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opencv/modules/ts/src/ts_perf.cpp
Alexander Alekhin f1873bbca1 perf tests: allow to skip performance tests
2013-11-13 14:17:56 +04:00

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#include "precomp.hpp"
#ifdef HAVE_CUDA
#include "opencv2/core/gpumat.hpp"
#endif
#ifdef ANDROID
# include <sys/time.h>
#endif
using namespace perf;
int64 TestBase::timeLimitDefault = 0;
unsigned int TestBase::iterationsLimitDefault = (unsigned int)(-1);
int64 TestBase::_timeadjustment = 0;
// Item [0] will be considered the default implementation.
static std::vector<std::string> available_impls;
static std::string param_impl;
static enum PERF_STRATEGY param_strategy = PERF_STRATEGY_BASE;
static double param_max_outliers;
static double param_max_deviation;
static unsigned int param_min_samples;
static unsigned int param_force_samples;
static uint64 param_seed;
static double param_time_limit;
static int param_threads;
static bool param_write_sanity;
static bool param_verify_sanity;
#ifdef HAVE_CUDA
static int param_cuda_device;
#endif
#ifdef ANDROID
static int param_affinity_mask;
static bool log_power_checkpoints;
#include <sys/syscall.h>
#include <pthread.h>
static void setCurrentThreadAffinityMask(int mask)
{
pid_t pid=gettid();
int syscallres=syscall(__NR_sched_setaffinity, pid, sizeof(mask), &mask);
if (syscallres)
{
int err=errno;
err=err;//to avoid warnings about unused variables
LOGE("Error in the syscall setaffinity: mask=%d=0x%x err=%d=0x%x", mask, mask, err, err);
}
}
#endif
namespace {
class PerfEnvironment: public ::testing::Environment
{
public:
void TearDown()
{
cv::setNumThreads(-1);
}
};
} // namespace
static void randu(cv::Mat& m)
{
const int bigValue = 0x00000FFF;
if (m.depth() < CV_32F)
{
int minmax[] = {0, 256};
cv::Mat mr = cv::Mat(m.rows, (int)(m.cols * m.elemSize()), CV_8U, m.ptr(), m.step[0]);
cv::randu(mr, cv::Mat(1, 1, CV_32S, minmax), cv::Mat(1, 1, CV_32S, minmax + 1));
}
else if (m.depth() == CV_32F)
{
//float minmax[] = {-FLT_MAX, FLT_MAX};
float minmax[] = {-bigValue, bigValue};
cv::Mat mr = m.reshape(1);
cv::randu(mr, cv::Mat(1, 1, CV_32F, minmax), cv::Mat(1, 1, CV_32F, minmax + 1));
}
else
{
//double minmax[] = {-DBL_MAX, DBL_MAX};
double minmax[] = {-bigValue, bigValue};
cv::Mat mr = m.reshape(1);
cv::randu(mr, cv::Mat(1, 1, CV_64F, minmax), cv::Mat(1, 1, CV_64F, minmax + 1));
}
}
/*****************************************************************************************\
* inner exception class for early termination
\*****************************************************************************************/
class PerfEarlyExitException: public cv::Exception {};
/*****************************************************************************************\
* ::perf::Regression
\*****************************************************************************************/
Regression& Regression::instance()
{
static Regression single;
return single;
}
Regression& Regression::add(TestBase* test, const std::string& name, cv::InputArray array, double eps, ERROR_TYPE err)
{
if(test) test->setVerified();
return instance()(name, array, eps, err);
}
Regression& Regression::addKeypoints(TestBase* test, const std::string& name, const std::vector<cv::KeyPoint>& array, double eps, ERROR_TYPE err)
{
int len = (int)array.size();
cv::Mat pt (len, 1, CV_32FC2, len ? (void*)&array[0].pt : 0, sizeof(cv::KeyPoint));
cv::Mat size (len, 1, CV_32FC1, len ? (void*)&array[0].size : 0, sizeof(cv::KeyPoint));
cv::Mat angle (len, 1, CV_32FC1, len ? (void*)&array[0].angle : 0, sizeof(cv::KeyPoint));
cv::Mat response(len, 1, CV_32FC1, len ? (void*)&array[0].response : 0, sizeof(cv::KeyPoint));
cv::Mat octave (len, 1, CV_32SC1, len ? (void*)&array[0].octave : 0, sizeof(cv::KeyPoint));
cv::Mat class_id(len, 1, CV_32SC1, len ? (void*)&array[0].class_id : 0, sizeof(cv::KeyPoint));
return Regression::add(test, name + "-pt", pt, eps, ERROR_ABSOLUTE)
(name + "-size", size, eps, ERROR_ABSOLUTE)
(name + "-angle", angle, eps, ERROR_ABSOLUTE)
(name + "-response", response, eps, err)
(name + "-octave", octave, eps, ERROR_ABSOLUTE)
(name + "-class_id", class_id, eps, ERROR_ABSOLUTE);
}
Regression& Regression::addMatches(TestBase* test, const std::string& name, const std::vector<cv::DMatch>& array, double eps, ERROR_TYPE err)
{
int len = (int)array.size();
cv::Mat queryIdx(len, 1, CV_32SC1, len ? (void*)&array[0].queryIdx : 0, sizeof(cv::DMatch));
cv::Mat trainIdx(len, 1, CV_32SC1, len ? (void*)&array[0].trainIdx : 0, sizeof(cv::DMatch));
cv::Mat imgIdx (len, 1, CV_32SC1, len ? (void*)&array[0].imgIdx : 0, sizeof(cv::DMatch));
cv::Mat distance(len, 1, CV_32FC1, len ? (void*)&array[0].distance : 0, sizeof(cv::DMatch));
return Regression::add(test, name + "-queryIdx", queryIdx, DBL_EPSILON, ERROR_ABSOLUTE)
(name + "-trainIdx", trainIdx, DBL_EPSILON, ERROR_ABSOLUTE)
(name + "-imgIdx", imgIdx, DBL_EPSILON, ERROR_ABSOLUTE)
(name + "-distance", distance, eps, err);
}
void Regression::Init(const std::string& testSuitName, const std::string& ext)
{
instance().init(testSuitName, ext);
}
void Regression::init(const std::string& testSuitName, const std::string& ext)
{
if (!storageInPath.empty())
{
LOGE("Subsequent initialization of Regression utility is not allowed.");
return;
}
const char *data_path_dir = getenv("OPENCV_TEST_DATA_PATH");
const char *path_separator = "/";
if (data_path_dir)
{
int len = (int)strlen(data_path_dir)-1;
