Improve grammar

docs/docs/configuration/license_plate_recognition.md

@@ -381,6 +381,7 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
      ```yaml
      lpr:
        enabled: true
+       device: CPU
        debug_save_plates: true
      ```
 

frigate/config/camera/review.py

@@ -108,12 +108,13 @@ class GenAIReviewConfig(FrigateBaseModel):
         default="""### Normal Activity Indicators (Level 0)
 - Known/verified people in any zone at any time
 - People with pets in residential areas
+- Routine residential vehicle access during daytime/evening (6 AM - 10 PM): entering, exiting, loading/unloading items — normal commute and travel patterns
 - Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
 - Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
 - Activity confined to public areas only (sidewalks, streets) without entering property at any time
 
 ### Suspicious Activity Indicators (Level 1)
-- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
+- **Checking or probing vehicle/building access**: trying handles without entering, peering through windows, examining multiple vehicles, or possessing break-in tools — Level 1
 - **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
 - Taking items that don't belong to them (packages, objects from porches/driveways)
 - Climbing or jumping fences/barriers to access property
@@ -133,8 +134,8 @@ Evaluate in this order:
 1. **If person is verified/known** → Level 0 regardless of time or activity
 2. **If person is unidentified:**
    - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
-   - Check actions: If testing doors/handles, taking items, climbing → Level 1
+   - Check actions: If probing access (trying handles without entering, checking multiple vehicles), taking items, climbing → Level 1
-   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
+   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service, routine vehicle access) → Level 0
 3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
 
 The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.""",

frigate/data_processing/real_time/custom_classification.py

@@ -97,7 +97,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
-        self.labelmap = load_labels(labelmap_path, prefill=0)
+        self.labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
         self.classifications_per_second.start()
 
     def __update_metrics(self, duration: float) -> None:
@@ -398,7 +398,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
-        self.labelmap = load_labels(labelmap_path, prefill=0)
+        self.labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
 
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()

frigate/embeddings/maintainer.py

@@ -633,7 +633,7 @@ class EmbeddingMaintainer(threading.Thread):
 
         camera, frame_name, _, _, motion_boxes, _ = data
 
-        if not camera or len(motion_boxes) == 0 or camera not in self.config.cameras:
+        if not camera or camera not in self.config.cameras:
             return
 
         camera_config = self.config.cameras[camera]
@@ -660,8 +660,10 @@ class EmbeddingMaintainer(threading.Thread):
             return
 
         for processor in self.realtime_processors:
-            if dedicated_lpr_enabled and isinstance(
+            if (
-                processor, LicensePlateRealTimeProcessor
+                dedicated_lpr_enabled
+                and len(motion_boxes) > 0
+                and isinstance(processor, LicensePlateRealTimeProcessor)
             ):
                 processor.process_frame(camera, yuv_frame, True)
 

frigate/genai/__init__.py

@@ -100,7 +100,7 @@ When forming your description:
 
 Your response MUST be a flat JSON object with:
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, in chronological order. Start by describing how the sequence begins, then describe the progression of events. **Describe all significant movements and actions in the order they occur.** For example, if a vehicle arrives and then a person exits, describe both actions sequentially. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
-- `title` (string): A concise title in the format "[Subject] [action verb] [context]" that matches your scene description. Use names from "Objects in Scene" when you visually observe them.
+- `title` (string): A concise, grammatically complete title in the format "[Subject] [action verb] [context]" that matches your scene description. Use names from "Objects in Scene" when you visually observe them.
 - `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. Should capture the key activity and context without full detail. This should be a condensed version of the scene description above.
 - `confidence` (float): 0-1 confidence in your analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous. Lower confidence when the sequence is unclear, objects are partially obscured, or context is ambiguous.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined in "Normal Activity Patterns for This Property" above. Your threat level must be consistent with your scene description and the guidance above.

frigate/util/builtin.py

@@ -129,7 +129,7 @@ def get_ffmpeg_arg_list(arg: Any) -> list:
     return arg if isinstance(arg, list) else shlex.split(arg)
 
 
-def load_labels(path: Optional[str], encoding="utf-8", prefill=91):
+def load_labels(path: Optional[str], encoding="utf-8", prefill=91, indexed: bool | None = None):
     """Loads labels from file (with or without index numbers).
     Args:
       path: path to label file.
@@ -146,11 +146,12 @@ def load_labels(path: Optional[str], encoding="utf-8", prefill=91):
         if not lines:
             return {}
 
-        if lines[0].split(" ", maxsplit=1)[0].isdigit():
+        if indexed != False and lines[0].split(" ", maxsplit=1)[0].isdigit():
             pairs = [line.split(" ", maxsplit=1) for line in lines]
             labels.update({int(index): label.strip() for index, label in pairs})
         else:
             labels.update({index: line.strip() for index, line in enumerate(lines)})
+
         return labels