Add recording keyframe analysis to camera probe dialog (#23453)

* backend: endpoint and util funcs

* tests

* frontend and i18n

* update openapi spec

* add tip to docs

frigate/util/services.py

@@ -879,6 +879,131 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
     return result
 
 
+KEYFRAME_PROBE_WINDOW_SECONDS = 20
+KEYFRAME_GAP_WARNING_SECONDS = 4.0
+
+
+def parse_keyframe_packets(output: str) -> Tuple[List[float], Optional[float]]:
+    """Parse ffprobe CSV `pts_time,flags` output.
+
+    Returns the presentation timestamps of keyframes (flags containing "K")
+    and the maximum timestamp observed across all packets.
+    """
+    keyframe_pts: List[float] = []
+    max_pts: Optional[float] = None
+
+    for line in output.splitlines():
+        parts = line.split(",")
+        if len(parts) < 2:
+            continue
+        try:
+            pts = float(parts[0])
+        except ValueError:
+            continue
+        if max_pts is None or pts > max_pts:
+            max_pts = pts
+        if "K" in parts[1]:
+            keyframe_pts.append(pts)
+
+    return keyframe_pts, max_pts
+
+
+def classify_keyframe_gaps(
+    keyframe_pts: List[float], segment_time: int
+) -> dict[str, Any]:
+    """Classify keyframe spacing for recording suitability.
+
+    A camera using a smart/+ codec or a long/variable GOP produces large or
+    irregular gaps between keyframes, which breaks time-based recording
+    segmentation. Severity:
+      - "unknown" when fewer than two keyframes were observed
+      - "error" when the longest gap exceeds the record segment length
+      - "warning" when the longest gap exceeds the warning threshold
+      - "ok" otherwise
+    """
+    thresholds = {
+        "warning": KEYFRAME_GAP_WARNING_SECONDS,
+        "error": segment_time,
+    }
+
+    if len(keyframe_pts) < 2:
+        return {
+            "keyframe_count": len(keyframe_pts),
+            "max_gap": None,
+            "mean_gap": None,
+            "min_gap": None,
+            "segment_time": segment_time,
+            "severity": "unknown",
+            "thresholds": thresholds,
+        }
+
+    gaps = [b - a for a, b in zip(keyframe_pts, keyframe_pts[1:])]
+    max_gap = max(gaps)
+
+    if max_gap > segment_time:
+        severity = "error"
+    elif max_gap > KEYFRAME_GAP_WARNING_SECONDS:
+        severity = "warning"
+    else:
+        severity = "ok"
+
+    return {
+        "keyframe_count": len(keyframe_pts),
+        "max_gap": round(max_gap, 2),
+        "mean_gap": round(sum(gaps) / len(gaps), 2),
+        "min_gap": round(min(gaps), 2),
+        "segment_time": segment_time,
+        "severity": severity,
+        "thresholds": thresholds,
+    }
+
+
+async def analyze_record_keyframes(
+    ffmpeg, url: str, segment_time: int, window: int = KEYFRAME_PROBE_WINDOW_SECONDS
+) -> dict[str, Any]:
+    """Probe a stream for ~`window` seconds and classify its keyframe spacing.
+
+    Reads video packet flags via ffprobe to find keyframes, then measures the
+    gaps between them. On timeout or failure returns an "unknown" result rather
+    than a false all-clear.
+    """
+    clean_url = escape_special_characters(url)
+    cmd = [
+        ffmpeg.ffprobe_path,
+        "-v",
+        "error",
+        "-select_streams",
+        "v:0",
+        "-read_intervals",
+        f"%+{window}",
+        "-show_entries",
+        "packet=pts_time,flags",
+        "-of",
+        "csv=p=0",
+        clean_url,
+    ]
+
+    try:
+        proc = await asyncio.create_subprocess_exec(
+            *cmd,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.PIPE,
+        )
+        stdout, _ = await asyncio.wait_for(proc.communicate(), timeout=window + 15)
+    except asyncio.TimeoutError:
+        logger.warning("Keyframe probe timed out for record stream")
+        proc.kill()
+        return classify_keyframe_gaps([], segment_time)
+    except OSError as err:
+        logger.error("Keyframe probe failed: %s", err)
+        return classify_keyframe_gaps([], segment_time)
+
+    keyframe_pts, max_pts = parse_keyframe_packets(stdout.decode("utf-8", "replace"))
+    result = classify_keyframe_gaps(keyframe_pts, segment_time)
+    result["duration_observed"] = round(max_pts, 2) if max_pts is not None else None
+    return result
+
+
 def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
     """Run vainfo."""
     if not device_name: