Relative-Intensity Pattern Registration (RIPR)

This Python package runs the same registration operation as the Java Fiji/ImageJ plugin. Java is the default engine because it is much faster; a Python/NumPy/SciPy engine remains available as a fallback. It does not launch ImageJ. On PyPI it is Relative-Intensity-Pattern-Registration; the import package and terminal command are both ripr.

Install

python -m pip install Relative-Intensity-Pattern-Registration

The install name is the project's full name; everything you type afterwards is ripr:

import ripr

On Windows, install into a virtual environment whose path is short. OpenCV ships a DLL whose full path can exceed the 260-character limit from a deeply nested folder, and it fails at import with DLL load failed while importing cv2: The filename or extension is too long, which names cv2 rather than the real cause. A shorter path, or long paths enabled in Windows, fixes it.

From a checkout

From this folder:

python -m pip install -e .

For development and tests:

python -m pip install -e ".[test]"
pytest

Quick start

For a TIFF, only the input filename is required. The output is written beside it as recording_registered.tif:

import ripr

ripr.register("recording.tif")

Choose a destination with output_path="registered/recording.tif"; for path input, ripr.register("recording.tif", "registered/recording.tif") is also accepted.

For a NumPy array, only the array is required when it is shaped T, Y, X:

result = ripr.register(stack)
corrected = result.corrected

The normal interface has three choices:

result = ripr.register(
    stack,
    recipe="landmarks",  # or "bright_dim" / "moving_cells"
    channel=1,           # one-based
    longitudinal=True,
)

They default to the benchmark-backed Landmarks category for phase contrast, channel 1, and whole-recording longitudinal processing. Bright/dim selects the accepted fluorescence or bioluminescence category. Moving cells is the separate biological-foreground route and is used with longitudinal=False. With longitudinal=False, the selected category is resolved to a concrete automatic recipe; inspect result.automatic_selection.recipe and result.provenance for its ID. Java is preferred for execution.

Expert recording settings

from ripr import register

result = register(
    "recording.tif",
    output_path="recording_registered.tif",
    recipe="landmarks",
    channel=1,
    longitudinal=False,
    image_type="phase_contrast",
    motion_type="subpixel_random_walk",
    max_shift=20,
    max_iterations=50,
    interpolation="bilinear",
    backend="python",
)

print([(t.dx, t.dy, t.theta) for t in result.transforms])  # theta is radians
print(result.registration.log2_gain)       # bleaching/lamp-drift trace
print(result.median_residual_before, result.median_residual_after)

Every LogRatioParameters field can be passed directly. image_type and motion_type choose the starting recipe; the remaining fields override one expert setting at a time. Use a parameters object only when you want to reuse or inspect a complete bundle.

Use ripr.rank_channels(array, axes="TCZYX") to rank estimation channels by localisability before a run. Values below ripr.WARN_BELOW carry the same poor-localisability warning threshold as the ImageJ plugin.

The input is never modified. For hyperstacks, pass axes explicitly, for example TCZYX. channel, slice, and reference_frame in LogRatioParameters are one-based like ImageJ; slice=0 maximum- projects Z for movement estimation. The estimated transform is applied unchanged to every channel and Z plane.

Run the estimation in Java, at Java speed

The registration in this package and the registration in the Fiji plugin are the same operation, and on a preset recipe they produce the same transforms bit for bit. They do not take the same amount of time. Java aligns frame pairs across a thread pool, which is the one place this problem parallelises well, and the NumPy engine here runs them one after another.

Java is used automatically when a Java runtime and the plugin jar are both present:

result = register(stack, parameters, axes="TYX")

Measured on one 40-frame 448x768 recording, 16 cores, identical settings and identical output:

Engine	Time
backend="java"	16.6 s
backend="python"	over 900 s

The optional backend override takes:

• omitted — prefer Java; warn clearly before falling back to Python
• "java" — require Java and raise if it cannot run
• "python" — deliberately use NumPy and do not warn about Java
• "auto" — prefer Java; warn clearly before falling back to Python

Set RIPR_BACKEND to java, auto, or python to choose the policy for a whole process. An explicit java setting is strict. A fallback warning includes the reason Java could not be used and how to fix it; it is never silent.

Only transforms cross the process boundary; warping happens here either way, so the choice changes how long a run takes and not what it gives back. Two consequences worth knowing:

• result.registration.pairs is empty under the Java backend. Per-pair fits are not carried across, because moving them costs more than a caller asking for a fast path wants to spend. Everything reported per frame is present and is the Java engine's own value.
• The Java runner rebuilds the recipe from the image type, motion type and selection mode you name. That is exact for a preset recipe and wrong for a customised one, so a recipe that differs from its preset in any other field stays on the Python engine. ripr.registration.java_incompatibilities() lists what is blocking it; backend="java" raises rather than silently running something else. The backend finds its pieces from the environment: RIPR_JAVA or JAVA_HOME or java on PATH for the runtime, and RIPR_JAR or a jars/ directory beside the package or RIPR_FIJI for the plugin. ripr.java_backend.available() reports whether it can run at all.

