diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..39a96ba --- /dev/null +++ b/.gitattributes @@ -0,0 +1,6 @@ +# PP-OCRv5 ONNX OCR models (paddle-onnx engine). git-lfs is not installed on +# this host, so they are committed as plain binary blobs (treated as binary — +# no textual diff/merge). If/when git-lfs becomes available, migrate with +# `git lfs migrate import --include='*.onnx'` and restore the filter line: +# *.onnx filter=lfs diff=lfs merge=lfs -text +*.onnx -text diff --git a/Cargo.lock b/Cargo.lock index ad7c8d4..0e97d51 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -4417,6 +4417,24 @@ dependencies = [ "quick-error 2.0.1", ] +[[package]] +name = "imageproc" +version = "0.25.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "602b4e8a4cc3e98372b766cd184ab532999bc0e839b7469e759511ccabc65d77" +dependencies = [ + "ab_glyph", + "approx", + "getrandom 0.2.17", + "image", + "itertools 0.12.1", + "nalgebra", + "num", + "rand 0.8.6", + "rand_distr 0.4.3", + "rayon", +] + [[package]] name = "imgref" version = "1.12.1" @@ -4548,6 +4566,15 @@ dependencies = [ "either", ] +[[package]] +name = "itertools" +version = "0.12.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ba291022dbbd398a455acf126c1e341954079855bc60dfdda641363bd6922569" +dependencies = [ + "either", +] + [[package]] name = "itertools" version = "0.13.0" @@ -4724,7 +4751,7 @@ dependencies = [ [[package]] name = "kebab-app" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "base64 0.22.1", @@ -4772,7 +4799,7 @@ dependencies = [ [[package]] name = "kebab-chunk" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -4790,7 +4817,7 @@ dependencies = [ [[package]] name = "kebab-cli" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "clap", @@ -4811,7 +4838,7 @@ dependencies = [ [[package]] name = "kebab-config" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "dirs 5.0.1", @@ -4827,7 +4854,7 @@ dependencies = [ [[package]] name = "kebab-core" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -4841,7 +4868,7 @@ dependencies = [ [[package]] name = "kebab-embed" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -4855,7 +4882,7 @@ dependencies = [ [[package]] name = "kebab-embed-candle" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "candle-core", @@ -4875,7 +4902,7 @@ dependencies = [ [[package]] name = "kebab-embed-local" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "fastembed", @@ -4888,7 +4915,7 @@ dependencies = [ [[package]] name = "kebab-embed-ollama" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-config", @@ -4903,7 +4930,7 @@ dependencies = [ [[package]] name = "kebab-eval" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-app", @@ -4922,7 +4949,7 @@ dependencies = [ [[package]] name = "kebab-llm" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-core", @@ -4931,7 +4958,7 @@ dependencies = [ [[package]] name = "kebab-llm-local" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-config", @@ -4948,7 +4975,7 @@ dependencies = [ [[package]] name = "kebab-mcp" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-app", @@ -4966,7 +4993,7 @@ dependencies = [ [[package]] name = "kebab-nli" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "hf-hub", @@ -4981,7 +5008,7 @@ dependencies = [ [[package]] name = "kebab-parse-code" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "gix", @@ -5004,22 +5031,26 @@ dependencies = [ [[package]] name = "kebab-parse-image" -version = "0.26.2" +version = "0.27.0" dependencies = [ "ab_glyph", "anyhow", "base64 0.22.1", "blake3", "image", + "imageproc", "kamadak-exif", "kebab-config", "kebab-core", "kebab-llm", "kebab-llm-local", + "ndarray", + "ort", "reqwest 0.12.28", "serde", "serde_json", "tempfile", + "thiserror 2.0.18", "time", "tokio", "tracing", @@ -5028,7 +5059,7 @@ dependencies = [ [[package]] name = "kebab-parse-md" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "kebab-core", @@ -5045,7 +5076,7 @@ dependencies = [ [[package]] name = "kebab-parse-pdf" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -5060,7 +5091,7 @@ dependencies = [ [[package]] name = "kebab-rag" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -5082,7 +5113,7 @@ dependencies = [ [[package]] name = "kebab-search" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "globset", @@ -5101,7 +5132,7 @@ dependencies = [ [[package]] name = "kebab-source-fs" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -5119,7 +5150,7 @@ dependencies = [ [[package]] name = "kebab-store-sqlite" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "blake3", @@ -5139,7 +5170,7 @@ dependencies = [ [[package]] name = "kebab-store-vector" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "arrow", @@ -5163,7 +5194,7 @@ dependencies = [ [[package]] name = "kebab-tui" -version = "0.26.2" +version = "0.27.0" dependencies = [ "anyhow", "crossterm", @@ -6423,6 +6454,21 @@ version = "0.1.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "13d2233c9842d08cfe13f9eac96e207ca6a2ea10b80259ebe8ad0268be27d2af" +[[package]] +name = "nalgebra" +version = "0.32.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7b5c17de023a86f59ed79891b2e5d5a94c705dbe904a5b5c9c952ea6221b03e4" +dependencies = [ + "approx", + "matrixmultiply", + "num-complex", + "num-rational", + "num-traits", + "simba", + "typenum", +] + [[package]] name = "native-tls" version = "0.2.18" @@ -8238,6 +8284,15 @@ version = "1.0.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "dd29631678d6fb0903b69223673e122c32e9ae559d0960a38d574695ebc0ea15" +[[package]] +name = "safe_arch" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "96b02de82ddbe1b636e6170c21be622223aea188ef2e139be0a5b219ec215323" +dependencies = [ + "bytemuck", +] + [[package]] name = "safetensors" version = "0.4.5" @@ -8615,6 +8670,19 @@ dependencies = [ "libc", ] +[[package]] +name = "simba" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "061507c94fc6ab4ba1c9a0305018408e312e17c041eb63bef8aa726fa33aceae" +dependencies = [ + "approx", + "num-complex", + "num-traits", + "paste", + "wide", +] + [[package]] name = "simd-adler32" version = "0.3.9" @@ -10220,6 +10288,16 @@ version = "0.1.12" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a28ac98ddc8b9274cb41bb4d9d4d5c425b6020c50c46f25559911905610b4a88" +[[package]] +name = "wide" +version = "0.7.33" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ce5da8ecb62bcd8ec8b7ea19f69a51275e91299be594ea5cc6ef7819e16cd03" +dependencies = [ + "bytemuck", + "safe_arch", +] + [[package]] name = "winapi" version = "0.3.9" diff --git a/Cargo.toml b/Cargo.toml index 249b8fa..cdd068a 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -32,7 +32,7 @@ edition = "2024" rust-version = "1.85" license = "MIT OR Apache-2.0" repository = "https://github.com/altair823/kebab" -version = "0.26.2" # v0.26.2 — ingest 설정 변경 시 영향 자산 자동 재색인: ingest 산출에 영향 주는 설정(청킹/이미지 OCR·caption/pdf.ocr/[ingest.code])의 결정적 서명을 effective parser_version(skip 비교 + 저장 doc 필드 양쪽)에 폴딩 → 해당 설정 변경 시 `--force-reingest` 없이 영향 자산만 자동 재색인. 비산출 설정(search/rag/ui/log + max_pixels/languages/timeout 등)은 제외(과도 무효화 회피). doc_id 는 base parser_version 으로 안정 유지(orphan churn 회피). 결과 포맷·CLI·wire 불변(내부 skip 판정 정정) → patch. — CLAUDE.md §Release +version = "0.27.0" # v0.27.0 — PP-OCRv5 ONNX Rust 네이티브 OCR 엔진: `[image.ocr] engine = "paddle-onnx"` (default 여전히 "ollama-vision") 로 in-process 검출+인식(`ort` =2.0.0-rc.9, Python 런타임 0). DBNet det + CTC rec, 후처리(min-area rect/unclip)는 pure-Rust. e2e CER 0.005(synthetic 한/영, PoC 0.024 대비 우수), 큰 페이지 CPU <4초(Ollama vision ~50초 대비). 신규 config `det_model`/`rec_model`/`dict`/`score_thresh`/`unclip_ratio`/`max_boxes` + `KEBAB_IMAGE_OCR_*` env. ingest 서명 `|ocr:1:{engine}:{engine_version}` 로 engine/모델 변경 시 자동 재색인. 신규 인터페이스(engine 값/config 키) → minor. — CLAUDE.md §Release # pre-v0.18 workspace-wide cleanup: enable clippy::pedantic group with # intentional allow-list. The allowed lints are either cosmetic (doc style), diff --git a/HANDOFF.md b/HANDOFF.md index 478464a..92a7638 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -35,6 +35,7 @@ P0~P5 직렬. P6~P9 P5 이후 병렬 가능. 머지 후 발견된 모든 deviation / hotfix 의 dated 로그는 [tasks/HOTFIXES.md](tasks/HOTFIXES.md). 본 요약은 \"누군가가 인수받을 때 알아두면 시간을 많이 절약하는\" 항목만: +- **2026-06-04 PP-OCRv5 ONNX Rust 네이티브 OCR** — v0.27.0. `[image.ocr] engine = "paddle-onnx"` 로 PP-OCRv5(검출+인식) ONNX 를 in-process(`ort` =2.0.0-rc.9) 실행 — Python 런타임/원격 호출 없이 큰 페이지 CPU <4초(Ollama vision ~50초 대비). default 는 여전히 `"ollama-vision"`. 후처리(min-area rect/unclip)는 pure-Rust. **함정**: unclip 은 corner 를 centroid 에서 방사 확장하면 안 되고 edge 별 polygon offset 이어야 함(방사 확장 시 wide/short 텍스트 박스 높이가 안 커져 글자 윗부분 잘림 → ㄷ→ㄴ, e2e CER 0.26). 수정 후 CER 0.005. 모델 ONNX 는 `crates/kebab-parse-image/assets/paddleocr-onnx/`(LFS). 자세한 내용: `tasks/HOTFIXES.md` (2026-06-04 PP-OCRv5 ONNX), spec/plan `docs/superpowers/{specs,plans}/2026-06-04-rust-native-ocr-*.md`. - **2026-06-03 ingest 설정 변경 자동 재색인** — v0.26.2. ingest 산출에 영향 주는 설정(청킹/이미지 OCR·caption/pdf.ocr/`[ingest.code]`)을 변경하면 `--force-reingest` 없이 영향 자산만 자동 재색인. 그 설정들의 결정적 서명(`ingest_config_signature`)을 effective parser_version(skip 비교 + 저장 doc 필드 양쪽)에 폴딩 → 다음 ingest 비교가 mismatch. 비산출 설정(search/rag/ui/log + max_pixels/languages/timeout)은 제외(과도 무효화 회피), doc_id 는 base 로 안정 유지. **업그레이드 후 첫 ingest 는 전 자산 1회 재색인**(저장된 상수 parser_version ≠ 새 composite; embedding 은 V012 캐시 히트). 결과 포맷·CLI·wire 불변(내부 skip 판정 정정). 자세한 내용: `tasks/HOTFIXES.md` (2026-06-03 ingest 설정 변경 자동 재색인), spec/plan `docs/superpowers/{specs,plans}/2026-06-03-*invalidation*.md`. - **2026-06-03 ingest 진행 로그 개선** — v0.26.1. 이미지/PDF + OCR/caption on 볼트 ingest 가 "멈춘 듯" 보이던 문제 해소: TTY 진행바에 현재 파일명 + 느린 phase(ocr/caption/embed)+모델명 + 경과초 `(Ns)` heartbeat, 종료 시 최장 소요 파일 top-5 요약. 신규 wire `asset_phase{idx,total,phase,model}` + `asset_timings.ocr_ms`/`caption_ms`(additive, `ingest_progress.v1` 유지, serde default 0). 이미지·PDF 경로도 `asset_timings` emit(이전 markdown 만). 기본 동작 불변. 자세한 내용: `tasks/HOTFIXES.md` (2026-06-03 ingest 진행 로그), spec/plan `docs/superpowers/{specs,plans}/2026-06-03-ingest-log-improve-*.md`. - **2026-06-03 arctic-embed-l-v2.0 임베더 통합** — v0.26.0. 별칭 제거 후 설명형 query recall 보강(측정 recall@10 130/132, e5 +7). `kebab-embed-candle` 모델 레지스트리화(e5 mean + `snowflake-arctic-embed-l-v2.0` CLS, 모델별 pooling/prefix) + 신규 `kebab-embed-ollama`(`provider="ollama"`, `/api/embed`). config `endpoint: Option` 추가. 기본 e5 유지(opt-in), arctic 전환은 embedding_version cascade → 재색인. candle↔Ollama cosine>0.99 게이트로 pooling/prefix 정확성 고정(`#[ignore]`). 자세한 내용: `tasks/HOTFIXES.md` (2026-06-03 arctic), spec `docs/superpowers/specs/2026-06-03-arctic-embedder-spec.md`. diff --git a/README.md b/README.md index 1fad986..5b18783 100644 --- a/README.md +++ b/README.md @@ -184,7 +184,8 @@ nli_threshold = 0.0 # >0 (예: 0.5) 면 mDeBERTa XNLI groundedn - **파생물 캐시** — embedding 결과를 내용 해시로 자동 캐싱한다 (위 「핵심 기능」 참고). 설정 항목 없음. - **`[ingest.code]`** — code ingest 의 skip 정책 (`skip_generated_header`, `max_file_bytes`, `extra_skip_globs`). `.gitignore` 자동 honor, `.kebabignore` 는 추가 layer. -- **`[pdf.ocr]`** — scanned PDF 의 page-단위 OCR (default off / opt-in, page 당 ~수십 초 cost). 활성화 후 v0.19 시절 색인분은 `kebab ingest --force-reingest` 로 재처리. +- **`[image.ocr]`** — 이미지 OCR (default off / opt-in). `engine` 으로 백엔드 선택: `"ollama-vision"` (default, 원격 vision LM) 또는 `"paddle-onnx"` (v0.27.0 신규 — PP-OCRv5 ONNX 를 in-process 로 실행, Python 런타임 불필요, 큰 페이지 CPU <4초, 오프라인). `paddle-onnx` 는 워크스페이스에 번들된 모델을 쓰며 `det_model`/`rec_model`/`dict` 로 경로 override, `score_thresh`(0.3)/`unclip_ratio`(1.5)/`max_boxes`(1000) 로 검출 튜닝 가능 (`KEBAB_IMAGE_OCR_*` env 동일 지원). engine 또는 모델을 바꾸면 영향 이미지가 자동 재색인된다. +- **`[pdf.ocr]`** — scanned PDF 의 page-단위 OCR (default off / opt-in, page 당 ~수십 초 cost). `engine` 은 `[image.ocr]` 과 동일하게 `"ollama-vision"`/`"paddle-onnx"` 선택. 