seiza (星座)

Star detection, WCS fitting, plate solving — hinted and blind — and calibrated batch/live image stacking for astrophotography, in Rust. Stacking includes linear FITS output and RGB, LRGB, and narrowband composition from mono stacks. Built to power object overlays and astrometric features in tenrankai and PSF Guard.

It is fast. A typical hinted solve of a real telescope frame finishes in 0.2–0.4 seconds, and a blind solve — no position hint at all — takes about half a second with the prebuilt index. In side-by-side benchmarks seiza matched or beat ASTAP on every workload we tested, up to 19x faster on blind solves. The numbers and caveats are in Performance.

Try it without installing anything: go to seiza.fyi, upload an image, and get a solution and object overlay in your browser. The site runs seiza-server; the CLI can submit to the same server with seiza worker --server.

Contents: Native apps · Install · Ways to use it · Feature matrix · Quick start · Image stacking · Performance · Catalogs and data · Status · N.I.N.A. · Siril · Layout

Built on seiza: Seiza for Mac · Seiza for Windows · seiza-server / seiza.fyi · PSF Guard · tenrankai

Native apps

The same Rust engine drives two real desktop apps through seiza-cabi:

• 🍎 Seiza for Mac — a fast, native FITS viewer and plate solver. Browse whole folders with thumbnails, stretch live with a reorderable stack editor, subtract gradients, deconvolve, blind solve on your machine with no uploads, install and verify catalogs, and export images with overlays. Quick Look previews included. macOS 15+, Apple silicon and Intel.
• 🪟 Seiza for Windows — a first-class native WinUI 3 viewer and solver. GPU-accelerated pan and zoom, per-channel color rendering, gradient subtraction, deconvolution, background plate solving with star, deep-sky, and motion overlays, and built-in catalog install, verify, and repair. Windows 11.

Install

• 🍎 macOS app — brew install --cask theatrus/seiza/seiza-mac, or download the DMG from the Seiza for Mac releases.
• Homebrew (macOS / Linux) — brew install theatrus/seiza/seiza builds the CLI from source via the theatrus/homebrew-seiza tap.
• Windows — download the MSI installer from the releases. It puts seiza on your PATH and offers to download catalogs for you when it finishes.
• Fedora / Ubuntu — install the RPM or deb from the same releases page.
• Python — pip install seiza for the library with binary wheels.
• Anywhere with Rust — cargo install seiza-cli (requires Rust 1.89 or newer; see MSRV).

Building from source requires Rust 1.89 or newer. Repository checkouts pin Rust 1.97.1 through rust-toolchain.toml so local builds and CI use the same compiler, formatter, and linter.

Ways to use it

• 🍎🪟 On your desktop — the native Mac and Windows apps above: browse, stretch, solve, and export without touching a terminal.
• As N.I.N.A.'s plate solver — seiza answers ASTAP's command line, so select ASTAP in N.I.N.A. and point it at seiza.exe. No plugin needed. Steps below.
• As Siril's plate solver — seiza also answers astrometry.net's solve-field command line. Point Siril's astrometry.net path at a copy of seiza named solve-field and solve as usual, SIP distortion included. Steps below.
• In Tenrankai — its gallery server uses seiza for astrometric solutions and object overlays.
• In PSF Guard — seiza provides solved WCS and catalog context for image-quality and spatial analysis, plus calibrated batch stacking of selected frames.
• In a browser — seiza.fyi lets you upload an image for a hosted solution and object overlay, with nothing to install.
• From your own application — run seiza worker to keep the catalogs and blind index open between solves, and send it one JSON request per line. It can also forward solves to a seiza-server, local or hosted. Wire protocol.
• From Python — pip install seiza: detection, hinted and blind solving with optional SIP distortion, WCS transforms, FITS WCS keyword output, verified catalog downloads, and native batch/live image stacking with calibration-master construction, robust background extraction, parameterized display stretching, and NumPy support. Binary wheels for Linux x86_64 and aarch64, macOS, and Windows cover every CPython from 3.9 up, with type stubs included (seiza-py).
• From native applications — seiza-cabi exposes FITS/XISF/raster rendering, robust background fitting and correction, incremental image stacking, solving, overlays, and catalog setup through one generated C header for Swift, .NET, C, and C++ consumers.
• From Rust — use the crates directly: seiza (detection, WCS, solving, catalogs), seiza-fits (FITS reading and linear f32 writing), seiza-xisf (XISF reading into the same astronomy image representation), seiza-background (robust gradient models), seiza-deconvolution (experimental, conservative linear-image restoration), seiza-stretch (parameterized display curves), seiza-imgproc (pure-Rust image processing primitives with OpenCV-compatible semantics), seiza-stacking (linear calibration, registration, and additive live stacking), seiza-download (catalog download and caching), seiza-satellites (single-exposure satellite track prediction), and seiza-sources (raw upstream data for custom catalog builds).

