Miscellaneous Fixes (#21102)

* ensure audio events display timeline entries in tracking details

* tweak tracking details layout for small desktop sizes

* update transcription docs

* Update classification docs for training recommendations

* Make number of classification images to be kept configurable

* Add bird to classification reference

* Fix incorrect averaging of the segments so it correctly only uses the most recent segments

* fix trigger logic

* add ability to download clean snapshot

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

frigate/api/event.py

@@ -1731,37 +1731,40 @@ def create_trigger_embedding(
             if event.data.get("type") != "object":
                 return
 
-            if thumbnail := get_event_thumbnail_bytes(event):
-                cursor = context.db.execute_sql(
-                    """
-                    SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
-                    """,
-                    [body.data],
+            # Get the thumbnail
+            thumbnail = get_event_thumbnail_bytes(event)
+
+            if thumbnail is None:
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": f"Failed to get thumbnail for {body.data} for {body.type} trigger",
+                    },
+                    status_code=400,
                 )
 
-                row = cursor.fetchone() if cursor else None
+            # Try to reuse existing embedding from database
+            cursor = context.db.execute_sql(
+                """
+                SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
+                """,
+                [body.data],
+            )
 
-                if row:
-                    query_embedding = row[0]
-                    embedding = np.frombuffer(query_embedding, dtype=np.float32)
+            row = cursor.fetchone() if cursor else None
+
+            if row:
+                query_embedding = row[0]
+                embedding = np.frombuffer(query_embedding, dtype=np.float32)
             else:
-                # Extract valid thumbnail
-                thumbnail = get_event_thumbnail_bytes(event)
-
-                if thumbnail is None:
-                    return JSONResponse(
-                        content={
-                            "success": False,
-                            "message": f"Failed to get thumbnail for {body.data} for {body.type} trigger",
-                        },
-                        status_code=400,
-                    )
-
+                # Generate new embedding
                 embedding = context.generate_image_embedding(
                     body.data, (base64.b64encode(thumbnail).decode("ASCII"))
                 )
 
-        if not embedding:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,
@@ -1896,7 +1899,9 @@ def update_trigger_embedding(
                 body.data, (base64.b64encode(thumbnail).decode("ASCII"))
             )
 
-        if not embedding:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,

frigate/config/classification.py

@@ -105,6 +105,11 @@ class CustomClassificationConfig(FrigateBaseModel):
     threshold: float = Field(
         default=0.8, title="Classification score threshold to change the state."
     )
+    save_attempts: int | None = Field(
+        default=None,
+        title="Number of classification attempts to save in the recent classifications tab. If not specified, defaults to 200 for object classification and 100 for state classification.",
+        ge=0,
+    )
     object_config: CustomClassificationObjectConfig | None = Field(default=None)
     state_config: CustomClassificationStateConfig | None = Field(default=None)
 

frigate/data_processing/real_time/custom_classification.py

@@ -250,6 +250,11 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if self.interpreter is None:
             # When interpreter is None, always save (score is 0.0, which is < 1.0)
             if self._should_save_image(camera, "unknown", 0.0):
+                save_attempts = (
+                    self.model_config.save_attempts
+                    if self.model_config.save_attempts is not None
+                    else 100
+                )
                 write_classification_attempt(
                     self.train_dir,
                     cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
@@ -257,6 +262,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                     now,
                     "unknown",
                     0.0,
+                    max_files=save_attempts,
                 )
             return
 
@@ -277,6 +283,11 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         detected_state = self.labelmap[best_id]
 
         if self._should_save_image(camera, detected_state, score):
+            save_attempts = (
+                self.model_config.save_attempts
+                if self.model_config.save_attempts is not None
+                else 100
+            )
             write_classification_attempt(
                 self.train_dir,
                 cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
@@ -284,6 +295,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 now,
                 detected_state,
                 score,
+                max_files=save_attempts,
             )
 
         if score < self.model_config.threshold:
@@ -482,6 +494,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
+            save_attempts = (
+                self.model_config.save_attempts
+                if self.model_config.save_attempts is not None
+                else 200
+            )
             write_classification_attempt(
                 self.train_dir,
                 cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
@@ -489,6 +506,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 now,
                 "unknown",
                 0.0,
+                max_files=save_attempts,
             )
             return
 
@@ -506,6 +524,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
+        save_attempts = (
+            self.model_config.save_attempts
+            if self.model_config.save_attempts is not None
+            else 200
+        )
         write_classification_attempt(
             self.train_dir,
             cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
@@ -513,7 +536,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             now,
             self.labelmap[best_id],
             score,
-            max_files=200,
+            max_files=save_attempts,
         )
 
         if score < self.model_config.threshold:

frigate/storage.py

@@ -5,7 +5,7 @@ import shutil
 import threading
 from pathlib import Path
 
-from peewee import fn
+from peewee import SQL, fn
 
 from frigate.config import FrigateConfig
 from frigate.const import RECORD_DIR
@@ -44,13 +44,19 @@ class StorageMaintainer(threading.Thread):
                     )
                 }
 
-                # calculate MB/hr
+                # calculate MB/hr from last 100 segments
                 try:
-                    bandwidth = round(
-                        Recordings.select(fn.AVG(bandwidth_equation))
+                    # Subquery to get last 100 segments, then average their bandwidth
+                    last_100 = (
+                        Recordings.select(bandwidth_equation.alias("bw"))
                         .where(Recordings.camera == camera, Recordings.segment_size > 0)
+                        .order_by(Recordings.start_time.desc())
                         .limit(100)
-                        .scalar()
+                        .alias("recent")
+                    )
+
+                    bandwidth = round(
+                        Recordings.select(fn.AVG(SQL("bw"))).from_(last_100).scalar()
                         * 3600,
                         2,
                     )

frigate/util/classification.py

@@ -330,7 +330,7 @@ def collect_state_classification_examples(
     1. Queries review items from specified cameras
     2. Selects 100 balanced timestamps across the data
     3. Extracts keyframes from recordings (cropped to specified regions)
-    4. Selects 20 most visually distinct images
+    4. Selects 24 most visually distinct images
     5. Saves them to the dataset directory
 
     Args:
@@ -660,7 +660,6 @@ def collect_object_classification_examples(
     Args:
         model_name: Name of the classification model
         label: Object label to collect (e.g., "person", "car")
-        cameras: List of camera names to collect examples from
     """
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
     temp_dir = os.path.join(dataset_dir, "temp")