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cellseg: Multiclass Cell Segmentation \n\n[![PyPI version](https://badge.fury.io/py/cellseg.svg)](https://badge.fury.io/py/cellseg) \n![Stage](https://www.repostatus.org/badges/latest/wip.svg)\n[![Codecov](https://codecov.io/gh/Nelson-Gon/cellseg/branch/main/graph/badge.svg)](https://codecov.io/gh/Nelson-Gon/cellseg?branch=main)\n[![Test Install](https://github.com/Nelson-Gon/cellseg/actions/workflows/build-package.yaml/badge.svg)](https://github.com/Nelson-Gon/cellseg/actions/workflows/build-package.yaml)\n[![PyPI license](https://img.shields.io/pypi/l/cellseg.svg)](https://pypi.python.org/pypi/cellseg/) \n[![Documentation Status](https://readthedocs.org/projects/cellseg/badge/?version=latest)](https://cellseg.readthedocs.io/en/latest/?badge=latest)\n[![Total Downloads](https://pepy.tech/badge/cellseg)](https://pepy.tech/project/cellseg)\n[![Monthly Downloads](https://pepy.tech/badge/cellseg/month)](https://pepy.tech/project/cellseg)\n[![Weekly Downloads](https://pepy.tech/badge/cellseg/week)](https://pepy.tech/project/cellseg)\n[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Nelson-Gon/cellseg/graphs/commit-activity)\n[![GitHub last commit](https://img.shields.io/github/last-commit/Nelson-Gon/cellseg.svg)](https://github.com/Nelson-Gon/cellseg/commits/main)\n[![GitHub issues](https://img.shields.io/github/issues/Nelson-Gon/cellseg.svg)](https://GitHub.com/Nelson-Gon/cellseg/issues/)\n[![GitHub issues-closed](https://img.shields.io/github/issues-closed/Nelson-Gon/cellseg.svg)](https://GitHub.com/Nelson-Gon/cellseg/issues?q=is%3Aissue+is%3Aclosed)\n\n\n# Development stage\n\n- [x] Read Tiff Images\n\n- [x] Read Non Tiff Images\n\n- [x] Write Data Transformers and Loaders\n\n- [ ] Write functional model plus scripts  \n\n- [ ] Modify model weights/layers\n\n- [ ] Read stacked tiff images/videos \n\n# Introduction\n\n`cellseg` is a PyTorch (`torch`) based deep learning package aimed at multiclass cell segmentation. \n\n# Installation\n\n```shell\npip install cellseg \n```\nOr if you want to build from source \n\n```shell\ngit clone git@github.com:Nelson-Gon/cellseg.git\ncd cellseg\npython setup.py install \n\n```\n\n\n\n\n\n\n# Usage\n\n## Script mode \n\n**View images**\n\n```shell\npython -m cellseg -d data/train -t \"image\" -n 4 -s 512\n```\n\nTo get help \n\n```shell\npython -m cellseg --help\n#usage: __main__.py [-h] -d IMAGE_DIRECTORY -s IMAGE_SIZE -t TARGET -n NUMBER\n#\n#optional arguments:\n#  -h, --help            show this help message and exit\n#  -d IMAGE_DIRECTORY, --image-directory IMAGE_DIRECTORY\n#                        Path to image directory containing images and\n#                        masks/labels\n#  -s IMAGE_SIZE, --image-size IMAGE_SIZE\n#                        Size of images\n#  -t TARGET, --target TARGET\n#                        Target images to show\n#  -n NUMBER, --number NUMBER\n#                        Number of images to show\n```\n\n## Programming mode \n\n**Importing relevant modules** \n\n```shell\n\nfrom cellseg.data import DataProcessor\nfrom cellseg.model import CellNet\nfrom cellseg.utils import DataProcessor, show_images\n```\n\n**Creating a a model object**\n\n```shell\nmy_model = CellNet()\n```\n\n**Load training data**\n\n```shell\ntrain_data = DataProcessor(image_dir=\"data/train/images\", label_dir=\"data/train/images\", image_suffix=\"tif\")\n```\n\n**View loaded images or masks**\n\n```shell\nshow_images(train_data, number = 8, target=\"image\")\n```\n\n**Training**\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnelson-gon%2Fcellseg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnelson-gon%2Fcellseg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnelson-gon%2Fcellseg/lists"}