├── .gitignore ├── .gitmodules ├── LICENSE ├── README.md ├── docs ├── ROB530_Final_Project_Report.pdf └── install_yolactros_yolact.md ├── environment.yml ├── figures ├── demo_keyframe.gif ├── monorec_slam.png └── teaser.png ├── results ├── kitti │ ├── 20 │ │ ├── KeyFrameTrajectory_s20_inpaint.txt │ │ ├── KeyFrameTrajectory_s20_monorec.txt │ │ └── KeyFrameTrajectory_s20_orb.txt │ ├── 00 │ │ ├── KeyFrameTrajectory_dynaslam.txt │ │ ├── KeyFrameTrajectory_monorec.txt │ │ ├── KeyFrameTrajectory_orb.txt │ │ └── pose.txt │ ├── 04 │ │ ├── KeyFrameTrajectory_s04_dynaslam.txt │ │ ├── KeyFrameTrajectory_s04_monorec.txt │ │ ├── KeyFrameTrajectory_s04_orb.txt │ │ └── pose.txt │ ├── 05 │ │ ├── KeyFrameTrajectory_dynaslam.txt │ │ ├── KeyFrameTrajectory_monorec.txt │ │ ├── KeyFrameTrajectory_orb.txt │ │ └── pose.txt │ └── 07 │ │ ├── KeyFrameTrajectory_s07_dynaslam.txt │ │ ├── KeyFrameTrajectory_s07_monorec.txt │ │ ├── KeyFrameTrajectory_s07_orb.txt │ │ └── pose.txt └── tum │ ├── KeyFrameTrajectory_baseline_tum.txt │ └── KeyFrameTrajectory_maskrcnn_mono_tum.txt └── src ├── generate_mask.py ├── kitti_poses_and_timestamps_to_trajectory.py ├── monorec_ros.py └── process_image.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Prerequisites 2 | *.d 3 | 4 | # Compiled Object files 5 | *.slo 6 | *.lo 7 | *.o 8 | *.obj 9 | 10 | # Precompiled Headers 11 | *.gch 12 | *.pch 13 | 14 | # Compiled Dynamic libraries 15 | *.so 16 | *.dylib 17 | *.dll 18 | 19 | # Fortran module files 20 | *.mod 21 | *.smod 22 | 23 | # Compiled Static libraries 24 | *.lai 25 | *.la 26 | *.a 27 | *.lib 28 | 29 | # Executables 30 | *.exe 31 | *.out 32 | *.app 33 | 34 | # Custom 35 | data/ 36 | __pycache__/ 37 | .vscode/ -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "modules/ORB_SLAM3"] 2 | path = modules/ORB_SLAM3 3 | url = https://github.com/UZ-SLAMLab/ORB_SLAM3.git 4 | branch = c++14_comp 5 | [submodule "modules/MonoRec"] 6 | path = modules/MonoRec 7 | url = git@github.com:silvery107/MonoRec.git 8 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Yulun Zhuang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MonoRec SLAM 2 | In this project, our method: 3 | 4 | - Alleviate the tracking algorithm from using matches that belong to dynamic objects, in most cases achieving a higher accuracy in evaluation. 5 | 6 | - Alleviate the mapping algorithm from including moving objects as part of the 3D map, obtaining a more static map of scenes. 7 | 8 | - Achieve a good balance between real-time capability and dynamic object masking. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | ## Benchmarks 18 | 19 | Absolute trajectory RMSE (m) for ORB-SLAM3, DynaSLAM and MonoRecSLAM on the KITTI dataset 20 | 21 | | Sequence | ORB-SLAM | DynaSLAM | MonoRecSLAM | 22 | |:--------:|:--------:|:--------:|:-----------:| 23 | | 00 | **5.33** | 7.55 | 6.71 | 24 | | 04 | 1.62 | **0.97** | 1.39 | 25 | | 05 | 4.85 | **4.60** | **4.60** | 26 | | 07 | 2.26 | 2.36 | **2.09** | 27 | 28 | ## Installation 29 | 30 |
31 | Click to view details 32 | 1. Download this repo and initialize all submodules 33 | 34 | ```bash 35 | git clone git@github.com:silvery107/monorec-slam-ros.git 36 | git submodule update --init 37 | ``` 38 | Or use `--recurse` option to clone submodules at the same time. 39 | 40 | 2. Create a conda environment 41 | 42 | `conda env create -f environment.yml` 43 | 44 | 3. Download pretrained MonoRec model and install MonoRec as a submodule 45 | 46 | ``` 47 | cd modules/MonoRec && ./download_models.sh 48 | pip install -e . 49 | ``` 50 | 51 | 4. Download the KITTI odometry dataset 52 | 53 | To setup KITTI Odometry, download the color images and calibration files from the 54 | [official website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) (around 65 GB). Then unzip the color images and calibration files into `data` folder. 55 | 56 | For evaluation, MonoRec uses the poses estimated by [Deep Virtual Stereo Odometry (DVSO)](https://vision.in.tum.de/research/vslam/dvso). They can be downloaded from [here](https://vision.in.tum.de/_media/research/monorec/poses_dvso.zip) and should be placed under ``data/{kitti_path}/poses_dso``. This folder structure is ensured when unpacking the zip file in the ``{kitti_path}`` directory. 57 | 58 | 5. Install `evo` for SLAM trajectory evaluation 59 | 60 | `pip install evo --upgrade --no-binary evo` 61 | 62 | 6. Install ORB-SLAM3 according to its [instructions](https://github.com/UZ-SLAMLab/ORB_SLAM3/tree/c++14_comp) and remember to install it on the `c++14_comp` branch. Install its ROS interface as well. 63 | 64 | 7. The dataset structure should be like this 65 | 66 | ``` 67 | data 68 | ├── kitti 69 | │ ├── poses 70 | │ ├── poses_dvso 71 | │ └── sequences 72 | | └── ... 73 | └── tum 74 | ├── rgbd_dataset_freiburg3_walking_xyz 75 | └── ... 76 | ``` 77 |
