Merge 2b6fcf38c0 into c244e6582a

docs/docs/integrations/third_party_extensions.md

@@ -39,10 +39,6 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.
 
-## [kiosk-monitor](https://github.com/extremeshok/kiosk-monitor)
-
-[kiosk-monitor](https://github.com/extremeshok/kiosk-monitor) is a Raspberry Pi watchdog that runs Chromium fullscreen on a Frigate dashboard (optionally with VLC on a second monitor for an RTSP camera stream), auto-restarts on frozen screens or unreachable URLs, and ships a Birdseye-aware Chromium helper that auto-sizes the grid to the display.
-
 ## [Periscope](https://github.com/maksz42/periscope)
 
 [Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.

frigate/util/classification.py

@@ -24,12 +24,8 @@ 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, load_event_snapshot_image
+from frigate.util.file import get_event_thumbnail_bytes
-from frigate.util.image import (
+from frigate.util.image import get_image_from_recording
-    calculate_region,
-    get_image_from_recording,
-    relative_box_to_absolute,
-)
 from frigate.util.process import FrigateProcess
 
 BATCH_SIZE = 16
@@ -717,7 +713,7 @@ 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. Crops each event's clean snapshot around the object bounding box
+    3. Retrieves thumbnails for selected events (with 33% center crop applied)
     4. Selects 24 most visually distinct thumbnails
     5. Saves to dataset directory
 
@@ -836,80 +832,29 @@ def _select_balanced_events(
 
 def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
     """
-    Extract a training image for each event.
+    Extract thumbnails from events and save to disk.
-
-    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 crops
+        output_dir: Directory to save thumbnails
 
     Returns:
-        List of paths to successfully extracted images
+        List of paths to successfully extracted thumbnail images
     """
-    image_paths = []
+    thumbnail_paths = []
 
     for idx, event in enumerate(events):
         try:
-            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
 
+                    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"]
@@ -917,6 +862,7 @@ def _load_event_classification_crop(event: Event) -> np.ndarray | None:
                         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:
@@ -932,10 +878,20 @@ def _load_event_classification_crop(event: Event) -> np.ndarray | None:
 
                     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]
+                    x2 = x1 + crop_width
-    if cropped.size == 0:
+                    y2 = y1 + crop_height
-        return None
 
-    return cropped
+                    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)
+
+        except Exception as e:
+            logger.debug(f"Failed to extract thumbnail for event {event.id}: {e}")
+            continue
+
+    return thumbnail_paths

frigate/util/services.py

@@ -711,11 +711,8 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
     else:
         format_entries = None
 
-    def run(rtsp_transport: Optional[str] = None) -> sp.CompletedProcess:
+    ffprobe_cmd = [
-        cmd = [ffmpeg.ffprobe_path]
+        ffmpeg.ffprobe_path,
-        if rtsp_transport:
-            cmd += ["-rtsp_transport", rtsp_transport]
-        cmd += [
         "-timeout",
         "1000000",
         "-print_format",
@@ -723,32 +720,14 @@ def ffprobe_stream(ffmpeg, path: str, detailed: bool = False) -> sp.CompletedPro
         "-show_entries",
         f"stream={stream_entries}",
     ]
+
+    # Add format entries for detailed mode
     if detailed and format_entries:
-            cmd.extend(["-show_entries", f"format={format_entries}"])
+        ffprobe_cmd.extend(["-show_entries", f"format={format_entries}"])
-        cmd.extend(["-loglevel", "error", clean_path])
-        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()
+    ffprobe_cmd.extend(["-loglevel", "error", clean_path])
 
-    # For RTSP: retry with explicit TCP transport if the first attempt failed
+    return sp.run(ffprobe_cmd, capture_output=True)
-    # (default UDP may be blocked)
-    if result.returncode != 0 and clean_path.startswith("rtsp://"):
-        result = run(rtsp_transport="tcp")
-
-    return result
 
 
 def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
@@ -845,23 +824,11 @@ 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
             )
-            try:
+            stdout, _ = await proc.communicate()
-                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/package-lock.json

@@ -9642,9 +9642,9 @@
       "license": "MIT"
     },
     "node_modules/lodash-es": {
-      "version": "4.18.1",
+      "version": "4.17.23",
-      "resolved": "https://registry.npmjs.org/lodash-es/-/lodash-es-4.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.23.tgz",
-      "integrity": "sha512-J8xewKD/Gk22OZbhpOVSwcs60zhd95ESDwezOFuA3/099925PdHJ7OFHNTGtajL3AlZkykD32HykiMo+BIBI8A==",
+      "integrity": "sha512-LgVTMpQtIopCi79SJeDiP0TfWi5CNEc/L/aRdTh3yIvmZXTnheWpKjSZhnvMl8iXbC1tFg9gdHHDMLoV7CnG+w==",
       "license": "MIT"
     },
     "node_modules/lodash.merge": {

web/public/locales/en/views/settings.json

@@ -415,7 +415,7 @@
         "audioCodecGood": "Audio codec is {{codec}}.",
         "resolutionHigh": "A resolution of {{resolution}} may cause increased resource usage.",
         "resolutionLow": "A resolution of {{resolution}} may be too low for reliable detection of small objects.",
-        "resolutionUnknown": "The resolution of this stream could not be probed. You should manually set the detect resolution in Settings or your config.",
+        "resolutionUnknown": "The resolution of this stream could not be probed. This will cause issues on startup. You should manually set the detect resolution in Settings or your config.",
         "noAudioWarning": "No audio detected for this stream, recordings will not have audio.",
         "audioCodecRecordError": "The AAC audio codec is required to support audio in recordings.",
         "audioCodecRequired": "An audio stream is required to support audio detection.",

web/src/components/card/AnimatedEventCard.tsx

@@ -17,9 +17,6 @@ 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";
@@ -130,11 +127,6 @@ export function AnimatedEventCard({
     true,
   );
 
-  const threatLevel = useMemo<ThreatLevel>(
-    () => (event.data.metadata?.potential_threat_level ?? 0) as ThreatLevel,
-    [event],
-  );
-
   const aspectRatio = useMemo(() => {
     if (
       !config ||
@@ -160,15 +152,7 @@ export function AnimatedEventCard({
           <Tooltip>
             <TooltipTrigger asChild>
               <Button
-                className={cn(
+                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"
-                  "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 () => {
@@ -176,13 +160,7 @@ 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", "Escape"], (key, modifiers) => {
+  useKeyboardListener(["m"], (key, modifiers) => {
     if (!modifiers.down) {
       return true;
     }
@@ -407,12 +407,6 @@ export default function LiveCameraView({
           return true;
         }
         break;
-      case "Escape":
-        if (!fullscreen) {
-          navigate(-1);
-          return true;
-        }
-        break;
     }
 
     return false;