YOLO v5 ONNX Runtime C++ inference code.

Overview

yolov5-onnxruntime

C++ YOLO v5 ONNX Runtime inference code for object detection.

Dependecies:

  • OpenCV 4.5+
  • ONNXRuntime 1.7+
  • OS: Windows 10 or Ubuntu 20.04
  • CUDA 11+ [Optional]

Build

To build the project you should run the following commands, don't forget to change ONNXRUNTIME_DIR cmake option:

mkdir build
cd build
cmake .. -DONNXRUNTIME_DIR=path_to_onnxruntime
cmake --build .

Run

Before running the executable you should convert you PyTorch model to ONNX if you haven't done it yet. Check the official tutorial.

To run the executable you should add OpenCV and ONNX Runtime libraries to your environment path or put all needed libraries near the executable.

Run from CLI:

./yolo_ort --model_path yolov5.onnx --image bus.jpg --class_names coco.names --gpu

Demo

TODO

  • refactoring;
  • add Python implementation of the project;
  • add dynamic input shape inference;
  • add C++ letterbox implementation and scaling;
  • add device selection for inference;
  • add Linux compatibility;
  • read class names from file;
  • better visualization with class names and boxes;
  • create YOLO class for easy deployment;

References

Issues
  • I encountered a bug in detect

    I encountered a bug in detect

    Hello, when I used the same onnx model to detect the original yolov5 project and this project, I encountered the problem of different results. The original yolov5 project category result is correct, but only part of the category of this project is correct, the recognition box The same, but the confidence level is also different. How to solve this problem?

    opened by p110120p1 6
  • How to find onnxruntime_cxx_api.h?

    How to find onnxruntime_cxx_api.h?

    Hi, I have built onnxruntime on macOS, but under MacOS/Release folder, there is no onnxruntime_cxx_api such file at all. Also, there is no such lib sub folder under it, it just inside MacOS/Release/libonnxruntime.dylib.

    How should I set them under macOS?

    just got 2 such file in src:

    /libs/onnxruntime//cmake/external/onnxruntime-extensions/includes/onnxruntime/onnxruntime_cxx_api.h
    /libs/onnxruntime//include/onnxruntime/core/session/onnxruntime_cxx_api.h
    
    opened by jinfagang 4
  • How to change imgsz?

    How to change imgsz?

    Hello, I had an experiment where all the images were high pixels, so scaling to 640*640 would cause the target to be too small. I tried to modify the c++ file in the src folder to change 640 to 1280, but after compiling, I still need 640 input, so how should I modify the project?

    opened by p110120p1 3
  • fatal error LNK 1104

    fatal error LNK 1104

    Hello, I am trying to run this example, but when I'm writing "cmake --build ." in the terminal, I always get this error:

    LINK : fatal error LNK1104: Datei "onnxruntime-win-x64\onnxruntime-win-x64\lib\onnxruntime.lib.lib" kann nicht geöffnet werden. [...\build\yolo_ort.vcxproj]
    

    Maybe the "lib.lib" from onnxruntime.lib.lib is the problem, but I don't know how to solve it. I am using the win-x64-1.10.0 onnxruntime version.

    Thanks for your help.

    opened by Defa6 2
  • Dynamic input shape

    Dynamic input shape

    There seem to be an issue handling an input other than 640x640. When I try to feed a 320x1296 input it throws an error: Got invalid dimensions for input: images for the following indices index: 2 Got: 1296 Expected: 640 index: 3 Got: 320 Expected: 640

    I think it has to do with the dynamic input shape checking of the code, which I think it is not doing its job correctly. Can someone point me where should I look at, to make it able to excecute multiple input shape images? Thanks!

    opened by emanef13 2
  • Half precision

    Half precision

    Official yolov5 PyTorch repo uses half precision. I try the onnx model with half precision on python, and speed increased. Can this repo support half precision?

    opened by guishilike 1
  • cvtColor does not take effect

    cvtColor does not take effect

    In preprocessing

    void YOLODetector::preprocessing(cv::Mat &image, float*& blob, std::vector<int64_t>& inputTensorShape)
    {
        cv::Mat resizedImage, floatImage;
        cv::cvtColor(image, resizedImage, cv::COLOR_BGR2RGB);
        utils::letterbox(image, resizedImage, this->inputImageShape,
                         cv::Scalar(114, 114, 114), this->isDynamicInputShape,
                         false, true, 32);
    
        inputTensorShape[2] = resizedImage.rows;
        inputTensorShape[3] = resizedImage.cols;
    
        resizedImage.convertTo(floatImage, CV_32FC3, 1 / 255.0);
        blob = new float[floatImage.cols * floatImage.rows * floatImage.channels()];
        cv::Size floatImageSize {floatImage.cols, floatImage.rows};
    
        // hwc -> chw
        std::vector<cv::Mat> chw(floatImage.channels());
        for (int i = 0; i < floatImage.channels(); ++i)
        {
            chw[i] = cv::Mat(floatImageSize, CV_32FC1, blob + i * floatImageSize.width * floatImageSize.height);
        }
        cv::split(floatImage, chw);
    }
    
    opened by guishilike 1
  • ort batch inference

    ort batch inference

    hello,I have tested ort c++ inference successfully! but I didn't make batch inference works! could you please give a batch inference c++ example?? Thank you very much!

    opened by yang-gis 1
  • Integrate with TensorRT?

    Integrate with TensorRT?

    I tried out your sample - very cool! I get 110 FPS with a YOLOv5s running CUDA 11.5 on my 1080ti. I am curious what it would take to evaluate performance with TensorRT. Have you tried this? Any pointers? Thanks.

    opened by rtrahms 1
Owner
Fidan
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