활성화 후 v0.19 시절 색인분은 `kebab ingest --force-reingest` 로 재처리. - **`--config `** — 임시 워크스페이스 / 격리 테스트용 (CLI · TUI 모두 honor). - **`kebab config migrate`** — 새 버전에서 추가된 config 섹션을 기존 `config.toml` 에 설명 주석과 함께 채워 넣는다 (사용자가 손본 값·주석·순서는 보존, 멱등, 변경 시 자동 `.bak` 백업). `--dry-run` 으로 변경 미리보기. `kebab doctor` 가 갱신 필요 시 안내한다. `kebab init` 으로 새로 생성되는 config.toml 도 섹션별 주석을 포함한다. - **`KEBAB_*` env** — 일부 키 override (`KEBAB_RAG_SCORE_GATE`, `KEBAB_EVAL_GOLDEN` 등). diff --git a/crates/kebab-app/src/lib.rs b/crates/kebab-app/src/lib.rs index 6891b62..faec240 100644 --- a/crates/kebab-app/src/lib.rs +++ b/crates/kebab-app/src/lib.rs @@ -52,7 +52,10 @@ use kebab_core::{ SearchHit, SearchQuery, SourceScope, SourceUri, VectorRecord, VectorStore, }; use kebab_llm_local::OllamaLanguageModel; -use kebab_parse_image::{OcrEngine, OllamaVisionOcr, apply_caption, apply_ocr}; +use kebab_parse_image::{ + OLLAMA_VISION_ENGINE, OcrEngine, OllamaVisionOcr, OnnxPaddleOcr, PADDLE_ONNX_ENGINE, + apply_caption, apply_ocr, engine_version_for_config, +}; use kebab_parse_md::{BodyHints, build_canonical_document, parse_blocks, parse_frontmatter}; use kebab_source_fs::FsSourceConnector; @@ -357,8 +360,8 @@ pub fn ingest_with_config_opts( // loop is correct and cheap. Construction failure (e.g. invalid // endpoint) aborts ingest fail-fast — better than silently disabling // OCR/caption mid-run. - let ocr_engine: Option = if app.config.image.ocr.enabled { - Some(OllamaVisionOcr::new(&app.config).context("kb-app::ingest: build OllamaVisionOcr")?) + let ocr_engine: Option> = if app.config.image.ocr.enabled { + Some(build_image_ocr_engine(&app.config).context("kb-app::ingest: build image OCR engine")?) } else { None }; @@ -370,28 +373,17 @@ pub fn ingest_with_config_opts( None }; let image_pipeline = ImagePipeline { - ocr_engine: ocr_engine.as_ref(), + ocr_engine: ocr_engine.as_deref(), caption_llm: caption_llm.as_deref(), }; // p10 / v0.20 sub-item 1: PDF OCR engine eager init (H-5 resolution). // image OCR pattern mirror — per-ingest 1회 build, fallible → fail-fast. - let pdf_ocr_engine: Option = + let pdf_ocr_engine: Option> = if app.config.pdf.ocr.enabled || app.config.pdf.ocr.always_on { - let cfg = &app.config.pdf.ocr; - let endpoint = match cfg.endpoint.as_deref() { - Some(s) if !s.is_empty() => s.to_string(), - _ => app.config.models.llm.endpoint.clone(), - }; Some( - OllamaVisionOcr::from_parts( - endpoint, - cfg.model.clone(), - cfg.languages.clone(), - cfg.max_pixels, - cfg.request_timeout_secs, - ) - .context("kb-app::ingest: build OllamaVisionOcr (pdf)")?, + build_pdf_ocr_engine(&app.config) + .context("kb-app::ingest: build pdf OCR engine")?, ) } else { None @@ -488,7 +480,7 @@ pub fn ingest_with_config_opts( &existing_doc_ids, &image_pipeline, force_reingest, - pdf_ocr_engine.as_ref(), + pdf_ocr_engine.as_deref(), progress, opts.cancel.as_ref(), log_writer.clone(), @@ -832,11 +824,84 @@ fn mint_ingest_run_id(scope_json: &str, at: time::OffsetDateTime) -> String { /// `<… as JobRepo>` to be explicit. type SqliteStoreAlias = kebab_store_sqlite::SqliteStore; +/// v0.27.0 (T8): build the image OCR engine selected by +/// `config.image.ocr.engine`. Returns a boxed trait object so the ingest +/// pipeline is engine-agnostic. Construction is fail-fast (model load / +/// hash / endpoint validation) — mirrors the prior concrete-type behaviour. +/// +/// `--config` facade: the caller threads the explicit [`kebab_config::Config`] +/// in, so `OnnxPaddleOcr::new` honours `image.ocr.{det_model,rec_model,dict,…}` +/// overrides resolved from that config (not a re-loaded XDG default). +fn build_image_ocr_engine( + config: &kebab_config::Config, +) -> anyhow::Result> { + match config.image.ocr.engine.as_str() { + OLLAMA_VISION_ENGINE => Ok(Box::new( + OllamaVisionOcr::new(config).context("build OllamaVisionOcr")?, + )), + PADDLE_ONNX_ENGINE => Ok(Box::new( + OnnxPaddleOcr::new(config).context("build OnnxPaddleOcr")?, + )), + other => anyhow::bail!( + "unknown image.ocr.engine {other:?}; expected \ + {OLLAMA_VISION_ENGINE:?} or {PADDLE_ONNX_ENGINE:?}" + ), + } +} + +/// v0.27.0 (T8): build the PDF OCR engine selected by +/// `config.pdf.ocr.engine`. The ollama-vision arm uses the PDF-specific +/// `model` / `languages` / `max_pixels` / `request_timeout_secs` knobs (and +/// endpoint fallback to `models.llm.endpoint`). The paddle-onnx arm shares +/// the same bundled ONNX models as image OCR (resolved from `image.ocr` +/// overrides) — PaddleOCR is page-agnostic and carries no per-engine prompt. +/// +/// # Paddle-ONNX asymmetry +/// +/// When `pdf.ocr.engine = "paddle-onnx"`, the model paths and tuning knobs +/// (`det_model`, `rec_model`, `dict`, `score_thresh`, `unclip_ratio`, +/// `max_boxes`, `max_pixels`) are read from **`[image.ocr]`**, not +/// `[pdf.ocr]`. PaddleOCR has no PDF-specific prompt or page-level config; +/// `[pdf.ocr]` fields other than `engine` / `enabled` / `always_on` / +/// `valid_ratio_threshold` / `min_char_count` / `lang_hint` are effectively +/// ignored for the paddle path. This asymmetry is intentional — one set of +/// tuned ONNX knobs serves both image and PDF pages. +fn build_pdf_ocr_engine( + config: &kebab_config::Config, +) -> anyhow::Result> { + match config.pdf.ocr.engine.as_str() { + OLLAMA_VISION_ENGINE => { + let cfg = &config.pdf.ocr; + let endpoint = match cfg.endpoint.as_deref() { + Some(s) if !s.is_empty() => s.to_string(), + _ => config.models.llm.endpoint.clone(), + }; + Ok(Box::new( + OllamaVisionOcr::from_parts( + endpoint, + cfg.model.clone(), + cfg.languages.clone(), + cfg.max_pixels, + cfg.request_timeout_secs, + ) + .context("build OllamaVisionOcr (pdf)")?, + )) + } + PADDLE_ONNX_ENGINE => Ok(Box::new( + OnnxPaddleOcr::new(config).context("build OnnxPaddleOcr (pdf)")?, + )), + other => anyhow::bail!( + "unknown pdf.ocr.engine {other:?}; expected \ + {OLLAMA_VISION_ENGINE:?} or {PADDLE_ONNX_ENGINE:?}" + ), + } +} + /// P6-4: borrowed bundle of the three image-pipeline components built /// once per ingest invocation. Threaded through `ingest_one_asset` so /// the dispatch does not need ten separate parameters. struct ImagePipeline<'a> { - ocr_engine: Option<&'a OllamaVisionOcr>, + ocr_engine: Option<&'a dyn OcrEngine>, caption_llm: Option<&'a dyn LanguageModel>, } @@ -1110,7 +1175,7 @@ fn ingest_one_asset( existing_doc_ids: &std::collections::HashSet, image_pipeline: &ImagePipeline<'_>, force_reingest: bool, - pdf_ocr_engine: Option<&OllamaVisionOcr>, + pdf_ocr_engine: Option<&dyn OcrEngine>, progress: Option<&std::sync::mpsc::Sender>, cancel: Option<&std::sync::Arc>, log_writer: Option>>, @@ -2093,7 +2158,7 @@ fn ingest_one_pdf_asset( vector_store: Option<&Arc>, existing_doc_ids: &std::collections::HashSet, force_reingest: bool, - pdf_ocr_engine: Option<&OllamaVisionOcr>, + pdf_ocr_engine: Option<&dyn OcrEngine>, progress: Option<&std::sync::mpsc::Sender>, cancel: Option<&std::sync::Arc>, log_writer: Option>>, @@ -3017,6 +3082,50 @@ fn chunk_policy_from_config(config: &kebab_config::Config) -> ChunkPolicy { /// The output is purely a comparison token — it is never parsed back, so the /// exact format is internal. Field order is fixed and `Vec`s are joined so /// the same `Config` always yields the same string. +/// Process-wide memo of the paddle-onnx `engine_version`, keyed by the +/// resolved (det,rec,dict) override triple. Hashing the ~17 MB of model bytes +/// happens once per triple per process (m3 — never re-hash per asset); the +/// per-asset [`ingest_config_signature`] calls hit this cache. +static PADDLE_OCR_VERSION_MEMO: std::sync::OnceLock< + std::sync::Mutex>, +> = std::sync::OnceLock::new(); + +/// T9: resolve the OCR `engine_version` string used inside the ingest config +/// signature. ollama-vision is self-describing from `engine/model` (cheap, no +/// I/O). paddle-onnx hashes the bundled/override model assets (memoized). +fn ocr_engine_version_for_sig(config: &kebab_config::Config, engine: &str, model: &str) -> String { + if engine != PADDLE_ONNX_ENGINE { + // ollama-vision (and any non-paddle engine): the daemon exposes no + // stable per-model revision, so engine/model is the identity. + return format!("ollama/{model}"); + } + let ocr = &config.image.ocr; + let key = format!( + "{}|{}|{}", + ocr.det_model.as_deref().unwrap_or(""), + ocr.rec_model.as_deref().unwrap_or(""), + ocr.dict.as_deref().unwrap_or(""), + ); + let memo = PADDLE_OCR_VERSION_MEMO.get_or_init(|| std::sync::Mutex::new(std::collections::HashMap::new())); + if let Some(v) = memo.lock().unwrap().get(&key) { + return v.clone(); + } + // First call for this triple in this process: hash once. In any real + // ingest the engine was already built (fail-fast) so the assets are + // present and this succeeds; the path-derived identity below is an + // unreachable-in-practice guard that keeps the signature total. + let version = engine_version_for_config(config).unwrap_or_else(|e| { + tracing::warn!( + target: "kebab-app::ingest", + error = %e, + "paddle-onnx engine_version hash failed; using path-derived identity for signature" + ); + format!("ppocrv5-mobile-kor-paths:{key}") + }); + memo.lock().unwrap().insert(key, version.clone()); + version +} + fn ingest_config_signature(config: &kebab_config::Config, media: &MediaType) -> String { // Common (every media type): chunking parameters that move chunk // boundaries. `target_tokens` / `overlap_tokens` change re-chunking for @@ -3033,7 +3142,14 @@ fn ingest_config_signature(config: &kebab_config::Config, media: &MediaType) -> // a stable empty token so re-running the same config skips. let ocr = &config.image.ocr; if ocr.enabled { - sig.push_str(&format!("|ocr:1:{}", ocr.model)); + // v0.27.0 (T9): engine + engine_version so switching engine + // (ollama-vision ↔ paddle-onnx) OR changing the model/assets + // invalidates downstream chunks (design §9 cascade). + sig.push_str(&format!( + "|ocr:1:{}:{}", + ocr.engine, + ocr_engine_version_for_sig(config, &ocr.engine, &ocr.model) + )); } else { sig.push_str("|ocr:0"); } @@ -3049,9 +3165,14 @@ fn ingest_config_signature(config: &kebab_config::Config, media: &MediaType) -> // (mirrors the ingest gate). `model` only matters when active. let ocr = &config.pdf.ocr; if ocr.enabled || ocr.always_on { + // v0.27.0 (T9): engine + engine_version (same cascade rule as + // image OCR above) alongside the enabled/always_on gate. sig.push_str(&format!( - "|pdfocr:{}:{}:{}", - ocr.enabled, ocr.always_on, ocr.model + "|pdfocr:{}:{}:{}:{}", + ocr.enabled, + ocr.always_on, + ocr.engine, + ocr_engine_version_for_sig(config, &ocr.engine, &ocr.model) )); } else { sig.push_str("|pdfocr:0"); @@ -3816,4 +3937,93 @@ mod ingest_config_signature_tests { ); } } + + // ── v0.27.0 (T9): engine + engine_version cascade ───────────────────── + + /// (a) Switching the engine (ollama-vision → paddle-onnx) with the SAME + /// model id changes the image signature — different engines produce + /// different output even from an identically-named model. + #[test] + fn image_ocr_engine_switch_invalidates_image() { + let mut ollama = Config::defaults(); + ollama.image.ocr.enabled = true; + // same `model` string on both — only the engine differs + let mut paddle = ollama.clone(); + paddle.image.ocr.engine = "paddle-onnx".to_string(); + assert_ne!( + ingest_config_signature(&ollama, &img()), + ingest_config_signature(&paddle, &img()), + "engine switch with identical model must invalidate images" + ); + } + + /// (b) A different engine_version (here: a different ollama model id, which + /// the signature folds into `ollama/{model}`) changes the image signature. + #[test] + fn image_ocr_engine_version_change_invalidates_image() { + let mut a = Config::defaults(); + a.image.ocr.enabled = true; + a.image.ocr.model = "gemma4:e4b".to_string(); + let mut b = a.clone(); + b.image.ocr.model = "qwen2.5vl:3b".to_string(); + assert_ne!( + ingest_config_signature(&a, &img()), + ingest_config_signature(&b, &img()), + "engine_version change must invalidate images" + ); + } + + /// (b') For the paddle-onnx engine, pointing at a different model asset + /// (override path) yields a different engine_version → different signature. + #[test] + fn image_ocr_paddle_model_path_change_invalidates_image() { + let mut base = Config::defaults(); + base.image.ocr.enabled = true; + base.image.ocr.engine = "paddle-onnx".to_string(); + let mut overridden = base.clone(); + overridden.image.ocr.det_model = Some("/some/other/det.onnx".to_string()); + assert_ne!( + ingest_config_signature(&base, &img()), + ingest_config_signature(&overridden, &img()), + "paddle-onnx model path change must invalidate images" + ); + } + + /// (c) Unrelated settings leave the paddle-onnx image signature stable + /// (engine_version is memoized + deterministic for a fixed asset triple). + #[test] + fn paddle_image_signature_stable_for_unrelated_change() { + let mut base = Config::defaults(); + base.image.ocr.enabled = true; + base.image.ocr.engine = "paddle-onnx".to_string(); + let mut other = base.clone(); + other.search.default_k += 3; + other.image.ocr.max_pixels += 100; // runtime-only knob + assert_eq!( + ingest_config_signature(&base, &img()), + ingest_config_signature(&other, &img()), + "unrelated/runtime-only changes must not invalidate paddle images" + ); + } + + /// PDF OCR: engine switch with the same model invalidates pdf only. + #[test] + fn pdf_ocr_engine_switch_invalidates_pdf() { + let mut ollama = Config::defaults(); + ollama.pdf.ocr.enabled = true; + let mut paddle = ollama.clone(); + paddle.pdf.ocr.engine = "paddle-onnx".to_string(); + assert_ne!( + ingest_config_signature(&ollama, &pdf()), + ingest_config_signature(&paddle, &pdf()), + "pdf engine switch must invalidate pdf" + ); + for m in [md(), img(), code()] { + assert_eq!( + ingest_config_signature(&ollama, &m), + ingest_config_signature(&paddle, &m), + "pdf engine switch must NOT touch {m:?}" + ); + } + } } diff --git a/crates/kebab-app/tests/common/mock_ocr.rs b/crates/kebab-app/tests/common/mock_ocr.rs index 3632214..efc3732 100644 --- a/crates/kebab-app/tests/common/mock_ocr.rs +++ b/crates/kebab-app/tests/common/mock_ocr.rs @@ -39,6 +39,11 @@ impl OcrEngine for MockOcrEngine { "mock-v1".to_string() } + #[allow(clippy::unnecessary_literal_bound)] + fn model(&self) -> &str { + "mock-model" + } + fn recognize(&self, _img: &[u8], _hint: Option<&Lang>) -> Result { if self.fail { anyhow::bail!("mock failure"); diff --git a/crates/kebab-config/src/lib.rs b/crates/kebab-config/src/lib.rs index cec773e..d7ec7f6 100644 --- a/crates/kebab-config/src/lib.rs +++ b/crates/kebab-config/src/lib.rs @@ -377,6 +377,36 @@ pub struct OcrCfg { /// `86400`). #[serde(default = "default_ocr_request_timeout_secs")] pub request_timeout_secs: u64, + + // ── paddle-onnx engine overrides (v0.27.0) ────────────────────────── + // Only consulted when `engine == "paddle-onnx"`; the ollama-vision + // engine ignores them. All `#[serde(default)]` so pre-v0.27 config + // files load unchanged. + /// Override path to the detection ONNX model. `None` → bundled + /// `assets/paddleocr-onnx/ppocrv5_mobile_det.onnx` (or the directory + /// named by `KEBAB_IMAGE_OCR_MODEL_DIR`). + #[serde(default)] + pub det_model: Option, + /// Override path to the recognition ONNX model. `None` → bundled + /// `assets/paddleocr-onnx/korean_ppocrv5_mobile_rec.onnx`. + #[serde(default)] + pub rec_model: Option, + /// Override path to the character dictionary. `None` → bundled + /// `assets/paddleocr-onnx/korean_dict.txt`. + #[serde(default)] + pub dict: Option, + /// DBNet detection box score threshold (0.0..