Contents Menu Expand Light mode Dark mode Auto light/dark, in light mode Auto light/dark, in dark mode Skip to content
JuSPICE Documentation 1.0 documentation
Logo
JuSPICE Documentation 1.0 documentation
  • Home

Getting Started

  • Library Structure
    • Fixed Structure
    • Auto-Generated Folders
  • Installation
    • Linux Installation
      • Linux — Install Everything at Once
      • Linux — Step-by-Step Installation
      • Linux Troubleshooting
    • macOS Installation
      • macOS — Install Everything at Once
      • macOS — Step-by-Step Installation
      • macOS Troubleshooting
    • Windows Installation
      • Windows — Install Everything at Once
      • Windows — Step-by-Step Installation
      • Windows Troubleshooting
    • IOPaint Installation
      • Linux
      • macOS
      • Windows
  • Modules
    • I/O and Tracking
    • Preprocessing
    • Clustering
    • Segmentation
    • Feature Extraction
    • Synthetic Data
    • Physics-Based Image Generation
    • Inpainting
  • Notebook Examples
    • I/O and History Tracking
      • From an Image Folder to a Time Series
      • Loading a Multi-Channel AFM Scan (.spm)
      • Grouping AFM Pixels by All Channels at Once
      • Loading a TEM Image (.dm3)
      • Loading a STEM Image (.ser)
      • Loading an FEI/TIA Project File (.emi)
    • Image Preprocessing
      • Image Preprocessing: From Raw Image to Labelled Regions
    • Synthetic Data Generation
      • More Training Data by Augmentation
      • Synthetic Training Images from Particle Models
      • Generating Microscopy Images with a GAN
      • Varying Real Images with Stable Diffusion
    • Segmentation and Tracking
      • Training a U-Net to Segment SEM Images
      • Segmentation Without Training: Segment Anything (SAM)
      • Segmentation Without Training: SAM2
      • Segmentation with a Microscopy-Pretrained Model (NASA MicroNet)
      • Tracking Particles and Measuring How They Move
    • Clustering
      • Splitting an Image into Regions with Clustering
    • Inpainting
      • Repairing Damaged Image Regions (Inpainting)
    • Feature Extraction
      • Describing Images with Feature Maps
    • Physics-Based Image Generation
      • Building Synthetic Microscopy Images from Simple Shapes
  • Sample Data
    • Sample_data/em/ — Electron Microscopy
    • Sample_data/afm/ — Atomic Force Microscopy
    • EBC1 — Environmental Barrier Coating
    • TiO₂ EM Dataset
    • SiO₂ Lithiation
    • Bulk Water

Domain Background

  • Electrochemical Imaging
    • What Is Electrochemical Imaging?
    • Key Imaging Techniques
      • Scanning Electron Microscopy (SEM)
      • Transmission Electron Microscopy (TEM)
      • Atomic Force Microscopy (AFM)
      • Spectroscopy, Tomography, and Other Techniques
      • Temporal / In-Situ and Operando Imaging
    • Typical Image Characteristics
    • Why Machine Learning?
  • Image Processing
    • Digital Image Processing Basics
      • What Is a Digital Image?
      • Histograms
      • Spatial Filtering
      • Edge Detection
      • Image Transformations
      • Thresholding and Binarization
      • Morphological Operations
      • Connected Components
    • Image Preprocessing
      • Normalization and Standardization
      • Denoising
      • Contrast Enhancement
      • Background Correction
      • Edge Detection and Gradient Images
      • Binarization and Thresholding
      • Morphological Post-Processing
    • Feature Extraction
      • Gaussian
      • Sobel
      • Scharr
      • Laplace
      • Hessian
      • Frangi
      • Meijering
      • Sato
      • Gabor
      • Local Binary Pattern (LBP)
      • Local Entropy
      • Structure Tensor Coherence
    • Image Clustering
      • Watershed
      • SLIC Superpixels
      • Felzenszwalb
      • Otsu Multi-Threshold
      • Quickshift
      • K-means
    • Synthetic Data Generation
      • Classical Augmentation
      • Physics-Based Synthesis
      • Deep Convolutional GAN (DCGAN)
      • Stable Diffusion Image-to-Image
    • Image Segmentation
      • U-Net
      • Segment Anything Model (SAM v1)
      • SAM 2
      • NASA MicroNet
      • TrackPy

Developer Reference

  • Developer Guide
    • Architecture overview
    • Tracker architecture
    • Keeping tracking consistent
    • Documentation workflow
    • Contributing documentation
  • API Reference
    • Core I/O and Tracking
      • juspice.io
      • juspice.tracking
    • Processing Modules (Core Install)
      • juspice.preprocess_module
      • juspice.feature_extract
      • juspice.inpainting_module
      • juspice.clustering_module
      • juspice.shape_based — Physics-Based Image Generation
    • Processing Modules (Require [dl])
      • juspice.segmentation_module
      • juspice.synth_data_module
      • juspice.dcgan
      • juspice.stable_diff
    • Utilities
      • juspice.utils

Project

  • About JuSPICE
  • How to Collaborate
  • Contact
  • Funding
  • Bibliography
Back to top

Core I/O and Tracking¶

← Back to API Reference

  • juspice.io — load_data(), load_frame_sequence(), load_video(), save_data(), SPICEData

  • juspice.tracking — Tracker, DatasetHistory, track_dataset_operation()

See also

  • Processing Modules (Core Install) — the core-install processing modules

  • API Reference — every API section

Next
juspice.io
Previous
API Reference
Copyright © 2026, Amir Omidvarnia, Forschungszentrum Jülich GmbH