| Name | deep-image-matching JSON |
| Version |
1.2.4
JSON |
| download |
| home_page | None |
| Summary | Multiview matching with deep-learning and hand-crafted local features for COLMAP and other SfM software. |
| upload_time | 2024-04-13 16:39:42 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | # LICENSE BSD 3-Clause License Copyright (c) 2018, the respective contributors, as shown by the AUTHORS file. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
image-matching
deep-learning
local-features
multiview-matching
sfm
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
[](https://github.com/colmap/colmap) [](https://github.com/openMVG/openMVG) [](https://github.com/micmacIGN/micmac) 
[](https://github.com/kornia/kornia) [](https://github.com/kornia/kornia) [](https://github.com/3DOM-FBK/deep-image-matching/releases) [](https://3dom-fbk.github.io/deep-image-matching/)
</div>
# DEEP-IMAGE-MATCHING
| SIFT | DISK | IMAGES ORIENTATION | DENSE WITH ROMA |
| ----------------------------------------------------- | ------------------------------------------------------- | --------------------------------------------------------- | --------------------------------------------------- |
| <img src='docs/assets/matches_sift.gif' height="100"> | <img src='docs/assets/matches_joined.gif' height="100"> | <img src='docs/assets/orientation_deep.gif' height="100"> | <img src='docs/assets/roma_dense.gif' height="100"> |
| SIFT | SUPERGLUE |
| ----------------------------------------------------- | --------------------------------------------------------- |
| <img src='docs/assets/temple_rsift.gif' height="165"> | <img src='docs/assets/temple_superglue.gif' height="165"> |
Multivew matcher for SfM software. Support both deep-learning based and hand-crafted local features and matchers and export keypoints and matches directly in a COLMAP database or to Agisoft Metashape by importing the reconstruction in Bundler format. Now, it supports both OpenMVG and MicMac. Feel free to collaborate!
While `dev` branch is more frequently updated, `master` is the default more stable branch and is updated from `dev` less frequently. If you are looking for the newest developments, please switch to `dev`.
For how to use DIM, check the <a href="https://3dom-fbk.github.io/deep-image-matching/">Documentation</a> (updated for the master branch).
**Please, note that `deep-image-matching` is under active development** and it is still in an experimental stage. If you find any bug, please open an issue.
Key features:
- Multiview
- Large format images
- SOTA deep-learning and hand-crafted features
- Support for image rotations
- Compatibility with several SfM software
- Support image retrieval with deep-learning local features
| Supported Extractors | Supported Matchers |
| ---------------------------------- | --------------------------------------------------------- |
| ✓ SuperPoint | ✓ Lightglue (with Superpoint, Disk, and ALIKED) |
| ✓ DISK | ✓ SuperGlue (with Superpoint) |
| ☐ Superpoint free | ✓ Nearest neighbor (with KORNIA Descriptor Matcher) |
| ✓ ALIKE | ✓ LoFTR (only GPU) |
| ✓ ALIKED | ✓ SE2-LoFTR (no tiling and only GPU) |
| ✓ KeyNet + OriNet + HardNet8 | ✓ RoMa |
| ✓ DeDoDe (only GPU) | ☐ GlueStick |
| ✓ SIFT (from Opencv) |
| ✓ ORB (from Opencv) |
| Supported SfM software |
| --------------------------------------------- |
| ✓ COLMAP |
| ✓ OpenMVG |
| ✓ MICMAC |
| ✓ Agisoft Metashape |
| ✓ Software that supports bundler format |
## Colab demo and notebooks
Want to run on a sample dataset? ➡️ [](https://colab.research.google.com/github/3DOM-FBK/deep-image-matching/blob/master/notebooks/colab_run_from_bash_example.ipynb)
Want to run on your images? ➡️ [](https://colab.research.google.com/github/3DOM-FBK/deep-image-matching/blob/master/notebooks/colab_run_from_bash_custom_images.ipynb)
DIM can also be utilized as a library instead of being executed through the Command Line Interface (refer to the `Usage Instructions`). For an illustrative example, please see `notebooks/sfm_pipeline.ipynb`.
## Local Installation
For installing deep-image-matching, first create a conda environment:
```bash
conda create -n deep-image-matching python=3.9
conda activate deep-image-matching
pip install --upgrade pip
```
Then, you can install deep-image-matching using pip with the following command:
```bash
pip install deep-image-matching
```
If there is any issue with the installation, you can also install the package from the source code.
