feat(agent): temporal video dedup — drop near-duplicate frames before the GPU

Near-static videos are the dominant GPU load: sampled into up to 64 frames, each
re-runs the whole detect→CCIP→SigLIP chain on ~identical content. Add a CPU
perceptual-hash frame dedup upstream of the GPU so the redundant frames are never
processed at all (not just their embeds).

- media.dedupe_frames() + _dhash(): 8×8 difference-hash (64-bit) per frame; greedy
  keep — a frame survives only if its hash differs from every kept frame by
  >= min_distance bits (Hamming). A static run collapses to one frame; genuinely
  distinct scenes all survive. Order + frame_time preserved.
- Called in worker._download_decode right after sample_frames, so it runs in the
  decode stage on the downloader thread (CPU) — the GPU consumers only ever see
  deduped frames, and buffered video items shrink (less RAM too).
- Env-tunable FRAME_DEDUPE_DISTANCE (default 8; higher keeps more frames for brief
  localized changes an 8×8 hash can miss; 0 disables). Logs `video frames N→M`
  when it drops any, so video load reduction is visible.

Complements the spatial per-frame crop dedup (2026-07-01.2); this is the temporal
axis. Build marker 2026-07-01.3.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

agent/fc_agent/media.py

@@ -25,6 +25,47 @@ def is_video(mime: str) -> bool:
     return bool(mime) and (mime.startswith("video/") or mime in {"image/gif"})
 
 
+def _dhash(img: Image.Image, size: int = 8) -> int:
+    """Difference hash: compare adjacent pixels of a (size+1 × size) grayscale
+    thumbnail → a `size*size`-bit fingerprint. Cheap (64 comparisons on a 72-px
+    thumbnail) and robust to scaling/compression noise — near-identical frames
+    hash within a few bits, a real scene change moves many."""
+    small = img.convert("L").resize((size + 1, size))
+    px = list(small.getdata())
+    bits = 0
+    for row in range(size):
+        base = row * (size + 1)
+        for col in range(size):
+            bits = (bits << 1) | int(px[base + col] > px[base + col + 1])
+    return bits
+
+
+def dedupe_frames(
+    frames: list[tuple[float, Image.Image]], min_distance: int
+) -> list[tuple[float, Image.Image]]:
+    """Drop visually near-duplicate frames. A near-static video sampled into many
+    frames re-runs the WHOLE detect→CCIP→SigLIP chain on ~identical frames — the
+    dominant video load. Greedy perceptual-hash dedup: keep a frame only if its
+    dHash differs from every already-kept frame by >= min_distance bits (Hamming),
+    so a static run collapses to one frame while genuinely distinct scenes all
+    survive. Order + timestamps preserved. CPU-only (64-bit int XORs), so it runs
+    in the decode stage and spares the GPU the skipped frames entirely.
+
+    min_distance is the coarseness dial: higher keeps more frames (safer for brief
+    localized changes an 8×8 hash can miss), 0 disables. The first frame is always
+    kept (nothing to compare against)."""
+    if min_distance <= 0 or len(frames) <= 1:
+        return frames
+    kept: list[tuple[float, Image.Image]] = []
+    hashes: list[int] = []
+    for t, frame in frames:
+        h = _dhash(frame)
+        if all(bin(h ^ k).count("1") >= min_distance for k in hashes):
+            hashes.append(h)
+            kept.append((t, frame))
+    return kept
+
+
 def to_rgb(img: Image.Image) -> Image.Image:
     """RGB, flattening any transparency onto white first. A naive convert('RGB')
     on a palette-with-transparency image (common for character PNGs on a clear