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‎README.md‎

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# YOLOX-ROS
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[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) + ROS2 Foxy demo
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[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) + ROS2 Humble demo
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![yolox_s_result](https://github.com/Ar-Ray-code/RenderTexture2ROS2Image/blob/main/images_for_readme/unity-demo.gif?raw=true)
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> rclcpp is recommended due to its extended support.
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| Base | ROS2 C++ | ROS2 Python |
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| --------------- | -------- | ----------- |
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| PyTorch | | ✅ |
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| TensorRT (CUDA) | ✅ | |
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| OpenVINO | ✅ | ✅ |
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| ONNX Runtime | ✅ | ✅ |
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| TFLite | ✅ | ✅ |
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| Base | ROS2 C++ |
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| --------------- | -------- |
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| TensorRT (CUDA) | ✅ |
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| OpenVINO | ✅ |
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| ONNX Runtime | |
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| TFLite | |
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<!-- ### Raspberry Pi4 🍓 + NCS2 + YOLOX-ROS
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Check [GitHub Wiki](https://github.com/Ar-Ray-code/YOLOX-ROS/wiki/YOLOX-ROS---Raspbian-(NCS2)) to try YOLOX-ROS. -->
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## Installation & Demo
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<details>
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<summary>Python (PyTorch)</summary>
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## Requirements
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- ROS2 Foxy
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- OpenCV 4
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- Python 3.8 (Ubuntu 20.04 Default)
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- PyTorch >= v1.7
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- [YOLOX v0.3.0](https://github.com/Megvii-BaseDetection/YOLOX)
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- [bbox_ex_msgs](https://github.com/Ar-Ray-code/bbox_ex_msgs)
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## Installation
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Install the dependent packages based on all tutorials.
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### STEP 1 : Download from GitHub
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```bash
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mkdir -p ~/ros2_ws/src
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cd ~/ros2_ws/src
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git clone https://github.com/Ar-Ray-code/yolox_ros.git --recursive
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```
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### STEP 2 : YOLOX Installation (yolox_ros_py)
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For more information, see the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX#quick-start) installation documentation.
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```bash
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pip3 install yolox
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```
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### STEP 3 : Install YOLOX-ROS
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```bash
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source /opt/ros/foxy/setup.bash
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sudo apt install ros-foxy-v4l2-camera
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# source /opt/intel/openvino_2021/bin/setupvars.sh # <- Using OpenVINO
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colcon build --symlink-install # weights (YOLOX-Nano) files will be installed automatically.
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```
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**Automatic download weights**
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- yolox_nano.onnx by [Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
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- yolox_nano.pth by [Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
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- model.onnx by [Kazuhito00](https://github.com/Kazuhito00/Person-Detection-using-RaspberryPi-CPU)
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- model.tflite by [Kazuhito00](https://github.com/Kazuhito00/Person-Detection-using-RaspberryPi-CPU)
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### (Step 3) Using CUDA
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If you have NVIDIA Graphics, you can run YOLOX-ROS on GPU.
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**Additional installing lists**
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- NVIDIA Graphics Driver
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- CUDA toolkit (11.0)
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- torch+cuda
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```bash
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source /opt/ros/foxy/setup.bash
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sudo apt install ros-foxy-v4l2-camera
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colcon build --symlink-install # weights (YOLOX-Nano) files will be installed automatically.
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```
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### Step : Demo
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Connect your web camera.
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```bash
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source /opt/ros/foxy/setup.bash
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source ~/ros2_ws/install/local_setup.bash
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ros2 launch yolox_ros_py yolox_nano_torch_cpu_camera.launch.py # <- CPU (PyTorch)
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# ros2 launch yolox_ros_py yolox_nano_torch_gpu_camera.launch.py # <- GPU (PyTorch)
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# ros2 launch yolox_ros_py yolox_nano_onnx_camera.launch.py # <- ONNXRuntime
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# OpenVINO -------------------------------------
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# source /opt/intel/openvino_2021/bin/setupvars.sh
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# ros2 launch yolox_ros_py yolox_nano_openvino.launch.py
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```
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</details>
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<details>
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<summary>C++</summary>
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<br>
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<!--
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## YOLOX-ROS + ?
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<details>
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![](./images_for_readme/yolox_ydl.png)
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</details>
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</details> -->
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<br>
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‎yolox_ros_cpp/README.md‎

