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Add performance test for detection in ROI; refactor soft cascade performance tests
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marina.kolpakova
parent
eb91593c08
commit
dd595376ba
@ -41,20 +41,197 @@
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//M*/
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#include <test_precomp.hpp>
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#include <time.h>
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#ifdef HAVE_CUDA
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using cv::gpu::GpuMat;
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TEST(SoftCascade, readCascade)
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// show detection results on input image with cv::imshow
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//#define SHOW_DETECTIONS
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#if defined SHOW_DETECTIONS
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# define SHOW(res) \
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cv::imshow(#res, result);\
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cv::waitKey(0);
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#else
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# define SHOW(res)
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#endif
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#define GPU_TEST_P(fixture, name, params) \
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class fixture##_##name : public fixture { \
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public: \
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fixture##_##name() {} \
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protected: \
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virtual void body(); \
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}; \
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TEST_P(fixture##_##name, name /*none*/){ body();} \
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INSTANTIATE_TEST_CASE_P(/*none*/, fixture##_##name, params); \
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void fixture##_##name::body()
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typedef std::tr1::tuple<std::string, std::string, int> roi_fixture_t;
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struct SoftCascadeTest : public ::testing::TestWithParam<roi_fixture_t>
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{
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typedef cv::gpu::SoftCascade::Detection detection_t;
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static cv::Rect getFromTable(int idx)
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{
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static const cv::Rect rois[] =
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{
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cv::Rect( 65, 20, 35, 80),
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cv::Rect( 95, 35, 45, 40),
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cv::Rect( 45, 35, 45, 40),
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cv::Rect( 25, 27, 50, 45),
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cv::Rect(100, 50, 45, 40),
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cv::Rect( 60, 30, 45, 40),
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cv::Rect( 40, 55, 50, 40),
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cv::Rect( 48, 37, 72, 80),
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cv::Rect( 48, 32, 85, 58),
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cv::Rect( 48, 0, 32, 27)
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};
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return rois[idx];
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}
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static std::string itoa(long i)
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{
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static char s[65];
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sprintf(s, "%ld", i);
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return std::string(s);
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}
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static std::string getImageName(int level)
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{
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time_t rawtime;
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struct tm * timeinfo;
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char buffer [80];
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time ( &rawtime );
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timeinfo = localtime ( &rawtime );
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strftime (buffer,80,"%Y-%m-%d--%H-%M-%S",timeinfo);
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return "gpu_rec_level_" + itoa(level)+ "_" + std::string(buffer) + ".png";
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}
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static void print(std::ostream &out, const detection_t& d)
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{
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out << "\x1b[32m[ detection]\x1b[0m ("
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<< std::setw(4) << d.x
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<< " "
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<< std::setw(4) << d.y
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<< ") ("
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<< std::setw(4) << d.w
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<< " "
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<< std::setw(4) << d.h
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<< ") "
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<< std::setw(12) << d.confidence
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<< std::endl;
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}
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static void printTotal(std::ostream &out, int detbytes)
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{
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out << "\x1b[32m[ ]\x1b[0m Total detections " << (detbytes / sizeof(detection_t)) << std::endl;
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}
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static void writeResult(const cv::Mat& result, const int level)
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{
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std::string path = cv::tempfile(getImageName(level).c_str());
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cv::imwrite(path, result);
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std::cout << "\x1b[32m" << "[ ]" << std::endl << "[ stored in]"<< "\x1b[0m" << path << std::endl;
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}
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};
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GPU_TEST_P(SoftCascadeTest, detectInROI,
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testing::Combine(
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testing::Values(std::string("../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 5)))
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{
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(1));
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ASSERT_FALSE(coloredCpu.empty());
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(cvtest::TS::ptr()->get_data_path() + GET_PARAM(0)));
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GpuMat colored(coloredCpu), objectBoxes(1, 16384, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
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rois.setTo(0);
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int nroi = GET_PARAM(2);
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cv::RNG rng;
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for (int i = 0; i < nroi; ++i)
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{
