Further Info

1. Image Utilities

A quick and easy way to save annotations (a napari labels layer) and corresponding images to corresponding folders. Best if the images are opened with the neuralDev reader (using bioio under the hood) -- which can be as simple as drag and drop opening by setting the appropriate default reader for each file type in Preferences -> Plugins--in order to utilize the metadata present for saving the image-label pairs.

Quick uniform adjustments to a folder of images, saving the output. Currently supports selecting channels, slicing Z, cropping/downsampling in XY, and doing a max projection of the sliced/cropped image data. To be added: alternative projection types, slicing in T, and compatibility with non TCZYX images (but this is not a priority since bioio currently always extracts images as TCZYX even if a dim is only length 1.

2. Workflow Widget

Batch pre-processing/processing images using napari-workflows. Images are processed outside the napari-viewer using bioio as both reader and writer. Prior to passing the images to napari-workflows, the user selects the correct images as the roots (inputs) and thus napari-workflows matches the processing to create the outputs. The advantage of using napari-workflows for batch processing is that it provides an incredibly flexible processing interface without writing a novel widget for small changes to processing steps like specific filters, segmentation, or measurements. Currently only intended for use with images as inputs and images as outputs from napari-workflows, though there is future potential to have other outputs possible, such as .csv measurement arrays.

3. APOC Widget

Utilizes the excellent accelerated-pixel-and-object-classification (apoc) in a similar fashion to napari-apoc, but intended for batch training and prediction with a napari widget instead of scripting. Recognizes pre established feature set, and custom feature sets (a string of filters and radii) can be generated with a corresponding widget. Also contains a Custom Feature Set widget which allows application of all the features to a layer in the viewer, for improved visualization.

4. Measure Widget

Batch measurements using scikit-image's regionprops. This can measure features of a label such as area, eccentricity, and more but also can measure various intensity metrics. Attempts to support post-processing of measurements, grouping, and more to make downstream analyses easier for users. Will be updated in the future to include nyxus.