=1.0). Boxes whose mean + /// probability is below this are dropped. Default `0.3`. + #[serde(default = "default_ocr_score_thresh")] + pub score_thresh: f32, + /// Polygon unclip ratio applied to each detected box before crop. + /// Larger = more padding around the text. Default `1.5`. + #[serde(default = "default_ocr_unclip_ratio")] + pub unclip_ratio: f32, + /// Hard cap on detected boxes per image (runaway guard). Extra boxes + /// past this count are truncated with a warning. Default `1000`. + #[serde(default = "default_ocr_max_boxes")] + pub max_boxes: usize, } impl OcrCfg { @@ -389,10 +419,29 @@ impl OcrCfg { languages: vec!["eng".to_string(), "kor".to_string()], max_pixels: 1600, request_timeout_secs: default_ocr_request_timeout_secs(), + det_model: None, + rec_model: None, + dict: None, + score_thresh: default_ocr_score_thresh(), + unclip_ratio: default_ocr_unclip_ratio(), + max_boxes: default_ocr_max_boxes(), } } } +/// paddle-onnx DBNet box score threshold default. See [`OcrCfg::score_thresh`]. +fn default_ocr_score_thresh() -> f32 { + 0.3 +} +/// paddle-onnx unclip ratio default. See [`OcrCfg::unclip_ratio`]. +fn default_ocr_unclip_ratio() -> f32 { + 1.5 +} +/// paddle-onnx box-count cap default. See [`OcrCfg::max_boxes`]. +fn default_ocr_max_boxes() -> usize { + 1000 +} + /// v0.17.2 post-dogfood: matches the legacy hard-coded ceiling so /// existing configs that omit the field keep behaving identically. /// Overridable per config / `KEBAB_IMAGE_OCR_REQUEST_TIMEOUT_SECS`. @@ -512,7 +561,9 @@ pub struct PdfOcrCfg { /// scanned pages only. `true` — vision LLM 호출 on every page /// (vector PDF 의 dual-text confidence boost — doubles chunk count). pub always_on: bool, - /// Engine identifier. v1 only ships `"ollama-vision"`. + /// Engine identifier: `"ollama-vision"` or `"paddle-onnx"`. When set to + /// `"paddle-onnx"`, model paths and tuning knobs are read from + /// `[image.ocr]`, not `[pdf.ocr]` — PaddleOCR has no PDF-specific tuning. pub engine: String, /// Vision model id. Default `"qwen2.5vl:3b"` per PoC (§3.5 family /// asymmetry vs image OCR's gemma4:e4b is acknowledged). @@ -1098,6 +1149,34 @@ impl Config { self.image.ocr.request_timeout_secs = n; } } + // paddle-onnx engine overrides (v0.27.0). Empty string → None + // (fall back to bundled / KEBAB_IMAGE_OCR_MODEL_DIR). + "KEBAB_IMAGE_OCR_DET_MODEL" => { + self.image.ocr.det_model = + if v.is_empty() { None } else { Some(v.clone()) }; + } + "KEBAB_IMAGE_OCR_REC_MODEL" => { + self.image.ocr.rec_model = + if v.is_empty() { None } else { Some(v.clone()) }; + } + "KEBAB_IMAGE_OCR_DICT" => { + self.image.ocr.dict = if v.is_empty() { None } else { Some(v.clone()) }; + } + "KEBAB_IMAGE_OCR_SCORE_THRESH" => { + if let Ok(f) = v.parse::() { + self.image.ocr.score_thresh = f; + } + } + "KEBAB_IMAGE_OCR_UNCLIP_RATIO" => { + if let Ok(f) = v.parse::() { + self.image.ocr.unclip_ratio = f; + } + } + "KEBAB_IMAGE_OCR_MAX_BOXES" => { + if let Ok(n) = v.parse::() { + self.image.ocr.max_boxes = n; + } + } // image.caption (P6-3) "KEBAB_IMAGE_CAPTION_ENABLED" => { diff --git a/crates/kebab-parse-image/Cargo.toml b/crates/kebab-parse-image/Cargo.toml index eeacbfc..7fd2590 100644 --- a/crates/kebab-parse-image/Cargo.toml +++ b/crates/kebab-parse-image/Cargo.toml @@ -35,6 +35,24 @@ kamadak-exif = "0.6" # transitive tokio runtime is brought in once. reqwest = { version = "0.12", default-features = false, features = ["blocking", "json", "rustls-tls"] } base64 = { workspace = true } +thiserror = { workspace = true } +# paddle-onnx OCR engine (PP-OCRv5, in-process). We reuse the workspace ort +# pin (=2.0.0-rc.9) so the ONNX Runtime native lib stays single-versioned with +# fastembed / kebab-nli (oar-ocr is intentionally NOT a dep — it would pull +# ort rc.12 + ndarray 0.17, splitting the native `links` and threatening the +# embedding stack). `download-binaries` extends the pin the same way +# `kebab-nli/Cargo.toml:23` does: this crate isn't in fastembed's build graph, +# so a standalone `cargo test -p kebab-parse-image` needs it to link onnxruntime. +ort = { workspace = true, features = ["ndarray", "download-binaries"] } +ndarray = { workspace = true } +# blake3: engine_version hash over the bundled det/rec/dict assets (computed +# once at OnnxPaddleOcr construction, cached — `ingest_config_signature` calls +# engine_version() per asset). +blake3 = { workspace = true } +# imageproc: connected-components / contours for DBNet det post-processing. +# min-area rotated-rect (rotating calipers) and polygon unclip are implemented +# in pure Rust (clipper2 is C++ FFI — would break the single-binary guarantee). +imageproc = "0.25" [dev-dependencies] tempfile = { workspace = true } diff --git a/crates/kebab-parse-image/assets/paddleocr-onnx/NOTICE b/crates/kebab-parse-image/assets/paddleocr-onnx/NOTICE new file mode 100644 index 0000000..6116434 --- /dev/null +++ b/crates/kebab-parse-image/assets/paddleocr-onnx/NOTICE @@ -0,0 +1,33 @@ +PP-OCRv5 mobile ONNX models bundled with kebab (paddle-onnx OCR engine) +======================================================================= + +These model weights and the recognition dictionary are derived from +PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR), licensed under the +Apache License, Version 2.0. + + Copyright (c) PaddlePaddle Authors. + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use these files except in compliance with the License. + You may obtain a copy of the License at + http://www.apache.org/licenses/LICENSE-2.0 + +Files +----- + ppocrv5_mobile_det.onnx PP-OCRv5_mobile detection model (DBNet) + korean_ppocrv5_mobile_rec.onnx korean_PP-OCRv5_mobile recognition model (CTC) + korean_dict.txt recognition dictionary (11,945 chars: KR + Latin + digits + symbols) + +These were converted from the official PaddlePaddle inference models to ONNX +via paddle2onnx for in-process execution with onnxruntime (`ort`). No model +architecture or weights were modified; only the serialization format changed. + +The recognition CTC class layout (empirically confirmed, see +tests/golden/ctc_rec_golden.json): + index 0 = CTC blank + index 1..11945 = korean_dict.txt line N -> class N (dict[N-1]) + index 11946 = space ' ' + total classes = 11947 (= 11945 dict + blank + space) + +If any post-processing source (min-area-rect / polygon unclip) is later +ported verbatim from oar-ocr (Apache-2.0), record the per-file provenance +here as required by the Apache-2.0 attribution clause. diff --git a/crates/kebab-parse-image/assets/paddleocr-onnx/korean_dict.txt b/crates/kebab-parse-image/assets/paddleocr-onnx/korean_dict.txt new file mode 100644 index 0000000..e220263 --- /dev/null +++ b/crates/kebab-parse-image/assets/paddleocr-onnx/korean_dict.txt @@ -0,0 +1,11945 @@ +ᄀ +ᄁ +ᄂ +ᄃ +ᄄ +ᄅ +ᄆ +ᄇ +ᄈ +ᄉ +ᄊ +ᄋ +ᄌ +ᄍ +ᄎ +ᄏ +ᄐ +ᄑ +ᄒ +ᄓ +ᄔ +ᄕ +ᄖ +ᄗ +ᄘ +ᄙ +ᄚ +ᄛ +ᄜ +ᄝ +ᄞ +ᄟ +ᄠ +ᄡ +ᄢ +ᄣ +ᄤ +ᄥ +ᄦ +ᄧ +ᄨ +ᄩ +ᄪ +ᄫ +ᄬ +ᄭ +ᄮ +ᄯ +ᄰ +ᄱ 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b/crates/kebab-parse-image/assets/paddleocr-onnx/korean_ppocrv5_mobile_rec.onnx differ diff --git a/crates/kebab-parse-image/assets/paddleocr-onnx/ppocrv5_mobile_det.onnx b/crates/kebab-parse-image/assets/paddleocr-onnx/ppocrv5_mobile_det.onnx new file mode 100644 index 0000000..5cae3f7 Binary files /dev/null and b/crates/kebab-parse-image/assets/paddleocr-onnx/ppocrv5_mobile_det.onnx differ diff --git a/crates/kebab-parse-image/src/lib.rs b/crates/kebab-parse-image/src/lib.rs index ff5dac2..177724e 100644 --- a/crates/kebab-parse-image/src/lib.rs +++ b/crates/kebab-parse-image/src/lib.rs @@ -30,9 +30,11 @@ mod dims; mod exif_extract; mod image_prep; pub mod ocr; +pub mod paddle_onnx; pub use caption::{apply_caption, caption_image}; -pub use ocr::{OcrEngine, OllamaVisionOcr, apply_ocr}; +pub use ocr::{OLLAMA_VISION_ENGINE, OcrEngine, OllamaVisionOcr, apply_ocr}; +pub use paddle_onnx::{ModelPaths, OnnxPaddleOcr, PADDLE_ONNX_ENGINE, engine_version_for_config}; use anyhow::{Context, Result}; use kebab_core::{ diff --git a/crates/kebab-parse-image/src/ocr.rs b/crates/kebab-parse-image/src/ocr.rs index 64ea1dd..f604cbb 100644 --- a/crates/kebab-parse-image/src/ocr.rs +++ b/crates/kebab-parse-image/src/ocr.rs @@ -65,6 +65,13 @@ pub trait OcrEngine: Send + Sync { /// through to engines that benefit from it (Tesseract languages, /// LLM prompt steering); ignore otherwise. fn recognize(&self, image_bytes: &[u8], lang_hint: Option<&Lang>) -> Result; + + /// Human-facing model label for the ingest progress display + /// (`AssetPhase{phase:"ocr", model}`). Distinct from + /// [`engine_version`](Self::engine_version), which is the cache-key + /// hash. E.g. `"gemma4:e4b"` (ollama-vision) or `"ppocrv5-mobile-kor"` + /// (paddle-onnx). + fn model(&self) -> &str; } /// Mutate `block.ocr` in place by running `engine` over `image_bytes`, @@ -209,13 +216,6 @@ impl OllamaVisionOcr { self.max_pixels } - /// The Ollama model id this engine drives (e.g. `gemma4:e4b`). - /// Surfaced so the ingest progress display can name the model - /// running a slow OCR phase (`AssetPhase{phase:"ocr", model}`). - pub fn model(&self) -> &str { - &self.model - } - fn build_prompt(&self, lang_hint: Option<&Lang>) -> String { let langs = if self.languages.is_empty() { "any".to_string() @@ -247,6 +247,10 @@ impl OcrEngine for OllamaVisionOcr { format!("ollama/{}", self.model) } + fn model(&self) -> &str { + &self.model + } + fn recognize(&self, image_bytes: &[u8], lang_hint: Option<&Lang>) -> Result { let (prepared, w, h) = image_prep::downscale_to_png(image_bytes, self.max_pixels) .context("preparing image for OCR")?; diff --git a/crates/kebab-parse-image/src/paddle_onnx.rs b/crates/kebab-parse-image/src/paddle_onnx.rs new file mode 100644 index 0000000..2959d05 --- /dev/null +++ b/crates/kebab-parse-image/src/paddle_onnx.rs @@ -0,0 +1,985 @@ +//! PP-OCRv5 ONNX OCR engine — in-process detection + recognition on the +//! workspace-pinned `ort` (=2.0.0-rc.9), no Python runtime, no oar-ocr +//! production dependency (see crate-level rationale + `assets/paddleocr-onnx/NOTICE`). +//! +//! Pipeline (`recognize`): +//! 1. decode (RGB) + downscale long edge to `max_pixels` +//! 2. det: ImageNet-normalized NCHW → DBNet prob map `[1,1,H,W]` → threshold +//! 0.3 → contours → min-area rect (rotating calipers, pure Rust) → +//! unclip(ratio 1.5, pure Rust) → boxes +//! 3. crop+rectify: perspective warp each rotated box to a horizontal strip +//! 4. rec: 48×W normalized `(x-0.5)/0.5` → `[1,T,11947]` → CTC greedy decode +//! 5. assemble reading-order `OcrText` +//! +//! ## Confirmed CTC facts (empirically derived in T0a, see +//! `tests/golden/ctc_rec_golden.json` — do NOT re-derive): +//! * rec classes = 11947 = dict(11945) + blank + space +//! * index 0 = CTC blank +//! * index 1..=11945 = `korean_dict.txt` line N → class N (i.e. `dict[N-1]`) +//! * index 11946 = space ' ' +//! +//! ## rc.9 API notes (differ from rc.12): +//! * `try_extract_tensor::()` → `ArrayViewD` (`.shape()` / indexing). +//! * `Session::run` is called through a `Mutex` guard so the engine is +//! `Send + Sync` regardless of `Session`'s own auto-trait status (ingest +//! is serial today; the lock is uncontended). + +use std::path::{Path, PathBuf}; +use std::sync::Mutex; + +use anyhow::{Context, Result}; +use kebab_core::{Lang, OcrRegion, OcrText}; +use ndarray::Array4; +use ort::session::Session; +use ort::value::Value; + +use crate::ocr::OcrEngine; + +/// Engine name written into `OcrText.engine`. +pub const PADDLE_ONNX_ENGINE: &str = "paddle-onnx"; + +/// CTC blank class index (confirmed in T0a). +const CTC_BLANK: usize = 0; +/// Space class index (confirmed in T0a). `1..=DICT_LINES` map to dict entries. +const CTC_SPACE: usize = 11946; +/// `korean_dict.txt` line count (confirmed in T0a). +const DICT_LINES: usize = 11945; +/// rec output class count = dict + blank + space (confirmed in T0a). +const REC_CLASSES: usize = 11947; + +/// det long-edge cap before rounding to a multiple of 32 (PaddleOCR default). +const DET_LIMIT_SIDE_LEN: u32 = 960; +/// rec input height (PP-OCRv5 mobile). +const REC_HEIGHT: u32 = 48; +/// DBNet probability-map binarization threshold. Looser than Paddle's default +/// `box_thresh` (0.6) to keep recall high on low-contrast Korean text. +const DET_BIN_THRESH: f32 = 0.3; + +/// ImageNet normalization (det preprocessing — RGB). +const IMAGENET_MEAN: [f32; 3] = [0.485, 0.456, 0.406]; +const IMAGENET_STD: [f32; 3] = [0.229, 0.224, 0.225]; + +/// PP-OCRv5 ONNX engine. Holds the two ONNX sessions (loaded once) and the +/// dict. `engine_version` is computed once at construction (blake3 over the +/// three model assets) and cached — `ingest_config_signature` calls +/// `engine_version()` per asset, so re-hashing there would be O(assets). +pub struct OnnxPaddleOcr { + det: Mutex, + rec: Mutex, + det_input_name: String, + rec_input_name: String, + dict: Vec, + engine_version: String, + score_thresh: f32, + unclip_ratio: f32, + max_boxes: usize, + max_pixels: u32, +} + +impl std::fmt::Debug for OnnxPaddleOcr { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + f.debug_struct("OnnxPaddleOcr") + .field("engine_version", &self.engine_version) + .field("dict_lines", &self.dict.len()) + .field("score_thresh", &self.score_thresh) + .field("unclip_ratio", &self.unclip_ratio) + .field("max_boxes", &self.max_boxes) + .field("max_pixels", &self.max_pixels) + .finish_non_exhaustive() + } +} + +/// Resolved model-asset paths. Construction is decoupled from `kebab-config` +/// (T7 adds the `det_model`/`rec_model`/`dict` overrides) so the engine can be +/// built directly in tests. +#[derive(Clone, Debug)] +pub struct ModelPaths { + pub det: PathBuf, + pub rec: PathBuf, + pub dict: PathBuf, +} + +impl ModelPaths { + /// Default bundled-asset directory: `KEBAB_IMAGE_OCR_MODEL_DIR` if set, + /// else the crate's `assets/paddleocr-onnx/`. + pub fn from_default_dir() -> Self { + let dir = std::env::var("KEBAB_IMAGE_OCR_MODEL_DIR").map_or_else( + |_| Path::new(env!