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Commit ec3f22ce authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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renamed OpenCL kernel filename; made some final changes

parent b7553d4e
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Showing with 12 additions and 337 deletions
modules/objdetect/src/cascadedetect.cpp
View file @ ec3f22ce
......@@ -1133,7 +1133,7 @@ bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processin
bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
int yStep, double factor, Size sumSize0 )
{
const int VECTOR_SIZE = 4;
const int VECTOR_SIZE = 1;
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
......@@ -1142,7 +1142,7 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
if( cascadeKernel.empty() )
{
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::haarobjectdetect_oclsrc,
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc,
format("-D VECTOR_SIZE=%d", VECTOR_SIZE));
if( cascadeKernel.empty() )
return false;
......
modules/objdetect/src/opencl/haarobjectdetect.cl → modules/objdetect/src/opencl/cascadedetect.cl
View file @ ec3f22ce
......@@ -40,7 +40,7 @@ __kernel void runHaarClassifierStump(
int2 imgsize, int xyscale, float factor,
int4 normrect, int2 windowsize, int maxFaces)
{
int ix = get_global_id(0)*xyscale*VECTOR_SIZE;
int ix = get_global_id(0)*xyscale;
int iy = get_global_id(1)*xyscale;
sumstep /= sizeof(int);
sqsumstep /= sizeof(int);
......@@ -94,7 +94,6 @@ __kernel void runHaarClassifierStump(
if( stageIdx == nstages )
{
int nfaces = atomic_inc(facepos);
//printf("detected face #d!!!!\n", nfaces);
if( nfaces < maxFaces )
{
volatile __global int* face = facepos + 1 + nfaces*4;
......
modules/objdetect/src/opencl/haarobjectdetect_scaled2.cl deleted 100644 → 0
View file @ b7553d4e
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Wu Xinglong, wxl370@126.com
// Sen Liu, swjtuls1987@126.com
// Peng Xiao, pengxiao@outlook.com
// Erping Pang, erping@multicorewareinc.com
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#define CV_HAAR_FEATURE_MAX 3
typedef int sumtype;
typedef float sqsumtype;
typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
{
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned(64)));
float weight[CV_HAAR_FEATURE_MAX] /*__attribute__((aligned (16)))*/;
float threshold /*__attribute__((aligned (4)))*/;
float alpha[3] __attribute__((aligned(16)));
int left __attribute__((aligned(4)));
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
//typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned(4)));
// GpuHidHaarTreeNode *node __attribute__((aligned(8)));
// float *alpha __attribute__((aligned(8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned(4)));
float threshold __attribute__((aligned(4)));
int two_rects __attribute__((aligned(4)));
int reserved0 __attribute__((aligned(8)));
int reserved1 __attribute__((aligned(8)));
int reserved2 __attribute__((aligned(8)));
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
//typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned(4)));
// int is_stump_based __attribute__((aligned(4)));
// int has_tilted_features __attribute__((aligned(4)));
// int is_tree __attribute__((aligned(4)));
// int pq0 __attribute__((aligned(4)));
// int pq1 __attribute__((aligned(4)));
// int pq2 __attribute__((aligned(4)));
// int pq3 __attribute__((aligned(4)));
// int p0 __attribute__((aligned(4)));
// int p1 __attribute__((aligned(4)));
// int p2 __attribute__((aligned(4)));
// int p3 __attribute__((aligned(4)));
// float inv_window_area __attribute__((aligned(4)));
//} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier *stagecascadeptr_,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr_,
global const int *restrict sum,
global const float *restrict sqsum,
global int4 *candidate,
const int rows,
const int cols,
const int step,
const int loopcount,
const int start_stage,
const int split_stage,
const int end_stage,
const int startnode,
global int4 *p,
global float *correction,
const int nodecount)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
int grpnumx = get_num_groups(0);
int grpidx = get_group_id(0);
int lclidx = get_local_id(0);
int lclidy = get_local_id(1);
int lcl_id = mad24(lclidy, grpszx, lclidx);
__local int glboutindex[1];
__local int lclcount[1];
__local int lcloutindex[64];
glboutindex[0] = 0;
int outputoff = mul24(grpidx, 256);
candidate[outputoff + (lcl_id << 2)] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 1] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 2] = (int4)0;
candidate[outputoff + (lcl_id << 2) + 3] = (int4)0;
int max_idx = rows * cols - 1;
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1 = info[scalei];
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
float correction_t = correction[scalei];
float ystep = max(2.0f, factor);
for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx)
{
int4 cascadeinfo = p[scalei];
int grpidy = grploop / grpnumperline;
int grpidx = grploop - mul24(grpidy, grpnumperline);
int ix = mad24(grpidx, grpszx, lclidx);
int iy = mad24(grpidy, grpszy, lclidy);
int x = round(ix * ystep);
int y = round(iy * ystep);
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int nodecounter;
float mean, variance_norm_factor;
//if((ix < width) && (iy < height))