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cv::Rect r = getFromTable(rng(10));
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GpuMat sub(rois, r);
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sub.setTo(1);
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}
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cascade.detectMultiScale(colored, rois, objectBoxes);
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///
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cv::Mat dt(objectBoxes);
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typedef cv::gpu::SoftCascade::Detection detection_t;
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detection_t* dts = (detection_t*)dt.data;
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cv::Mat result(coloredCpu);
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printTotal(std::cout, dt.cols);
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for (int i = 0; i < (int)(dt.cols / sizeof(detection_t)); ++i)
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{
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detection_t d = dts[i];
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print(std::cout, d);
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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SHOW(result);
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}
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GPU_TEST_P(SoftCascadeTest, detectInLevel,
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testing::Combine(
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testing::Values(std::string("../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 47)
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))
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + GET_PARAM(0);
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(1));
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ASSERT_FALSE(coloredCpu.empty());
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typedef cv::gpu::SoftCascade::Detection detection_t;
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GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(detection_t), CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
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rois.setTo(1);
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int level = GET_PARAM(2);
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cascade.detectMultiScale(colored, rois, objectBoxes, 1, level);
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cv::Mat dt(objectBoxes);
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detection_t* dts = (detection_t*)dt.data;
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cv::Mat result(coloredCpu);
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printTotal(std::cout, dt.cols);
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for (int i = 0; i < (int)(dt.cols / sizeof(detection_t)); ++i)
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{
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detection_t d = dts[i];
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print(std::cout, d);
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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writeResult(result, level);
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SHOW(result);
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}
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TEST(SoftCascadeTest, readCascade)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/icf-template.xml";
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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}
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TEST(SoftCascade, detect)
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TEST(SoftCascadeTest, detect)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SoftCascade cascade;
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@ -71,67 +248,4 @@ TEST(SoftCascade, detect)
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cascade.detectMultiScale(colored, rois, objectBoxes);
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}
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class SCSpecific : public ::testing::TestWithParam<std::tr1::tuple<std::string, int> > {
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};
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namespace {
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std::string itoa(long i)
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{
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static char s[65];
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sprintf(s, "%ld", i);
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return std::string(s);
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}
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}
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TEST_P(SCSpecific, detect)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SoftCascade cascade;
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ASSERT_TRUE(cascade.load(xml));
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std::string path = GET_PARAM(0);
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + path);
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ASSERT_FALSE(coloredCpu.empty());
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GpuMat colored(coloredCpu), objectBoxes(1, 1000, CV_8UC1), rois(cascade.getRoiSize(), CV_8UC1);
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rois.setTo(0);
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GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
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sub.setTo(cv::Scalar::all(1));
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int level = GET_PARAM(1);
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cascade.detectMultiScale(colored, rois, objectBoxes, 1, level);
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cv::Mat dt(objectBoxes);
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typedef cv::gpu::SoftCascade::Detection detection_t;
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detection_t* dts = (detection_t*)dt.data;
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cv::Mat result(coloredCpu);
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std::cout << "Total detections " << (dt.cols / sizeof(detection_t)) << std::endl;
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for(int i = 0; i < (int)(dt.cols / sizeof(detection_t)); ++i)
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{
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detection_t d = dts[i];
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std::cout << "detection: [" << std::setw(4) << d.x << " " << std::setw(4) << d.y
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<< "] [" << std::setw(4) << d.w << " " << std::setw(4) << d.h << "] "
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<< std::setw(12) << d.confidence << std::endl;
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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std::cout << "Result stored in " << "/home/kellan/gpu_res_1_oct_" + itoa(level) << "_"
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+ itoa((dt.cols / sizeof(detection_t))) + ".png" << std::endl;
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cv::imwrite("/home/kellan/gpu_res_1_oct_" + itoa(level) + "_" + itoa((dt.cols / sizeof(detection_t))) + ".png",
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result);
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cv::imshow("res", result);
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cv::waitKey(0);
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}
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INSTANTIATE_TEST_CASE_P(inLevel, SCSpecific,
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testing::Combine(
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 47)
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));
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#endif
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