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)
82 lines
1.8 KiB
C++
82 lines
1.8 KiB
C++
// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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#pragma once
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#include <opencv2/opencv.hpp>
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#include "grpc_client.h"
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class Image
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{
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public:
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Image() = default;
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static void preprocess(cv::Mat* image, std::vector<uint16_t>& triton_data, int input_w, int input_h);
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};
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struct struct_yolo_output
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{
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std::vector<int> num_dets, det_classes;
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std::vector<float> det_boxes, det_scores;
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};
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struct BoundingBox {
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float x, y, w, h;
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float score;
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int class_id;
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};
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struct detection_struct
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{
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cv::Rect bbox;
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int class_id;
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std::string name;
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double confidence_score;
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};
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// C-compatible declarations
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#ifdef __cplusplus
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extern "C" {
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#endif
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int getDetectionsFromTritonRawData(
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std::vector<float>& detection_results,
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std::vector<struct detection_struct>& tespitler,
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std::vector<std::string>& object_class_list,
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float confidence_threshold,
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int image_width,
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int image_height
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);
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std::vector<BoundingBox> NMS(const std::vector<BoundingBox>& boxes, float iou_threshold);
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float IoU(const BoundingBox& box1, const BoundingBox& box2);
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#ifdef __cplusplus
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}
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#endif
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namespace tc = triton::client;
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class TritonCommunication
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{
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private:
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std::unique_ptr<tc::InferenceServerGrpcClient> client;
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std::string triton_url;
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std::vector<int64_t> shape;
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tc::InferOptions options;
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size_t input_byte_size;
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size_t output_byte_size;
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std::shared_ptr<tc::InferResult> results_ptr;
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public:
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std::vector<float> output_raw_data;
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TritonCommunication(std::string triton_address,
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std::string model_name,
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std::string model_version,
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int image_channel,
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int image_width,
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int image_height,
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int class_count);
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void infer(uint16_t* triton_data);
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};
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