moirestrain 0.1.0

Sampling moire analysis for full-field micro-displacement and micro-strain measurement from grating images.

moirestrain

NumPy-first tools for phase-shifted sampling moire analysis of periodic grating images. moirestrain targets full-field micro-displacement and micro-strain measurement from reference/deformed grid images.

moirestrain estimates phase fields from periodic grating images and converts reference/deformed phase differences into displacement and small-strain fields. The current high-level workflow focuses on two-dimensional square-marker grid targets, such as regularly spaced black squares on a white background.

Highlights

• Generate phase-shifted sampling moire images from a single grating image.
• Estimate wrapped phase with a phase-shifting formula.
• Calculate displacement from reference/deformed phase difference.
• Analyze square-grid targets in both x/y directions.
• Output u, v, exx, eyy, and gamma_xy fields.
• Detect partial grating ROIs inside larger images.
• Rectify tilted planar grating regions from four corner points.
• Save analysis arrays, valid ROI masks, and PNG summaries.

The numerical core is NumPy-based. Runtime dependencies also include imageio and matplotlib for CLI image I/O and PNG reporting. scikit-image is used only by optional examples and development tests.

Results

Partial-Grid Strain Recovery

This example detects a square-grid patch inside a larger image, crops the valid ROI, and compares measured strain against the known synthetic truth.

ROI Detection In A Full Image

The same workflow also reports the full image, grating-energy map, detected mask, cropped ROI, and strain field.

Natural-Image Background

This example places a square-grid strain target on a natural-image background, detects the grid ROI, rectifies the patch, and outputs the measured strain fields.

Benchmark

The benchmark compares measured fields with ground truth and sweeps target period and strain presets.

Installation

For local development:

poetry install

Run tests:

poetry run pytest

Build distributions:

poetry build

Quick Start

from moirestrain import analyze_grid, recommended_strain_smoothing_window

period = 16
strain_window = recommended_strain_smoothing_window(period, cycles=3)

result = analyze_grid(
    reference_image,
    deformed_image,
    period=period,
    strain_smooth_window=strain_window,
)

u = result.x.displacement
v = result.y.displacement
exx = result.strain.exx
eyy = result.strain.eyy
gamma_xy = result.strain.gamma_xy

For one-direction grating analysis:

from moirestrain import phase_shifted_sampling_moire

result = phase_shifted_sampling_moire(
    reference_image,
    deformed_image,
    period=8,
    axis="x",
    grating_pitch=8.0,
)

u = result.displacement

CLI

Analyze a manually cropped square-grid ROI:

moirestrain analyze-grid ref.png def.png \
  --period 16 \
  --roi y0,x0,y1,x1 \
  --out result.npz \
  --figure result.png

Detect the dominant grating ROI automatically:

moirestrain analyze-grid ref.png def.png \
  --period 16 \
  --auto-roi \
  --out result.npz \
  --figure result.png

Create a four-corner calibration, rectify an image pair, then analyze:

moirestrain make-calibration \
  --image-points x0,y0,x1,y1,x2,y2,x3,y3 \
  --output-shape 240,320 \
  --period 16 \
  --out calibration.json

moirestrain rectify-pair ref.png def.png \
  --calibration calibration.json \
  --reference-out rectified_ref.npy \
  --deformed-out rectified_def.npy \
  --metadata-out rectification_metadata.json

moirestrain analyze-grid ref.png def.png \
  --period 16 \
  --calibration calibration.json \
  --out result.npz \
  --figure result.png

If physical dimensions are known, pass --world-points and --unit to make-calibration. The analysis output then includes u_physical, v_physical, pixel_spacing, and physical-coordinate strain fields.

Use --no-figure for batch jobs that only need .npz arrays.

Examples

Generate synthetic data, run benchmarks, and build figures:

PYTHONPATH=src python examples/benchmark_microstrain.py
PYTHONPATH=src python examples/benchmark_sweep.py
PYTHONPATH=src python examples/partial_grid_detection_analysis.py
PYTHONPATH=src python examples/skimage_natural_grating_strain.py

Runnable learning-oriented workflows are under tutorials/:

PYTHONPATH=src python tutorials/01_square_grid_analysis.py
PYTHONPATH=src python tutorials/02_partial_grid_detection.py
PYTHONPATH=src python tutorials/03_four_corner_rectification.py

Other examples:

PYTHONPATH=src python examples/generate_experiment.py
PYTHONPATH=src python examples/microstrain_square_grid.py
PYTHONPATH=src python examples/strain_distribution.py
PYTHONPATH=src python examples/perspective_rectification.py

Build documentation assets and Sphinx HTML:

PYTHONPATH=src python scripts/build_docs_assets.py
poetry run sphinx-build -b html docs docs/_build/html

Notes For Experiments

• Use a camera-space grid pitch around 12-16 px or larger when possible.
• Square-marker targets contain strong harmonics; strain estimation needs smoothing before differentiation.
• Evaluate and display only a valid inner ROI near image boundaries.
• For tilted planar targets, rectify from four corner points before analysis.
• For real images, check illumination, blur, noise, grid pitch calibration, and registration between reference/deformed frames.

Citation

License

MIT License. See LICENSE.