Refactor project structure
This commit is contained in:
935
modules/ANSODEngine/ANSYOLOV12RTOD.cpp
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935
modules/ANSODEngine/ANSYOLOV12RTOD.cpp
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@@ -0,0 +1,935 @@
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#include "ANSYOLOV12RTOD.h"
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#include "Utility.h"
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#include <opencv2/cudaimgproc.hpp>
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#include <future>
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namespace ANSCENTER
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{
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bool ANSYOLOV12RTOD::OptimizeModel(bool fp16, std::string& optimizedModelFolder) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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if (!ANSODBase::OptimizeModel(fp16, optimizedModelFolder)) {
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return false;
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}
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if (!FileExist(_modelFilePath)) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::OptimizeModel", "Raw model file path does not exist", __FILE__, __LINE__);
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return false;
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}
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try {
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_fp16 = fp16;
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optimizedModelFolder = GetParentFolder(_modelFilePath);
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// Check if the engine already exists to avoid reinitializing
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if (!m_trtEngine) {
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m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
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m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
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m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
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m_options.maxInputHeight = _modelConfig.maxInputHeight;
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m_options.minInputHeight = _modelConfig.minInputHeight;
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m_options.optInputHeight = _modelConfig.optInputHeight;
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m_options.maxInputWidth = _modelConfig.maxInputWidth;
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m_options.minInputWidth = _modelConfig.minInputWidth;
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m_options.optInputWidth = _modelConfig.optInputWidth;
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m_options.engineFileDir = optimizedModelFolder;
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// Use FP16 or FP32 precision based on the input flag
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m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
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// Create the TensorRT inference engine
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m_trtEngine = std::make_unique<Engine<float>>(m_options);
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}
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// Build the TensorRT engine
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auto succ = m_trtEngine->buildWithRetry(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE);
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if (!succ) {
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const std::string errMsg =
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"Error: Unable to build the TensorRT engine. "
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"Try increasing TensorRT log severity to kVERBOSE.";
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this->_logger.LogError("ANSYOLOV12RTOD::OptimizeModel", errMsg, __FILE__, __LINE__);
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_modelLoadValid = false;
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return false;
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}
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_modelLoadValid = true;
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return true;
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}
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catch (const std::exception& e) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::OptimizeModel", e.what(), __FILE__, __LINE__);
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optimizedModelFolder.clear();
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return false;
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}
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}
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bool ANSYOLOV12RTOD::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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try {
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bool result = ANSODBase::LoadModel(modelZipFilePath, modelZipPassword);
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if (!result) return false;
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_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
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_modelConfig.modelType = ModelType::TENSORRT;
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_modelConfig.inpHeight = 640;
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_modelConfig.inpWidth = 640;
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if (_modelConfig.modelMNSThreshold < 0.2)
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_modelConfig.modelMNSThreshold = 0.5;
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if (_modelConfig.modelConfThreshold < 0.2)
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_modelConfig.modelConfThreshold = 0.5;
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if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133) // 133 = COCO wholebody max
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_modelConfig.numKPS = 17;
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if (_modelConfig.kpsThreshold == 0)_modelConfig.kpsThreshold = 0.5; // If not define
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// if (_modelConfig.precisionType == PrecisionType::FP16)_fp16 = true;
