| Name | pointextract JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Polar to cartesian transforms using annular point sampling. |
| upload_time | 2024-10-19 20:03:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2024, CWRU SDLE Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. 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 |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pointextract
Polar to cartesian transforms using annular point sampling
Designed to unwrap 2D cross section images of 3D X-ray computed tomography scans.
The topological transformation enables the surface of a circular or elliptical object to be aligned for downsteam analysis.
<img src="./docs/workflow.png" width="700">
## Installation
You can install the package with:
```bash
pip install pointextract
```
## Example
Simple example:
```python
import pointextract
from skimage import io, filters
img_arr = io.imread('./data/sample.png')
img_thresh = img_arr > filters.threshold_otsu(img_arr)
ellipse = ellipse_detect(img_thresh)
img_unwrap = unwrap_image(img_arr, ellipse, radial_distance=20, num_points=800)
```
## Questions
This package is still in early development. Please feel free to post to the GitHub Issues page with questions.
## Acknowledgements
This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE).
<cite> [Case Western Reserve University, SDLElab] [1]</cite>
[1]: http://sdle.case.edu
Raw data
{
"_id": null,
"home_page": null,
"name": "pointextract",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": "Thomas Ciardi <thomas.ciardi@case.edu>, \"Roger H. French\" <rxf131@case.edu>",
"download_url": "https://files.pythonhosted.org/packages/18/98/4f361d60fea339dfd54392ae33a99c1087605917122808a62b3093d112a9/pointextract-0.1.2.tar.gz",
"platform": null,
"description": "# pointextract\nPolar to cartesian transforms using annular point sampling\n\nDesigned to unwrap 2D cross section images of 3D X-ray computed tomography scans.\nThe topological transformation enables the surface of a circular or elliptical object to be aligned for downsteam analysis.\n\n<img src=\"./docs/workflow.png\" width=\"700\">\n\n## Installation\n\nYou can install the package with:\n```bash\npip install pointextract\n```\n\n## Example\n\nSimple example:\n```python\nimport pointextract\nfrom skimage import io, filters\n\nimg_arr = io.imread('./data/sample.png')\n\nimg_thresh = img_arr > filters.threshold_otsu(img_arr)\nellipse = ellipse_detect(img_thresh)\n\nimg_unwrap = unwrap_image(img_arr, ellipse, radial_distance=20, num_points=800)\n```\n\n## Questions\nThis package is still in early development. Please feel free to post to the GitHub Issues page with questions.\n\n## Acknowledgements\nThis material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE).\n\n<cite> [Case Western Reserve University, SDLElab] [1]</cite>\n\n[1]: http://sdle.case.edu\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2024, CWRU SDLE Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. 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. ",
"summary": "Polar to cartesian transforms using annular point sampling.",
"version": "0.1.2",
"project_urls": {
"Bug Tracker": "https://github.com/cwru-sdle/pointextract/issues",
"Source Code": "https://github.com/cwru-sdle/pointextract"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "5c36ba42583dfebf8f7eb6efede33f9ec993057d4c47d2d3c0211d06495663e3",
"md5": "748aad773e88c5d781393d16c666af91",
"sha256": "7733bcdb013e2654aa3bc8c2a3d8e900265af4f9a2adc8f772b4bf71b5740669"
},
"downloads": -1,
"filename": "pointextract-0.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "748aad773e88c5d781393d16c666af91",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 5297,
"upload_time": "2024-10-19T20:03:38",
"upload_time_iso_8601": "2024-10-19T20:03:38.150206Z",
"url": "https://files.pythonhosted.org/packages/5c/36/ba42583dfebf8f7eb6efede33f9ec993057d4c47d2d3c0211d06495663e3/pointextract-0.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "18984f361d60fea339dfd54392ae33a99c1087605917122808a62b3093d112a9",
"md5": "ba5c04d18c7df8c75e1a9d6e5437697d",
"sha256": "5f269322a85caf3ca38c5424530b236768a9d0c235d829d552632911985a303a"
},
"downloads": -1,
"filename": "pointextract-0.1.2.tar.gz",
"has_sig": false,
"md5_digest": "ba5c04d18c7df8c75e1a9d6e5437697d",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 337475,
"upload_time": "2024-10-19T20:03:40",
"upload_time_iso_8601": "2024-10-19T20:03:40.535882Z",
"url": "https://files.pythonhosted.org/packages/18/98/4f361d60fea339dfd54392ae33a99c1087605917122808a62b3093d112a9/pointextract-0.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-19 20:03:40",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "cwru-sdle",
"github_project": "pointextract",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "pointextract"
}
