Refactor project structure
This commit is contained in:
279
modules/ANSODEngine/RetinaFaceDetector.cpp
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279
modules/ANSODEngine/RetinaFaceDetector.cpp
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#include "RetinaFaceDetector.h"
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#include "Utility.h"
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namespace ANSCENTER {
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bool ANSRETINAFD::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
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bool result = ANSFDBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
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// We do not need to check for the license
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_licenseValid = true;
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if (!_licenseValid) return false;
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try {
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labelMap = "Face";
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_modelConfig = modelConfig;
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_modelConfig.modelType = ModelType::FACEDETECT;
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_modelConfig.detectionType = DetectionType::FACEDETECTOR;
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// We need to get the modelfolder from here
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std::string onnxfile = CreateFilePath(_modelFolder, "retinaface.onnx");
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if (std::filesystem::exists(onnxfile)) {
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_modelFilePath = onnxfile;
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this->_logger.LogDebug("ANSRETINAFD::Initialize. Loading retina weight", _modelFilePath, __FILE__, __LINE__);
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}
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else {
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this->_logger.LogError("ANSRETINAFD::Initialize. Model retinaface.onnx file is not exist", _modelFilePath, __FILE__, __LINE__);
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return false;
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}
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std::string params_file;
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std::string TrtCacheFile = CreateFilePath(_modelFolder, "retinaface.cache");;
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auto option = fastdeploy::RuntimeOption();
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// Check if the system has NVIDIA GPU
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EngineType engineType = ANSLicenseHelper::CheckHardwareInformation();
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if (engineType == EngineType::NVIDIA_GPU) // NVIDIA CUDA
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{
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option.UseGpu();
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option.UseTrtBackend();
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option.trt_option.SetShape("images", { 1, 3, 640, 640 });
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option.trt_option.enable_fp16 = false;
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option.trt_option.serialize_file = TrtCacheFile;
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}
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else {
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// Otherwise it use CPU
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option.UseCpu();
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option.UseOrtBackend();
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}
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auto format = fastdeploy::ModelFormat::ONNX;
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this->_faceDectector = std::make_unique<fastdeploy::vision::facedet::RetinaFace>(onnxfile, params_file, option, format);
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if (!_faceDectector->Initialized()) {
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this->_logger.LogFatal("ANSRETINAFD::Initialize", "Failed to initialize face detector model", __FILE__, __LINE__);
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return false;
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}
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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("ANSRETINAFD::Initialize", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSRETINAFD::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword) {
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try {
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// We need to get the _modelFolder
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bool result = ANSFDBase::LoadModel(modelZipFilePath, modelZipPassword);
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if (!result) return false;
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// We need to get the modelfolder from here
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std::string onnxfile = CreateFilePath(_modelFolder, "retinaface.onnx");
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if (std::filesystem::exists(onnxfile)) {
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_modelFilePath = onnxfile;
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this->_logger.LogDebug("ANSRETINAFD::LoadModel. Loading retinaface weight", _modelFilePath, __FILE__, __LINE__);
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}
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else {
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this->_logger.LogError("ANSRETINAFD::LoadModel. Model retinaface.onnx file is not exist", _modelFilePath, __FILE__, __LINE__);
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return false;
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}
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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("ANSRETINAFD::LoadModel", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSRETINAFD::OptimizeModel(bool fp16, std::string& optimizedModelFolder){
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if (!FileExist(_modelFilePath)) {
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optimizedModelFolder = "";
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return false;
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}
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optimizedModelFolder = GetParentFolder(_modelFilePath);
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std::string params_file;
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std::string TrtCacheFile = CreateFilePath(_modelFolder, "retinaface.cache");;
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auto option = fastdeploy::RuntimeOption();
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EngineType engineType = ANSLicenseHelper::CheckHardwareInformation();
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if (engineType == EngineType::NVIDIA_GPU) // NVIDIA CUDA
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{
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option.UseGpu();
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option.UseTrtBackend();
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option.trt_option.SetShape("images", { 1, 3, 640, 640 });
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option.trt_option.enable_fp16 = fp16;
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option.trt_option.serialize_file = TrtCacheFile;
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}
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else {
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option.UseCpu();
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option.UseOrtBackend();
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}
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auto format = fastdeploy::ModelFormat::ONNX;
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auto model = fastdeploy::vision::facedet::RetinaFace(_modelFilePath, params_file, option, format);
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if (!model.Initialized()) {
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this->_logger.LogError("ANSRETINAFD::OptimizeModel.", "Failed to initialize", __FILE__, __LINE__);
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return false;
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}
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return true;
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}
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std::vector<Object> ANSRETINAFD::RunInference(const cv::Mat& input) {
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std::vector<Object> output;
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output.clear();
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if (!_licenseValid) {
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this->_logger.LogError("ANSRETINAFD::RunInference", "Invalid license", __FILE__, __LINE__);
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return output;
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}
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if (!_isInitialized) {
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this->_logger.LogError("ANSRETINAFD::RunInference", "Model is not initialized", __FILE__, __LINE__);
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return output;
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}
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try {
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bool croppedFace = false; // Check if the image is cropped face image
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cv::Mat im = input.clone();
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// We know that the image sizes <=300 px, it is likely that image is cropped for face only
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if ((input.size[0] <= 300) || (input.size[1] <= 300)) croppedFace = true;
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if (croppedFace) cv::copyMakeBorder(input, im, 200, 200, 200, 200, cv::BORDER_REPLICATE);
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fastdeploy::vision::FaceDetectionResult res;
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if (!_faceDectector->Predict(&im, &res)) {
