Changelog

v2.0 - 2022-05-12

fixed a bug that lead to invalid dimensions for input images with certain aspect ratios
ported pipeline code to nextflow dsl2
bumped required nextflow version to 22.04.0
moved hosting of pretrained models and test data to Azure (thanks @ebirn)
added CITATION.cff
added version pinning for R 4.1.3
dropped r-shinythemes dependency as r-shiny now has bootstrap 5 support
updated r-shiny 1.6.0 > 1.7.1
updated tensorflow-base 2.4.1 > 2.7.1
updated imagemagick dependency 7.0.11_12 > 7.1.0_33
updated r-corrplot dependency 0.88 > 0.92
updated r-jpeg dependency 0.1_8.1 > 0.1_9

fixed illegible text in Markdown documents when browsing Github in Dark Mode (thanks @greymonroe for pointing this out)
added config profile for BioHPC Genomics cluster at LRZ
removed unneeded tensorflow-estimator dependency
updated tensorflow-base 2.0.0 > 2.4.1
updated imagemagick dependency 7.0.10_28 > 7.0.11_12
updated shiny dependency 1.5.0 > 1.6.0
updated tidyverse dependency 1.3.0 > 1.3.1
updated shinythemes dependency 1.1.2 > 1.2.0
updated corrplot dependency 0.84 > 0.88
changed container base to micromamba for smaller container images and faster builds
added CI tests for conda profile on macOS and additional container engines on linux (charliecloud and podman)

changed container registry from docker.io to quay.io
improved image visualization and plot rendering in Shiny app (see #54) thanks @dschneiderch for input
updated scikit-image 0.17.2 > 0.18.1
updated imagemagick dependency 7.0.10_23 > 7.0.10_28
updated shinycssloaders dependency 0.3 > 1.0.0
updated slickr dependency 0.4.9 > 0.5.0
added publication doi to pipeline manifest
changed (back) download links in pipeline to fetch pretrained models from Dropbox instead of Zenodo (#53)
added charliecloud profile (needs nextflow >= 20.12.0-edge)
fixed plotting of leaf states over time in Shiny app (when appropriate metadata are provided)
added config for LRZ coolmuc2

updated pipeline to fetch trained models from the deposited Zenodo record instead of Dropbox
fixed an issue where the DPP addon produced sub-par segmentation results compared to the tools implementation in Deep Plant Phenomics.
updated shiny app to show visualizations sorted by filename

updated shiny dependency 1.4.0 > 1.5.0
added a more informative log message if pipeline fails on systems with insufficient memory
added --ignore_label parameter to exclude a segmentation class for trait calculation.
added --label_spec parameter to allow for mapping of segmentation classes to pixel values of user-supplied segmentation masks. This is a requirement for the --masks parameter now.
updated base.config to avoid out-of-memory issues when running with --multiscale
added Dockerfile + Conda environment for DPP v2.1.0
factored out Shiny dependencies into separate container (should be easier to deploy as a hosted Shiny app now)
added --model 'DPP' and --dpp_checkpoint to allow for custom segmentation models, trained using the Deep Plant Phenomics framework
added --masks parameter to skip semantic segmentation and run trait extraction using user-supplied masks
fixed an issue where the pipeline would crash if the input image contains an alpha channel
updated configuration for CBE cluster
added log message to show current parameter settings when starting a pipeline run
updated scikit-image 0.16.2 > 0.17.2
updated imagemagick dependency 7.0.9_27 > 7.0.10_23

disabled task error strategy for pipeline runs on local computers
fixed an issue where the network address of the shiny application was not correctly displayed on computers running MacOS

Initial pipeline release