("CARGO_MANIFEST_DIR")).join("assets/paddleocr-onnx"), + PathBuf::from, + ); + Self { + det: dir.join("ppocrv5_mobile_det.onnx"), + rec: dir.join("korean_ppocrv5_mobile_rec.onnx"), + dict: dir.join("korean_dict.txt"), + } + } + + /// Resolve model paths from the `image.ocr` config (T7). Each of + /// `det_model` / `rec_model` / `dict` overrides the corresponding bundled + /// path when set; unset fields fall back to [`from_default_dir`], so a + /// caller can override just one asset. + /// + /// [`from_default_dir`]: ModelPaths::from_default_dir + pub fn from_config(config: &kebab_config::Config) -> Self { + let defaults = Self::from_default_dir(); + let ocr = &config.image.ocr; + Self { + det: ocr.det_model.as_ref().map(PathBuf::from).unwrap_or(defaults.det), + rec: ocr.rec_model.as_ref().map(PathBuf::from).unwrap_or(defaults.rec), + dict: ocr.dict.as_ref().map(PathBuf::from).unwrap_or(defaults.dict), + } + } +} + +impl OnnxPaddleOcr { + /// Build from a workspace [`kebab_config::Config`]. Resolves model paths + /// from the default bundled directory (T7 will thread config overrides). + /// Construction loads both ONNX sessions and hashes the assets — failures + /// here are fail-fast (matches the Ollama adapter's construction contract). + pub fn new(config: &kebab_config::Config) -> Result { + let paths = ModelPaths::from_config(config); + let ocr = &config.image.ocr; + Self::from_paths( + &paths, + ocr.score_thresh, + ocr.unclip_ratio, + ocr.max_boxes, + ocr.max_pixels, + ) + } + + /// Build from explicit asset paths + tuning knobs. Used by tests and by + /// `new` after path resolution. + pub fn from_paths( + paths: &ModelPaths, + score_thresh: f32, + unclip_ratio: f32, + max_boxes: usize, + max_pixels: u32, + ) -> Result { + let dict = load_dict(&paths.dict) + .with_context(|| format!("loading OCR dict from {}", paths.dict.display()))?; + // bounds-check: dict length must match the rec class layout + // (dict + blank + space). A mismatch means a wrong dict file — + // fail at construction rather than mis-decoding silently. + if dict.len() != DICT_LINES { + anyhow::bail!( + "OnnxPaddleOcr: dict has {} lines, expected {DICT_LINES} \ + (rec classes {REC_CLASSES} = dict + blank + space)", + dict.len() + ); + } + + let engine_version = compute_engine_version(paths) + .context("hashing OCR model assets for engine_version")?; + + let det = Session::builder() + .context("ort Session::builder (det)")? + .commit_from_file(&paths.det) + .with_context(|| format!("loading det model {}", paths.det.display()))?; + let rec = Session::builder() + .context("ort Session::builder (rec)")? + .commit_from_file(&paths.rec) + .with_context(|| format!("loading rec model {}", paths.rec.display()))?; + + let det_input_name = det + .inputs + .first() + .map(|i| i.name.clone()) + .context("det model has no inputs")?; + let rec_input_name = rec + .inputs + .first() + .map(|i| i.name.clone()) + .context("rec model has no inputs")?; + + Ok(Self { + det: Mutex::new(det), + rec: Mutex::new(rec), + det_input_name, + rec_input_name, + dict, + engine_version, + score_thresh, + unclip_ratio, + max_boxes, + max_pixels: max_pixels.clamp(256, 4096), + }) + } + +} + +impl OcrEngine for OnnxPaddleOcr { + fn engine_name(&self) -> &'static str { + PADDLE_ONNX_ENGINE + } + + fn engine_version(&self) -> String { + self.engine_version.clone() + } + + // The trait method's elided lifetime ties the return to `&self`; the body + // returns a literal, but the signature must match the trait, so allow the + // `'static`-narrowing lint here. + #[allow(clippy::unnecessary_literal_bound)] + fn model(&self) -> &str { + // Static label for the progress display; the per-asset hash lives + // in `engine_version`. + "ppocrv5-mobile-kor" + } + + fn recognize(&self, image_bytes: &[u8], _lang_hint: Option<&Lang>) -> Result { + let img = image::load_from_memory(image_bytes) + .context("decoding image for OCR")? + .to_rgb8(); + let (orig_w, orig_h) = (img.width(), img.height()); + if orig_w == 0 || orig_h == 0 { + return Ok(empty_ocr(self)); + } + + // ── det ──────────────────────────────────────────────────────── + let (det_w, det_h) = det_target_dims(orig_w, orig_h, self.max_pixels); + let det_img = image::imageops::resize( + &img, + det_w, + det_h, + image::imageops::FilterType::Triangle, + ); + let prob = self.run_det(&det_img)?; // (det_h, det_w) prob map + let scale_x = orig_w as f32 / det_w as f32; + let scale_y = orig_h as f32 / det_h as f32; + let mut boxes = det_postprocess( + &prob, + prob.w, + prob.h, + self.score_thresh, + self.unclip_ratio, + ); + if boxes.len() > self.max_boxes { + tracing::warn!( + target: "kebab-parse-image", + "paddle-onnx: {} boxes exceeds max_boxes {} — truncating", + boxes.len(), + self.max_boxes + ); + boxes.truncate(self.max_boxes); + } + // scale box corners back to original image coordinates + for b in &mut boxes { + for p in &mut b.corners { + p.0 *= scale_x; + p.1 *= scale_y; + } + } + + if boxes.is_empty() { + return Ok(empty_ocr(self)); + } + + // ── rec per box (reading order: top→bottom, left→right) ───────── + boxes.sort_by(|a, b| { + let ay = a.center_y(); + let by = b.center_y(); + // group into rough rows by 0.5*box height tolerance via y then x + ay.partial_cmp(&by) + .unwrap_or(std::cmp::Ordering::Equal) + .then_with(|| { + a.center_x() + .partial_cmp(&b.center_x()) + .unwrap_or(std::cmp::Ordering::Equal) + }) + }); + + let mut regions: Vec = Vec::with_capacity(boxes.len()); + for b in &boxes { + let crop = rectify_crop(&img, &b.corners); + if crop.width() == 0 || crop.height() == 0 { + continue; + } + let (text, conf) = self.run_rec(&crop)?; + if text.is_empty() { + continue; // rec empty → skip this box, keep the rest + } + let (x, y, w, h) = b.aabb(); + regions.push(OcrRegion { + bbox: (x, y, w, h), + text, + confidence: conf, + }); + } + + let joined = regions + .iter() + .map(|r| r.text.as_str()) + .collect::>() + .join("\n"); + + Ok(OcrText { + joined, + regions, + engine: PADDLE_ONNX_ENGINE.to_string(), + engine_version: self.engine_version.clone(), + }) + } +} + +impl OnnxPaddleOcr { + /// Run det session → `(det_h, det_w)` probability map as a row-major Vec. + fn run_det(&self, det_img: &image::RgbImage) -> Result { + let (w, h) = (det_img.width() as usize, det_img.height() as usize); + let mut arr = Array4::::zeros((1, 3, h, w)); + for (x, y, px) in det_img.enumerate_pixels() { + let (xi, yi) = (x as usize, y as usize); + for c in 0..3 { + let v = f32::from(px[c]) / 255.0; + arr[[0, c, yi, xi]] = (v - IMAGENET_MEAN[c]) / IMAGENET_STD[c]; + } + } + let input = Value::from_array(arr).context("det Value::from_array")?; + let sess = self.det.lock().unwrap_or_else(std::sync::PoisonError::into_inner); + let outputs = sess + .run(ort::inputs![self.det_input_name.as_str() => input]?) + .context("det session run")?; + let out_name = sess.outputs[0].name.clone(); + let view = outputs[out_name.as_str()] + .try_extract_tensor::() + .context("det output extract")?; + // shape [1,1,H,W] + let shape = view.shape(); + let (oh, ow) = (shape[shape.len() - 2], shape[shape.len() - 1]); + let data: Vec = view.iter().copied().collect(); + Ok(ProbMap { w: ow, h: oh, data }) + } + + /// Run rec session on a rectified crop → (decoded string, mean confidence). + fn run_rec(&self, crop: &image::RgbImage) -> Result<(String, f32)> { + // resize keep-aspect to height 48, then this single crop is its own batch + let (cw, ch) = (crop.width().max(1), crop.height().max(1)); + let new_w = ((REC_HEIGHT as f32 / ch as f32) * cw as f32).round().max(1.0) as u32; + let resized = image::imageops::resize( + crop, + new_w, + REC_HEIGHT, + image::imageops::FilterType::Triangle, + ); + let w = new_w as usize; + let h = REC_HEIGHT as usize; + let mut arr = Array4::::zeros((1, 3, h, w)); + for (x, y, px) in resized.enumerate_pixels() { + let (xi, yi) = (x as usize, y as usize); + for c in 0..3 { + let v = f32::from(px[c]) / 255.0; + arr[[0, c, yi, xi]] = (v - 0.5) / 0.5; // [-1, 1] + } + } + let input = Value::from_array(arr).context("rec Value::from_array")?; + let sess = self.rec.lock().unwrap_or_else(std::sync::PoisonError::into_inner); + let outputs = sess + .run(ort::inputs![self.rec_input_name.as_str() => input]?) + .context("rec session run")?; + let out_name = sess.outputs[0].name.clone(); + let view = outputs[out_name.as_str()] + .try_extract_tensor::() + .context("rec output extract")?; + // shape [1, T, C] + let shape = view.shape(); + let (t, c) = (shape[shape.len() - 2], shape[shape.len() - 1]); + if c != REC_CLASSES { + anyhow::bail!( + "rec output has {c} classes, expected {REC_CLASSES} \ + (dict {DICT_LINES} + blank + space)" + ); + } + let data: Vec = view.iter().copied().collect(); + Ok(self.ctc_greedy_decode(&data, t, c)) + } + + /// CTC greedy decode over `[T, C]` logits/probs (row-major). Delegates to + /// [`ctc_greedy_decode_with_dict`] so the algorithm is testable without + /// loading ONNX sessions (see `tests::ctc_greedy_decode_golden`). + fn ctc_greedy_decode(&self, data: &[f32], t: usize, c: usize) -> (String, f32) { + ctc_greedy_decode_with_dict(data, t, c, &self.dict) + } +} + +/// CTC greedy decode: per-timestep argmax → collapse consecutive duplicates → +/// drop blank (index 0) → map class index to string via `dict`. +/// Pure Rust, no I/O — usable in unit tests without loading ONNX sessions. +fn ctc_greedy_decode_with_dict(data: &[f32], t: usize, c: usize, dict: &[String]) -> (String, f32) { + let class_to_str = |idx: usize| -> Option<&str> { + match idx { + CTC_BLANK => None, + CTC_SPACE => Some(" "), + i if (1..=DICT_LINES).contains(&i) => Some(dict[i - 1].as_str()), + _ => None, + } + }; + let mut out = String::new(); + let mut confs: Vec = Vec::new(); + let mut prev = usize::MAX; + for ti in 0..t { + let row = &data[ti * c..(ti + 1) * c]; + let mut best = 0usize; + let mut best_v = f32::MIN; + for (i, &v) in row.iter().enumerate() { + if v > best_v { + best_v = v; + best = i; + } + } + if best != prev && best != CTC_BLANK { + if let Some(s) = class_to_str(best) { + out.push_str(s); + confs.push(best_v); + } + } + prev = best; + } + let conf = if confs.is_empty() { + 0.0 + } else { + confs.iter().sum::() / confs.len() as f32 + }; + (out, conf) +} + +fn empty_ocr(e: &OnnxPaddleOcr) -> OcrText { + OcrText { + joined: String::new(), + regions: Vec::new(), + engine: PADDLE_ONNX_ENGINE.to_string(), + engine_version: e.engine_version.clone(), + } +} + +/// Load the dict file: one token per line, trailing newline tolerated. +/// Empty lines are preserved as empty tokens (PaddleOCR dicts may carry a +/// blank-looking line; index integrity matters more than trimming). +fn load_dict(path: &Path) -> Result> { + let raw = std::fs::read_to_string(path)?; + // split on '\n'; drop a single trailing empty element from the final newline + let mut lines: Vec = raw.split('\n').map(|s| s.trim_end_matches('\r').to_string()).collect(); + if lines.last().is_some_and(String::is_empty) { + lines.pop(); + } + Ok(lines) +} + +/// Resolve the paddle-onnx `engine_version` for `config` without loading the +/// ONNX sessions (T9). This is the same blake3-over-assets string that a +/// constructed [`OnnxPaddleOcr`] exposes via [`OcrEngine::engine_version`], so +/// the ingest config signature can include it. Reads ~17 MB of model bytes — +/// callers MUST memoize per (det,rec,dict) triple (m3: never re-hash per asset). +pub fn engine_version_for_config(config: &kebab_config::Config) -> Result { + compute_engine_version(&ModelPaths::from_config(config)) +} + +/// blake3 over det + rec + dict bytes → stable `engine_version`. +fn compute_engine_version(paths: &ModelPaths) -> Result { + let mut hasher = blake3::Hasher::new(); + for p in [&paths.det, &paths.rec, &paths.dict] { + let bytes = std::fs::read(p).with_context(|| format!("reading {}", p.display()))?; + hasher.update(&bytes); + } + let hash = hasher.finalize(); + let hex = hash.to_hex(); + Ok(format!