Refactor HVideoTool's configuration and project management: updated project handling in core/project.py, streamlined restoration settings in config.py, and improved documentation in CLAUDE.md. Removed unused parameters and enhanced type hints for better clarity.

hvideotool/core/restore/deepmosaics.py

@@ -1,20 +1,22 @@
-"""DeepMosaics restorer — real generative mosaic removal, in-process.
+"""DeepMosaics restorers — real generative mosaic removal, in-process.
 
-The DeepMosaics network code (GPL-3.0) is vendored under ``_deepmosaics/`` (see
-its NOTICE/LICENSE). We load the models **once** and run the per-frame clean path
-in-process — far faster than spawning a subprocess per frame (which reloaded the
-models every time). Only the model *weights* are user-supplied.
+The DeepMosaics network code (GPL-3.0) is vendored under ``_deepmosaics/`` (see its
+NOTICE/LICENSE). We load the models **once** and run in-process — far faster than
+spawning a subprocess per frame (which reloaded the models every time). Only the model
+*weights* are user-supplied. Both engines locate the mosaic themselves (BiSeNet
+``mosaic_position.pth``); detections are not passed to them.
 
-Per-frame clean = DeepMosaics' ``cleanmosaic_img_server`` logic, reimplemented
-here (so we don't pull in their video/ffmpeg modules):
-  locate mosaic (BiSeNet ``mosaic_position.pth``) → run the clean generator on the
-  crop → feather it back. DeepMosaics finds the mosaic itself; our detections are
-  used for navigation, not passed to it.
+Two engines:
+  - :class:`DeepMosaicsRestorer` (per-frame): reproduces ``cleanmosaic_img_server`` —
+    locate mosaic → run the image generator on the crop → feather it back. Image weights
+    ``clean_youknow_resnet_9blocks.pth``.
+  - :class:`DeepMosaicsVideoRestorer` (temporal/BVDNet): reproduces
+    ``cleanmosaic_video_fusion`` — a window of neighbouring frames + recurrence, for
+    temporal coherence. Video weights ``clean_youknow_video.pth``; needs a contiguous
+    sequence (see :meth:`Restorer.restore_sequence`).
 
-Setup (see README → Восстановление): download the **image** clean weights
-``clean_youknow_resnet_9blocks.pth`` + ``mosaic_position.pth`` into one folder and
-point the app at the clean-model file. The video model ``clean_youknow_video.pth``
-(BVDNet) needs neighbour frames and does NOT work per-frame.
+Setup (see README → Восстановление): drop the chosen ``clean_*.pth`` + ``mosaic_position.pth``
+into one folder (``models/deepmosaics``) and pick it in the restore dialog.
 """
 
 from __future__ import annotations
@@ -31,8 +33,8 @@ from .base import (
     Cancelled,
     DetGetter,
     FrameGetter,
-    ResultSink,
     Restorer,
+    ResultSink,
 )
 
 _VENDOR = Path(__file__).parent / "_deepmosaics"
@@ -94,9 +96,8 @@ def _netg_kind(model_name: str) -> str:
 class DeepMosaicsRestorer(Restorer):
     def __init__(
         self,
-        deepmosaics_dir: str | None,   # kept for factory/config compatibility (weights hint)
+        deepmosaics_dir: str | None,   # optional extra dir to find mosaic_position.pth
         model_path: str | None,
-        python_exe: str | None = None,  # unused now (in-process)
         gpu_id: str = "0",
     ) -> None:
         if not model_path or not Path(model_path).is_file():
@@ -136,12 +137,13 @@ class DeepMosaicsRestorer(Restorer):
         # Fall back to CPU when CUDA isn't available: DeepMosaics calls `.cuda()`
         # whenever gpu_id != "-1", which raises "Torch not compiled with CUDA enabled"
         # on a CPU-only torch build.
-        import torch  # noqa: E402
+        import torch
         if self._gpu != "-1" and not torch.cuda.is_available():
             self._gpu = "-1"
 
-        from models import loadmodel, runmodel  # type: ignore  # noqa: E402
-        import util.image_processing as impro    # type: ignore  # noqa: E402
+        import util.image_processing as impro  # type: ignore
+
+        from models import loadmodel, runmodel  # type: ignore
 
         self._runmodel = runmodel
         self._impro = impro
@@ -206,7 +208,6 @@ class DeepMosaicsVideoRestorer(Restorer):
         self,
         deepmosaics_dir: str | None,
         model_path: str | None,
-        python_exe: str | None = None,  # unused (in-process); kept for factory parity
         gpu_id: str = "0",
     ) -> None:
         chosen: Path | None = None
@@ -244,13 +245,14 @@ class DeepMosaicsVideoRestorer(Restorer):
         if str(_VENDOR) not in sys.path:
             sys.path.insert(0, str(_VENDOR))
 
-        import torch  # noqa: E402
+        import torch
         if self._gpu != "-1" and not torch.cuda.is_available():
             self._gpu = "-1"  # CPU fallback (see DeepMosaicsRestorer for why)
 
-        from models import loadmodel, runmodel  # type: ignore  # noqa: E402
-        import util.data as data                 # type: ignore  # noqa: E402
-        import util.image_processing as impro     # type: ignore  # noqa: E402
+        import util.data as data  # type: ignore
+        import util.image_processing as impro  # type: ignore
+
+        from models import loadmodel, runmodel  # type: ignore
 
         self._torch = torch
         self._runmodel = runmodel