Enhance HVideoTool's detection and restoration features: added support for tracking the model used in detections, improved temporal coherence by allowing the use of already-restored frames in the restoration process, and updated the UI to reflect these changes with new indicators and configuration options. Documentation in CLAUDE.md has been updated accordingly.

hvideotool/core/restore/deepmosaics.py

@@ -209,7 +209,12 @@ class DeepMosaicsVideoRestorer(Restorer):
         deepmosaics_dir: str | None,
         model_path: str | None,
         gpu_id: str = "0",
+        feed_restored: bool = True,
     ) -> None:
+        # When True, already-restored PAST frames are fed into the temporal window
+        # (instead of the original mosaic frames). Future neighbours and the centre
+        # frame stay original — they aren't restored yet / are what we're cleaning.
+        self._feed_restored = feed_restored
         chosen: Path | None = None
         if model_path and Path(model_path).is_file() and "video" in Path(model_path).name.lower():
             chosen = Path(model_path)
@@ -299,6 +304,18 @@ class DeepMosaicsVideoRestorer(Restorer):
         self._ensure_loaded()
         torch, data, impro, opt = self._torch, self._data, self._impro, self._opt
         N, T, S, SZ = self._N, self._T, self._S, self._INPUT_SIZE
+        reach = N * S  # how far back/forward the window samples (frames)
+
+        # Rolling cache of already-restored frames, used as window neighbours when
+        # ``feed_restored`` is on. Only the last ``reach`` frames are ever needed.
+        restored: dict[int, np.ndarray] = {}
+
+        def remember(idx: int, frame: np.ndarray) -> None:
+            if not self._feed_restored:
+                return
+            restored[idx] = frame
+            for old in [k for k in restored if k < idx - reach]:
+                restored.pop(old, None)
 
         previous = None  # recurrent state: the network's previous output (a tensor)
         for i in range(count):
@@ -307,13 +324,20 @@ class DeepMosaicsVideoRestorer(Restorer):
             img_origin = get_frame(i)
             x, y, size, mask = self._runmodel.get_mosaic_position(img_origin, self._netM, opt)
             if size <= 50:
-                emit(i, img_origin.copy())  # no mosaic here; recurrence carries over
+                clean = img_origin.copy()  # no mosaic here; recurrence carries over
+                emit(i, clean)
+                remember(i, clean)
                 continue
 
             stream = []
             for k in range(T):
                 j = min(max(i + (k - N) * S, 0), count - 1)  # clamp window to range edges
-                frame = img_origin if j == i else get_frame(j)
+                if j == i:
+                    frame = img_origin
+                elif self._feed_restored and j < i and j in restored:
+                    frame = restored[j]   # already-restored past neighbour
+                else:
+                    frame = get_frame(j)  # original (future neighbour / not yet cached)
                 crop = frame[y - size:y + size, x - size:x + size]
                 stream.append(impro.resize(crop, SZ)[:, :, ::-1])  # BGR→RGB, SZ×SZ
 
@@ -326,4 +350,6 @@ class DeepMosaicsVideoRestorer(Restorer):
                 pred = self._netG(tensor, previous)
             previous = pred
             img_fake = data.tensor2im(pred, rgb2bgr=True)
-            emit(i, impro.replace_mosaic(img_origin.copy(), img_fake, mask, x, y, size, opt.no_feather))
+            result = impro.replace_mosaic(img_origin.copy(), img_fake, mask, x, y, size, opt.no_feather)
+            emit(i, result)
+            remember(i, result)