Clone the repository and install deep-image-matching in editable mode:
```bash
git clone https://github.com/3DOM-FBK/deep-image-matching.git
cd deep-image-matching
pip install -e .
```
Install pycolmap (optional, but recommended):
```bash
pip install pycolmap==0.6.1
```
Pycolmap is optional to run reconstruction directly in DIM. If pycolmap is not available, matches will be written both in a h5 and colmap database for later processing with COLMAP GUI or API, or other processing.
Try to run the tests to check if deep-image-matching is correctly installed, try to import the package in a Python shell:
```python
import deep_image_matching as dim
```
For more information, check the [documentation](https://3dom-fbk.github.io/deep-image-matching/installation/).
## Docker Installation
If you prefer using Docker, first, build the image:
```
docker build --tag deep-image-matching .
```
Note that the first time you run the command, it will take a while to download the base image and install all the dependencies.
Once the image is built, you can run it with the following commands.
On Linux:
```
docker run --name run-deep-image-matching --mount type=bind,source=/home/username/data,target=/workspace/data --gpus all -it deep-image-matching
```
On Windows (please use Powershell):
```
docker run --name run-deep-image-matching --mount type=bind,source=D:\data,target=/workspace/data --gpus all -it deep-image-matching
```
**replace** `/home/username/data` (on Linux) or `D:\data` (on Winows) with the desired path for mounting a shared volume between the local OS and the docker container. Make sure to use absolute paths. This folder will be used to store alll the input data (images) and outputs.
Include the `--detach` option to run the container in background and/or `--rm` to remove container on exit.
Once the container is running, you can then open the repo cloned inside the container directly in VSCode using `ctrl+alt+O` and selecting the option "attach to running container" (make sure to have the Docker extension installed in VSCode), then enjoy!
If you face any issues, especially on Linux when using the `gpus all` setting, please refer to the [documentation](https://3dom-fbk.github.io/deep-image-matching/installation/).
## Usage instructions
<!-- You can run deep-image-matching from the command line or from the GUI. -->
Use the following command to see all the available options from the CLI:
```
python main.py --help
```
For example, to run the matching with SuperPoint and LightGlue on a dataset, you can use the following command:
```
python main.py --dir assets/example_cyprus --pipeline superpoint+lightglue
```
The `--dir` parameter defines the processing directory, where all the results will be saved. This directory must constain a subfolder named **images** in which all the images must be stored. We currentely do not support image retrieval from multiple directories or subdirectories, but we may add this feature in the future.
The `--pipeline` parameter defines the combaination of local feature extractor and matcher.
For all the usage instructions and configurations, refer to the [documenation](https://3dom-fbk.github.io/deep-image-matching/getting_started) or check the example notebooks.
<!-- To run the GUI, you can use the following command:
```bash
python main.py --gui
``` -->
## Advanced usage
For advanced usage, please refer to the [documentation](https://3dom-fbk.github.io/deep-image-matching/) and/or check the `scripts` directory.
### Merging databases with different local features
To run the matching with different local features and/or matchers and marging together the results, you can use scripts in the `./scripts` directory for merging the COLMAP databases.
```
python ./join_databases.py --help
python ./join_databases.py --input path/to/dir/with/databases --output path/to/output/dir
```
### Exporting the solution to Metashape
To export the solution to Metashape, you can export the COLMAP database to Bundler format and then import it into Metashape.
This can be done from Metashape GUI, by first importing the images and then use the function `Import Cameras` (File -> Import -> Import Cameras) to select Bundler file (e.g., bundler.out) and the image list file (e.g., bundler_list.txt).
Alternatevely, you can use the `export_to_metashape.py` script to automatically create a Metashape project from a reconstruction saved in Bundler format.
The script `export_to_metashape.py` takes as input the solution in Bundler format and the images and it exports the solution to Metashape.
It requires to install Metashape as a Python module in your environment and to have a valid license.
Please, refer to the instructions at [https://github.com/franioli/metashape](https://github.com/franioli/metashape).
## How to contribute
Any contribution to this repo is really welcome!
If you want to contribute to the project, please, check the [contributing guidelines](./CONTRIBUTING.md).
## To Do List:
See the [TODO list](notes.md) for the list of features and improvements that are planned for the future.
