Merge b5a360be39 into 1a6d04fde7

* Use escape key to go back to main camera dashboard

* Add icon showing when review item is needing review

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.file import get_event_thumbnail_bytes, load_event_snapshot_image
-from frigate.util.image import get_image_from_recording
+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
 
@@ -832,29 +836,80 @@ 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
+
+            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 image for event {event.id}: {e}")
+            continue
+
+    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
 
-            if thumbnail_bytes:
     nparr = np.frombuffer(thumbnail_bytes, np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    if img is None or img.size == 0:
+        return None
 
-                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"]
@@ -862,7 +917,6 @@ def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]
         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:
@@ -878,20 +932,10 @@ def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]
 
     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
+    cropped = img[y1 : y1 + crop_height, x1 : x1 + crop_width]
-                    y2 = y1 + crop_height
+    if cropped.size == 0:
+        return None
 
-                    cropped = img[y1:y2, x1:x2]
+    return cropped
-                    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)
-
-        except Exception as e:
-            logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
-            continue
-
-    return thumbnail_paths

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
 

web/src/components/card/AnimatedEventCard.tsx

@@ -17,6 +17,9 @@ import { useUserPersistence } from "@/hooks/use-user-persistence";
 import { Skeleton } from "../ui/skeleton";
 import { Button } from "../ui/button";
 import { FaCircleCheck } from "react-icons/fa6";
+import { FaExclamationTriangle } from "react-icons/fa";
+import { MdOutlinePersonSearch } from "react-icons/md";
+import { ThreatLevel } from "@/types/review";
 import { cn } from "@/lib/utils";
 import { useTranslation } from "react-i18next";
 import { getTranslatedLabel } from "@/utils/i18n";
@@ -127,6 +130,11 @@ export function AnimatedEventCard({
     true,
   );
 
+  const threatLevel = useMemo<ThreatLevel>(
+    () => (event.data.metadata?.potential_threat_level ?? 0) as ThreatLevel,
+    [event],
+  );
+
   const aspectRatio = useMemo(() => {
     if (
       !config ||
@@ -152,7 +160,15 @@ export function AnimatedEventCard({
           <Tooltip>
             <TooltipTrigger asChild>
               <Button
-                className="pointer-events-none absolute left-2 top-1 z-40 bg-gray-500 bg-gradient-to-br from-gray-400 to-gray-500 opacity-0 transition-opacity group-hover:pointer-events-auto group-hover:opacity-100"
+                className={cn(
+                  "absolute left-2 top-1 z-40 transition-opacity",
+                  threatLevel === ThreatLevel.SECURITY_CONCERN &&
+                    "pointer-events-auto bg-severity_alert opacity-100 hover:bg-severity_alert",
+                  threatLevel === ThreatLevel.NEEDS_REVIEW &&
+                    "pointer-events-auto bg-severity_detection opacity-100 hover:bg-severity_detection",
+                  threatLevel === ThreatLevel.NORMAL &&
+                    "pointer-events-none bg-gray-500 bg-gradient-to-br from-gray-400 to-gray-500 opacity-0 group-hover:pointer-events-auto group-hover:opacity-100",
+                )}
                 size="xs"
                 aria-label={t("markAsReviewed")}
                 onClick={async () => {
@@ -160,7 +176,13 @@ export function AnimatedEventCard({
                   updateEvents();
                 }}
               >
+                {threatLevel === ThreatLevel.SECURITY_CONCERN ? (
+                  <FaExclamationTriangle className="size-3 text-white" />
+                ) : threatLevel === ThreatLevel.NEEDS_REVIEW ? (
+                  <MdOutlinePersonSearch className="size-3 text-white" />
+                ) : (
                   <FaCircleCheck className="size-3 text-white" />
+                )}
               </Button>
             </TooltipTrigger>
             <TooltipContent>{t("markAsReviewed")}</TooltipContent>

web/src/views/live/LiveCameraView.tsx

@@ -389,7 +389,7 @@ export default function LiveCameraView({
     return "mse";
   }, [lowBandwidth, mic, webRTC, isRestreamed]);
 
-  useKeyboardListener(["m"], (key, modifiers) => {
+  useKeyboardListener(["m", "Escape"], (key, modifiers) => {
     if (!modifiers.down) {
       return true;
     }
@@ -407,6 +407,12 @@ export default function LiveCameraView({
           return true;
         }
         break;
+      case "Escape":
+        if (!fullscreen) {
+          navigate(-1);
+          return true;
+        }
+        break;
     }
 
     return false;