if (len < 0) len = 0;
std::string path_base = (data_path_dir[0] == 0 ? std::string(".") : std::string(data_path_dir))
+ (data_path_dir[len] == '/' || data_path_dir[len] == '\\' ? "" : path_separator)
+ "perf"
+ path_separator;
storageInPath = path_base + testSuitName + ext;
storageOutPath = path_base + testSuitName;
}
else
{
storageInPath = testSuitName + ext;
storageOutPath = testSuitName;
}
suiteName = testSuitName;
try
{
if (storageIn.open(storageInPath, cv::FileStorage::READ))
{
rootIn = storageIn.root();
if (storageInPath.length() > 3 && storageInPath.substr(storageInPath.length()-3) == ".gz")
storageOutPath += "_new";
storageOutPath += ext;
}
}
catch(cv::Exception&)
{
LOGE("Failed to open sanity data for reading: %s", storageInPath.c_str());
}
if(!storageIn.isOpened())
storageOutPath = storageInPath;
}
Regression::Regression() : regRNG(cv::getTickCount())//this rng should be really random
{
}
Regression::~Regression()
{
if (storageIn.isOpened())
storageIn.release();
if (storageOut.isOpened())
{
if (!currentTestNodeName.empty())
storageOut << "}";
storageOut.release();
}
}
cv::FileStorage& Regression::write()
{
if (!storageOut.isOpened() && !storageOutPath.empty())
{
int mode = (storageIn.isOpened() && storageInPath == storageOutPath)
? cv::FileStorage::APPEND : cv::FileStorage::WRITE;
storageOut.open(storageOutPath, mode);
if (!storageOut.isOpened())
{
LOGE("Could not open \"%s\" file for writing", storageOutPath.c_str());
storageOutPath.clear();
}
else if (mode == cv::FileStorage::WRITE && !rootIn.empty())
{
//TODO: write content of rootIn node into the storageOut
}
}
return storageOut;
}
std::string Regression::getCurrentTestNodeName()
{
const ::testing::TestInfo* const test_info =
::testing::UnitTest::GetInstance()->current_test_info();
if (test_info == 0)
return "undefined";
std::string nodename = std::string(test_info->test_case_name()) + "--" + test_info->name();
size_t idx = nodename.find_first_of('/');
if (idx != std::string::npos)
nodename.erase(idx);
const char* type_param = test_info->type_param();
if (type_param != 0)
(nodename += "--") += type_param;
const char* value_param = test_info->value_param();
if (value_param != 0)
(nodename += "--") += value_param;
for(size_t i = 0; i < nodename.length(); ++i)
if (!isalnum(nodename[i]) && '_' != nodename[i])
nodename[i] = '-';
return nodename;
}
bool Regression::isVector(cv::InputArray a)
{
return a.kind() == cv::_InputArray::STD_VECTOR_MAT || a.kind() == cv::_InputArray::STD_VECTOR_VECTOR;
}
double Regression::getElem(cv::Mat& m, int y, int x, int cn)
{
switch (m.depth())
{
case CV_8U: return *(m.ptr<unsigned char>(y, x) + cn);
case CV_8S: return *(m.ptr<signed char>(y, x) + cn);
case CV_16U: return *(m.ptr<unsigned short>(y, x) + cn);
case CV_16S: return *(m.ptr<signed short>(y, x) + cn);
case CV_32S: return *(m.ptr<signed int>(y, x) + cn);
case CV_32F: return *(m.ptr<float>(y, x) + cn);
case CV_64F: return *(m.ptr<double>(y, x) + cn);
default: return 0;
}
}
void Regression::write(cv::Mat m)
{
if (!m.empty() && m.dims < 2) return;
double min, max;
cv::minMaxIdx(m, &min, &max);
write() << "min" << min << "max" << max;
write() << "last" << "{" << "x" << m.size.p[1] - 1 << "y" << m.size.p[0] - 1
<< "val" << getElem(m, m.size.p[0] - 1, m.size.p[1] - 1, m.channels() - 1) << "}";
int x, y, cn;
x = regRNG.uniform(0, m.size.p[1]);
y = regRNG.uniform(0, m.size.p[0]);
cn = regRNG.uniform(0, m.channels());
write() << "rng1" << "{" << "x" << x << "y" << y;
if(cn > 0) write() << "cn" << cn;
write() << "val" << getElem(m, y, x, cn) << "}";
x = regRNG.uniform(0, m.size.p[1]);
y = regRNG.uniform(0, m.size.p[0]);
cn = regRNG.uniform(0, m.channels());
write() << "rng2" << "{" << "x" << x << "y" << y;
if (cn > 0) write() << "cn" << cn;
write() << "val" << getElem(m, y, x, cn) << "}";
}
void Regression::verify(cv::FileNode node, cv::Mat actual, double eps, std::string argname, ERROR_TYPE err)
{
if (!actual.empty() && actual.dims < 2) return;
double expect_min = (double)node["min"];
double expect_max = (double)node["max"];
if (err == ERROR_RELATIVE)
eps *= std::max(std::abs(expect_min), std::abs(expect_max));
double actual_min, actual_max;
cv::minMaxIdx(actual, &actual_min, &actual_max);
ASSERT_NEAR(expect_min, actual_min, eps)
<< argname << " has unexpected minimal value" << std::endl;
ASSERT_NEAR(expect_max, actual_max, eps)
<< argname << " has unexpected maximal value" << std::endl;
cv::FileNode last = node["last"];
double actual_last = getElem(actual, actual.size.p[0] - 1, actual.size.p[1] - 1, actual.channels() - 1);
int expect_cols = (int)last["x"] + 1;
int expect_rows = (int)last["y"] + 1;
ASSERT_EQ(expect_cols, actual.size.p[1])
<< argname << " has unexpected number of columns" << std::endl;
ASSERT_EQ(expect_rows, actual.size.p[0])
<< argname << " has unexpected number of rows" << std::endl;
double expect_last = (double)last["val"];
ASSERT_NEAR(expect_last, actual_last, eps)
<< argname << " has unexpected value of the last element" << std::endl;
cv::FileNode rng1 = node["rng1"];
int x1 = rng1["x"];
int y1 = rng1["y"];
int cn1 = rng1["cn"];
double expect_rng1 = (double)rng1["val"];
// it is safe to use x1 and y1 without checks here because we have already
// verified that mat size is the same as recorded
double actual_rng1 = getElem(actual, y1, x1, cn1);
ASSERT_NEAR(expect_rng1, actual_rng1, eps)
<< argname << " has unexpected value of the ["<< x1 << ":" << y1 << ":" << cn1 <<"] element" << std::endl;