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## Usage
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### Requirements
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- ROS2 Foxy
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- ROS2 Humble
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- OpenCV 4.x
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- OpenVINO 2021.*
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- TensorRT 8.x *
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- ONNXRuntime *
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- Tensorflow Lite *
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<!-- - ONNXRuntime * -->
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<!-- - Tensorflow Lite * -->
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※ Either one of OpenVINO or TensorRT or ONNXRuntime or Tensorflow Lite is required.
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※ ONNXRuntime support CPU or CUDA execute provider.
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<!-- ※ ONNXRuntime support CPU or CUDA execute provider.
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※ Tensorflow Lite support XNNPACK Delegate only.
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※ Tensorflow Lite support float model and does not support integer model.
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※ Tensorflow Lite support float model and does not support integer model. -->
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※ Model convert script is not supported OpenVINO 2022.*
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※ Jetson + TensorRT docker support (Jetpack 4.6 r32.6.1). Tested with Jetson Nano 4GB.
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### Execute with docker
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<details>
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<summary>Execute with docker</summary>
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#### OpenVINO
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```bash
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/bin/bash
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```
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</details>
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### Clone YOLOX-ROS
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```bash
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git clone --recursive https://github.com/fateshelled/YOLOX-ROS -b dev_cpp
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git clone --recursive https://github.com/Ar-Ray-code/YOLOX-ROS -b humble
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```
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### Model Convert or Download
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#### OpenVINO
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```bash
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# Download onnx file and convert to IR format.
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./src/YOLOX-ROS/weights/openvino/install.bash yolox_nano
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./src/YOLOX-ROS/weights/openvino/install.bash yolox_tiny
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```
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#### TensorRT
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# Download onnx model and convert to TensorRT engine.
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# 1st arg is model name. 2nd is workspace size.
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./src/YOLOX-ROS/weights/tensorrt/convert.bash yolox_nano 16
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./src/YOLOX-ROS/weights/tensorrt/convert.bash yolox_tiny 16
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#### ONNXRuntime
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# Download onnx model
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./src/YOLOX-ROS/weights/onnx/download.bash yolox_nano
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./src/YOLOX-ROS/weights/onnx/download.bash yolox_tiny
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#### Tensorflow Lite
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<!-- #### Tensorflow Lite
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- `curl -s https://raw.githubusercontent.com/PINTO0309/PINTO_model_zoo/main/132_YOLOX/download_nano.sh | bash`
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- ONNX model copy to weight dir
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- `cp resouces_new/saved_model_yolox_nano_480x640/yolox_nano_480x640.onnx ./src/YOLOX-ROS/weights/onnx/`
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- `cp resouces_new/saved_model_yolox_tiny_480x640/yolox_tiny_480x640.onnx ./src/YOLOX-ROS/weights/onnx/`
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- `./src/YOLOX-ROS/weights/tensorrt/convert.bash yolox_nano_480x640`
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- `./src/YOLOX-ROS/weights/tensorrt/convert.bash yolox_tiny_480x640`
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- tflite model copy to weight dir
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- `cp resouces_new/saved_model_yolox_nano_480x640/model_float32.tflite ./src/YOLOX-ROS/weights/tflite/`
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- `cp resouces_new/saved_model_yolox_tiny_480x640/model_float32.tflite ./src/YOLOX-ROS/weights/tflite/` -->
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### build packages
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```bash
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# # If use openvino
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# source /opt/intel/openvino_2021/bin/setupvars.sh
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cd ~/ros2_ws
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source /opt/ros/foxy/setup.bash
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colcon build --symlink-install
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source ./install/setup.bash
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```
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#### build yolox_ros_cpp with tflite
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<!-- #### build yolox_ros_cpp with tflite
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``` -->
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### Build
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#### OpenVINO
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```bash
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# build with openvino
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source /opt/ros/humble/setup.bash
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source /opt/intel/openvino_2021/bin/setupvars.sh
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colcon build --cmake-args -DYOLOX_USE_OPENVINO=ON
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```
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#### TensorRT
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```bash
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# build with tensorrt
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source /opt/ros/humble/setup.bash
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colcon build --cmake-args -DYOLOX_USE_TENSORRT=ON
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##### build ros package with tflite
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<!-- ##### build ros package with tflite
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```
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``` -->
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### Run
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```
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If you want to show image with bounding box drawn, subscribe from host jetson or other PC.
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### Parameter
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#### OpenVINO example
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- `model_path`: ./install/yolox_ros_cpp/share/yolox_ros_cpp/weights/openvino/yolox_nano.xml
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- `model_path`: ./install/yolox_ros_cpp/share/yolox_ros_cpp/weights/openvino/yolox_tiny.xml
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- `model_path`: ./install/yolox_ros_cpp/share/yolox_ros_cpp/weights/tensorrt/yolox_tiny.trt
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- `model_path`: ./install/yolox_ros_cpp/share/yolox_ros_cpp/weights/onnx/yolox_tiny.onnx
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- `p6`: false
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- `class_labels_path`: ""
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- `num_classes`: 80

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