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_fp16 = true; // Load Model from Here
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// Load Model from Here
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TOP_K = 100;
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SEG_CHANNELS = 32;
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PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
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NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
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SEGMENTATION_THRESHOLD = 0.5f;
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SEG_H = 160;
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SEG_W = 160;
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NUM_KPS = _modelConfig.numKPS;
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KPS_THRESHOLD = _modelConfig.kpsThreshold;
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SEG_CHANNELS = 32; // For segmentation
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if (!m_trtEngine) {
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// Fixed batch size of 1 for this model
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m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
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m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
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m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
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m_options.maxInputHeight = _modelConfig.maxInputHeight;
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m_options.minInputHeight = _modelConfig.minInputHeight;
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m_options.optInputHeight = _modelConfig.optInputHeight;
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m_options.maxInputWidth = _modelConfig.maxInputWidth;
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m_options.minInputWidth = _modelConfig.minInputWidth;
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m_options.optInputWidth = _modelConfig.optInputWidth;
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m_options.engineFileDir = _modelFolder;
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// Use FP16 or FP32 precision based on the input flag
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m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
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// Create the TensorRT inference engine
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m_trtEngine = std::make_unique<Engine<float>>(m_options);
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}
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// 0. Check if the configuration file exist
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if (FileExist(_modelConfigFile)) {
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ModelType modelType;
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std::vector<int> inputShape;
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_classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
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if (inputShape.size() == 2) {
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if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
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if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
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}
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}
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else {// This is old version of model zip file
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_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
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_classFilePath = CreateFilePath(_modelFolder, "classes.names");
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std::ifstream isValidFileName(_classFilePath);
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if (!isValidFileName)
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{
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from string", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromString();
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}
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else {
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from file", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromFile();
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}
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}
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// Load the TensorRT engine file
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if (this->_loadEngineOnCreation) {
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auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
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if (!succ) {
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const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
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this->_logger.LogError("ANSYOLOV12RTOD::Initialize", errMsg, __FILE__, __LINE__);
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_modelLoadValid = false;
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return false;
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}
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}
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_modelLoadValid = true;
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_isInitialized = true;
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return true;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::LoadModel", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSYOLOV12RTOD::LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName,std::string className, const std::string& modelFolder, std::string& labelMap) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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try {
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bool result = ANSODBase::LoadModelFromFolder(licenseKey, modelConfig,modelName, className,modelFolder, labelMap);
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if (!result) return false;
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std::string _modelName = modelName;
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if (_modelName.empty()) {
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_modelName = "train_last";
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}
