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.

CLAUDE.md

@@ -160,9 +160,9 @@ hvideotool/
   команду установки" (`_copy_install_command` → the recommended cu121/cu118 command, picked by
   `recommend_channel` from the driver's CUDA) and "Проверить заново" (re-runs `_probe_device`).
   `core/torch_info.py` is pure (no Qt); subprocess uses `CREATE_NO_WINDOW` on Windows.
-- **Projects (`core/project.py`).** `MainWindow._project` is the open `Project`;
-  `_folder` is kept as a synonym for `project.frames_dir` so the rest of the code
-  (navigation, cache, tags) didn't need rewiring. Entry points: "Создать проект…"
+- **Projects (`core/project.py`).** `MainWindow._project` is the open `Project`; its
+  frames come from `project.frames_dir` (navigation/cache/tags work off `_files`). Entry
+  points: "Создать проект…"
   (`_create_project`), "Открыть проект…" (`_open_project_dialog`), "Импортировать папку
   как проект…" (`_import_folder_as_project` — copies images into a new project's
   `frames/`, carries over an old `.hvideotool_detections.json` sidecar if present), and

hvideotool/config.py

@@ -49,9 +49,8 @@ class AppConfig:
 
     # --- restoration ("расцензурить") ---
     restorer: str = "deepmosaics"      # "deepmosaics" | "deepmosaics_video"
-    dm_dir: str | None = None          # DeepMosaics repo dir (contains deepmosaic.py)
+    dm_dir: str | None = None          # optional extra dir to search for mosaic_position.pth
     dm_model: str | None = None        # DeepMosaics clean weights (clean_*.pth)
-    dm_python: str | None = None       # python exe for DeepMosaics (None = current)
     dm_gpu: str = "0"                  # CUDA device id, "-1" for CPU
 
 

hvideotool/core/detection/types.py

@@ -3,10 +3,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from enum import Enum
+from enum import StrEnum
 
 
-class CensorType(str, Enum):
+class CensorType(StrEnum):
     """Kind of already-applied censorship a detection represents."""
 
     MOSAIC = "mosaic"
@@ -33,7 +33,7 @@ class Detection:
         }
 
     @classmethod
-    def from_dict(cls, data: dict) -> "Detection":
+    def from_dict(cls, data: dict) -> Detection:
         return cls(
             type=CensorType(data["type"]),
             score=float(data["score"]),

hvideotool/core/detection/yolo.py

@@ -16,8 +16,6 @@ from __future__ import annotations
 
 import os
 
-import numpy as np
-
 from ...config import DetectionConfig
 from ..video.frame import Frame
 from .base import Detector
@@ -61,7 +59,7 @@ class YoloDetector(Detector):
             import torch
 
             cuda_ok = torch.cuda.is_available()
-        except Exception:  # noqa: BLE001
+        except Exception:
             cuda_ok = False
         device = self.cfg.yolo_device
         if device is None:

hvideotool/core/project.py

@@ -41,7 +41,6 @@ _SETTING_KEYS = (
     "restorer",
     "dm_dir",
     "dm_model",
-    "dm_python",
     "dm_gpu",
 )
 
@@ -93,7 +92,7 @@ class Project:
         name: str | None = None,
         settings: dict | None = None,
         source: str | None = None,
-    ) -> "Project":
+    ) -> Project:
         """Create a new project folder (with ``frames/``) and write ``project.json``."""
         root = Path(root)
         proj = cls(
@@ -108,7 +107,7 @@ class Project:
         return proj
 
     @classmethod
-    def load(cls, path: Path | str) -> "Project":
+    def load(cls, path: Path | str) -> Project:
         """Load a project from its folder or directly from its ``project.json``."""
         path = Path(path)
         root = path.parent if path.name == PROJECT_FILE else path

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

hvideotool/core/restore/factory.py

@@ -20,20 +20,20 @@ if TYPE_CHECKING:  # avoid importing AppConfig at runtime here (not needed)
     from ...config import AppConfig
 