Feature matrix

Every surface runs the same engine; pick the one that fits.

Feature	CLI	Python	C ABI	🍎 Mac app	🪟 Windows app
Plate solving — hinted and blind	✓	✓	✓	✓	✓
Star, deep-sky, and motion overlays	✓	—	✓	✓	✓
Image browsing and native rendering	—	—	✓	✓	✓
Display stretching	✓	✓	✓	✓	✓
Batch and live stacking	✓	✓	✓	—	—
Calibration masters	✓	✓	—	—	—
Background extraction	✓	✓	✓	✓	✓
Deconvolution (experimental)	✓	✓	✓	✓	✓
RGB, LRGB, and narrowband color	✓	✓	—	—	—
Satellite track prediction	✓	✓	—	—	—
Catalog install and verify	✓	✓	✓	✓	✓
Export with overlays	✓	—	—	✓	✓

The C ABI renders; its host app does the browsing. For stacking on the desktop, PSF Guard stacks the frames it selects with this engine.

Quick start

These are CLI instructions for core use, scripting, and integration. If you just want to view, solve, and process images, install a native Mac or Windows app instead — nothing below is needed there.

Download the ready-made catalogs once, then solve:

cargo install seiza-cli
seiza download-data prebuilt --output data       # SHA-256-verified from downloads.seiza.fyi
seiza solve-blind image.jpg --data data --min-scale 0.5 --max-scale 15
seiza solve image.fits --data data --scale 1.26 --objects data
seiza solve integration.xisf --data data --scale 1.26 --objects data
seiza solve image.fits --data data --scale 1.26 --satellites-celestrak --annotate tracks.png
seiza catalog object --data data "Andromeda Galaxy"
seiza catalog objects --data data --ra 10.6848 --dec 41.2691 --radius 3 --format json
seiza catalog star --data data "TYC 5949-2777-1" --format json
seiza master bias bias/*.fits --output master-bias.fits
seiza stack light-001.fits light-002.fits light-003.fits --output stack.fits \
  --preview stack.png --report stack-report.json
seiza background stack.fits --output stack-bg.fits \
  --model-output background.fits --diagnostics background.json
seiza deconvolve stack-bg.fits --output stack-light-dc.fits \
  --psf-fwhm 3.1 --iterations 4 --amount 0.35

--data takes a file or a directory: a directory picks the right catalog automatically (the deepest star catalog present, the blind pattern index when one is there). After seiza setup, every --data and --index can be omitted entirely — the standard catalog locations are searched.

Satellite overlays are opt-in and apply only to one shutter-open exposure, not a stack. The solver reads DATE-BEG/DATE-END, DATE-AVG plus EXPTIME, DATE-OBS plus EXPTIME, or a lone DATE-END plus EXPTIME, and standard OBSGEO-* observer coordinates from FITS. Explicit --time, --exposure-seconds, and --observer-lat/--observer-lon remain available. The annotation is a predicted path, not a claim that a trail was detected. For historical images, the seiza-satellites library resolves epoch- appropriate TLEs from its durable cache, the Seiza rolling mirror, or the public IAU SatChecker fallback. Current CelesTrak and historical responses share a cache-only-replayable history with a configurable 5 GiB default cap. See the satellite track design. Mirror operators should follow the satellite publication runbook.