cv::FileNode rng2 = node["rng2"];
int x2 = rng2["x"];
int y2 = rng2["y"];
int cn2 = rng2["cn"];
double expect_rng2 = (double)rng2["val"];
double actual_rng2 = getElem(actual, y2, x2, cn2);
ASSERT_NEAR(expect_rng2, actual_rng2, eps)
<< argname << " has unexpected value of the ["<< x2 << ":" << y2 << ":" << cn2 <<"] element" << std::endl;
}
void Regression::write(cv::InputArray array)
{
write() << "kind" << array.kind();
write() << "type" << array.type();
if (isVector(array))
{
int total = (int)array.total();
int idx = regRNG.uniform(0, total);
write() << "len" << total;
write() << "idx" << idx;
cv::Mat m = array.getMat(idx);
if (m.total() * m.channels() < 26) //5x5 or smaller
write() << "val" << m;
else
write(m);
}
else
{
if (array.total() * array.channels() < 26) //5x5 or smaller
write() << "val" << array.getMat();
else
write(array.getMat());
}
}
static int countViolations(const cv::Mat& expected, const cv::Mat& actual, const cv::Mat& diff, double eps, double* max_violation = 0, double* max_allowed = 0)
{
cv::Mat diff64f;
diff.reshape(1).convertTo(diff64f, CV_64F);
cv::Mat expected_abs = cv::abs(expected.reshape(1));
cv::Mat actual_abs = cv::abs(actual.reshape(1));
cv::Mat maximum, mask;
cv::max(expected_abs, actual_abs, maximum);
cv::multiply(maximum, cv::Vec<double, 1>(eps), maximum, CV_64F);
cv::compare(diff64f, maximum, mask, cv::CMP_GT);
int v = cv::countNonZero(mask);
if (v > 0 && max_violation != 0 && max_allowed != 0)
{
int loc[10];
cv::minMaxIdx(maximum, 0, max_allowed, 0, loc, mask);
*max_violation = diff64f.at<double>(loc[1], loc[0]);
}
return v;
}
void Regression::verify(cv::FileNode node, cv::InputArray array, double eps, ERROR_TYPE err)
{
int expected_kind = (int)node["kind"];
int expected_type = (int)node["type"];
ASSERT_EQ(expected_kind, array.kind()) << " Argument \"" << node.name() << "\" has unexpected kind";
ASSERT_EQ(expected_type, array.type()) << " Argument \"" << node.name() << "\" has unexpected type";
cv::FileNode valnode = node["val"];
if (isVector(array))
{
int expected_length = (int)node["len"];
ASSERT_EQ(expected_length, (int)array.total()) << " Vector \"" << node.name() << "\" has unexpected length";
int idx = node["idx"];
cv::Mat actual = array.getMat(idx);
if (valnode.isNone())
{
ASSERT_LE((size_t)26, actual.total() * (size_t)actual.channels())
<< " \"" << node.name() << "[" << idx << "]\" has unexpected number of elements";
verify(node, actual, eps, cv::format("%s[%d]", node.name().c_str(), idx), err);
}
else
{
cv::Mat expected;
valnode >> expected;
if(expected.empty())
{
ASSERT_TRUE(actual.empty())
<< " expected empty " << node.name() << "[" << idx<< "]";
}
else
{
ASSERT_EQ(expected.size(), actual.size())
<< " " << node.name() << "[" << idx<< "] has unexpected size";
cv::Mat diff;
cv::absdiff(expected, actual, diff);
if (err == ERROR_ABSOLUTE)
{
if (!cv::checkRange(diff, true, 0, 0, eps))
{
if(expected.total() * expected.channels() < 12)
std::cout << " Expected: " << std::endl << expected << std::endl << " Actual:" << std::endl << actual << std::endl;
double max;
cv::minMaxIdx(diff.reshape(1), 0, &max);
FAIL() << " Absolute difference (=" << max << ") between argument \""
<< node.name() << "[" << idx << "]\" and expected value is greater than " << eps;
}
}
else if (err == ERROR_RELATIVE)
{
double maxv, maxa;
int violations = countViolations(expected, actual, diff, eps, &maxv, &maxa);
if (violations > 0)
{
if(expected.total() * expected.channels() < 12)
std::cout << " Expected: " << std::endl << expected << std::endl << " Actual:" << std::endl << actual << std::endl;
FAIL() << " Relative difference (" << maxv << " of " << maxa << " allowed) between argument \""
<< node.name() << "[" << idx << "]\" and expected value is greater than " << eps << " in " << violations << " points";
}
}
}
}
}
else
{
if (valnode.isNone())
{
ASSERT_LE((size_t)26, array.total() * (size_t)array.channels())
<< " Argument \"" << node.name() << "\" has unexpected number of elements";
verify(node, array.getMat(), eps, "Argument \"" + node.name() + "\"", err);
}
else
{
cv::Mat expected;
valnode >> expected;
cv::Mat actual = array.getMat();
if(expected.empty())
{
ASSERT_TRUE(actual.empty())
<< " expected empty " << node.name();
}
else
{
ASSERT_EQ(expected.size(), actual.size())
<< " Argument \"" << node.name() << "\" has unexpected size";
cv::Mat diff;
cv::absdiff(expected, actual, diff);
if (err == ERROR_ABSOLUTE)
{
if (!cv::checkRange(diff, true, 0, 0, eps))
{
if(expected.total() * expected.channels() < 12)
std::cout << " Expected: " << std::endl << expected << std::endl << " Actual:" << std::endl << actual << std::endl;
double max;
cv::minMaxIdx(diff.reshape(1), 0, &max);
FAIL() << " Difference (=" << max << ") between argument1 \"" << node.name()
<< "\" and expected value is greater than " << eps;
}
}
else if (err == ERROR_RELATIVE)
{
double maxv, maxa;
int violations = countViolations(expected, actual, diff, eps, &maxv, &maxa);
if (violations > 0)
{
if(expected.total() * expected.channels() < 12)
std::cout << " Expected: " << std::endl << expected << std::endl << " Actual:" << std::endl << actual << std::endl;
FAIL() << " Relative difference (" << maxv << " of " << maxa << " allowed) between argument \"" << node.name()
<< "\" and expected value is greater than " << eps << " in " << violations << " points";
}
}
}
}
}
}
Regression& Regression::operator() (const std::string& name, cv::InputArray array, double eps, ERROR_TYPE err)
