# 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/)
Raw data
{
"_id": null,
"home_page": "https://github.com/Defense-Circuits-Lab/findmycells",
"name": "findmycells",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "",
"keywords": "nbdev jupyter notebook python",
"author": "DSegebarth",
"author_email": "d.segebarth@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/79/f8/cf56d0bec1cdb09a6b31463263c1bdfd0f3630144c79522e01b0dc2fb8ef/findmycells-0.1.4.tar.gz",
"platform": null,
"description": "# findmycells\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n# Hi there!\n\n<p>\n<img src=\"https://github.com/Defense-Circuits-Lab/findmycells/blob/main/media/findmycells_logo_low_res.png?raw=true\" style=\"float:right;width:300px;\">\n<font size=\"3\"> Over the past years, deep-learning-based tools have\nbecome increasingly popular and abundant, particularly in the image\nprocessing domain. In fact, even the image shown next to this text was\ncreated by such a tool - with nothing but a few keywords as input (go\ncheckout <a href=\"https://starryai.com/\">starryai</a>). Similarly,\ndeep-learning-based image analysis tools also have a growing impact on\nbiomedical research. However, such deep-learning-powered scientific\nsoftware tools are rarely as user-friendly as starryai (or\n<a href=\"http://www.mackenziemathislab.org/deeplabcut\">DeepLabCut</a>,\nto name at least one positive exception). And make no mistake, also\n<i>findmycells</i> will not be able to make such a giant leap forward.\nInstead, it was developed to narrow the gap by bringing state-of-the-art\ndeep-learning-based bioimage analysis tools to users with little or even\nno coding experience. This is achieved, as it integrates them in a full\nend-to-end bioimage analysis pipeline that comes with an intuitive and\ninteractive graphical user interface that runs directly in Jupyter\nNotebooks. But enough introduction - please feel free to test it\nyourself! Either follow the installation instructions below, or head\nover for instance to the\n<a href=\"https://defense-circuits-lab.github.io/findmycells/tutorials/gui_tutorial.html\">GUI\ntutorial</a> to get a first impression! </font>\n</p>\n\n# Installation guide\n\n*findmycells* is currently only available via pip. *findmycells* was so\nfar tested in Windows 7, MacOS Ventura and in a Linux subsystem run\nunder Windows (Ubuntu 20.04.5 in WSL2 on both Windows 10 and Windows\n11). Moreover, having a CUDA-capable GPU is highly recommended when\nusing [deepflash2](https://github.com/matjesg/deepflash2) or\n[cellpose](https://github.com/MouseLand/cellpose) for the segmentation\nof your images. You can run *findmycells* on [Google Colab](https://colab.research.google.com/github/Defense-Circuits-Lab/findmycells/blob/main/findmycells_colab.ipynb) \nas well. For local installation, please follow the steps below:\n\nWe highly recommend that you use\n[Anaconda](https://www.anaconda.com/products/distribution). In your\nterminal (command line, or Anaconda Prompt), run the following line to\ncreate a new conda environment, called \u201cfindmycells\u201d:\n\n> conda create -n findmycells\n\nOnce the environment is created, activate it as suggested by conda by\nrunning:\n\n> conda activate findmycells\n\nYou should now see `(findmycells)` at the beginning of the line.\nContinue by installing pip:\n\n> conda install pip\n\nOnce the installations are done, all you need to do is run:\n\n> pip install findmycells\n\nAnd all required packages should come with it.\n\n## Confirming that your GPU is accessible:\n\nIf you would like to use your GPU (again: using a GPU is highly\nrecommended, if you want to use the integrated segmentation tools\ndeepflash2 and cellpose), you can test whether it is accessible for\npython by running the following commands again in the terminal (with the\nfindmycells environment still activated):\n\n> python\n\nThis will now launch a python interface right in your terminal. Now\ncontinue with importing torch:\n\n> import torch\n\nAs soon as torch is imported, you can check for GPU accessability by\nrunning:\n\n> torch.cuda.is_available()\n\nThis will now output either `True` (whoop whoop - everything is\nworking - well done!) or `False` ( :( ). In case you have a local\nCUDA-capable GPU installed & you still got a `False`, confirm that you\nhave the latest GPU driver installed, and that you are using the latest\navailable Windows, WSL2, and Linux builds. If you\u2019re still not able to\naccess the GPU, it may sometimes help to start by installing pytorch\nfirst (mix & match the correct versions for you\n[here](https://pytorch.org/get-started/locally/)), and to first confirm\nthat the GPU is indeed accessible. If it is, you can now continue with\nthe installation of findmycells & hope that nothing breaks! Fingers\ncrossed, that you can find a version that satisfies your local\nrequirements & also those of deepflash2 & cellpose!\n\n# For developers\n\nThis package is developed using [nbdev](https://nbdev.fast.ai/)\n",
"bugtrack_url": null,
"license": "GNU General Public License v3",
"summary": "An end-to-end bioimage analysis pipeline with state-of-the-art tools for non-coding experts",
"version": "0.1.4",
"project_urls": {
"Homepage": "https://github.com/Defense-Circuits-Lab/findmycells"
},
"split_keywords": [
"nbdev",
"jupyter",
"notebook",
"python"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "693f27e3648a90b53c639405af9278af27439fa0c350605fae544604da5e0804",
"md5": "905c16e735a4f798042267b2739a0940",
"sha256": "ee4ccfd019c369a2664a0343d97696d66ad1915ed8caa11182a22482afa51ad3"
},
"downloads": -1,
"filename": "findmycells-0.1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "905c16e735a4f798042267b2739a0940",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 100989,
"upload_time": "2023-12-08T10:33:49",
"upload_time_iso_8601": "2023-12-08T10:33:49.174690Z",
"url": "https://files.pythonhosted.org/packages/69/3f/27e3648a90b53c639405af9278af27439fa0c350605fae544604da5e0804/findmycells-0.1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "79f8cf56d0bec1cdb09a6b31463263c1bdfd0f3630144c79522e01b0dc2fb8ef",
"md5": "775f385a4676fa6102e90d5a84d9c80e",
"sha256": "26ed5507b3d8417341cf0e548d1a017925b13228527ec0e24755888037295dd5"
},
"downloads": -1,
"filename": "findmycells-0.1.4.tar.gz",
"has_sig": false,
"md5_digest": "775f385a4676fa6102e90d5a84d9c80e",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 94042,
"upload_time": "2023-12-08T10:33:51",
"upload_time_iso_8601": "2023-12-08T10:33:51.173129Z",
"url": "https://files.pythonhosted.org/packages/79/f8/cf56d0bec1cdb09a6b31463263c1bdfd0f3630144c79522e01b0dc2fb8ef/findmycells-0.1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-12-08 10:33:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Defense-Circuits-Lab",
"github_project": "findmycells",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "findmycells"
}
JSON
