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use perf test replace performance sample

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yao
2013-03-27 12:04:48 +08:00
parent 55c9a7c87d
commit 5539e85a11
26 changed files with 3791 additions and 14305 deletions
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198
modules/ocl/perf/perf_haar.cpp
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@@ -10,12 +10,12 @@
// 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, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Jia Haipeng, jiahaipeng95@gmail.com
// Fangfang Bai, fangfang@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
@@ -30,7 +30,7 @@
// * 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
// 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,
@@ -42,133 +42,97 @@
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/objdetect/objdetect.hpp"
#include "precomp.hpp"
#ifdef HAVE_OPENCL
///////////// Haar ////////////////////////
namespace cv
{
namespace ocl
{
using namespace cvtest;
using namespace testing;
using namespace std;
using namespace cv;
extern std::string workdir;
struct getRect
{
Rect operator ()(const CvAvgComp &e) const
Rect operator()(const CvAvgComp &e) const
{
return e.rect;
}
};
PARAM_TEST_CASE(HaarTestBase, int, int)
class CascadeClassifier_GPU : public OclCascadeClassifier
{
//std::vector<cv::ocl::Info> oclinfo;
cv::ocl::OclCascadeClassifier cascade, nestedCascade;
cv::CascadeClassifier cpucascade, cpunestedCascade;
// Mat img;
double scale;
int index;
virtual void SetUp()
public:
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())
{
scale = 1.0;
index = 0;
string cascadeName = "../../../data/haarcascades/haarcascade_frontalface_alt.xml";
if( (!cascade.load( cascadeName )) || (!cpucascade.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:\\");
(void)maxSize;
MemStorage storage(cvCreateMemStorage(0));
//CvMat img=image;
CvSeq *objs = oclHaarDetectObjects(image, storage, scaleFactor, minNeighbors, flags, minSize);
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(objs).copyTo(vecAvgComp);
faces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
}
};
////////////////////////////////faceDetect/////////////////////////////////////////////////
struct Haar : HaarTestBase {};
TEST_F(Haar, FaceDetect)
{
string imgName = workdir + "lena.jpg";
Mat img = imread( imgName, 1 );
if(img.empty())
{
std::cout << imgName << std::endl;
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 );
t = (double)cvGetTickCount();
for(int k = 0; k < LOOP_TIMES; k++)
{
cpucascade.detectMultiScale( smallImg, faces, 1.1,
3, 0
| CV_HAAR_SCALE_IMAGE
, Size(30, 30), Size(0, 0) );
}
t = (double)cvGetTickCount() - t ;
printf( "cpudetection time = %g ms\n", t / (LOOP_TIMES * (double)cvGetTickFrequency() * 1000.) );
cv::ocl::oclMat image;
CvSeq *_objects=NULL;
t = (double)cvGetTickCount();
for(int k = 0; k < LOOP_TIMES; k++)
{
image.upload(smallImg);
_objects = cascade.oclHaarDetectObjects( image, storage, 1.1,
3, 0
| CV_HAAR_SCALE_IMAGE
, Size(30, 30), Size(0, 0) );
}
t = (double)cvGetTickCount() - t ;
printf( "ocldetection time = %g ms\n", t / (LOOP_TIMES * (double)cvGetTickFrequency() * 1000.) );
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
oclfaces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), oclfaces.begin(), getRect());
//for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
//{
// 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();
}
#endif // HAVE_OPENCL
}
TEST(Haar)
{
Mat img = imread(abspath("basketball1.png"), CV_LOAD_IMAGE_GRAYSCALE);
if (img.empty())
{
throw runtime_error("can't open basketball1.png");
}
CascadeClassifier faceCascadeCPU;
if (!faceCascadeCPU.load(abspath("haarcascade_frontalface_alt.xml")))
{
throw runtime_error("can't load haarcascade_frontalface_alt.xml");
}
vector<Rect> faces;
SUBTEST << img.cols << "x" << img.rows << "; scale image";
CPU_ON;
faceCascadeCPU.detectMultiScale(img, faces,
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
CPU_OFF;
ocl::CascadeClassifier_GPU faceCascade;
if (!faceCascade.load(abspath("haarcascade_frontalface_alt.xml")))
{
throw runtime_error("can't load haarcascade_frontalface_alt.xml");
}
ocl::oclMat d_img(img);
faces.clear();
WARMUP_ON;
faceCascade.detectMultiScale(d_img, faces,
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
WARMUP_OFF;
faces.clear();
GPU_ON;
faceCascade.detectMultiScale(d_img, faces,
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
;
GPU_OFF;
GPU_FULL_ON;
d_img.upload(img);
faceCascade.detectMultiScale(d_img, faces,
1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
GPU_FULL_OFF;
}