# YOLOv8-ONNX-RKNN-HORIZON-TensorRT-Segmentation

**Repository Path**: djboy1021/YOLOv8-ONNX-RKNN-HORIZON-TensorRT-Segmentation

## Basic Information

- **Project Name**: YOLOv8-ONNX-RKNN-HORIZON-TensorRT-Segmentation
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 2
- **Created**: 2023-11-29
- **Last Updated**: 2024-08-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# YOLOv8-ONNX-RKNN-HORIZON-TensorRT-Segmentation
***Remark: This repo only support 1 batch size***
![!YOLOv8 ONNX RKNN Segmentation Picture](https://github.com/laitathei/YOLOv8-ONNX-RKNN-Segmentation/blob/master/doc/visual_image.jpg)
![!YOLOv8 ONNX RKNN Segmentation Video](https://github.com/laitathei/YOLOv8-ONNX-RKNN-Segmentation/blob/master/doc/result.gif)

Video source: https://www.youtube.com/watch?v=n3Dru5y3ROc&t=0s
```
git clone --recursive https://github.com/laitathei/YOLOv8-ONNX-RKNN-HORIZON-TensorRT-Segmentation.git
```
## 0. Environment Setting
```
# For onnx, rknn, horizon
torch: 1.10.1+cu102
torchvision: 0.11.2+cu102
onnx: 1.10.0
onnxruntime: 1.10.0

# For tensorrt
torch: 1.11.0+cu113
torchvision: 0.12.0+cu113
TensorRT: 8.6.1
```

## 1. Yolov8 Prerequisite
```
pip3 install ultralytics==8.0.147
pip3 install numpy==1.23.5
```

## 2. Convert Pytorch model to ONNX
Remember to change the variable to your setting.
```
python3 pytorch2onnx.py
```

## 3. RKNN Prerequisite
Install the wheel according to your python version
```
cd rknn-toolkit2/packages
pip3 install rknn_toolkit2-1.5.0+1fa95b5c-cpxx-cpxx-linux_x86_64.whl
```

## 4. Convert ONNX model to RKNN
Remember to change the variable to your setting
To improve perfermance, you can change ```./config/yolov8x-seg-xxx-xxx.quantization.cfg``` layer type.
Please follow [official document](https://github.com/rockchip-linux/rknn-toolkit2/blob/master/doc/Rockchip_User_Guide_RKNN_Toolkit2_EN-1.5.0.pdf) hybrid quatization part and reference to [example program](https://github.com/rockchip-linux/rknn-toolkit2/tree/master/examples/functions/hybrid_quant) to modify your codes.
```
python3 onnx2rknn_step1.py
python3 onnx2rknn_step2.py
```

## 5. RKNN-Lite Inference
```
python3 rknn_lite_inference.py
```

## 6. Horizon Prerequisite
```
wget -c ftp://xj3ftp@vrftp.horizon.ai/ai_toolchain/ai_toolchain.tar.gz --ftp-password=xj3ftp@123$%
tar -xvf ai_toolchain.tar.gz
cd ai_toolchain/
pip3 install h*
```

## 7. Convert ONNX model to Horizon
Remember to change the variable to your setting include ```yolov8seg_config.yaml``` and get onnx file from ```python3 pytorch2onnx.py``` and replace
```
model.export(format="onnx", imgsz=[input_height,input_width], opset=11)
```

```
sh 01_check.sh
sh 02_preprocess.sh
sh 03_build.sh
```

## 8. Horizon Inference
```
python3 horizion_simulator_inference.py
python3 horizion_onboard_inference.py
```

## 9. Onnx Runtime Inference
```
python3 onnxruntime_inference.py
```

## 10. Convert ONNX model to TensorRT 
Remember to change the variable to your setting
```
python3 onnx2trt.py
```

## 11. TensorRT Inference
```
python3 tensorrt_inference.py
```

## 12. Blob Inference
Convert model from onnx to blob format via ```https://blobconverter.luxonis.com/```
```
python3 blob_inference.py
```

## Reference
```
https://blog.csdn.net/magic_ll/article/details/131944207
https://blog.csdn.net/weixin_45377629/article/details/124582404#t18
https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation
```