 
-def build_restorer(name: str = "deepmosaics", config: "AppConfig | None" = None) -> Restorer:
+def build_restorer(name: str = "deepmosaics", config: AppConfig | None = None) -> Restorer:
     if config is None:
         raise ValueError("Для DeepMosaics нужны настройки (config).")
     if name == "deepmosaics":
         from .deepmosaics import DeepMosaicsRestorer
 
         return DeepMosaicsRestorer(
-            config.dm_dir, config.dm_model, config.dm_python, config.dm_gpu
+            config.dm_dir, config.dm_model, config.dm_gpu
         )
     if name == "deepmosaics_video":
         from .deepmosaics import DeepMosaicsVideoRestorer
 
         return DeepMosaicsVideoRestorer(
-            config.dm_dir, config.dm_model, config.dm_python, config.dm_gpu
+            config.dm_dir, config.dm_model, config.dm_gpu
         )
     if name == "lada":
         raise ValueError(

hvideotool/core/torch_info.py

@@ -44,7 +44,7 @@ def _run_nvidia_smi() -> dict:
                     out["gpus"].append(parts[0])
                     if len(parts) > 1 and parts[1]:
                         out["driver_version"] = parts[1]
-    except Exception:  # noqa: BLE001 - any failure => "not found"
+    except Exception:
         return out
     # Max CUDA version the driver supports (only in the plain header).
     try:
@@ -54,7 +54,7 @@ def _run_nvidia_smi() -> dict:
         m = re.search(r"CUDA Version:\s*([\d.]+)", r2.stdout)
         if m:
             out["cuda_driver"] = m.group(1)
-    except Exception:  # noqa: BLE001
+    except Exception:
         pass
     return out
 
@@ -76,23 +76,23 @@ def gather() -> dict:
     }
     try:
         import torch
-    except Exception as exc:  # noqa: BLE001 - report any import failure
+    except Exception as exc:
         info["import_error"] = str(exc)
     else:
         info["installed"] = True
         info["version"] = getattr(torch, "__version__", None)
         try:
             info["built_cuda"] = torch.version.cuda
-        except Exception:  # noqa: BLE001
+        except Exception:
             info["built_cuda"] = None
         try:
             info["cuda_available"] = bool(torch.cuda.is_available())
-        except Exception:  # noqa: BLE001
+        except Exception:
             info["cuda_available"] = False
         if info["cuda_available"]:
             try:
                 info["device_name"] = torch.cuda.get_device_name(0)
-            except Exception:  # noqa: BLE001
+            except Exception:
                 info["device_name"] = None
 
     smi = _run_nvidia_smi()
@@ -103,10 +103,6 @@ def gather() -> dict:
     return info
 
 
-def device_label(info: dict) -> str:
-    return "CUDA" if info.get("cuda_available") else "CPU"
-
-
 def _ver_tuple(v: str | None) -> tuple[int, ...]:
     try:
         return tuple(int(x) for x in str(v).split(".")[:2])

hvideotool/core/video/extract.py

@@ -18,8 +18,8 @@ from __future__ import annotations
 
 import shutil
 import subprocess
+from collections.abc import Callable
 from pathlib import Path
-from typing import Callable
 
 import cv2
 
@@ -38,7 +38,7 @@ def _find_ffmpeg() -> str | None:
         import imageio_ffmpeg
 
         return imageio_ffmpeg.get_ffmpeg_exe()
-    except Exception:  # noqa: BLE001 - package missing or no bundled binary
+    except Exception:
         return None
 
 

hvideotool/core/video/frame.py

@@ -1,4 +1,4 @@
-"""Decoded video frame passed from the reader to the detector."""
+"""Image passed to the detector — the ``Detector.detect`` input type."""
 
