Openpiv

OpenPIV

Overview

OpenPIV (Open Particle Image Velocimetry) analyzes fluid flow from PIV image pairs. It covers preprocessing, cross-correlation, vector validation, outlier replacement, smoothing, and scaling to physical units.

Everything below is verified against openpiv 0.25.4. The API moves between releases — check inspect.signature() before trusting a snippet against a different version.

When to use

Use this skill when working with experimental PIV or flow-visualization image pairs: measuring 2D velocity fields, tuning interrogation-window parameters, validating vectors, or deriving vorticity, strain rate, and turbulence statistics. For simulating flow rather than measuring it, use a CFD skill instead.

Quick Start

Install OpenPIV:

uv pip install openpiv

# Pin it when the analysis needs to be reproducible -- this is the version every
# snippet below was checked against.
uv pip install "openpiv==0.25.4"

Run PIV analysis on an image pair:

import numpy as np
from openpiv import tools, pyprocess, validation, filters, scaling

frame_a = tools.imread("image_a.bmp")
frame_b = tools.imread("image_b.bmp")

# Cross-correlate. Returns (u, v, s2n) whenever sig2noise_method is not None.
u, v, s2n = pyprocess.extended_search_area_piv(
    frame_a.astype(np.int32),
    frame_b.astype(np.int32),
    window_size=32,
    overlap=12,
    dt=0.02,
    search_area_size=38,
    correlation_method="linear",   # required for search_area_size > window_size
    sig2noise_method="peak2peak",
)

x, y = pyprocess.get_coordinates(
    image_size=frame_a.shape,
    search_area_size=38,
    overlap=12,
)

# flags is a boolean array: True marks a spurious vector.
flags = validation.sig2noise_val(s2n, threshold=1.05)
u, v = filters.replace_outliers(u, v, flags, method="localmean", max_iter=3, kernel_size=2)

# Scale to physical units, then flip to image coordinates for plotting.
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
x, y, u, v = tools.transform_coordinates(x, y, u, v)

tools.save("vectors.txt", x, y, u, v, flags)

Or use the bundled CLI, which wraps exactly that pipeline:

python skills/openpiv/scripts/runner.py \
    --image frame_a.bmp --image frame_b.bmp --output_dir results --verbose

Core Concepts

PIV Fundamentals

Particle Image Velocimetry is an optical method for measuring fluid velocity by tracking illuminated tracer particles between two images.

Process flow:

1. Capture an image pair (frame_a, frame_b) separated by a known time dt.
2. Divide the images into interrogation windows.
3. Cross-correlate matching windows to find peak displacement.
4. Validate vectors (signal-to-noise, global range, local median).
5. Replace spurious vectors with interpolated values.
6. Scale pixel displacements to physical units.

Interrogation Window Parameters

`window_size` — correlation window in pixels (typically 16–128). Larger windows give better correlation but coarser spatial resolution.

`overlap` — pixels shared between adjacent windows (typically 50–75% of window_size). Higher overlap raises vector density and cost, but adjacent vectors become correlated rather than independent.

`search_area_size` — the window searched in the second frame. Must be ≥ window_size; a few pixels larger accommodates larger displacements. Pair an extended search area with correlation_method="linear" — the default "circular" relies on FFT wrap-around and aliases large displacements into small ones. See references/advanced_algorithms.md.

Rules of thumb: keep the largest displacement under about a quarter of window_size, and aim for 5–10 particles per window.

Signal-to-Noise Ratio

s2n measures how distinct the correlation peak is. sig2noise_method controls how it is computed — "peak2mean" (the function default) or "peak2peak". The two are on different scales, so a threshold tuned for one is meaningless for the other. Typical peak2peak thresholds are 1.05–1.3.

flags = validation.sig2noise_val(s2n, threshold=1.05)
# flags is bool: True == spurious. `~flags` selects the good vectors.

Common Operations

Dynamic Masking

Masking lives in openpiv.preprocess, not in an openpiv.masking module. It returns an (image, mask) tuple and expects a float image.

from openpiv import preprocess

# method="edges" for dark, sharp-edged objects; "intensity" for high-contrast objects.
frame_a_masked, mask_a = preprocess.dynamic_masking(
    frame_a.astype(np.float64), method="intensity", filter_size=7, threshold=0.005
)
frame_b_masked, mask_b = preprocess.dynamic_masking(
    frame_b.astype(np.float64), method="intensity", filter_size=7, threshold=0.005
)

Feed the returned image into the correlation step — it already has the masked region zeroed. Do not multiply the original frame by mask: masking is already applied, and for method="edges" the mask comes back as uint8 0/255 rather than boolean, so multiplying rescales the image by 255.

Multi-Pass Processing

Multi-pass (window deformation) lives in openpiv.windef, driven by a PIVSettings dataclass. pyprocess has no multi-pass entry point.

import numpy as np
from openpiv import scaling, windef

settings = windef.PIVSettings()
settings.windowsizes = (64, 32, 16)   # one entry per pass, decreasing (this is also the default)
settings.overlap = (32, 16, 8)        # same length as windowsizes
settings.num_iterations = 3           # number of passes to actually run
settings.sig2noise_threshold = 1.05

x, y, u, v, flags = windef.simple_multipass(
    frame_a.astype(np.int32), frame_b.astype(np.int32), settings
)

# Output is in PIXELS PER FRAME -- convert yourself. scaling.uniform only divides
# by scaling_factor, so apply dt separately.
dt = 0.02
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
u, v = u / dt, v / dt

simple_multipass already validates, replaces outliers, fills remaining NaNs with zeros, and calls transform_coordinates — do not repeat those steps.

Units trap: PIVSettings has dt and scaling_factor fields, but windef never uses either — first_pass calls extended_search_area_piv without dt, so the whole multi-pass chain works in pixels per frame. Setting settings.dt = 0.02 changes nothing about the returned values. Convert after the fact, as above.

For control over individual passes, windef.first_pass and windef.multipass_img_deform are the lower-level building blocks.

Validation and Post-Processing

Validation Methods

Every validator returns a boolean array where True marks a spurious vector.

# Signal-to-noise
flags = validation.sig2noise_val(s2n, threshold=1.05)

# Global range -- takes (min, max) TUPLES, positionally or as u_thresholds/v_thresholds.
flags = validation.global_val(u, v, (-300, 300), (-300, 300))

# Local median -- u_threshold and v_threshold are REQUIRED; size is the neighbourhood half-width.
flags = validation.local_median_val(u, v, u_threshold=30.0, v_threshold=30.0, size=1)

# Combine with boolean OR (not np.maximum -- these are bool arrays).
flags = (
    validation.sig2noise_val(s2n, threshold=1.05)
    | validation.global_val(u, v, (-300, 300), (-300, 300))
    | validation.local_median_val(u, v, u_threshold=30.0, v_threshold=30.0)
)

Set these thresholds in the units of `u` and `v`, not in pixels per frame. extended_search_area_piv divides by dt, so with dt=0.02 a 3 px/frame displacement arrives as 150 px/s. The thresholds above suit that case; the (-30, 30) figure that PIV literature and PIVSettings.min_max_u_disp use is a px/frame limit, and applying it to px/s output rejects the entire field. Either validate before scaling, or scale the thresholds by 1/dt too.

Outlier Replacement

u, v = filters.replace_outliers(
    u, v, flags, method="localmean", max_iter=3, tol=1e-3, kernel_size=2
)