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std::string modelFullName = _modelName + ".onnx";
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_modelConfig = modelConfig;
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_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
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_modelConfig.modelType = ModelType::TENSORRT;
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_modelConfig.inpHeight = 640;
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_modelConfig.inpWidth = 640;
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if (_modelConfig.modelMNSThreshold < 0.2)
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_modelConfig.modelMNSThreshold = 0.5;
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if (_modelConfig.modelConfThreshold < 0.2)
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_modelConfig.modelConfThreshold = 0.5;
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if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133) // 133 = COCO wholebody max
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_modelConfig.numKPS = 17;
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if (_modelConfig.kpsThreshold == 0)_modelConfig.kpsThreshold = 0.5; // If not define
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// if (_modelConfig.precisionType == PrecisionType::FP16)_fp16 = true;
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_fp16 = true; // Load Model from Here
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// Load Model from Here
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TOP_K = 100;
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SEG_CHANNELS = 32;
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PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
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NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
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SEGMENTATION_THRESHOLD = 0.5f;
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SEG_H = 160;
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SEG_W = 160;
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NUM_KPS = _modelConfig.numKPS;
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KPS_THRESHOLD = _modelConfig.kpsThreshold;
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SEG_CHANNELS = 32; // For segmentation
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if (!m_trtEngine) {
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// Fixed batch size of 1 for this model
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m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
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m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
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m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
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m_options.maxInputHeight = _modelConfig.maxInputHeight;
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m_options.minInputHeight = _modelConfig.minInputHeight;
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m_options.optInputHeight = _modelConfig.optInputHeight;
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m_options.maxInputWidth = _modelConfig.maxInputWidth;
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m_options.minInputWidth = _modelConfig.minInputWidth;
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m_options.optInputWidth = _modelConfig.optInputWidth;
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m_options.engineFileDir = _modelFolder;
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// Use FP16 or FP32 precision based on the input flag
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m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
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// Create the TensorRT inference engine
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m_trtEngine = std::make_unique<Engine<float>>(m_options);
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}
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// 0. Check if the configuration file exist
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if (FileExist(_modelConfigFile)) {
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ModelType modelType;
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std::vector<int> inputShape;
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_classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
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if (inputShape.size() == 2) {
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if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
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if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
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}
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}
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else {// This is old version of model zip file
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_modelFilePath = CreateFilePath(_modelFolder, modelFullName);
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_classFilePath = CreateFilePath(_modelFolder, className);
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std::ifstream isValidFileName(_classFilePath);
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if (!isValidFileName)
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{
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from string", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromString();
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}
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else {
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from file", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromFile();
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}
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}
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// 1. Load labelMap and engine
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labelMap.clear();
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if (!_classes.empty())
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labelMap = VectorToCommaSeparatedString(_classes);
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// Load the TensorRT engine file
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if (this->_loadEngineOnCreation) {
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auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
