| Name | fastlbp-baseline-imbg JSON |
| Version |
0.0.7
JSON |
| download |
| home_page | |
| Summary | A wrapper for Ben's LBP bio pipeline |
| upload_time | 2023-10-26 22:43:01 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | |
| keywords |
lbp
texture
features
image
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ImageTextureFinder
A project to create an easy-to-use way of finding areas of common patterns and structures within an image. Should work on any image, designed for use on any biological images including DAPI, IMC and H&E.
See `sample_run.sh` for details.
- Branch `baseline` is the most stable. It is ready for pip packaging.
- Branch `pip` is stale at the moment
- Branch `dev` is unstable and for dev purposes only.
## Container
Image tag is `mkrooted/imbg-fastlbp`. Hosted on Docker Hub (https://hub.docker.com/repository/docker/mkrooted/imbg-fastlbp/general).
See https://github.com/imbg-ua/fastLBP-sandbox for details
---
# Guides
## How to build and deploy a pip package
Src: https://packaging.python.org/en/latest/tutorials/packaging-projects/
- Add your access token to `.pypirc`
```
# ~/.pypirc
[pypi]
username = __token__
password = pypi-TOKEN_FROM_YOUR_PYPI_SETTINGS_GOES_HERE
```
- Ensure that your Python is 3.8 because the package targets Python 3.8 and thus requires to be build using this Python version
```
python --version
# Should show Python 3.8.something
```
- Install prerequisites (`twine` and `build`)
```
pip install --upgrade twine build
```
- Edit project version in `pyproject.toml`
- Build and upload the project
```
# while in root project directory
python -m build # .whl and .gz output will be at ./dist directory
python3 -m twine upload dist/* # note that this can accidentally upload unneeded builds
```
---
## Algorithm notes
Step 1 performs an LBP and creates histograms for each **method**.
**Method** is a combination of the following parameters:
- image name
- image channel
- LBP radius
- LBP number of points
Every method's result got saved into the separate `.npy` file. There is a correspondence betweeen a method and a computational job.
Step 2 collects all the results and concatenate them along the features dimension.
That means that feature vector of a patch is a concatenation of all LBP codes from all channels and all LBP radii.
Raw data
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