The microEye

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The microEye is a Python toolkit for fluorescence microscopy that supports super-resolution single-molecule localization microscopy and single-particle tracking. It features hardware control, data analysis, and visualization.

This toolkit is compatible with the hardware used in our microscope. For further details, refer to the miEye microscope paper and OSF project.

What's New in microEye 2.4

• Stage Control Overhaul

  Stage management has been completely redesigned with a unified controller that handles all stage types through a single, consistent interface. A new Stage Manager lets you add, remove, and assign stages to axes on the fly. Per-axis soft limits, configurable center positions, and a new Calibrate action make setup safer and more repeatable. Dedicated XY and Z Center buttons in the Controller dock move stages to their configured center positions in one click.

• SmarAct MCS2 Support

  Full support for SmarAct MCS2 multi-channel controllers, including guided setup, calibration, homing, and soft-limit configuration for reliable multi-axis motion.

• Slides Widget

  A new Slides dock lets you visualize and navigate multi-channel slide layouts (Sticky-Slide VI, µ-Slide VI, 8/18 Well, and more). Click any channel to move the stage to its center, with live position and delta readouts, position overlays, and context menu options for axis orientation and inversion. Layout settings persist across sessions.

• Focus Stabilization Improvements

  The focus stabilizer has been thoroughly refactored. Each axis now has independent PID gains and calibration. New stabilization methods are supported — including Reflection peak fitting, Fiducial/Fourier, and a Hybrid XYZ mode. Frame statistics (count, mean, median) are now tracked and the stabilizer waits for a sufficient number of frames before locking, reducing false engagements. Plots dynamically show only the data relevant to the active method, and all settings persist across sessions.

• New Analysis & Calibration Tools

  Three new calibration workflows are available from the Multi-Viewer Tools menu:

  • Dark Calibration — directory-based mean/variance analysis with results cache, import/export, and dataset comparison.
  • Photon Transfer Curve (PTC) — dataset import, run/cache workflows, cache comparison, and Dark-Cal JSON export.
  • Sphere Power Calibration — calibration utilities integrated into the PTC workflow.

  A new Image Registration tool for aligning and merging multi-channel stacks (with transformation export/import) is also available in the Multi-Viewer Tools menu.

• Expanded Camera Support

  Cameras from Basler, Allied Vision (Vimba X SDK), IDS Peak, Thorlabs, PCO, and PycroManager now share unified ROI, exposure, and framerate handling. Notable additions:

  • DCAM API v4 Support: Full support for Hamamatsu sCMOS cameras via the DCAM API v4, tested with the ORCA-Fusion BT.
  • IDS Peak is newly supported with binning, flash, and full ROI and acquisition flow.
  • Basler is newly supported.
  • Vimba X gains black level, gain, correction, and binning controls, plus CTI auto-discovery via VIMBA_X_HOME / VIMBA_X_CTI.
  • All camera configurations are automatically saved and restored on next launch when detected.

• Laser & Device Controls

  The Controller dock now shows color-coded, wavelength-aware indicators for each laser and relay. Clicking an indicator toggles the laser state directly from the main UI. Relay mode labels (ON/F1/F2) are shown alongside each laser.

• Experiment Protocol Designer

  The protocol action list gains a new ListLoop action type for repeating sequences of steps. The parameter adjustment UI now sorts target objects, shows readable labels, and correctly handles integer and float editors.

• Program-Wide Logging

  Structured logging replaces scattered print statements across the entire codebase. Log level, file output, and console output are configurable via the launcher UI or CLI flags (--log-level, --no-log-file, --no-log-console). Logs are written per session under ./logs for easier troubleshooting.

• Image I/O & Data Handling

  Image handling has been refactored into a dedicated microEye.images package. Zarr V3 is now fully supported for streaming and saving acquisition data, with both zipped and directory store support.

• Multi-Viewer Improvements

  The Multi-Viewer gains an "Opened Files" dock for quick switching between open datasets, a Registration Tool for stack alignment, and new menu entries for the Dark Calibration and PTC tools — all accessible as MDI subwindows.

• Display & Performance

  Focus widget plots have been rewritten to eliminate GUI freezes at high framerates (tested at ~165 Hz). A new optional crosshair overlay can be enabled on any camera display. OpenGL rendering is now used for image display. Application startup is faster thanks to deferred initialization of heavy components.