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if (!succ) {
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const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
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this->_logger.LogError("ANSYOLOV12RTOD::Initialize", errMsg, __FILE__, __LINE__);
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_modelLoadValid = false;
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return false;
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}
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}
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_modelLoadValid = true;
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_isInitialized = true;
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return true;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::LoadModel", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSYOLOV12RTOD::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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try {
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const bool engineAlreadyLoaded = _modelLoadValid && _isInitialized && m_trtEngine != nullptr;
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_modelLoadValid = false;
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bool result = ANSODBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
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if (!result) return false;
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// Parsing for YOLO only here
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_modelConfig = modelConfig;
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_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
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_modelConfig.modelType = ModelType::TENSORRT;
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_modelConfig.inpHeight = 640;
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_modelConfig.inpWidth = 640;
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if (_modelConfig.modelMNSThreshold < 0.2)
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_modelConfig.modelMNSThreshold = 0.5;
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if (_modelConfig.modelConfThreshold < 0.2)
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_modelConfig.modelConfThreshold = 0.5;
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if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133) // 133 = COCO wholebody max
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_modelConfig.numKPS = 17;
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if (_modelConfig.kpsThreshold == 0)_modelConfig.kpsThreshold = 0.5; // If not define
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// if (_modelConfig.precisionType == PrecisionType::FP16)_fp16 = true;
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_fp16 = true; // Load Model from Here
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// Load Model from Here
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TOP_K = 100;
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SEG_CHANNELS = 32;
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PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
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NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
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SEGMENTATION_THRESHOLD = 0.5f;
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SEG_H = 160;
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SEG_W = 160;
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NUM_KPS = _modelConfig.numKPS;
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KPS_THRESHOLD = _modelConfig.kpsThreshold;
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SEG_CHANNELS = 32; // For segmentation
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if (!m_trtEngine) {
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// Fixed batch size of 1 for this model
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m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
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m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
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m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
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m_options.engineFileDir = _modelFolder;
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// Use FP16 or FP32 precision based on the input flag
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m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
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// Create the TensorRT inference engine
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m_trtEngine = std::make_unique<Engine<float>>(m_options);
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}
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// 0. Check if the configuration file exist
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if (FileExist(_modelConfigFile)) {
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ModelType modelType;
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std::vector<int> inputShape;
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_classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
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if (inputShape.size() == 2) {
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if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
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if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
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}
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}
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else {// This is old version of model zip file
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_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
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_classFilePath = CreateFilePath(_modelFolder, "classes.names");
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std::ifstream isValidFileName(_classFilePath);
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if (!isValidFileName)
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{
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from string", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromString();
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}
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else {