{
// exit if current test is already failed
if(::testing::UnitTest::GetInstance()->current_test_info()->result()->Failed()) return *this;
if(!array.empty() && array.depth() == CV_USRTYPE1)
{
ADD_FAILURE() << " Can not check regression for CV_USRTYPE1 data type for " << name;
return *this;
}
std::string nodename = getCurrentTestNodeName();
cv::FileNode n = rootIn[nodename];
if(n.isNone())
{
if(param_write_sanity)
{
if (nodename != currentTestNodeName)
{
if (!currentTestNodeName.empty())
write() << "}";
currentTestNodeName = nodename;
write() << nodename << "{";
}
// TODO: verify that name is alphanumeric, current error message is useless
write() << name << "{";
write(array);
write() << "}";
}
else if(param_verify_sanity)
{
ADD_FAILURE() << " No regression data for " << name << " argument";
}
}
else
{
cv::FileNode this_arg = n[name];
if (!this_arg.isMap())
ADD_FAILURE() << " No regression data for " << name << " argument";
else
verify(this_arg, array, eps, err);
}
return *this;
}
/*****************************************************************************************\
* ::perf::performance_metrics
\*****************************************************************************************/
performance_metrics::performance_metrics()
{
clear();
}
void performance_metrics::clear()
{
bytesIn = 0;
bytesOut = 0;
samples = 0;
outliers = 0;
gmean = 0;
gstddev = 0;
mean = 0;
stddev = 0;
median = 0;
min = 0;
frequency = 0;
terminationReason = TERM_UNKNOWN;
}
/*****************************************************************************************\
* ::perf::TestBase
\*****************************************************************************************/
void TestBase::Init(int argc, const char* const argv[])
{
std::vector<std::string> plain_only;
plain_only.push_back("plain");
TestBase::Init(plain_only, argc, argv);
}
void TestBase::Init(const std::vector<std::string> & availableImpls,
int argc, const char* const argv[])
{
available_impls = availableImpls;
const std::string command_line_keys =
"{ |perf_max_outliers |8 |percent of allowed outliers}"
"{ |perf_min_samples |10 |minimal required numer of samples}"
"{ |perf_force_samples |100 |force set maximum number of samples for all tests}"
"{ |perf_seed |809564 |seed for random numbers generator}"
"{ |perf_threads |-1 |the number of worker threads, if parallel execution is enabled}"
"{ |perf_write_sanity |false |create new records for sanity checks}"
"{ |perf_verify_sanity |false |fail tests having no regression data for sanity checks}"
"{ |perf_impl |" + available_impls[0] +
"|the implementation variant of functions under test}"
"{ |perf_list_impls |false |list available implementation variants and exit}"
"{ |perf_run_cpu |false |deprecated, equivalent to --perf_impl=plain}"
"{ |perf_strategy |default |specifies performance measuring strategy: default, base or simple (weak restrictions)}"
#ifdef ANDROID
"{ |perf_time_limit |6.0 |default time limit for a single test (in seconds)}"
"{ |perf_affinity_mask |0 |set affinity mask for the main thread}"
"{ |perf_log_power_checkpoints | |additional xml logging for power measurement}"
#else
"{ |perf_time_limit |3.0 |default time limit for a single test (in seconds)}"
#endif
"{ |perf_max_deviation |1.0 |}"
"{h |help |false |print help info}"
#ifdef HAVE_CUDA
"{ |perf_cuda_device |0 |run GPU test suite onto specific CUDA capable device}"
"{ |perf_cuda_info_only |false |print an information about system and an available CUDA devices and then exit.}"
#endif
;
cv::CommandLineParser args(argc, argv, command_line_keys.c_str());
if (args.get<bool>("help"))
{
args.printParams();
printf("\n\n");
return;
}
::testing::AddGlobalTestEnvironment(new PerfEnvironment);
param_impl = args.get<bool>("perf_run_cpu") ? "plain" : args.get<std::string>("perf_impl");
std::string perf_strategy = args.get<std::string>("perf_strategy");
if (perf_strategy == "default")
{
// nothing
}
else if (perf_strategy == "base")
{
param_strategy = PERF_STRATEGY_BASE;
}
else if (perf_strategy == "simple")
{
param_strategy = PERF_STRATEGY_SIMPLE;
}
else
{
printf("No such strategy: %s\n", perf_strategy.c_str());
exit(1);
}
param_max_outliers = std::min(100., std::max(0., args.get<double>("perf_max_outliers")));
param_min_samples = std::max(1u, args.get<unsigned int>("perf_min_samples"));
param_max_deviation = std::max(0., args.get<double>("perf_max_deviation"));
param_seed = args.get<uint64>("perf_seed");
param_time_limit = std::max(0., args.get<double>("perf_time_limit"));
param_force_samples = args.get<unsigned int>("perf_force_samples");
param_write_sanity = args.get<bool>("perf_write_sanity");
param_verify_sanity = args.get<bool>("perf_verify_sanity");
param_threads = args.get<int>("perf_threads");
#ifdef ANDROID
param_affinity_mask = args.get<int>("perf_affinity_mask");
log_power_checkpoints = args.get<bool>("perf_log_power_checkpoints");
#endif
bool param_list_impls = args.get<bool>("perf_list_impls");
if (param_list_impls)
{
fputs("Available implementation variants:", stdout);
for (size_t i = 0; i < available_impls.size(); ++i) {
putchar(' ');
fputs(available_impls[i].c_str(), stdout);
}
putchar('\n');
exit(0);
}
if (std::find(available_impls.begin(), available_impls.end(), param_impl) == available_impls.end())
{
printf("No such implementation: %s\n", param_impl.c_str());
exit(1);
}
#ifdef HAVE_CUDA
bool printOnly = args.get<bool>("perf_cuda_info_only");