## References
If you find the repository useful for your work consider citing the papers:
```bibtex
@article{morelli2024_deep_image_matching,
AUTHOR = {Morelli, L. and Ioli, F. and Maiwald, F. and Mazzacca, G. and Menna, F. and Remondino, F.},
TITLE = {DEEP-IMAGE-MATCHING: A TOOLBOX FOR MULTIVIEW IMAGE MATCHING OF COMPLEX SCENARIOS},
JOURNAL = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {XLVIII-2/W4-2024},
YEAR = {2024},
PAGES = {309--316},
DOI = {10.5194/isprs-archives-XLVIII-2-W4-2024-309-2024}
}
```
```bibtex
@article{morelli2022photogrammetry,
title={PHOTOGRAMMETRY NOW AND THEN--FROM HAND-CRAFTED TO DEEP-LEARNING TIE POINTS--},
author={Morelli, Luca and Bellavia, Fabio and Menna, Fabio and Remondino, Fabio},
journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
volume={48},
pages={163--170},
year={2022},
publisher={Copernicus GmbH}
}
```
```bibtex
@article{ioli2024,
title={Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier
Dynamics Monitoring},
author={Ioli, Francesco and Dematteis, Nicolò and Giordan, Daniele and Nex, Francesco and Pinto Livio},
journal={PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
year={2024},
DOI = {10.1007/s41064-023-00272-w}
}
```
Depending on the options used, consider citing the corresponding work of:
- [KORNIA](https://github.com/kornia/kornia)
- [HLOC](https://github.com/cvg/Hierarchical-Localization)
- [COLMAP](https://github.com/colmap/colmap)
- [OpenMVG](https://github.com/openMVG/openMVG)
- [MICMAC](https://github.com/micmacIGN/micmac)
- used local features and matchers
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"description": "<div align=\"center\">\n \n [](https://github.com/colmap/colmap) [](https://github.com/openMVG/openMVG) [](https://github.com/micmacIGN/micmac)  \n \n [](https://github.com/kornia/kornia) [](https://github.com/kornia/kornia) [](https://github.com/3DOM-FBK/deep-image-matching/releases) [](https://3dom-fbk.github.io/deep-image-matching/)\n\n</div>\n\n# DEEP-IMAGE-MATCHING\n\n| SIFT | DISK | IMAGES ORIENTATION | DENSE WITH ROMA |\n| ----------------------------------------------------- | ------------------------------------------------------- | --------------------------------------------------------- | --------------------------------------------------- |\n| <img src='docs/assets/matches_sift.gif' height=\"100\"> | <img src='docs/assets/matches_joined.gif' height=\"100\"> | <img src='docs/assets/orientation_deep.gif' height=\"100\"> | <img src='docs/assets/roma_dense.gif' height=\"100\"> |\n\n| SIFT | SUPERGLUE |\n| ----------------------------------------------------- | --------------------------------------------------------- |\n| <img src='docs/assets/temple_rsift.gif' height=\"165\"> | <img src='docs/assets/temple_superglue.gif' height=\"165\"> |\n\nMultivew matcher for SfM software. Support both deep-learning based and hand-crafted local features and matchers and export keypoints and matches directly in a COLMAP database or to Agisoft Metashape by importing the reconstruction in Bundler format. Now, it supports both OpenMVG and MicMac. Feel free to collaborate!\n\nWhile `dev` branch is more frequently updated, `master` is the default more stable branch and is updated from `dev` less frequently. If you are looking for the newest developments, please switch to `dev`. \n\n\nFor how to use DIM, check the <a href=\"https://3dom-fbk.github.io/deep-image-matching/\">Documentation</a> (updated for the master branch). \n\n\n**Please, note that `deep-image-matching` is under active development** and it is still in an experimental stage. If you find any bug, please open an issue.\n\nKey features:\n\n- Multiview\n- Large format images\n- SOTA deep-learning and hand-crafted features\n- Support for image rotations\n- Compatibility with several SfM software\n- Support image retrieval with deep-learning local features\n\n| Supported Extractors | Supported Matchers |\n| ---------------------------------- | --------------------------------------------------------- |\n| ✓ SuperPoint | ✓ Lightglue (with Superpoint, Disk, and ALIKED) |\n| ✓ DISK | ✓ SuperGlue (with Superpoint) |\n| ☐ Superpoint free | ✓ Nearest neighbor (with KORNIA Descriptor Matcher) |\n| ✓ ALIKE | ✓ LoFTR (only GPU) |\n| ✓ ALIKED | ✓ SE2-LoFTR (no tiling