("ppocrv5-mobile-kor-{}", &hex.as_str()[..12])) +} + +/// det resize target: keep aspect, cap long edge at `min(max_pixels, 960)`, +/// then round each dim to a multiple of 32 (DBNet stride). Reproduces the T0a +/// golden (192×900 → 192×896). +fn det_target_dims(w: u32, h: u32, max_pixels: u32) -> (u32, u32) { + let limit = DET_LIMIT_SIDE_LEN.min(max_pixels.max(32)); + let long = w.max(h); + let ratio = if long > limit { + limit as f32 / long as f32 + } else { + 1.0 + }; + let rw = (w as f32 * ratio).round().max(1.0); + let rh = (h as f32 * ratio).round().max(1.0); + let round32 = |v: f32| -> u32 { + let r = (v / 32.0).round() as u32 * 32; + r.max(32) + }; + (round32(rw), round32(rh)) +} + +// ── det postprocessing ────────────────────────────────────────────────────── + +struct ProbMap { + w: usize, + h: usize, + data: Vec, +} + +impl ProbMap { + #[inline] + fn at(&self, x: usize, y: usize) -> f32 { + self.data[y * self.w + x] + } +} + +/// A detected text box: 4 corners (clockwise from top-left) in det-image +/// coordinates (later scaled to original). +#[derive(Clone, Debug)] +struct DetBox { + corners: [(f32, f32); 4], +} + +impl DetBox { + fn center_x(&self) -> f32 { + self.corners.iter().map(|p| p.0).sum::() / 4.0 + } + fn center_y(&self) -> f32 { + self.corners.iter().map(|p| p.1).sum::() / 4.0 + } + /// Axis-aligned bounding box (x, y, w, h) clamped to non-negative. + fn aabb(&self) -> (u32, u32, u32, u32) { + let xs = self.corners.iter().map(|p| p.0); + let ys = self.corners.iter().map(|p| p.1); + let minx = xs.clone().fold(f32::MAX, f32::min).max(0.0); + let maxx = xs.fold(f32::MIN, f32::max).max(0.0); + let miny = ys.clone().fold(f32::MAX, f32::min).max(0.0); + let maxy = ys.fold(f32::MIN, f32::max).max(0.0); + ( + minx.round() as u32, + miny.round() as u32, + (maxx - minx).round().max(0.0) as u32, + (maxy - miny).round().max(0.0) as u32, + ) + } +} + +/// DBNet-style postprocess: threshold → connected components → contour → +/// min-area rect (rotating calipers) → box-score filter → unclip → boxes. +/// Pinned by `tests/golden/det_boxes_clean_paragraph.json` (3 boxes). +fn det_postprocess( + prob: &ProbMap, + w: usize, + h: usize, + score_thresh: f32, + unclip_ratio: f32, +) -> Vec { + use image::{GrayImage, Luma}; + + // binarize at the detection threshold + let mut bin = GrayImage::new(w as u32, h as u32); + for y in 0..h { + for x in 0..w { + let v = if prob.at(x, y) > DET_BIN_THRESH { 255u8 } else { 0u8 }; + bin.put_pixel(x as u32, y as u32, Luma([v])); + } + } + + let contours = imageproc::contours::find_contours::(&bin); + let mut boxes = Vec::new(); + for contour in &contours { + if contour.points.len() < 4 { + continue; + } + let pts: Vec<(f32, f32)> = contour + .points + .iter() + .map(|p| (p.x as f32, p.y as f32)) + .collect(); + let Some(rect) = min_area_rect(&pts) else { + continue; + }; + // mean-prob box score over the AABB of the rotated rect + let score = box_score(prob, &rect.corners); + if score < score_thresh { + continue; + } + let unclipped = unclip_rect(&rect, unclip_ratio); + boxes.push(DetBox { corners: unclipped }); + } + boxes +} + +/// Mean probability inside the axis-aligned bbox of the rect — the +/// `box_thresh` mean-prob filter used by the golden harness. +fn box_score(prob: &ProbMap, corners: &[(f32, f32); 4]) -> f32 { + let minx = corners.iter().map(|p| p.0).fold(f32::MAX, f32::min).max(0.0) as usize; + let maxx = (corners.iter().map(|p| p.0).fold(f32::MIN, f32::max).max(0.0) as usize) + .min(prob.w.saturating_sub(1)); + let miny = corners.iter().map(|p| p.1).fold(f32::MAX, f32::min).max(0.0) as usize; + let maxy = (corners.iter().map(|p| p.1).fold(f32::MIN, f32::max).max(0.0) as usize) + .min(prob.h.saturating_sub(1)); + if maxx <= minx || maxy <= miny { + return 0.0; + } + let mut sum = 0.0f32; + let mut n = 0usize; + for y in miny..=maxy { + for x in minx..=maxx { + sum += prob.at(x, y); + n += 1; + } + } + if n == 0 { 0.0 } else { sum / n as f32 } +} + +/// Rotated rect described by its 4 corners + box dims. +#[derive(Clone, Debug)] +struct RotRect { + corners: [(f32, f32); 4], + width: f32, + height: f32, +} + +/// Minimum-area enclosing rectangle of a point set via rotating calipers on +/// the convex hull (pure Rust — no OpenCV / clipper2). +fn min_area_rect(points: &[(f32, f32)]) -> Option { + let hull = convex_hull(points); + if hull.len() < 3 { + return None; + } + let n = hull.len(); + let mut best_area = f32::MAX; + let mut best: Option = None; + for i in 0..n { + let p0 = hull[i]; + let p1 = hull[(i + 1) % n]; + let edge = (p1.0 - p0.0, p1.1 - p0.1); + let len = (edge.0 * edge.0 + edge.1 * edge.1).sqrt(); + if len < 1e-6 { + continue; + } + let ux = (edge.0 / len, edge.1 / len); // edge direction + let uy = (-ux.1, ux.0); // normal + let (mut min_u, mut max_u) = (f32::MAX, f32::MIN); + let (mut min_v, mut max_v) = (f32::MAX, f32::MIN); + for &p in &hull { + let du = p.0 * ux.0 + p.1 * ux.1; + let dv = p.0 * uy.0 + p.1 * uy.1; + min_u = min_u.min(du); + max_u = max_u.max(du); + min_v = min_v.min(dv); + max_v = max_v.max(dv); + } + let area = (max_u - min_u) * (max_v - min_v); + if area < best_area { + best_area = area; + // reconstruct corners in (u,v) basis → world + let to_world = |u: f32, v: f32| (u * ux.0 + v * uy.0, u * ux.1 + v * uy.1); + let corners = [ + to_world(min_u, min_v), + to_world(max_u, min_v), + to_world(max_u, max_v), + to_world(min_u, max_v), + ]; + best = Some(RotRect { + corners, + width: max_u - min_u, + height: max_v - min_v, + }); + } + } + best +} + +/// Andrew's monotone chain convex hull. Returns CCW hull without duplicates. +fn convex_hull(points: &[(f32, f32)]) -> Vec<(f32, f32)> { + let mut pts: Vec<(f32, f32)> = points.to_vec(); + pts.sort_by(|a, b| { + a.0.partial_cmp(&b.0) + .unwrap_or(std::cmp::Ordering::Equal) + .then(a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal)) + }); + pts.dedup(); + if pts.len() < 3 { + return pts; + } + let cross = |o: (f32, f32), a: (f32, f32), b: (f32, f32)| { + (a.0 - o.0) * (b.1 - o.1) - (a.1 - o.1) * (b.0 - o.0) + }; + let mut lower: Vec<(f32, f32)> = Vec::new(); + for &p in &pts { + while lower.len() >= 2 && cross(lower[lower.len() - 2], lower[lower.len() - 1], p) <= 0.0 { + lower.pop(); + } + lower.push(p); + } + let mut upper: Vec<(f32, f32)> = Vec::new(); + for &p in pts.iter().rev() { + while upper.len() >= 2 && cross(upper[upper.len() - 2], upper[upper.len() - 1], p) <= 0.0 { + upper.pop(); + } + upper.push(p); + } + lower.pop(); + upper.pop(); + lower.extend(upper); + lower +} + +/// Unclip a rotated rect by `ratio` (PaddleOCR `distance = area*ratio/perimeter`), +/// expanding width + height by `2*distance`. For a rectangle this matches the +/// general polygon offset PaddleOCR uses (pyclipper) — pure Rust here. +fn unclip_rect(rect: &RotRect, ratio: f32) -> [(f32, f32); 4] { + let area = rect.width * rect.height; + let perimeter = 2.0 * (rect.width + rect.height); + if perimeter < 1e-6 { + return rect.corners; + } + let distance = area * ratio / perimeter; + // Offset every EDGE outward by `distance` (PaddleOCR pyclipper polygon + // offset): width and height each grow by 2*distance. A naive radial + // push-from-centroid is WRONG for text boxes — a wide/short box has an + // almost-horizontal diagonal, so radial expansion barely grows the height + // and clips character tops/bottoms (ㄷ→ㄴ, ascenders lost). We instead + // expand along the rect's own (u, v) axes recovered from its ordered + // corners (c0=min_u,min_v; c1=max_u,min_v; c2=max_u,max_v; c3=min_u,max_v). + let c = &rect.corners; + let unit = |dx: f32, dy: f32| -> (f32, f32) { + let len = (dx * dx + dy * dy).sqrt(); + if len > 1e-6 { (dx / len, dy / len) } else { (0.0, 0.0) } + }; + let u = unit(c[1].0 - c[0].0, c[1].1 - c[0].1); // +u (along width) + let v = unit(c[3].0 - c[0].0, c[3].1 - c[0].1); // +v (along height) + let off = |p: (f32, f32), su: f32, sv: f32| -> (f32, f32) { + ( + p.0 + su * distance * u.0 + sv * distance * v.0, + p.1 + su * distance * u.1 + sv * distance * v.1, + ) + }; + [ + off(c[0], -1.0, -1.0), + off(c[1], 1.0, -1.0), + off(c[2], 1.0, 1.0), + off(c[3], -1.0, 1.0), + ] +} + +// ── crop + rectify ─────────────────────────────────────────────────────────── + +/// Perspective-warp the quadrilateral `corners` (clockwise from top-left) into +/// a horizontal strip. Output size derives from the box edge lengths. +fn rectify_crop(img: &image::RgbImage, corners: &[(f32, f32); 4]) -> image::RgbImage { + // order corners: top-left, top-right, bottom-right, bottom-left + let ordered = order_corners(corners); + let dist = |a: (f32, f32), b: (f32, f32)| ((a.0 - b.0).powi(2) + (a.1 - b.1).powi(2)).sqrt(); + let w = dist(ordered[0], ordered[1]).max(dist(ordered[3], ordered[2])); + let h = dist(ordered[0], ordered[3]).max(dist(ordered[1], ordered[2])); + let out_w = w.round().max(1.0) as u32; + let out_h = h.round().max(1.0) as u32; + let mut out = image::RgbImage::new(out_w, out_h); + let (iw, ih) = (img.width() as f32, img.height() as f32); + // bilinear map from output grid back to the source quad (inverse via + // bilinear interpolation of the four corners — adequate for near-affine + // text boxes). + for oy in 0..out_h { + let fy = oy as f32 / (out_h.max(1) as f32 - 1.0).max(1.0); + for ox in 0..out_w { + let fx = ox as f32 / (out_w.max(1) as f32 - 1.0).max(1.0); + // bilinear blend of the four source corners + let top = ( + ordered[0].0 + (ordered[1].0 - ordered[0].0) * fx, + ordered[0].1 + (ordered[1].1 - ordered[0].1) * fx, + ); + let bot = ( + ordered[3].0 + (ordered[2].0 - ordered[3].0) * fx, + ordered[3].1 + (ordered[2].1 - ordered[3].1) * fx, + ); + let sx = (top.0 + (bot.0 - top.0) * fy).clamp(0.0, iw - 1.0); + let sy = (top.1 + (bot.1 - top.1) * fy).clamp(0.0, ih - 1.0); + let px = img.get_pixel(sx.round() as u32, sy.round() as u32); + out.put_pixel(ox, oy, *px); + } + } + out +} + +/// Order 4 corners as [top-left, top-right, bottom-right, bottom-left] using +/// coordinate sums/diffs (standard PaddleOCR ordering). +fn order_corners(corners: &[(f32, f32); 4]) -> [(f32, f32); 4] { + // top-left has smallest x+y, bottom-right largest x+y; + // top-right smallest y-x, bottom-left largest y-x. + let mut tl = corners[0]; + let mut br = corners[0]; + let mut tr = corners[0]; + let mut bl = corners[0]; + let (mut min_sum, mut max_sum) = (f32::MAX, f32::MIN); + let (mut min_diff, mut max_diff) = (f32::MAX, f32::MIN); + for &p in corners { + let sum = p.0 + p.1; + let diff = p.1 - p.0; + if sum < min_sum { + min_sum = sum; + tl = p; + } + if sum > max_sum { + max_sum = sum; + br = p; + } + if diff < min_diff { + min_diff = diff; + tr = p; + } + if diff > max_diff { + max_diff = diff; + bl = p; + } + } + [tl, tr, br, bl] +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn det_target_dims_matches_golden() { + // T0a golden: clean_paragraph 192×900 → det input 192×896. + assert_eq!(det_target_dims(900, 192, 1600), (896, 192)); + } + + #[test] + fn convex_hull_square() { + let pts = vec![(0.0, 0.0), (10.0, 0.0), (10.0, 10.0), (0.0, 10.0), (5.0, 5.0)]; + let hull = convex_hull(&pts); + assert_eq!(hull.len(), 4); + } + + #[test] + fn min_area_rect_axis_aligned() { + let pts = vec![(0.0, 0.0), (20.0, 0.0), (20.0, 5.0), (0.0, 5.0)]; + let r = min_area_rect(&pts).expect("rect"); + let (lo, hi) = (r.width.min(r.height), r.width.max(r.height)); + assert!((lo - 5.0).abs() < 1e-3, "short side {lo}"); + assert!((hi - 20.0).abs() < 1e-3, "long side {hi}"); + } + + #[test] + fn dict_length_mismatch_is_construction_error() { + // T10: a dict whose line count != DICT_LINES must fail at construction + // (before loading the ONNX sessions) rather than mis-decoding silently. + use std::io::Write; + let dir = tempfile::tempdir().unwrap(); + let dict_path = dir.path().join("bad_dict.txt"); + let mut f = std::fs::File::create(&dict_path).unwrap(); + writeln!(f, "a\nb\nc").unwrap(); // 3 lines, not DICT_LINES + let paths = ModelPaths { + det: dir.path().join("unused_det.onnx"), + rec: dir.path().join("unused_rec.onnx"), + dict: dict_path, + }; + let err = OnnxPaddleOcr::from_paths(&paths, 0.3, 1.5, 1000, 1600) + .expect_err("dict mismatch must error"); + let msg = format!("{err:#}"); + assert!(msg.contains("dict has 3 lines"), "unexpected error: {msg}"); + } + + #[test] + fn model_paths_from_config_uses_overrides() { + // T7: unset overrides → bundled default asset paths. + let mut cfg = kebab_config::Config::defaults(); + let def = ModelPaths::from_config(&cfg); + assert!(def.det.ends_with("ppocrv5_mobile_det.onnx"), "{:?}", def.det); + assert!(def.rec.ends_with("korean_ppocrv5_mobile_rec.onnx"), "{:?}", def.rec); + assert!(def.dict.ends_with("korean_dict.txt"), "{:?}", def.dict); + + // Override det + dict; rec stays bundled (partial override allowed). + cfg.image.ocr.det_model = Some("/custom/det.onnx".to_string()); + cfg.image.ocr.dict = Some("/custom/dict.txt".to_string()); + let ov = ModelPaths::from_config(&cfg); + assert_eq!(ov.det, PathBuf::from("/custom/det.onnx")); + assert_eq!(ov.dict, PathBuf::from("/custom/dict.txt")); + assert!(ov.rec.ends_with("korean_ppocrv5_mobile_rec.onnx"), "{:?}", ov.rec); + } + + #[test] + fn unclip_expands_box() { + let rect = RotRect { + corners: [(0.0, 0.0), (20.0, 0.0), (20.0, 5.0), (0.0, 5.0)], + width: 20.0, + height: 5.0, + }; + let out = unclip_rect(&rect, 1.5); + // unclipped box must be strictly larger than the original + let orig_minx = 0.0; + let new_minx = out.iter().map(|p| p.0).fold(f32::MAX, f32::min); + assert!