if (printOnly)
exit(0);
#endif
if (available_impls.size() > 1)
printf("[----------]\n[ INFO ] \tImplementation variant: %s.\n[----------]\n", param_impl.c_str()), fflush(stdout);
#ifdef HAVE_CUDA
param_cuda_device = std::max(0, std::min(cv::gpu::getCudaEnabledDeviceCount(), args.get<int>("perf_cuda_device")));
if (param_impl == "cuda")
{
cv::gpu::DeviceInfo info(param_cuda_device);
if (!info.isCompatible())
{
printf("[----------]\n[ FAILURE ] \tDevice %s is NOT compatible with current GPU module build.\n[----------]\n", info.name().c_str()), fflush(stdout);
exit(-1);
}
cv::gpu::setDevice(param_cuda_device);
printf("[----------]\n[ GPU INFO ] \tRun test suite on %s GPU.\n[----------]\n", info.name().c_str()), fflush(stdout);
}
#endif
// if (!args.check())
// {
// args.printErrors();
// return;
// }
timeLimitDefault = param_time_limit == 0.0 ? 1 : (int64)(param_time_limit * cv::getTickFrequency());
iterationsLimitDefault = param_force_samples == 0 ? (unsigned)(-1) : param_force_samples;
_timeadjustment = _calibrate();
}
void TestBase::RecordRunParameters()
{
::testing::Test::RecordProperty("cv_implementation", param_impl);
::testing::Test::RecordProperty("cv_num_threads", param_threads);
#ifdef HAVE_CUDA
if (param_impl == "cuda")
{
cv::gpu::DeviceInfo info(param_cuda_device);
::testing::Test::RecordProperty("cv_cuda_gpu", info.name());
}
#endif
}
std::string TestBase::getSelectedImpl()
{
return param_impl;
}
enum PERF_STRATEGY TestBase::getPerformanceStrategy()
{
return param_strategy;
}
enum PERF_STRATEGY TestBase::setPerformanceStrategy(enum PERF_STRATEGY strategy)
{
enum PERF_STRATEGY ret = param_strategy;
param_strategy = strategy;
return ret;
}
int64 TestBase::_calibrate()
{
class _helper : public ::perf::TestBase
{
public:
performance_metrics& getMetrics() { return calcMetrics(); }
virtual void TestBody() {}
virtual void PerfTestBody()
{
//the whole system warmup
SetUp();
cv::Mat a(2048, 2048, CV_32S, cv::Scalar(1));
cv::Mat b(2048, 2048, CV_32S, cv::Scalar(2));
declare.time(30);
double s = 0;
for(declare.iterations(20); startTimer(), next(); stopTimer())
s+=a.dot(b);
declare.time(s);
//self calibration
SetUp();
for(declare.iterations(1000); startTimer(), next(); stopTimer()){}
}
};
_timeadjustment = 0;
_helper h;
h.PerfTestBody();
double compensation = h.getMetrics().min;
if (param_strategy == PERF_STRATEGY_SIMPLE)
{
CV_Assert(compensation < 0.01 * cv::getTickFrequency());
compensation = 0.0f; // simple strategy doesn't require any compensation
}
LOGD("Time compensation is %.0f", compensation);
return (int64)compensation;
}
#ifdef _MSC_VER
# pragma warning(push)
# pragma warning(disable:4355) // 'this' : used in base member initializer list
#endif
TestBase::TestBase(): declare(this)
{
}
#ifdef _MSC_VER
# pragma warning(pop)
#endif
void TestBase::declareArray(SizeVector& sizes, cv::InputOutputArray a, int wtype)
{
if (!a.empty())
{
sizes.push_back(std::pair<int, cv::Size>(getSizeInBytes(a), getSize(a)));
warmup(a, wtype);
}
else if (a.kind() != cv::_InputArray::NONE)
ADD_FAILURE() << " Uninitialized input/output parameters are not allowed for performance tests";
}
void TestBase::warmup(cv::InputOutputArray a, int wtype)
{
if (a.empty()) return;
if (a.kind() != cv::_InputArray::STD_VECTOR_MAT && a.kind() != cv::_InputArray::STD_VECTOR_VECTOR)
warmup_impl(a.getMat(), wtype);
else
{
size_t total = a.total();
for (size_t i = 0; i < total; ++i)
warmup_impl(a.getMat((int)i), wtype);
}
}
int TestBase::getSizeInBytes(cv::InputArray a)
{
if (a.empty()) return 0;
int total = (int)a.total();
if (a.kind() != cv::_InputArray::STD_VECTOR_MAT && a.kind() != cv::_InputArray::STD_VECTOR_VECTOR)
return total * CV_ELEM_SIZE(a.type());
int size = 0;
for (int i = 0; i < total; ++i)
size += (int)a.total(i) * CV_ELEM_SIZE(a.type(i));
return size;
}
cv::Size TestBase::getSize(cv::InputArray a)
{
if (a.kind() != cv::_InputArray::STD_VECTOR_MAT && a.kind() != cv::_InputArray::STD_VECTOR_VECTOR)
return a.size();
return cv::Size();
}
bool TestBase::next()
{
static int64 lastActivityPrintTime = 0;
if (currentIter != (unsigned int)-1)
{
if (currentIter + 1 != times.size())
ADD_FAILURE() << " next() is called before stopTimer()";
}
else
{
lastActivityPrintTime = 0;
metrics.clear();
}
cv::theRNG().state = param_seed; //this rng should generate same numbers for each run
++currentIter;
bool has_next = false;
do {
assert(currentIter == times.size());
if (currentIter == 0)
{
has_next = true;
break;
}
if (param_strategy == PERF_STRATEGY_BASE)
{
has_next = currentIter < nIters && totalTime < timeLimit;
}
else
{
assert(param_strategy == PERF_STRATEGY_SIMPLE);
if (totalTime - lastActivityPrintTime >= cv::getTickFrequency() * 10)
{
std::cout << '.' << std::endl;
lastActivityPrintTime = totalTime;
}
if (currentIter >= nIters)
{
has_next = false;
break;
}
if (currentIter < param_min_samples)
{
has_next = true;
break;
}
calcMetrics();
double criteria = 0.03; // 3%
if (fabs(metrics.mean) > 1e-6)
has_next = metrics.stddev > criteria * fabs(metrics.mean);
else
has_next = true;
}
} while (false);
#ifdef ANDROID
if (log_power_checkpoints)
{
timeval tim;
gettimeofday(&tim, NULL);
unsigned long long t1 = tim.tv_sec * 1000LLU + (unsigned long long)(tim.tv_usec / 1000.f);
if (currentIter == 1) RecordProperty("test_start", cv::format("%llu",t1).c_str());
if (!has_next) RecordProperty("test_complete", cv::format("%llu",t1).c_str());
}
#endif
if (has_next)