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this->_logger.LogDebug("ANSYOLOV12RTOD::Initialize. Load classes from file", _classFilePath, __FILE__, __LINE__);
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LoadClassesFromFile();
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}
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}
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// 1. Load labelMap and engine
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labelMap.clear();
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if (!_classes.empty())
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labelMap = VectorToCommaSeparatedString(_classes);
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// Load the TensorRT engine file
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if (this->_loadEngineOnCreation && !engineAlreadyLoaded) {
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auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
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if (!succ) {
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const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
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this->_logger.LogError("ANSYOLOV12RTOD::Initialize", errMsg, __FILE__, __LINE__);
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_modelLoadValid = false;
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return false;
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}
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}
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_modelLoadValid = true;
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_isInitialized = true;
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return true;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::Initialize", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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std::vector<Object> ANSYOLOV12RTOD::RunInference(const cv::Mat& inputImgBGR) {
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return RunInference(inputImgBGR, "TensorRT12Cam");
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}
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std::vector<Object> ANSYOLOV12RTOD::RunInference(const cv::Mat& inputImgBGR, const std::string& camera_id) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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// Validate model, license, and initialization
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if (!_modelLoadValid) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::RunInference",
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"Cannot load the TensorRT model. Please check if it is exist", __FILE__, __LINE__);
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return {};
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}
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if (!_licenseValid) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::RunInference",
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"Runtime license is not valid or expired. Please contact ANSCENTER", __FILE__, __LINE__);
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return {};
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}
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if (!_isInitialized) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::RunInference",
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"Model is not initialized", __FILE__, __LINE__);
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return {};
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}
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// Validate input
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if (inputImgBGR.empty() || inputImgBGR.cols < 10 || inputImgBGR.rows < 10) {
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return {};
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}
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try {
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return DetectObjects(inputImgBGR, camera_id);
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}
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catch (const std::exception& e) {
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this->_logger.LogFatal("ANSYOLOV12RTOD::RunInference", e.what(), __FILE__, __LINE__);
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return {};
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}
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}
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ANSYOLOV12RTOD::~ANSYOLOV12RTOD() {
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try {
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Destroy();
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}
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catch (std::exception& e) {
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this->_logger.LogError("ANSYOLOV12RTOD::~ANSYOLOV12RTOD()", e.what(), __FILE__, __LINE__);
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}
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}
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bool ANSYOLOV12RTOD::Destroy() {
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try {
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m_trtEngine.reset();
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m_nv12Helper.destroy();
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return true;
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}
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catch (std::exception& e) {
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this->_logger.LogError("ANSYOLOV12RTOD::~ANSYOLOV12RTOD()", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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// private
|
||||
std::vector<Object> ANSYOLOV12RTOD::DetectObjects(const cv::Mat& inputImage, const std::string& camera_id) {
|
||||
// Phase 1: Preprocess under brief lock — try NV12 fast path first
|
||||
ImageMetadata meta;
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> input;
|
||||
bool usedNV12 = false;
|
||||
float bgrFullResScaleX = 1.0f, bgrFullResScaleY = 1.0f;
|
||||
{
|
||||
std::lock_guard<std::recursive_mutex> lock(_mutex);
|
||||
const int inferenceGpu = m_trtEngine ? m_trtEngine->getPreferredDeviceIndex() : 0;
|
||||
const auto& inputDims = m_trtEngine->getInputDims();
|
||||
const int inputW = inputDims[0].d[2];
|
||||
const int inputH = inputDims[0].d[1];
|
||||
|
||||
auto nv12 = m_nv12Helper.tryNV12(inputImage, inferenceGpu, inputW, inputH,
