单目3D初始代码

examples/YOLO11-Triton-CPP/README.md

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+# YOLO11 Triton Inference Server C++ Client
+
+[![Ultralytics](https://img.shields.io/badge/Ultralytics-YOLO11-orange)](https://github.com/ultralytics/ultralytics)
+[![Triton](https://img.shields.io/badge/NVIDIA-Triton-green)](https://github.com/triton-inference-server/server)
+
+This example demonstrates how to perform object detection using Ultralytics YOLO11 models deployed on the NVIDIA Triton Inference Server. The implementation highlights efficient image preprocessing, FP16 (half-precision) data conversion, seamless communication with the Triton server via gRPC, and visualization of detection results with bounding boxes and confidence scores.
+
+## ⚡ Features
+
+- **High-Performance Inference**: Utilizes FP16 (half-precision) data format for optimized memory usage and accelerated inference.
+- **Non-Maximum Suppression (NMS)**: Removes duplicate detections to ensure precise object detection results.
+- **Seamless Triton Integration**: Communicates with the NVIDIA Triton Inference Server via gRPC for efficient and scalable model serving.
+- **Detection Visualization**: Annotates images with bounding boxes, class labels, and confidence scores for intuitive result interpretation.
+
+## 🛠️ Dependencies
+
+Ensure you have the following dependencies installed before proceeding:
+
+| Dependency              | Version | Description                                   |
+| ----------------------- | ------- | --------------------------------------------- |
+| Triton Inference Server | 22.06   | Running with a deployed FP16 YOLO11 model     |
+| Triton Client libraries | 2.23    | Required for communication with Triton Server |
+| C++ compiler            | C++ 17+ | For compiling the C++ client application      |
+| OpenCV library          | 3.4.15  | For image processing and visualization        |
+| CMake                   | 3.5+    | For building the project                      |
+
+For more information on Triton, see the [NVIDIA Triton Inference Server documentation](https://github.com/triton-inference-server/server) and explore [model deployment options with Ultralytics](https://docs.ultralytics.com/guides/model-deployment-options/).
+
+## 🏗️ Building the Project
+
+1. **Install the Triton Client libraries:**
+
+   ```bash
+   wget https://github.com/triton-inference-server/server/releases/download/v2.23.0/v2.23.0_ubuntu2004.clients.tar.gz
+   mkdir tritonclient
+   tar -xvf v2.23.0_ubuntu2004.clients.tar.gz -C tritonclient
+   rm -f v2.23.0_ubuntu2004.clients.tar.gz
+   ```
+
+2. **Clone the Ultralytics repository:**
+
+   ```bash
+   git clone https://github.com/ultralytics/ultralytics.git
+   cd ultralytics/examples/YOLO11-Triton-CPP
+   ```
+
+3. **Configure and build the project using CMake:**
+
+   ```bash
+   mkdir build
+   cd build
+   cmake .. -DTRITON_CLIENT_DIR=/path/to/tritonclient
+   make
+   ```
+
+For additional guidance on integrating Ultralytics YOLO models with various platforms, check out the [Ultralytics integrations documentation](https://docs.ultralytics.com/integrations/).
+
+## 🚀 Usage
+
+1. **Deploy your FP16 (half-precision) YOLO11 model on a Triton Inference Server.**
+   Learn more about deploying models with [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/).
+
+2. **Run the YOLO11-Triton-CPP application:**
+
+   ```bash
+   ./YOLO11TritonCPP
+   ```
+
+By default, the application will:
+
+- Connect to the Triton server at `localhost:8001`
+- Use the model named `yolo11` with version `1`
+- Process the image file `test.jpg`
+- Save detection results to `output.jpg`
+
+For more on object detection workflows, see [Ultralytics object detection tasks](https://docs.ultralytics.com/tasks/detect/).
+
+## ⚙️ Configuration
+
+You can modify the following parameters in [main.cpp](main.cpp):
+
+```cpp
+std::string triton_address = "localhost:8001";
+std::string model_name = "yolo11";
+std::string model_version = "1";
+std::string image_path = "test.jpg";
+std::string output_path = "output.jpg";
+std::vector<std::string> object_class_list = {"class1", "class2"};
+```
+
+To learn more about configuring and customizing YOLO models, visit the [Ultralytics configuration guide](https://docs.ultralytics.com/usage/cfg/).
+
+## 🌟 Contributors
+
+Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the [main Ultralytics repository](https://github.com/ultralytics/ultralytics).
+
+- Ahmet Selim Demirel
+- Doğan Mehmet Başoğlu
+- Enes Uzun
+- Elif Cansu Ada
+- Mevlüt Ardıç
+- Serhat Karaca
+
+[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
+
+---
+
+For more resources, explore the [Ultralytics documentation](https://docs.ultralytics.com/) and [Ultralytics blog](https://www.ultralytics.com/blog).
+
+**We encourage your contributions to help improve this project.**