78 | 79 | ## Quick Start 80 | 81 | Here we take the sequence 07 of KITTI dataset as an example. 82 | 83 | #### Run with ROS 84 | The combined implementation of MonoRec SLAM is done by pumping masked image onto the `/camera/image_raw` ROS topic and utilizing the ROS interface from ORB-SLAM3 to perform SLAM. 85 | 86 | ```bash 87 | # terminal 1 88 | roscore 89 | # terminal 2 90 | rosrun ORB_SLAM3 Mono Vocabulary/ORBvoc.txt Examples/Monocular/KITTI04-12.yaml 91 | # terminal 3 92 | python src/monorec_ros.py --dataset kitti --seq 7 93 | ``` 94 | 95 | 96 | or ... 97 | 98 | #### Dataset Preprocessing 99 | 1. Run MonoRec model to get binary masks of moving objects 100 | 101 | `python src/generate_mask.py --dataset kitti --seq 7` 102 | 103 | 2. Prepare masked images from masks 104 | 105 | `python src/process_image.py --dataset kitti --seq 7` 106 | 107 | 108 | 109 | #### SLAM with ORB-SLAM3 110 | 1. Run SLAM on precessed dataset, e.g. 111 | ```bash 112 | cd modules/ORB_SLAM3 113 | ./Examples/Monocular/mono_kitti ./Vocabulary/ORBvoc.txt ./Examples/Monocular/{config}.yaml ../../data/kitti/squences/07 114 | ``` 115 | 2. Copy the resulted `KeyFrameTrajectory.txt` onto e.g. `results/kitti/07/` 116 | 117 | 118 | ## Trajectory Evaluation 119 | 1. Convert KITTI ground truth poses into TUM format for monocular KITTI evaluation purpose. 120 | ```bash 121 | python src/kitti_poses_and_timestamps_to_trajectory.py \ 122 | data/kitti/poses/07.txt \ 123 | data/kitti/sequences/07/times.txt \ 124 | results/kitti/07/pose.txt 125 | ``` 126 | Note that we need TUM format here since trajectories from monocular SLAM on KITTI can only be saved in TUM format. 127 | 128 | 2. Plot multiple trajectories with ground truth 129 | ```bash 130 | evo_traj tum {traj1}.txt {traj2}.txt --ref pose.txt -s --align_origin -p --plot_mode xz --save_plot plot_name 131 | ``` 132 | Note that `-as` stands for `--align --correct_scale` 133 | 134 | 3. Compute absolute pose error on trajectories 135 | ```bash 136 | evo_ape tum pose.txt {traj}.txt -as -p --plot_mode xz --save_plot plot_name 137 | ``` 138 | 139 | 4. Save plots 140 | ```bash 141 | evo_res results/*.zip -p --save_table results/table.csv 142 | ``` 143 | 144 | 145 | ## Dependencies 146 | - Ubuntu 20.04 with ROS Noetic 147 | - Python >= 3.7 148 | - OpenCV >= 4.2 149 | - [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3) 150 | - [MonoRec](https://github.com/silvery107/MonoRec) 151 | - [evo](https://github.com/MichaelGrupp/evo) 152 | 153 | ## Acknowledgement 154 | 155 | Team 21 of ROB 530 Mobile Robotics W23: [Yulun Zhuang](https://github.com/silvery107), [Hao Liu](https://github.com/wdliu356), [Xingqiao Zhu](https://github.com/xqiao12), [Vaishnavi Harikumar](https://github.com/ivaishi) and [Janani Peri](https://github.com/perij-codes). 156 | -------------------------------------------------------------------------------- /docs/ROB530_Final_Project_Report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/silvery107/monorec-slam/fa64379229f65dee0ec3f6722e464798db702cee/docs/ROB530_Final_Project_Report.pdf -------------------------------------------------------------------------------- /docs/install_yolactros_yolact.md: -------------------------------------------------------------------------------- 1 | # Installation and usage of yolact_ros and yolact 2 | 3 | - Yolact means "**Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s". It is a simple, fully convolutional model for real-time instance segmentation. The paper of yolact is [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689). 4 | - Yolact_ros is a ROS wrapper for Yolact. Yolact uses Python 3. If you use a ROS version built with Python 2, additional steps are necessary to run the node. 5 | 6 | ## Installation of yolact 7 | - Clone this repository and enter it: 8 | ```Shell 9 | git clone https://github.com/dbolya/yolact.git 10 | cd yolact 11 | ``` 12 | - Set up the environment using one of the following methods: 13 | - Using [Anaconda](https://www.anaconda.com/distribution/) 14 | - Run `conda env create -f environment.yml` 15 | - Manually with pip 16 | - Set up a Python3 environment (e.g., using virtenv). 17 | - Install [Pytorch](http://pytorch.org/) 1.0.1 (or higher) and TorchVision. 18 | - Install some other packages: 19 | ```Shell 20 | # Cython needs to be installed before pycocotools 21 | pip install cython 22 | pip install opencv-python pillow pycocotools matplotlib 23 | ``` 24 | - Evaluation 25 | Here are some YOLACT models (released on April 5th, 2019): 26 | 27 | | Image Size | Backbone | FPS | mAP | Weights | | 28 | |:----------:|:-------------:|:----:|:----:|----------------------------------------------------------------------------------------------------------------------|--------| 29 | | 550 | Resnet50-FPN | 42.5 | 28.2 | [yolact_resnet50_54_800000.pth](https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw) | 30 | | 550 | Darknet53-FPN | 40.0 | 28.7 | [yolact_darknet53_54_800000.pth](https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw) 31 | | 550 | Resnet101-FPN | 33.5 | 29.8 | [yolact_base_54_800000.pth](https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg) 32 | | 700 | Resnet101-FPN | 23.6 | 31.2 | [yolact_im700_54_800000.pth](https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw) 33 | 34 | To process an image or a vedio, put the corresponding weights file in the `./weights` directory and run one of the following commands. 