Not sure which catalogs you need? Run the guided setup — the same one the Windows installer offers, available on every platform:

seiza setup

It walks you through use-case-based choices: lightweight hinted solving, denser Gaia solving, deep blind solving, or the complete bundle. Every choice includes object search, Solar System objects, active transients, and at least one plate-solving catalog. All downloads are versioned and SHA-256 verified.

Set SEIZA_CATALOG_DIR to choose the default setup and ASTAP-compatible catalog directory. The all-users Windows installer sets it system-wide to the shared %ProgramData%\Seiza\catalogs directory.

Solving many images from your own application? Start a worker so the catalogs and blind index stay open instead of being reloaded for every solve:

seiza worker --data data --index data

Send it one JSON request per line on stdin; it writes one response per line on stdout, takes FITS or normal image paths, and exits cleanly at EOF. The full request and response format is in the worker protocol.

The same worker can send solves to a seiza-server instead of solving locally — your own, or the hosted one at seiza.fyi. It converts each FITS to a lossless 8-bit PNG before upload to keep transfers small:

seiza worker --server http://solver-host:8080

If the server needs an API key, pass --server-token or set SEIZA_SERVER_TOKEN. To upload the original FITS instead of the PNG (for example, to preserve headers or full bit depth), pass --server-upload fits. Remote solves give up after five minutes; change that with --server-timeout SECONDS. Local and remote workers speak the same JSON protocol, so your application code does not change.

Image stacking

seiza stack calibrates and registers linear FITS or XISF light frames, optionally applies global or tiled local normalization, and integrates them with online delta-sigma rejection. Differently sized or cropped frames are mapped onto the first frame's fixed output grid. Frames acquired after a German-equatorial-mount meridian flip are handled automatically: a transform near 180 degrees is accepted under the normal rotation tolerance and the pixels are rotated back onto the reference orientation before integration.

Color remains color. Three-plane FITS/XISF inputs are stacked as linear RGB; raw one-shot-color frames carrying BAYERPAT are calibrated in their native CFA sampling and then debayered. Registration detects stars from a luminance view, but the resulting transform, per-channel normalization, rejection, and accumulation retain all three channels. The result is an unstretched three-plane float32 RGB FITS, and --preview produces an RGB display image.

seiza stack lights/*.fits --output stack.fits \
  --bias master-bias.fits --dark master-dark.fits --flat master-flat.fits \
  --normalization local --preview stack.png --report stack-report.json

Eight 300-second H-alpha frames stacked on the first frame's pixel grid. The JPEG uses a display-only stretch; the stack itself remains linear f32 FITS.

The Rust crate, Python wheel, and C ABI expose the same incremental LiveStacker engine. Live handles can be atomically checkpointed to a versioned .seiza-stack context, reopened in a later process, and continue accepting frames without resetting their registration or rejection history. See the CLI stacking guide, Python API, and stacking design.

Light deconvolution (experimental)

seiza deconvolve provides a deliberately restrained classical restoration experiment for calibrated/stacked linear FITS/XISF images. Supply a stellar FWHM in pixels; Seiza applies four damped Richardson-Lucy iterations by default and blends 35% of the result back into the input while preserving per-channel flux. The CLI keeps NaN registration borders masked instead of treating them as image data.

seiza deconvolve stack-bg.fits --output stack-light-dc.fits \
  --psf-fwhm 3.1 --iterations 4 --amount 0.35

The Python wheel exposes the same operation as seiza.deconvolve(image, psf_fwhm=3.1). Native consumers can call seiza_deconvolve_in_place from the generated C ABI header.

This is an explicit symmetric-Gaussian PSF model, not blind sharpening or a learned reconstruction. Use it before display stretching and inspect for noise, rings, and field-dependent failures. Raw Bayer mosaics are rejected. See the seiza-deconvolution crate and design note for the guardrails and limitations. The AstroBin corpus trial shows four input/conservative/strong/external-reference comparisons with measured PSF and background changes, including a ringing failure hidden by aggregate FWHM; the model-based restoration plan explains how synthetic degradations and registered expert pairs could train a later ML operation without treating attractive edits as ground truth.