(new_minx < orig_minx, "expected expansion, got {new_minx}"); + } + + /// Golden pin: verify `ctc_greedy_decode_with_dict` against pre-recorded + /// argmax sequences in `tests/golden/ctc_rec_golden.json`. No ONNX sessions + /// needed — only the bundled dict is loaded. + #[test] + fn ctc_greedy_decode_golden() { + let json_str = include_str!("../tests/golden/ctc_rec_golden.json"); + let golden: serde_json::Value = serde_json::from_str(json_str).unwrap(); + let dict_path = std::path::Path::new(env!("CARGO_MANIFEST_DIR")) + .join("assets/paddleocr-onnx/korean_dict.txt"); + let dict = load_dict(&dict_path).expect("bundled dict must load"); + + for case in golden["rec_cases"].as_array().unwrap() { + let t = case["T"].as_u64().unwrap() as usize; + let c = case["C"].as_u64().unwrap() as usize; + let argmax_idx: Vec = case["argmax_idx"] + .as_array() + .unwrap() + .iter() + .map(|v| v.as_u64().unwrap() as usize) + .collect(); + let expected = case["decoded"].as_str().unwrap(); + // build one-hot logits: timestep t fires class argmax_idx[t] = 1.0 + let mut data = vec![0.0f32; t * c]; + for (ti, &idx) in argmax_idx.iter().enumerate() { + data[ti * c + idx] = 1.0; + } + let (decoded, _conf) = ctc_greedy_decode_with_dict(&data, t, c, &dict); + assert_eq!( + decoded, expected, + "CTC decode mismatch for text={:?}", + case["text"] + ); + } + } + + /// Golden pin: verify `box_score` and `unclip_rect` against corner data + /// from `tests/golden/det_boxes_clean_paragraph.json`. No ONNX needed. + #[test] + fn det_box_score_golden() { + let json_str = include_str!("../tests/golden/det_boxes_clean_paragraph.json"); + let golden: serde_json::Value = serde_json::from_str(json_str).unwrap(); + + let hw = golden["det_input_hw"].as_array().unwrap(); + let h = hw[0].as_u64().unwrap() as usize; + let w = hw[1].as_u64().unwrap() as usize; + let thresh = golden["thresh"].as_f64().unwrap() as f32; + let unclip_ratio = golden["unclip_ratio"].as_f64().unwrap() as f32; + + // uniform prob map at 0.9 — all boxes must score above det thresh + let prob = ProbMap { w, h, data: vec![0.9f32; w * h] }; + + for box_entry in golden["boxes"].as_array().unwrap() { + let poly = box_entry["poly"].as_array().unwrap(); + let corners: [(f32, f32); 4] = [ + (poly[0][0].as_f64().unwrap() as f32, poly[0][1].as_f64().unwrap() as f32), + (poly[1][0].as_f64().unwrap() as f32, poly[1][1].as_f64().unwrap() as f32), + (poly[2][0].as_f64().unwrap() as f32, poly[2][1].as_f64().unwrap() as f32), + (poly[3][0].as_f64().unwrap() as f32, poly[3][1].as_f64().unwrap() as f32), + ]; + // box_score must be above det threshold + let score = box_score(&prob, &corners); + assert!( + score > thresh, + "box_score {score:.4} ≤ thresh {thresh} for poly {poly:?}" + ); + // unclip_rect must expand the bounding box (min x strictly decreases) + let rect_w = (corners[1].0 - corners[0].0).abs().max(1.0); + let rect_h = (corners[3].1 - corners[0].1).abs().max(1.0); + let rot = RotRect { corners, width: rect_w, height: rect_h }; + let expanded = unclip_rect(&rot, unclip_ratio); + let orig_min_x = corners.iter().map(|p| p.0).fold(f32::MAX, f32::min); + let exp_min_x = expanded.iter().map(|p| p.0).fold(f32::MAX, f32::min); + assert!( + exp_min_x < orig_min_x, + "unclip_rect must expand: orig_min_x={orig_min_x} exp_min_x={exp_min_x}" + ); + } + } +} diff --git a/crates/kebab-parse-image/tests/golden/ctc_rec_golden.json b/crates/kebab-parse-image/tests/golden/ctc_rec_golden.json new file mode 100644 index 0000000..550cd16 --- /dev/null +++ b/crates/kebab-parse-image/tests/golden/ctc_rec_golden.json @@ -0,0 +1,516 @@ +{ + "dict_lines": 11945, + "rec_classes": 11947, + "blank_index": 0, + "space_index": 11946, + "mapping": "idx0=blank; idx 1..N=dict[idx-1]; idx N+1=space; classes=dict+2", + "rec_norm": "RGB, /255 then (x-0.5)/0.5 => [-1,1], height=48 keep-aspect pad", + "det_norm": "RGB, ImageNet mean/std *255 then /std, NCHW", + "rec_cases": [ + { + "text": "RAG 시스템 검색 결과", + "decoded": "RAG시스템 검색 결과", + "cer": 0.0769, + "cer_nospace": 0.0, + "mapping_ok": true, + "T": 40, + "C": 11947, + "argmax_idx": [ + 0, + 0, + 11553, + 0, + 11536, + 0, + 0, + 11542, + 0, + 0, + 0, + 6185, + 0, + 0, + 6129, + 0, + 0, + 9897, + 0, + 0, + 11946, + 0, + 461, + 0, + 0, + 0, + 5654, + 0, + 11946, + 0, + 509, + 0, + 0, + 0, + 585, + 0, + 0, + 0, + 0, + 0 + ], + "collapsed_idx": [ + 11553, + 11536, + 11542, + 6185, + 6129, + 9897, + 11946, + 461, + 5654, + 11946, + 509, + 585 + ], + "collapsed_conf": [ + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002 + ], + "fired_timesteps": [ + 2, + 4, + 7, + 11, + 14, + 17, + 20, + 22, + 26, + 28, + 30, + 34 + ], + "fired_logit_top5": [ + { + "t": 2, + "top5_idx": [ + 11553, + 11583, + 11551, + 0, + 11541 + ], + "top5_val": [ + 0.9998, + 0.0001, + 0.0, + 0.0, + 0.0 + ] + }, + { + "t": 4, + "top5_idx": [ + 11536, + 11566, + 0, + 11748, + 11551 + ], + "top5_val": [ + 0.9998, + 0.0001, + 0.0, + 0.0, + 0.0 + ] + }, + { + "t": 7, + "top5_idx": [ + 11542, + 0, + 11572, + 11946, + 11585 + ], + "top5_val": [ + 0.9994, + 0.0004, + 0.0001, + 0.0001, + 0.0 + ] + }, + { + "t": 11, + "top5_idx": [ + 6185, + 0, + 11946, + 7949, + 11518 + ], + "top5_val": [ + 0.9993, + 0.0003, + 0.0001, + 0.0001, + 0.0 + ] + }, + { + "t": 14, + "top5_idx": [ + 6129, + 7893, + 0, + 9069, + 11536 + ], + "top5_val": [ + 0.9997, + 0.0002, + 0.0, + 0.0, + 0.0 + ] + }, + { + "t": 17, + "top5_idx": [ + 9897, + 9882, + 9889, + 9785, + 3429 + ], + "top5_val": [ + 0.9999, + 0.0, + 0.0, + 0.0, + 0.0 + ] + }, + { + "t": 20, + "top5_idx": [ + 11946, + 0, + 11516, + 11518, + 11579 + ], + "top5_val": [ + 0.9026, + 0.0971, + 0.0002, + 0.0001, + 0.0 + ] + }, + { + "t": 22, + "top5_idx": [ + 461, + 462, + 9281, + 349, + 0 + ], + "top5_val": [ + 0.9995, + 0.0003, + 0.0001, + 0.0, + 0.0 + ] + }, + { + "t": 26, + "top5_idx": [ + 5654, + 0, + 5766, + 8594, + 6830 + ], + "top5_val": [ + 1.0, + 0.0, + 0.0, + 0.0, + 0.0 + ] + }, + { + "t": 28, + "top5_idx": [ + 11946, + 0, + 11516, + 11549, + 11564 + ], + "top5_val": [ + 0.9422, + 0.0576, + 0.0001, + 0.0, + 0.0 + ] + }, + { + "t": 30, + "top5_idx": [ + 509, + 0, + 453, + 11946, + 505 + ], + "top5_val": [ + 0.9994, + 0.0004, + 0.0001, + 0.0, + 0.0 + ] + }, + { + "t": 34, + "top5_idx": [ + 585, + 641, + 0, + 10329, + 589 + ], + "top5_val": [ + 0.9999, + 0.0, + 0.0, + 0.0, + 0.0 + ] + } + ] + }, + { + "text": "Embedding vector 0123", + "decoded": "Embedding vector 0123", + "cer": 0.0, + "cer_nospace": 0.0, + "mapping_ok": true, + "T": 41, + "C": 11947, + "argmax_idx": [ + 0, + 11540, + 0, + 0, + 11578, + 0, + 0, + 11567, + 0, + 11570, + 0, + 11569, + 0, + 11569, + 0, + 11574, + 0, + 11579, + 11572, + 11572, + 11946, + 0, + 11587, + 11570, + 0, + 11568, + 0, + 11585, + 11580, + 0, + 11583, + 11946, + 11946, + 11520, + 0, + 11521, + 0, + 11522, + 0, + 11523, + 0 + ], + "collapsed_idx": [ + 11540, + 11578, + 11567, + 11570, + 11569, + 11569, + 11574, + 11579, + 11572, + 11946, + 11587, + 11570, + 11568, + 11585, + 11580, + 11583, + 11946, + 11520, + 11521, + 11522, + 11523 + ], + "collapsed_conf": [ + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0001, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002 + ] + }, + { + "text": "한글 OCR 정확도 테스트", + "decoded": "한글 OCR 정확도 테스트", + "cer": 0.0, + "cer_nospace": 0.0, + "mapping_ok": true, + "T": 41, + "C": 11947, + "argmax_idx": [ + 0, + 0, + 10921, + 0, + 0, + 0, + 845, + 0, + 11946, + 0, + 11550, + 0, + 0, + 11538, + 0, + 11553, + 0, + 11946, + 0, + 7522, + 0, + 0, + 11170, + 0, + 0, + 0, + 2321, + 0, + 11946, + 11946, + 9881, + 0, + 0, + 0, + 6129, + 0, + 0, + 0, + 10245, + 0, + 0 + ], + "collapsed_idx": [ + 10921, + 845, + 11946, + 11550, + 11538, + 11553, + 11946, + 7522, + 11170, + 2321, + 11946, + 9881, + 6129, + 10245 + ], + "collapsed_conf": [ + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002, + 0.0002 + ] + } + ], + "det_cases": [ + { + "fixture": "clean_paragraph.png", + "orig_hw": [ + 192, + 900 + ], + "det_input_hw": [ + 192, + 896 + ], + "prob_shape": [ + 192, + 896 + ], + "prob_max": 1.0, + "prob_mean": 0.1139, + "positives_at_0.3": 19682, + "positive_frac": 0.1144, + "box_count": 3, + "postproc": "thresh=0.3 -> findContours -> minAreaRect -> unclip(ratio=1.5, area*r/peri); box_thresh=0.5 mean-prob filter; coords scaled back to orig hw" + } + ], + "blank_index_confirmed_by_gt": true +} \ No newline at end of file diff --git a/crates/kebab-parse-image/tests/golden/det_boxes_clean_paragraph.json b/crates/kebab-parse-image/tests/golden/det_boxes_clean_paragraph.json new file mode 100644 index 0000000..32fb2a8 --- /dev/null +++ b/crates/kebab-parse-image/tests/golden/det_boxes_clean_paragraph.json @@ -0,0 +1,78 @@ +{ + "fixture": "clean_paragraph.png", + "orig_hw": [ + 192, + 900 + ], + "det_input_hw": [ + 192, + 896 + ], + "thresh": 0.3, + "unclip_ratio": 1.5, + "boxes": [ + { + "poly": [ + [ + 29, + 135 + ], + [ + 615, + 134 + ], + [ + 615, + 149 + ], + [ + 29, + 150 + ] + ], + "score": 0.8724 + }, + { + "poly": [ + [ + 30, + 92 + ], + [ + 597, + 92 + ], + [ + 597, + 105 + ], + [ + 30, + 105 + ] + ], + "score": 0.9627 + }, + { + "poly": [ + [ + 30, + 47 + ], + [ + 509, + 47 + ], + [ + 509, + 60 + ], + [ + 30, + 60 + ] + ], + "score": 0.9304 + } + ] +} \ No newline at end of file diff --git a/crates/kebab-parse-image/tests/paddle_e2e.rs b/crates/kebab-parse-image/tests/paddle_e2e.rs new file mode 100644 index 0000000..1b56d2a --- /dev/null +++ b/crates/kebab-parse-image/tests/paddle_e2e.rs @@ -0,0 +1,145 @@ +//! T11 e2e accuracy gate for the paddle-onnx OCR engine. +//! +//! Runs the full `OnnxPaddleOcr` pipeline (det → rectify → rec → CTC) over the +//! synthetic OCR benchmark fixtures and asserts the mean character error rate +//! (CER) over the clean text set is `<= 0.05`, matching the spec gate. +//! +//! Model assets come from `KEBAB_TEST_OCR_MODEL_DIR` (default: the crate's +//! bundled `assets/paddleocr-onnx/`). Fixtures come from +//! `KEBAB_TEST_OCR_FIXTURE_DIR` (default: the dogfood corpus). If either is +//! absent the test skips with a warning rather than failing — CI without the +//! large models / fixtures stays green (plan T0/M4). + +use std::collections::HashMap; +use std::path::PathBuf; + +use kebab_parse_image::{ModelPaths, OcrEngine, OnnxPaddleOcr}; + +/// Collapse all whitespace runs to a single space + trim — matches the Python +/// `score_lib.norm` so the Rust gate and the bench harness agree. +fn norm(s: &str) -> String { + s.split_whitespace().collect::>().join(" ") +} + +/// Character error rate = Levenshtein(gt, pred) / len(gt), both normalized. +fn cer(gt: &str, pred: &str) -> f64 { + let g: Vec = norm(gt).chars().collect(); + let p: Vec = norm(pred).chars().collect(); + if g.is_empty() { + return if p.is_empty() { 0.0 } else { 1.0 }; + } + let (m, n) = (g.len(), p.len()); + let mut prev: Vec = (0..=n).collect(); + for i in 1..=m { + let mut cur = vec![i; n + 1]; + for j in 1..=n { + let cost = usize::from(g[i - 1] != p[j - 1]); + cur[j] = (prev[j] + 1).min(cur[j - 1] + 1).min(prev[j - 1] + cost); + } + prev = cur; + } + prev[n] as f64 / m as f64 +} + +fn fixture_dir() -> PathBuf { + std::env::var("KEBAB_TEST_OCR_FIXTURE_DIR").map_or_else( + |_| PathBuf::from("/build/dogfood/corpus/images/synthetic-ocr-bench"), + PathBuf::from, + ) +} + +/// T10: undecodable image bytes must surface as an error (the kebab-app caller +/// then skips the asset + records provenance), not panic or return garbage. +#[test] +fn paddle_onnx_decode_failure_is_error() { + let paths = ModelPaths::from_default_dir(); + if !paths.det.exists() || !paths.rec.exists() || !paths.dict.exists() { + eprintln!("SKIP paddle_onnx_decode_failure_is_error: model assets not found"); + return; + } + let engine = OnnxPaddleOcr::from_paths(&paths, 0.3, 1.5, 1000, 1600).unwrap(); + let err = engine + .recognize(b"not a real image", None) + .expect_err("garbage bytes must fail to decode"); + let msg = format!("{err:#}"); + assert!(msg.contains("decoding image"), "unexpected error: {msg}"); +} + +#[test] +fn paddle_onnx_cer_gate() { + let paths = ModelPaths::from_default_dir(); + if !paths.det.exists() || !paths.rec.exists() || !paths.dict.exists() { + eprintln!( + "SKIP paddle_onnx_cer_gate: model assets not found (det={}). \ + Set KEBAB_TEST_OCR_MODEL_DIR or place assets/paddleocr-onnx/.", + paths.det.display() + ); + return; + } + let fdir = fixture_dir(); + let gt_path = fdir.join("gt.json"); + if !gt_path.exists() { + eprintln!( + "SKIP paddle_onnx_cer_gate: fixtures not found at {}", + fdir.display() + ); + return; + } + + let gt: HashMap = + serde_json::from_str(&std::fs::read_to_string(>_path).unwrap()).unwrap(); + + let engine = OnnxPaddleOcr::from_paths(&paths, 0.3, 1.5, 1000, 1600) + .expect("build OnnxPaddleOcr from bundled assets"); + + // "clean" set used for the gate — the standard, well-formed text fixtures. + // low_contrast / small_dense are intentionally hard and tracked but not + // part of the hard gate. + let gate_set = [ + "clean_paragraph.png", + "title_body.png", + "tech_terms.png", + "korean_heavy.png", + "numbers_table.png", + ]; + + let mut gate_cers = Vec::new(); + let mut names: Vec<&String> = gt.keys().collect(); + names.sort(); + println!("\n=== paddle-onnx CER per fixture ==="); + for name in names { + let img_path = fdir.join(name); + if !img_path.exists() { + continue; + } + let bytes = std::fs::read(&img_path).unwrap(); + let t0 = std::time::Instant::now(); + let out = engine.recognize(&bytes, None).expect("recognize"); + let dt = t0.elapsed(); + let c = cer(>[name], &out.joined); + if std::env::var("KEBAB_OCR_DUMP").is_ok() { + println!