Metadata-Version: 2.1
Name: findmycells
Version: 1.0.0
Summary: An end-to-end bioimage analysis pipeline with state-of-the-art tools for non-coding experts
Home-page: https://github.com/Defense-Circuits-Lab/findmycells
Author: DSegebarth
Author-email: d.segebarth@gmail.com
License: GNU General Public License v3
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# findmycells

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

# Hi there!

<p>
<img src="https://github.com/Defense-Circuits-Lab/findmycells/blob/main/media/findmycells_logo_low_res.png?raw=true" style="float:right;width:300px;">
<font size="3"> Over the past years, deep-learning-based tools have
become increasingly popular and abundant, particularly in the image
processing domain. In fact, even the image shown next to this text was
created by such a tool - with nothing but a few keywords as input (go
checkout <a href="https://starryai.com/">starryai</a>). Similarly,
deep-learning-based image analysis tools also have a growing impact on
biomedical research. However, such deep-learning-powered scientific
software tools are rarely as user-friendly as starryai (or
<a href="http://www.mackenziemathislab.org/deeplabcut">DeepLabCut</a>,
to name at least one positive exception). And make no mistake, also
<i>findmycells</i> will not be able to make such a giant leap forward.
Instead, it was developed to narrow the gap by bringing state-of-the-art
deep-learning-based bioimage analysis tools to users with little or even
no coding experience. This is achieved, as it integrates them in a full
end-to-end bioimage analysis pipeline that comes with an intuitive and
interactive graphical user interface that runs directly in Jupyter
Notebooks. But enough introduction - please feel free to test it
yourself! Either follow the installation instructions below, or head
over for instance to the
<a href="https://defense-circuits-lab.github.io/findmycells/tutorials/gui_tutorial.html">GUI
tutorial</a> to get a first impression! </font>
</p>

# Installation guide

*findmycells* is currently only available via pip. *findmycells* was so
far tested in Windows 7, MacOS Ventura and in a Linux subsystem run
under Windows (Ubuntu 20.04.5 in WSL2 on both Windows 10 and Windows
11). Moreover, having a CUDA-capable GPU is highly recommended when
using [deepflash2](https://github.com/matjesg/deepflash2) or
[cellpose](https://github.com/MouseLand/cellpose) for the segmentation
of your images. You can run *findmycells* on [Google Colab](https://colab.research.google.com/github/Defense-Circuits-Lab/findmycells/blob/main/findmycells_colab.ipynb) 
as well. For local installation, please follow the steps below:

We highly recommend that you use
[Anaconda](https://www.anaconda.com/products/distribution). In your
terminal (command line, or Anaconda Prompt), run the following line to
create a new conda environment, called “findmycells”:

> conda create -n findmycells

Once the environment is created, activate it as suggested by conda by
running:

> conda activate findmycells

You should now see `(findmycells)` at the beginning of the line.
Continue by installing pip:

> conda install pip

Once the installations are done, all you need to do is run:

> pip install findmycells

And all required packages should come with it.

## Confirming that your GPU is accessible:

If you would like to use your GPU (again: using a GPU is highly
recommended, if you want to use the integrated segmentation tools
deepflash2 and cellpose), you can test whether it is accessible for
python by running the following commands again in the terminal (with the
findmycells environment still activated):

> python

This will now launch a python interface right in your terminal. Now
continue with importing torch:

> import torch

As soon as torch is imported, you can check for GPU accessability by
running:

> torch.cuda.is_available()

This will now output either `True` (whoop whoop - everything is
working - well done!) or `False` ( :( ). In case you have a local
CUDA-capable GPU installed & you still got a `False`, confirm that you
have the latest GPU driver installed, and that you are using the latest
available Windows, WSL2, and Linux builds. If you’re still not able to
access the GPU, it may sometimes help to start by installing pytorch
first (mix & match the correct versions for you
[here](https://pytorch.org/get-started/locally/)), and to first confirm
that the GPU is indeed accessible. If it is, you can now continue with
the installation of findmycells & hope that nothing breaks! Fingers
crossed, that you can find a version that satisfies your local
requirements & also those of deepflash2 & cellpose!

# For developers

This package is developed using [nbdev](https://nbdev.fast.ai/)
