| Name | forement JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | None |
| Summary | A collection of unsupervised clustering tools for foreground segmentation |
| upload_time | 2024-08-27 21:42:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2024 Brandon Hastings Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
foreground segmentation
color clustering
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# FOREground segMENTation
A collection of unsupervised clustering tools for foreground segmentation of an image to isolate an object of interest.
### Requirements:
- ultralytics
- opencv-python
- orchard-bouman
Developed in Python 3.10, but anything over Python 3.7 should work
### Installation:
```
pip install forement
```
### Usage:
To use from the command line use the command:
```
python forement.py -image_path -method -k -channel -output_dir -save_image -show_image
```
Arguments:
- image_path, type=str, help=Input the path to the image to perform clustering on.
- method, type=str, help=Method to segment images by, options include "sam", "kmeans", "ob", "em", "ob-em".
- k, type=int | None, default=None, help=How many clusters to generate. For method "ob" or "ob-em" clusters is 2^k.
- channel, type=int, default=0, help=Color channel to use when running mode counts on images.
- output_dir, type=str | None, default=None, help=Folder to save images to. If None, created images are saved in the folder of the original image.
- save_image, type=bool, default=False, help=Whether or not to save the segmented image.
- show_image, type=bool, default=False, help=Whether or not to display the segmented image.
Available methods:
- kmeans: "kmeans"
- Orchard Bouman: "ob"
- Expectation Maximization: "em"
- Expectation Maximization using Orchard Bouman: "ob-em"
- SAM (Segment anything model): "sam"
In addition to the command line call, all functions can be imported to your own scripts and used as part of a larger program.
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"description": "# FOREground segMENTation\nA collection of unsupervised clustering tools for foreground segmentation of an image to isolate an object of interest.\n\n### Requirements:\n- ultralytics\n- opencv-python\n- orchard-bouman\n\nDeveloped in Python 3.10, but anything over Python 3.7 should work\n\n### Installation:\n```\npip install forement\n```\n\n### Usage:\nTo use from the command line use the command:\n```\npython forement.py -image_path -method -k -channel -output_dir -save_image -show_image\n```\nArguments:\n- image_path, type=str, help=Input the path to the image to perform clustering on.\n- method, type=str, help=Method to segment images by, options include \"sam\", \"kmeans\", \"ob\", \"em\", \"ob-em\".\n- k, type=int | None, default=None, help=How many clusters to generate. For method \"ob\" or \"ob-em\" clusters is 2^k.\n- channel, type=int, default=0, help=Color channel to use when running mode counts on images.\n- output_dir, type=str | None, default=None, help=Folder to save images to. If None, created images are saved in the folder of the original image.\n- save_image, type=bool, default=False, help=Whether or not to save the segmented image.\n- show_image, type=bool, default=False, help=Whether or not to display the segmented image.\n\nAvailable methods:\n- kmeans: \"kmeans\"\n- Orchard Bouman: \"ob\"\n- Expectation Maximization: \"em\"\n- Expectation Maximization using Orchard Bouman: \"ob-em\"\n- SAM (Segment anything model): \"sam\"\n\n\nIn addition to the command line call, all functions can be imported to your own scripts and used as part of a larger program.\n",
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