openptv2

Particle Tracking Velocimetry, single Cython 3 pure-Python engine

Overview

openptv2 is a single-engine PTV library: the same src/openptv2/algorithms/ modules run interpreted in development and compiled through Cython 3 when built, with no separate C core, no Cython bindings to an external library, and no runtime engine selector.

Algorithms (src/openptv2/algorithms/) - Cython 3 pure-Python particle tracking, correspondence, and calibration code
Plugins (src/openptv2/plugins/) - pluggable tracker implementations (fast, MyPTV/ProPTV-informed trackers)
GUI (src/openptv2/gui/) - TraitsUI/Chaco desktop application
Batch pipeline (openptv2-batch) - headless sequence/tracking runner for scripting and cloud use

What's Inside (and Where the Tracking Concepts Come From)

openptv2 is a self-contained PTV engine: detection, camera calibration, correspondence/stereo-matching, and the classic forward tracking loop are all implemented from scratch here in src/openptv2/algorithms/.

On top of that native engine, openptv2 ships plugins that bring in tracking concepts from two external open-source projects:

MyPTV — MIT licensed (© 2022 Ron Shnapp). openptv2's myptv_2d_tracking / nearest_hungarian_3d plugins adapt MyPTV's algorithm ideas (2D per-camera image-space tracking with multi-camera consensus, and 3D kinematic prediction + linear-assignment matching) onto openptv2's own data structures and assignment machinery.
proPTV — MIT licensed (© 2023 DLR, Robin Barta). openptv2 vendors the small pure-numpy core of proPTV (src/openptv2/plugins/proptv/): the Gaussian-Mixture-Model / basis approximation and Savitzky-Golay smoothing routines used in proPTV's track prediction. The predictive_gmm_3d plugin wires those routines into openptv2's tracker.

Important: openptv2 incorporates and adapts parts of these projects as plugins — not their full frameworks. The complete tracking capabilities of each project — e.g. proPTV's 2D-image triangulation pipeline, Soloff calibration, backtracking/repair and Eulerian field estimation, or MyPTV's full photogrammetry, trajectory smoothing and anisotropic-particle tools — live in and are available only from their own repositories:

MyPTV: https://github.com/ronshnapp/MyPTV

proPTV: https://github.com/RobinBarta/proPTV

If you need those advanced features, please use those projects directly. Both are permissively MIT-licensed (see License).

Key Features

Single runtime: one codebase, no C/Cython vs. Python fallback split to keep in sync
Pluggable trackers: swap tracking algorithms via the plugin architecture
Easy installation: pip install openptv2 for headless/batch use, openptv2[gui] to add the desktop GUI

Installation

There are three install profiles:

Command	For	Includes
`pip install openptv2`	scripting + `openptv2-batch` (default)	headless algorithms/API + sequence/tracking pipeline
`pip install openptv2[gui]`	desktop users	default plus the TraitsUI/Chaco/PySide6 desktop GUI
`pip install openptv2[dev]`	contributors	everything: GUI, tests, lint, type-check, notebooks, docs

Default (headless / batch)

The bare install is the headless batch runtime — the library, the openptv2-batch sequence + tracking pipeline, no GUI:

uv pip install openptv2
# or
pip install openptv2

Don't have uv? curl -LsSf https://astral.sh/uv/install.sh | sh

GUI (desktop users)

uv pip install openptv2[gui]
# or
pip install openptv2[gui]

Verify Installation

python -c "import openptv2; print(f'openptv2 version: {openptv2.__version__}')"
python -c "from openptv2 import Tracker; print('Tracker imported successfully')"

Note: launching the GUI (openptv2-gui) requires the [gui] extra. A default install that then launches the GUI will fail with ModuleNotFoundError: No module named 'traitsui' (and chaco/PySide6) — that is expected; install openptv2[gui].

Docker (no Python setup required)

A single image serves both the GUI and batch. It bakes in a trimmed test_cavity demo at /demo/test_cavity so the first run works with no data.

# Build once
docker build -t openptv2 -f docker/Dockerfile .

# GUI on the host X display (Linux/X11), current folder mounted at /data
./docker/run-gui.sh
#   in the GUI, open /demo/test_cavity to try the baked demo

# Headless batch on your own data
docker run --rm -v "$PWD:/data" openptv2 \
  openptv2-batch /data/<experiment>/parameters_Run1.yaml <first> <last>

X11 notes: run-gui.sh handles xhost and mounts /tmp/.X11-unix. On Wayland run xhost +local:root in an XWayland session; on macOS/Windows use an X server (XQuartz / VcXsrv) and set DISPLAY accordingly.