Automatic background extraction

seiza background estimates a smooth gradient from robust sample windows in a linear mono or RGB FITS/XISF image. The default quadratic model is fit per channel at shared, deterministically selected positions; locally noisy samples and samples inconsistent with the fitted surface are rejected. Output remains linear float32 and retains a valid input WCS.

seiza background stack.fits --output corrected.fits \
  --model-output background.fits --diagnostics background.json

# A conservative plane for a simple additive gradient
seiza background stack.fits --output corrected.fits --degree 1

# Multiplicative illumination correction
seiza background stack.fits --output corrected.fits --mode divide

Fitting itself retains only compact samples and coefficients. Correction can run in place; a full-resolution model is allocated only when requested. Rust and Python expose the same fit/apply split and accept an exclusion mask for extended structures. See the background-extraction design for the ADBE-inspired sampling strategy, correction math, memory behavior, and limits.

Color from mono stacks

Mono stacks can be turned into RGB/LRGB or narrowband quick looks without changing the linear stack accumulator:

seiza color lrgb --luminance l.fits --red r.fits --green g.fits --blue b.fits \
  --output lrgb.fits --preview lrgb.png

seiza color lrgb --luminance l.fits --red r.fits --green g.fits --blue b.fits \
  --luminance-mode super --output super-lrgb.fits --preview super-lrgb.png

seiza color narrowband --ha ha.fits --oiii oiii.fits --sii sii.fits \
  --palette sho --output sho.fits --preview sho.png

seiza color narrowband --ha ha.fits --oiii oiii.fits --sii sii.fits \
  --palette foraxx-sho --preview foraxx.png

RGB, LRGB, additive super-LRGB (L + R + G + B), synthetic super-RGB (R + G + B, no luminance stack), SHO/HOO, every direct three-filter permutation, and custom Rust mixing matrices retain linear-light samples. Foraxx-SHO and Foraxx-HOO use the published dynamic formula on internally stretched working channels and are explicitly marked display-referred in FITS metadata. See the color-composition design for normalization, equations, and the distinction between linear CIE-luminance replacement and display palettes. The CLI automatically registers filter stacks onto L, R, or H-alpha before composition; pass --no-register only for masters already sharing one grid.

Direct linear SHO	Foraxx-SHO quick look

Sh2-132 from twelve 300-second Askar107PHQ frames per H-alpha, OIII, and SII channel. Seiza calibrated and stacked all 36 frames, handled the meridian-flip orientation automatically, registered the three filter masters, and rendered both previews from the same data. These README images are cropped, downscaled quick-look JPEGs; the composition outputs remain full-resolution f32 FITS.

Performance

Seiza is built to solve inside an imaging loop. On our Windows 11 test machine (Intel Core i7-12700H, release builds, no GPU), process startup, image loading, star detection, catalog access, solving, and result output are all included:

• A real hinted FITS solve usually finishes in 0.2-0.4 seconds.
• Blind solving with a prebuilt index took 0.53 seconds median across 13 real FITS frames ranging from 26 to 61 megapixels.
• Compact u8 detection cut peak detector memory from 1.08 GiB to 543 MiB on a 94 MP JPEG, and from 590 MiB to 179 MiB on a 61 MP FITS frame.

Compared with ASTAP

ASTAP is a mature, highly regarded plate solver and a serious reference point. There is no universal winner: different search strategies do better on different fields. In our repeated real-FITS comparison, using the catalog recommended for each solver:

Workload	seiza	ASTAP	Result
Accurate hint, 26 MP narrow field	0.25-0.27 s	0.27-0.29 s	Roughly tied; seiza 9-11% faster
Accurate hint, 61 MP wide field	0.42 s	0.63 s	seiza 1.5x faster
Position blind, 26 MP narrow field	1.61 s	31.28 s	seiza 19x faster
Position blind, 61 MP wide field	0.65 s	1.09 s	seiza 1.7x faster

The 61 MP position-blind row was rerun after the blind-pipeline improvements (three runs each: seiza 0.62-0.65 s, ASTAP 1.08-1.16 s).

On a separate set of 25 heavily processed JPEGs, seiza solved all 25 with a hint and all 25 position-blind. ASTAP solved 13 and 12 respectively. Among images both programs solved, seiza was 3.5x faster hinted and 6.5x faster position-blind by median wall-time ratio. Seiza also solved all 25 with no position or scale hint in 0.90 seconds median. This is deliberately unusual input for a plate solver, so it measures robustness on processed web images rather than ASTAP's normal FITS workflow.

These are measurements on one system, not universal promises. Runs used the normal OS file cache and included complete command-line wall time. See the FITS comparison and blind/detection follow-up for the images, catalogs, repetitions, correctness checks, and caveats.

Catalogs and data

Most users only need seiza download-data prebuilt or seiza setup from the Quick start; this section is the detail behind them — what is in each hosted bundle and how the compatibility paths work.

V4-capable clients use one complete, versioned v4 catalog-bundle manifest. New clients never combine files from different bundle versions: stars-lite-tycho2.bin (2.5M stars, 25 MB), stars-gaia.bin (Gaia DR3 G≤15, 36.7M stars, 367 MB), stars-deep-gaia17.bin (Gaia DR3 G≤17, 154.1M stars, 1.54 GB), blind-gaia16.idx (the memory-mapped G≤16 blind pattern index, 1.63 GB), stars-lite-tycho2.ids.bin (2.7M numeric identifiers and 387k names, 100 MB), objects.bin (315k objects), minor-bodies.bin (comets and asteroids), and transients.bin (active supernovae/novae, refreshed nightly). download-data prebuilt combines the bundle into one local data directory. Current manifests may offer zstd-compressed transports; new clients stream-decompress them into the normal uncompressed mmap cache, while older v4 clients continue to use the retained uncompressed artifacts. The deep catalog and maintained index enable blind solving of small, fine-scale fields whose brightest detections are fainter than the G≤15 catalog's small-field pattern tiers without rebuilding the whole-sky index for every process.

Applications can install only the catalogs they need without invoking the CLI:

// Enable seiza's non-default `downloads` feature first.
let manager = seiza::downloads::CatalogManager::builder().build()?;
let files = manager
    .ensure(&seiza::downloads::CatalogSet::solver_lite()
        .with(seiza::downloads::Dataset::Objects))
    .await?;
let stars = seiza::catalog::TileCatalog::open(
    files.path(seiza::downloads::Dataset::StarsLiteTycho2)?,
)?;

seiza-download owns the async, verified runtime bundle cache. seiza-sources separately owns raw Gaia, VizieR, MPC, OpenNGC, and other catalog-building downloads, keeping those large and rate-limited workflows out of application integrations.

Status

Working today:

• Star detection — tile-based background/noise estimation (median + MAD), sigma thresholding, connected components, flux-weighted sub-pixel centroids.
• WCS — TAN (gnomonic) projection with a CD matrix: pixel ↔ world transforms, scale/footprint helpers.
• Hinted plate solving — triangle matching over FOV-sized windows, affine candidate voting, iterative least-squares refinement, seeded by an approximate center and pixel scale. Solves real telescope images in tens of milliseconds with sub-arcsecond RMS.
• Blind plate solving — no position hint, only a plausible pixel-scale range: a disc-anchored whole-sky 4-star pattern index, hypothesis voting with smoothing and non-max suppression, parallel verification through the hinted solver. The hosted G≤16 index is versioned, SHA-256 verified, and memory-mapped (seiza solve-blind image.jpg --data data --index data --min-scale 0.1 --max-scale 15).
• Star catalogs — memory-mappable tile formats with cone search. Use the prebuilt sets from download-data prebuilt unless you need a custom depth or epoch: building from primary sources stays fully supported (Tycho-2; Gaia DR3 via ESA TAP with --max-mag and --chunks for deeper sets; ASTAP .1476 databases) but the Gaia download alone can take many hours against the ESA archive. An optional memory-mapped identifier sidecar resolves TYC/HIP/HR/HD/SAO/FK5, IAU and Bayer/Flamsteed names, GCVS variables, and WDS double-star designations without a network request or plate solve. Its normalized name index also supports prefix completion for interactive search. Catalog readers keep normal opens non-exhaustive; run seiza catalog validate --data FILE when a deliberate full integrity scan is needed.

# custom build from primary sources (the prebuilt sets skip all this)
seiza download-data gaia --output raw/gaia --max-mag 17 --chunks 3072
seiza build-data gaia --input raw/gaia --output stars-deep.bin --max-mag 17
seiza build-blind-index --data stars-deep.bin --output blind-gaia16.idx --index-mag-limit 16

• Object catalogs — OpenNGC (NGC/IC/Messier), Sharpless, Barnard, UGC, LDN, LBN, Cederblad, vdB, PGC, Green's Galactic supernova remnants, Wolf-Rayet stars, IAU named and HD stars, and live transient (supernova/nova) lists built into a memory-mapped object store. Its embedded tile and normalized-name indices page in only relevant records for viewport, exact-name/ID, and prefix queries. Query a known sky cone or ordered image footprint without plate solving (seiza catalog objects ...), resolve a name or alias with seiza catalog object ..., or query a solved image with projected pixel and ellipse geometry (seiza solve ... --objects objects.bin). The current extensible v4 container also preserves every contributing upstream record, typed relations, preferred facet selections, source-qualified geometry (including hand-drawn OpenNGC outlines), pinned build provenance, and externally curated corrections; seiza catalog object --all-sources audits all of it. Earlier SEIZAOB1 and SEIZAOB3 files remain readable.
• FITS — streaming reading with typed headers, exact histogram statistics, planar RGB (NAXIS3) support, OSC debayering (BAYERPAT), and bounded-memory streaming into native pixel storage, plus atomic linear f32 output, in the seiza-fits crate. FITS files plate-solve directly, with RA/DEC hints read from headers.
• XISF — monolithic grayscale, planar RGB, and Bayer image reading with typed integer/float samples, attached image blocks, FITS-compatible metadata, and zlib, LZ4/LZ4HC, or zstd compression with optional byte shuffling in seiza-xisf. XISF uses the same linear image path as FITS for solving, rendering, background work, deconvolution, and stacking. Atomic Float32 XISF output mirrors the FITS writer; a .xisf output path selects it in every CLI save command.
• Parameterized stretching — reusable identity, linear, asinh, percentile-asinh, MTF, manual GHS, and existing median/MAD Auto-MTF models in seiza-stretch. Analysis, curve resolution, and application are separate so interactive and full-resolution pipeline stages can share an exact plan.
• Background extraction — deterministic low-noise sample selection, robust rejection, weighted polynomial surfaces, additive subtraction, and multiplicative correction in seiza-background. Model fitting is compact; rendering a full model image is explicit.
• Image stacking — master bias/dark/flat construction, CFA-aware OSC calibration, registration onto the first frame's fixed grid with meridian-flip handling, global or tiled local normalization, and online delta-sigma rejection in seiza-stacking. The same incremental LiveStacker engine serves batch and live use, with atomic on-disk contexts for exact process-to-process resumption.
• Deconvolution (experimental) — damped Richardson-Lucy restoration from a measured Gaussian PSF FWHM with per-channel flux preservation in seiza-deconvolution. NaN registration borders stay masked instead of being treated as image data.
• Color from mono stacks — RGB, LRGB with native or additive super-luminance modes, SHO/HOO and every direct three-filter permutation, Foraxx quick looks, and custom mixing matrices, all in linear light.
• Satellite track prediction — single-exposure tracks from current or historical OMM/TLE element sets, with annotated overlays, in seiza-satellites.
• Packages & CI — crates.io releases, a guided Windows MSI installer, Fedora RPMs and Ubuntu debs on GitHub releases, and an integration suite that solves real hosted camera frames against known-good solutions on every PR.

Both solvers can fit SIP distortion polynomials (orders 2-5, forward and inverse) on the accepted solution with --sip-order; the linear solution is kept whenever the polynomial does not improve the residual beyond what its extra coefficients buy for free. On real wide-field images this cuts the astrometric residual by a third to a half (measurements).

