🖼️ Format bbox label with fixed precision for ortcpp-example (#4409)

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

examples/YOLOv8-ONNXRuntime-CPP/CMakeLists.txt

@@ -11,20 +11,22 @@ set(CMAKE_CXX_EXTENSIONS ON)
 set(CMAKE_INCLUDE_CURRENT_DIR ON)
 
 
-# OpenCV
+# -------------- OpenCV  ------------------#
 find_package(OpenCV REQUIRED)
 include_directories(${OpenCV_INCLUDE_DIRS})
 
 
 # -------------- Compile CUDA for FP16 inference if needed  ------------------#
 option(USE_CUDA "Enable CUDA support" ON)
-if (USE_CUDA)
+if (NOT APPLE AND USE_CUDA)
     find_package(CUDA REQUIRED)
     include_directories(${CUDA_INCLUDE_DIRS})
     add_definitions(-DUSE_CUDA)
+else ()
+    set(USE_CUDA OFF)
 endif ()
 
-# ONNXRUNTIME
+# -------------- ONNXRUNTIME  ------------------#
 
 # Set ONNXRUNTIME_VERSION
 set(ONNXRUNTIME_VERSION 1.15.1)
@@ -84,3 +86,11 @@ endif ()
 # Download https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml
 # and put it in the same folder of the executable file
 configure_file(coco.yaml ${CMAKE_CURRENT_BINARY_DIR}/coco.yaml COPYONLY)
+
+# Copy yolov8n.onnx file to the same folder of the executable file
+configure_file(yolov8n.onnx ${CMAKE_CURRENT_BINARY_DIR}/yolov8n.onnx COPYONLY)
+
+# Create folder name images in the same folder of the executable file
+add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${CMAKE_CURRENT_BINARY_DIR}/images
+)

examples/YOLOv8-ONNXRuntime-CPP/README.md

@@ -1,14 +1,19 @@
-# YOLOv8 OnnxRuntime C++
+<h1 align="center">YOLOv8 OnnxRuntime C++</h1>
+
+<p align="center">
+  <img alt="C++" src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B">
+  <img alt="Onnx-runtime" src="https://img.shields.io/badge/OnnxRuntime-717272.svg?logo=Onnx&logoColor=white"></img>
+</p>
 
 This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
 
-## Benefits
+## Benefits ✨
 
 - Friendly for deployment in the industrial sector.
 - Faster than OpenCV's DNN inference on both CPU and GPU.
 - Supports FP32 and FP16 CUDA acceleration.
 
-## Exporting YOLOv8 Models
+## Exporting YOLOv8 Models 📦
 
 To export YOLOv8 models, use the following Python script:
 
@@ -28,25 +33,50 @@ Alternatively, you can use the following command for exporting the model in the
 yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
 ```
 
-## Download COCO.yaml file
+## Download COCO.yaml file 📂
 
 In order to run example, you also need to download coco.yaml. You can download the file manually from [here](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml)
 
-## Dependencies
+## Dependencies ⚙️
 
-| Dependency                       | Version       |
-| -------------------------------- | ------------- |
-| Onnxruntime(linux,windows,macos) | >=1.14.1      |
-| OpenCV                           | >=4.0.0       |
-| C++                              | >=17          |
-| Cmake                            | >=3.5         |
-| Cuda (Optional)                  | >=11.4,\<12.0 |
-| cuDNN (Cuda required)            | =8            |
+| Dependency                       | Version        |
+| -------------------------------- | -------------- |
+| Onnxruntime(linux,windows,macos) | >=1.14.1       |
+| OpenCV                           | >=4.0.0        |
+| C++ Standard                     | >=17           |
+| Cmake                            | >=3.5          |
+| Cuda (Optional)                  | >=11.4  \<12.0 |
+| cuDNN (Cuda required)            | =8             |
 
 Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
+
 Note (2): Due to ONNX Runtime, we need to use CUDA 11 and cuDNN 8. Keep in mind that this requirement might change in the future.
 
-## Usage
+## Build 🛠️
+
+1. Clone the repository to your local machine.
+1. Navigate to the root directory of the repository.
+1. Create a build directory and navigate to it:
+
+```console
+mkdir build && cd build
+```
+
+4. Run CMake to generate the build files:
+
+```console
+cmake ..
+```
+
+5. Build the project:
+
+```console
+make
+```
+
+6. The built executable should now be located in the `build` directory.
+
+## Usage 🚀
 
 ```c++
 // CPU inference

examples/YOLOv8-ONNXRuntime-CPP/main.cpp

@@ -1,4 +1,5 @@
 #include <iostream>
+#include <iomanip>
 #include "inference.h"
 #include <filesystem>
 #include <fstream>
@@ -18,16 +19,31 @@ void file_iterator(DCSP_CORE *&p) {
                 cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));
 
                 cv::rectangle(img, re.box, color, 3);
-                std::string label = p->classes[re.classId] + " " + std::to_string(re.confidence);
+
+                float confidence = floor(100 * re.confidence) / 100;
+                std::cout << std::fixed << std::setprecision(2);
+                std::string label = p->classes[re.classId] + " " +
+                                    std::to_string(confidence).substr(0, std::to_string(confidence).size() - 4);
+
+                cv::rectangle(
+                        img,
+                        cv::Point(re.box.x, re.box.y - 25),
+                        cv::Point(re.box.x + label.length() * 15, re.box.y),
+                        color,
+                        cv::FILLED
+                );
+
                 cv::putText(
                         img,
                         label,
                         cv::Point(re.box.x, re.box.y - 5),
                         cv::FONT_HERSHEY_SIMPLEX,
                         0.75,
-                        color,
+                        cv::Scalar(0, 0, 0),
                         2
                 );
+
+
             }
             std::cout << "Press any key to exit" << std::endl;
             cv::imshow("Result of Detection", img);