|
||||
NV12PreprocessHelper::defaultYOLOLauncher(),
|
||||
_logger, "ANSYOLOV12RTOD");
|
||||
if (nv12.succeeded) {
|
||||
meta.imgWidth = nv12.metaWidth;
|
||||
meta.imgHeight = nv12.metaHeight;
|
||||
meta.ratio = nv12.ratio;
|
||||
input = {{ std::move(nv12.gpuRGB) }};
|
||||
usedNV12 = true;
|
||||
}
|
||||
else if (nv12.useBgrFullRes) {
|
||||
input = Preprocess(nv12.bgrFullResImg, meta);
|
||||
usedNV12 = !input.empty();
|
||||
bgrFullResScaleX = nv12.bgrFullResScaleX;
|
||||
bgrFullResScaleY = nv12.bgrFullResScaleY;
|
||||
}
|
||||
|
||||
if (input.empty()) {
|
||||
input = Preprocess(inputImage, meta);
|
||||
}
|
||||
m_nv12Helper.tickInference();
|
||||
}
|
||||
if (input.empty()) return {};
|
||||
|
||||
// Phase 2: Inference - mutex released; pool dispatches to idle GPU slot
|
||||
std::vector<std::vector<std::vector<float>>> featureVectors;
|
||||
auto succ = m_trtEngine->runInference(input, featureVectors);
|
||||
if (!succ) {
|
||||
this->_logger.LogFatal("ANSYOLOV12RTOD::DetectObjects", "Error running inference", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
// Phase 3: Postprocess under brief lock
|
||||
std::lock_guard<std::recursive_mutex> lock(_mutex);
|
||||
std::vector<float> featureVector;
|
||||
Engine<float>::transformOutput(featureVectors, featureVector);
|
||||
auto ret = Postprocess(featureVector, camera_id, meta);
|
||||
|
||||
// Rescale coords from full-res to display-res (BGR full-res path)
|
||||
if (bgrFullResScaleX != 1.0f || bgrFullResScaleY != 1.0f) {
|
||||
for (auto& obj : ret) {
|
||||
obj.box.x = static_cast<int>(obj.box.x * bgrFullResScaleX);
|
||||
obj.box.y = static_cast<int>(obj.box.y * bgrFullResScaleY);
|
||||
obj.box.width = static_cast<int>(obj.box.width * bgrFullResScaleX);
|
||||
obj.box.height = static_cast<int>(obj.box.height * bgrFullResScaleY);
|
||||
for (auto& pt : obj.polygon) {
|
||||
pt.x *= bgrFullResScaleX;
|
||||
pt.y *= bgrFullResScaleY;
|
||||
}
|
||||
for (size_t k = 0; k + 2 < obj.kps.size(); k += 3) {
|
||||
obj.kps[k] *= bgrFullResScaleX;
|
||||
obj.kps[k + 1] *= bgrFullResScaleY;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (_trackerEnabled) {
|
||||
ret = ApplyTracking(ret, camera_id);
|
||||
if (_stabilizationEnabled) ret = StabilizeDetections(ret, camera_id);
|
||||
}
|
||||
return ret;
|
||||
}
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> ANSYOLOV12RTOD::Preprocess(const cv::Mat& inputImage, ImageMetadata& outMeta) {
|
||||
|
||||
try {
|
||||
if (!_licenseValid) {
|
||||
this->_logger.LogFatal("ANSYOLOV12RTOD::Preprocess", "Invalid license", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
const auto& inputDims = m_trtEngine->getInputDims();
|
||||
cv::cuda::Stream stream;
|
||||
cv::cuda::GpuMat img;
|
||||
|
||||
// Upload to GPU
|
||||
if (inputImage.channels() == 1) {
|
||||
cv::Mat img3Channel;
|
||||
cv::cvtColor(inputImage, img3Channel, cv::COLOR_GRAY2BGR);
|
||||
img.upload(img3Channel, stream);
|
||||
}
|
||||
else {
|
||||
img.upload(inputImage, stream);
|
||||
}
|
||||
|
||||
// Convert BGR to RGB
|
||||
cv::cuda::GpuMat imgRGB;
|
||||
cv::cuda::cvtColor(img, imgRGB, cv::COLOR_BGR2RGB, 0, stream);
|
||||
stream.waitForCompletion();
|
||||
|
||||
outMeta.imgHeight = imgRGB.rows;
|
||||
outMeta.imgWidth = imgRGB.cols;
|
||||
if (outMeta.imgHeight > 0 && outMeta.imgWidth > 0) {
|
||||
outMeta.ratio = 1.f / std::min(inputDims[0].d[2] / static_cast<float>(imgRGB.cols),
|
||||
inputDims[0].d[1] / static_cast<float>(imgRGB.rows));
|
||||
|
||||
cv::cuda::GpuMat resized = imgRGB;
|
||||
|
||||
// Resize to the model's expected input size while maintaining aspect ratio with padding
|
||||
if (resized.rows != inputDims[0].d[1] || resized.cols != inputDims[0].d[2]) {
|
||||
resized = Engine<float>::resizeKeepAspectRatioPadRightBottom(imgRGB, inputDims[0].d[1], inputDims[0].d[2]);
|
||||
}
|
||||
|
||||
// Convert to format expected by our inference engine
|
||||
std::vector<cv::cuda::GpuMat> input{ std::move(resized) };
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> inputs{ std::move(input) };
|
||||
return inputs;
|
||||
}
|
||||
else {
|
||||
this->_logger.LogFatal("TENSORRTCL::Preprocess",
|
||||
"Image height or width is zero after processing (Width: " + std::to_string(outMeta.imgWidth) +
|
||||
", Height: " + std::to_string(outMeta.imgHeight) + ")",
|
||||
__FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
this->_logger.LogFatal("ANSYOLOV12RTOD::Preprocess", e.what(), __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
std::vector<Object> ANSYOLOV12RTOD::Postprocess(std::vector<float>& featureVector, const std::string& camera_id, const ImageMetadata& meta) {
|
||||
try {
|
||||
const auto& outputDims = m_trtEngine->getOutputDims();
|
||||
auto numChannels = outputDims[0].d[1];// similar to detection_attribute_size
|
||||
auto numAnchors = outputDims[0].d[2]; // similar to num_detections
|
||||
|
||||
auto numClasses = _classes.size();
|
||||
|
||||
std::vector<cv::Rect> bboxes;
|
||||
std::vector<float> scores;
|
||||
std::vector<int> labels;
|
||||
std::vector<int> indices;
|
||||
|
||||
cv::Mat output = cv::Mat(numChannels, numAnchors, CV_32F, featureVector.data());
|
||||
output = output.t();
|
||||
|
||||
// Get all the YOLO proposals
|
||||
for (int i = 0; i < numAnchors; i++) {
|
||||
auto rowPtr = output.row(i).ptr<float>();
|
||||
auto bboxesPtr = rowPtr;
|
||||
auto scoresPtr = rowPtr + 4;
|
||||
auto maxSPtr = std::max_element(scoresPtr, scoresPtr + numClasses);
|
||||
float score = *maxSPtr;
|
||||
if (score > this->_modelConfig.detectionScoreThreshold) {
|
||||
float x = *bboxesPtr++;
|
||||
float y = *bboxesPtr++;
|
||||
float w = *bboxesPtr++;
|
||||
float h = *bboxesPtr;
|
||||
|
||||
float x0 = std::clamp((x - 0.5f * w) * meta.ratio, 0.f, meta.imgWidth);
|
||||
float y0 = std::clamp((y - 0.5f * h) * meta.ratio, 0.f, meta.imgHeight);
|
||||
float x1 = std::clamp((x + 0.5f * w) * meta.ratio, 0.f, meta.imgWidth);
|
||||
float y1 = std::clamp((y + 0.5f * h) * meta.ratio, 0.f, meta.imgHeight);
|
||||
|
||||
int label = maxSPtr - scoresPtr;
|
||||
cv::Rect_<float> bbox;
|
||||
bbox.x = x0;
|
||||
bbox.y = y0;
|
||||
bbox.width = x1 - x0;
|
||||
bbox.height = y1 - y0;
|
||||
bbox.x = std::clamp(bbox.x, 0.f, meta.imgWidth);
|
||||
bbox.y = std::clamp(bbox.y, 0.f, meta.imgHeight);
|
||||
bbox.width = std::clamp(bbox.width, 0.f, meta.imgWidth);
|
||||
bbox.height = std::clamp(bbox.height, 0.f, meta.imgHeight);
|
||||
bboxes.push_back(bbox);
|
||||
labels.push_back(label);
|
||||
scores.push_back(score);
|
||||
}
|
||||
}
|
||||
|
||||
// Run NMS
|
||||
