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)
62 lines
2.5 KiB
C++
62 lines
2.5 KiB
C++
// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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#ifndef YOLO_INFERENCE_H_
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#define YOLO_INFERENCE_H_
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#include <string>
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#include <vector>
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#include <opencv2/imgproc.hpp>
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#include <openvino/openvino.hpp>
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namespace yolo {
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struct Detection {
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short class_id;
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float confidence;
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cv::Rect box;
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};
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class Inference {
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public:
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Inference() {}
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// Constructor to initialize the model with default input shape
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Inference(const std::string &model_path, const float &model_confidence_threshold, const float &model_NMS_threshold);
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// Constructor to initialize the model with specified input shape
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Inference(const std::string &model_path, const cv::Size model_input_shape, const float &model_confidence_threshold, const float &model_NMS_threshold);
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void RunInference(cv::Mat &frame);
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private:
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void InitializeModel(const std::string &model_path);
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void Preprocessing(const cv::Mat &frame);
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void PostProcessing(cv::Mat &frame);
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cv::Rect GetBoundingBox(const cv::Rect &src) const;
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void DrawDetectedObject(cv::Mat &frame, const Detection &detections) const;
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cv::Point2f scale_factor_; // Scaling factor for the input frame
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cv::Size2f model_input_shape_; // Input shape of the model
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cv::Size model_output_shape_; // Output shape of the model
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ov::InferRequest inference_request_; // OpenVINO inference request
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ov::CompiledModel compiled_model_; // OpenVINO compiled model
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float model_confidence_threshold_; // Confidence threshold for detections
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float model_NMS_threshold_; // Non-Maximum Suppression threshold
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std::vector<std::string> classes_ {
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"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
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"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
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"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
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"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
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"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
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"scissors", "teddy bear", "hair drier", "toothbrush"
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};
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};
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} // namespace yolo
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#endif // YOLO_INFERENCE_H_
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