【毕业设计】使用TensorRT+Cpp部署SuperPoint+SuperGlue算法模型源码+运行环境说明.zip

# SuperPoint SuperGlue TensorRT SuperPoint and SuperGlue with TensorRT. Deploy with C++. ## Demo <img src="image/superpoint_superglue_tensorrt.gif" width = "640" height = "240" alt="match_image" border="10" /> * This demo was tested on the Quadro P620 GPU. ## Baseline | Image Size: 320 x 240 | RTX3080 | Quadro P620 | |:----------------------:|:-------:|:-----------:| | SuperPoint (250 points)| 1.04 MS | 13.61 MS | | SuperPoint (257 points)| 1.03 MS | 13.32 MS | | SuperGlue (256 dims) | 7.47 MS | 58.83 MS | | FPS (superpoint one time + superglue one time) | $\approx$ 117 FPS | $\approx$ 13 FPS | ## Docker(Recommand) ```bash docker pull yuefan2022/tensorrt-ubuntu20.04-cuda11.6: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-ubuntu20.04-cuda11.6:latest /bin/bash ``` ## Environment required * CUDA==11.6 * TensorRT==8.4.1.5 * 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 and SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork), you do not need to go through this step. ```bash python convert2onnx/convert_superpoint_to_onnx.py --weight_file superpoint_pth_file_path --output_dir superpoint_onnx_file_dir python convert2onnx/convert_superglue_to_onnx.py --weight_file superglue_pth_file_path --output_dir superglue_onnx_file_dir ``` ## Build and run ```bash git clone https://github.com/yuefanhao/SuperPoint-SuperGlue-TensorRT.git cd SuperPoint-SuperGlue-TensorRT mkdir build cd build cmake .. make # test on image pairs 100 times, the output image will be saved in the build dir ./superpointglue_image ../config/config.yaml ../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 ./superpointglue_sequence ../config/config.yaml ../weights/ ${PWD}/../image/freiburg_sequence/ ${PWD}/../image/freiburg_sequence/match_images/ ``` The default image size param is 320x240, if you need to modify the image size in the config file, you should delete the old .engine file in the weights dir. ## Samples ```c++ #include "super_point.h" #include "super_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 superglue matcher auto superpoint = std::make_shared<SuperPoint>(configs.superpoint_config); auto superglue = std::make_shared<SuperGlue>(configs.superglue_config); // build engine superpoint->build(); superglue->build(); // infer superpoint Eigen::Matrix<double, 259, Eigen::Dynamic> feature_points0, feature_points1; superpoint->infer(image0, feature_points0); superpoint->infer(image1, feature_points1) // infer superglue std::vector<cv::DMatch> superglue_matches; superglue->matching_points(feature_points0, feature_points1, superglue_matches); ``` ## Acknowledgements [SuperPoint](https://github.com/magicleap/SuperPointPretrainedNetwork) [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork) [TensorRT](https://github.com/NVIDIA/TensorRT) [AirVO](https://github.com/xukuanHIT/AirVO)