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revise accuracy and perf tests

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yao
2013-06-19 13:03:35 +08:00
parent 843094a07f
commit 2c198f6cd6
14 changed files with 392 additions and 614 deletions
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modules/ocl/test/test_objdetect.cpp Normal file
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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// 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
// Yao Wang, bitwangyaoyao@gmail.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.
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// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// 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*/
#include "precomp.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/objdetect/objdetect.hpp"
using namespace cv;
using namespace testing;
#ifdef HAVE_OPENCL
extern string workdir;
///////////////////// HOG /////////////////////////////
PARAM_TEST_CASE(HOG, Size, int)
{
Size winSize;
int type;
Mat img_rgb;
virtual void SetUp()
{
winSize = GET_PARAM(0);
type = GET_PARAM(1);
img_rgb = readImage(workdir + "../gpu/road.png");
if(img_rgb.empty())
{
std::cout << "Couldn't read road.png" << std::endl;
}
}
};
TEST_P(HOG, GetDescriptors)
{
// Convert image
Mat img;
switch (type)
{
case CV_8UC1:
cvtColor(img_rgb, img, CV_BGR2GRAY);
break;
case CV_8UC4:
default:
cvtColor(img_rgb, img, CV_BGR2BGRA);
break;
}
ocl::oclMat d_img(img);
// HOGs
ocl::HOGDescriptor ocl_hog;
ocl_hog.gamma_correction = true;
HOGDescriptor hog;
hog.gammaCorrection = true;
// Compute descriptor
ocl::oclMat d_descriptors;
ocl_hog.getDescriptors(d_img, ocl_hog.win_size, d_descriptors, ocl_hog.DESCR_FORMAT_COL_BY_COL);
Mat down_descriptors;
d_descriptors.download(down_descriptors);
down_descriptors = down_descriptors.reshape(0, down_descriptors.cols * down_descriptors.rows);
hog.setSVMDetector(hog.getDefaultPeopleDetector());
std::vector<float> descriptors;
switch (type)
{
case CV_8UC1:
hog.compute(img, descriptors, ocl_hog.win_size);
break;
case CV_8UC4:
default:
hog.compute(img_rgb, descriptors, ocl_hog.win_size);
break;
}
Mat cpu_descriptors(descriptors);
EXPECT_MAT_SIMILAR(down_descriptors, cpu_descriptors, 1e-2);
}
TEST_P(HOG, Detect)
{
// Convert image
Mat img;
switch (type)
{
case CV_8UC1:
cvtColor(img_rgb, img, CV_BGR2GRAY);
break;
case CV_8UC4:
default:
cvtColor(img_rgb, img, CV_BGR2BGRA);
break;
}
ocl::oclMat d_img(img);
// HOGs
if ((winSize != Size(48, 96)) && (winSize != Size(64, 128)))
winSize = Size(64, 128);
ocl::HOGDescriptor ocl_hog(winSize);
ocl_hog.gamma_correction = true;
HOGDescriptor hog;
hog.winSize = winSize;
hog.gammaCorrection = true;
if (winSize.width == 48 && winSize.height == 96)
{
// daimler's base
ocl_hog.setSVMDetector(ocl_hog.getPeopleDetector48x96());
hog.setSVMDetector(hog.getDaimlerPeopleDetector());
}
else if (winSize.width == 64 && winSize.height == 128)
{
ocl_hog.setSVMDetector(ocl_hog.getPeopleDetector64x128());
hog.setSVMDetector(hog.getDefaultPeopleDetector());
}
else
{
ocl_hog.setSVMDetector(ocl_hog.getDefaultPeopleDetector());
hog.setSVMDetector(hog.getDefaultPeopleDetector());
}
// OpenCL detection
std::vector<Rect> d_found;
ocl_hog.detectMultiScale(d_img, d_found, 0, Size(8, 8), Size(0, 0), 1.05, 6);
// CPU detection
std::vector<Rect> found;
switch (type)
{
case CV_8UC1:
hog.detectMultiScale(img, found, 0, Size(8, 8), Size(0, 0), 1.05, 6);
break;
case CV_8UC4:
default:
hog.detectMultiScale(img_rgb, found, 0, Size(8, 8), Size(0, 0), 1.05, 6);
break;
}
EXPECT_LT(checkRectSimilarity(img.size(), found, d_found), 1.0);
}
INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine(
testing::Values(Size(64, 128), Size(48, 96)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4))));
///////////////////////////// Haar //////////////////////////////
IMPLEMENT_PARAM_CLASS(CascadeName, std::string);
CascadeName cascade_frontalface_alt(std::string("haarcascade_frontalface_alt.xml"));
CascadeName cascade_frontalface_alt2(std::string("haarcascade_frontalface_alt2.xml"));
struct getRect
{
Rect operator ()(const CvAvgComp &e) const
{
return e.rect;
}
};
PARAM_TEST_CASE(Haar, int, CascadeName)
{
ocl::OclCascadeClassifier cascade, nestedCascade;
CascadeClassifier cpucascade, cpunestedCascade;
int flags;
std::string cascadeName;
vector<Rect> faces, oclfaces;
Mat img;
ocl::oclMat d_img;
virtual void SetUp()
{
flags = GET_PARAM(0);
cascadeName = (workdir + "../../data/haarcascades/").append(GET_PARAM(1));
if( (!cascade.load( cascadeName )) || (!cpucascade.load(cascadeName)) )
{
std::cout << "ERROR: Could not load classifier cascade" << std::endl;
return;
}
img = readImage(workdir + "lena.jpg", IMREAD_GRAYSCALE);
if(img.empty())
{
std::cout << "Couldn't read lena.jpg" << std::endl;
return ;
}
equalizeHist(img, img);
d_img.upload(img);
}
};
TEST_P(Haar, FaceDetect)
{
MemStorage storage(cvCreateMemStorage(0));
CvSeq *_objects;
_objects = cascade.oclHaarDetectObjects(d_img, storage, 1.1, 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(img, faces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0));
EXPECT_LT(checkRectSimilarity(img.size(), faces, oclfaces), 1.0);
}
TEST_P(Haar, FaceDetectUseBuf)
{
ocl::OclCascadeClassifierBuf cascadebuf;
if(!cascadebuf.load(cascadeName))
{
std::cout << "ERROR: Could not load classifier cascade for FaceDetectUseBuf!" << std::endl;
return;
}
cascadebuf.detectMultiScale(d_img, oclfaces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0));
cpucascade.detectMultiScale(img, faces, 1.1, 3,
flags,
Size(30, 30), Size(0, 0));
// intentionally run ocl facedetect again and check if it still works after the first run
cascadebuf.detectMultiScale(d_img, oclfaces, 1.1, 3,
flags,
Size(30, 30));
cascadebuf.release();
EXPECT_LT(checkRectSimilarity(img.size(), faces, oclfaces), 1.0);
}
INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, Haar,
Combine(Values(CV_HAAR_SCALE_IMAGE, 0),
Values(cascade_frontalface_alt/*, cascade_frontalface_alt2*/)));
#endif //HAVE_OPENCL