 from __future__ import annotations
 
@@ -10,5 +10,4 @@ import numpy as np
 @dataclass
 class Frame:
     image: np.ndarray  # BGR, HxWx3, uint8 (OpenCV convention)
-    index: int         # 0-based frame counter since the last open/seek
-    pts: float         # presentation timestamp, seconds
+    index: int = 0     # 0-based position in the sequence (informational)

hvideotool/settings_store.py

@@ -39,7 +39,7 @@ def apply(config: AppConfig) -> None:
         config.default_threshold = float(data["threshold"])
     if data.get("restorer"):
         config.restorer = data["restorer"]
-    for key in ("dm_dir", "dm_model", "dm_python", "dm_gpu"):
+    for key in ("dm_dir", "dm_model", "dm_gpu"):
         if key in data:
             setattr(config, key, data[key])
 
@@ -54,7 +54,6 @@ def save(config: AppConfig) -> None:
         restorer=config.restorer,
         dm_dir=config.dm_dir,
         dm_model=config.dm_model,
-        dm_python=config.dm_python,
         dm_gpu=config.dm_gpu,
     )
     _write(data)

hvideotool/ui/image_view.py

@@ -1,9 +1,9 @@
 """Widget that renders an image and draws detection overlays.
 
-Overlay visibility and the confidence threshold are applied at paint time, so
-toggling them is instant. One detection can be *highlighted* (selected in the
-detail table) — it is drawn boldly even if below the threshold, while the others
-dim, so the user can inspect exactly what the detector found.
+The confidence threshold is applied at paint time, so changing it is instant. One
+detection can be *highlighted* (selected in the detail table) — it is drawn boldly
+even if below the threshold, while the others dim, so the user can inspect exactly
+what the detector found.
 """
 
 from __future__ import annotations
@@ -24,7 +24,6 @@ class ImageView(QWidget):
         self._cfg = overlay_cfg
         self._qimage: QImage | None = None
         self._dets: list[Detection] = []
-        self._overlay_enabled = True
         self._threshold = 0.0
         self._highlight: int | None = None
         self.setMinimumSize(480, 360)
@@ -42,10 +41,6 @@ class ImageView(QWidget):
         self._highlight = None
         self.update()
 
-    def set_overlay_enabled(self, enabled: bool) -> None:
-        self._overlay_enabled = enabled
-        self.update()
-
     def set_threshold(self, threshold: float) -> None:
         self._threshold = threshold
         self.update()
@@ -59,13 +54,13 @@ class ImageView(QWidget):
         r, g, b = self._cfg.colors.get(ctype.value, (255, 0, 0))
         return QColor(r, g, b)
 
-    def paintEvent(self, event) -> None:  # noqa: N802 - Qt signature
+    def paintEvent(self, event) -> None:
         painter = QPainter(self)
         painter.fillRect(self.rect(), QColor(18, 18, 18))
 
         if self._qimage is None:
             painter.setPen(QColor(160, 160, 160))
-            painter.drawText(self.rect(), Qt.AlignCenter, "Откройте папку с картинками (Файл → Открыть папку…)")
+            painter.drawText(self.rect(), Qt.AlignCenter, "Откройте проект (Файл → Открыть проект…)")
             painter.end()
             return
 
@@ -77,7 +72,7 @@ class ImageView(QWidget):
         painter.setRenderHint(QPainter.SmoothPixmapTransform, True)
         painter.drawImage(QRectF(ox, oy, dw, dh), self._qimage)
 
-        if self._overlay_enabled and self._dets:
+        if self._dets:
             painter.setRenderHint(QPainter.Antialiasing, True)
             for i, d in enumerate(self._dets):
                 highlighted = i == self._highlight

hvideotool/ui/main_window.py

@@ -5,23 +5,24 @@ A project is a folder (``project.json`` + ``frames/`` + ``detections.json`` +
 model, threshold, restore engine) live in ``project.json``; the global
 ``settings.json`` only seeds defaults for new projects.
 
