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Refactor project structure

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This commit is contained in:
Tuan Nghia Nguyen
2026-03-28 19:56:39 +11:00
parent 1d267378b2
commit 8a2e721058
511 changed files with 59 additions and 48 deletions
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modules/ANSFR/ARCFaceRT.h Normal file
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#ifndef ARCFACERT_H
#define ARCFACERT_H
#pragma once
#include "ANSFRCommon.h"
//#include "fastdeploy/vision.h"
#include "hnswlib/hnswlib.h"
#include <faiss/IndexFlat.h>
#include "engine.h"
namespace ANSCENTER {
class ArcFace : public ANSFRBase {
public:
virtual bool Initialize(std::string licenseKey,
ModelConfig modelConfig,
const std::string& modelZipFilePath,
const std::string& modelZipPassword,
std::string& labelMap) override;
virtual bool LoadModel(const std::string& modelZipFilePath,
const std::string& modelZipPassword) override;
bool OptimizeModel(bool fp16, std::string& optimizedModelFolder);
// Single face feature (uses RunArcFace)
std::vector<float> Feature(const cv::Mat& image, const ANSCENTER::Object& bBox);
// Main entry: run recognition for all faces in bBox
std::vector<FaceResultObject> Match(const cv::Mat& input,
const std::vector<ANSCENTER::Object>& bBox,
const std::map<std::string, std::string>& userDict);
cv::Mat GetCropFace(const cv::Mat& input, const ANSCENTER::Object& bBox);
void Init();
void AddEmbedding(const std::string& className, float embedding[]);
void AddEmbedding(const std::string& className, const std::vector<float>& embedding);
bool UpdateParamater(double knownPersonThreshold) {
_modelConfig.unknownPersonThreshold = knownPersonThreshold;
m_knownPersonThresh = _modelConfig.unknownPersonThreshold;
return true;
}
~ArcFace();
bool Destroy();
private:
bool LoadEngine(const std::string onnxModelPath, bool engineOptimisation = true);
// Batched forward: one embedding per Object.mask
std::vector<std::vector<float>> Forward(const cv::Mat& input,
const std::vector<ANSCENTER::Object>& outputBbox);
// Search embeddings in FAISS index
std::tuple<std::vector<std::string>, std::vector<float>>
SearchForFaces(const std::vector<std::vector<float>>& detectedEmbeddings);
// Single-face inference (kept for Feature)
std::vector<float> RunArcFace(const cv::Mat& input);
// New: batched inference helper
std::vector<std::vector<float>> RunArcFaceBatch(const std::vector<cv::Mat>& faceROIs);
protected:
std::vector<std::string> classNames;
ModelConfig _modelConfig;
std::string _modelFilePath;
float m_knownPersonThresh = 0.35f;
const int FACE_WIDTH = 112;
const int FACE_HEIGHT = 112;
const int FACE_EMBEDDING_SIZE = 512;
std::unique_ptr<faiss::IndexFlatL2> faiss_index;
ANSCENTER::Options m_options;
std::recursive_mutex _mutex;
Engine<float> m_trtEngine;
float m_ratio = 1.0f;
float m_imgWidth = 0.0f;
float m_imgHeight = 0.0f;
const std::array<float, 3> SUB_VALS{ 0.5f, 0.5f, 0.5f };
const std::array<float, 3> DIV_VALS{ 0.5f, 0.5f, 0.5f };
const bool NORMALIZE = true;
};
// Using FastDeploy Enging to perform facial recognition
// class ArcFace :public ANSFRBase {
// public:
// virtual bool Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) override;
// virtual bool LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword)override;
// bool OptimizeModel(bool fp16, std::string& optimizedModelFolder);
// std::vector<float> Feature(const cv::Mat& input, ANSCENTER::Object bBox); // Run inference and get embedding information from a cropped image
// std::vector<FaceResultObject> Match(const cv::Mat& input, std::vector<ANSCENTER::Object> bBox, std::map<std::string, std::string>userDict); // Run inference and get embedding information from a cropped image (the first bbox)
// cv::Mat GetCropFace(const cv::Mat& input, ANSCENTER::Object bBox);
// void Init();
// void AddEmbedding(const std::string& className, float embedding[]);
// void AddEmbedding(const std::string& className, const std::vector<float>& embedding);
// bool UpdateParamater(double knownPersonThreshold) {
// _modelConfig.unknownPersonThreshold = knownPersonThreshold;
// _modelConfig.unknownPersonThreshold = knownPersonThreshold;
// m_knownPersonThresh = _modelConfig.unknownPersonThreshold;
// return true;
// }
//~ArcFace();
// bool Destroy();
// private:
// bool LoadEngine(const std::string engineFile, bool engineOptimisation = true);
// std::vector<std::vector<float>> Forward(const cv::Mat& input, std::vector<ANSCENTER::Object> outputBbox);
// std::tuple<std::vector<std::string>, std::vector<float>> SearchForFaces(const std::vector<std::vector<float>>& detectedEmbeddings);
//std::vector<float> RunArcFace(const cv::Mat& input);
// protected:
// std::vector<std::string> classNames;
// ModelConfig _modelConfig;
// std::string _modelFilePath;
// float m_knownPersonThresh;
// const int FACE_WIDTH = 112;
// const int FACE_HEIGHT = 112;
// const int FACE_EMBEDDING_SIZE = 512;
// std::unique_ptr<faiss::IndexFlatL2> faiss_index; // Use shared_ptr
// ANSCENTER::Options m_options;
// std::recursive_mutex _mutex;
// Engine<float> m_trtEngine;
// float m_ratio = 1;
// float m_imgWidth = 0;
// float m_imgHeight = 0;
// const std::array<float, 3> SUB_VALS{ 0.5f, 0.5f, 0.5f };
// const std::array<float, 3> DIV_VALS{ 0.5f, 0.5f, 0.5f };
// const bool NORMALIZE = true;
// };
}
#endif