Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
97 lines
1.9 KiB
C++
97 lines
1.9 KiB
C++
// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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#pragma once
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#define RET_OK nullptr
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#ifdef _WIN32
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#include <Windows.h>
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#include <direct.h>
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#include <io.h>
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#endif
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#include <string>
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#include <vector>
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#include <cstdio>
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#include <opencv2/opencv.hpp>
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#include "onnxruntime_cxx_api.h"
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#ifdef USE_CUDA
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#include <cuda_fp16.h>
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#endif
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enum MODEL_TYPE
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{
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//FLOAT32 MODEL
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YOLO_DETECT_V8 = 1,
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YOLO_POSE = 2,
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YOLO_CLS = 3,
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//FLOAT16 MODEL
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YOLO_DETECT_V8_HALF = 4,
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YOLO_POSE_V8_HALF = 5,
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YOLO_CLS_HALF = 6
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};
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typedef struct _DL_INIT_PARAM
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{
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std::string modelPath;
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MODEL_TYPE modelType = YOLO_DETECT_V8;
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std::vector<int> imgSize = { 640, 640 };
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float rectConfidenceThreshold = 0.6;
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float iouThreshold = 0.5;
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int keyPointsNum = 2;//Note:kpt number for pose
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bool cudaEnable = false;
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int logSeverityLevel = 3;
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int intraOpNumThreads = 1;
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} DL_INIT_PARAM;
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typedef struct _DL_RESULT
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{
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int classId;
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float confidence;
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cv::Rect box;
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std::vector<cv::Point2f> keyPoints;
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} DL_RESULT;
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class YOLO_V8
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{
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public:
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YOLO_V8();
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~YOLO_V8();
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public:
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char* CreateSession(DL_INIT_PARAM& iParams);
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char* RunSession(cv::Mat& iImg, std::vector<DL_RESULT>& oResult);
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char* WarmUpSession();
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template<typename N>
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char* TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::vector<int64_t>& inputNodeDims,
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std::vector<DL_RESULT>& oResult);
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char* PreProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg);
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std::vector<std::string> classes{};
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private:
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Ort::Env env;
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Ort::Session* session;
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bool cudaEnable;
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Ort::RunOptions options;
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std::vector<const char*> inputNodeNames;
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std::vector<const char*> outputNodeNames;
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MODEL_TYPE modelType;
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std::vector<int> imgSize;
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float rectConfidenceThreshold;
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float iouThreshold;
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float resizeScales;//letterbox scale
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};
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