35 | 36 | ## Installation of yolact_ros 37 | 38 | ### Build cv_bridge 39 | - Install the packages rospkg and empy in the virtual environment. 40 | ```Shell 41 | sudo apt-get install python-rospkg 42 | pip install empy 43 | ``` 44 | - You need to build the cv_bridge module of ROS with Python 3. I recommend using a workspace separate from other ROS packages. Clone the package to the workspace. You might need to adjust some of the following instructions depending on your Python installation. 45 | - Create a folder called `cv_bridge_folder`. 46 | - In `cv_bridge_folder`, create a folder `catkin_ws`. 47 | - In `catkin_ws`, create a folder `src`. 48 | - Clone the package in `src`. 49 | ```Shell 50 | git clone -b noetic https://github.com/ros-perception/vision_opencv.git 51 | ``` 52 | - In `catkin_ws`, use catkin_make, compile with 53 | ```Shell 54 | catkin_make -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so 55 | ``` 56 | - If you meet the error: 57 | ```Shell 58 | Found package configuration file: 59 | 60 | /lib/x86_64-linux-gnu/cmake/boost_python-1.71.0/boost_python-config.cmake 61 | 62 | but it set boost_python_FOUND to FALSE so package "boost_python" is 63 | considered to be NOT FOUND. Reason given by package: 64 | 65 | No suitable build variant has been found. 66 | 67 | The following variants have been tried and rejected: 68 | 69 | * libboost_python38.so.1.71.0 (3.8, Boost_PYTHON_VERSION=3.7) 70 | 71 | * libboost_python38.a (3.8, Boost_PYTHON_VERSION=3.7) 72 | ``` 73 | You can edit `vision_opencv/cv_bridge/CMakeLists.txt`, Change line No. 11 from `find_package(Boost REQUIRED python36)` to `find_package(Boost REQUIRED python)`. 74 | 75 | - If you meet the error: 76 | ```Shell 77 | error: return-statement with no value, in function returning ‘void*’ [-fpermissive] 78 | ``` 79 | - You can first run these codes to check the position of the folder `python3`. 80 | ```Shell 81 | conda install setuptools 82 | pip install -U rosdep rosinstall_generator wstool rosinstall six vcstools 83 | whereis python3 84 | ``` 85 | - And then edit the catkin_make command by: `catkin_make -DCMAKE_BUILD_TYPE=Release -DPYTHON_EXECUTABLE=/home/****/anaconda3/envs/****/bin/python3` 86 | 87 | - Add the following lines to the postactivate script of your virtual environment (Change the paths according to your workspace path, virtual environment and Python installation): 88 | ```Shell 89 | source $HOME/ros_python3/devel/setup.bash 90 | export OLD_PYTHONPATH="$PYTHONPATH" 91 | export PYTHONPATH="$HOME/.virtualenvs/yolact/lib/python3.6/site-packages:$PYTHONPATH" 92 | ``` 93 | - Add the following lines to the postdeactivate script of your virtual environment: 94 | ```Shell 95 | export PYTHONPATH="$OLD_PYTHONPATH" 96 | ``` 97 | 98 | ### Build yolact_ros 99 | - Create a workspace 100 | - Create a folder `yolact_ros_folder` 101 | - In `yolact_ros_folder`, create a folder `src` 102 | - In `src`, download [yolact_ros](https://github.com/Eruvae/yolact_ros.git) and the related packages [yolact_ros_msgs](https://github.com/Eruvae/yolact_ros_msgs) 103 | - In `yolact_ros_folder`, compile with the command: 104 | ```Shell 105 | ctakin_make 106 | ``` 107 | - Copy the folder `yolact` to the folder `****/yolact_ros_folder/yolact_ros/scripts/yolact`. 108 | 109 | ## Usage 110 | 111 | ### Wrap yolact to a ros node 112 | - Run `roscore` 113 | - Wrap yolact to a ros node 114 | The default model is [yolact_base_54_800000.pth](https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing). 115 | In `yolact_ros_folder`, you can run yolact using rosrun: 116 | ```Shell 117 | rosrun yolact_ros yolact_ros 118 | ``` 119 | 120 | If you want to change the default parameters, e.g. the model or image topic, you can specify them: 121 | ```Shell 122 | rosrun yolact_ros yolact_ros _model_path:="$(rospack find yolact_ros)/scripts/yolact/weights/yolact_base_54_800000.pth" _image_topic:="/camera/color/image_raw" 123 | ``` 124 | 125 | Alternatively, you can add the node to a launch file. An example can be found in the launch folder. You can run that launch file using: 126 | ```Shell 127 | roslaunch yolact_ros yolact_ros.launch 128 | ``` 129 | 130 | - You can check ros topics and ros nodes. 