• Stability & Compatibility

  • GPU imports are safely guarded on CPU-only systems.
  • Qt binding selection is configurable at launch via CLI or environment variable.
  • Spatial filters (DoG, PointGaussFilter, Fourier) are faster via FFT convolution and Numba acceleration.
  • Qt6 compatibility fixes for mouse event handling.
  • PiezoConcept FOC stage adapter fixes a float/int mismatch that caused random Z jumps.

Table of Contents

• The microEye
  • What's New in microEye 2.4
  • Table of Contents
  • How to Install microEye
    • Troubleshooting Installation
  • microEye Launcher
    • Usage
  • Modules
    • The miEye Module
      • Experiment Designer (Beta)
    • The Multi Viewer Module
  • Uses Packages
  • Microscope Scheme
  • Hardware
    • Supported Cameras
    • Additional Hardware
  • Pycro-Manager Integration
    • Features
    • Integrated Hardware
    • How to Use
    • Step-by-step Instructions
  • Authors
  • People Involved
  • Acknowledgement
  • Citation

How to Install microEye

1. Install Python:

   Download and install the latest Python stable release (version ≥3.11.9).

2. Install microEye package:

   Open a terminal and execute the following command to install microEye using pip:

   pip install microEye --upgrade
   

   Or for a specific version:

   pip install microEye==version
   

3. Install specific hardware drivers: (Optional)

   • Allied Vision CMOS cameras:

     Install the Vimba X SDK (avoid installing it inside the Program Files directory). After installation, navigate to the Python API directory (e.g., C:\Allied Vision\Vimba X\api\python) where the vmbpy wheel file (.whl) is located, and install it using:

     python -m pip install vmbpy-1.1.1-py3-none-win_amd64.whl
     

     Steps

     1. Auto (recommended)

        Set the VIMBA_X_HOME environment variable to your Vimba X installation directory. microEye will read VIMBA_X_HOME at startup and use it to point the Vimba X transport-layer (.cti) configuration (e.g., VIMBA_X_HOME/cti).

     2. Manual

        If the automatic method is not prefered, create the environment variable VIMBA_X_CTI and point it to the directory (or directories) that contain the Vimba X .cti transport-layer files. Use directory paths (not individual .cti files). Multiple directories are allowed — separate them with the platform-specific path separator.

• • Hamamatsu CMOS cameras: Install DCAM API v4.
  • Basler CMOS cameras: Install pylon (avoid installing it inside the Program Files directory).
  • IDS CMOS cameras:
    • Install IDS Software Suite 4.96.1 for Windows 32/64-bit.
    • Alternatively, IDS Peak Extended Setup is prefered as it support old uEye models and the new uEye+ ones.

• • Integrated Optics Lasers: Install Laser control software.
  • PCO cameras: Install pco.sdk, pco.runtime.

• • Thorlabs CMOS cameras: Install Thorcam in its default directory. Note: Some Thorlabs cameras may be identified as IDS uEye cameras by Windows and may run without Thorcam.

  • Thorlabs hardware, install Kinesis® Software and Elliptec™ Software.

1. Open a terminal and execute microEye: 🥳

   usage: microEye.exe [-h] [--module {mieye,viewer}] [--QT_API {PySide6,PyQt6,PyQt5}] [--theme {None,qdarktheme,qdarkstyle,...}]
                    [--log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG}] [--no-log-file] [--no-log-console]
   
   options:
     -h, --help            show this help message and exit
     --module {mieye,viewer}
                           The module to launch [mieye|viewer], If not specified, launcher is executed.
     --QT_API {PySide6,PyQt6,PyQt5}
                           Select QT API [PySide6|PyQT6|PyQt5], If not specified, the environment variable QT_API is used.
     --theme {None,qdarktheme,qdarkstyle,...}
                           The theme of the app, if not specified, the environment variable MITHEME is used.
     --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG}
                           Root logging level (default WARNING).
     --no-log-file         Disable logging to file.
     --no-log-console      Disable logging to console.
   

