mirror of
https://github.com/azaion/autopilot.git
synced 2026-04-22 21:46:33 +00:00
Tuomas Järvinen
683f2d538b
- refactoring - can use normal YOLOv8 files converted to ONNX format - does not work with azaion ONNX files!
52 lines
2.0 KiB
C++
52 lines
2.0 KiB
C++
#pragma once
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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 = false);
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std::vector<Detection> runInference(const cv::Mat &input);
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cv::Mat drawLabels(const cv::Mat &image, const std::vector<Detection> &detections);
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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 = false;
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cv::dnn::Net net;
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};
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