cv::dnn::NMSBoxesBatched(bboxes, scores, labels, PROBABILITY_THRESHOLD, NMS_THRESHOLD, indices);
|
||||
int classNameSize = static_cast<int>(_classes.size());
|
||||
std::vector<Object> objects;
|
||||
|
||||
// Choose the top k detections
|
||||
for (auto& chosenIdx : indices) {
|
||||
if (scores[chosenIdx] > _modelConfig.detectionScoreThreshold) {
|
||||
Object obj{};
|
||||
obj.confidence = scores[chosenIdx];
|
||||
obj.classId = labels[chosenIdx];
|
||||
obj.box = bboxes[chosenIdx];
|
||||
obj.polygon = ANSUtilityHelper::RectToNormalizedPolygon(obj.box, meta.imgWidth, meta.imgHeight);
|
||||
if (!_classes.empty()) {
|
||||
if (obj.classId < classNameSize) {
|
||||
obj.className = _classes[obj.classId];
|
||||
}
|
||||
else {
|
||||
obj.className = _classes[classNameSize - 1]; // Use last valid class name if out of range
|
||||
}
|
||||
}
|
||||
else {
|
||||
obj.className = "Unknown"; // Fallback if _classes is empty
|
||||
}
|
||||
obj.cameraId = camera_id;
|
||||
objects.push_back(obj);
|
||||
}
|
||||
}
|
||||
//EnqueueDetection(objects, camera_id);
|
||||
return objects;
|
||||
}
|
||||
catch (std::exception& e) {
|
||||
this->_logger.LogFatal("ANSYOLOV12RTOD::Postproces", e.what(), __FILE__, __LINE__);
|
||||
std::vector<Object> result;
|
||||
result.clear();
|
||||
return result;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
std::vector<std::vector<Object>> ANSYOLOV12RTOD::DetectObjectsBatch(const std::vector<cv::Mat>& inputImages, const std::string& camera_id) {
|
||||
// Validate under brief lock
|
||||
{
|
||||
std::lock_guard<std::recursive_mutex> lock(_mutex);
|
||||
if (inputImages.empty()) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::DetectObjectsBatch", "Empty input images vector", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
|
||||
// Auto-split if batch exceeds engine capacity
|
||||
const int maxBatch = m_options.maxBatchSize > 0 ? m_options.maxBatchSize : 1;
|
||||
if (static_cast<int>(inputImages.size()) > maxBatch) {
|
||||
const size_t numImages = inputImages.size();
|
||||
std::vector<std::vector<Object>> allResults;
|
||||
allResults.reserve(numImages);
|
||||
// Process chunks sequentially to avoid GPU contention on the same engine
|
||||
for (size_t start = 0; start < numImages; start += static_cast<size_t>(maxBatch)) {
|
||||
const size_t end = std::min(start + static_cast<size_t>(maxBatch), numImages);
|
||||
std::vector<cv::Mat> chunk(inputImages.begin() + start, inputImages.begin() + end);
|
||||
auto chunkResults = DetectObjectsBatch(chunk, camera_id);
|
||||
if (chunkResults.size() == chunk.size()) {
|
||||
for (auto& r : chunkResults) allResults.push_back(std::move(r));
|
||||
}
|
||||
else {
|
||||
_logger.LogError("ANSYOLOV12RTOD::DetectObjectsBatch",
|
||||
"Chunk returned " + std::to_string(chunkResults.size()) +
|
||||
" results, expected " + std::to_string(chunk.size()) +
|
||||
". Padding with empty results.", __FILE__, __LINE__);
|
||||
for (auto& r : chunkResults) allResults.push_back(std::move(r));
|
||||
for (size_t pad = chunkResults.size(); pad < chunk.size(); ++pad) {
|
||||
allResults.push_back({});
|
||||
}
|
||||
}
|
||||
}
|
||||
return allResults;
|
||||
}
|
||||
|
||||
_logger.LogDebug("ANSYOLOV12RTOD::DetectObjectsBatch",
|
||||
"Processing batch of " + std::to_string(inputImages.size()) + " images",
|
||||
__FILE__, __LINE__);
|
||||
|
||||
// Phase 1: Preprocess under brief lock
|
||||
BatchMetadata metadata;
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> inputs;
|
||||
{
|
||||
std::lock_guard<std::recursive_mutex> lock(_mutex);
|
||||
inputs = PreprocessBatch(inputImages, metadata);
|
||||
}
|
||||
if (inputs.empty() || inputs[0].empty()) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::DetectObjectsBatch", "Preprocessing failed", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
// Phase 2: Inference - mutex released; pool dispatches to idle GPU slot
|
||||
std::vector<std::vector<std::vector<float>>> featureVectors;
|
||||
auto succ = m_trtEngine->runInference(inputs, featureVectors);
|
||||
if (!succ) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::DetectObjectsBatch", "Error running inference", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
// Phase 3: Parallel postprocessing - each image is independent
|
||||
const size_t numBatch = featureVectors.size();
|
||||
std::vector<std::vector<Object>> batchDetections(numBatch);
|
||||
std::vector<std::future<std::vector<Object>>> postFutures;
|
||||
postFutures.reserve(numBatch);
|
||||
|
||||
for (size_t batchIdx = 0; batchIdx < numBatch; ++batchIdx) {
|
||||
const auto& batchOutput = featureVectors[batchIdx];
|
||||
std::vector<float> featureVector =
|
||||
batchOutput.empty() ? std::vector<float>{} : batchOutput[0];
|
||||
postFutures.push_back(std::async(std::launch::async,
|
||||
[this, fv = std::move(featureVector), cid = camera_id,
|
||||
idx = batchIdx, &metadata]() mutable {
|
||||
return PostprocessBatch(fv, cid, idx, metadata);
|
||||
}));
|
||||
}
|
||||
// Gather results in original order; metadata stays alive until all futures joined
|
||||
for (size_t i = 0; i < numBatch; ++i)
|
||||
batchDetections[i] = postFutures[i].get();
|
||||
|
||||
_logger.LogDebug("ANSYOLOV12RTOD::DetectObjectsBatch",
|
||||
"Batch processing complete. Images: " + std::to_string(numBatch),
|
||||
__FILE__, __LINE__);
|
||||
return batchDetections;
|
||||
}
|
||||
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> ANSYOLOV12RTOD::PreprocessBatch(const std::vector<cv::Mat>& inputImages, BatchMetadata& outMetadata) {
|
||||
|
||||
try {
|
||||
if (!_licenseValid) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::PreprocessBatch", "Invalid license", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
const auto& inputDims = m_trtEngine->getInputDims();
|
||||
const int inputH = inputDims[0].d[1];
|
||||
const int inputW = inputDims[0].d[2];
|
||||
|
||||
// Store original image dimensions for each image in batch
|
||||
outMetadata.imgHeights.resize(inputImages.size());
|
||||
outMetadata.imgWidths.resize(inputImages.size());
|
||||
outMetadata.ratios.resize(inputImages.size());
|
||||
|
||||
std::vector<cv::cuda::GpuMat> batchProcessed;
|
||||
batchProcessed.reserve(inputImages.size());
|
||||
|
||||
cv::cuda::Stream stream;
|
||||
|
||||
// Process each image
|
||||
for (size_t i = 0; i < inputImages.size(); ++i) {
|
||||
const auto& inputImage = inputImages[i];
|
||||
|
||||
if (inputImage.empty()) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::PreprocessBatch",
|
||||
"Empty input image at index " + std::to_string(i), __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
