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提交 fd83f2f5 编写于 作者: V Vadim Pisarevsky 提交者： OpenCV Buildbot
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Merge pull request #819 from bitwangyaoyao:2.4_haarBuf

上级 c549ec83 f788d010
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modules/ocl/include/opencv2/ocl/ocl.hpp
浏览文件 @ fd83f2f5
......@@ -802,6 +802,44 @@ namespace cv
int minNeighbors, int flags, CvSize minSize = cvSize(0, 0), CvSize maxSize = cvSize(0, 0));
};
class CV_EXPORTS OclCascadeClassifierBuf : public cv::CascadeClassifier
{
public:
OclCascadeClassifierBuf() :
m_flags(0), initialized(false), m_scaleFactor(0), buffers(NULL) {}
~OclCascadeClassifierBuf() {}
void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
Size minSize = Size(), Size maxSize = Size());
void release();
private:
void Init(const int rows, const int cols, double scaleFactor, int flags,
const int outputsz, const size_t localThreads[],
CvSize minSize, CvSize maxSize);
void CreateBaseBufs(const int datasize, const int totalclassifier, const int flags, const int outputsz);
void CreateFactorRelatedBufs(const int rows, const int cols, const int flags,
const double scaleFactor, const size_t localThreads[],
CvSize minSize, CvSize maxSize);
void GenResult(CV_OUT std::vector<cv::Rect>& faces, const std::vector<cv::Rect> &rectList, const std::vector<int> &rweights);
int m_rows;
int m_cols;
int m_flags;
int m_loopcount;
int m_nodenum;
bool findBiggestObject;
bool initialized;
double m_scaleFactor;
Size m_minSize;
Size m_maxSize;
vector<CvSize> sizev;
vector<float> scalev;
oclMat gimg1, gsum, gsqsum;
void * buffers;
};
/////////////////////////////// Pyramid /////////////////////////////////////
......
modules/ocl/src/haar.cpp
浏览文件 @ fd83f2f5
此差异已折叠。
modules/ocl/src/opencl/haarobjectdetect.cl
浏览文件 @ fd83f2f5
......@@ -112,7 +112,7 @@ typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
} GpuHidHaarClassifierCascade;
__kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCascade(//constant GpuHidHaarClassifierCascade * cascade,
__kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCascade(
global GpuHidHaarStageClassifier * stagecascadeptr,
global int4 * info,
global GpuHidHaarTreeNode * nodeptr,
......@@ -128,12 +128,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
const int splitnode,
const int4 p,
const int4 pq,
const float correction
//const int width,
//const int height,
//const int grpnumperline,
//const int totalgrp
)
const float correction)
{
int grpszx = get_local_size(0);
int grpszy = get_local_size(1);
......@@ -145,13 +140,8 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int lcl_sz = mul24(grpszx,grpszy);
int lcl_id = mad24(lclidy,grpszx,lclidx);
//assume lcl_sz == 256 or 128 or 64
//int lcl_sz_shift = (lcl_sz == 256) ? 8 : 7;
//lcl_sz_shift = (lcl_sz == 64) ? 6 : lcl_sz_shift;
__local int lclshare[1024];
#define OFF 0
__local int* lcldata = lclshare + OFF;//for save win data
__local int* lcldata = lclshare;//for save win data
__local int* glboutindex = lcldata + 28*28;//for save global out index
__local int* lclcount = glboutindex + 1;//for save the numuber of temp pass pixel
__local int* lcloutindex = lclcount + 1;//for save info of temp pass pixel
......@@ -181,7 +171,6 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int totalgrp = scaleinfo1.y & 0xffff;
int imgoff = scaleinfo1.z;
float factor = as_float(scaleinfo1.w);
//int ystep =1;// factor > 2.0 ? 1 : 2;
__global const int * sum = sum1 + imgoff;
__global const float * sqsum = sqsum1 + imgoff;
......@@ -191,8 +180,6 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int grpidx = grploop - mul24(grpidy, grpnumperline);
int x = mad24(grpidx,grpszx,lclidx);
int y = mad24(grpidy,grpszy,lclidy);
//candidate_result.x = convert_int_rtn(x*factor);
//candidate_result.y = convert_int_rtn(y*factor);
int grpoffx = x-lclidx;
