Developer API¶
Outputs diagnostic information to help with debugging SLEAP installation. |
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Data structures for all labeled data contained with a SLEAP project. |
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Module with classes for sending and receiving messages between processes. |
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Handles SLEAP preferences. |
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Module with RangeList class for manipulating a list of range intervals. |
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Implementation of skeleton data structure and API. |
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A miscellaneous set of utility functions. |
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Current version of SLEAP package. |
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Main GUI application for labeling, training/inference, and proofreading. |
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Logic for determining what color/width to draw instance nodes/edges. |
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Module for gui command context and commands objects. |
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Data table widgets and view models used in GUI app. |
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Module for checking for new releases on GitHub. |
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Class for accessing/setting keyboard shortcuts. |
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Module with object for storing and accessing gui state variables. |
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Module for generating lists of suggested frames (for labeling or reviewing). |
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Dialog for deleting various subsets of instances in dataset. |
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Dialog for exporting clip; shows message depending on available encoder. |
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Wrappers for Qt File Dialogs. |
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Widgets and dialogues for YAML-based forms. |
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Interface to handle the UI for importing videos. |
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Gui for merging two labels files with options to resolve conflicts. |
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Module to show a non-blocking modal dialog box with a string message. |
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Dialog/widgets for showing metrics on trained models. |
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Gui for prompting the user to locate one or more missing files. |
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GUI for viewing/modifying keyboard shortcuts. |
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Find, load, and show lists of saved TrainingJobConfig. |
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Preview of training data (i.e., confidence maps and part affinity fields). |
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Dialogs for running training and/or inference in GUI. |
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Widget for previewing receptive field on sample image using model hyperparams. |
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Run training/inference in background process via CLI. |
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Conversion between flat (form data) and hierarchical (config object) dicts. |
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Overlay for showing negative training sample anchors (currently unused). |
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Base classes for overlays. |
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Overlay for confidence maps. |
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Overlay for showing instances. |
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Overlay for part affinity fields. |
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Track trail and track list overlays. |
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Qt widget for showing images from a directory. |
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Widget which wraps Matplotlib canvas. |
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Module for Qt Widget to show multiple checkboxes for selecting. |
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Drop-in replacement for QSlider with additional features. |
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Module for showing and manipulating skeleton instances within a video. |
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Functions to align instances. |
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Module for generating lists of frames using frame features, pca, kmeans, etc. |
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Command line utility which prints data about labels file. |
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Module for producing prediction metrics for SLEAP datasets. |
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Module for getting a series which gives some statistic based on labeling data for each frame of some labeled video. |
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CLI for TrackCleaner (mostly deprecated). |
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Generate an HDF5 file with track occupancy and point location data. |
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Support for loading video frames (by chunk) in background process. |
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Command line utility for converting between various dataset formats. |
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A SLEAP dataset collects labeled video frames, together with required metadata. |
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Module for legacy LEAP dataset. |
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Utilities for working with file paths. |
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Video reading and writing interfaces for different formats. |
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Module for writing avi/mp4 videos. |
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Module for generating videos with visual annotation overlays. |
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File format adaptor base class. |
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Adaptor for reading COCO keypoint detection datasets. |
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Adaptor for reading DeepLabCut datasets. |
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Adaptor for reading DeepPoseKit datasets (HDF5). |
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Dispatcher for dynamically supporting multiple dataset file formats. |
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File object which can be passed to adaptors. |
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Adaptor for reading and writing any generic JSON file. |
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Adaptor for reading/writing SLEAP datasets as HDF5 (including .slp). |
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Adaptor for reading/writing old, JSON dataset format (kind of deprecated). |
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Adaptor to read (not write) LEAP MATLAB data files. |
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Read/write for multiple dataset formats. |
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Adaptor to read and write analysis HDF5 files. |
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Adaptor for reading and writing any generic text file. |
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Training-related tf.keras callbacks. |
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Evaluation utilities for measuring pose estimation accuracy. |
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Model head definitions for defining model output types. |
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Inference pipelines and utilities. |
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Custom loss functions and metrics. |
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This module defines the main SLEAP model class for defining a trainable model. |
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GUI for monitoring training progress interactively. |
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This module provides a set of utilities for grouping peaks based on PAFs. |
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This module contains TensorFlow-based peak finding methods. |
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Utilities for working with the physical system (e.g., GPUs). |
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Tracking tools for linking grouped instances over time. |
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Training functionality and high level APIs. |
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This module contains generic utilities used for training and inference. |
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Visualization and plotting utilities. |
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Common utilities for architecture and model building. |
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Generic encoder-decoder fully convolutional backbones. |
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This module provides a generalized implementation of (stacked) hourglass. |
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(Higher)HRNet backbone. |
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This module provides a generalized implementation of the LEAP CNN. |
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ResNet-based backbones. |
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This module provides a generalized implementation of UNet. |
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This module defines common upsampling layer stack configurations. |
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Serializable configuration classes for specifying all training job parameters. |
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Utilities for config building and validation. |
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Transformers for applying data augmentation. |
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Transformers for confidence map generation. |
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Transformers for dataset (multi-example) operations, e.g., shuffling and batching. |
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Transformers for generating edge confidence maps and part affinity fields. |
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General purpose transformers for common pipeline processing tasks. |
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Group inference results (“examples”) by frame. |
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Transformers for performing inference. |
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Transformers for finding instance centroids. |
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Transformers for cropping instances for topdown processing. |
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Transformers for normalizing data formats. |
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This module defines high level pipeline configurations from providers/transformers. |
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Data providers for pipeline I/O. |
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Transformers for image resizing and padding. |
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Transformers and utilities for training-related operations. |
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Miscellaneous utility functions for data processing. |
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Functions/classes used by multiple trackers. |
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Module to use Kalman filters for tracking instance identities. |