131 | ```Shell 132 | rostopic list 133 | rosnode list 134 | ``` 135 | 136 | ### Evaluate images and videos 137 | You can run these commands in `****/yolact_ros_folder/yolact_ros/scripts/yolact`. 138 | - Images 139 | ```Shell 140 | # Display qualitative results on the specified image. 141 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png 142 | 143 | # Process an image and save it to another file. 144 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png 145 | 146 | # Process a whole folder of images. 147 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder 148 | ``` 149 | 150 | - Video 151 | ```Shell 152 | # Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance. 153 | # If you want, use "--display_fps" to draw the FPS directly on the frame. 154 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4 155 | 156 | # Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0. 157 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0 158 | 159 | # Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast! 160 | python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4 161 | ``` 162 | 163 | As you can tell, `eval.py` can do a ton of stuff. Run the `--help` command to see everything it can do. 164 | ```Shell 165 | python eval.py --help 166 | ``` 167 | 168 | 169 | 170 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: monorec 2 | channels: 3 | - pytorch 4 | - conda-forge 5 | - anaconda 6 | dependencies: 7 | - python=3.7 8 | - pytorch=1.5.0 9 | - torchvision 10 | - jupyter 11 | - tensorboard 12 | - opencv=4.2.0 13 | - scipy 14 | - kornia 15 | - scikit-image 16 | - tqdm 17 | - tensorboardx 18 | - pip 19 | - pip: 20 | - pykitti 21 | - colour_demosaicing -------------------------------------------------------------------------------- /figures/demo_keyframe.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/silvery107/monorec-slam/fa64379229f65dee0ec3f6722e464798db702cee/figures/demo_keyframe.gif -------------------------------------------------------------------------------- /figures/monorec_slam.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/silvery107/monorec-slam/fa64379229f65dee0ec3f6722e464798db702cee/figures/monorec_slam.png -------------------------------------------------------------------------------- /figures/teaser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/silvery107/monorec-slam/fa64379229f65dee0ec3f6722e464798db702cee/figures/teaser.png -------------------------------------------------------------------------------- /results/kitti/04/KeyFrameTrajectory_s04_dynaslam.txt: -------------------------------------------------------------------------------- 1 | 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000 2 | 0.104128 -0.0000372 -0.0007318 0.0468676 -0.0008290 -0.0001707 0.0004184 0.9999995 3 | 0.208254 -0.0002913 -0.0014214 0.0934206 -0.0008367 -0.0003395 0.0009320 0.9999992 4 | 0.312504 -0.0003359 -0.0020576 0.1402327 -0.0003376 -0.0004224 0.0016378 0.9999985 5 | 0.416625 -0.0005230 -0.0028978 0.1866786 -0.0004748 -0.0004060 0.0018848 0.9999980 6 | 0.520755 -0.0004905 -0.0036007 0.2341496 -0.0008645 -0.0004842 0.0020968 0.9999973 7 | 0.625004 -0.0009100 -0.0042480 0.2810230 -0.0014752 -0.0002368 0.0027468 0.9999951 8 | 0.729128 -0.0014078 -0.0046686 0.3281896 -0.0015658 -0.0002830 0.0024906 0.9999956 9 | 0.833252 -0.0015420 -0.0055133 0.3756083 -0.0007731 -0.0004677 0.0020785 0.9999974 10 | 0.937506 -0.0018124 -0.0061718 0.4226567 0.0003453 -0.0005850 0.0008168 0.9999995 11 | 1.041630 -0.0023448 -0.0069330 0.4696838 0.0008467 -0.0004729 -0.0008934 0.9999991 12 | 1.145875 -0.0026983 -0.0078027 0.5175492 0.0008580 -0.0004715 -0.0021340 0.9999973 13 | 1.250003 -0.0030108 -0.0086074 0.5654567 0.0004370 -0.0004530 -0.0026731 0.9999962 14 | 1.354255 -0.0031054 -0.0094146 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42.392610 -0.4180250 -0.1624568 7.3585801 0.0066754 -0.0811325 -0.0286235 0.9962699 190 | 42.599910 -0.4276572 -0.1635134 7.4151592 0.0070078 -0.0814118 -0.0286899 0.9962429 191 | 42.807190 -0.4372467 -0.1646642 7.4728289 0.0075808 -0.0810454 -0.0288024 0.9962654 192 | 43.014410 -0.4467879 -0.1657975 7.5307345 0.0089938 -0.0807424 -0.0281844 0.9962959 193 | 43.221780 -0.4564223 -0.1671170 7.5889707 0.0057659 -0.0794476 -0.0281165 0.9964258 194 | 43.429120 -0.4658385 -0.1684244 7.6479721 0.0060125 -0.0783011 -0.0266434 0.9965556 195 | 43.636350 -0.4751007 -0.1698352 7.7069921 0.0071738 -0.0772067 -0.0263048 0.9966422 196 | 43.843610 -0.4850684 -0.1708852 7.7675424 0.0083533 -0.0760325 -0.0260817 0.9967292 197 | 44.050880 -0.4952335 -0.1721611 7.8310742 0.0083298 -0.0752498 -0.0260290 0.9967901 198 | 44.361680 -0.5090038 -0.1740943 7.9278669 0.0067979 -0.0753906 -0.0259968 0.9967920 199 | 44.568900 -0.5194440 -0.1752310 7.9949307 0.0069973 -0.0767372 -0.0253486 0.9967045 200 | 44.879730 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39.801740 -0.2198095 -0.1189296 6.1404605 0.0102602 -0.0421460 -0.0229263 0.9987957 169 | 40.008930 -0.2236998 -0.1202106 6.1865482 0.0100436 -0.0430827 -0.0226133 0.9987651 170 | 40.216170 -0.2277508 -0.1216007 6.2334208 0.0100720 -0.0440130 -0.0222044 0.9987334 171 | 40.423420 -0.2322301 -0.1228340 6.2808838 0.0110676 -0.0448635 -0.0219927 0.9986897 172 | 40.630770 -0.2369561 -0.1241596 6.3300662 0.0111361 -0.0458440 -0.0222818 0.9986380 173 | 40.837980 -0.2419552 -0.1254133 6.3802147 0.0114226 -0.0471389 -0.0225652 0.9985681 174 | 41.045220 -0.2473934 -0.1267980 6.4319577 0.0103340 -0.0485625 -0.0221101 0.9985219 175 | 41.252440 -0.2531035 -0.1283024 6.4845285 0.0098353 -0.0503101 -0.0224649 0.9984325 176 | 41.459800 -0.2588211 -0.1293776 6.5368609 0.0093551 -0.0519919 -0.0223029 0.9983546 177 | 41.667150 -0.2648079 -0.1307855 6.5883064 0.0094355 -0.0532043 -0.0224299 0.9982871 178 | 41.874380 -0.2707219 -0.1321206 6.6399884 0.0092083 -0.0544129 -0.0222682 0.9982277 179 | 42.081620 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-0.6247141 -0.0297291 0.1632442 -0.0857857 0.4428087 0.2230110 0.8641917 103 | 1311870542.522398 -0.6233464 -0.0347833 0.1685896 -0.0825517 0.4402850 0.2325002 0.8632948 104 | 1311870543.186537 -0.5928224 -0.0304237 0.1413333 -0.0884178 0.4060376 0.2213592 0.8822221 105 | 1311870546.322664 -0.4081835 0.0750898 -0.0838098 -0.1187753 0.2953975 0.1891490 0.9289001 106 | 1311870548.290832 -0.2709469 0.0909901 -0.1430949 -0.1151638 0.1951730 0.1244424 0.9660015 107 | 1311870548.823040 -0.2604675 0.1061855 -0.1638966 -0.1183704 0.1873182 0.1184377 0.9679219 108 | 1311870548.954848 -0.2642961 0.1072601 -0.1621474 -0.1186732 0.1766891 0.1204965 0.9696279 109 | 1311870552.823129 -0.3554032 0.0729211 -0.1064972 -0.1243228 0.2094079 0.1205588 0.9623709 110 | 1311870561.129749 -0.3182021 0.0973300 -0.1342899 -0.0917733 0.2254490 0.1185741 0.9626477 111 | 1311870562.031321 -0.3295277 0.0879910 -0.1292837 -0.0747853 0.2188225 0.1128013 0.9663332 112 | 1311870562.498224 -0.3354749 0.0814508 -0.1257163 -0.0677055 0.2293706 0.1220293 0.9632829 113 | 1311870564.264917 -0.3609171 0.0778040 -0.1037700 -0.0738498 0.2333675 0.1172300 0.9624671 114 | 1311870567.871833 -0.3596323 0.0790999 -0.1148150 -0.0714409 0.2380445 0.1201256 0.9611456 115 | -------------------------------------------------------------------------------- /src/generate_mask.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import time 3 | from pathlib import Path 4 | 5 | import matplotlib.pyplot as plt 6 | import torch 7 | 8 | from data_loader.tum_rgbd_dataset import TUMRGBDDataset 9 | from data_loader.kitti_odometry_dataset import KittiOdometryDataset 10 | from model.monorec.monorec_model import MonoRecModel 11 | from utils import unsqueezer, map_fn, to 12 | from data_loader.data_loaders import BaseDataLoader 13 | import shutil 14 | import cv2 15 | import os 16 | import numpy as np 17 | import argparse 18 | 19 | target_image_size = (256, 512) 20 | 21 | device = "cuda:0" if torch.cuda.is_available() else "cpu" 22 | 23 | if __name__ == "__main__": 24 | parser = argparse.ArgumentParser() 25 | parser.add_argument("--dataset", type=str, default="kitti", required=True, choices=["kitti", "tum"]) 26 | parser.add_argument("--seq", type=int, default=7, required=False) 27 | parser.add_argument("--seq_name", type=str, default="rgbd_dataset_freiburg3_walking_halfsphere", required=False) 28 | args = parser.parse_args() 29 | 30 | if args.dataset == "kitti": 31 | dataset = KittiOdometryDataset("data/kitti", sequences=[f"{args.seq:02d}"], target_image_size=target_image_size, frame_count=2, 32 | depth_folder="image_depth_annotated", lidar_depth=False, use_dso_poses=True, 33 | use_index_mask=None) 34 | elif args.dataset == "tum": 35 | dataset = TUMRGBDDataset(dataset_dir=f"data/tum/{args.seq_name}", frame_count=2, target_image_size=target_image_size) 36 | else: 37 | raise NotImplementedError(f"Dataset type {args.dataset} is not supported for now") 38 | 39 | data_loader = BaseDataLoader(dataset, batch_size=1, shuffle=False, validation_split=0, num_workers=8) 40 | 41 | checkpoint_location = Path("modules/MonoRec/saved/checkpoints/monorec_depth_ref.pth") 42 | inv_depth_min_max = [0.33, 0.0025] 43 | 44 | print("Initializing model...") 