fix classification wizard finding no sample images on large databases

The wizard grouped tracked objects by camera and 6 hour block, oldest first, then kept the first 100. With more than 100 groups that was always the oldest objects, which often have no snapshot or thumbnail left on disk, so no examples were generated. Shuffle the selection before truncating, and keep extracting from the remaining tracked objects until 100 usable images are found.

frigate/util/classification.py

@@ -747,8 +747,14 @@ def collect_object_classification_examples(
     selected_events = _select_balanced_events(events, target_count=100)
     logger.debug(f"Selected {len(selected_events)} events")
 
-    # Step 3: Extract thumbnails from events
-    thumbnails = _extract_event_thumbnails(selected_events, temp_dir)
+    # Step 3: Extract thumbnails from events, falling back to the remaining
+    # events when the selected ones have no image on disk
+    selected_ids = {e.id for e in selected_events}
+    remaining_events = [e for e in events if e.id not in selected_ids]
+    random.shuffle(remaining_events)
+    thumbnails = _extract_event_thumbnails(
+        selected_events + remaining_events, temp_dir, target_count=100
+    )
     logger.debug(f"Successfully extracted {len(thumbnails)} thumbnails")
 
     # Step 4: Select 24 most visually distinct thumbnails
@@ -833,10 +839,16 @@ def _select_balanced_events(
         else:
             selected.extend(remaining)
 
+    # groups are ordered oldest first, so truncating unshuffled keeps only the
+    # oldest events, which are the least likely to still have images on disk
+    random.shuffle(selected)
+
     return selected[:target_count]
 
 
-def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]:
+def _extract_event_thumbnails(
+    events: list[Event], output_dir: str, target_count: int = 100
+) -> list[str]:
     """
     Extract a training image for each event.
 
@@ -850,8 +862,9 @@ def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]
     using a step ladder sized from the box/region area ratio.
 
     Args:
-        events: List of Event objects
+        events: List of Event objects, in order of preference
         output_dir: Directory to save crops
+        target_count: Number of images to extract before stopping
 
     Returns:
         List of paths to successfully extracted images
@@ -859,6 +872,9 @@ def _extract_event_thumbnails(events: list[Event], output_dir: str) -> list[str]
     image_paths = []
 
     for idx, event in enumerate(events):
+        if len(image_paths) >= target_count:
+            break
+
         try:
             img = _load_event_classification_crop(event)
             if img is None: