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