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52 lines
2.0 KiB
52 lines
2.0 KiB
#ifndef INFERENCE_H |
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#define INFERENCE_H |
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// Cpp native |
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#include <fstream> |
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#include <vector> |
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#include <string> |
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#include <random> |
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// OpenCV / DNN / Inference |
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#include <opencv2/imgproc.hpp> |
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#include <opencv2/opencv.hpp> |
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#include <opencv2/dnn.hpp> |
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struct Detection |
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{ |
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int class_id{0}; |
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std::string className{}; |
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float confidence{0.0}; |
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cv::Scalar color{}; |
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cv::Rect box{}; |
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}; |
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class Inference |
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{ |
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public: |
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Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape = {640, 640}, const std::string &classesTxtFile = "", const bool &runWithCuda = true); |
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std::vector<Detection> runInference(const cv::Mat &input); |
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private: |
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void loadClassesFromFile(); |
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void loadOnnxNetwork(); |
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cv::Mat formatToSquare(const cv::Mat &source); |
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std::string modelPath{}; |
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std::string classesPath{}; |
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bool cudaEnabled{}; |
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std::vector<std::string> classes{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"}; |
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cv::Size2f modelShape{}; |
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float modelConfidenceThreshold {0.25}; |
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float modelScoreThreshold {0.45}; |
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float modelNMSThreshold {0.50}; |
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bool letterBoxForSquare = true; |
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cv::dnn::Net net; |
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}; |
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#endif // INFERENCE_H
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