-Layout: a toolbar (new/open project · from-video · detector · model · calc-frame ·
-detect-all · threshold), then a splitter with three panes — left: collection
-controls + the file list; center: the image with overlays; right: a detail table
-of every detection. Collection controls sit by the file list (they act on its
-selection), keeping the toolbar to detection/entry actions only.
+Layout: a toolbar (new/open project · from-video · model · calc-frame · detect-all ·
+restore · threshold), then a splitter with three panes — left: collection controls +
+the file list; center: the image with overlays; right: a detail table of every
+detection. Collection controls sit by the file list (they act on its selection),
+keeping the toolbar to detection/entry actions only.
 
-Viewing and detecting are decoupled, so browsing a big project stays instant even
-with a slow (CPU) detector:
+Detection is YOLO-only; restoration is DeepMosaics-only. Viewing and detecting are
+decoupled, so browsing a big project stays instant even with a slow (CPU) detector:
 - selecting a file just **shows** it (with its cached result, if any);
 - **double-clicking** a file, or "Рассчитать кадр", runs the detector on it;
 - "Детектировать все" runs the whole project.
 Both project loading and detect-all show a progress bar. Results are cached in the
-project; switching detector/model clears the cache.
+project; switching the model clears the cache.
 """
 
 from __future__ import annotations
 
+import contextlib
 import shutil
 from pathlib import Path
 
@@ -77,13 +78,12 @@ class MainWindow(QMainWindow):
         self._detector = None
         self._detector_key = None
         self._project: Project | None = None  # the open project (None until one is opened)
-        self._folder: Path | None = None       # == project.frames_dir while a project is open
         self._files: list[Path] = []
         self._results: dict[str, list[Detection]] = {}  # path -> detections (cache)
         self._current: Path | None = None
         self._restorer = None           # un-censor engine, built lazily from config
         self._restorer_key = None
-        self._restored: dict[str, "object"] = {}    # path -> restored image (BGR ndarray)
+        self._restored: dict[str, object] = {}    # path -> restored image (BGR ndarray)
         self._showing_restored = False
         self._nav_sync = False  # guard against slider<->list signal loops
         self._busy = False      # a long operation is running
@@ -547,7 +547,7 @@ class MainWindow(QMainWindow):
         if Project.is_project(p):
             try:
                 self._open_project(Project.load(p))
-            except (OSError, ValueError) as exc:  # noqa: BLE001 - surface to the user
+            except (OSError, ValueError) as exc:
                 QMessageBox.warning(self, "Ошибка", f"Не удалось открыть проект:\n{exc}")
         elif p.is_dir():
             QMessageBox.information(
@@ -562,10 +562,8 @@ class MainWindow(QMainWindow):
         """On startup, reopen the last project if it still exists (best-effort)."""
         last = settings_store.last_project()
         if last and Project.is_project(last):
-            try:
+            with contextlib.suppress(OSError, ValueError):
                 self._open_project(Project.load(last))
-            except (OSError, ValueError):
-                pass
 
     def _new_project_root(self, default_name: str = "") -> Path | None:
         """Prompt for a parent dir + name; return a fresh (empty) project root or None."""
@@ -662,10 +660,8 @@ class MainWindow(QMainWindow):
         # Carry over an old sidecar detection cache (basename-keyed) if present.
         old_sidecar = src / ".hvideotool_detections.json"
         if old_sidecar.is_file():
-            try:
+            with contextlib.suppress(OSError):
                 shutil.copy2(str(old_sidecar), str(project.cache_path))
-            except OSError:
-                pass
 
