Merge 317d1acfe1 into 8eace9c3e7

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

@@ -726,20 +726,7 @@ 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])
-        try:
+        return sp.run(cmd, capture_output=True)
-            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()
 
@@ -845,23 +832,11 @@ 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