// Upload to GPU
|
||||
cv::cuda::GpuMat img;
|
||||
|
||||
// Convert grayscale to BGR if needed
|
||||
if (inputImage.channels() == 1) {
|
||||
cv::Mat img3Channel;
|
||||
cv::cvtColor(inputImage, img3Channel, cv::COLOR_GRAY2BGR);
|
||||
img.upload(img3Channel, stream);
|
||||
}
|
||||
else {
|
||||
img.upload(inputImage, stream);
|
||||
}
|
||||
|
||||
// Convert BGR to RGB
|
||||
cv::cuda::GpuMat imgRGB;
|
||||
cv::cuda::cvtColor(img, imgRGB, cv::COLOR_BGR2RGB, 0, stream);
|
||||
|
||||
// Store original dimensions
|
||||
outMetadata.imgHeights[i] = imgRGB.rows;
|
||||
outMetadata.imgWidths[i] = imgRGB.cols;
|
||||
|
||||
if (outMetadata.imgHeights[i] <= 0 || outMetadata.imgWidths[i] <= 0) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::PreprocessBatch",
|
||||
"Image " + std::to_string(i) + " has invalid dimensions (Width: " +
|
||||
std::to_string(outMetadata.imgWidths[i]) + ", Height: " +
|
||||
std::to_string(outMetadata.imgHeights[i]) + ")",
|
||||
__FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
|
||||
// Calculate ratio for this image
|
||||
outMetadata.ratios[i] = 1.f / std::min(inputW / static_cast<float>(imgRGB.cols),
|
||||
inputH / static_cast<float>(imgRGB.rows));
|
||||
|
||||
// Resize with padding
|
||||
cv::cuda::GpuMat resized = imgRGB;
|
||||
if (resized.rows != inputH || resized.cols != inputW) {
|
||||
resized = Engine<float>::resizeKeepAspectRatioPadRightBottom(imgRGB, inputH, inputW);
|
||||
}
|
||||
|
||||
batchProcessed.push_back(std::move(resized));
|
||||
}
|
||||
|
||||
stream.waitForCompletion();
|
||||
|
||||
// Return as required format
|
||||
std::vector<std::vector<cv::cuda::GpuMat>> inputs;
|
||||
inputs.push_back(std::move(batchProcessed));
|
||||
|
||||
return inputs;
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::PreprocessBatch", e.what(), __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<Object> ANSYOLOV12RTOD::PostprocessBatch(std::vector<float>& featureVector,
|
||||
const std::string& camera_id,
|
||||
size_t batchIdx,
|
||||
const BatchMetadata& metadata) {
|
||||
|
||||
try {
|
||||
const auto& outputDims = m_trtEngine->getOutputDims();
|
||||
auto numChannels = outputDims[0].d[1]; // detection_attribute_size
|
||||
auto numAnchors = outputDims[0].d[2]; // num_detections
|
||||
|
||||
auto numClasses = _classes.size();
|
||||
|
||||
// Get the ratio and dimensions for this specific image in the batch
|
||||
float ratio = metadata.ratios[batchIdx];
|
||||
int imgWidth = metadata.imgWidths[batchIdx];
|
||||
int imgHeight = metadata.imgHeights[batchIdx];
|
||||
|
||||
std::vector<cv::Rect> bboxes;
|
||||
std::vector<float> scores;
|
||||
std::vector<int> labels;
|
||||
std::vector<int> indices;
|
||||
|
||||
cv::Mat output = cv::Mat(numChannels, numAnchors, CV_32F, featureVector.data());
|
||||
output = output.t();
|
||||
|
||||
// Get all the YOLO proposals
|
||||
for (int i = 0; i < numAnchors; i++) {
|
||||
auto rowPtr = output.row(i).ptr<float>();
|
||||
auto bboxesPtr = rowPtr;
|
||||
auto scoresPtr = rowPtr + 4;
|
||||
auto maxSPtr = std::max_element(scoresPtr, scoresPtr + numClasses);
|
||||
float score = *maxSPtr;
|
||||
|
||||
if (score > _modelConfig.detectionScoreThreshold) {
|
||||
float x = *bboxesPtr++;
|
||||
float y = *bboxesPtr++;
|
||||
float w = *bboxesPtr++;
|
||||
float h = *bboxesPtr;
|
||||
|
||||
// Use batch-specific ratio and dimensions
|
||||
float x0 = std::clamp((x - 0.5f * w) * ratio, 0.f, static_cast<float>(imgWidth));
|
||||
float y0 = std::clamp((y - 0.5f * h) * ratio, 0.f, static_cast<float>(imgHeight));
|
||||
float x1 = std::clamp((x + 0.5f * w) * ratio, 0.f, static_cast<float>(imgWidth));
|
||||
float y1 = std::clamp((y + 0.5f * h) * ratio, 0.f, static_cast<float>(imgHeight));
|
||||
|
||||
int label = maxSPtr - scoresPtr;
|
||||
cv::Rect_<float> bbox;
|
||||
bbox.x = x0;
|
||||
bbox.y = y0;
|
||||
bbox.width = x1 - x0;
|
||||
bbox.height = y1 - y0;
|
||||
|
||||
// Clamp bbox to image boundaries
|
||||
bbox.x = std::clamp(bbox.x, 0.f, static_cast<float>(imgWidth));
|
||||
bbox.y = std::clamp(bbox.y, 0.f, static_cast<float>(imgHeight));
|
||||
bbox.width = std::clamp(bbox.width, 0.f, static_cast<float>(imgWidth));
|
||||
bbox.height = std::clamp(bbox.height, 0.f, static_cast<float>(imgHeight));
|
||||
|
||||
bboxes.push_back(bbox);
|
||||
labels.push_back(label);
|
||||
scores.push_back(score);
|
||||
}
|
||||
}
|
||||
|
||||
// Run NMS
|
||||
cv::dnn::NMSBoxesBatched(bboxes, scores, labels, PROBABILITY_THRESHOLD, NMS_THRESHOLD, indices);
|
||||
|
||||
int classNameSize = static_cast<int>(_classes.size());
|
||||
std::vector<Object> objects;
|
||||
|
||||
// Choose the top k detections
|
||||
for (auto& chosenIdx : indices) {
|
||||
if (scores[chosenIdx] > _modelConfig.detectionScoreThreshold) {
|
||||
Object obj{};
|
||||
obj.confidence = scores[chosenIdx];
|
||||
obj.classId = labels[chosenIdx];
|
||||
obj.box = bboxes[chosenIdx];
|
||||
obj.polygon = ANSUtilityHelper::RectToNormalizedPolygon(obj.box, imgWidth, imgHeight);
|
||||
|
||||
if (!_classes.empty()) {
|
||||
if (obj.classId < classNameSize) {
|
||||
obj.className = _classes[obj.classId];
|
||||
}
|
||||
else {
|
||||
obj.className = _classes[classNameSize - 1];
|
||||
}
|
||||
}
|
||||
else {
|
||||
obj.className = "Unknown";
|
||||
}
|
||||
|
||||
obj.cameraId = camera_id;
|
||||
objects.push_back(obj);
|
||||
}
|
||||
}
|
||||
|
||||
return objects;
|
||||
}
|
||||
catch (std::exception& e) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::PostprocessBatch", e.what(), __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<std::vector<Object>> ANSYOLOV12RTOD::RunInferencesBatch(
|
||||
const std::vector<cv::Mat>& inputs, const std::string& camera_id)
|
||||
{
|
||||
{
|
||||
std::lock_guard<std::recursive_mutex> lock(_mutex);
|
||||
if (!_modelLoadValid) {
|
||||
_logger.LogError("ANSYOLOV12RTOD::RunInferencesBatch", "Model not loaded", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
if (!_licenseValid) {
|
||||
_logger.LogError("ANSYOLOV12RTOD::RunInferencesBatch", "Invalid license", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
if (!_isInitialized) {
|
||||
_logger.LogError("ANSYOLOV12RTOD::RunInferencesBatch", "Engine not initialized", __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
if (inputs.empty()) return {};
|
||||
}
|
||||
try {
|
||||
return DetectObjectsBatch(inputs, camera_id);
|
||||
}
|
||||
catch (const std::exception& e) {
|
||||
_logger.LogFatal("ANSYOLOV12RTOD::RunInferencesBatch", e.what(), __FILE__, __LINE__);
|
||||
return {};
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user