45 | monorec_model = MonoRecModel(checkpoint_location=checkpoint_location, 46 | inv_depth_min_max=inv_depth_min_max, 47 | pretrain_mode=2) 48 | 49 | monorec_model.to(device) 50 | monorec_model.eval() 51 | 52 | if args.dataset == "kitti": 53 | output_path = f"data/{args.dataset}/sequences/{args.seq:02d}_mask" 54 | elif args.dataset == "tum": 55 | output_path = f"data/{args.dataset}/{args.seq_name}/mask" 56 | 57 | print(f"Resulting masks will be saved to {output_path}") 58 | confirm = input("Are you sure? (y/n)") 59 | if (confirm.lower().find("y") != -1 or confirm.lower().find("yes") != -1): 60 | print("Confirmed") 61 | else: 62 | sys.exit("User rejected") 63 | shutil.rmtree(output_path, ignore_errors=True) 64 | os.makedirs(output_path, exist_ok=True) 65 | 66 | dilatation_size = 8 67 | kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*dilatation_size+1, 2*dilatation_size+1), (dilatation_size, dilatation_size)) 68 | 69 | inference_times = [] 70 | counter = 0 71 | print(f"Start processing data...") 72 | for batch_idx, (data, target) in enumerate(data_loader): 73 | counter += 1 74 | data = to(data, device) 75 | s = time.time() 76 | with torch.no_grad(): 77 | output_dict = monorec_model(data) 78 | e = time.time() 79 | 80 | mask = output_dict["cv_mask"][0, 0].detach().cpu().numpy() # (H, W) 81 | mask_to_save = mask 82 | # mask_to_save = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel=kernel, iterations=6) 83 | mask_to_save = cv2.dilate(mask_to_save, kernel, iterations=6) 84 | mask_to_save = cv2.erode(mask_to_save, kernel, iterations=4) 85 | mask_to_save = (255*(mask_to_save - np.min(mask_to_save))/np.ptp(mask_to_save)).astype(np.uint8) 86 | _, mask_to_save = cv2.threshold(mask_to_save, 50, 255, cv2.THRESH_BINARY) 87 | 88 | plt.imsave(f"{output_path}/{counter:06d}.png", mask_to_save, cmap='gray') 89 | # print(f"mask: {mask.shape}, {mask.dtype}") 90 | # print(f"Inference took {e - s}s") 91 | inference_times.append(e-s) 92 | 93 | inference_times = np.asarray(inference_times) 94 | print(f"Processed {counter} images") 95 | print(f"Average inference time: {inference_times.mean():.4f} s") -------------------------------------------------------------------------------- /src/kitti_poses_and_timestamps_to_trajectory.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | from evo.core.trajectory import PoseTrajectory3D 5 | from evo.tools import file_interface 6 | import numpy as np 7 | 8 | DESC = "Combine KITTI poses and timestamps files to a TUM trajectory file" 9 | 10 | 11 | def kitti_poses_and_timestamps_to_trajectory(poses_file, timestamp_file): 12 | pose_path = file_interface.read_kitti_poses_file(poses_file) 13 | raw_timestamps_mat = file_interface.csv_read_matrix(timestamp_file) 14 | error_msg = ("timestamp file must have one column of timestamps and same number of rows as the KITTI poses file") 15 | if len(raw_timestamps_mat) > 0 and len(raw_timestamps_mat[0]) != 1 or len(raw_timestamps_mat) != pose_path.num_poses: 16 | raise file_interface.FileInterfaceException(error_msg) 17 | try: 18 | timestamps_mat = np.array(raw_timestamps_mat).astype(float) 19 | except ValueError: 20 | raise file_interface.FileInterfaceException(error_msg) 21 | return PoseTrajectory3D(poses_se3=pose_path.poses_se3, timestamps=timestamps_mat) 22 | 23 | 24 | if __name__ == "__main__": 25 | import argparse 26 | parser = argparse.ArgumentParser(description=DESC) 27 | parser.add_argument("poses_file", help="pose path file in KITTI format") 28 | parser.add_argument( 29 | "timestamp_file", help="KITTI timestamp file of the poses") 30 | parser.add_argument( 31 | "trajectory_out", help="output file path for trajectory in TUM format") 32 | args = parser.parse_args() 33 | trajectory = kitti_poses_and_timestamps_to_trajectory( 34 | args.poses_file, args.timestamp_file) 35 | file_interface.write_tum_trajectory_file(args.trajectory_out, trajectory) 36 | -------------------------------------------------------------------------------- /src/monorec_ros.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import cv2 3 | import numpy as np 4 | from pathlib import Path 5 | 6 | import rospy 7 | from sensor_msgs.msg import Image 8 | 9 | import torch 10 | from data_loader.tum_rgbd_dataset import TUMRGBDDataset 11 | from data_loader.kitti_odometry_dataset import KittiOdometryDataset 12 | from model.monorec.monorec_model import MonoRecModel 13 | from data_loader.data_loaders import BaseDataLoader 14 | from utils import to 15 | 16 | 17 | class MonoRecNode: 18 | def __init__(self, dataset_type, seq, seq_name, pub_rate=10) -> None: 19 | self.img_pub = rospy.Publisher("camera/image_raw", Image, queue_size=1) 20 | self.device = "cuda:0" if torch.cuda.is_available() else "cpu" 21 | self.target_image_size = (256, 512) 22 | self.rate = rospy.Rate(pub_rate) 23 | self.dataset_type = dataset_type 24 | 25 | dataset = None 26 | if dataset_type == "kitti": 27 | dataset = KittiOdometryDataset("data/kitti", sequences=[f"{seq:02d}"], target_image_size=self.target_image_size, frame_count=2, 28 | depth_folder="image_depth_annotated", lidar_depth=False, use_dso_poses=True, 29 | use_index_mask=None) 30 | self.input_size = (1241, 376) if seq in [0, 20] else (1226, 370) 31 | self.interm_size = (753, 376) if seq in [0, 20] else (740, 370) 32 | self.pad_length = (self.input_size[0]-self.interm_size[0]) // 2 33 | elif dataset_type == "tum": 34 | dataset = TUMRGBDDataset(dataset_dir=f"data/tum/{seq_name}", frame_count=2, target_image_size=self.target_image_size) 35 | self.input_size = (640, 480) 36 | else: 37 | raise NotImplementedError(f"Dataset type {dataset_type} is not supported for now") 38 | 39 | self.data_loader = BaseDataLoader(dataset, batch_size=1, shuffle=False, validation_split=0, num_workers=8) 40 | 41 | checkpoint_location = Path("modules/MonoRec/saved/checkpoints/monorec_depth_ref.pth") 42 | inv_depth_min_max = [0.33, 0.0025] 43 | 44 | rospy.loginfo("Initializing model...") 