral[ty * pitch + tx];
anchors[1] = integral[ty * pitch + tx + fw];
anchors[0] -= anchors[1];
anchors[2] = integral[ty * pitch + tx + fw * 2];
anchors[1] -= anchors[2];
anchors[2] -= integral[ty * pitch + tx + fw * 3];
ty += fh;
anchors[3] = integral[ty * pitch + tx];
anchors[4] = integral[ty * pitch + tx + fw];
anchors[3] -= anchors[4];
anchors[5] = integral[ty * pitch + tx + fw * 2];
anchors[4] -= anchors[5];
anchors[5] -= integral[ty * pitch + tx + fw * 3];
anchors[0] -= anchors[3];
anchors[1] -= anchors[4];
anchors[2] -= anchors[5];
// 0 - 2 contains s0 - s2
ty += fh;
anchors[6] = integral[ty * pitch + tx];
anchors[7] = integral[ty * pitch + tx + fw];
anchors[6] -= anchors[7];
anchors[8] = integral[ty * pitch + tx + fw * 2];
anchors[7] -= anchors[8];
anchors[8] -= integral[ty * pitch + tx + fw * 3];
anchors[3] -= anchors[6];
anchors[4] -= anchors[7];
anchors[5] -= anchors[8];
// 3 - 5 contains s3 - s5
anchors[0] -= anchors[4];
anchors[1] -= anchors[4];
anchors[2] -= anchors[4];
anchors[3] -= anchors[4];
anchors[5] -= anchors[4];
int response = (~(anchors[0] >> 31)) & 4;
response |= (~(anchors[1] >> 31)) & 2;;
response |= (~(anchors[2] >> 31)) & 1;
shift = (~(anchors[5] >> 31)) & 16;
shift |= (~(anchors[3] >> 31)) & 1;
ty += fh;
anchors[0] = integral[ty * pitch + tx];
anchors[1] = integral[ty * pitch + tx + fw];
anchors[0] -= anchors[1];
anchors[2] = integral[ty * pitch + tx + fw * 2];
anchors[1] -= anchors[2];
anchors[2] -= integral[ty * pitch + tx + fw * 3];
anchors[6] -= anchors[0];
anchors[7] -= anchors[1];
anchors[8] -= anchors[2];
// 0 -2 contains s6 - s8
anchors[6] -= anchors[4];
anchors[7] -= anchors[4];
anchors[8] -= anchors[4];
shift |= (~(anchors[6] >> 31)) & 2;
shift |= (~(anchors[7] >> 31)) & 4;
shift |= (~(anchors[8] >> 31)) & 8;
return response;
}
// for texture fetchrd integral matrix
__device__ __forceinline__ int operator() (int ty, int tx, int fh, int fw, int& shift) const
{
int anchors[9];
@ -143,9 +217,9 @@ namespace cv { namespace gpu { namespace device
struct Classifier
{
__host__ __device__ __forceinline__ Classifier(const Stage* _stages, const ClNode* _nodes, const float* _leaves, const int* _subsets, const uchar4* _features,
__host__ __device__ __forceinline__ Classifier(const int* _integral, const int _pitch, const Stage* _stages, const ClNode* _nodes, const float* _leaves, const int* _subsets, const uchar4* _features,
const int _nstages, const int _clWidth, const int _clHeight, const float _scale, const int _step, const int _subsetSize)
: stages(_stages), nodes(_nodes), leaves(_leaves), subsets(_subsets), features(_features), nstages(_nstages), clWidth(_clWidth), clHeight(_clHeight),
: integral(_integral), pitch(_pitch), stages(_stages), nodes(_nodes), leaves(_leaves), subsets(_subsets), features(_features), nstages(_nstages), clWidth(_clWidth), clHeight(_clHeight),
scale(_scale), step(_step), subsetSize(_subsetSize){}
__device__ __forceinline__ void operator() (int y, int x, DevMem2D_<int4> objects, const unsigned int maxN, unsigned int* n) const
@ -163,7 +237,8 @@ namespace cv { namespace gpu { namespace device
uchar4 feature = features[node.featureIdx];
int shift;
int c = evaluator(y + feature.y, x + feature.x, feature.w, feature.z, shift);
// int c = evaluator(y + feature.y, x + feature.x, feature.w, feature.z, shift);
int c = evaluator(integral, pitch, (y + feature.y), x + feature.x, feature.w, feature.z, shift);
int idx = (subsets[ current_node * subsetSize + c] & ( 1 << shift)) ? current_leave : current_leave + 1;
sum += leaves[idx];
@ -189,6 +264,9 @@ namespace cv { namespace gpu { namespace device
objects(0, res) = rect;
}
const int* integral;
const int pitch;
const Stage* stages;
const ClNode* nodes;
const float* leaves;
@ -292,24 +370,24 @@ namespace cv { namespace gpu { namespace device
}
}
void classifyStump(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)
{
int blocks = ceilf(workHeight / (float)step);
int threads = ceilf(workWidth / (float)step);
// void classifyStump(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)
// {
// int blocks = ceilf(workHeight / (float)step);
// int threads = ceilf(workWidth / (float)step);
Classifier clr((Stage*)(mstages.ptr()), (ClNode*)(mnodes.ptr()), mleaves.ptr(), msubsets.ptr(), (uchar4*)(mfeatures.ptr()), nstages, clWidth, clHeight, scale, step, subsetSize);
lbp_classify_stump<<<blocks, threads>>>(clr, objects, objects.cols, classified);
}
// Classifier clr((Stage*)(mstages.ptr()), (ClNode*)(mnodes.ptr()), mleaves.ptr(), msubsets.ptr(), (uchar4*)(mfeatures.ptr()), nstages, clWidth, clHeight, scale, step, subsetSize);
// lbp_classify_stump<<<blocks, threads>>>(clr, objects, objects.cols, classified);
// }
void classifyStumpFixed(const DevMem2Db& mstages, const int nstages, const DevMem2Di& mnodes, const DevMem2Df& mleaves, const DevMem2Di& msubsets, const DevMem2Db& mfeatures,
void classifyStumpFixed(const DevMem2Di& integral, const int pitch, 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 THREADS_BLOCK = 256;
int work_amount = ceilf(workHeight / (float)step) * ceilf(workWidth / (float)step);
int blocks = divUp(work_amount, THREADS_BLOCK);
Classifier clr((Stage*)(mstages.ptr()), (ClNode*)(mnodes.ptr()), mleaves.ptr(), msubsets.ptr(), (uchar4*)(mfeatures.ptr()), nstages, clWidth, clHeight, scale, step, subsetSize);
Classifier clr(integral.ptr(), pitch, (Stage*)(mstages.ptr()), (ClNode*)(mnodes.ptr()), mleaves.ptr(), msubsets.ptr(), (uchar4*)(mfeatures.ptr()), nstages, clWidth, clHeight, scale, step, subsetSize);
lbp_classify_stump<<<blocks, THREADS_BLOCK>>>(clr, objects, objects.cols, classified, workWidth >> 1);
}

Write Preview
Loading…
Cancel
Save