Enhance documentation and UI for HVideoTool: added restoration feature for detected regions, updated layout for collection controls, and improved navigation bar. Clarified tool capabilities and limitations in README and CLAUDE.md.

hvideotool/core/restore/__init__.py

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+

hvideotool/core/restore/base.py

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+"""Restorer interface — "un-censor" detected regions of an image.
+
+A Restorer takes an image plus the detected censored regions and returns a new
+image with those regions reconstructed/filled. This mirrors the ``Detector``
+abstraction so different engines (classic inpaint now; a generative model like
+DeepMosaics / LADA later) plug in behind the same interface.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+from ..detection.types import Detection
+
+
+class Restorer(ABC):
+    @property
+    def name(self) -> str:
+        return type(self).__name__
+
+    @abstractmethod
+    def restore(self, image: np.ndarray, detections: list[Detection]) -> np.ndarray:
+        """Return a copy of ``image`` with the detected regions reconstructed."""
+        raise NotImplementedError

hvideotool/core/restore/factory.py

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+"""Restorer factory: build a Restorer by name.
+
+Currently only the cv2 inpaint baseline is wired. Generative engines
+(DeepMosaics / LADA BasicVSR++) are placeholders — they need model weights and a
+CUDA GPU, and raise a clear, actionable error until integrated. See README.
+"""
+
+from __future__ import annotations
+
+from .base import Restorer
+from .inpaint import InpaintRestorer
+
+
+def build_restorer(name: str = "inpaint", model_path: str | None = None) -> Restorer:
+    if name == "inpaint":
+        return InpaintRestorer()
+    if name in ("deepmosaics", "lada"):
+        raise ValueError(
+            "Генеративное восстановление пока не подключено.\n"
+            "Нужна модель (DeepMosaics / LADA) и GPU (CUDA). См. README → Восстановление."
+        )
+    raise ValueError(f"Неизвестный режим восстановления: {name!r}")

hvideotool/core/restore/inpaint.py

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+"""Classic inpainting restorer (cv2) — the always-available baseline.
+
+HONEST LIMITATION: cv2 inpainting fills the masked region by propagating
+surrounding pixels. It removes the mosaic/bar but does NOT reconstruct the hidden
+detail — it smooths/guesses. For real reconstruction a generative model
+(DeepMosaics / LADA) is needed; this is the no-weights, no-GPU fallback so the
+"Расцензурить кадр" flow works end-to-end today.
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from ..detection.types import Detection
+from .base import Restorer
+from .mask import detections_to_mask
+
+
+class InpaintRestorer(Restorer):
+    def __init__(self, radius: int = 3, dilate: int = 2, method: str = "telea") -> None:
+        self.radius = radius
+        self.dilate = dilate
+        self.method = method
+
+    @property
+    def name(self) -> str:
+        return f"InpaintRestorer({self.method})"
+
+    def restore(self, image: np.ndarray, detections: list[Detection]) -> np.ndarray:
+        if not detections:
+            return image.copy()
+        mask = detections_to_mask(image.shape, detections, dilate=self.dilate)
+        flags = cv2.INPAINT_TELEA if self.method == "telea" else cv2.INPAINT_NS
+        return cv2.inpaint(image, mask, self.radius, flags)

hvideotool/core/restore/mask.py

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+"""Build a binary mask of the censored regions from detections."""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from ..detection.types import Detection
+
+
+def detections_to_mask(
+    shape: tuple[int, int], detections: list[Detection], dilate: int = 0
+) -> np.ndarray:
+    """White (255) over every detected region (polygon if present, else bbox)."""
+    h, w = shape[:2]
+    mask = np.zeros((h, w), np.uint8)
+    for d in detections:
+        if len(d.polygon) >= 3:
+            cv2.fillPoly(mask, [np.array(d.polygon, np.int32)], 255)
+        else:
+            x, y, bw, bh = d.bbox
+            cv2.rectangle(mask, (x, y), (x + bw, y + bh), 255, -1)
+    if dilate > 0:
+        k = np.ones((dilate * 2 + 1, dilate * 2 + 1), np.uint8)
+        mask = cv2.dilate(mask, k)
+    return mask