startTimer(); // really we should measure activity from this moment, so reset start time
return has_next;
}
void TestBase::warmup_impl(cv::Mat m, int wtype)
{
switch(wtype)
{
case WARMUP_READ:
cv::sum(m.reshape(1));
return;
case WARMUP_WRITE:
m.reshape(1).setTo(cv::Scalar::all(0));
return;
case WARMUP_RNG:
randu(m);
return;
default:
return;
}
}
unsigned int TestBase::getTotalInputSize() const
{
unsigned int res = 0;
for (SizeVector::const_iterator i = inputData.begin(); i != inputData.end(); ++i)
res += i->first;
return res;
}
unsigned int TestBase::getTotalOutputSize() const
{
unsigned int res = 0;
for (SizeVector::const_iterator i = outputData.begin(); i != outputData.end(); ++i)
res += i->first;
return res;
}
void TestBase::startTimer()
{
lastTime = cv::getTickCount();
}
void TestBase::stopTimer()
{
int64 time = cv::getTickCount();
if (lastTime == 0)
ADD_FAILURE() << " stopTimer() is called before startTimer()/next()";
lastTime = time - lastTime;
totalTime += lastTime;
lastTime -= _timeadjustment;
if (lastTime < 0) lastTime = 0;
times.push_back(lastTime);
lastTime = 0;
}
performance_metrics& TestBase::calcMetrics()
{
CV_Assert(metrics.samples <= (unsigned int)currentIter);
if ((metrics.samples == (unsigned int)currentIter) || times.size() == 0)
return metrics;
metrics.bytesIn = getTotalInputSize();
metrics.bytesOut = getTotalOutputSize();
metrics.frequency = cv::getTickFrequency();
metrics.samples = (unsigned int)times.size();
metrics.outliers = 0;
if (metrics.terminationReason != performance_metrics::TERM_INTERRUPT && metrics.terminationReason != performance_metrics::TERM_EXCEPTION)
{
if (currentIter == nIters)
metrics.terminationReason = performance_metrics::TERM_ITERATIONS;
else if (totalTime >= timeLimit)
metrics.terminationReason = performance_metrics::TERM_TIME;
else
metrics.terminationReason = performance_metrics::TERM_UNKNOWN;
}
std::sort(times.begin(), times.end());
TimeVector::const_iterator start = times.begin();
TimeVector::const_iterator end = times.end();
if (param_strategy == PERF_STRATEGY_BASE)
{
//estimate mean and stddev for log(time)
double gmean = 0;
double gstddev = 0;
int n = 0;
for(TimeVector::const_iterator i = times.begin(); i != times.end(); ++i)
{
double x = static_cast<double>(*i)/runsPerIteration;
if (x < DBL_EPSILON) continue;
double lx = log(x);
++n;
double delta = lx - gmean;
gmean += delta / n;
gstddev += delta * (lx - gmean);
}
gstddev = n > 1 ? sqrt(gstddev / (n - 1)) : 0;
//filter outliers assuming log-normal distribution
//http://stackoverflow.com/questions/1867426/modeling-distribution-of-performance-measurements
if (gstddev > DBL_EPSILON)
{
double minout = exp(gmean - 3 * gstddev) * runsPerIteration;
double maxout = exp(gmean + 3 * gstddev) * runsPerIteration;
while(*start < minout) ++start, ++metrics.outliers;
do --end, ++metrics.outliers; while(*end > maxout);
++end, --metrics.outliers;
}
}
else if (param_strategy == PERF_STRATEGY_SIMPLE)
{
metrics.outliers = static_cast<int>(times.size() * param_max_outliers / 100);
for (unsigned int i = 0; i < metrics.outliers; i++)
--end;
}
else
{
assert(false);
}
int offset = static_cast<int>(start - times.begin());
metrics.min = static_cast<double>(*start)/runsPerIteration;
//calc final metrics
unsigned int n = 0;
double gmean = 0;
double gstddev = 0;
double mean = 0;
double stddev = 0;
unsigned int m = 0;
for(; start != end; ++start)
{
double x = static_cast<double>(*start)/runsPerIteration;
if (x > DBL_EPSILON)
{
double lx = log(x);
++m;
double gdelta = lx - gmean;
gmean += gdelta / m;
gstddev += gdelta * (lx - gmean);
}
++n;
double delta = x - mean;
mean += delta / n;
stddev += delta * (x - mean);
}
metrics.mean = mean;
metrics.gmean = exp(gmean);
metrics.gstddev = m > 1 ? sqrt(gstddev / (m - 1)) : 0;
metrics.stddev = n > 1 ? sqrt(stddev / (n - 1)) : 0;
metrics.median = (n % 2
? (double)times[offset + n / 2]
: 0.5 * (times[offset + n / 2] + times[offset + n / 2 - 1])
) / runsPerIteration;
return metrics;
}
void TestBase::validateMetrics()
{
performance_metrics& m = calcMetrics();
if (HasFailure()) return;
ASSERT_GE(m.samples, 1u)
<< " No time measurements was performed.\nstartTimer() and stopTimer() commands are required for performance tests.";
if (param_strategy == PERF_STRATEGY_BASE)
{
EXPECT_GE(m.samples, param_min_samples)
<< " Only a few samples are collected.\nPlease increase number of iterations or/and time limit to get reliable performance measurements.";
if (m.gstddev > DBL_EPSILON)
{
EXPECT_GT(/*m.gmean * */1., /*m.gmean * */ 2 * sinh(m.gstddev * param_max_deviation))
<< " Test results are not reliable ((mean-sigma,mean+sigma) deviation interval is greater than measured time interval).";
}
EXPECT_LE(m.outliers, std::max((unsigned int)cvCeil(m.samples * param_max_outliers / 100.), 1u))
<< " Test results are not reliable (too many outliers).";
}
else if (param_strategy == PERF_STRATEGY_SIMPLE)
{
double mean = metrics.mean * 1000.0f / metrics.frequency;
double stddev = metrics.stddev * 1000.0f / metrics.frequency;
double percents = stddev / mean * 100.f;
printf(" samples = %d, mean = %.2f, stddev = %.2f (%.1f%%)\n", (int)metrics.samples, mean, stddev, percents);
}
else
{
assert(false);
}
}
void TestBase::reportMetrics(bool toJUnitXML)
{
performance_metrics& m = calcMetrics();
if (m.terminationReason == performance_metrics::TERM_SKIP_TEST)
{
if (toJUnitXML)
{
RecordProperty("custom_status", "skipped");
}
}
else if (toJUnitXML)
{
RecordProperty("bytesIn", (int)m.bytesIn);