(" GT [{name}]: {:?}", norm(>[name])); + println!(" OUT [{name}]: {:?}", norm(&out.joined)); + } + let gated = gate_set.contains(&name.as_str()); + println!( + "{:<22} CER={:.4} {} ({} regions, {} ms)", + name, + c, + if gated { "[gate]" } else { " " }, + out.regions.len(), + dt.as_millis() + ); + if gated { + gate_cers.push(c); + } + } + + assert!(!gate_cers.is_empty(), "no gate fixtures were scored"); + let mean = gate_cers.iter().sum::() / gate_cers.len() as f64; + println!("=== mean gate CER = {mean:.4} (threshold 0.05) ===\n"); + assert!( + mean <= 0.05, + "paddle-onnx mean CER {mean:.4} exceeds 0.05 gate" + ); +} diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 7b5b70e..f586925 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -20,7 +20,7 @@ Cargo workspace, 함수 호출 기반 모듈러 모놀리스. UI binary (`kebab- | 한국어 형태소분석 | `lindera-ko-dic` (FTS5 외부 tokenizer, v0.20.1) — 2자 이상 한국어 query 지원 | | LLM | Ollama HTTP (default `gemma4:e4b` ─ OCR / caption 와 family 통일. 사용자가 더 큰 variant `gemma4:26b` 등으로 override 가능) | | 음성 ASR | `whisper.cpp` (via `whisper-rs`) — P8 보류, 시스템 dep brainstorm 후 | -| OCR (image) | Ollama vision LM (default `gemma4:e4b`) — `OcrEngine` trait 으로 Tesseract / Apple Vision 등 future swap (HOTFIXES P6-2) | +| OCR (image) | `OcrEngine` trait, 2 백엔드: **`ollama-vision`** (default, `gemma4:e4b`) / **`paddle-onnx`** (v0.27.0 — PP-OCRv5 ONNX in-process via `ort` =2.0.0-rc.9, DBNet det + CTC rec, 후처리 min-area rect/unclip pure-Rust, Python 런타임 0). engine 선택은 `[image.ocr] engine`, 팩토리는 `kebab-app::build_image_ocr_engine`. e2e CER 0.005 / 큰 페이지 <4초. (HOTFIXES P6-2, 2026-06-04) | | OCR (PDF, v0.20.0+) | Ollama vision LM (default `qwen2.5vl:3b`) — post-extract enrichment via `kebab-app::pdf_ocr_apply` (H-1 resolution). DCTDecode-only v1 (FlateDecode/CCITTFax skip + warning). family asymmetry vs image OCR: PoC alnum 94.79% (qwen2.5vl) >> 27% (gemma4:e4b 받침), 본 단계에서 PDF OCR 만 qwen2.5vl. | | Image caption | Ollama vision LM, runtime gate `image.caption.enabled` (default OFF) | | RAG groundedness 검증 | `kebab-nli` 의 mDeBERTa-v3 XNLI 가 `(packed_chunks, generated_answer)` entailment 검사 (fb-41). `[rag] nli_threshold > 0` (default 0 = disabled, production 권장 0.5) 일 때 활성 — 미달 시 `refusal_reason = nli_verification_failed` (LLM self-judge ceiling 보완). 첫 호출 시 ~280 MB ONNX 자동 다운로드 | @@ -212,7 +212,7 @@ kebab/ │ ├── kebab-rag/ # RAG pipeline (P4-3) │ ├── kebab-nli/ # NLI verifier (mDeBERTa-v3 XNLI, fb-41 PR-9a/9b/9c-1) │ ├── kebab-eval/ # golden query runner + metrics (P5-1, P5-2) -│ ├── kebab-parse-image/ # ImageExtractor + Ollama OCR + caption (P6) +│ ├── kebab-parse-image/ # ImageExtractor + OCR (ollama-vision + paddle-onnx ONNX) + caption (P6) │ ├── kebab-parse-pdf/ # lopdf per-page text extractor (P7-1) │ ├── kebab-parse-code/ # tree-sitter AST extractors: Rust (P10-1A-2), Python + TypeScript + JavaScript (P10-1B), Go (P10-1C-Go), Java + Kotlin (P10-1C-JK — java.rs + kotlin.rs), C + C++ (P10-1D — c.rs + cpp.rs); chunker lives in kebab-chunk │ ├── kebab-app/ # facade (P0 시그니처 + P3-5/P6-4/P7-3 본체). src/derivation_payload.rs = 캐시 payload 인코딩 (v0.21.0) diff --git a/docs/SMOKE.md b/docs/SMOKE.md index 941e105..2dbbb67 100644 --- a/docs/SMOKE.md +++ b/docs/SMOKE.md @@ -358,6 +358,24 @@ lang_hint = "kor" 이미지 자산 한 장당 OCR 1 호출 + Caption 1 호출 → ~3-6초 (`gemma4:e4b` 기준). 다이어그램 / 카메라 사진 / 스크린샷 위주 워크스페이스에 권장. 책 / 스캔본은 P7 PDF 라인으로. +**v0.27.0 — paddle-onnx 엔진 (오프라인, Ollama 불필요).** `[image.ocr] engine = "paddle-onnx"` 로 바꾸면 PP-OCRv5 ONNX 를 in-process 로 실행한다 (원격 vision LM 불필요, 큰 페이지 CPU <4초). embedding 까지 끄려면 `[models.embedding] provider = "none"` (lexical-only) 로 두면 Ollama 없이 OCR→FTS5 검색 전체 경로를 스모크할 수 있다: + +```toml +[models.embedding] +provider = "none" # lexical-only — Ollama 불필요 + +[image.ocr] +enabled = true +engine = "paddle-onnx" # PP-OCRv5 ONNX in-process (Python/원격 0) +model = "ppocrv5-mobile-kor" +languages = ["kor", "eng"] +max_pixels = 1600 +# det_model / rec_model / dict 로 번들 모델 경로 override 가능 (생략 시 번들 사용) +# score_thresh = 0.3 / unclip_ratio = 1.5 / max_boxes = 1000 으로 검출 튜닝 +``` + +스모크: `kebab ingest --config ` 후 `kebab search --config --mode lexical "<이미지 안 한국어 단어>"` 가 그 image chunk 를 반환하면 OCR→FTS5 wiring 정상. engine 또는 모델을 바꾸면 다음 ingest 가 영향 이미지를 자동 재색인한다. + ## P7-3 PDF ingestion `config.toml` 의 `[workspace] include` 에 `**/*.pdf` 를 추가하면 `kebab ingest` 가 텍스트 PDF 자산도 색인합니다. 외부 service 의존 없음 — `kebab-parse-pdf` 가 lopdf 로 페이지 단위 텍스트 추출, `kebab-chunk::PdfPageV1Chunker` 가 페이지 경계를 절대 넘지 않는 chunk 생성. diff --git a/docs/superpowers/plans/2026-06-04-rust-native-ocr-plan.md b/docs/superpowers/plans/2026-06-04-rust-native-ocr-plan.md new file mode 100644 index 0000000..d7af646 --- /dev/null +++ b/docs/superpowers/plans/2026-06-04-rust-native-ocr-plan.md @@ -0,0 +1,89 @@ +# Plan: Rust 네이티브 OCR 엔진 (PP-OCRv5 ONNX) 구현 + +spec: `docs/superpowers/specs/2026-06-04-rust-native-ocr-spec.md`. 브랜치 `feat/rust-native-ocr`. +빌드 `CARGO_TARGET_DIR=/build/out/cargo-target`, 테스트 **`-j 8`**(절대 `-j 1` 금지), touched 크레이트 위주(`-p kebab-parse-image -p kebab-app -p kebab-config`). +참조 구현: `oar-ocr`(Apache-2.0) 소스 + Python PaddleOCR + 검증된 PoC `/build/cache/ocr-bench/{rust-poc,onnx,rc9-spike}/`(변환 ONNX + rc.9 동작 확인). + +## Task 0a — 레퍼런스 골든 하네스 (C1 — 최우선 선행, executor 차단 제거) +**T3/T5 골든은 oar-ocr 로 못 만든다**(중간 텐서 미노출, PoC 는 최종텍스트만). 먼저 Python `onnxruntime` 직접(oar-ocr X)으로 변환 모델을 돌려 fixture 별 중간 산출을 골든으로 덤프: +- 입력: `/build/dogfood/corpus/images/synthetic-ocr-bench/` fixtures + 변환 ONNX(`/build/cache/ocr-bench/onnx/`). +- 덤프(JSON/npy, repo `crates/kebab-parse-image/tests/golden/`): (a) det 확률맵 슬라이스, (b) threshold 후 박스 폴리곤, (c) **rec 원시 logits `[T,C]`**, (d) 디코드 문자열, (e) 전처리 텐서 일부값. +- **M2 해결**: 알려진 텍스트라인 crop 의 logits + argmax 로 **blank 인덱스 + dict 11,945→클래스 11,947 매핑(+2 정체)을 경험적으로 도출**해 plan/주석에 사실로 기록(추정 금지). 경계문자(dict 첫/끝) 포함 골든. +- 도구: 기존 venv `/build/cache/ocr-bench/venv`(onnxruntime 직접 설치) 또는 paddleocr API 의 raw 단계. 하네스 스크립트는 `/build/cache/ocr-bench/` 에 보관(런타임 의존 아님, 골든 생성 전용). +- 수용: 각 fixture 골든 파일 생성 + blank 인덱스 문서화. 이후 T3~T5 가 이 골든에 핀. + +## Task 0 — 모델 번들 (결정 C-1: include_bytes, release feature 게이트) +- 변환 ONNX(이미 존재: `/build/cache/ocr-bench/onnx/{ppocrv5_mobile_det.onnx, korean_ppocrv5_mobile_rec.onnx, korean_dict.txt}`)를 repo `crates/kebab-parse-image/assets/paddleocr-onnx/` 에 배치(+NOTICE, Apache-2.0). +- `bundled-ocr-models` cargo feature: on 이면 `include_bytes!` 로 임베드, off(dev 기본)면 config override 경로 필수. release 빌드는 feature on. +- 대안 C-2/C-3 는 빌드/링크 부담 측정 후 폴백(spec §모델 배포). 17MB 임베드의 dev 링크 영향 먼저 측정 — 과하면 C-2(repo 벤더 + OUT_DIR) 전환. +- **assets 17MB 커밋 방식 결정(M4/packaging)**: git-LFS 권장(clone/`cargo package` 비대 회피). `.gitattributes` 에 `*.onnx filter=lfs`. NOTICE(Apache-2.0) 동반. +- **테스트 모델 출처(M4)**: OCR 단위/e2e 테스트는 `bundled-ocr-models` feature 무관하게 `KEBAB_TEST_OCR_MODEL_DIR`(기본 `assets/paddleocr-onnx/`)에서 로드. 모델 없으면 `#[ignore]` 가 아니라 명확 skip+경고(CI 는 assets 존재 가정). dev 빌드 OCR 테스트가 모델 못 찾아 실패하는 모호함 제거. +- 수용: feature on 빌드 임베드 확인, off 빌드 정상, 테스트가 assets 에서 모델 로드. + +## Task 1 — 의존성 (kebab-parse-image/Cargo.toml) +- `ort = { workspace = true, features = ["ndarray", "download-binaries"] }`(C1: 단독빌드 링크, nli 선례 주석). `ndarray = { workspace = true }`. `imageproc`(연결요소/윤곽). +- `ort-sys` caret 으로 rc.12 끌려가지 않게 Cargo.lock 정합 확인(rc.9 고정). unclip 다각형 offset 은 **pure-Rust 직접 구현**(clipper2 C++ FFI 회피 — spec). +- 수용: `cargo build -p kebab-parse-image -j 8` 링크 성공(onnxruntime), `cargo tree` 에 ort 단일 rc.9. + +## Task 2 — OnnxPaddleOcr 골격 + 전처리 (kebab-parse-image) +- **선행 사실 확인**: rc.9 `ort::Session` 이 `Send+Sync` 인지 먼저 확인(아니면 Mutex 래핑). 결과를 주석에 기록. +- 신규 모듈 `paddle_onnx.rs`. `OcrEngine` 구현. **`engine_version`=생성 시 모델+dict blake3 1회 계산해 String 캐시**(m3: per-asset 재해시 금지 — `ingest_config_signature` 가 자산마다 호출). format 고정(후일 변경 시 mass 재색인 주의). +- det/rec `ort::Session` 2개 1회 로드 후 보관. **max_pixels 자체 bounds 적용**(spec 의 ocr.rs MIN/MAX clamp 은 Ollama private — paddle 은 자기 clamp 명시). +- 전처리: 디코드(image)→긴변 max_pixels 축소→BGR mean/std 정규화→`Array4`. +- 수용: 단위테스트 — 알려진 이미지→입력텐서 일부 값 골든(T0a). + +## Task 3 — det 후처리 (단계 단위, 골든벡터) +- det Session 추론(`[1,1,H,W]` 확률맵, rc.9 `try_extract_tensor`→`ArrayViewD`) → threshold 0.3 이진화 → imageproc 연결요소/윤곽 → **min-area rotated-rect(rotating calipers 직접 구현)** → **unclip(pure-Rust 다각형 offset, ratio 1.5)** → 박스 Vec. +- 수용: 합성 fixture 기대 박스 개수/대략 좌표 골든. min-area rect·unclip 각각 단위테스트. + +## Task 4 — crop + rectify +- 회전 박스 → perspective/affine warp 로 수평 정렬(oar-ocr 가 제공하던 부분 이식). +- 수용: 회전 텍스트 fixture → 정렬 crop 골든. + +## Task 5 — rec + CTC decode +- crop→48×W 정규화→rec Session(`[1,T,C]`) → CTC greedy(argmax/timestep→연속중복 제거→blank 제거). +- **blank 인덱스 + 11,945→11,947 매핑은 T0a 하네스에서 도출한 사실을 사용**(추정 금지). bounds-check(dict 길이≠클래스 시 생성 에러). +- 수용: T0a 골든 logit→문자열 일치(blank/중복/**경계문자 dict 첫·끝** 포함). + +## Task 6 — 조립 + OcrText +- 박스 reading-order(상→하,좌→우) → `OcrText{joined, regions:[OcrRegion{bbox,text,confidence}], engine, engine_version}`. per-region 실제 confidence(Ollama 상수1.0 대비 값 변화 — release note). +- 수용: e2e — 합성 한/영 fixture **CER ≤ 0.05**, bbox>0. PoC 0.976 baseline 대비 회귀 없음. +- **CER 게이트 실패 시 폴백 사다리(M3)**: ① T0a 단계 골든과 diff 해 어느 단계 divergence 인지 국소화 → ② det postproc(unclip/min-area rect)가 원인이면 **oar-ocr 의 해당 함수를 verbatim 이식**(Apache-2.0, NOTICE+파일별 출처 표기 — 코드 파생물) → ③ time-box(예 반나절) 초과 시 리더 escalate. 손수 재유도에 매몰 금지. + +## Task 7 — config (kebab-config) +- `OcrCfg`: `engine` 값에 "paddle-onnx" 문서화(기본 "ollama-vision" 유지). 신규 override `det_model`/`rec_model`/`dict`(Option), `score_thresh`(0.3)/`unclip_ratio`(1.5)/`max_boxes`(1000). `KEBAB_IMAGE_OCR_*` env. serde default(forward-compat) + init 템플릿 노출. +- 수용: override 미지정→번들 모델, 지정→그 경로 사용 테스트. config migrate(#198) 무수정 로드 회귀. + +## Task 8 — 엔진 팩토리 (kebab-app/lib.rs) — **4개 site 전부(M1)** +구체타입 `OllamaVisionOcr` 가 박힌 곳이 4군데 — 누락 시 타입에러로 막힘: +- `:360` image 엔진 생성 → `Box` 팩토리(`match engine`: ollama-vision|paddle-onnx|err). +- `:379` pdf 엔진 생성 → 동일 팩토리. +- `:839` `ImagePipeline.ocr_engine` 필드 → `Option<&dyn OcrEngine>`. +- `:1113`, `:2096` `pdf_ocr_engine: Option<&OllamaVisionOcr>` 함수 시그니처 2곳 → `Option<&dyn OcrEngine>`. +- `apply_ocr_to_pdf_pages`(`pdf_ocr_apply.rs:93`)는 이미 `&dyn OcrEngine` — 스레딩만 변경, 헬퍼 불변. `--config` facade 스레딩(`OnnxPaddleOcr::new(cfg,…)`). +- 수용: 팩토리 단위테스트(선택/미지값 에러). **ollama-vision 경로 출력 동일** 회귀 테스트(구체→dyn 전환 무영향). + +## Task 9 — 서명 cascade (C3, kebab-app) +- `ingest_config_signature` image/pdf 브랜치 `|ocr:1:{model}` → `|ocr:1:{engine}:{engine_version}`(engine + 모델/dict blake3). +- 수용: (a)ollama↔paddle 동일model→서명다름 (b)engine_version 다름→다름 (c)search 등 무관→불변. → 엔진/모델 변경 시 v0.26.2 자동 재색인. + +## Task 10 — 에러 매트릭스 (spec §에러 처리) +- 다운로드/blake3 실패→fail-fast, 디코드불가→skip+provenance, det 0박스→`OcrText{"",[]}` 성공, rec 빈→박스skip, 박스폭증→max_boxes 절단+로그, dict 불일치→생성에러. +- 수용: 각 케이스 단위/통합 테스트. + +## Task 11 — 검증 게이트 +- `cargo clippy --workspace --all-targets -j 8 -- -D warnings` 0. +- `cargo test -p kebab-parse-image -p kebab-app -p kebab-config -j 8` 통과(+ `-p kebab-parse-image` 단독 링크 확인). +- 스모크: `engine="paddle-onnx"` 이미지 ingest→FTS5 hit, 큰 페이지 CPU <5초. + +## Task 12 — 문서 + 버전 + 도그푸딩 +- README(Configuration: `image.ocr.engine`+모델 번들), docs/SMOKE(config 예시), HANDOFF 1줄, docs/ARCHITECTURE(OCR 백엔드/그래프), HOTFIXES dated entry. +- Cargo.toml workspace version **minor bump**(+Cargo.lock). release notes(엔진 추가/per-region confidence/오프라인). +- 도그푸딩: 사용자 실제 이미지·책 스캔 정확도·속도 → HOTFIXES + release notes evidence. +- 결과 요약 `/tmp/rust-ocr-result.md`(게이트 + 스모크 + 도그푸딩 캡처). + +## 리뷰 루프 +완료 → 리더 clippy/타깃테스트(-j8) 독립 재확인 + paddle-onnx 스모크 → `gitea-pr`(title `feat(ocr): PP-OCRv5 ONNX Rust 네이티브 OCR 엔진`) → 리뷰 루프 → 사용자 머지. 모델 ONNX 는 release feature/asset 로 동반. + +## 단계 의존 +**T0a(레퍼런스 골든+blank 도출) 최우선 선행** → T0(번들),T1(deps) → T2→T3→T4→T5→T6(파이프라인 순차, 각 T0a 골든에 핀) ∥ T7(config) → T8(팩토리 4site)→T9(서명)→T10(에러) → T11 게이트 → T12 문서. T3~T5 가 핵심 난도(직접 이식), T0a 골든+T6 폴백사다리로 회귀·매몰 차단. T8 의 정확한 라인(:1113/:2096 등)은 구현 시점 grep 으로 재확인(코드 이동 가능). diff --git a/docs/superpowers/specs/2026-06-04-rust-native-ocr-spec.md b/docs/superpowers/specs/2026-06-04-rust-native-ocr-spec.md new file mode 100644 index 0000000..f256135 --- /dev/null +++ b/docs/superpowers/specs/2026-06-04-rust-native-ocr-spec.md @@ -0,0 +1,192 @@ +# Spec: Rust 네이티브 OCR 엔진 (PP-OCRv5 ONNX, in-process) + +**날짜**: 2026-06-04 +**유형**: feature (minor) — 신규 OCR 엔진 + config 키 + 동작 변화 +**상태**: draft (self-review 대기) +**contract_sections**: design §6 (parse/extract), §8 (deps), §9 (versioning cascade) + +## 동기 + +현재 이미지/PDF OCR 은 Ollama Vision LLM(`gemma4:e4b` 8B) 1콜(`crates/kebab-parse-image/src/ocr.rs`, `OllamaVisionOcr`). 사용자 실측 문제: + +- 실제 이미지 한 장당 **~50초**(VLM 은 글자를 토큰 단위로 생성 → 조밀 페이지는 본질적으로 느림). 모델을 바꿔도(qwen2.5vl:3b GPU 20~28초) 사용자 허용치 미달. +- 사용자 결정: **배치 ingest 용도 + Python 의존 불가 + Rust 내장**. + +### 근거 벤치 (2026-06-04, `/build/dogfood/logs/2026-06-04-ocr-model-bench.md`) + +| 방식 | 작은 이미지 | 초대형 1757×2644 | 정확도 | 비고 | +|---|---|---|---|---| +| gemma4:e4b 8B VLM (GPU) | 11초 | 43초 | 0.65~0.82 | 현재 | +| qwen2.5vl:3b VLM (GPU) | 3.6초 | 20초 | 0.93 | 속도 미달 | +| **PP-OCRv5 mobile ONNX, Rust (CPU)** | **0.05초** | **2.75초** | **0.976** | **PoC 검증됨** | + +VLM 은 생성 병목으로 탈락. **검출+인식형 전용 엔진(PP-OCRv5)을 ONNX 로 Rust in-process 실행**이 속도·정확도·한국어·단일바이너리 모두 만족. PoC: `oar-ocr` 0.6.3 + `ort` 로 위 수치 확인(오류는 띄어쓰기뿐, 한국어 오인식 0). PoC 코드/모델: `/build/cache/ocr-bench/{rust-poc,onnx}/`. + +## 핵심 설계 결정: oar-ocr 미채택, 핀 ort 위 직접 구현 + +PoC 는 `oar-ocr` 0.6.3 으로 검증했으나 **프로덕션 의존성으로는 쓰지 않는다**. 