Troubleshooting Installation

If you encounter any issues during the installation process, please check the following:

• Ensure that you have the latest version of Python installed (3.11.9).
• Try installing the required packages repeating step (2) or individually using pip install <package_name>.
• Check the system requirements for specific hardware drivers and follow the installation instructions provided by the manufacturers.
• Refer to the project's issue tracker on GitHub for any known installation issues and solutions.

microEye Launcher

The microEye Launcher allows you to launch either the miEye Module or the Viewer Module, and provides options to select the Qt API and theme for the application.

Usage

Upon running the launcher, you will be presented with the following interface:

• miEye Module: Launches the miEye module for microscope control and acquisition.
• Viewer Module: Launches the viewer module for image/data anlysis and visualization.
• QT API (dropdown): Select the Qt API to use. Options are PySide6, PyQt6, or PyQt5.
• Theme (dropdown): Select the theme for the application. Options are None (default), qdarktheme, or qdarkstyle.

To launch a module, simply click on the respective button (miEye Module or Viewer Module). If you wish to change the Qt API or theme, select the desired option from the dropdown menus before launching.

Modules

The miEye Module

The miEye_module provides the primary graphical user interface (GUI) for microscope control and data acquisition, combining the functionalities of the deprecated Acquisition and Control modules.

miEye module (NOT UP TO DATE)	Acquisition Camera

How to use:

To launch the miEye module, run the following command:

microEye --module mieye

Experiment Designer (Beta)

The Experiment Designer is a new feature within the miEye Module that allows users to create and manage complex acquisition protocols through a graphical interface.

Key features include:

• GUI-based design of acquisition protocols;
• Support for loops and device parameter adjustments/actions;
• Export and import capabilities for protocols;
• Threaded execution with pause functionality;
• Configurable delays between protocol steps
• Context menu for easy adjustments
• Nested protocol structure
• Acquisition action wait option
• Cell reordering and deletion via keyboard shortcuts
• Real-time execution progress view

To access the Experiment Designer:

1. Launch the miEye module.
2. Look for the Protocols view in the interface.

Note: This feature is currently in beta. We value your feedback to enhance its functionality and improve the user experience.

The Multi Viewer Module

The multi_viewer Module is an improved GUI that replaces the deprecated tiff_viewer module. It allows users to process multiple files and provides data analysis and visualization tools for super-resolution single-molecule localization microscopy and single-particle tracking.

Raw Data	Localizations

How to use:

To launch the Multi Viewer module, run the following command:

microEye --module viewer

Uses Packages

The microEye uses the following Python packages:

Microscope Scheme

Schematic overview of the miEye instrument:

• A) Single-Mode Fiber (SMF): Excitation path for TIRF-, HILO-, and Epi-mode.
• B) Multi-Mode Fiber (MMF): Excitation path for Epi-mode when imaging MMF output on the sample plane.
• C) Fluorescence Emission: Path for fluorescence emission.
• D) Automatic Focus Stabilization: the automatic focus stabilization path using an IR laser in TIRF setting.

Scheme 1	Scheme 2

Key Components: AC: Achromat lens, AS: Aspheric lens, BFP: Back-focal plane, TL: Tube lens, B: B-coated N-BK7 optics, BS: Beamsplitter.

Hardware

Supported Cameras

Additional Hardware

Pycro-Manager Integration

Integration with Pycro-Manager is now implemented, providing support for managing headless instances and Core instances. This allows for seamless control and configuration of hardware devices through the Pycro-Manager framework.

Features

• Headless Manager: Manage multiple headless Micro-Manager instances (JavaBackend), including starting, stopping, and saving configurations.
• Core Instances Manager: Handle multiple PycroCore instances for device communication and control.

Integrated Hardware

The following table lists the hardware devices that are integrated and supported by the miEye module:

How to Use

1. Headless Manager:

   • Open the miEye_module and navigate to Tools -> Micro-Manager Headless Manager in the main menu.
   • Start a new headless instance using a specific configuration file.
   • Stop individual running instances or terminate all instances simultaneously.
   • Save configurations for future use or load previously saved configurations for headless instances.

2. Core Instances:

   • Open the miEye_module and navigate to Tools -> Micro-Manager Core Bridges in the main menu.

   • Click on Add Core Bridge and provide the port address in the dialog box.

   • A list of connected devices will be displayed, allowing you to view each device's properties.

   • Close the window and refresh the camera list to access Pycro-Manager cameras.

