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ac5cd48279
opencv/modules/gpu/test/test_softcascade.cpp
marina.kolpakova ac5cd48279 add DeviceInfo parameter to the soft cascade tests
2012-11-10 05:12:13 +04:00

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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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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.
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include <test_precomp.hpp>
#include <time.h>
#ifdef HAVE_CUDA
using cv::gpu::GpuMat;
// show detection results on input image with cv::imshow
#define SHOW_DETECTIONS
#if defined SHOW_DETECTIONS
# define SHOW(res) \
cv::imshow(#res, result);\
cv::waitKey(0);
#else
# define SHOW(res)
#endif
#define GPU_TEST_P(fixture, name, params) \
class fixture##_##name : public fixture { \
public: \
fixture##_##name() {} \
protected: \
virtual void body(); \
}; \
TEST_P(fixture##_##name, name /*none*/){ body();} \
INSTANTIATE_TEST_CASE_P(/*none*/, fixture##_##name, params); \
void fixture##_##name::body()
namespace {
typedef cv::gpu::SoftCascade::Detection Detection;
static cv::Rect getFromTable(int idx)
{
static const cv::Rect rois[] =
{
cv::Rect( 65, 20, 35, 80),
cv::Rect( 95, 35, 45, 40),
cv::Rect( 45, 35, 45, 40),
cv::Rect( 25, 27, 50, 45),
cv::Rect(100, 50, 45, 40),
cv::Rect( 60, 30, 45, 40),
cv::Rect( 40, 55, 50, 40),
cv::Rect( 48, 37, 72, 80),
cv::Rect( 48, 32, 85, 58),
cv::Rect( 48, 0, 32, 27)
};
return rois[idx];
}
static std::string itoa(long i)
{
static char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
static std::string getImageName(int level)
{
time_t rawtime;
struct tm * timeinfo;
char buffer [80];
time ( &rawtime );
timeinfo = localtime ( &rawtime );
strftime (buffer,80,"%Y-%m-%d--%H-%M-%S",timeinfo);
return "gpu_rec_level_" + itoa(level)+ "_" + std::string(buffer) + ".png";
}
static void print(std::ostream &out, const Detection& d)
{
out << "\x1b[32m[ detection]\x1b[0m ("
<< std::setw(4) << d.x
<< " "
<< std::setw(4) << d.y
<< ") ("
<< std::setw(4) << d.w
<< " "
<< std::setw(4) << d.h
<< ") "
<< std::setw(12) << d.confidence
<< std::endl;
}
static void printTotal(std::ostream &out, int detbytes)
{
out << "\x1b[32m[ ]\x1b[0m Total detections " << (detbytes / sizeof(Detection)) << std::endl;
}
static void writeResult(const cv::Mat& result, const int level)
{
std::string path = cv::tempfile(getImageName(level).c_str());
cv::imwrite(path, result);
std::cout << "\x1b[32m" << "[ ]" << std::endl << "[ stored in]"<< "\x1b[0m" << path << std::endl;
}
}
typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SoftCascadeTestRoi;
GPU_TEST_P(SoftCascadeTestRoi, detect,
testing::Combine(
ALL_DEVICES,
testing::Values(std::string("../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
testing::Range(0, 5)))
{
cv::gpu::setDevice(GET_PARAM(0).deviceID());
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
ASSERT_FALSE(coloredCpu.empty());
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(cvtest::TS::ptr()->get_data_path() + GET_PARAM(1)));
GpuMat colored(coloredCpu), objectBoxes(1, 16384, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1), trois;
rois.setTo(0);
int nroi = GET_PARAM(3);
cv::Mat result(coloredCpu);
cv::RNG rng;
for (int i = 0; i < nroi; ++i)
{
cv::Rect r = getFromTable(rng(10));
GpuMat sub(rois, r);
sub.setTo(1);
r.x *= 4; r.y *= 4; r.width *= 4; r.height *= 4;
cv::rectangle(result, r, cv::Scalar(0, 0, 255, 255), 1);
}
cv::gpu::transpose(rois, trois);
cascade.detectMultiScale(colored, trois, objectBoxes);
cv::Mat dt(objectBoxes);
typedef cv::gpu::SoftCascade::Detection Detection;
Detection* dts = (Detection*)dt.data;
printTotal(std::cout, dt.cols);
for (int i = 0; i < (int)(dt.cols / sizeof(Detection)); ++i)
{
Detection d = dts[i];
print(std::cout, d);
cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
}
SHOW(result);
}
typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SoftCascadeTestLevel;
GPU_TEST_P(SoftCascadeTestLevel, detect,
testing::Combine(
ALL_DEVICES,
testing::Values(std::string("../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
testing::Range(0, 47)
))
{
cv::gpu::setDevice(GET_PARAM(0).deviceID());
std::string xml = cvtest::TS::ptr()->get_data_path() + GET_PARAM(1);
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
ASSERT_FALSE(coloredCpu.empty());
typedef cv::gpu::SoftCascade::Detection Detection;
GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
rois.setTo(1);
cv::gpu::GpuMat trois;
cv::gpu::transpose(rois, trois);
int level = GET_PARAM(3);
cascade.detectMultiScale(colored, trois, objectBoxes, 1, level);
cv::Mat dt(objectBoxes);
Detection* dts = (Detection*)dt.data;
cv::Mat result(coloredCpu);
printTotal(std::cout, dt.cols);
for (int i = 0; i < (int)(dt.cols / sizeof(Detection)); ++i)
{
Detection d = dts[i];
print(std::cout, d);
cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
}
writeResult(result, level);
SHOW(result);
}
TEST(SoftCascadeTest, readCascade)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/icf-template.xml";
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
}
typedef ::testing::TestWithParam<cv::gpu::DeviceInfo > SoftCascadeTestAll;
GPU_TEST_P(SoftCascadeTestAll, detect,
ALL_DEVICES
)
{
cv::gpu::setDevice(GetParam().deviceID());
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(coloredCpu.empty());
GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
rois.setTo(0);
GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
sub.setTo(cv::Scalar::all(1));
cv::gpu::GpuMat trois;
cv::gpu::transpose(rois, trois);
cascade.detectMultiScale(colored, trois, objectBoxes);
typedef cv::gpu::SoftCascade::Detection Detection;
cv::Mat detections(objectBoxes);
ASSERT_EQ(detections.cols / sizeof(Detection) ,3670U);
}
GPU_TEST_P(SoftCascadeTestAll, detectOnIntegral,
ALL_DEVICES
)
{
cv::gpu::setDevice(GetParam().deviceID());
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SoftCascade cascade;
ASSERT_TRUE(cascade.load(xml));
std::string intPath = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/integrals.xml";
cv::FileStorage fs(intPath, cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
GpuMat hogluv(121 * 10, 161, CV_32SC1);
for (int i = 0; i < 10; ++i)
{
cv::Mat channel;
fs[std::string("channel") + itoa(i)] >> channel;
GpuMat gchannel(hogluv, cv::Rect(0, 121 * i, 161, 121));
gchannel.upload(channel);
}
GpuMat objectBoxes(1, 100000, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
rois.setTo(1);
cv::gpu::GpuMat trois;
cv::gpu::transpose(rois, trois);
cascade.detectMultiScale(hogluv, trois, objectBoxes);
typedef cv::gpu::SoftCascade::Detection Detection;
cv::Mat detections(objectBoxes);
ASSERT_EQ(detections.cols / sizeof(Detection) ,2042U);
}
#endif
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