Welcome to cellseg’s documentation!

cellseg

data module

class data.DataProcessor(image_dir, dir_type='image', target_size=(512, 512), image_suffix='tif')[source]

Bases: Generic[torch.utils.data.dataset.T_co]

to_uint8(image)[source]
transform(image)[source]
Parameters

  • image – Stacked image eg (71, 1200, 1200)

  • mask – Stacked mask eg (71, 1200, 1200)

Returns

Transformed images that have been randomly cropped, flipped, and resized to the target size.

data.convertScaleAbs(src[, dst[, alpha[, beta]]]) → dst

. @brief Scales, calculates absolute values, and converts the result to 8-bit. . . On each element of the input array, the function convertScaleAbs . performs three operations sequentially: scaling, taking an absolute . value, conversion to an unsigned 8-bit type: . f[texttt{dst} (I)= texttt{saturate_cast<uchar>} (| texttt{src} (I)* texttt{alpha} + texttt{beta} |)f] . In case of multi-channel arrays, the function processes each channel . independently. When the output is not 8-bit, the operation can be . emulated by calling the Mat::convertTo method (or by using matrix . expressions) and then by calculating an absolute value of the result. . For example: . @code{.cpp} . Mat_<float> A(30,30); . randu(A, Scalar(-100), Scalar(100)); . Mat_<float> B = A*5 + 3; . B = abs(B); . // Mat_<float> B = abs(A*5+3) will also do the job, . // but it will allocate a temporary matrix . @endcode . @param src input array. . @param dst output array. . @param alpha optional scale factor. . @param beta optional delta added to the scaled values. . @sa Mat::convertTo, cv::abs(const Mat&)

utils module

utils.get_thresholds(image, method='li')[source]
Parameters

  • image – A stacked image of class DataProcessor

  • method – One of li, watershed or multiotsu

Returns

Thresholded methods based on the method.

utils.show_images(dataset_object, stack_number=0, number=None, fig_size=(20, 20), target='image')[source]
Parameters

  • dataset_object – An object of class DataLoader

  • stack_number – Frame number for tiff images. Defaults to zero.

  • number – Number of images to plot from the frame

  • target – Type of images to show. One of “image” or “mask”

  • fig_size – Figure size, defaults to (20, 20)

Returns

A plot showing images from the stack number chosen.

utils.show_thresholded(original, otsu, li, number=28)[source]
Parameters

  • original – Original Image of class DataProcessor

  • otsu – Otsu/watershed/li thresholded image

  • li – Otsu/watershed/li thresholded image

  • number – Frame number to view

Returns

Three images side by side

cellseg: Multiclass Cell Segmentation

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Introduction

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

Installation

pip install cellseg

Or if you want to build from source

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

Development stage

  • [x] Read Tiff Images

  • [x] Read Non Tiff Images

  • [x] Write Data Transformers and Loaders

  • [ ] Write functional model

  • [ ] Modify model weights/layers

Usage

# imports data, utils, model
from cellseg import *

cellseg: Multiclass Cell Segmentation

version 0.0.0

  • Preserve name on PyPI

  • Fixed issues with show_images showing blank images for masks (labels).

  • Fixed issues with uint16 not working with PIL.

  • DataProcessor can now transform images to a given target size.

  • Renamed DataLoader class to DataProcessor to avoid conflicts with torch.utils.data.DataLoader

  • Added initial simple CNN model with a single layer

  • Added show_images in utils.py to allow quick visualization of a given number of images from a given stack of images.

  • Implemented data loaders.

  • Conceptualized project

Contributing to cellseg

This document provides guidelines for contributions to cellseg.

Kinds of contribution

  • Typo fixes

  • Documentation enhancements

  • Pull requests

Fixing typos and enhancing documentation

To fix typos and/or grammatical errors, please edit the corresponding .py or .md file that generates the documentation.

Please also update the docs using sphinx

Pull Requests

  • Please raise an issue for discussion and reproducibility checks at issues

  • Once the bug/enhancement is approved, please create a Git branch for the pull request.

  • Make changes and ensure that builds are passing the necessary checks on Travis.

  • Update changelog.md to reflect the changes made.

  • Do the following:

bash scripts/mkdocs.sh #projectnamehere

  • Releasing

bash scripts/release.sh

The above does the following:

  • Makes dist with python setup.py sdist at the very minimum. Ensure everything necessary is included in Manifest.in.

  • Uploads dist to test.pypi.org with twine upload --repository-url https://test.pypi.org/legacy/ dist/*

  • If everything looks good, asks you to upload to pypi.org with twine upload dist/*

Please note that the ‘cellseg’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

See also for a guide on Sphinx documentation.

Indices and tables