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YOLOs-CPP

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License C++ ONNX Runtime OpenCV CMake

Overview

YOLOs-CPP provides single c++ headers with high-performance application designed for real-time object detection using various YOLO (You Only Look Once) models from Ultralytics. Leveraging the power of ONNX Runtime and OpenCV, this project provides seamless integration with unified YOLOv(5,7,8,10,11) implementation for image, video, and live camera inference. Whether you're developing for research, production, or hobbyist projects, this application offers flexibility and efficiency also provide.

Video example of object detection output with bounding boxes and labels. [Click on image!]

Watch the Demo Video

Vidoe source

Integration in your c++ projects

// Include necessary headers
#include <opencv2/opencv.hpp>
#include <iostream>
#include <string>

#include "YOLO11.hpp" // Ensure YOLO11.hpp or other version is in your include path

int main()
{
    // Configuration parameters
    const std::string labelsPath = "../models/coco.names";       // Path to class labels
    const std::string modelPath  = "../models/yolo11n.onnx";     // Path to YOLO11 model
    const std::string imagePath  = "../data/dogs.jpg";           // Path to input image
    bool isGPU = true;                                           // Set to false for CPU processing

    // Initialize the YOLO11 detector
    YOLO11Detector detector(modelPath, labelsPath, isGPU);

    // Load an image
    cv::Mat image = cv::imread(imagePath);

    // Perform object detection to get bboxs
    std::vector<Detection> detections = detector.detect(image);

    // Draw bounding boxes on the image
    detector.drawBoundingBoxMask(image, detections);

    // Display the annotated image
    cv::imshow("YOLO11 Detections", image);
    cv::waitKey(0); // Wait indefinitely until a key is pressed

    return 0;
}

Note: For more usage, check the source files: camera_inference.cpp, image_inference.cpp, video_inference.cpp.

Features

  • Multiple YOLO Models: Supports YOLOv5, YOLOv7, YOLOv8, YOLOv10, and YOLOv11 with standard and quantized ONNX models for flexibility in use cases.

  • ONNX Runtime Integration: Leverages ONNX Runtime for optimized inference on both CPU and GPU, ensuring high performance.

    • Dynamic Shapes Handling: Adapts automatically to varying input sizes for improved versatility.
    • Graph Optimization: Enhances performance using model optimization with ORT_ENABLE_ALL.
    • Execution Providers: Configures sessions for CPU or GPU (e.g., CUDAExecutionProvider for GPU support).
    • Input/Output Shape Management: Manages dynamic input tensor shapes per model specifications.
    • Optimized Memory Allocation: Utilizes Ort::MemoryInfo for efficient memory management during tensor creation.
    • Batch Processing: Supports processing multiple images, currently focused on single-image input.
    • Output Tensor Extraction: Extracts output tensors dynamically for flexible result handling.

  • OpenCV Integration: Uses OpenCV for image processing and rendering bounding boxes and labels (note: cv::dnn modules are not used).

  • Real-Time Inference: Capable of processing images, videos, and live camera feeds instantly.

  • Efficient Detection Handling: Employs Non-Maximum Suppression (NMS) for effective processing (note: some models are NMS free e.g. YOLO10).

  • Cross-Platform Support: Fully compatible with Linux, macOS, and Windows environments.

  • Easy-to-Use Scripts: Includes shell scripts for straightforward building and running of different inference modes.

Requirements

Before building the project, ensure that the following dependencies are installed on your system:

  • C++ Compiler: Compatible with C++14 standard (e.g., g++, clang++, or MSVC).
  • CMake: Version 3.0.0 or higher.
  • OpenCV: Version 4.5.5 or higher.
  • ONNX Runtime: Tested with version 1.16.3 and 1.19.2, backward compatibility [Installed and linked automatically during the build].
  • Python (optional): For running the quantization script (yolos_quantization.py).

Installation

Clone Repository

First, clone the repository to your local machine:

git clone https://github.com/Geekgineer/YOLOs-CPP
cd YOLOs-CPP

Configure

  1. make sure you have opencv c++ installed

  2. set the ONNX Runtime version you need e.g. ONNXRUNTIME_VERSION="1.16.3" in build.sh to download ONNX Runtime headers also set GPU.

  3. Ensure that the you commented and uncomment the yolo version you need in the script you will run, select the data or the camera source in the code:

    camera_inference.cpp

    image_inference.cpp

    video_inference.cpp

  4. Optional: control the debugging and timing using Config.hpp

Build the Project

Execute the build script to compile the project using CMake:

./build.sh

This script will download onnxruntime headers, create a build directory, configure the project, and compile the source code. Upon successful completion, the executable files (camera_inference, image_inference, video_inference) will be available in the build directory.

Usage

After building the project, you can perform object detection on images, videos, or live camera feeds using the provided shell scripts.

Run Image Inference

To perform object detection on a single image:

./run_image.sh

This command will process dog.jpg using e.g. YOLOv5-n6 model and display the output image with detected bounding boxes and labels.

Run Video Inference

To perform object detection on a video file:

./run_video.sh

Example:

./run_video.sh

The above command will process SIG_experience_center.mp4 using the YOLO11n model and save the output video with detected objects.

Run Camera Inference

To perform real-time object detection using a usb cam:

./run_camera.sh

This command will activate your usb and display the video feed with real-time object detection.

Models

The project includes various pertained standard YOLO models stored in the models and quantized_models directories:

Model Type Model Name
Standard Models yolo5-n6.onnx
yolo7-tiny.onnx
yolo8n.onnx
yolo10n.onnx
yolo11n.onnx
Quantized Models yolo5-n6_uint8.onnx
yolo7-tiny-uint8.onnx
yolo8n_uint8.onnx
yolo10n_uint8.onnx
yolo11n_uint8.onnx

You can use your custom yolo version with custom classes also!

Class Names:

  • coco.names: Contains the list of class labels used by the models.

Quantization

The quantized_models directory includes quantized versions of the YOLO models optimized for lower precision inference. Additionally, the quantized_models/yolos_quantization.py script can be used to perform custom quantization on your custom YOLOs models.

Note: Quantized models offer reduced model size and potentially faster inference with a slight trade-off in accuracy.

Contributing

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the Repository: Click the "Fork" button at the top-right corner of this repository to create a personal copy.
  2. Clone Your Fork: 
    git clone https://github.com/Geekgineer/YOLOs-CPP
cd YOLOs-CPP
  3. Create a New Branch: 
    git checkout -b feature/YourFeatureName
  4. Make Your Changes: Implement your feature or bug fix.
  5. Commit Your Changes: 
    git commit -m "Add feature: YourFeatureName"
  6. Push to Your Fork: 
    git push origin feature/YourFeatureName
  7. Create a Pull Request: Navigate to the original repository and click "New Pull Request" to submit your changes for review.

License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software in accordance with the terms of the license.

Acknowledgment