Headless cloud batch: docker/Dockerfile.cloud is a slim, no-GUI, free-threaded 3.14t image for servers/Cloud Run. See docs/cloud-batch.md for the one-command install, openptv2-batch usage, and measured timings.

Zarr + HDF5 Cloud Storage: OpenPTV2 includes a native, high-performance Zarr storage engine (res/run.zarr) replacing thousands of per-frame text files with a cloud-native chunked format. See docs/zarr-hdf5-storage.md for usage, terminal inspection, and Flowtracks HDF5 export.

For Developers (Build from Source)

Prerequisites:

Python 3.11–3.14 (free-threaded 3.14t works too, see docker/Dockerfile.cloud)
C compiler (gcc on Linux, clang on macOS, MSVC Build Tools on Windows) — needed to build the Cython 3 extensions, no CMake involved
uv (recommended) or pip

System Dependencies

Linux (Debian/Ubuntu):

sudo apt-get update
sudo apt-get install -y build-essential python3-dev

Linux (Fedora/RHEL):

sudo dnf install -y gcc gcc-c++ python3-devel

macOS:

xcode-select --install

Windows:

Install Microsoft Visual C++ Build Tools

Step 1: Clone the Repository

git clone https://github.com/alexlib/openptv2.git
cd openptv2

Step 2: Install Dependencies and Build

Using uv (recommended):

# Sync all dependencies and build the package
uv sync --extra dev

Using pip:

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install build dependencies
pip install setuptools cython numpy

# Install in development mode
pip install -e ".[dev]"

The default editable install builds the Cython 3 pure-Python modules:

uv pip install -e .

Step 3: Verify Build

Always run verification inside your virtual environment to ensure the compiled libraries are correctly found:

# Test imports
uv run python -c "import openptv2; print(f'openptv2 version: {openptv2.__version__}')"
uv run python -c "from openptv2 import Tracker; print('Tracker imported successfully')"

# Run core tests
uv run pytest tests/unit/ -v

GUI Dependencies

The [gui] extra includes:

traitsui, enable, chaco (Enthought framework + visualization)
PySide6 (Qt bindings)
matplotlib, pandas, flowtracks (analysis)

(scikit-image and numpy are core dependencies, installed either way.)

Installing from Binary Wheels

Pre-built wheels are published to PyPI:

# Using pip
pip install openptv2

# Using uv
uv pip install openptv2

Troubleshooting Installation

Common Issues

1. "C compiler not found"

# Linux: sudo apt-get install build-essential
# macOS: xcode-select --install
# Windows: Install MSVC Build Tools

2. "Cython not found"

pip install cython>=3.0.0

3. "NumPy version mismatch"

pip install numpy>=2.0.0

4. Cython extensions not rebuilt after editing src/openptv2/algorithms/

uv run python setup.py build_ext --inplace

Usage

Basic Tracking

The quickest way to run a full detection → correspondence → tracking pipeline is the batch CLI (see Batch Processing below) or the GUI. For scripting against the library directly — loading calibrations/parameters and driving openptv2.Tracker — see docs/tutorials/.

Runtime

import openptv2

print(openptv2.get_runtime_info())
# {'engine': 'cython3-pure-python', 'compiled': True/False, 'package': 'openptv2'}

GUI

Launch the graphical interface using the unified console scripts. Ensure your virtual environment is activated (source .venv/bin/activate) or prefix the commands with uv run:

# Launch the GUI using the standard openptv2-gui or shorter pyptv_gui shortcut
uv run pyptv_gui

# Launch the GUI in the single-engine runtime
uv run pyptv_gui --workdir=./test_data/test_cavity

Batch Processing

Run high-throughput processing sequences using command-line batch utilities (ensure your virtual environment is activated or use uv run):

# Run batch processing with the single runtime
uv run pyptv_batch --workdir=./test_data/test_cavity --first=10000 --last=10005

# Run with legacy positional arguments (for backward compatibility)
uv run pyptv_batch ./test_data/test_cavity/parameters_Run1.yaml 10000 10004

# Parallel batch processing (distributes frame chunks to multiple cores)
uv run python -m openptv2.batch.pyptv_batch_parallel test_data/test_cavity/parameters_Run1.yaml 10000 10004 4 --mode sequence

### Parallel Processing Config
OpenPTV2 supports multi-core parallel processing during image preprocessing and target detection (Approach C). This is highly effective for accelerating execution on multi-core systems.