Use with N.I.N.A. (ASTAP-compatible mode)

seiza speaks ASTAP's command-line contract, so N.I.N.A. can use it as its plate solver with no plugin:

1. Grab the Windows MSI from the releases (or use the portable ZIP or cargo install seiza-cli).

2. Install the catalogs you want. On Windows, let the installer launch catalog setup when it finishes or open Seiza Catalog Setup from the Start menu later. On every platform, the equivalent command is seiza setup. It writes a complete usable selection into Seiza's standard catalog directory, which ASTAP-compatible mode discovers automatically.

   For a manual or portable layout, download the prebuilt bundle into one directory and configure that directory once:

   seiza download-data prebuilt --output C:\seiza-data
   setx SEIZA_CATALOG_DIR C:\seiza-data
   

3. In N.I.N.A.: Options → Plate Solving → Plate Solver: ASTAP, and point the ASTAP path at seiza.exe. It works in the blind-solver slot too.

seiza auto-detects ASTAP-style invocations (-f image.fits -fov … -ra … -spd …), solves hinted or blind accordingly, and writes the .ini result file N.I.N.A. reads — including the full CD matrix, so pixel scale, rotation, and flip all come through. Catalog discovery selects the right star catalog and blind index from the configured directory; advanced single-file overrides remain available through SEIZA_STAR_DATA and SEIZA_BLIND_INDEX. A copy of the binary renamed astap.exe behaves identically. Details: docs/design/astap-mode.md.

Use with Siril (solve-field compatible mode)

seiza speaks astrometry.net's solve-field command line — and on Windows it also answers Siril's bin/bash launch wrapper itself, so no cygwin is needed. Siril's existing astrometry.net integration drives it with no plugin on every platform:

1. Run seiza install-solve-field --dir <dir> — it installs the complete layout (solve-field, bin/bash, tmp/) Siril expects.
2. Install catalogs with Seiza Catalog Setup on Windows or seiza setup on any platform. For a manual or portable layout, populate one directory with seiza download-data prebuilt --output <directory> and point SEIZA_CATALOG_DIR at it. Solve-field mode selects the star catalog and blind index from that directory automatically.
3. In Siril: Preferences → Astrometry → astrometry.net install dir, set it to that directory, and pick the astrometry.net solver when plate solving.

Siril hands seiza its own detected star list and reads the solution back from the standard .wcs file — no pixels are exchanged, and the SIP distortion order Siril requests is fitted by seiza's solver.

Siril reports fitted PSF amplitudes rather than photometric flux, which would defeat the matcher's brightness ranking on stretched images — so when the source image is present next to the star table (the normal Siril case), seiza automatically re-measures star flux from the pixels and solves in the table's exact frame. Contract details: docs/design/solve-field-mode.md.

Layout

• seiza/ — library crate: detect, wcs, catalog, objects, solve
• seiza-fits/ — FITS reading and atomic linear f32 writing; re-exports the statistics and MTF autostretch that now live in seiza-stretch
• seiza-xisf/ — XISF metadata and attached-pixel reading into FitsImage, plus atomic linear f32 writing
• seiza-background/ — format-independent robust background sampling, polynomial fitting, diagnostics, and linear correction
• seiza-stretch/ — parameterized, format-independent display analysis, transfer plans, and mono/RGB application
• seiza-stacking/ — linear FITS/XISF calibration, local registration, normalization, additive integration, and rejection
• seiza-cabi/ — shared native C ABI for rendering, background extraction, live stacking, solving, overlays, and catalog setup
• seiza-cli/ — the seiza command-line tool: solving, ASTAP mode, the JSON-RPC worker, guided seiza setup, and dataset building
• seiza-download/ — async, verified runtime catalog-bundle cache
• seiza-sources/ — raw upstream catalog acquisition for custom builds
• seiza-py/ — Python bindings (pip install seiza), outside the cargo workspace so workspace builds never need libpython
• packaging/windows/ — the WiX MSI installer
• docs/ — design notes and benchmark reports

Minimum supported Rust version

seiza builds on Rust 1.89 or newer. This is a real floor set by the dependency tree (currently nalgebra), not just the 2024 edition's own 1.85 requirement, and it is declared as rust-version in Cargo.toml and checked in CI against a pinned 1.89 toolchain. A bump here is treated as a routine change, not a breaking one.

License

Apache-2.0