opencv/modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp
Anatoliy Talamanov 6c235c8edb
Merge pull request #23211 from TolyaTalamanov:at/pipeline-modeling-tool-perf-alignment
[G-API] Pipeline modeling tool: Refactor calculating performance statistics

* Add warmup execution

* Align perf metrics

* Add busy wait mode for source

* Small fix for late frames

* pl_fn to src_fn

* Change show statistics

* Correct warm-up iteration

* Properly calculate drop frames

* Enable frame dropping for streaming mode

* Enable frame dropping for streaming mode

* Fix comments to review

* Fix typos

* Cosmetic
2023-02-15 14:04:14 +03:00

113 lines
3.8 KiB
C++

#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP
#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP
#include <thread>
#include <memory>
#include <chrono>
#include <opencv2/gapi.hpp>
#include <opencv2/gapi/streaming/cap.hpp> // cv::gapi::wip::IStreamSource
#include "utils.hpp"
class DummySource final: public cv::gapi::wip::IStreamSource {
public:
using WaitStrategy = std::function<void(std::chrono::microseconds)>;
using Ptr = std::shared_ptr<DummySource>;
using ts_t = std::chrono::microseconds;
template <typename DurationT>
DummySource(const DurationT latency,
const OutputDescr& output,
const bool drop_frames,
WaitStrategy&& wait);
bool pull(cv::gapi::wip::Data& data) override;
cv::GMetaArg descr_of() const override;
private:
int64_t m_latency;
cv::Mat m_mat;
bool m_drop_frames;
int64_t m_next_tick_ts = -1;
int64_t m_curr_seq_id = 0;
WaitStrategy m_wait;
};
template <typename DurationT>
DummySource::DummySource(const DurationT latency,
const OutputDescr& output,
const bool drop_frames,
WaitStrategy&& wait)
: m_latency(std::chrono::duration_cast<ts_t>(latency).count()),
m_drop_frames(drop_frames),
m_wait(std::move(wait)) {
utils::createNDMat(m_mat, output.dims, output.precision);
utils::generateRandom(m_mat);
}
bool DummySource::pull(cv::gapi::wip::Data& data) {
using namespace std::chrono;
using namespace cv::gapi::streaming;
// NB: Wait m_latency before return the first frame.
if (m_next_tick_ts == -1) {
m_next_tick_ts = utils::timestamp<ts_t>() + m_latency;
}
int64_t curr_ts = utils::timestamp<ts_t>();
if (curr_ts < m_next_tick_ts) {
/*
* curr_ts
* |
* ------|----*-----|------->
* ^
* m_next_tick_ts
*
*
* NB: New frame will be produced at the m_next_tick_ts point.
*/
m_wait(ts_t{m_next_tick_ts - curr_ts});
} else if (m_latency != 0) {
/*
* curr_ts
* +1 +2 |
* |----------|----------|----------|----*-----|------->
* ^ ^
* m_next_tick_ts ------------->
*
*/
// NB: Count how many frames have been produced since last pull (m_next_tick_ts).
int64_t num_frames =
static_cast<int64_t>((curr_ts - m_next_tick_ts) / m_latency);
// NB: Shift m_next_tick_ts to the nearest tick before curr_ts.
m_next_tick_ts += num_frames * m_latency;
// NB: if drop_frames is enabled, update current seq_id and wait for the next tick, otherwise
// return last written frame (+2 at the picture above) immediately.
if (m_drop_frames) {
// NB: Shift tick to the next frame.
m_next_tick_ts += m_latency;
// NB: Wait for the next frame.
m_wait(ts_t{m_next_tick_ts - curr_ts});
// NB: Drop already produced frames + update seq_id for the current.
m_curr_seq_id += num_frames + 1;
}
}
// NB: Just increase reference counter not to release mat memory
// after assigning it to the data.
cv::Mat mat = m_mat;
data.meta[meta_tag::timestamp] = utils::timestamp<ts_t>();
data.meta[meta_tag::seq_id] = m_curr_seq_id++;
data = mat;
m_next_tick_ts += m_latency;
return true;
}
cv::GMetaArg DummySource::descr_of() const {
return cv::GMetaArg{cv::descr_of(m_mat)};
}
#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP