# CLAUDE.md

Guidance for Claude Code (and other agents) working in this repository.

## Memory: EchoVault (read this first)

This project uses the **EchoVault** MCP server for persistent, cross-session memory.
Prior sessions store architectural decisions, fixed bugs, and gotchas there. Follow
this protocol every session:

1. **At session start — load context.** Call `memory_context` (project is
   auto-detected from cwd) before doing any work. Use `memory_search` for specific
   topics (e.g. "detector model", "classic-cv", "false positives").
2. **During work — search before re-deciding.** When the task touches an area that
   may have prior context, `memory_search` it first instead of re-deriving decisions.
3. **Before ending a session — save what matters.** Call `memory_save` when you made
   a design decision, fixed a bug (include root cause + fix), found a non-obvious
   gotcha, or the user corrected/clarified a requirement. Pick the right `category`
   (`decision` / `bug` / `pattern` / `learning` / `context`). Do **not** save trivia,
   things obvious from the code, or duplicates.

EchoVault is the source of truth for *why* things are the way they are; this file is
the stable, high-level map. When they disagree, trust on-disk code first, then
EchoVault, then this file — and update whichever is stale.

## What this project is

**HVideoTool** is a Windows-first desktop GUI utility that **detects already-applied
censorship** (mosaic, pixelation, blur, black bars) in **images**, and draws outlines
over the detected censored regions. You open a folder of images; it runs each through
a detector, draws the regions, and shows a detailed per-image list of what it found.

> **Scope was deliberately narrowed (this session).** It used to extract frames from
> video, detect, and play back with overlays (a "project model" with worker threads).
> That whole video pipeline was **removed** — the tool is now a simple **image-folder
> inspector** for viewing/debugging detector output on test images. Pre-extract video
> to frames externally if you need that.

Keep this scope sharp:

- Primary job is **detection + overlay/inspection**. A **restoration** ("расцензурить")
  step was added later (user-requested): per-frame, on-demand, behind a `Restorer`
  interface. The shipped engine is a cv2 **inpaint baseline** (fills, does NOT truly
  reconstruct); a generative engine (DeepMosaics / LADA BasicVSR++) is the intended
  real engine but needs weights + a CUDA GPU and is not wired yet.
- Still **no diffusion / ControlNet / SDXL**. (`xinsir/controlnet-union-sdxl-1.0` was
  rejected early — a generative *conditioning* model, not a censorship restorer. Don't
  reintroduce it.) Restoration, if upgraded, uses a mosaic-removal model (DeepMosaics/
  LADA), not a general text-to-image diffusion pipeline.
- It detects **already-censored** regions, not "content that should be censored"
  (i.e. not an NSFW classifier).
- Video is only a one-shot frame-extraction convenience (see below); detection and
  restoration operate on image folders.

## Target environment

- **OS:** Windows 11 x64 (primary). Use PowerShell syntax in commands.
- **Python:** 3.11+.
- **GPU:** NVIDIA + CUDA via PyTorch, only for the YOLO detector. CPU fallback works
  but is slow. The `classic` detector needs no torch and no GPU.

## Tech stack (decided)

| Concern        | Choice                                      |
|----------------|---------------------------------------------|
| GUI            | PySide6 (Qt 6) — LGPL                        |
| Image IO       | OpenCV (`opencv-python`) + NumPy, unicode-safe via `core/imageio.py` |
| Detector       | classic-CV heuristic; Ultralytics YOLO (LADA) behind a pluggable interface |

Torch/CUDA + Ultralytics enter only with the YOLO detector. Keep that dependency
optional (the `yolo` extra in `pyproject.toml` pulls only Ultralytics; torch is
installed separately per the README). The classic detector must keep running with no
torch present.

## Architecture (as implemented)

> This reflects the actual code on disk. It is a synchronous, single-threaded GUI app
> — no worker threads, no project/cache files. The only video touch is a one-shot
> "Создать из ролика…" that decodes a clip to a folder of JPGs via the **ffmpeg CLI**
> (cv2.VideoCapture fallback; NOT PyAV); detection still works on image folders only.
> Detection runs on the GUI
> thread (lazily per image, or via "Детектировать все"). When code and this file
> disagree, trust the code.

```
hvideotool/
├── __main__.py          # entry point + CLI (optional folder arg, --detector, --model)
├── app.py               # QApplication bootstrap; run(config, folder=None)
├── config.py            # AppConfig + DetectionConfig/OverlayConfig (thresholds live here)
├── settings_store.py    # persist detector/model/threshold/last_dir to ~/HVideoTool/settings.json
├── ui/
│   ├── main_window.py   # the whole UI: toolbar + [file list | image view | detail table]
│   └── image_view.py    # renders an image + draws polygon/bbox overlays (QPainter); can highlight one
└── core/
    ├── imageio.py       # unicode-safe imread/imwrite (np.fromfile + imdecode)
    ├── video/
    │   ├── extract.py   # extract_frames(): ffmpeg CLI (cv2 fallback) -> JPGs; keyframe/step modes + downscale
    │   └── frame.py     # Frame dataclass (image BGR, index, pts) — the detector input type
    ├── restore/         # "un-censor" detected regions (per-frame)
    │   ├── base.py      #   Restorer ABC: restore(image, detections) -> image
    │   ├── factory.py   #   build_restorer(name, config) -> inpaint | deepmosaics (lada = TODO)
    │   ├── inpaint.py   #   InpaintRestorer (cv2) — baseline, fills not reconstructs
    │   ├── deepmosaics.py #  DeepMosaicsRestorer — shells out to user-installed DeepMosaics
    │   └── mask.py      #   detections_to_mask(shape, dets, dilate)
    └── detection/
        ├── base.py      # Detector ABC: detect(frame) -> list[Detection]
        ├── factory.py   # build_detector(config) -> classic | yolo | combined
        ├── types.py     # Detection (+ to_dict/from_dict), CensorType enum
        ├── classic_cv.py  # ClassicCVDetector — heuristic; accepts a `types` filter
        ├── yolo.py      # YoloDetector — Ultralytics YOLO-seg; lazy-imports torch/ultralytics
        └── composite.py # CompositeDetector — merge detectors + IoU dedup
```

### How it works

- `MainWindow` holds the config, builds the detector lazily via `build_detector`
  (cached by detector+model+conf in `_make_detector`), and keeps `_results: dict[path
  -> list[Detection]]` as the detection cache.
- "Открыть папку" lists image files (`_IMAGE_EXTS`) with a progress bar (bulk insert
  with `setUpdatesEnabled(False)` + periodic `processEvents`), so a big folder doesn't
  freeze silently.
- **Viewing and detecting are decoupled on purpose** (so browsing stays instant even
  with a slow CPU detector): selecting a file only *shows* it with its cached result
  (header reads "не рассчитано" if none). Detection runs on **double-click**, the
  "Рассчитать кадр" action (Space → `_recompute_current`, force-recomputes current), or
  "Детектировать все" (whole folder, progress bar). Do NOT re-add auto-detect-on-select.
  Results cache in `_results`; the file-list row gets a count suffix when computed.
  Switching detector/model clears the cache (`_invalidate_results`).
- **Collections (curation).** A combo in the left pane (next to the file list, since it
  acts on the list selection — not on the toolbar, to keep that uncluttered)
  (`collection_combo`) picks the active destination; `_refresh_collections()` repopulates it from sibling folders of the
  opened folder (`_collections_base()` = the opened folder's parent, else
  `~/HVideoTool/collections`) on load/create, so previously-made collections are
  reselectable. Items carry the path in itemData; "— не выбрана —" = None,
  "Выбрать папку…" = `"__browse__"` sentinel → `_browse_collection()` for an arbitrary
  folder (kept in the combo even if outside base). "Создать…" makes a new one and
  selects it. The file list is `ExtendedSelection`; "В коллекцию" / Ctrl+M **moves**
  (`shutil.move`, not copy) the selected frames there, removing them from
  list/`_files`/cache. `_unique_dest` avoids clobbering (`foo.jpg` → `foo (1).jpg`).
  Use case: sort frames into a training/example set while inspecting detections.
- **Restoration ("Расцензурить кадр").** Toolbar action runs `self._restorer` (built via
  `build_restorer`) on the current frame's detections (computing them first if needed),
  caches the result in `_restored[path]`, and shows it overlay-free. "Показать оригинал/
  результат" toggles (`_showing_restored`); "Сохранить результат" writes
  `<stem>_restored.jpg` into the active collection (or beside the frame). The baseline
  is cv2 inpaint; the real engine is **DeepMosaics** (`restore/deepmosaics.py`), which
  shells out to a user-installed DeepMosaics repo (`deepmosaic.py --mode clean`, stdin
  closed so its error `input()` can't hang; reads the newest image from a temp
  result_dir). The engine + paths are set in `RestoreDialog` (Файл → Движок
  восстановления…), persisted, and built lazily/cached in `_make_restorer` (like
  `_make_detector`). NOTE: DeepMosaics locates mosaics itself (its `mosaic_position.pth`)
  — our detections aren't passed to it. `_show` resets `_showing_restored` +
  `_update_restore_actions`. To add another engine (e.g. LADA), implement
  `core/restore/base.Restorer` and register it in `restore/factory.build_restorer`.
- **Navigation bar** under the image (`_build_nav_bar`): prev/next frame (◀ ▶, keys
  `,`/`.`), a scrubber `frame_slider` across the whole sequence, a `pos_label`
  ("row / n"), and jump-to-detection (◀ детекция / детекция ▶, keys `[`/`]`,
  `_step_hit` scans `_results` for the next non-empty frame). The slider and file list
  are kept in sync via `_update_nav` guarded by `_nav_sync` (avoids signal loops); all
  navigation ultimately drives `file_list.setCurrentRow`.
- `image_view.ImageView` draws the image scaled-to-fit plus overlays. Overlay
  visibility/threshold are applied at paint time. Selecting a row in the detail table
  calls `set_highlight(i)` — that detection is drawn boldly (even below threshold) and
  the rest dim. The detail table lists ALL detections (sorted by score), so sub-threshold
  hits are still visible for debugging; the threshold only affects what's drawn.

### Separation of concerns

- `ui/` must not import `torch` / `ultralytics` directly. It builds detectors only via
  `core/detection/factory.build_detector` and talks to `core/` through the `Detector`
  interface and the `Detection`/`CensorType` types.
- New detector kinds: implement `core/detection/base.Detector`, register the string in
  `core/detection/factory.build_detector`, and add it to `_DETECTORS` in
  `ui/main_window.py`.

## Commands

```powershell
python -m venv .venv; .\.venv\Scripts\Activate.ps1
pip install -e .                       # classic detector needs no torch/CUDA

python -m hvideotool                   # open a folder in-app
python -m hvideotool "C:\path\to\images" --detector yolo --model models\lada_mosaic_detection_model_v4_accurate.pt

pip install -e ".[yolo]"               # + install torch separately, see README
```

No formal test suite. Headless sanity check: set `QT_QPA_PLATFORM=offscreen`, build a
`MainWindow`, `open_path(folder)`, drive `file_list.setCurrentRow(...)`, and read
`detail_table` / `detail_header`. Or run `build_detector(config).detect(...)` on a
frame directly.

## Conventions

- Match the style of surrounding code; keep `core/` free of Qt where reasonable.
- Type hints on public functions and the `Detector` interface.
- Model weights (`.pt`) and large media are **not** committed — keep them in `models/`
  and `.gitignore`d.
- User-facing strings / README are in Russian; code identifiers and this file in English.

## Gotchas

- **Wrong YOLO model = "noise".** The YOLO detector needs a **censorship** model
  (LADA `models\lada_mosaic_detection_model_v4_accurate.pt`). If `model_path` points at
  a generic COCO model (e.g. the `yolo11n-seg.pt` in the repo root, which Ultralytics
  auto-downloads / is the training base), it detects people/objects and maps them to
  `CensorType.UNKNOWN` → purple boxes that look like noise. This was a real user trap.
- **classic-CV is approximate and noisy on real video.** Its mosaic heuristic (low
  block-reconstruction residual + 2D gradient + contrast) fires on textured real
  footage (skin/hair/fabric/JPEG) → many false positives, while simultaneously missing
  real mosaic after the `proc_max_dim=720` downscale softens block edges (measured:
  contrast/grad fall below `mosaic_contrast_min`/`mosaic_grad_min`). For real-video
  mosaic use `yolo`/`combined` + LADA. For anime there is no good public model.
- **Domain matters.** LADA is trained on REAL video (JAV). It detects some anime mosaic
  but not all. The real anime fix is *retraining* a YOLO11-seg (see `scripts/training/`),
  not tuning more classic thresholds.
- **YOLO detector = LADA weights** ([HF `ladaapp/lada`](https://huggingface.co/ladaapp/lada)).
  YOLO **segmentation** model, classes `{0: mosaic_nsfw, 1: mosaic_sfw_head}` → both map
  to `CensorType.MOSAIC` (`_name_to_type` matches "mosaic" in the class name). Detects
  mosaic only; black bars / blur stay with classic. Weights + Ultralytics are AGPL-3.0
  (accepted). `yolo.py` lazy-imports `torch`/`ultralytics`.
- **No model weights in the repo.** Code must fail with a clear, actionable message
  when the model path is missing — not a raw stack trace (`factory._require_model`,
  `YoloDetector.__init__`).
- **CUDA/torch install is environment-specific.** Don't add torch to core deps; it
  stays out (the `yolo` extra pulls only Ultralytics) and is installed separately.
- **QImage from a numpy buffer must be `.copy()`d** (see `ImageView.set_image`),
  otherwise it aliases a buffer that gets freed → garbage/crash.
- **Always use `core/imageio.py`** (`imread_unicode`/`imwrite_unicode`) for images —
  `cv2.imread`/`imwrite` silently fail on non-ASCII Windows paths.
- Don't reintroduce any generative / ControlNet dependency, nor the removed video
  *pipeline* (PyAV, project/cache, worker threads, playback). The one allowed video
  touch is `core/video/extract.py` (one-shot decode → JPG folder, behind "Создать из
  ролика…"): ffmpeg CLI — `_find_ffmpeg()` prefers PATH, else the binary bundled by
  the `imageio-ffmpeg` dep, else cv2 fallback. Keyframe-only `-skip_frame nokey` is
  ~10× faster than every-frame; `-hwaccel` does NOT help (GPU transfer overhead). Use
  ffmpeg/cv2, not PyAV, and keep it synchronous. Decoding every frame is the inherent
  cost — the speed lever is decoding *fewer* frames (keyframes).