SuperPoint-LightGlue-TensorRT

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SuperPoint-LightGlue-TensorRT

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SuperPoint and LightGlue with TensorRT. Deploy with C++.

SuperPoint-LightGlue-TensorRT 최신버젼 다운로드

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# SuperPoint LightGlue TensorRT

SuperPoint and LightGlue with TensorRT. Deploy with C++. (Release tag v0.1.0 is available, the main branch has some bugs to fix.)

## Demo

- [x] TODO

## Baseline

| Image Size: 320 x 240 | RTX3080 |
|:-----------------------------------------:|:-------:|
| SuperPoint (250 points)Time | 0.95 MS | 
| SuperPoint (256 points)Time | 0.96 MS | 
| SuperGlue (256 dims)Time | 7.47 MS | 
| LightGlue (256 dims)Time | 2.54 MS |
| (SuperPoint Once+SuperGlue)FPS | 118 FPS | 
| (SuperPoint Once+SuperGlue)FPS | 286 FPS |

## Docker(Recommand)

```bash
docker pull yuefan2022/tensorrt-ubuntu22.04-cuda12.1:latest
docker run -it --env DISPLAY=$DISPLAY --volume /tmp/.X11-unix:/tmp/.X11-unix --privileged --runtime nvidia --gpus all --volume ${PWD}:/workspace --workdir /workspace --name tensorrt yuefan2022/tensorrt-ubuntu22.04-cuda12.1:latest /bin/bash
```

## Environment Required

* CUDA==12.1
* TensorRT==8.6.1.6
* OpenCV>=4.0
* EIGEN
* yaml-cpp

## Convert Model(Optional)

The converted model is already provided in the [weights](./weights) folder, if you are using the pretrained model
officially provided by [SuperPoint](https://github.com/magicleap/SuperGluePretrainedNetwork) and [LightGlue](https://github.com/cvg/LightGlue), you do not
need to go through this step.

```bash
python3 convert2onnx/convert_superpoint_to_onnx.py --weight_file superpoint_pth_file_path --output_dir superpoint_onnx_file_dir
python3 convert2onnx/convert_lightglue_to_onnx.py --weight_file lightglue_pth_file_path --output_dir lightglue_onnx_file_dir
# use polygraphy check onnx model(optional)
polygraphy run --trt --onnxrt onnx_file_name.onnx
```

## Build and Run

```bash
git clone https://github.com/yuefanhao/SuperPoint-LightGlue-TensorRT.git
cd SuperPoint-LightGlue-TensorRT
mkdir build
cd build
cmake ..
make
export CUDA_MODULE_LOADING=LAZY
# test on image pairs 100 times, the output image will be saved in the build dir
./superpoint_lightglue_image  ${PWD}/../config/config.yaml ${PWD}/../weights/ ${PWD}/../image/image0.png ${PWD}/../image/image1.png
# test on the folder with image sequence, output images will be saved in the param assigned dir
./superpoint_lightglue_sequence  ${PWD}/../config/config.yaml ${PWD}/../weights/ ${PWD}/../image/freiburg_sequence/ ${PWD}/../image/freiburg_sequence/match_images/
```

## Samples
```c++
#include "super_point.h"
#include "light_glue.h"

// read image
cv::Mat image0 = cv::imread("../image/image0.png", cv::IMREAD_GRAYSCALE);
cv::Mat image1 = cv::imread("../image/image1.png", cv::IMREAD_GRAYSCALE);

// read config from file
Configs configs("../config/config.yaml", "../weights/");

// create superpoint detector and superpoint-lightglue matcher
auto superpoint = std::make_shared<SuperPoint>(configs.superpoint_config);
auto superpoint_lightglue = std::make_shared<SuperPointLightGlue>(configs.superpoint_lightglue_config);

// build engine
superpoint->build();
superpoint_lightglue->build();

// infer superpoint
Eigen::Matrix<double, 258, Eigen::Dynamic> feature_points0, feature_points1;
Eigen::Matrix<double, 1, Eigen::Dynamic> feature_scores0, feature_scores1;
superpoint->infer(image0, feature_points0, feature_scores0);
superpoint->infer(image1, feature_points1, feature_scores1)

// infer lightglue
std::vector<cv::DMatch> lightglue_matches;
superpoint_lightglue->matching_points(feature_points0, feature_points1, lightglue_matches);
 
```

## Acknowledgements
[SuperPoint](https://github.com/magicleap/SuperPointPretrainedNetwork) [LightGlue](https://github.com/cvg/LightGlue) [TensorRT](https://github.com/NVIDIA/TensorRT)

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