[Example] YOLO-Series(v5-11) ONNXRuntime Rust (#17311)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

examples/README.md

@@ -21,6 +21,7 @@ This directory features a collection of real-world applications and walkthroughs
 | [YOLOv8 OpenCV INT8 TFLite Python](./YOLOv8-TFLite-Python)                                                                                | Python             | [Wamiq Raza](https://github.com/wamiqraza)                                                |
 | [YOLOv8 All Tasks ONNXRuntime Rust](./YOLOv8-ONNXRuntime-Rust)                                                                            | Rust/ONNXRuntime   | [jamjamjon](https://github.com/jamjamjon)                                                 |
 | [YOLOv8 OpenVINO CPP](./YOLOv8-OpenVINO-CPP-Inference)                                                                                    | C++/OpenVINO       | [Erlangga Yudi Pradana](https://github.com/rlggyp)                                        |
+| [YOLOv5-YOLO11 ONNXRuntime Rust](./YOLO-Series-ONNXRuntime-Rust)                                                                          | Rust/ONNXRuntime   | [jamjamjon](https://github.com/jamjamjon)                                                 |
 
 ### How to Contribute
 

examples/YOLO-Series-ONNXRuntime-Rust/Cargo.toml

@@ -0,0 +1,12 @@
+[package]
+name = "YOLO-ONNXRuntime-Rust"
+version = "0.1.0"
+edition = "2021"
+authors = ["Jamjamjon <xxyydzml@outlook.com>"]
+
+[dependencies]
+anyhow = "1.0.92"
+clap = "4.5.20"
+tracing = "0.1.40"
+tracing-subscriber = "0.3.18"
+usls = { version = "0.0.19", features = ["auto"] }

examples/YOLO-Series-ONNXRuntime-Rust/README.md

@@ -0,0 +1,94 @@
+# YOLO-Series ONNXRuntime Rust Demo for Core YOLO Tasks
+
+This repository provides a Rust demo for key YOLO-Series tasks such as `Classification`, `Segmentation`, `Detection`, `Pose Detection`, and `OBB` using ONNXRuntime. It supports various YOLO models (v5 - 11) across multiple vision tasks.
+
+## Introduction
+
+- This example leverages the latest versions of both ONNXRuntime and YOLO models.
+- We utilize the [usls](https://github.com/jamjamjon/usls/tree/main) crate to streamline YOLO model inference, providing efficient data loading, visualization, and optimized inference performance.
+
+## Features
+
+- **Extensive Model Compatibility**: Supports `YOLOv5`, `YOLOv6`, `YOLOv7`, `YOLOv8`, `YOLOv9`, `YOLOv10`, `YOLO11`, `YOLO-world`, `RTDETR`, and others, covering a wide range of YOLO versions.
+- **Versatile Task Coverage**: Includes `Classification`, `Segmentation`, `Detection`, `Pose`, and `OBB`.
+- **Precision Flexibility**: Works with `FP16` and `FP32` ONNX models.
+- **Execution Providers**: Accelerated support for `CPU`, `CUDA`, `CoreML`, and `TensorRT`.
+- **Dynamic Input Shapes**: Dynamically adjusts to variable `batch`, `width`, and `height` dimensions for flexible model input.
+- **Flexible Data Loading**: The `DataLoader` handles images, folders, videos, and video streams.
+- **Real-Time Display and Video Export**: `Viewer` provides real-time frame visualization and video export functions, similar to OpenCV’s `imshow()` and `imwrite()`.