     Accessing other types of devices is under development!

Step-by-step Instructions

For comprehensive step-by-step instructions, please refer to the Pycro-Manager Integration Guide.

Authors

Mohammad Nour Alsamsam

People Involved

PhD supervision:

Dr. Marijonas Tutkus

Sample preparation, experiments and testing:

Aurimas Kopūstas Tutkus Lab Team

Acknowledgement

Research and access to intruments and samples is credited to:

• Vilnius University, Lithuania.
• Center For Physical Sciences and Technology, Vilnius, Lithuania.
• Research Council of Lithuania.

Special thanks to the following projects and libraries that make this work possible:

• SMAP/fit3Dcspline: The original code, provided as a part of the Ries group SMAP software. The pyfit3Dcspline module in our project is a Python adaptation of this functionality, offering both CPU and GPU accelerated fitting of Single-Molecule Localization Microscopy (SMLM) data. For more details, refer to the pyfit3Dcspline README.

• ACCéNT: a partial implementation of the photon free calibration within the acquisition pipeline which generates pixel-wise mean and variance images.

  Robin Diekmann, Joran Deschamps, Yiming Li, Aline Tschanz, Maurice Kahnwald, Ulf Matti, Jonas Ries, "Photon-free (s)CMOS camera characterization for artifact reduction in high- and super-resolution microscopy", bioRxiv 2021.04.16.440125. doi: 2021.04.16.440125

• Phasor Fit: We have implemented the 2D phasor fitting algorithm in Python for fast pre-fitting visualization of localizations.

  K.J.A. Martens, A.N. Bader, S. Baas, B. Rieger, J. Hohlbein. "Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): an algorithm for MHz localization rates using standard CPUs," bioRxiv, 2017. DOI: 10.1101/191957.

• Endesfelder Lab/SMLMComputational: A numba accelerated adaptation of Drift Correction, Fourier Ring Correlation (FRC) structural resolution and Nearest Neighbour Analysis (NeNA) for localization precision from Endesfelder Lab.

  Raw data to results: a hands-on introduction and overview of computational analysis for single-molecule localization microscopy", Martens et al., (2022), Frontiers in Bioinformatics. Paper

• TARDIS (Temporal Analysis of Relative Distances): We have developed a partial Python implementation of TARDIS without fitting for now. For more information, refer to the TARDIS software releases. The underlying algorithms and scientific details of TARDIS are detailed in the manuscript:

  Martens et al., “Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking”, Nature Methods (2024). Article

• RCC (Robust Cross-Correlation): We have implemented the RCC algorithm in Python for drift correction in SMLM data.

  Y. Wang, J. Schnitzbauer, Z. Hu, X. Li, Y. Cheng, Z.-L. Huang, and B. Huang, “Localization events-based sample drift correction for localization microscopy with redundant cross-correlation algorithm,” Opt. Express 22(13), 15982 (2014). Paper GitHub

• AIM (Adaptive Intersection Maximization): We have implemented the AIM algorithm in Python for drift correction in SMLM data.

  H. Ma, M. Chen, P. Nguyen, and Y. Liu, “Toward drift-free high-throughput nanoscopy through adaptive intersection maximization,” Sci. Adv. 10(21), eadm7765 (2024). Paper GitHub

• COMET (Cost-function Optimized Maximal overlap drift EsTimation): We have implemented the COMET algorithm in Python for drift correction in SMLM data.

  L. Reinkensmeier, S. Aufmkolk, I. Farabella, A. Egner, and M. Bates, “Cost-function Optimized Maximal Overlap Drift Estimation for Single Molecule Localization Microscopy,” (2026). Paper GitHub

Citation

If you find our work or software helpful in your research or project, we kindly request that you cite it appropriately. Here is the suggested citation format:

miEye (2022) HardwareX

M.N. Alsamsam, A. Kopūstas, M. Jurevičiūtė, and M. Tutkus, “The miEye: Bench-top super-resolution microscope with cost-effective equipment,” HardwareX 12, e00368 (2022). Paper

Additionally, we would appreciate it if you could provide a link to our GitHub repository or any relevant publication associated with the software.

Software Citation

Alsamsam, M. N. microEye, https://github.com/samhitech/microEye [Computer software]

Thank you for your support!