* **GUI Configuration**: Open the **Main Parameters** dialog, navigate to the **Sequence** tab, check the **Parallel Pre-processing** box, and set the **Number of workers** (e.g., `4` or `0` for automatic core detection).
* **CLI/Environment Configuration**: Set environment variables before running any tracking or batch sequence:
  ```bash
  export OPENPTV_PARALLEL_PREPROCESS=True
  export OPENPTV_NUM_WORKERS=4  # Set worker processes (omitting uses all CPU cores)

Command-line Shortcuts and Running Without `uv`

You can run these command-line tools without prefixing them with uv run using any of the following approaches:

1. Activate the Virtual Environment (Standard Workflow)

By activating the project's virtual environment, the environment's bin/ directory is added to your shell's PATH. This registers all entry points (like pyptv_gui, pyptv, pyptv_batch) directly in your terminal:

source .venv/bin/activate

# Now run directly without uv
pyptv_gui -w ./test_data/test_cavity

2. Execute via Direct Path

If you do not wish to activate the virtual environment, you can run the built executable directly from the local .venv folder:

./.venv/bin/pyptv_gui -w ./test_data/test_cavity

3. Define Shell Aliases (Global Access)

To run these shortcuts cleanly from any directory without manual paths, add alias entries to your shell profile (e.g., ~/.bashrc or ~/.zshrc):

# Add these lines to ~/.bashrc or ~/.zshrc
alias pyptv_gui='/home/user/Documents/GitHub/openptv2/.venv/bin/pyptv_gui'
alias pyptv_batch='/home/user/Documents/GitHub/openptv2/.venv/bin/pyptv_batch'

# Reload shell profile
source ~/.bashrc

# Now launch cleanly from any folder
pyptv_gui -w ./test_data/test_cavity

Single Runtime Behavior

OpenPTV2 now ships a single runtime:

One codebase: algorithms/*.py is the only implementation path.
Two execution modes: the same modules run interpreted during development and compiled when built through Cython 3.
No engine switching: OPENPTV_ENGINE, set_engine(), and optv dispatching are no longer part of the runtime model.

Short vs. Long Options

Both styles are fully supported across all command-line scripts. Choose based on your context:

Short Options (-e, -w, -f, -l): Best for manual terminal use, quick debugging, and live interactive typing.
Long Options (--engine, --workdir, --first, --last): Best for automation scripts, documentation, and config files to maximize readability.

Documentation

OpenPTV³ Auto-Research Dashboard

Live, zero-install demo of the differentiable PTV pipeline (docs/plans/differentiable_ptv_nextgen_plan.md): drag the Stage-1 intensity threshold and watch the gradient flow through to the Lagrangian physics loss, live acceleration PDF, and velocity power spectrum.

Repository Structure

openptv2/
├── src/openptv2/
│   ├── algorithms/    # Cython 3 pure-Python engine: calibration,
│   │                  # correspondences, orientation, tracking, etc.
│   │                  # (the only algorithm implementation path)
│   ├── plugins/       # Pluggable tracker/sequence implementations
│   │                  # (fast, MyPTV/ProPTV-inspired trackers, rembg, ...)
│   ├── batch/         # openptv2-batch / pyptv_batch headless pipeline
│   ├── storage/       # Zarr frame store (res/run.zarr)
│   ├── gui/           # TraitsUI/Chaco desktop application
│   ├── tracker.py, calibration.py, correspondences.py, ...  # public API
│   └── __init__.py
├── tests/             # Test suite (unit, parity, perf, integration, gui)
├── docs/              # Documentation, tutorials, developer guide
├── scripts/           # Build/analysis/benchmark helper scripts
├── docker/            # Dockerfiles for GUI and cloud batch images
├── test_data/         # Calibration files, parameter files, fixtures
├── pyproject.toml     # Python project config (build, deps, entry points)
└── README.md

Testing

# All tests
uv run pytest

# By marker
uv run pytest -m unit
uv run pytest -m "not slow"

# GUI tests (headless)
uv run pytest tests/gui/ -v

# Hot-path smoke test (tracking + correspondences)
uv run pytest tests/unit/test_track.py tests/unit/test_track3d.py tests/unit/test_correspondences.py -v --tb=short

