openptv2

Unified OpenPTV: Particle Tracking Velocimetry with dual-engine support

Overview

openptv2 combines the best of three repositories into a single, maintainable package:

C core library (lib/) - High-performance particle tracking algorithms
Cython bindings (bindings/) - Python interface to C library
Python/Numba fallback (algorithms/) - Pure Python implementation for debugging
TraitsUI GUI (gui/) - Full-featured graphical interface

Key Features

Dual-engine architecture: Use fast C/Cython (optv) or debuggable Python/Numba (python)
Identical results: Both engines produce the same output (within floating-point tolerance)
Backward compatible: Works with existing optv and pyptv code
Easy installation: Pre-built wheels for Linux, Windows, macOS

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 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 .

# 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: 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.

For Developers (Build from Source)

Prerequisites:

Python 3.11, 3.12, or 3.13
CMake 3.15+
C compiler (gcc on Linux, clang on macOS, MSVC on Windows)
Cython 3.0+
NumPy 2.0+
uv (recommended) or pip

System Dependencies

Linux (Debian/Ubuntu):

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

Linux (Fedora/RHEL):

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

macOS:

# Install Xcode Command Line Tools
xcode-select --install

# Install cmake via Homebrew (optional, if not using system cmake)
brew install cmake

Windows:

Install Microsoft Visual C++ Build Tools
Install CMake

Step 1: Clone the Repository

git clone https://github.com/openptv/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 scikit-build-core 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:

traits, traitsui (Enthought framework)
enable, chaco (visualization)
PySide6 (Qt bindings)
scikit-image, pandas, matplotlib (analysis)

Installing from Binary Wheels

Pre-built manylinux wheels are available for Linux:

# Using pip
pip install openptv2

# Using uv
uv pip install openptv2

The wheels are compatible with glibc 2.17+ (CentOS 7, Ubuntu 14.04, Debian 8, etc.)

Building Binary Wheels from Source

See BUILDING_BINARY_WHEELS.md for detailed instructions on building portable binary wheels using cibuildwheel.

Troubleshooting Installation

Common Issues

1. "CMake not found"

# Install CMake
# Linux: sudo apt-get install cmake
# macOS: brew install cmake
# Windows: Download from https://cmake.org/download/

2. "C compiler not found"

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

3. "Cython not found"

pip install cython>=3.0.0

4. "NumPy version mismatch"

pip install numpy>=2.0.0

5. "optv module not found" (after cloning)

# The optv package is built by CMake - you need to build the package
uv sync --extra dev
# or
pip install -e ".[dev]"

Usage

Basic Tracking

import openptv2
from openptv2 import Tracker, detect_targets

# Load images
from skimage import io
images = [io.imread(f"cam1_{i:04d}.tif") for i in range(100)]

# Detect particles
targets = [detect_targets(img) for img in images]

# Track particles
tracker = Tracker()
tracks = tracker.track([t.coordinates for t in targets])

print(f"Found {len(tracks)} tracks")

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

Repository Structure

openptv2/
├── algorithms/        # Python/Numba fallback engine
│   ├── calibration.py
│   ├── correspondences.py
│   ├── image_processing.py
│   ├── orientation.py
│   ├── parameters.py
│   ├── segmentation.py
│   ├── track.py
│   └── ...
├── bindings/          # Cython bindings source
│   ├── optv/          # Cython .pyx, .pxd files
│   ├── tests/         # Binding tests
│   └── pyproject.toml # scikit-build-core config
├── gui/               # TraitsUI GUI application
│   ├── pyptv/         # Main GUI package
│   ├── plugins/       # GUI plugins
│   └── tests/         # GUI tests
├── lib/               # C core library
│   ├── include/       # C headers
│   ├── src/           # C source files
│   ├── tests/         # C library tests
│   └── CMakeLists.txt
├── openptv2/          # Main Python package
│   ├── __init__.py
│   ├── calibration.py
│   ├── correspondence.py
│   ├── engine.py      # Engine selector
│   ├── tracker.py
│   └── ...
├── tests/             # Integration tests
│   ├── engine_comparison/
│   ├── fixtures/
│   └── integration/
├── docs/              # Documentation
│   ├── algorithms/
│   ├── developer_guide/
│   ├── sphinx/
│   └── tutorials/
├── scripts/           # Build helpers
├── CMakeLists.txt     # Root CMake build config
├── pyproject.toml     # Python project config
└── README.md

Engine Comparison

Feature	optv (C/Cython)	python (Numba)
Speed	Fastest	Fast (JIT compiled)
Debugging	Harder	Easy
Visualization	Limited	Full
Use case	Production	Development

Testing

# All tests
pytest

# C library tests
cd lib && mkdir build && cd build && cmake .. && ctest

# Engine comparison
pytest tests/engine_comparison/ --validate-engine

# GUI tests (headless)
pytest gui/tests/ --headless

# Integration tests
pytest tests/integration/ -v

Migration from optv/pyptv

openptv2 maintains backward compatibility:

# Old optv code (still works after installation)
from optv.tracking_framebuf import Target
from optv.tracker import Tracker

# New openptv2 code
from openptv2 import Target, Tracker

# Both work identically

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

LGPL-3.0 or later. See LICENSE for details.

Acknowledgments

openptv2 combines work from:

openptv - C library and bindings
pyptv - Python GUI
openptv-python - Python/Numba engine

Contact

Mailing list: openptv@googlegroups.com
GitHub: https://github.com/openptv/openptv2
Issues: https://github.com/openptv/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
`scripts/Dockerfile.slim`	Slim Docker image for testing

See BUILDING_BINARY_WHEELS.md for detailed usage.

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Release history Release notifications | RSS feed

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

This release

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 0.2.1

openptv2

Overview

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

Building Binary Wheels from Source

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

Repository Structure

Engine Comparison

Testing

Migration from optv/pyptv

Contributing

Development Workflow

License

Acknowledgments

Contact

Helper Scripts

Key dates

1 maintainer

Credits

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Provides Extra

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Command-line Shortcuts and Running Without `uv`