Register a TIFF or folder

from ripr import register, register_batch

register("recording.ome.tif")
register_batch("input_folder", "output_folder")

register_file remains available as a compatibility alias for older code.

Or from a shell:

ripr recording.tif

ripr fluorescence.tif --recipe bright_dim --channel 2

ripr rotating_recording.tif --no-longitudinal --fit-rotation --max-rotation-degrees 10

ripr remounted_recording.tif remounted_registered.tif `
  --no-longitudinal --rotation-mode known_events --rotation-events 25,51 `
  --rotation-event-window 3 --max-rotation-degrees 10

ripr input_folder output_folder --recursive

Folder batches create log_ratio_batch_report.csv, skip existing outputs unless --overwrite is set, and continue after a damaged or incompatible input. The existing report columns are followed by the resolved rotation mode, one-based event list, window and compact event diagnostics.

Java-to-Python interface map

Java plugin/API	Python package
RelativeIntensityPatternRegistration.register(ImagePlus, ...)	ripr.register(ndarray, ..., axes=...)
RelativeIntensityPatternRegistration.estimate(...)	ripr.estimate(...)
RelativeIntensityPatternParameters	ripr.LogRatioParameters
RelativeIntensityPatternRecommendations.forTypes(...)	ripr.recommendation(...)
StackWarper.apply(...)	ripr.apply_transforms(...)
batch plugin	ripr.register_batch(...)
TIFF input/output	ripr.register(path, output_path=...) (register_file is a compatibility alias)

Set fit_rotation=True and max_rotation_degrees=<bound> on LogRatioParameters to estimate bounded in-plane rotation as well as translation. The public bound is in degrees; returned Transform.theta values are radians. Both log-ratio and area-correlation estimators support the rigid search. Automatic is a fixed declared image-and-motion rule and never inspects the recording to choose a recipe. Dense and low-light fluorescence use single_channel_emission_max_accuracy_r04_a208: dense fluorescence uses median-filtered previous-image Enhanced Correlation Coefficient, while sparse/low-light fluorescence or bioluminescence uses the tuned log-ratio preset. Other image types retain recording_adaptive_selector_v1_user_approved_fixed_policy_v1.

For long recordings with slow drift, gentle shake, isolated stage movements and major light changes, choose the separate whole-recording route:

from ripr import ImageType, LogRatioParameters, SelectionMode

parameters = LogRatioParameters(
    image_type=ImageType.SPARSE_LOW_LIGHT_FLUORESCENCE,
    selection_mode=SelectionMode.LONGITUDINAL_ACCURACY,
    channel=1,
)

This route uses bright/dim same-channel references for fluorescence or bioluminescence and edge/dark landmarks for phase contrast or brightfield/DIC. It suppresses returning pulse-linked excursions while retaining persistent and near-dark final jumps. It never reads another channel. Use Automatic instead for repeated oscillation or continuous rotation.

For recordings that rotate only when they are removed and replaced, use the experimental event mode:

from ripr import LogRatioParameters, RotationMode

parameters = LogRatioParameters.manual(
    rotation_mode=RotationMode.KNOWN_EVENTS,
    rotation_event_frames=(25, 51),  # one-based first frames after remounting
    rotation_event_window=3,
    max_rotation_degrees=10,
)

Each boundary uses all available before/after cross-pairs in the window and needs at least three usable rigid fits. One robust angular jump is held exactly until the next event while translation remains free. The final composed transforms are applied to the original pixels once. Event diagnostics are available as result.registration.event_rotations; they include the event frame, incremental and cumulative angle, candidate/usable/inlier counts, circular spread, contributing ranges and status. Large disagreement is a warning; insufficient support stops the run. This mode uses the log-ratio estimator, does not support a rolling reference, and remains opt-in pending validation on independent real remount recordings.

Interpolation.NONE remains the default: pure translations are rounded to whole pixels and applied through a bit-exact block copy. A non-zero rotation cannot use that path, so NONE uses nearest-neighbour sampling; bilinear and Catmull-Rom bicubic interpolation are opt-in for smoother intensity images. Interpolation.FOURIER uses padded Fourier shifts for sharp, band-limited interpolation and represents rotation as three Fourier shears. It can ring near hard edges. Cropping defaults to the field containing real pixels in every registered frame.

This is a standalone Python package, separate from the Java plugin. It has no Swing dialogs or ImageJ macro recorder; its settings are exposed through the Python API and command-line interface. The Java plugin and Python package can continue to be used independently.

Agent control

Automation clients can use the stable ripr.actions registry or its JSON-only one-shot runner at .claude/skills/ripr/scripts/ripr_runner.py (the Codex bridge is mirrored under .codex/skills/ripr/). The runner exposes TIFF inspection, channel ranking, recommendations, estimation, single-image registration, and folder batches. It returns diagnostics plus an equivalent Python script; it never returns full pixel arrays. Existing output files need explicit confirm_overwrite=true in runner requests. Concise public orientation is available through ripr.context.read() and ripr.context.search(...), which return versioned structured envelopes for named topics and searches.