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this->_logger.LogFatal("ANSRETINAFD::RunInference", "Failed to predict.", __FILE__, __LINE__);
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return output;
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}
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if (res.boxes.size() > 0) {
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// Peform face alignment
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std::vector<cv::Mat> detectedFaces =
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fastdeploy::vision::utils::AlignFaceWithFivePoints(im, res);
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if (res.boxes.size() == detectedFaces.size()) {
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for (int i = 0; i < res.boxes.size(); i++)
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{
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Object result;
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float confidence = res.scores[i];
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if (confidence >= _modelConfig.detectionScoreThreshold) {
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int x_min = res.boxes[i][0];
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int y_min = res.boxes[i][1];
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int x_max = res.boxes[i][2];
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int y_max = res.boxes[i][3];
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result.classId = 0;
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result.className = "Face";
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result.confidence = confidence;
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result.box.x = x_min;
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result.box.y = y_min;
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if (croppedFace) {
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if (x_min <= 200) x_min = 200;
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if (y_min <= 200) y_min = 200;
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result.box.x = x_min - 200;
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result.box.y = y_min - 200;
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}
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result.box.width = x_max - x_min;
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result.box.height = y_max - y_min;
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result.mask = detectedFaces.at(i).clone();
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result.cameraId = "RETINAFACECAM";
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output.push_back(result);
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}
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}
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}
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//detectedFaces.clear();
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}
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im.release();
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res.Clear();
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res.Free();
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EnqueueDetection(output, "RETINAFACECAM");
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return output;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSRETINAFD::RunInference", e.what(), __FILE__, __LINE__);
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return output;
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}
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}
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std::vector<Object> ANSRETINAFD::RunInference(const cv::Mat& input, const std::string& camera_id) {
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std::vector<Object> output;
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output.clear();
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if (!_licenseValid) {
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this->_logger.LogError("ANSRETINAFD::RunInference", "Invalid license", __FILE__, __LINE__);
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return output;
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}
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if (!_isInitialized) {
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this->_logger.LogError("ANSRETINAFD::RunInference", "Model is not initialized", __FILE__, __LINE__);
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return output;
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}
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try {
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bool croppedFace = false; // Check if the image is cropped face image
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cv::Mat im = input.clone();
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// We know that the image sizes <=300 px, it is likely that image is cropped for face only
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if ((input.size[0] <= 300) || (input.size[1] <= 300)) croppedFace = true;
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if (croppedFace) cv::copyMakeBorder(input, im, 200, 200, 200, 200, cv::BORDER_REPLICATE);
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fastdeploy::vision::FaceDetectionResult res;
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if (!_faceDectector->Predict(&im, &res)) {
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this->_logger.LogFatal("ANSRETINAFD::RunInference", "Failed to predict.", __FILE__, __LINE__);
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return output;
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}
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if (res.boxes.size() > 0) {
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// Peform face alignment
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std::vector<cv::Mat> detectedFaces =
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fastdeploy::vision::utils::AlignFaceWithFivePoints(im, res);
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if (res.boxes.size() == detectedFaces.size()) {
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for (int i = 0; i < res.boxes.size(); i++)
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{
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Object result;
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float confidence = res.scores[i];
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if (confidence >= _modelConfig.detectionScoreThreshold) {
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int x_min = res.boxes[i][0];
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int y_min = res.boxes[i][1];
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int x_max = res.boxes[i][2];
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int y_max = res.boxes[i][3];
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result.classId = 0;
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result.className = "Face";
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result.confidence = confidence;
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result.box.x = x_min;
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result.box.y = y_min;
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if (croppedFace) {
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if (x_min <= 200) x_min = 200;
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if (y_min <= 200) y_min = 200;
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result.box.x = x_min - 200;
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result.box.y = y_min - 200;
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}
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result.box.width = x_max - x_min;
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result.box.height = y_max - y_min;
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result.mask = detectedFaces.at(i).clone();
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result.cameraId = camera_id;
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output.push_back(result);
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}
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}
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}
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//detectedFaces.clear();
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}
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im.release();
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res.Clear();
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res.Free();
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EnqueueDetection(output,camera_id);
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return output;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSRETINAFD::RunInference", e.what(), __FILE__, __LINE__);
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return output;
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}
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}
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ANSRETINAFD::~ANSRETINAFD() {
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try {
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// if (_faceDectector) {
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// delete _faceDectector;
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//_faceDectector = nullptr;
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// }
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSRETINAFD::Destroy", e.what(), __FILE__, __LINE__);
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}
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}
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bool ANSRETINAFD::Destroy() {
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try {
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//if (_faceDectector) {
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// delete _faceDectector;
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// _faceDectector = nullptr;
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//}
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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("ANSRETINAFD::Destroy", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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}
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