.. dcp documentation master file, created by sphinx-quickstart on Sun Feb 11 18:53:28 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Data Centric Platform =============================== *A data centric platform for all-kinds segmentation in microscopy imaging* .. image:: https://img.shields.io/badge/stability-work_in_progress-lightgrey.svg :alt: stability-wip .. image:: https://github.com/HelmholtzAI-Consultants-Munich/data-centric-platform/actions/workflows/test.yml/badge.svg?event=push :alt: tests .. image:: https://codecov.io/gh/HelmholtzAI-Consultants-Munich/data-centric-platform/branch/main/graph/badge.svg :target: https://codecov.io/gh/HelmholtzAI-Consultants-Munich/data-centric-platform How to use it? ---------------- The client and server communicate via the `bentoml `_ library. The client interacts with the server every time we run model inference or training. For full functionality of the software the server should be running, either locally or remotely. To install and start the server side follow the instructions described in :ref:`DCP Server`. To run the client GUI follow the instructions described in :ref:`DCP Client` DCP handles all kinds of **segmentation tasks**! Try it out if you need to do: - **Instance** segmentation - **Semantic** segmentation - **Multi-class instance** segmentation Toy data -------- Our github repo includes the ``data/`` directory with some toy data which you can use as the *Uncurated dataset* folder. You can create (empty) folders for the other two directories required in the welcome window and start playing around. Enabling data centric development ---------------------------------- Our platform encourages the use of data centric practices. With the user friendly client interface you can: - **Detect and remove outliers** from your training data: only confirmed samples are used to train our models - **Detect and correct labeling errors**: editing labels with the integrated napari visualisation tool - **AI-assisted labelling:** Apply any changes to your labels faster using SAM prompts - **Auto cleanup**: Left over pixels remaining after using the eraser tool or an object your forgot to add a class label to? DCP will let you know and clean everything up for you! - **Establish consensus**: allows for multiple annotators before curated label is passed to train model - **Focus on data curation**: no interaction with model parameters during training and inference *Get more with less!* DCP Imaging Conventions ----------------------- DCP currently follows the imaging conventions described below: - Only 2D images are accepted - The accepted imaging formats are: ``(".jpg", ".jpeg", ".png", ".tiff", ".tif")`` - RGB and RGBA images are accepted, however they will be converted to grayscale after read into DCP. The dims can be [C, H, W] or [H, W, C] - Existing segementations can be used, however they need to be TIFF files and have the same name as the corresponding image followed by '_seg', e.g. image1_seg.tiff\ .. toctree:: :maxdepth: 3 :caption: Contents: dcp_client_installation dcp_server_installation dcp_server dcp_client