* feat(image-enhancement): add analysis and correction types
* feat(image-enhancement): implement auto-enhance analysis and correction engine
* test(image-enhancement): add unit tests for auto-enhance engine
* feat(image-enhancement): add API route with analyze endpoint and register in constants/i18n
* feat(image-enhancement): add UI component with mode selector, intensity slider, and analysis badges
* test(image-enhancement): add integration and e2e tests
* fix(image-enhancement): use modulate instead of gamma for exposure correction
Sharp's gamma() only accepts values between 1.0 and 3.0, but brightening
underexposed images computed gamma < 1.0. Switch to modulate({ brightness })
which handles both brightening and darkening correctly.
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
Add 12 previously undocumented routes to the OpenAPI 3.1 specification:
content-aware-resize, edit-metadata (+ inspect), stitch, pdf-to-image
(+ info, preview), gif-tools/info, remove-background/effects, preview,
pipeline/tools, and pipeline/batch. Fix license from MIT to AGPL-3.0,
correct DELETE /files response from 204 to 200 with body, and update
VitePress API docs (rest.md tool table, ai.md model parameters). Also
register the sharpen operation in the image-engine OPERATION_MAP so it
can be used as a standalone pipeline step.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
- Route resize steps with contentAware=true to the content-aware-resize
tool in both single-file and batch pipeline paths
- Update HEIC filename extension to .png after decoding so downstream
tools don't attempt double-decoding
- Wrap per-step errors with step number and tool name for clarity
(e.g. "Step 1 (resize): Resize requires width, height, or percentage")
- Show first file's step-level error in batch failure messages
Reject non-SVG files with a clear error message instead of letting Sharp
fail with cryptic HEIF/corrupt header errors. Applies to both single-file
and batch endpoints.
Add user-configurable DPI (36-1200) and quality (1-100) instead of
hardcoded values. Support avif, tiff, gif, heif output in addition to
png, jpg, webp. Width is now optional, defaulting to SVG intrinsic size
at the given DPI. Generate browser-previewable webp thumbnails for
non-browser formats (tiff, heif). Remove unused _contentType variable.
Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
Expand the svg-to-raster dedicated endpoint test suite from 3 to 10 tests:
- All 7 output formats (png, jpg, webp, avif, tiff, gif, heif)
- Quality setting verification (low vs high quality jpg file sizes)
- DPI setting verification (72 vs 300 dpi png file sizes)
- Preview URL generation for non-browser formats (tiff)
HEIF test skips gracefully if heif-enc is not installed (422 response).
Switch from manual fetch to useToolProcessor hook for consistency and
batch support. Add scale/custom sizing modes, DPI presets, 7-format
button grid, quality slider, transparent/color background toggle with
preset swatches, and ProgressCard during processing.
- Add clientJobId field support for SSE progress correlation
- Add updateJobProgress calls matching generic batch route pattern
- Use sanitizeFilename() instead of basename() for security
- Map Zod errors to {path, message} format for consistency
- Include errors array in all-failed response body
The generic batch route validates image buffers by magic bytes, which
fails for SVG input. This adds a dedicated /batch endpoint that sanitizes
each SVG individually before conversion, using p-queue for concurrency
and streaming the results as a ZIP with X-File-Results header.
Also extracts shared conversion logic into a reusable convertSvg helper
to avoid duplication between single-file and batch endpoints.
Sharp cannot encode HEVC directly. Use encodeHeic() (same as convert tool)
to go through heif-enc. Also decode HEIF before Sharp for preview generation.
Hoist NON_PREVIEWABLE set to module scope.
Add user-configurable DPI (36-1200) and quality (1-100) instead of
hardcoded values. Support avif, tiff, gif, heif output in addition to
png, jpg, webp. Width is now optional, defaulting to SVG intrinsic size
at the given DPI. Generate browser-previewable webp thumbnails for
non-browser formats (tiff, heif). Remove unused _contentType variable.
The edit-metadata integration tests require exiftool (libimage-exiftool-perl)
which was missing from the CI test runner, causing 4 tests to fail with 422.
Update erase-object pipeline, eraser canvas, and inpainting Python script.
Add LaMa model download script and update Dockerfile for model support.
Update multi-file tool routes for consistency.
Replace the confusing 2-mode smart crop with a clear 3-mode system:
- Subject Focus: Sharp attention/entropy saliency crop with social media presets
- Face Focus: MediaPipe face detection with headshot framing presets
- Auto Trim: Border removal with optional pad-to-square
Adds detectFaces() to AI package, face preset constants, backward
compatibility for old mode names, and comprehensive integration tests.
- Pin PaddlePaddle to 3.0.0 on ARM64 to fix segfault in PIR inference
engine (3.1+ crashes on aarch64 Debian Bookworm)
- Fix text extraction for PaddleOCR 3.4.x result format (rec_texts)
- Add Node.js-level fallback chain (best -> balanced -> fast) when
Python subprocess crashes
- Add multi-image OCR: processes all uploaded files sequentially with
per-file progress and filename headers in combined output
- Convert input images to PNG via Sharp before OCR so HEIC, AVIF, WebP,
TIFF all work transparently
- Implement real auto-detect language using Tesseract multi-lang script
detection (analyzes Unicode ranges for Hangul, CJK, Kana, Latin)
- Default enhance to off (hurts clean digital images)
- Replace Auto/AI/Fast buttons with Fast/Balanced/Best (consistent with other tools)
- Rename "Denoise" to "Noise Reduction" with explanatory subtitle
- Change output format from 3 buttons to dropdown with all formats (PNG, JPG, WebP, AVIF, TIFF, GIF, HEIC, HEIF)
- Add HEIC/HEIF input decoding (was missing unlike other tools)
- Add HEIC/HEIF/AVIF output conversion via Sharp and heif-enc
- Generate browser-compatible WebP preview for non-previewable output formats
- Fix torchvision compatibility shim so Real-ESRGAN actually loads (was silently falling back to Lanczos)
- Fix denoise crash: Image.fromarray() instead of type(img).fromarray()
- Redirect stdout for entire AI pipeline to prevent library messages corrupting JSON output
- Add GFPGAN model download for face enhancement
- Use batch endpoint for multi-file uploads (enables Download All ZIP)
- Fix multi-image: process selected file, not always first
- Fix progress bar: asymptotic fill prevents visual stalling
- Fix slider: write results to captured index, not current selection
- Add model selection (Auto/AI/Fast), face enhancement, denoise
- Add output format (PNG/JPEG/WebP) with quality control
- Add Upscale All for sequential batch processing with queue
- More granular Python progress stages for smoother UX
- Fix temp file leak: clean up preprocessed image in finally block
- Log warning instead of silently swallowing preprocessing failures
- Simplify auto_detect_language to honest default (was a stub that
wasted time loading a model but always returned "en")
Ultra quality (People only):
- BiRefNet-matting ONNX (928MB) for true alpha matting with per-pixel
transparency on hair wisps and fine edges
- Custom rembg session class, zero new Python dependencies
- Model pre-downloaded in Docker build for immediate availability
Quality tier labels: Fast / HD / Max / Ultra (shorter, fits 4-col grid)