and only GPU) |\n| ✓ KeyNet + OriNet + HardNet8 | ✓ RoMa |\n| ✓ DeDoDe (only GPU) | ☐ GlueStick |\n| ✓ SIFT (from Opencv) | \n| ✓ ORB (from Opencv) |\n\n| Supported SfM software |\n| --------------------------------------------- |\n| ✓ COLMAP |\n| ✓ OpenMVG |\n| ✓ MICMAC |\n| ✓ Agisoft Metashape |\n| ✓ Software that supports bundler format |\n\n## Colab demo and notebooks\n\nWant to run on a sample dataset? \u27a1\ufe0f [](https://colab.research.google.com/github/3DOM-FBK/deep-image-matching/blob/master/notebooks/colab_run_from_bash_example.ipynb)\n\nWant to run on your images? \u27a1\ufe0f [](https://colab.research.google.com/github/3DOM-FBK/deep-image-matching/blob/master/notebooks/colab_run_from_bash_custom_images.ipynb)\n\nDIM can also be utilized as a library instead of being executed through the Command Line Interface (refer to the `Usage Instructions`). For an illustrative example, please see `notebooks/sfm_pipeline.ipynb`.\n\n## Local Installation\n\nFor installing deep-image-matching, first create a conda environment:\n\n```bash\nconda create -n deep-image-matching python=3.9\nconda activate deep-image-matching\npip install --upgrade pip\n```\n\nThen, you can install deep-image-matching using pip with the following command:\n\n```bash\npip install deep-image-matching\n```\n\nIf there is any issue with the installation, you can also install the package from the source code.\nClone the repository and install deep-image-matching in editable mode:\n\n```bash\ngit clone https://github.com/3DOM-FBK/deep-image-matching.git\ncd deep-image-matching\npip install -e .\n```\n\nInstall pycolmap (optional, but recommended):\n\n```bash\npip install pycolmap==0.6.1\n```\nPycolmap is optional to run reconstruction directly in DIM. If pycolmap is not available, matches will be written both in a h5 and colmap database for later processing with COLMAP GUI or API, or other processing.\n\nTry to run the tests to check if deep-image-matching is correctly installed, try to import the package in a Python shell:\n\n```python\nimport deep_image_matching as dim\n```\n\nFor more information, check the [documentation](https://3dom-fbk.github.io/deep-image-matching/installation/).\n\n## Docker Installation\n\nIf you prefer using Docker, first, build the image:\n\n```\ndocker build --tag deep-image-matching .\n```\n\nNote that the first time you run the command, it will take a while to download the base image and install all the dependencies.\n\nOnce the image is built, you can run it with the following commands.\nOn Linux:\n\n```\ndocker run --name run-deep-image-matching --mount type=bind,source=/home/username/data,target=/workspace/data --gpus all -it deep-image-matching\n```\n\nOn Windows (please use Powershell):\n\n```\ndocker run --name run-deep-image-matching --mount type=bind,source=D:\\data,target=/workspace/data --gpus all -it deep-image-matching\n```\n\n**replace** `/home/username/data` (on Linux) or `D:\\data` (on Winows) with the desired path for mounting a shared volume between the local OS and the docker container. Make sure to use absolute paths. This folder will be used to store alll the input data (images) and outputs.\n\nInclude the `--detach` option to run the container in background and/or `--rm` to remove container on exit.\n\nOnce the container is running, you can then open the repo cloned inside the container directly in VSCode using `ctrl+alt+O` and selecting the option \"attach to running container\" (make sure to have the Docker extension installed in VSCode), then enjoy!\n\nIf you face any issues, especially on Linux when using the `gpus all` setting, please refer to the [documentation](https://3dom-fbk.github.io/deep-image-matching/installation/).\n\n## Usage instructions\n\n<!-- You can run deep-image-matching from the command line or from the GUI. -->\n\nUse the following command to see all the available options from the CLI:\n\n```\npython main.py --help\n```\n\nFor example, to run the matching with SuperPoint and LightGlue on a dataset, you can use the following command:\n\n```\npython main.py --dir assets/example_cyprus --pipeline superpoint+lightglue\n```\n\nThe `--dir` parameter defines the processing directory, where all the results will be saved. This directory must constain a subfolder named **images** in which all the images must be stored. We currentely do not support image retrieval from multiple directories or subdirectories, but we may add this feature in the future. \n\nThe `--pipeline` parameter defines the combaination of local feature extractor and matcher. \n\nFor all the usage instructions and configurations, refer to the [documenation](https://3dom-fbk.github.io/deep-image-matching/getting_started) or check the example notebooks.