Merge 317d1acfe1 into 1a6d04fde7

pandas 3.0 changed DatetimeIndex internal storage from datetime64[ns]
(nanoseconds) to datetime64[us] (microseconds). The motion activity
endpoint in review.py converted DatetimeIndex to epoch seconds using:

    df.index = df.index.astype(int) // (10**9)

This assumed nanosecond resolution, dividing by 10^9 to get seconds.
With microsecond resolution the division produces values ~1000x too
small (e.g. 1774785 instead of 1774785600), causing every entry to
have a start_time near zero. The frontend timeline could not match
these timestamps to the visible range, so motion indicator bars
disappeared entirely — despite the underlying recording data being
correct.

Replace the resolution-dependent integer division with pandas
Timedelta arithmetic:

    df.index = (df.index - _EPOCH) // _ONE_SECOND

This is resolution-independent (produces correct results on
datetime64[s], [ms], [us], and [ns]), ~148x faster than the
per-element .timestamp() alternative, produces native Python int
types that serialize cleanly to JSON, and is backwards-compatible
with older pandas versions.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

frigate/api/review.py

@@ -40,6 +40,11 @@ from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
 
+# Pre-computed constants for resolution-independent datetime-to-epoch conversion
+# (pandas 3.0+ stores datetime64 as microseconds, not nanoseconds)
+_EPOCH = pd.Timestamp("1970-01-01")
+_ONE_SECOND = pd.Timedelta("1s")
+
 router = APIRouter(tags=[Tags.review])
 
 
@@ -659,7 +664,7 @@ def motion_activity(
             df.iloc[i : i + chunk, 0] = 0.0
 
     # change types for output
-    df.index = df.index.astype(int) // (10**9)
+    df.index = (df.index - _EPOCH) // _ONE_SECOND
     normalized = df.reset_index().to_dict("records")
     return JSONResponse(content=normalized)
 

frigate/util/classification.py

@@ -24,8 +24,12 @@ from frigate.log import redirect_output_to_logger, suppress_stderr_during
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
-from frigate.util.file import get_event_thumbnail_bytes
-from frigate.util.image import get_image_from_recording
+from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
+from frigate.util.image import (
+    calculate_region,
+    get_image_from_recording,
+    relative_box_to_absolute,
+)
 from frigate.util.process import FrigateProcess
 
 BATCH_SIZE = 16
@@ -713,7 +717,7 @@ def collect_object_classification_examples(
     This function:
     1. Queries events for the specified label
     2. Selects 100 balanced events across different cameras and times
-    3. Retrieves thumbnails for selected events (with 33% center crop applied)
+    3. Crops each event's clean snapshot around the object bounding box
     4. Selects 24 most visually distinct thumbnails
     5. Saves to dataset directory
 
@@ -832,66 +836,106 @@ def _select_balanced_events(
 
 def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
     """
-    Extract thumbnails from events and save to disk.
+    Extract a training image for each event.
+
+    Preferred path: load the full-frame clean snapshot and crop around the
+    stored bounding box with the same calculate_region(..., max(w, h), 1.0)
+    call the live ObjectClassificationProcessor uses, so wizard examples
+    are framed like inference-time inputs.
+
+    Fallback: if no clean snapshot exists (snapshots disabled, or only a
+    legacy annotated JPG is on disk), center-crop the stored thumbnail
+    using a step ladder sized from the box/region area ratio.
 
     Args:
         events: List of Event objects
-        output_dir: Directory to save thumbnails
+        output_dir: Directory to save crops
 
     Returns:
-        List of paths to successfully extracted thumbnail images
+        List of paths to successfully extracted images
     """
-    thumbnail_paths = []
+    image_paths = []
 
     for idx, event in enumerate(events):
         try:
-            thumbnail_bytes = get_event_thumbnail_bytes(event)
+            img = _load_event_classification_crop(event)
+            if img is None:
+                continue
 
-            if thumbnail_bytes:
-                nparr = np.frombuffer(thumbnail_bytes, np.uint8)
-                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-                if img is not None:
-                    height, width = img.shape[:2]
-
-                    crop_size = 1.0
-                    if event.data and "box" in event.data and "region" in event.data:
-                        box = event.data["box"]
-                        region = event.data["region"]
-
-                        if len(box) == 4 and len(region) == 4:
-                            box_w, box_h = box[2], box[3]
-                            region_w, region_h = region[2], region[3]
-
-                            box_area = (box_w * box_h) / (region_w * region_h)
-
-                            if box_area < 0.05:
-                                crop_size = 0.4
-                            elif box_area < 0.10:
-                                crop_size = 0.5
-                            elif box_area < 0.20:
-                                crop_size = 0.65
-                            elif box_area < 0.35:
-                                crop_size = 0.80
-                            else:
-                                crop_size = 0.95
-
-                    crop_width = int(width * crop_size)
-                    crop_height = int(height * crop_size)
-
-                    x1 = (width - crop_width) // 2
-                    y1 = (height - crop_height) // 2
-                    x2 = x1 + crop_width
-                    y2 = y1 + crop_height
-
-                    cropped = img[y1:y2, x1:x2]
-                    resized = cv2.resize(cropped, (224, 224))
-                    output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
-                    cv2.imwrite(output_path, resized)
-                    thumbnail_paths.append(output_path)
+            resized = cv2.resize(img, (224, 224))
+            output_path = os.path.join(output_dir, f"thumbnail_{idx:04d}.jpg")
+            cv2.imwrite(output_path, resized)
+            image_paths.append(output_path)
 