+- **Enhanced Annotation and Visualization**: The `Annotator` facilitates comprehensive result rendering, with support for bounding boxes (HBB), oriented bounding boxes (OBB), polygons, masks, keypoints, and text labels.
+
+## Setup Instructions
+
+### 1. ONNXRuntime Linking
+
+<details>
+<summary>You have two options to link the ONNXRuntime library:</summary>
+
+- **Option 1: Manual Linking**
+
+  - For detailed setup, consult the [ONNX Runtime linking documentation](https://ort.pyke.io/setup/linking).
+  - **Linux or macOS**:
+    1. Download the ONNX Runtime package from the [Releases page](https://github.com/microsoft/onnxruntime/releases).
+    2. Set up the library path by exporting the `ORT_DYLIB_PATH` environment variable:
+       ```shell
+       export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.19.0
+       ```
+
+- **Option 2: Automatic Download**
+  - Use the `--features auto` flag to handle downloading automatically:
+    ```shell
+    cargo run -r --example yolo --features auto
+    ```
+
+</details>
+
+### 2. \[Optional\] Install CUDA, CuDNN, and TensorRT
+
+- The CUDA execution provider requires CUDA version `12.x`.
+- The TensorRT execution provider requires both CUDA `12.x` and TensorRT `10.x`.
+
+### 3. \[Optional\] Install ffmpeg
+
+To view video frames and save video inferences, install `rust-ffmpeg`. For instructions, see:  
+[https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies](https://github.com/zmwangx/rust-ffmpeg/wiki/Notes-on-building#dependencies)
+
+## Get Started
+
+```Shell
+# customized
+cargo run -r -- --task detect --ver v8 --nc 6 --model xxx.onnx  # YOLOv8
+
+# Classify
+cargo run -r -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000  # YOLOv5
+cargo run -r -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000  # YOLOv8
+cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000  # YOLOv11
+
+# Detect
+cargo run -r -- --task detect --ver v5 --scale n  # YOLOv5
+cargo run -r -- --task detect --ver v6 --scale n  # YOLOv6
+cargo run -r -- --task detect --ver v7 --scale t  # YOLOv7
+cargo run -r -- --task detect --ver v8 --scale n  # YOLOv8
+cargo run -r -- --task detect --ver v9 --scale t  # YOLOv9
+cargo run -r -- --task detect --ver v10 --scale n  # YOLOv10
+cargo run -r -- --task detect --ver v11 --scale n  # YOLOv11
+cargo run -r -- --task detect --ver rtdetr --scale l  # RTDETR
+
+# Pose
+cargo run -r -- --task pose --ver v8 --scale n   # YOLOv8-Pose
+cargo run -r -- --task pose --ver v11 --scale n  # YOLOv11-Pose
+
+# Segment
+cargo run -r -- --task segment --ver v5 --scale n  # YOLOv5-Segment
+cargo run -r -- --task segment --ver v8 --scale n  # YOLOv8-Segment
+cargo run -r -- --task segment --ver v11 --scale n  # YOLOv8-Segment
+cargo run -r -- --task segment --ver v8 --model yolo/FastSAM-s-dyn-f16.onnx  # FastSAM
+
+# OBB
+cargo run -r -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png  # YOLOv8-Obb
+cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png  # YOLOv11-Obb
+```
+
+**`cargo run -- --help` for more options**
+
+For more details, please refer to [usls-yolo](https://github.com/jamjamjon/usls/tree/main/examples/yolo).