int grpoffy = y-lclidy;
......@@ -207,18 +194,11 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int glb_x = grpoffx + (lcl_x<<2);
int glb_y = grpoffy + lcl_y;
int glb_off = mad24(glb_y,pixelstep,glb_x);
int glb_off = mad24(min(glb_y, height - 1),pixelstep,glb_x);
int4 data = *(__global int4*)&sum[glb_off];
int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2);
#if OFF
lcldata[lcl_off] = data.x;
lcldata[lcl_off+1] = data.y;
lcldata[lcl_off+2] = data.z;
lcldata[lcl_off+3] = data.w;
#else
vstore4(data, 0, &lcldata[lcl_off]);
#endif
}
lcloutindex[lcl_id] = 0;
......@@ -231,11 +211,8 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int lcl_off = mad24(lclidy,readwidth,lclidx);
int4 cascadeinfo1, cascadeinfo2;
cascadeinfo1 = p;
cascadeinfo2 = pq;// + mad24(y, pixelstep, x);
cascadeinfo2 = pq;
//if((x < width) && (y < height))
{
cascadeinfo1.x +=lcl_off;
cascadeinfo1.z +=lcl_off;
mean = (lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.x)] - lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.z)] -
......@@ -251,8 +228,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
variance_norm_factor = variance_norm_factor * correction - mean * mean;
variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f;
//if( cascade->is_stump_based )
//{
for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ )
{
float stage_sum = 0.f;
......@@ -277,18 +253,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
//if((info3.z - info3.x) && (!stageinfo.z))
//{
info3.x +=lcl_off;
info3.z +=lcl_off;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
//}
stage_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
nodecounter++;
}
......@@ -308,8 +280,6 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
nodecounter = splitnode;
for(int stageloop = split_stage; stageloop< end_stage && queuecount>0; stageloop++)
{
//barrier(CLK_LOCAL_MEM_FENCE);
//if(lcl_id == 0)
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -322,14 +292,13 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int lcl_compute_win_id = (lcl_id >>(6-perfscale));
int lcl_loops = (stageinfo.x + lcl_compute_win -1) >> (6-perfscale);
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
for(int queueloop=0; queueloop<queuecount_loop/* && lcl_compute_win_id < queuecount*/; queueloop++)
for(int queueloop=0; queueloop<queuecount_loop; queueloop++)
{
float stage_sum = 0.f;
int temp_coord = lcloutindex[lcl_compute_win_id<<1];
float variance_norm_factor = as_float(lcloutindex[(lcl_compute_win_id<<1)+1]);
int queue_pixel = mad24(((temp_coord & (int)0xffff0000)>>16),readwidth,temp_coord & 0xffff);
//barrier(CLK_LOCAL_MEM_FENCE);
if(lcl_compute_win_id < queuecount)
{
......@@ -357,13 +326,12 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;
//if((info3.z - info3.x) && (!stageinfo.z))
//{
info3.x +=queue_pixel;
info3.z +=queue_pixel;
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;
//}
part_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
tempnodecounter +=lcl_compute_win;
}//end for(int lcl_loop=0;lcl_loop<lcl_loops;lcl_loop++)
......@@ -386,12 +354,12 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
}
barrier(CLK_LOCAL_MEM_FENCE);
}//end for(int queueloop=0;queueloop<queuecount_loop;queueloop++)
//barrier(CLK_LOCAL_MEM_FENCE);
queuecount = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter += stageinfo.x;
}//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++)
//barrier(CLK_LOCAL_MEM_FENCE);
if(lcl_id<queuecount)
{
int temp = lcloutindex[lcl_id<<1];
......@@ -406,9 +374,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
candidate[outputoff+temp+lcl_id] = candidate_result;
}
barrier(CLK_LOCAL_MEM_FENCE);
}//end if((x < width) && (y < height))
}//end for(int grploop=grpidx;grploop<totalgrp;grploop+=grpnumx)
//outputoff +=mul24(width,height);
}//end for(int scalei = 0; scalei <loopcount; scalei++)
}
......
modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
浏览文件 @ fd83f2f5
......@@ -16,6 +16,7 @@
//
// @Authors
// Wu Xinglong, wxl370@126.com
// Sen Liu, swjtuls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
......@@ -113,15 +114,15 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
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,
const int splitnode,
global int4 *p,
//const int4 * pq,
global float *correction,
const int nodecount)
{
......@@ -133,18 +134,16 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int lclidy = get_local_id(1);
int lcl_sz = mul24(grpszx, grpszy);
int lcl_id = mad24(lclidy, grpszx, lclidx);
__local int lclshare[1024];
__local int *glboutindex = lclshare + 0;
__local int *lclcount = glboutindex + 1;
__local int *lcloutindex = lclcount + 1;
__local float *partialsum = (__local float *)(lcloutindex + (lcl_sz << 1));
__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;
......@@ -168,30 +167,28 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int y = iy * ystep;
lcloutindex[lcl_id] = 0;
lclcount[0] = 0;
int result = 1, nodecounter;
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[mad24(cascadeinfo.y, step, cascadeinfo.x)] - sum[mad24(cascadeinfo.y, step, cascadeinfo.z)] -
sum[mad24(cascadeinfo.w, step, cascadeinfo.x)] + sum[mad24(cascadeinfo.w, step, cascadeinfo.z)])
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[mad24(cascadeinfo.y, step, cascadeinfo.x)] - sqsum[mad24(cascadeinfo.y, step, cascadeinfo.z)] -
sqsum[mad24(cascadeinfo.w, step, cascadeinfo.x)] + sqsum[mad24(cascadeinfo.w, step, cascadeinfo.z)];
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;
result = 1;
bool result = true;
nodecounter = startnode + nodecount * scalei;
for (int stageloop = start_stage; stageloop < end_stage && result; stageloop++)
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
int4 stageinfo = *(global int4 *)(stagecascadeptr + stageloop);
float stagethreshold = as_float(stageinfo.y);
for (int nodeloop = 0; nodeloop < stageinfo.x; nodeloop++)
int stagecount = stagecascadeptr[stageloop].count;
for (int nodeloop = 0; nodeloop < stagecount; nodeloop++)
{
__global GpuHidHaarTreeNode *currentnodeptr = (nodeptr + nodecounter);
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
......@@ -204,43 +201,41 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
info1.z += p_offset;
info2.x += p_offset;
info2.z += p_offset;
float classsum = (sum[mad24(info1.y, step, info1.x)] - sum[mad24(info1.y, step, info1.z)] -
sum[mad24(info1.w, step, info1.x)] + sum[mad24(info1.w, step, info1.z)]) * w.x;
classsum += (sum[mad24(info2.y, step, info2.x)] - sum[mad24(info2.y, step, info2.z)] -
sum[mad24(info2.w, step, info2.x)] + sum[mad24(info2.w, step, info2.z)]) * w.y;
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;
info3.x += p_offset;
info3.z += p_offset;
classsum += (sum[mad24(info3.y, step, info3.x)] - sum[mad24(info3.y, step, info3.z)] -
sum[mad24(info3.w, step, info3.x)] + sum[mad24(info3.w, step, info3.z)]) * w.z;
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;
stage_sum += classsum >= nodethreshold ? alpha2.y : alpha2.x;
nodecounter++;
}
result = (stage_sum >= stagethreshold);
result = (bool)(stage_sum >= stagecascadeptr[stageloop].threshold);
}
barrier(CLK_LOCAL_MEM_FENCE);
if (result && (ix < width) && (iy < height))
{
int queueindex = atomic_inc(lclcount);
lcloutindex[queueindex << 1] = (y << 16) | x;
lcloutindex[(queueindex << 1) + 1] = as_int(variance_norm_factor);
lcloutindex[queueindex] = (y << 16) | x;
}
barrier(CLK_LOCAL_MEM_FENCE);
int queuecount = lclcount[0];
nodecounter = splitnode + nodecount * scalei;
if (lcl_id < queuecount)
{
int temp = lcloutindex[lcl_id << 1];
int temp = lcloutindex[lcl_id];
int x = temp & 0xffff;
int y = (temp & (int)0xffff0000) >> 16;
temp = glboutindex[0];
temp = atomic_inc(glboutindex);
int4 candidate_result;
candidate_result.zw = (int2)convert_int_rtn(factor * 20.f);
candidate_result.x = x;
candidate_result.y = y;
atomic_inc(glboutindex);