         self.statusBar().showMessage(f"Импортировано {copied} картинок → {project.name}")
         self._open_project(project)
@@ -764,7 +760,7 @@ class MainWindow(QMainWindow):
                 str(video), str(out), step=step, keyframes_only=keyframes_only,
                 max_dim=max_dim, progress=cb,
             )
-        except Exception as exc:  # noqa: BLE001 - surface decode errors to the user
+        except Exception as exc:
             QMessageBox.warning(self, "Ошибка", f"Не удалось извлечь кадры:\n{exc}")
             return
         finally:
@@ -788,7 +784,6 @@ class MainWindow(QMainWindow):
         self.statusBar().showMessage(f"Сканирую папку: {folder}…")
         QApplication.processEvents()
         files = sorted(p for p in folder.iterdir() if p.suffix.lower() in _IMAGE_EXTS)
-        self._folder = folder
         self._files = files
         self._results.clear()
         self._current = None
@@ -850,7 +845,7 @@ class MainWindow(QMainWindow):
         img = imread_unicode(str(path))
         if img is None:
             raise RuntimeError(f"Не удалось прочитать: {Path(path).name}")
-        dets = detector.detect(Frame(image=img, index=0, pts=0.0))
+        dets = detector.detect(Frame(image=img))
         dets.sort(key=lambda d: d.score, reverse=True)
         return dets
 
@@ -1057,8 +1052,7 @@ class MainWindow(QMainWindow):
         self._start_job(fn, total, on_done=done)
 
     def _make_restorer(self):
-        key = (self._cfg.restorer, self._cfg.dm_dir, self._cfg.dm_model,
-               self._cfg.dm_python, self._cfg.dm_gpu)
+        key = (self._cfg.restorer, self._cfg.dm_dir, self._cfg.dm_model, self._cfg.dm_gpu)
         if key != self._restorer_key:
             self._restorer = build_restorer(self._cfg.restorer, self._cfg)  # may raise
             self._restorer_key = key
@@ -1266,7 +1260,7 @@ class MainWindow(QMainWindow):
         self.view.set_threshold(value)
         self._persist_settings()
 
-    def closeEvent(self, event) -> None:  # noqa: N802 - Qt override
+    def closeEvent(self, event) -> None:
         if self._job is not None:  # stop a running background job before tearing down
             self._job.cancel()
             self._pool.waitForDone(3000)

hvideotool/ui/marker_slider.py

@@ -27,11 +27,6 @@ class MarkerSlider(QSlider):
             self._marks = marks
             self.update()
 
-    def clear_marks(self) -> None:
-        if self._marks:
-            self._marks = set()
-            self.update()
-
     def paintEvent(self, event) -> None:
         super().paintEvent(event)
         if not self._marks or self.maximum() <= self.minimum():

hvideotool/ui/workers.py

@@ -33,7 +33,7 @@ class _Signals(QObject):
 class Job(QRunnable):
     """Runs ``fn(job)`` on a thread pool, marshaling progress/result to the GUI."""
 
-    def __init__(self, fn: Callable[["Job"], Any]) -> None:
+    def __init__(self, fn: Callable[[Job], Any]) -> None:
         super().__init__()
         self.setAutoDelete(False)  # the GUI keeps a reference until `done`/`failed`
         self.signals = _Signals()
@@ -56,12 +56,12 @@ class Job(QRunnable):
         self.signals.tick.emit(payload)
 
     # -- thread entry point --
-    def run(self) -> None:  # noqa: D401 - QRunnable override
+    def run(self) -> None:
         try:
             result = self._fn(self)
         except Cancelled:
             self.signals.done.emit(None)
-        except Exception as exc:  # noqa: BLE001 - surface engine/model errors to the GUI
+        except Exception as exc:
             self.signals.failed.emit(str(exc))
         else:
             self.signals.done.emit(result)

pyproject.toml

@@ -19,9 +19,9 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-# Trained-model detector (future work). PyTorch must be installed separately
-# with the correct CUDA build — see README. Installing this extra only pulls in
-# Ultralytics; it does NOT install torch.
+# YOLO detector (Ultralytics). PyTorch must be installed separately with the correct
+# CUDA build — see README. This extra only pulls in Ultralytics; it does NOT install
+# torch. Both detection (YOLO) and restoration (DeepMosaics) need torch at runtime.
 yolo = ["ultralytics>=8.0"]
 
 [project.scripts]