cellseg: Multiclass Cell Segmentation
=====================================


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Development stage
=================


* 
  [x] Read Tiff Images

* 
  [x] Read Non Tiff Images

* 
  [x] Write Data Transformers and Loaders

* 
  [ ] Write functional model plus scripts  

* 
  [ ] Modify model weights/layers

* 
  [ ] Read stacked tiff images/videos 

Introduction
============

``cellseg`` is a PyTorch (\ ``torch``\ ) based deep learning package aimed at multiclass cell segmentation. 

Installation
============

.. code-block:: shell

   pip install cellseg

Or if you want to build from source 

.. code-block:: shell

   git clone git@github.com:Nelson-Gon/cellseg.git
   cd cellseg
   python setup.py install

Usage
=====

Script mode
-----------

**View images**

.. code-block:: shell

   python -m cellseg -d data/train -t "image" -n 4 -s 512

To get help 

.. code-block:: shell

   python -m cellseg --help
   #usage: __main__.py [-h] -d IMAGE_DIRECTORY -s IMAGE_SIZE -t TARGET -n NUMBER
   #
   #optional arguments:
   #  -h, --help            show this help message and exit
   #  -d IMAGE_DIRECTORY, --image-directory IMAGE_DIRECTORY
   #                        Path to image directory containing images and
   #                        masks/labels
   #  -s IMAGE_SIZE, --image-size IMAGE_SIZE
   #                        Size of images
   #  -t TARGET, --target TARGET
   #                        Target images to show
   #  -n NUMBER, --number NUMBER
   #                        Number of images to show

Programming mode
----------------

**Importing relevant modules** 

.. code-block:: shell


   from cellseg.data import DataProcessor
   from cellseg.model import CellNet
   from cellseg.utils import DataProcessor, show_images

**Creating a a model object**

.. code-block:: shell

   my_model = CellNet()

**Load training data**

.. code-block:: shell

   train_data = DataProcessor(image_dir="data/train/images", label_dir="data/train/images", image_suffix="tif")

**View loaded images or masks**

.. code-block:: shell

   show_images(train_data, number = 8, target="image")

**Training**