candidate[outputoff + temp + lcl_id] = candidate_result;
}
......@@ -283,3 +278,4 @@ __kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuH
newnode[counter].alpha[0] = t1.alpha[0];
newnode[counter].alpha[1] = t1.alpha[1];
}
modules/ocl/test/test_haar.cpp
浏览文件 @ fd83f2f5
......@@ -16,6 +16,7 @@
//
// @Authors
// Jia Haipeng, jiahaipeng95@gmail.com
// Sen Liu, swjutls1987@126.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
......@@ -61,40 +62,31 @@ struct getRect
}
};
PARAM_TEST_CASE(HaarTestBase, int, int)
PARAM_TEST_CASE(Haar, double, int)
{
//std::vector<cv::ocl::Info> oclinfo;
cv::ocl::OclCascadeClassifier cascade, nestedCascade;
cv::ocl::OclCascadeClassifierBuf cascadebuf;
cv::CascadeClassifier cpucascade, cpunestedCascade;
// Mat img;
double scale;
int index;
int flags;
virtual void SetUp()
{
scale = 1.0;
index = 0;
scale = GET_PARAM(0);
flags = GET_PARAM(1);
string cascadeName = workdir + "../../data/haarcascades/haarcascade_frontalface_alt.xml";
if( (!cascade.load( cascadeName )) || (!cpucascade.load(cascadeName)))
if( (!cascade.load( cascadeName )) || (!cpucascade.load(cascadeName)) || (!cascadebuf.load( cascadeName )))
{
cout << "ERROR: Could not load classifier cascade" << endl;
return;
}
//int devnums = getDevice(oclinfo);
//CV_Assert(devnums>0);
////if you want to use undefault device, set it here
////setDevice(oclinfo[0]);
//cv::ocl::setBinpath("E:\\");
}
};
////////////////////////////////faceDetect/////////////////////////////////////////////////
struct Haar : HaarTestBase {};
TEST_F(Haar, FaceDetect)
TEST_P(Haar, FaceDetect)
{
string imgName = workdir + "lena.jpg";
Mat img = imread( imgName, 1 );
......@@ -105,59 +97,65 @@ TEST_F(Haar, FaceDetect)
return ;
}
//int i = 0;
//double t = 0;
vector<Rect> faces, oclfaces;
// const static Scalar colors[] = { CV_RGB(0, 0, 255),
// CV_RGB(0, 128, 255),
// CV_RGB(0, 255, 255),
// CV_RGB(0, 255, 0),
// CV_RGB(255, 128, 0),
// CV_RGB(255, 255, 0),
// CV_RGB(255, 0, 0),
// CV_RGB(255, 0, 255)
// } ;
Mat gray, smallImg(cvRound (img.rows / scale), cvRound(img.cols / scale), CV_8UC1 );
MemStorage storage(cvCreateMemStorage(0));
cvtColor( img, gray, CV_BGR2GRAY );
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
cv::ocl::oclMat image;
CvSeq *_objects;
image.upload(smallImg);
_objects = cascade.oclHaarDetectObjects( image, storage, 1.1,
3, 0
| CV_HAAR_SCALE_IMAGE
, Size(30, 30), Size(0, 0) );
3, flags, Size(30, 30), Size(0, 0) );
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
oclfaces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), oclfaces.begin(), getRect());
cpucascade.detectMultiScale( smallImg, faces, 1.1,
3, 0
| CV_HAAR_SCALE_IMAGE
, Size(30, 30), Size(0, 0) );
cpucascade.detectMultiScale( smallImg, faces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0) );
EXPECT_EQ(faces.size(), oclfaces.size());
/* for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
}
TEST_P(Haar, FaceDetectUseBuf)
{
string imgName = workdir + "lena.jpg";
Mat img = imread( imgName, 1 );
if(img.empty())
{
Mat smallImgROI;
Point center;
Scalar color = colors[i%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
} */
//namedWindow("result");
//imshow("result",img);
//waitKey(0);
//destroyAllWindows();
std::cout << "Couldn't read " << imgName << std::endl;
return ;
}
vector<Rect> faces, oclfaces;
Mat gray, smallImg(cvRound (img.rows / scale), cvRound(img.cols / scale), CV_8UC1 );
MemStorage storage(cvCreateMemStorage(0));
cvtColor( img, gray, CV_BGR2GRAY );
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
cv::ocl::oclMat image;
image.upload(smallImg);
cascadebuf.detectMultiScale( image, oclfaces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0) );
cascadebuf.release();
cpucascade.detectMultiScale( smallImg, faces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0) );
EXPECT_EQ(faces.size(), oclfaces.size());
}
INSTANTIATE_TEST_CASE_P(FaceDetect, Haar,
Combine(Values(1.0),
Values(CV_HAAR_SCALE_IMAGE, 0)));
#endif // HAVE_OPENCL
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