RecordProperty("bytesOut", (int)m.bytesOut);
RecordProperty("term", m.terminationReason);
RecordProperty("samples", (int)m.samples);
RecordProperty("outliers", (int)m.outliers);
RecordProperty("frequency", cv::format("%.0f", m.frequency).c_str());
RecordProperty("min", cv::format("%.0f", m.min).c_str());
RecordProperty("median", cv::format("%.0f", m.median).c_str());
RecordProperty("gmean", cv::format("%.0f", m.gmean).c_str());
RecordProperty("gstddev", cv::format("%.6f", m.gstddev).c_str());
RecordProperty("mean", cv::format("%.0f", m.mean).c_str());
RecordProperty("stddev", cv::format("%.0f", m.stddev).c_str());
}
else
{
const ::testing::TestInfo* const test_info = ::testing::UnitTest::GetInstance()->current_test_info();
const char* type_param = test_info->type_param();
const char* value_param = test_info->value_param();
#if defined(ANDROID) && defined(USE_ANDROID_LOGGING)
LOGD("[ FAILED ] %s.%s", test_info->test_case_name(), test_info->name());
#endif
if (type_param) LOGD("type = %11s", type_param);
if (value_param) LOGD("params = %11s", value_param);
switch (m.terminationReason)
{
case performance_metrics::TERM_ITERATIONS:
LOGD("termination reason: reached maximum number of iterations");
break;
case performance_metrics::TERM_TIME:
LOGD("termination reason: reached time limit");
break;
case performance_metrics::TERM_INTERRUPT:
LOGD("termination reason: aborted by the performance testing framework");
break;
case performance_metrics::TERM_EXCEPTION:
LOGD("termination reason: unhandled exception");
break;
case performance_metrics::TERM_UNKNOWN:
default:
LOGD("termination reason: unknown");
break;
};
LOGD("bytesIn =%11lu", (unsigned long)m.bytesIn);
LOGD("bytesOut =%11lu", (unsigned long)m.bytesOut);
if (nIters == (unsigned int)-1 || m.terminationReason == performance_metrics::TERM_ITERATIONS)
LOGD("samples =%11u", m.samples);
else
LOGD("samples =%11u of %u", m.samples, nIters);
LOGD("outliers =%11u", m.outliers);
LOGD("frequency =%11.0f", m.frequency);
if (m.samples > 0)
{
LOGD("min =%11.0f = %.2fms", m.min, m.min * 1e3 / m.frequency);
LOGD("median =%11.0f = %.2fms", m.median, m.median * 1e3 / m.frequency);
LOGD("gmean =%11.0f = %.2fms", m.gmean, m.gmean * 1e3 / m.frequency);
LOGD("gstddev =%11.8f = %.2fms for 97%% dispersion interval", m.gstddev, m.gmean * 2 * sinh(m.gstddev * 3) * 1e3 / m.frequency);
LOGD("mean =%11.0f = %.2fms", m.mean, m.mean * 1e3 / m.frequency);
LOGD("stddev =%11.0f = %.2fms", m.stddev, m.stddev * 1e3 / m.frequency);
}
}
}
void TestBase::SetUp()
{
cv::theRNG().state = param_seed; // this rng should generate same numbers for each run
if (param_threads >= 0)
cv::setNumThreads(param_threads);
#ifdef ANDROID
if (param_affinity_mask)
setCurrentThreadAffinityMask(param_affinity_mask);
#endif
verified = false;
lastTime = 0;
totalTime = 0;
runsPerIteration = 1;
nIters = iterationsLimitDefault;
currentIter = (unsigned int)-1;
timeLimit = timeLimitDefault;
times.clear();
}
void TestBase::TearDown()
{
if (metrics.terminationReason == performance_metrics::TERM_SKIP_TEST)
{
LOGI("\tTest was skipped");
GTEST_SUCCEED() << "Test was skipped";
}
else
{
if (!HasFailure() && !verified)
ADD_FAILURE() << "The test has no sanity checks. There should be at least one check at the end of performance test.";
validateMetrics();
if (HasFailure())
{
reportMetrics(false);
return;
}
}
const ::testing::TestInfo* const test_info = ::testing::UnitTest::GetInstance()->current_test_info();
const char* type_param = test_info->type_param();
const char* value_param = test_info->value_param();
if (value_param) printf("[ VALUE ] \t%s\n", value_param), fflush(stdout);
if (type_param) printf("[ TYPE ] \t%s\n", type_param), fflush(stdout);
reportMetrics(true);
}
std::string TestBase::getDataPath(const std::string& relativePath)
{
if (relativePath.empty())
{
ADD_FAILURE() << " Bad path to test resource";
throw PerfEarlyExitException();
}
const char *data_path_dir = getenv("OPENCV_TEST_DATA_PATH");
const char *path_separator = "/";
std::string path;
if (data_path_dir)
{
int len = (int)strlen(data_path_dir) - 1;
if (len < 0) len = 0;
path = (data_path_dir[0] == 0 ? std::string(".") : std::string(data_path_dir))
+ (data_path_dir[len] == '/' || data_path_dir[len] == '\\' ? "" : path_separator);
}
else
{
path = ".";
path += path_separator;
}
if (relativePath[0] == '/' || relativePath[0] == '\\')
path += relativePath.substr(1);
else
path += relativePath;
FILE* fp = fopen(path.c_str(), "r");
if (fp)
fclose(fp);
else
{
ADD_FAILURE() << " Requested file \"" << path << "\" does not exist.";
throw PerfEarlyExitException();
}
return path;
}
void TestBase::RunPerfTestBody()
{
try
{
this->PerfTestBody();
}
catch(PerfSkipTestException&)
{
metrics.terminationReason = performance_metrics::TERM_SKIP_TEST;
return;
}
catch(PerfEarlyExitException&)
{
metrics.terminationReason = performance_metrics::TERM_INTERRUPT;
return;//no additional failure logging
}
catch(cv::Exception& e)
{
metrics.terminationReason = performance_metrics::TERM_EXCEPTION;
#ifdef HAVE_CUDA
if (e.code == CV_GpuApiCallError)
cv::gpu::resetDevice();
#endif
FAIL() << "Expected: PerfTestBody() doesn't throw an exception.\n Actual: it throws cv::Exception:\n " << e.what();
}
catch(std::exception& e)
{
metrics.terminationReason = performance_metrics::TERM_EXCEPTION;
FAIL() << "Expected: PerfTestBody() doesn't throw an exception.\n Actual: it throws std::exception:\n " << e.what();
}
catch(...)