\n\n<!-- To run the GUI, you can use the following command:\n\n```bash\npython main.py --gui\n``` -->\n\n## Advanced usage\n\nFor advanced usage, please refer to the [documentation](https://3dom-fbk.github.io/deep-image-matching/) and/or check the `scripts` directory.\n\n### Merging databases with different local features\n\nTo run the matching with different local features and/or matchers and marging together the results, you can use scripts in the `./scripts` directory for merging the COLMAP databases.\n\n```\npython ./join_databases.py --help\npython ./join_databases.py --input path/to/dir/with/databases --output path/to/output/dir\n```\n\n### Exporting the solution to Metashape\n\nTo export the solution to Metashape, you can export the COLMAP database to Bundler format and then import it into Metashape.\nThis can be done from Metashape GUI, by first importing the images and then use the function `Import Cameras` (File -> Import -> Import Cameras) to select Bundler file (e.g., bundler.out) and the image list file (e.g., bundler_list.txt).\n\nAlternatevely, you can use the `export_to_metashape.py` script to automatically create a Metashape project from a reconstruction saved in Bundler format.\nThe script `export_to_metashape.py` takes as input the solution in Bundler format and the images and it exports the solution to Metashape.\nIt requires to install Metashape as a Python module in your environment and to have a valid license.\nPlease, refer to the instructions at [https://github.com/franioli/metashape](https://github.com/franioli/metashape).\n\n## How to contribute\n\nAny contribution to this repo is really welcome!\nIf you want to contribute to the project, please, check the [contributing guidelines](./CONTRIBUTING.md).\n\n## To Do List:\n\nSee the [TODO list](notes.md) for the list of features and improvements that are planned for the future.\n\n## References\n\nIf you find the repository useful for your work consider citing the papers:\n\n```bibtex\n@article{morelli2024_deep_image_matching,\n AUTHOR = {Morelli, L. and Ioli, F. and Maiwald, F. and Mazzacca, G. and Menna, F. and Remondino, F.},\n TITLE = {DEEP-IMAGE-MATCHING: A TOOLBOX FOR MULTIVIEW IMAGE MATCHING OF COMPLEX SCENARIOS},\n JOURNAL = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},\n VOLUME = {XLVIII-2/W4-2024},\n YEAR = {2024},\n PAGES = {309--316},\n DOI = {10.5194/isprs-archives-XLVIII-2-W4-2024-309-2024}\n}\n```\n\n```bibtex\n@article{morelli2022photogrammetry,\n title={PHOTOGRAMMETRY NOW AND THEN--FROM HAND-CRAFTED TO DEEP-LEARNING TIE POINTS--},\n author={Morelli, Luca and Bellavia, Fabio and Menna, Fabio and Remondino, Fabio},\n journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},\n volume={48},\n pages={163--170},\n year={2022},\n publisher={Copernicus GmbH}\n}\n```\n\n```bibtex\n@article{ioli2024,\n title={Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier\nDynamics Monitoring},\n author={Ioli, Francesco and Dematteis, Nicol\u00f2 and Giordan, Daniele and Nex, Francesco and Pinto Livio},\n journal={PFG \u2013 Journal of Photogrammetry, Remote Sensing and Geoinformation Science},\n year={2024},\n DOI = {10.1007/s41064-023-00272-w}\n}\n```\n\nDepending on the options used, consider citing the corresponding work of:\n- [KORNIA](https://github.com/kornia/kornia)\n- [HLOC](https://github.com/cvg/Hierarchical-Localization)\n- [COLMAP](https://github.com/colmap/colmap)\n- [OpenMVG](https://github.com/openMVG/openMVG)\n- [MICMAC](https://github.com/micmacIGN/micmac)\n- used local features and matchers\n",
"bugtrack_url": null,
"license": "# LICENSE BSD 3-Clause License Copyright (c) 2018, the respective contributors, as shown by the AUTHORS file. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
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