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Merge pull request #2007 from krodyush:pullreq/2.4-opt-131202-haar

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pull/1765/merge
Andrey Pavlenko 11 years ago committed by OpenCV Buildbot
parent 8c91a1af71 917b883cf0
commit b674cd8571
2 changed files with 62 additions and 33 deletions
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  1. 69
      modules/ocl/src/haar.cpp
  2. 26
      modules/ocl/src/opencl/haarobjectdetect.cl

69
modules/ocl/src/haar.cpp
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@ -866,16 +866,17 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
if(gcascade->is_stump_based && gsum.clCxt->supportsFeature(FEATURE_CL_INTEL_DEVICE))
{
//setup local group size
localThreads[0] = 8;
localThreads[1] = 16;
//setup local group size for "pixel step" = 1
localThreads[0] = 16;
localThreads[1] = 32;
localThreads[2] = 1;
//init maximal number of workgroups
//calc maximal number of workgroups
int WGNumX = 1+(sizev[0].width /(localThreads[0]));
int WGNumY = 1+(sizev[0].height/(localThreads[1]));
int WGNumZ = loopcount;
int WGNum = 0; //accurate number of non -empty workgroups
int WGNumTotal = 0; //accurate number of non-empty workgroups
int WGNumSampled = 0; //accurate number of workgroups processed only 1/4 part of all pixels. it is made for large images with scale <= 2
oclMat oclWGInfo(1,sizeof(cl_int4) * WGNumX*WGNumY*WGNumZ,CV_8U);
{
cl_int4* pWGInfo = (cl_int4*)clEnqueueMapBuffer(getClCommandQueue(oclWGInfo.clCxt),(cl_mem)oclWGInfo.datastart,true,CL_MAP_WRITE, 0, oclWGInfo.step, 0,0,0,&status);
@ -895,12 +896,16 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
if(gx>=(Width-cascade->orig_window_size.width))
continue; // no data to process
if(scaleinfo[z].factor<=2)
{
WGNumSampled++;
}
// save no-empty workgroup info into array
pWGInfo[WGNum].s[0] = scaleinfo[z].width_height;
pWGInfo[WGNum].s[1] = (gx << 16) | gy;
pWGInfo[WGNum].s[2] = scaleinfo[z].imgoff;
memcpy(&(pWGInfo[WGNum].s[3]),&(scaleinfo[z].factor),sizeof(float));
WGNum++;
pWGInfo[WGNumTotal].s[0] = scaleinfo[z].width_height;
pWGInfo[WGNumTotal].s[1] = (gx << 16) | gy;
pWGInfo[WGNumTotal].s[2] = scaleinfo[z].imgoff;
memcpy(&(pWGInfo[WGNumTotal].s[3]),&(scaleinfo[z].factor),sizeof(float));
WGNumTotal++;
}
}
}
@ -908,13 +913,8 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
pWGInfo = NULL;
}
// setup global sizes to have linear array of workgroups with WGNum size
globalThreads[0] = localThreads[0]*WGNum;
globalThreads[1] = localThreads[1];
globalThreads[2] = 1;
#define NODE_SIZE 12
// pack node info to have less memory loads
// pack node info to have less memory loads on the device side
oclMat oclNodesPK(1,sizeof(cl_int) * NODE_SIZE * nodenum,CV_8U);
{
cl_int status;
@ -963,8 +963,6 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
options += format(" -D WND_SIZE_X=%d",cascade->orig_window_size.width);
options += format(" -D WND_SIZE_Y=%d",cascade->orig_window_size.height);
options += format(" -D STUMP_BASED=%d",gcascade->is_stump_based);
options += format(" -D LSx=%d",localThreads[0]);
options += format(" -D LSy=%d",localThreads[1]);
options += format(" -D SPLITNODE=%d",splitnode);
options += format(" -D SPLITSTAGE=%d",splitstage);
options += format(" -D OUTPUTSZ=%d",outputsz);
@ -972,8 +970,39 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
// init candiate global count by 0
int pattern = 0;
openCLSafeCall(clEnqueueWriteBuffer(qu, candidatebuffer, 1, 0, 1 * sizeof(pattern),&pattern, 0, NULL, NULL));
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, localThreads, args, -1, -1, options.c_str());
if(WGNumTotal>WGNumSampled)
{// small images and each pixel is processed
// setup global sizes to have linear array of workgroups with WGNum size
int pixelstep = 1;
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
globalThreads[0] = LS[0]*(WGNumTotal-WGNumSampled);
globalThreads[1] = LS[1];
globalThreads[2] = 1;
string options1 = options;
options1 += format(" -D PIXEL_STEP=%d",pixelstep);
options1 += format(" -D WGSTART=%d",WGNumSampled);
options1 += format(" -D LSx=%d",LS[0]);
options1 += format(" -D LSy=%d",LS[1]);
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, LS, args, -1, -1, options1.c_str());
}
if(WGNumSampled>0)
{// large images each 4th pixel is processed
// setup global sizes to have linear array of workgroups with WGNum size
int pixelstep = 2;
size_t LS[3]={localThreads[0]/pixelstep,localThreads[1]/pixelstep,1};
globalThreads[0] = LS[0]*WGNumSampled;
globalThreads[1] = LS[1];
globalThreads[2] = 1;
string options2 = options;
options2 += format(" -D PIXEL_STEP=%d",pixelstep);
options2 += format(" -D WGSTART=%d",0);
options2 += format(" -D LSx=%d",LS[0]);
options2 += format(" -D LSy=%d",LS[1]);
// execute face detector
openCLExecuteKernel(gsum.clCxt, &haarobjectdetect, "gpuRunHaarClassifierCascadePacked", globalThreads, LS, args, -1, -1, options2.c_str());
}
//read candidate buffer back and put it into host list
openCLReadBuffer( gsum.clCxt, candidatebuffer, candidate, 4 * sizeof(int)*outputsz );
assert(candidate[0]<outputsz);

