Kiến trúc và triển khai HD Sat

// Luồng dữ liệu chính appsettings.json | v Env::Load() -> PluginManager::LoadPlugin() | v Pipeline::Wire() | +-- Per channel: | | IVideoSource | | | +--[FrameCallback]--> IMultiviewMonitor::SetChannelFrame() | | --> IOutput::SetInputFrame() | | --> IFilter::ProcessFrame() | | | +--[AlertCallback]--> IMultiviewMonitor::TriggerChannelAlert() | --> IOutput::TriggerAlert() | +-- Mỗi IFilter: +--[AlertCallback]--> IMultiviewMonitor::TriggerChannelAlert() | --> IOutput::TriggerAlert() +--[ClearCallback]--> IMultiviewMonitor::ClearChannelAlert() --> IOutput::ClearAlert()

// Thread safety trong Monitor plugin RegisterChannel() // Main thread - gọi trước StartAll(), không cần lock SetChannelFrame() // Capture thread - per-slot mutex cho frame buffer TriggerChannelAlert() // Capture thread - per-slot mutex cho alert state ClearChannelAlert() // Capture thread - per-slot mutex cho alert state // Render thread đọc dữ liệu với same per-slot mutex // Thread safety trong Filter plugins ProcessFrame() // Capture thread - state chỉ truy cập từ 1 thread SetAlertCallback() // Any thread - bảo vệ bởi std::mutex m_cbMutex SetClearAlertCallback() // Any thread - bảo vệ bởi std::mutex m_cbMutex

{ "monitor": { "type": "Monitor", "name": "Monitor", "enabled": true, "windowWidth": 1280, "windowHeight": 720, "vuWidth": 65, "alertBorder": 10 }, "processes": [ { "source": { "type": "DeckLink", "name": "SDI-1", "enabled": true, "deviceIndex": 0 }, "filters": [ { "type": "BlackVideoDetection", "name": "BlackDetect-SDI1", "enabled": true, "pixelBlackThreshold": 16, "maxNonBlackRatio": 0.001, "maxBlackSeconds": 3.0 }, { "type": "FreezeVideoDetection", "name": "FreezeDetect-SDI1", "enabled": true, "freezeThreshold": 4.0, "maxFreezeSeconds": 3.0 }, { "type": "AudioSilenceDetection", "name": "AudioSilence-SDI1", "enabled": true, "silenceThreshold": -60.0, "maxSilenceSeconds": 3.0 }, { "type": "AudioInversionDetection", "name": "AudioInversion-SDI1", "enabled": true, "inversionThreshold": -0.8, "maxInversionSeconds": 3.0, "minAudioLevelDb": -40.0 }, { "type": "AudioLimitDetector", "name": "AudioLimit-SDI1", "enabled": true, "limitThreshold": -3.0, "minDurationSeconds": 0.5 }, { "type": "DOVERQualityScore", "name": "DOVER-SDI1", "enabled": true, "modelPath": "models/dover.onnx", "minQualityScore": 50.0, "useCuda": false } ], "monitors": [ { "name": "CHANNEL 1", "enabled": true, "windowX": 0.0, "windowY": 0.0, "windowWidth": 0.5, "windowHeight": 0.5 } ], "outputs": [] } ] }

// Cấu trúc thư mục output bin/Release/ +-- HD.QCMonitor.App.exe +-- HD.QCMonitor.Core.dll +-- models/ | +-- dover.onnx +-- plugins/ +-- HD.QCMonitor.VideoSource.DeckLink.dll +-- HD.QCMonitor.VideoSource.FFmpeg.dll +-- HD.QCMonitor.Monitor.dll +-- HD.QCMonitor.Filter.BlackVideoDetection.dll +-- HD.QCMonitor.Filter.FreezeVideoDetection.dll +-- HD.QCMonitor.Filter.AudioSilenceDetection.dll +-- HD.QCMonitor.Filter.AudioInversionDetection.dll +-- HD.QCMonitor.Filter.AudioLimitDetector.dll +-- HD.QCMonitor.Filter.DOVERQualityScore.dll +-- HD.QCMonitor.Output.TelegramAlert.dll +-- HD.QCMonitor.Web.dll

// Ví dụ: Thêm Filter mới (phát hiện logo mất) // 1. Tạo project DLL mới trong Plugins/ // HD.QCMonitor.Filter.LogoDetection.vcxproj // 2. Implement IFilter class LogoDetectionFilter : public IFilter { public: wstring GetName() const override { return L"LogoDetection"; } bool Initialize(const PluginConfig& cfg) override { m_threshold = cfg.GetDouble("threshold", 0.8); return true; } void ProcessFrame(const Frame& frame) override { // Phân tích frame, gọi m_alertCb nếu phát hiện } // ... implement các phương thức khác }; // 3. Export CreatePlugin / DestroyPlugin extern "C" { IPlugin* CreatePlugin() { return new LogoDetectionFilter(); } void DestroyPlugin(IPlugin* p) { delete p; } } // 4. Thêm vào appsettings.json { "type": "LogoDetection", "name": "LogoDetect-SDI1", "enabled": true, "threshold": 0.8 }

# export_dover_onnx.py import torch from dover.models import DOVER class DOVERExportWrapper(torch.nn.Module): def forward(self, aesthetic, technical): scores = self.model([aesthetic, technical], inference=True) return scores[0], scores[1] model = DOVER() model.load_state_dict(torch.load("DOVER.pth", map_location="cpu")) model.eval() wrapper = DOVERExportWrapper(model) T, H, W = 32, 224, 224 aesthetic_in = torch.randn(1, 3, T, H, W) technical_in = torch.randn(1, 3, T, H, W) torch.onnx.export( wrapper, (aesthetic_in, technical_in), "dover.onnx", input_names=["aesthetic", "technical"], output_names=["aesthetic_score", "technical_score"], dynamic_axes={"aesthetic": {2: "T"}, "technical": {2: "T"}}, opset_version=17 )