Contributing

Fork the repository
Create a feature branch
Make your changes
Run tests: pytest
Submit a pull request

Development Workflow

# Set up development environment
uv sync --extra dev

# Make changes to source code

# Run tests
pytest tests/ -v

# Build documentation (optional)
cd docs && make html

License

MIT. See LICENSE for details.

As of 0.3.2, openptv2 is relicensed from LGPL-3.0 to MIT. The C/Cython core that carried the LGPL license now lives in the separate openptv repo; this codebase is a from-scratch pure-Python/Cython PTV engine plus a pluggable tracker architecture.

Third-party tracking code

openptv2 includes and adapts tracking code from two MIT-licensed projects, in accordance with their licenses:

MyPTV (MIT, © 2022 Ron Shnapp) — algorithm concepts adapted into the myptv_2d_tracking / nearest_hungarian_3d plugins. Their MIT notice is incorporated; see https://github.com/ronshnapp/MyPTV.
proPTV (MIT, © 2023 DLR / Robin Barta) — the GMM / Savitzky-Golay routines in src/openptv2/plugins/proptv/ are vendored from proPTV. proPTV's license requires that its copyright/permission notice be included in copies and that its underlying publication be cited. Accordingly:

MIT License, Copyright (c) 2023 DLR (Project owner: Robin Barta). GMM / Savitzky-Golay routines adapted from Barta, Robin, et al. "proPTV – A probabilistic particle tracking velocimetry framework." Journal of Computational Physics (2024), https://doi.org/10.1016/j.jcp.2024.113212.

The vendored modules at src/openptv2/plugins/proptv/ carry the MIT copyright/permission notice in their headers.

Using either project's advanced, project-specific features is out of scope here — refer to and use those projects directly.

Acknowledgments

openptv2 combines work from:

openptv - C library and bindings
pyptv - Python GUI
openptv-python - Python/Numba engine
MyPTV - 2D/3D tracking algorithm concepts adapted into the MyPTV plugins (MIT)
proPTV - GMM / Savitzky-Golay track-prediction routines vendored into src/openptv2/plugins/proptv/ (MIT, © 2023 DLR / Robin Barta)

See License for the licensing and citation details.

Contact

Mailing list: openptv@googlegroups.com
GitHub: https://github.com/alexlib/openptv2
Issues: https://github.com/alexlib/openptv2/issues

Helper Scripts

The project includes scripts for building and testing:

Script	Purpose
`scripts/build_wheel.sh`	Build binary wheel from source
`scripts/install_wheel.sh`	Install wheel in clean test environment
`scripts/run_tests.sh`	Run test suite in test environment
`docker/Dockerfile.slim`	Slim Docker image for testing

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Release history Release notifications | RSS feed

This release

0.5.0

Aug 15, 2026

0.4.1

Aug 11, 2026

0.4.0

Aug 10, 2026

0.3.0

Aug 1, 2026

0.2.2

Aug 1, 2026

0.2.1

Jul 12, 2026

0.1.7

Jun 19, 2026

0.1.6

Apr 6, 2026

0.1.5

Apr 6, 2026

openptv2 0.5.0

openptv2

Overview

What's Inside (and Where the Tracking Concepts Come From)

Key Features

Installation

Default (headless / batch)

GUI (desktop users)

Verify Installation

Docker (no Python setup required)

For Developers (Build from Source)

System Dependencies

Step 1: Clone the Repository

Step 2: Install Dependencies and Build

Step 3: Verify Build

GUI Dependencies

Installing from Binary Wheels

Troubleshooting Installation

Common Issues

Usage

Basic Tracking

Runtime

GUI

Batch Processing

Command-line Shortcuts and Running Without uv

1. Activate the Virtual Environment (Standard Workflow)

2. Execute via Direct Path

3. Define Shell Aliases (Global Access)

Single Runtime Behavior

Short vs. Long Options

Documentation

OpenPTV³ Auto-Research Dashboard

Repository Structure

Testing

Contributing

Development Workflow

License

Third-party tracking code

Acknowledgments

Contact

Helper Scripts

Key dates

1 maintainer

Credits

License expression

Requires

Provides Extra

Classifiers

Download files

Source Distribution

Built Distributions

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Command-line Shortcuts and Running Without `uv`