Uses of Class
org.anchoranalysis.core.exception.OperationFailedException

Packages that use OperationFailedException
Package
Description
Reading and writing annotations to the filesystem.
Generates output images to illustrate a Assignment between annotations.
Beans related to reading / writing / specifying annotations.
Beans that create lists or indices of colors according to particular schemes.
Collection-related classes with general applicability that don't belong elsewhere.
File formats and associated extensions.
Data-structures for directed and undirected graphs, with or without an edge payload.
A collection of named-elements, a NamedProvider and related operations.
Data structures like NamedProvider with supporting for adding new elements.
Classes to support getting / setting / specifying unique-integers (indices) for elements.
A range of indices from minimum to maximum.
Encapsulating values or sets of named-values.
Generic experiments that handle input and output in structured ways.
How a task is executed on multiple inputs.
Interfaces for replacing attributes of an experiment: input, output, task etc.
Helpful classes for logging.
A particular stack used in feature-calculations called a EnergyStack together with related classes.
Initialization parameters used for beans in org.anchoranalysis.feature.bean.
Strategies to determine which child-cache (among hierarchy) to employ for a particular feature calculation.
Strategies on whether to reuse or invalidate a feature-input's cache.
Base classes for performing operations on Channels.
Classes that are used elsewhere in this package but which aren't themselves AnchorBeans.
Classes that aren't AnchorBeans that pertain to the initialization of image-related beans.
Beans pertaining to ObjectMask and their collections.
Beans to define entities or operate that consider Euclidian space.
Aligning a BoundingBox to fit inside another BoundingBox.
Base classes for thresholding and calculating levels for thresholding.
Converting quantities of distance between units.
Conversions and operations involving Java AWT's BufferedImage.
Converts a channel to another data-type based upon another object to which it is attached.
Converts a channel to another data-type based upon a Histogram to which it is attached.
Suggestions on a size for an image.
Merges two or more entities.
Scaling object-collections, or lists of elements with object-representations.
Scaling elements with an associated ObjectMask either collectively or independently.
Operations that extract points from image data entities or vice versa.
Defines the key data object, Stack, and related classes.
A collection of Stacks, each with a unique identifier as a name.
Beans that define context (which feature, which energy-stack) for evaluating feature-inputs.
Inference of machine learning models on images.
Beans to reduce the number of results that are returned from inference, by removing overlaping entities etc.
Non-bean classes to reduce the number of results that are returned from inference, by removing overlapping entities etc.
Non-beans pertaining to segmentation of images via model inference.
How to represent an object-mask in a raster that is being drawn.
Non-bean classes for reading a Channel from the filesystem.
Non-bean classes for a mapping of names to Channels.
Non-bean classes for reading a Stack from the filesystem.
Base classes for generators that ultimately write only a bounding-box portion of a Stack to the filesystem.
Base classes for generators that ultimately write a Stack to the filesystem.
A time-sequence of Stacks.
Data-structures to store and manipulate image raster-data or voxels.
Converting Voxels to different data-types.
Routines for moving a KernelPointCursor around the neighboring voxels of the point.
Applying a kernel via convolution to voxels.
The fundamental data class that is an ObjectMask and related structures.
Predicates to match certain voxels as used in org.anchoranalysis.image.voxel.object.morphological.
Classes for calculating differnet kind of projections of voxel values across multiple buffers.
Statistics about aggregated voxel intensities.
Thresholding operations on voxels.
High-level classes for performing machine learning model inference.
Base-classes and utilities for generators, which write different types of objects to the file system.
Classes relating to creating inputs for an experiment / task.
Classes for reading comma-separated-values (CSV) files.
Solving or manipulating equations.
The Histogram data class and related operations.
Operations based on sets of points.
Proposing Marks or other data-structures with particular attributes.
Parameters used to initialize certain types of beans.
Non-beans for reading data structures related to marked point processes from the file system.
Beans relating to drawing an Overlay on an image.
Drawing an overlay on an image.
Strategies for how to annotate an image, such label per image etc.
Morphological grayscale-reconstruction algorithm.
Implementations of Thresholder that use FIJI.
Filters that perform blurring.
Thresholding of intensity values represented in at Histogram.
Implementations of ObjectFilter.
Implementations of ObjectFilter that assess each element in a collection collectively.
Implementations of ObjectFilter that combine two or more other ObjectFilters.
Implementations of ObjectFilter that independently assess each element in a collection.
Implementations of ObjectMatcher.
Implementations of ObjectCollectionProvider involving merging objects together.
Minima imposition for a Watershed transformation.
Grayscale reconstruction to support the Watershed transformation.
Strategies for reducing the number of elements with a list by merging/removing lower-confidence elements that overlap.
Implementations of ScaleCalculator for calculating a scaling-factor from dimensions.
Implementations of ThumbnailFromObjects.
Implementations of VoxelScore.
Implementations of CalculationPart that that process a single ObjectMask.
Implementations of CalculationPart that that process a single ObjectMask - with morphological operations.
Non-bean for operations or calculations relating to intensity.
Non-beans pertaining to ObjectMasks.
Merging ObjectMasks.
Conditions for merging ObjectMasks.
Assigning priority when merging ObjectMasks.
Combining multiple images together into a single image.
Source of rows in feature-tables with FeatureSource and derived classes.
Different approaches for converting sets of channels (RGB, independently etc.) to another image format.
Tasks that involved stacks (usually each channel from an image) that are somehow grouped-together.
Task(s) to export histograms of intensity values.
Selecting a subset of a set of channels.
Associating labels with images.
Tasks to scale an image.
Combines multiple Channels voxel-wise to form a single aggregated Channel.
Non-bean classes pertaining to Features as used in tasks.
Non-beans for calculating Features.
Non-bean classes about grouping channels or other inputs.
Non-bean classes about labelling images.
Non-bean classes pertaining to combining stacks into a single stack.
Non-bean classes pertaining to stacks and channels as used in tasks.
Implementations of Thresholder that call ImageJ.
Implementations of ChannelProvider that call ImageJ.
Non-bean classes involving ObjectMask that call ImageJ.
Rules for preserving or changing the naming of files when copying on the file-system.
Implementations of CopyFilesNaming that perform clustering of files.
Implementations of InputManager that process Stacks.
Summarizing a set of elements into a descriptive-string.
Implementations of Summarizer that summarize images.
Implementations of Summarizer that summarize generically InputFromManagers.
Implementations of Summarizer that summarize generically Paths.
Non-bean classes to help tasks in org.anchoranalysis.plugin.io.bean.file.copy.
Non-bean classes to help in processing paths.
Objects used in shared-state in Tasks in this plugin.
Converting from NamedChannelsInput to the input-type expected by a Task.
Beans related to Define involving Mark or related classes.
Decodes the outputted tensors from a Mask R-CNN implementation.
Decodes the outputted tensors from text-segmentation models.
Segmenting a Stack using the ONNX Runtime to produce an ObjectCollection.
Non-bean classes for running an inference model with the ONNX Runtime.
Plugins that call OpenCV.
Segmentation of a Stack using OpenCV.
Conversion to/from OpenCV data-structures.
Non-bean classes pertaining to segmentation.
Fitting geometric structures to points.
Non-bean convex hull operations.
Axis-aligned bounding-boxes and related operations.
Various methods to specify the orientation (general direction) of an entity.
Helper classes to execute Tasks in tests.
A mocked feature internally using a feature-calculation.