         except Exception as e:
-            logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
+            logger.debug(f"Failed to extract image for event {event.id}: {e}")
             continue
 
-    return thumbnail_paths
+    return image_paths
+
+
+def _load_event_classification_crop(event: Event) -> np.ndarray | None:
+    """Prefer a snapshot-based object crop; fall back to a center-cropped thumbnail."""
+    if event.data and "box" in event.data:
+        snapshot, _ = load_event_snapshot_image(event, clean_only=True)
+        if snapshot is not None:
+            abs_box = relative_box_to_absolute(snapshot.shape, event.data["box"])
+            if abs_box is not None:
+                xmin, ymin, xmax, ymax = abs_box
+                box_w = xmax - xmin
+                box_h = ymax - ymin
+                if box_w > 0 and box_h > 0:
+                    x1, y1, x2, y2 = calculate_region(
+                        snapshot.shape,
+                        xmin,
+                        ymin,
+                        xmax,
+                        ymax,
+                        max(box_w, box_h),
+                        1.0,
+                    )
+                    cropped = snapshot[y1:y2, x1:x2]
+                    if cropped.size > 0:
+                        return cropped
+
+    thumbnail_bytes = get_event_thumbnail_bytes(event)
+    if not thumbnail_bytes:
+        return None
+
+    nparr = np.frombuffer(thumbnail_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    if img is None or img.size == 0:
+        return None
+
+    height, width = img.shape[:2]
+    crop_size = 1.0
+
+    if event.data and "box" in event.data and "region" in event.data:
+        box = event.data["box"]
+        region = event.data["region"]
+
+        if len(box) == 4 and len(region) == 4:
+            box_w, box_h = box[2], box[3]
+            region_w, region_h = region[2], region[3]
+            box_area = (box_w * box_h) / (region_w * region_h)
+
+            if box_area < 0.05:
+                crop_size = 0.4
+            elif box_area < 0.10:
+                crop_size = 0.5
+            elif box_area < 0.20:
+                crop_size = 0.65
+            elif box_area < 0.35:
+                crop_size = 0.80
+            else:
+                crop_size = 0.95
+
+    crop_width = int(width * crop_size)
+    crop_height = int(height * crop_size)
+    x1 = (width - crop_width) // 2
+    y1 = (height - crop_height) // 2
+    cropped = img[y1 : y1 + crop_height, x1 : x1 + crop_width]
+    if cropped.size == 0:
+        return None
+
+    return cropped

frigate/util/services.py

@@ -726,7 +726,20 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
         if detailed and format_entries:
             cmd.extend(["-show_entries", f"format={format_entries}"])
         cmd.extend(["-loglevel", "error", clean_path])
-        return sp.run(cmd, capture_output=True)
+        try:
+            return sp.run(cmd, capture_output=True, timeout=6)
+        except sp.TimeoutExpired as e:
+            logger.info(
+                "ffprobe timed out while probing %s (transport=%s)",
+                clean_camera_user_pass(path),
+                rtsp_transport or "default",
+            )
+            return sp.CompletedProcess(
+                args=cmd,
+                returncode=1,
+                stdout=e.stdout or b"",
+                stderr=(e.stderr or b"") + b"\nffprobe timed out",
+            )
 
     result = run()
 
@@ -832,11 +845,23 @@ async def get_video_properties(
             "-show_streams",
             url,
         ]
+        proc = None
         try:
             proc = await asyncio.create_subprocess_exec(
                 *cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
             )
-            stdout, _ = await proc.communicate()
+            try:
+                stdout, _ = await asyncio.wait_for(proc.communicate(), timeout=6)
+            except asyncio.TimeoutError:
+                logger.info(
+                    "ffprobe timed out while probing %s (transport=%s)",
+                    clean_camera_user_pass(url),
+                    rtsp_transport or "default",
+                )
+                proc.kill()
+                await proc.wait()
+                return False, 0, 0, None, -1
+
             if proc.returncode != 0:
                 return False, 0, 0, None, -1