{
metrics.terminationReason = performance_metrics::TERM_EXCEPTION;
FAIL() << "Expected: PerfTestBody() doesn't throw an exception.\n Actual: it throws...";
}
}
/*****************************************************************************************\
* ::perf::TestBase::_declareHelper
\*****************************************************************************************/
TestBase::_declareHelper& TestBase::_declareHelper::iterations(unsigned int n)
{
test->times.clear();
test->times.reserve(n);
test->nIters = std::min(n, TestBase::iterationsLimitDefault);
test->currentIter = (unsigned int)-1;
test->metrics.clear();
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::time(double timeLimitSecs)
{
test->times.clear();
test->currentIter = (unsigned int)-1;
test->timeLimit = (int64)(timeLimitSecs * cv::getTickFrequency());
test->metrics.clear();
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::tbb_threads(int n)
{
cv::setNumThreads(n);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::runs(unsigned int runsNumber)
{
test->runsPerIteration = runsNumber;
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::in(cv::InputOutputArray a1, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->inputData, a1, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::in(cv::InputOutputArray a1, cv::InputOutputArray a2, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->inputData, a1, wtype);
TestBase::declareArray(test->inputData, a2, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::in(cv::InputOutputArray a1, cv::InputOutputArray a2, cv::InputOutputArray a3, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->inputData, a1, wtype);
TestBase::declareArray(test->inputData, a2, wtype);
TestBase::declareArray(test->inputData, a3, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::in(cv::InputOutputArray a1, cv::InputOutputArray a2, cv::InputOutputArray a3, cv::InputOutputArray a4, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->inputData, a1, wtype);
TestBase::declareArray(test->inputData, a2, wtype);
TestBase::declareArray(test->inputData, a3, wtype);
TestBase::declareArray(test->inputData, a4, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::out(cv::InputOutputArray a1, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->outputData, a1, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::out(cv::InputOutputArray a1, cv::InputOutputArray a2, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->outputData, a1, wtype);
TestBase::declareArray(test->outputData, a2, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::out(cv::InputOutputArray a1, cv::InputOutputArray a2, cv::InputOutputArray a3, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->outputData, a1, wtype);
TestBase::declareArray(test->outputData, a2, wtype);
TestBase::declareArray(test->outputData, a3, wtype);
return *this;
}
TestBase::_declareHelper& TestBase::_declareHelper::out(cv::InputOutputArray a1, cv::InputOutputArray a2, cv::InputOutputArray a3, cv::InputOutputArray a4, int wtype)
{
if (!test->times.empty()) return *this;
TestBase::declareArray(test->outputData, a1, wtype);
TestBase::declareArray(test->outputData, a2, wtype);
TestBase::declareArray(test->outputData, a3, wtype);
TestBase::declareArray(test->outputData, a4, wtype);
return *this;
}
TestBase::_declareHelper::_declareHelper(TestBase* t) : test(t)
{
}
/*****************************************************************************************\
* miscellaneous
\*****************************************************************************************/
namespace {
struct KeypointComparator
{
std::vector<cv::KeyPoint>& pts_;
comparators::KeypointGreater cmp;
KeypointComparator(std::vector<cv::KeyPoint>& pts) : pts_(pts), cmp() {}
bool operator()(int idx1, int idx2) const
{
return cmp(pts_[idx1], pts_[idx2]);
}
private:
const KeypointComparator& operator=(const KeypointComparator&); // quiet MSVC
};
}//namespace
void perf::sort(std::vector<cv::KeyPoint>& pts, cv::InputOutputArray descriptors)
{
cv::Mat desc = descriptors.getMat();
CV_Assert(pts.size() == (size_t)desc.rows);
cv::AutoBuffer<int> idxs(desc.rows);
for (int i = 0; i < desc.rows; ++i)
idxs[i] = i;
std::sort((int*)idxs, (int*)idxs + desc.rows, KeypointComparator(pts));
std::vector<cv::KeyPoint> spts(pts.size());
cv::Mat sdesc(desc.size(), desc.type());
for(int j = 0; j < desc.rows; ++j)
{
spts[j] = pts[idxs[j]];
cv::Mat row = sdesc.row(j);
desc.row(idxs[j]).copyTo(row);
}
spts.swap(pts);
sdesc.copyTo(desc);
}
/*****************************************************************************************\
* ::perf::GpuPerf
\*****************************************************************************************/
bool perf::GpuPerf::targetDevice()
{
return param_impl == "cuda";
}
/*****************************************************************************************\
* ::perf::PrintTo
\*****************************************************************************************/
namespace perf
{
void PrintTo(const MatType& t, ::std::ostream* os)
{
switch( CV_MAT_DEPTH((int)t) )
{
case CV_8U: *os << "8U"; break;
case CV_8S: *os << "8S"; break;
case CV_16U: *os << "16U"; break;
case CV_16S: *os << "16S"; break;
case CV_32S: *os << "32S"; break;
case CV_32F: *os << "32F"; break;
case CV_64F: *os << "64F"; break;
case CV_USRTYPE1: *os << "USRTYPE1"; break;
default: *os << "INVALID_TYPE"; break;
}
*os << 'C' << CV_MAT_CN((int)t);
}
} //namespace perf
/*****************************************************************************************\
* ::cv::PrintTo
\*****************************************************************************************/
namespace cv {
void PrintTo(const Size& sz, ::std::ostream* os)
{
*os << /*"Size:" << */sz.width << "x" << sz.height;
}
} // namespace cv
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