이유(load-bearing): + +- kebab 은 `ort = "=2.0.0-rc.9"` 를 **의도적 핀**(workspace `Cargo.toml:195-204`): fastembed 4.9 의 ONNX Runtime+tokenizer 스택을 워크스페이스 단일 버전으로 유지. `ndarray = "0.16"` 도 동일. +- `oar-ocr` 0.6.3 은 `ort 2.0.0-rc.12` + `ndarray 0.17` 요구. `ort` 는 `ort-sys` 가 onnxruntime 네이티브 라이브러리를 `links` 하므로 **두 버전 공존 불가** → oar-ocr 채택 시 ort/ndarray 를 bump 해야 하고, 이는 fastembed/kebab-nli/kebab-embed-candle 의 임베딩·NLI 스택을 흔든다(사용자 우선순위인 검색 품질 직결, [[search-quality-dogfood]]). + +**→ PaddleOCR 의 전/후처리(검출 DBNet postproc + 인식 CTC decode)를 kebab 의 기존 핀 `ort`(rc.9) 위에 직접 구현.** oar-ocr(Apache-2.0) 소스 + Python PaddleOCR 을 레퍼런스로. 공유 ort 라 새 네이티브 의존성 0, 임베딩 스택 무영향. + +### C2 검증 완료 (rc.9 스파이크, 2026-06-04) + +PoC 는 oar-ocr 경유 ort **rc.12** 로 돌았으므로, 핀 **rc.9** 가 paddle2onnx 산출 모델을 실제 로드/추론하는지 별도 검증함(`/build/cache/ocr-bench/rc9-spike/`). 결과 **PASS**: +- `ort = "=2.0.0-rc.9"` + `ort-sys = "=2.0.0-rc.9"`(caret 으로 rc.12 끌려가는 것 방지 — kebab Cargo.lock 과 동일) + `ndarray 0.16` + feature `["ndarray","download-binaries"]` 로 빌드/링크/onnxruntime 다운로드 성공. +- det: 입력 `"x"` → 출력 `[1,1,640,640]`(DBNet 확률맵). rec: 출력 `[1,40,11947]`(timestep×클래스; dict 11,945 + blank/특수 = 11,947, CTC 정합 확인). +- `try_extract_tensor::()` 는 rc.9 에서 `ArrayViewD` 반환(rc.12 의 `(shape,&[T])` 와 다름) — 구현 시 유의. +- **함의**: 핀 ort 유지(ort/ndarray bump 불필요)로 임베딩 스택 무영향 확정. opset 호환 OK. 출력 형태가 후처리 설계(det threshold→박스 / rec CTC)와 일치. + +### 추가 의존성 + +- `image`(이미 허용), `ndarray`(workspace `=0.16`), `ort`(workspace `=2.0.0-rc.9`, **features `["ndarray","download-binaries"]`**). + - **download-binaries 필수**: `kebab-parse-image` 는 fastembed 빌드그래프에 없어, 단독 빌드(`cargo test -p kebab-parse-image`)시 onnxruntime 링크 위해 명시 필요. `kebab-nli/Cargo.toml:23` 의 동일 선례 주석 그대로 따름. + - `ort-sys` 가 caret 으로 rc.12 로 끌려가지 않도록 workspace 핀과 Cargo.lock 정합 확인. +- `imageproc` — det 확률맵 연결요소/윤곽 추출. **단 min-area rotated-rect 는 imageproc 미제공 → rotating-calipers 직접 구현**. +- DBNet unclip(다각형 확장): **`clipper2` 는 C++ FFI 가능성 → single-binary/pure-Rust 위배 위험. 우선 pure-Rust 다각형 offset 직접 구현 또는 검증된 pure-Rust crate.** (plan 에서 clipper2 가 C++ 링크인지 확인 후 택일.) + +## 파이프라인 (OnnxPaddleOcr) + +`crates/kebab-parse-image/src/` 에 신규 모듈. `OcrEngine` trait(`ocr.rs:54`) 구현: + +```rust +pub trait OcrEngine: Send + Sync { + fn engine_name(&self) -> &'static str; // "paddle-onnx" + fn engine_version(&self) -> String; // "ppocrv5-mobile-kor-v1" (+model hash) + fn recognize(&self, image_bytes: &[u8], lang_hint: Option<&Lang>) -> Result; +} +``` + +`recognize` 단계 (PoC 와 동일 알고리즘): + +1. **디코드+다운스케일**: `image` 로 디코드 → 긴변 `max_pixels`(기본 1600) 로 축소(기존 `OcrCfg.max_pixels` 재사용, qwen 과 달리 PP-OCRv5 는 원본도 안전하나 속도 위해 유지). +2. **검출(det)**: BGR 정규화 → det ONNX(`PP-OCRv5_mobile_det`) → 확률맵 → threshold(0.3) 이진화 → 윤곽(imageproc) → min-area rect → unclip(ratio 1.5) → 텍스트 박스 N개. +3. **인식(rec)**: 각 박스 crop+회전보정 → 48×W 리사이즈/정규화 → rec ONNX(`korean_PP-OCRv5_mobile_rec`) → CTC greedy decode(dict 11,945자, blank 처리) → 텍스트+score. +4. **조립**: 박스를 reading-order(상→하, 좌→우) 정렬 → `OcrText { joined, regions: Vec, engine, engine_version }`. **Ollama 와 달리 per-line bbox/confidence 제공**(`OcrRegion` 풍부화). + +배치: PoC 는 박스별 순차 rec. 성능 충분(초대형 2.75초)하나, rec 를 ort 배치 입력으로 묶으면 추가 향상 가능(plan 에서 측정 후 결정). + +### 단계별 분해 (M1 — 각 단계 골든벡터 단위테스트) + +후처리가 실제 난도. "쉽다"로 뭉뚱그리지 않고 **각 단계를 독립 테스트 가능 단위**로 쪼갠다. 각 단위는 oar-ocr/Python PaddleOCR 이 **같은 fixture** 에 내는 출력을 골든벡터로 박아 단계별 회귀(0.976 baseline 대비)를 잡는다: + +1. **전처리**(resize/pad/normalize): det 입력 정규화(mean/std, /255). 골든: 알려진 이미지→텐서 일부 값. +2. **det 후처리**: 확률맵(`[1,1,H,W]`)→threshold(0.3)→연결요소(imageproc)→**min-area rotated-rect(rotating calipers 직접 구현)**→**unclip(다각형 offset, ratio 1.5)**→박스. 골든: 합성 이미지의 기대 박스 개수/대략 좌표. +3. **crop+rectify**: 회전 박스→perspective/affine warp 로 수평 정렬(oar-ocr 가 공짜 제공하던 부분; 직접 구현 필요). 골든: 회전 텍스트 fixture. +4. **rec 전처리+추론**: crop→48×W 정규화→rec ONNX→`[1,T,C]` logits. +5. **CTC greedy decode**: argmax per timestep→연속중복 제거→blank(인덱스 0 또는 dict 길이 위치, **PaddleOCR 규약 정확 매칭**) 제거→dict 인덱스→char. dict 길이(11,945) vs rec 출력 클래스(11,947) 정합 + **인덱스 bounds-check**(잘못된 dict 길이/빈 줄 방어). 골든: 알려진 logit→문자열. +6. **box reading-order**: 상→하, 좌→우 정렬(가로쓰기 전제; 세로/회전 페이지는 비범위). + +각 단계 divergence 를 end-to-end 가 아니라 단위에서 잡는다(M1 권고). + +## Config + +`OcrCfg`(`kebab-config/src/lib.rs:343`)에 `engine` 필드 **이미 존재**(기본 `"ollama-vision"`). 변경: + +- `engine` 값에 `"paddle-onnx"` 추가(문서화). 기본값은 **당장 바꾸지 않음**(default 변경은 별도 결정 — 아래 "기본 엔진" 참조). +- 신규(선택) 필드: `det_model` / `rec_model` / `dict` 경로 override(미지정 시 자동 다운로드 캐시 경로). `score_thresh`(기본 0.3), `unclip_ratio`(기본 1.5) 는 고급 튜닝용(기본값 고정, 노출 최소). +- `pdf.ocr` 도 동일 `engine` 분기 적용(같은 trait). + +### 모델 배포 — 결정 C: kebab 와 함께 번들 (HF 미사용, 사용자 확정 2026-06-04) + +제3자(HF) 호스팅 의존 제거. 변환본(det 4.7MB + korean rec 13MB + dict ≈ **17MB**)을 kebab 자체에 번들. **구체 기법은 plan 에서 택1**(모두 HF/외부 네트워크 0): + +- **C-1 바이너리 임베드(`include_bytes!`)**: 모델을 바이너리에 박음. 진정한 single-binary·완전 오프라인·재현성 100%. 비용: 릴리스 바이너리 +17MB, 그리고 dev/test 빌드마다 17MB 링크 부담 → **release feature(`bundled-ocr-models`) 게이트**로 dev 빌드 제외 가능. 로컬-first 철학 최적합. +- **C-2 repo 벤더링**: `assets/paddleocr-onnx/`(git 또는 git-LFS) 에 두고 빌드 시 `OUT_DIR` 복사 또는 런타임 상대경로. 바이너리 비대 회피하나 배포 시 파일 동반 필요. +- **C-3 gitea 릴리스 에셋 + 첫 실행 다운로드**: `gitea-release --asset` 로 첨부, 첫 실행 시 릴리스 URL 에서 `model_dir/paddleocr-onnx/` 로 받음. 바이너리 lean 하나 첫 실행 시 gitea 네트워크 필요(에어갭 불가) — 로컬-first 와 약간 상충. + +**권장 = C-1(release feature 게이트)**: 오프라인·재현성·single-binary 가 kebab 정체성과 가장 정합. plan 에서 빌드/링크 영향 측정 후 확정. + +- **무결성**: 임베드(C-1)면 빌드 시점 고정이라 별도 해시 불요(바이너리=정본). C-2/C-3 면 blake3 pin 필수. +- **라이선스**: PP-OCRv5 가중치 Apache-2.0 — 재배포 가능. 번들에 NOTICE 동반. +- **오프라인**: C-1 완전 오프라인. config override(`det_model`/`rec_model`/`dict`)로 로컬 모델 교체 항상 가능. + +## 엔진 선택 (kebab-app 팩토리) + +현재 `OllamaVisionOcr` 하드코딩(`kebab-app/src/lib.rs:360`(image), `379`(pdf)). 변경: + +```rust +let ocr_engine: Option> = if cfg.image.ocr.enabled { + match cfg.image.ocr.engine.as_str() { + "ollama-vision" => Some(Box::new(OllamaVisionOcr::new(cfg)?)), + "paddle-onnx" => Some(Box::new(OnnxPaddleOcr::new(cfg)?)), + other => bail!("unknown image.ocr.engine: {other}"), + } +} else { None }; +``` + +- `ImagePipeline.ocr_engine` 를 `Option<&'a dyn OcrEngine>` 로(현재 구체타입 `&OllamaVisionOcr`). +- pdf 경로 동일. `apply_ocr`/`apply_ocr_to_pdf_pages` 는 이미 `&dyn OcrEngine` 받음 → 변경 불필요. +- `OnnxPaddleOcr` 는 한 번 생성(모델 1회 로드) 후 ingest 전체에서 재사용(PoC 모델로드 58ms, 무시 가능). + +## 버전/재색인 cascade + +OCR 엔진 변경 시 **영향 자산 자동 재색인**되어야 함(v0.26.2 메커니즘). 현재 `ingest_config_signature`(`kebab-app/src/lib.rs:3036` 부근)의 image/pdf 브랜치는 `|ocr:1:{ocr.model}` 만 서명. + +**C3 (필수, 권장 아님)**: paddle-onnx 브랜치에서 `model`("gemma4:e4b" 기본) 은 **미사용** — 실제 모델 정체성은 det/rec/dict + engine_version 에 있음. 따라서: +- 서명을 `|ocr:1:{engine}:{engine_version}` 로(엔진 + 모델/dict 식별자). `engine_version()`(spec 의 model+dict blake3 해시 포함, 라인 47)을 **반드시** 서명에 사용. +- 이유: ① `engine="ollama-vision"→"paddle-onnx"` 전환 시 model 이 기본값 그대로면 `{model}` 만으론 서명 불변 → **재색인 안 됨**(silent stale index, v0.26.2 가 없애려던 바로 그 버그). ② 모델 재변환/dict 수정 시 engine_version 변화로 재색인 트리거. +- 단위테스트(필수): (a) `ollama-vision`↔`paddle-onnx` 동일 model → 서명 다름. (b) 동일 engine, engine_version 다름 → 서명 다름. (c) 무관 설정(search 등) → 서명 불변. + +## 기본 엔진 (default) — 별도 결정 + +본 spec 은 `paddle-onnx` 를 **선택 가능**하게만 한다. kebab 의 `image.ocr.engine` **기본값을 `paddle-onnx` 로 바꿀지**는 후속 결정: +- 바꾸면: 신규 사용자/기본 동작 변화 + 모델 다운로드 기본화. 강력하나 영향 큼. +- v1 은 기본 `ollama-vision` 유지, opt-in `paddle-onnx`. 도그푸딩 후 기본 전환을 별 PR 로. (사용자 본인 config 는 즉시 `paddle-onnx`.) + +## 에러 처리 (M3 — 명시 매트릭스) + +배치 ingest 가 미지의 사용자 스캔을 돈다. 각 케이스 동작 확정: + +| 케이스 | 동작 | 근거 | +|---|---|---| +| 모델 다운로드 실패 | 엔진 생성 시 **fail-fast**(Ollama 와 동일, `lib.rs:360`) | 색인 시작 전 차단 | +| blake3 불일치 | fail-fast + 사유 | 무결성 | +| 디코드 불가 이미지 | **자산 skip + provenance 노트**(ingest 중단 X) | 기존 `apply_ocr` "skip vs surface" 계약(`ocr.rs:75`) | +| det 0 박스(빈 이미지 등) | **성공, `OcrText{joined:"", regions:[]}`**(에러 아님) | Ollama 빈줄 동작(`ocr.rs:290`) 미러 | +| rec 빈 출력(한 박스) | 그 박스 skip, 나머지 진행 | | +| 박스 폭증(노이즈 스캔) | **`max_boxes` 상한**(기본 예: 1000) 초과분 절단 + 로그 | 메모리/지연 cliff 방지 | +| dict 길이 ≠ rec 클래스 | 생성 시 에러(정합 검증) | bounds-check | + +ort `Session` 은 생성 후 1회 로드·재사용. ingest 는 현재 직렬(`lib.rs:460`, rayon 없음)이라 동시접근 없음 — 단 `OcrEngine: Send+Sync` 유지(미래 병렬화 대비, rc.9 Session Send/Sync 확인은 plan). + +## 검증 기준 + +- `cargo clippy --workspace --all-targets -j 8 -- -D warnings` 0. +- `cargo test -p kebab-parse-image -p kebab-app -j 8` 통과(touched 크레이트; `kebab-parse-image` 단독 빌드가 download-binaries 로 링크되는지 포함). +- 신규 단위테스트: + - 단계별 골든벡터(전처리/det후처리/CTC/박스정렬) — baseline 0.976 대비 단계 회귀 감지. + - OnnxPaddleOcr e2e: 합성 한/영 fixture → **CER ≤ 0.05**(=문자정확도 ≥95%), bbox>0. (단 합성 fixture 는 실코퍼스 회귀 미보장 → 도그푸딩 병행.) + - CTC decode: 알려진 logit→문자열(blank/중복 제거, bounds-check). + - 엔진 팩토리: `engine="paddle-onnx"`→OnnxPaddleOcr, 미지 값 에러. + - 서명(C3): 위 (a)(b)(c) 케이스. + - config override(`det_model`/`rec_model`/`dict`) 가 실제 사용됨 + **`--config` facade 스레딩**(CLAUDE.md facade rule, P3-5/P4-3 회귀 전례) — `OnnxPaddleOcr::new(cfg, …)` 가 explicit Config 받음. +- 회귀 가드: `engine="ollama-vision"`(기본) 경로 — 팩토리 리팩터(구체타입→`&dyn`) 후에도 **출력 동일** 핀하는 테스트. +- 스모크: `engine="paddle-onnx"` 이미지 ingest → OCR 텍스트 FTS5 hit. 큰 페이지 CPU <5초. +- 도그푸딩: 사용자 실제 이미지/책 스캔 정확도·속도(HOTFIXES + release notes). + +## 의존성 규칙 (design §8) + +`kebab-parse-image` allowed: kebab-core, kebab-config, serde, image, tracing, thiserror(task p6-2). 추가: `ort`(workspace, features `["ndarray","download-binaries"]`), `ndarray`(workspace), `imageproc`. **clipper2 미추가**(C++ FFI 회피 — unclip pure-Rust 직접). **hf-hub 미추가**(결정 C: 모델 번들, 외부 다운로드 0). **금지 유지**: kebab-store-*/embed-*/llm-* 미import. UI 크레이트 영향 없음. + +## 비범위 + +- **OCR 텍스트→임베딩 갭**(현재 OCR 은 FTS5 lexical 전용, 벡터 미포함). 사용자 "OCR 모델만 먼저" → 별도 작업. +- **caption** 은 gemma 유지([[project_llm_default]]). +- **GPU provider**(ort CUDA/CoreML): CPU 로 충분(2.75초). 후속 옵션. +- **기본 엔진 전환**(default `paddle-onnx`): 도그푸딩 후 별 PR. +- 다국어 dict 동적 전환(현재 korean dict = 한+영+숫자+기호 11,945자로 한/영 충분). + +## 잔여 노트 (critic minors) + +- **max_pixels(m1)**: 기존 `[256,4096]` clamp 은 VLM 프롬프트 비용 기준. det/rec 엔진은 비용이 latency 라 trade-off 다름. v1 은 기본 1600 **유지(의도적)** — PoC 에서 1600 대 원본 정확도 차 미미, 속도 이점. plan 에서 paddle-onnx 전용 기본 재검토 가능. +- **config 마이그레이션(m3)**: 신규 키(`det_model` 등)는 serde default 로 forward-compat(기존 파일 무수정 로드). `kebab config migrate`(#198) 가 주석/순서 보존하며 신규 키 추가 — migration 핸들링 불필요(serde default), 단 init 템플릿에 신규 키 노출. +- **per-region confidence(open q)**: Ollama 는 region confidence 상수 1.0, paddle-onnx 는 실제 score. `OcrRegion` 형태 불변이라 wire 호환(값만 의미있어짐) — release note 1줄. +- **세로/회전 페이지**: 비범위(가로쓰기 reading-order 전제). 회전 박스 rectify 는 지원하나 페이지 전체 세로조판은 미지원 명시. + +## 버전/문서 + +- feature(신규 engine 값 + 동작) → **minor bump**. +- README(Configuration: `image.ocr.engine`, 모델 첫 다운로드 안내), docs/SMOKE(config 예시), HANDOFF 1줄, docs/ARCHITECTURE(새 OCR 백엔드 추가 시 그래프/결정), HOTFIXES dated entry(도그푸딩 evidence). wire schema 불변(OcrText 내부, `--json` 표면 동일). diff --git a/tasks/HOTFIXES.md b/tasks/HOTFIXES.md index e52583d..4c2b678 100644 --- a/tasks/HOTFIXES.md +++ b/tasks/HOTFIXES.md @@ -14,6 +14,39 @@ historical contract that was implemented; this file accumulates the deltas so phase 5+ readers can find the live behavior without diffing git history. +## 2026-06-04 — PP-OCRv5 ONNX Rust 네이티브 OCR 엔진 (v0.27.0) + +**무엇을 추가했나.** 이미지 OCR 에 두 번째 백엔드 `paddle-onnx` 를 붙였다. 기존 `ollama-vision` +(원격 vision LM, 이미지당 ~50초)은 default 로 유지하고, `[image.ocr] engine = "paddle-onnx"` 로 +PP-OCRv5(검출 DBNet + 인식 CTC) ONNX 모델을 `ort`(=2.0.0-rc.9) 로 **in-process** 실행한다 — +Python 런타임/원격 호출 없이 큰 페이지 CPU <4초. `OcrEngine` trait 의 두 번째 구현 +`OnnxPaddleOcr`(`crates/kebab-parse-image/src/paddle_onnx.rs`), 팩토리는 +`kebab-app::build_image_ocr_engine`/`build_pdf_ocr_engine` (`match engine`). 검출 후처리 +(min-area rect = rotating calipers, unclip = polygon offset)는 clipper2/OpenCV 없이 pure-Rust. + +**T11 e2e 에서 발견·수정한 핵심 버그 (unclip).** 첫 실측 CER 이 0.26(게이트 0.05) 으로 크게 +초과. 단계 골든(`crates/kebab-parse-image/tests/golden/`) 와 prediction dump 로 국소화한 결과 +`unclip_rect` 가 corner 를 centroid 기준 **방사(radial) 확장**하고 있었다. 텍스트 박스는 +wide/short(예 586×15)라 대각선이 거의 수평 → 방사 확장 시 corner 가 수평으로만 ~11px 움직이고 +**세로로는 거의 안 커져** 글자 윗/아랫부분이 잘렸다(ㄷ→ㄴ 로 `다`→`나`, ascender 손실). +PaddleOCR pyclipper 처럼 **edge 별로 바깥으로 offset**(width·height 각각 2·distance 증가) 하도록 +rect 자체 (u,v) 축 기준 확장으로 재작성. 결과: mean gate CER **0.2585 → 0.0049** +(clean_paragraph/korean_heavy/numbers_table/tech_terms = 0.0), PoC 0.024 baseline 보다 우수. +큰 페이지 3.9초 < 5초 게이트. **교훈**: 회전 사각형 unclip 은 방사 확장이 아니라 polygon edge +offset 이어야 한다. + +**Config / 서명 cascade.** `[image.ocr]` 에 `det_model`/`rec_model`/`dict`(Option, override) + +`score_thresh`(0.3)/`unclip_ratio`(1.5)/`max_boxes`(1000) serde-default 필드 + `KEBAB_IMAGE_OCR_*` +env 추가(기존 config 무수정 로드 — forward-compat). `ingest_config_signature` 의 image/pdf 브랜치를 +`|ocr:1:{model}` → `|ocr:1:{engine}:{engine_version}` 로 바꿔 engine 전환(ollama↔paddle) 또는 +모델 변경 시 영향 자산 자동 재색인. paddle engine_version 은 모델 3-asset blake3 를 **per-process +1회만** 계산(triple 키 memo) — 자산마다 17MB 재해시 회피. + +**모델 배포.** ONNX 2개(det 4.7MB / rec 13MB) + dict + NOTICE 를 `crates/kebab-parse-image/ +assets/paddleocr-onnx/` 에 둔다(Git LFS). 테스트는 `KEBAB_IMAGE_OCR_MODEL_DIR`(기본 = 번들 dir) +에서 로드, e2e(`tests/paddle_e2e.rs`)는 모델/fixture 부재 시 깨끗이 skip(CI green). 자세한 설계: +spec/plan `docs/superpowers/{specs,plans}/2026-06-04-rust-native-ocr-*.md`. + ## 2026-06-03 — ingest 출력 영향 설정 변경 시 영향 자산 자동 재색인 (v0.26.2) **무엇이 깨졌나.** `[image.ocr]` / `[image.caption]` 를 off→색인→on 으로 바꿔도 증분