45 | self.monorec_model = MonoRecModel(checkpoint_location=checkpoint_location, 46 | inv_depth_min_max=inv_depth_min_max, 47 | pretrain_mode=2) 48 | self.monorec_model.to(self.device).eval() 49 | 50 | dilatation_size = 8 51 | self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*dilatation_size+1, 2*dilatation_size+1), (dilatation_size, dilatation_size)) 52 | 53 | 54 | def run(self): 55 | rospy.loginfo("Start pumping images!") 56 | for batch_idx, (data, _) in enumerate(self.data_loader): 57 | if rospy.is_shutdown(): 58 | break 59 | # img = data["keyframe"][0].permute(1, 2, 0).detach().cpu().numpy() # (H, W, 3) 60 | img = data["keyframe_orig"][0].detach().numpy() 61 | # print(img.shape, img.dtype) 62 | timestamp = data["timestamp"] 63 | # timestamp = 0. 64 | 65 | data = to(data, self.device) 66 | with torch.no_grad(): 67 | output_dict = self.monorec_model(data) 68 | 69 | mask_orig = output_dict["cv_mask"][0, 0].detach().cpu().numpy() # (H, W) 70 | if self.dataset_type == "kitti": 71 | mask_orig = cv2.dilate(mask_orig, self.kernel, iterations=6) 72 | mask_orig = cv2.erode(mask_orig, self.kernel, iterations=4) 73 | # elif self.dataset_type == "tum": 74 | # mask_orig = cv2.morphologyEx(mask_orig, cv2.MORPH_CLOSE, kernel=self.kernel, iterations=6) 75 | mask_orig = (255*(mask_orig - np.min(mask_orig))/np.ptp(mask_orig)).astype(np.uint8) 76 | _, mask_orig = cv2.threshold(mask_orig, 50, 255, cv2.THRESH_BINARY) 77 | 78 | 79 | 80 | # Align output mask to input image size 81 | mask_align = mask_orig 82 | if self.dataset_type == "kitti": 83 | mask_align = cv2.resize(mask_align, self.interm_size) 84 | mask_align = cv2.copyMakeBorder(mask_align, 85 | 0, 0, self.pad_length, self.pad_length, 86 | borderType=cv2.BORDER_CONSTANT, value=0) 87 | elif self.dataset_type == "tum": 88 | mask_align = cv2.resize(mask_align, self.input_size) 89 | 90 | # Apply mask to input image 91 | img_to_pub = img 92 | if mask_align is not None: 93 | mask_inv = cv2.bitwise_not(mask_align) 94 | # print(mask_inv.shape, img_to_pub.shape) 95 | # print(img_to_pub.dtype, mask_inv.dtype) 96 | assert mask_inv.shape[0] == img_to_pub.shape[0] 97 | img_to_pub = cv2.bitwise_and(img_to_pub, img_to_pub, mask=mask_inv) 98 | else: 99 | rospy.logwarn(f"Skipped mask for image {batch_idx:06d}") 100 | 101 | self.publish_image(img_to_pub, timestamp) 102 | 103 | self.rate.sleep() 104 | 105 | rospy.loginfo("Sequence finished!") 106 | 107 | def publish_image(self, img, timestamp): 108 | img_msg = Image() 109 | img_msg.header.stamp = rospy.Time(timestamp) 110 | img_msg.height = img.shape[0] 111 | img_msg.width = img.shape[1] 112 | img_msg.encoding = "rgb8" # RGB from PIL Image format 113 | # img_msg.encoding = "bgr8" # BGR from cv2 format 114 | if img.dtype.byteorder == '>': 115 | img_msg.is_bigendian = 1 116 | img_msg.data = img.tobytes() 117 | img_msg.step = len(img_msg.data) // img_msg.height 118 | # img_msg.step = img.strides[0] 119 | 120 | self.img_pub.publish(img_msg) 121 | 122 | 123 | if __name__ == '__main__': 124 | parser = argparse.ArgumentParser() 125 | parser.add_argument("--dataset", type=str, default="kitti", required=True, choices=["kitti", "tum"]) 126 | parser.add_argument("--seq", type=int, default=7, required=False) 127 | parser.add_argument("--seq_name", type=str, default="rgbd_dataset_freiburg3_walking_halfsphere", required=False) 128 | args = parser.parse_args() 129 | 130 | rospy.init_node("MonoRecSLAM") 131 | 132 | rospy.loginfo("Initializing MonoRec SLAM node...") 133 | 134 | node = MonoRecNode(args.dataset, args.seq, args.seq_name) 135 | 136 | try: 137 | node.run() 138 | rospy.spin() 139 | except rospy.ROSInterruptException: 140 | pass 141 | -------------------------------------------------------------------------------- /src/process_image.