26
modules/ocl/src/opencl/haarobjectdetect.cl
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@ -126,13 +126,11 @@ __kernel void gpuRunHaarClassifierCascadePacked(
)
{
// this version used information provided for each workgroup
// no empty WG
int gid = (int)get_group_id(0);
int lid_x = (int)get_local_id(0);
int lid_y = (int)get_local_id(1);
int lid = lid_y*LSx+lid_x;
int4 WGInfo = pWGInfo[gid];
int4 WGInfo = pWGInfo[WGSTART+gid];
int GroupX = (WGInfo.y >> 16)&0xFFFF;
int GroupY = (WGInfo.y >> 0 )& 0xFFFF;
int Width = (WGInfo.x >> 16)&0xFFFF;
@ -140,8 +138,8 @@ __kernel void gpuRunHaarClassifierCascadePacked(
int ImgOffset = WGInfo.z;
float ScaleFactor = as_float(WGInfo.w);
#define DATA_SIZE_X (LSx+WND_SIZE_X)
#define DATA_SIZE_Y (LSy+WND_SIZE_Y)
#define DATA_SIZE_X (PIXEL_STEP*LSx+WND_SIZE_X)
#define DATA_SIZE_Y (PIXEL_STEP*LSy+WND_SIZE_Y)
#define DATA_SIZE (DATA_SIZE_X*DATA_SIZE_Y)
local int SumL[DATA_SIZE];
@ -165,9 +163,11 @@ __kernel void gpuRunHaarClassifierCascadePacked(
int4 info1 = p;
int4 info2 = pq;
{
int xl = lid_x;
int yl = lid_y;
// calc processed ROI coordinate in local mem
int xl = lid_x*PIXEL_STEP;
int yl = lid_y*PIXEL_STEP;
{// calc variance_norm_factor for all stages
int OffsetLocal = yl * DATA_SIZE_X + xl;
int OffsetGlobal = (GroupY+yl)* pixelstep + (GroupX+xl);
@ -194,13 +194,13 @@ __kernel void gpuRunHaarClassifierCascadePacked(
int result = (1.0f>0.0f);
for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
{// iterate until candidate is exist
{// iterate until candidate is valid
float stage_sum = 0.0f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int lcl_off = (yl*DATA_SIZE_X)+(xl);
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
for(int nodeloop = 0; nodeloop < stagecount; nodecounter++,nodeloop++ )
{
// simple macro to extract shorts from int
@ -212,7 +212,7 @@ __kernel void gpuRunHaarClassifierCascadePacked(
int4 n1 = pN[1];
int4 n2 = pN[2];
float nodethreshold = as_float(n2.y) * variance_norm_factor;
// calc sum of intensity pixels according to node information
// calc sum of intensity pixels according to classifier node information
float classsum =
(SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) +
(SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) +
@ -228,8 +228,8 @@ __kernel void gpuRunHaarClassifierCascadePacked(
int index = 1+atomic_inc((volatile global int*)candidate); //get index to write global data with face info
if(index<OUTPUTSZ)
{
int x = GroupX+lid_x;
int y = GroupY+lid_y;
int x = GroupX+xl;
int y = GroupY+yl;
int4 candidate_result;
candidate_result.x = convert_int_rtn(x*ScaleFactor);
candidate_result.y = convert_int_rtn(y*ScaleFactor);

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