{
const int p_offset = mad24(y, step, x);
cascadeinfo.x += p_offset;
cascadeinfo.z += p_offset;
mean = (sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)])
* correction_t;
variance_norm_factor = sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
- sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
+ sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)];
variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f;
bool result = true;
nodecounter = startnode + nodecount * scalei;
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
(((__global uchar*)stagecascadeptr_)+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
for (int nodeloop = 0; nodeloop < stagecount;)
{
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr_) + nodecounter * sizeof(GpuHidHaarTreeNode));
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0]));
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
float nodethreshold = w.w * variance_norm_factor;
info1.x += p_offset;
info1.z += p_offset;
info2.x += p_offset;
info2.z += p_offset;
info3.x += p_offset;
info3.z += p_offset;
float classsum = (sum[clamp(mad24(info1.y, step, info1.x), 0, max_idx)]
- sum[clamp(mad24(info1.y, step, info1.z), 0, max_idx)] -
sum[clamp(mad24(info1.w, step, info1.x), 0, max_idx)]
+ sum[clamp(mad24(info1.w, step, info1.z), 0, max_idx)]) * w.x;
classsum += (sum[clamp(mad24(info2.y, step, info2.x), 0, max_idx)]
- sum[clamp(mad24(info2.y, step, info2.z), 0, max_idx)] -
sum[clamp(mad24(info2.w, step, info2.x), 0, max_idx)]
+ sum[clamp(mad24(info2.w, step, info2.z), 0, max_idx)]) * w.y;
classsum += (sum[clamp(mad24(info3.y, step, info3.x), 0, max_idx)]
- sum[clamp(mad24(info3.y, step, info3.z), 0, max_idx)] -
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
bool passThres = (classsum >= nodethreshold) ? 1 : 0;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
nodecounter++;
nodeloop++;
#else
bool isRootNode = (nodecounter & 1) == 0;
if(isRootNode)
{
if( (passThres && currentnodeptr->right) ||
(!passThres && currentnodeptr->left))
{
nodecounter ++;
}
else
{
stage_sum += alpha3.x;
nodecounter += 2;
nodeloop ++;
}
}
else
{
stage_sum += (passThres ? alpha3.z : alpha3.y);
nodecounter ++;
nodeloop ++;
}
#endif
}
result = (stage_sum >= stageinfo->threshold) ? 1 : 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (result)
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex] = (y << 16) | x;
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
if (lcl_id < queuecount)
{
int temp = lcloutindex[lcl_id];
int x = temp & 0xffff;
int y = (temp & (int)0xffff0000) >> 16;
temp = atomic_inc(glboutindex);
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rte(factor * 20.f);
candidate_result.x = x;
candidate_result.y = y;
int i = outputoff+temp+lcl_id;
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
}
else
{
for(i=i+1;;i++)
{
if(candidate[i].z == 0)
{
candidate[i] = candidate_result;
break;
}
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum)
{
const int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*)
(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*)
(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode));
#pragma unroll
for (i = 0; i < 3; i++)
{
tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f);
tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f);
tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f);
tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f);
}
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]);
#pragma unroll
for (i = 0; i < 3; i++)
{
pNew->p[i][0] = tr_x[i];
pNew->p[i][1] = tr_y[i];
pNew->p[i][2] = tr_x[i] + tr_w[i];
pNew->p[i][3] = tr_y[i] + tr_h[i];
pNew->weight[i] = t1.weight[i] * weight_scale;
}
pNew->left = t1.left;
pNew->right = t1.right;
pNew->threshold = t1.threshold;
pNew->alpha[0] = t1.alpha[0];
pNew->alpha[1] = t1.alpha[1];
pNew->alpha[2] = t1.alpha[2];
}
samples/cpp/ufacedetect.cpp
View file @ ec3f22ce
......@@ -201,13 +201,12 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
t = (double)getTickCount();
cvtColor( img, gray, COLOR_BGR2GRAY );
resize( gray, smallImg, Size(), scale0, scale0, INTER_LINEAR );
cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
equalizeHist( smallImg, smallImg );
resize( img, smallImg, Size(), scale0, scale0, INTER_LINEAR );
cvtColor( smallImg, gray, COLOR_BGR2GRAY );
equalizeHist( gray, gray );
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
cascade.detectMultiScale( gray, faces,
1.1, 3, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
......@@ -215,8 +214,8 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
Size(30, 30) );
if( tryflip )
{
flip(smallImg, smallImg, 1);
cascade.detectMultiScale( smallImg, faces2,
flip(gray, gray, 1);
cascade.detectMultiScale( gray, faces2,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
......@@ -229,7 +228,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
}
}
t = (double)getTickCount() - t;
cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
smallImg.copyTo(canvas);
double fps = getTickFrequency()/t;
......@@ -257,7 +256,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
color, 3, 8, 0);
if( nestedCascade.empty() )
continue;
UMat smallImgROI = smallImg(*r);
UMat smallImgROI = gray(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
......
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