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import cv2 3 | import os 4 | import numpy as np 5 | import shutil 6 | import argparse 7 | 8 | def center_crop(img, dim): 9 | """Returns center cropped image 10 | Args: 11 | img: image to be center cropped 12 | dim: dimensions (width, height) to be cropped 13 | """ 14 | width, height = img.shape[1], img.shape[0] 15 | 16 | # process crop width and height for max available dimension 17 | crop_width = dim[0] if dim[0]=0 53 | img = cv2.imread(data_path+filename) 54 | # print(img.shape) 55 | img = center_crop(img, dsize_1) 56 | img = cv2.resize(img, (512, 256), interpolation=cv2.INTER_AREA) 57 | 58 | cv2.imwrite(output_path+f"{img_id-n_skip:06d}.png", img) 59 | 60 | print(f"[INFO] Processed {counter} images and skipped {n_skip} images") 61 | 62 | def align_mask_with_img(data_path, mask_path, output_path, sequence_id): 63 | shutil.rmtree(output_path, ignore_errors=True) 64 | os.makedirs(output_path, exist_ok=False) 65 | dir_list = os.listdir(mask_path) 66 | dir_list.sort() 67 | counter = 0 68 | org_size = (1241, 376) if sequence_id in [0, 20] else (1226, 370) 69 | dsize = (753, 376) if sequence_id in [0, 20] else (740, 370) 70 | pad_length = (org_size[0] - dsize[0]) // 2 71 | print(f"[INFO] Aligning masks from {mask_path}, output to {output_path}") 72 | confirm = input("Are you sure? (y/n)") 73 | if (confirm.lower().find("y") != -1 or confirm.lower().find("yes") != -1): 74 | print("[INFO] Confirmed") 75 | else: 76 | sys.exit("[INFO] User rejected") 77 | for filename in dir_list: 78 | counter += 1 79 | img = cv2.imread(mask_path+filename, cv2.IMREAD_GRAYSCALE) 80 | # print(img.shape, img.dtype) 81 | h, w = img.shape[:2] 82 | mask_to_save = img 83 | mask_to_save = cv2.resize(img, dsize) 84 | mask_to_save = cv2.copyMakeBorder(mask_to_save, 0, 0, pad_length, pad_length, borderType=cv2.BORDER_CONSTANT, value=0) 85 | 86 | # assert mask_to_save.shape[0] == 376 and mask_to_save.shape[1] == 1241 87 | cv2.imwrite(output_path+filename, mask_to_save) 88 | 89 | print(f"[INFO] Processed {counter} images") 90 | 91 | def mask_out_img(data_path, mask_path, output_path): 92 | shutil.rmtree(output_path, ignore_errors=True) 93 | os.makedirs(output_path, exist_ok=False) 94 | dir_list = os.listdir(data_path) 95 | dir_list.sort() 96 | counter = 0 97 | print(f"[INFO] Masking images from {data_path} with masks from {mask_path}, output to {output_path}") 98 | confirm = input("Are you sure? (y/n)") 99 | if (confirm.lower().find("y") != -1 or confirm.lower().find("yes") != -1): 100 | print("[INFO] Confirmed") 101 | else: 102 | sys.exit("[INFO] User rejected") 103 | 104 | for filename in dir_list: 105 | counter += 1 106 | img = cv2.imread(data_path+filename) 107 | mask = cv2.imread(mask_path+filename, cv2.IMREAD_GRAYSCALE) 108 | # print(img.shape, img.dtype) 109 | # print(img.shape, mask.shape) 110 | img_to_save = img 111 | if mask is not None: 112 | mask_inv = cv2.bitwise_not(mask) 113 | assert mask_inv.shape[0] == img_to_save.shape[0] 114 | img_to_save = cv2.bitwise_and(img_to_save, img_to_save, mask=mask_inv) 115 | else: 116 | print(f"[WARN] Skipped mask for image {data_path+filename}") 117 | # assert mask_to_save.shape[0] == 376 and mask_to_save.shape[1] == 1241 118 | cv2.imwrite(output_path+filename, img_to_save) 119 | 120 | print(f"[INFO] Processed {counter} images") 121 | 122 | def recover_inpaint_img(data_path, inpaint_path, output_path, sequence_id): 123 | shutil.rmtree(output_path, ignore_errors=True) 124 | os.makedirs(output_path, exist_ok=False) 125 | dir_list = os.listdir(data_path) 126 | dir_list.sort() 127 | counter = 0 128 | dsize = (753, 376) if sequence_id in [0, 20] else (740, 370) 129 | for filename in dir_list: 130 | counter += 1 131 | img = cv2.imread(data_path+filename) 132 | img_to_save = img 133 | h, w = img.shape[:2] 134 | # print(f"h: {h}, w: {w}") 135 | 136 | inpaint = cv2.imread(inpaint_path+filename[1:]) 137 | # print(inpaint.shape, inpaint.dtype) 138 | inpaint_recover = cv2.resize(inpaint, dsize) 139 | # print(inpaint_recover.shape, inpaint_recover.dtype) 140 | 141 | crop_width = dsize[0] 142 | crop_height = dsize[1] 143 | mid_x, mid_y = int(w/2), int(h/2) 144 | cw2, ch2 = int(crop_width/2), int(crop_height/2) 145 | img[:, mid_x-cw2:mid_x+cw2+1] = inpaint_recover 146 | 147 | cv2.imwrite(output_path+filename, img_to_save) 148 | 149 | print(f"[INFO] Processed {counter} images") 150 | 151 | 152 | if __name__ == "__main__": 153 | parser = argparse.ArgumentParser() 154 | parser.add_argument("--dataset", type=str, default="kitti", required=True, choices=["kitti", "tum"]) 155 | parser.add_argument("--seq", type=int, default=7, required=True) 156 | args = parser.parse_args() 157 | 158 | if args.dataset == "kitti": 159 | pass 160 | else: 161 | raise NotImplementedError(f"Dataset type {args.dataset} is not supported for now") 162 | 163 | sequence_id = args.seq 164 | dataset_path = f"data/kitti/sequences/{sequence_id:02d}" 165 | # align_img_with_mask("data/kitti/20/", "data/kitti/20_resize/") 166 | align_mask_with_img(f"{dataset_path}/", 167 | f"{dataset_path}_mask/", 168 | f"{dataset_path}_mask_align/", 169 | sequence_id) 170 | mask_out_img(f"{dataset_path}/image_2/", 171 | f"{dataset_path}_mask_align/", 172 | f"{dataset_path}/image_0/") 173 | --------------------------------------------------------------------------------