examples/YOLO-Series-ONNXRuntime-Rust/src/main.rs

@@ -0,0 +1,236 @@
+use anyhow::Result;
+use clap::Parser;
+
+use usls::{
+    models::YOLO, Annotator, DataLoader, Device, Options, Viewer, Vision, YOLOScale, YOLOTask,
+    YOLOVersion, COCO_SKELETONS_16,
+};
+
+#[derive(Parser, Clone)]
+#[command(author, version, about, long_about = None)]
+pub struct Args {
+    /// Path to the ONNX model
+    #[arg(long)]
+    pub model: Option<String>,
+
+    /// Input source path
+    #[arg(long, default_value_t = String::from("../../ultralytics/assets/bus.jpg"))]
+    pub source: String,
+
+    /// YOLO Task
+    #[arg(long, value_enum, default_value_t = YOLOTask::Detect)]
+    pub task: YOLOTask,
+
+    /// YOLO Version
+    #[arg(long, value_enum, default_value_t = YOLOVersion::V8)]
+    pub ver: YOLOVersion,
+
+    /// YOLO Scale
+    #[arg(long, value_enum, default_value_t = YOLOScale::N)]
+    pub scale: YOLOScale,
+
+    /// Batch size
+    #[arg(long, default_value_t = 1)]
+    pub batch_size: usize,
+
+    /// Minimum input width
+    #[arg(long, default_value_t = 224)]
+    pub width_min: isize,
+
+    /// Input width
+    #[arg(long, default_value_t = 640)]
+    pub width: isize,
+
+    /// Maximum input width
+    #[arg(long, default_value_t = 1024)]
+    pub width_max: isize,
+
+    /// Minimum input height
+    #[arg(long, default_value_t = 224)]
+    pub height_min: isize,
+
+    /// Input height
+    #[arg(long, default_value_t = 640)]
+    pub height: isize,
+
+    /// Maximum input height
+    #[arg(long, default_value_t = 1024)]
+    pub height_max: isize,
+
+    /// Number of classes
+    #[arg(long, default_value_t = 80)]
+    pub nc: usize,
+
+    /// Class confidence
+    #[arg(long)]
+    pub confs: Vec<f32>,
+
+    /// Enable TensorRT support
+    #[arg(long)]
+    pub trt: bool,
+
+    /// Enable CUDA support
+    #[arg(long)]
+    pub cuda: bool,
+
+    /// Enable CoreML support
+    #[arg(long)]
+    pub coreml: bool,
+
+    /// Use TensorRT half precision
+    #[arg(long)]
+    pub half: bool,
+
+    /// Device ID to use
+    #[arg(long, default_value_t = 0)]
+    pub device_id: usize,
+
+    /// Enable performance profiling
+    #[arg(long)]
+    pub profile: bool,
+
+    /// Disable contour drawing, for saving time
+    #[arg(long)]
+    pub no_contours: bool,
+
+    /// Show result
+    #[arg(long)]
+    pub view: bool,
+
+    /// Do not save output
+    #[arg(long)]
+    pub nosave: bool,
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+
+    // logger
+    if args.profile {
+        tracing_subscriber::fmt()
+            .with_max_level(tracing::Level::INFO)
+            .init();
+    }
+
+    // model path
+    let path = match &args.model {
+        None => format!(
+            "yolo/{}-{}-{}.onnx",
+            args.ver.name(),
+            args.scale.name(),
+            args.task.name()
+        ),
+        Some(x) => x.to_string(),
+    };
+
+    // saveout
+    let saveout = match &args.model {
+        None => format!(
+            "{}-{}-{}",
+            args.ver.name(),
+            args.scale.name(),
+            args.task.name()
+        ),
+        Some(x) => {
+            let p = std::path::PathBuf::from(&x);
+            p.file_stem().unwrap().to_str().unwrap().to_string()
+        }
+    };
+
+    // device
+    let device = if args.cuda {
+        Device::Cuda(args.device_id)
+    } else if args.trt {
+        Device::Trt(args.device_id)
+    } else if args.coreml {
+        Device::CoreML(args.device_id)
+    } else {
+        Device::Cpu(args.device_id)
+    };
+
+    // build options
+    let options = Options::new()
+        .with_model(&path)?
+        .with_yolo_version(args.ver)
+        .with_yolo_task(args.task)
+        .with_device(device)
+        .with_trt_fp16(args.half)
+        .with_ixx(0, 0, (1, args.batch_size as _, 4).into())
+        .with_ixx(0, 2, (args.height_min, args.height, args.height_max).into())
+        .with_ixx(0, 3, (args.width_min, args.width, args.width_max).into())
+        .with_confs(if args.confs.is_empty() {
+            &[0.2, 0.15]
+        } else {
+            &args.confs
+        })
+        .with_nc(args.nc)
+        .with_find_contours(!args.no_contours) // find contours or not
+        // .with_names(&COCO_CLASS_NAMES_80)  // detection class names
+        // .with_names2(&COCO_KEYPOINTS_17) // keypoints class names
+        // .exclude_classes(&[0])
+        // .retain_classes(&[0, 5])
+        .with_profile(args.profile);
+
+    // build model
+    let mut model = YOLO::new(options)?;
+
+    // build dataloader
+    let dl = DataLoader::new(&args.source)?
+        .with_batch(model.batch() as _)
+        .build()?;
+
+    // build annotator
+    let annotator = Annotator::default()
+        .with_skeletons(&COCO_SKELETONS_16)
+        .without_masks(true) // no masks plotting when doing segment task
+        .with_bboxes_thickness(3)
+        .with_keypoints_name(false) // enable keypoints names
+        .with_saveout_subs(&["YOLO"])
+        .with_saveout(&saveout);
+
+    // build viewer
+    let mut viewer = if args.view {
+        Some(Viewer::new().with_delay(5).with_scale(1.).resizable(true))
+    } else {
+        None
+    };
+
+    // run & annotate
+    for (xs, _paths) in dl {
+        let ys = model.forward(&xs, args.profile)?;
+        let images_plotted = annotator.plot(&xs, &ys, !args.nosave)?;
+
+        // show image
+        match &mut viewer {
+            Some(viewer) => viewer.imshow(&images_plotted)?,
+            None => continue,
+        }
+
+        // check out window and key event
+        match &mut viewer {
+            Some(viewer) => {
+                if !viewer.is_open() || viewer.is_key_pressed(usls::Key::Escape) {
+                    break;
+                }
+            }
+            None => continue,
+        }
+
+        // write video
+        if !args.nosave {
+            match &mut viewer {
+                Some(viewer) => viewer.write_batch(&images_plotted)?,
+                None => continue,
+            }
+        }
+    }
+
+    // finish video write
+    if !args.nosave {
+        if let Some(viewer) = &mut viewer {
+            viewer.finish_write()?;
+        }
+    }
+
+    Ok(())
+}

examples/YOLOv8-ONNXRuntime-Rust/Cargo.toml

@@ -12,7 +12,7 @@ clap = { version = "4.2.4", features = ["derive"] }
 image = { version = "0.25.2"}
 imageproc = { version = "0.25.0"}
 ndarray = { version = "0.16" }
-ort = { version = "2.0.0-rc.5", features = ["cuda", "tensorrt"]}
+ort = { version = "2.0.0-rc.5", features = ["cuda", "tensorrt", "load-dynamic", "copy-dylibs", "half"]}
 rusttype = { version = "0.9.3" }
 anyhow = { version = "1.0.75" }
 regex = { version = "1.5.4" }

examples/YOLOv8-ONNXRuntime-Rust/README.md

@@ -7,7 +7,7 @@ This repository provides a Rust demo for performing YOLOv8 tasks like `Classific
 - Add YOLOv8-OBB demo
 - Update ONNXRuntime to 1.19.x
 
-Newly updated YOLOv8 example code is located in this repository (https://github.com/jamjamjon/usls/tree/main/examples/yolo)
+Newly updated YOLOv8 example code is located in [this repository](https://github.com/jamjamjon/usls/tree/main/examples/yolo)
 
 ## Features
 
@@ -22,25 +22,16 @@ Newly updated YOLOv8 example code is located in this repository (https://github.
 
 Please follow the Rust official installation. (https://www.rust-lang.org/tools/install)
 
-### 2. Install ONNXRuntime
+### 2. ONNXRuntime Linking
 
-This repository use `ort` crate, which is ONNXRuntime wrapper for Rust. (https://docs.rs/ort/latest/ort/)
+- #### For detailed setup instructions, refer to the [ORT documentation](https://ort.pyke.io/setup/linking).
 
-You can follow the instruction with `ort` doc or simply do this:
+- #### For Linux or macOS Users:
-
+  - Download the ONNX Runtime package from the [Releases page](https://github.com/microsoft/onnxruntime/releases).
-- step1: Download ONNXRuntime(https://github.com/microsoft/onnxruntime/releases)
+  - Set up the library path by exporting the `ORT_DYLIB_PATH` environment variable:
-- setp2: Set environment variable `PATH` for linking.
+    ```shell
-
+    export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.19.0
-On ubuntu, You can do like this:
+    ```
-
-```bash
-vim ~/.bashrc
-
-# Add the path of ONNXRUntime lib
-export LD_LIBRARY_PATH=/home/qweasd/Documents/onnxruntime-linux-x64-gpu-1.16.3/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-
-source ~/.bashrc
-```
 
 ### 3. \[Optional\] Install CUDA & CuDNN & TensorRT
 

examples/YOLOv8-ONNXRuntime-Rust/src/lib.rs

@@ -118,16 +118,15 @@ pub fn check_font(font: &str) -> rusttype::Font<'static> {
     rusttype::Font::try_from_vec(buffer).unwrap()
 }
 
-
 use ab_glyph::FontArc;
-pub fn load_font() -> FontArc{
+pub fn load_font() -> FontArc {
     use std::path::Path;
     let font_path = Path::new("./font/Arial.ttf");
     match font_path.try_exists() {
         Ok(true) => {
             let buffer = std::fs::read(font_path).unwrap();
             FontArc::try_from_vec(buffer).unwrap()
-        },
+        }
         Ok(false) => {
             std::fs::create_dir_all("./font").unwrap();
             println!("Downloading font...");
@@ -153,9 +152,9 @@ pub fn load_font() -> FontArc{
             fd.write_all(&buffer).unwrap();
             println!("Font saved at: {:?}", font_path.display());
             FontArc::try_from_vec(buffer).unwrap()
-        },
+        }
         Err(e) => {
             panic!("Failed to load font {}", e);
-        },
+        }
     }
 }

examples/YOLOv8-ONNXRuntime-Rust/src/model.rs

@@ -8,7 +8,7 @@ use rand::{thread_rng, Rng};
 use std::path::PathBuf;
 
 use crate::{
-    load_font, gen_time_string, non_max_suppression, Args, Batch, Bbox, Embedding, OrtBackend,
+    gen_time_string, load_font, non_max_suppression, Args, Batch, Bbox, Embedding, OrtBackend,
     OrtConfig, OrtEP, Point2, YOLOResult, YOLOTask, SKELETON,
 };
 
@@ -463,7 +463,7 @@ impl YOLOv8 {
                         image::Rgb(self.color_palette[bbox.id()].into()),
                         bbox.xmin() as i32,
                         (bbox.ymin() - legend_size as f32) as i32,
-                        legend_size as  f32,
+                        legend_size as f32,
                         &font,
                         &legend,
                     );