Dedicated LPR improvements (#17453)

* remove license plate from attributes for dedicated lpr cameras

* ensure we always have a color

* use frigate+ models with dedicated lpr cameras

* docs

* docs clarity

* docs enrichments

* use license_plate as object type

frigate/camera/state.py

@@ -88,7 +88,9 @@ class CameraState:
                         thickness = 1
                     else:
                         thickness = 2
-                        color = self.config.model.colormap[obj["label"]]
+                        color = self.config.model.colormap.get(
+                            obj["label"], (255, 255, 255)
+                        )
                 else:
                     thickness = 1
                     color = (255, 0, 0)
@@ -110,7 +112,9 @@ class CameraState:
                     and obj["frame_time"] == frame_time
                 ):
                     thickness = 5
-                    color = self.config.model.colormap[obj["label"]]
+                    color = self.config.model.colormap.get(
+                        obj["label"], (255, 255, 255)
+                    )
 
                     # debug autotracking zooming - show the zoom factor box
                     if (

frigate/data_processing/common/license_plate/mixin.py

@@ -21,7 +21,6 @@ from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
     EventMetadataTypeEnum,
 )
-from frigate.config.camera.camera import CameraTypeEnum
 from frigate.const import CLIPS_DIR
 from frigate.embeddings.onnx.lpr_embedding import LPR_EMBEDDING_SIZE
 from frigate.util.builtin import EventsPerSecond
@@ -972,7 +971,7 @@ class LicensePlateProcessingMixin:
             (
                 now,
                 camera,
-                "car",
+                "license_plate",
                 event_id,
                 True,
                 plate_score,
@@ -994,9 +993,7 @@ class LicensePlateProcessingMixin:
         if not self.config.cameras[camera].lpr.enabled:
             return
 
-        if not dedicated_lpr and self.config.cameras[camera].type == CameraTypeEnum.lpr:
-            return
-
+        # dedicated LPR cam without frigate+
         if dedicated_lpr:
             id = "dedicated-lpr"
 
@@ -1050,8 +1047,11 @@ class LicensePlateProcessingMixin:
         else:
             id = obj_data["id"]
 
-            # don't run for non car objects
-            if obj_data.get("label") != "car":
+            # don't run for non car or non license plate (dedicated lpr with frigate+) objects
+            if (
+                obj_data.get("label") != "car"
+                and obj_data.get("label") != "license_plate"
+            ):
                 logger.debug(
                     f"{camera}: Not a processing license plate for non car object."
                 )
@@ -1131,26 +1131,34 @@ class LicensePlateProcessingMixin:
                     license_plate[0] : license_plate[2],
                 ]
             else:
-                # don't run for object without attributes
-                if not obj_data.get("current_attributes"):
+                # don't run for object without attributes if this isn't dedicated lpr with frigate+
+                if (
+                    not obj_data.get("current_attributes")
+                    and obj_data.get("label") != "license_plate"
+                ):
                     logger.debug(f"{camera}: No attributes to parse.")
                     return
 
-                attributes: list[dict[str, any]] = obj_data.get(
-                    "current_attributes", []
-                )
-                for attr in attributes:
-                    if attr.get("label") != "license_plate":
-                        continue
+                if obj_data.get("label") == "car":
+                    attributes: list[dict[str, any]] = obj_data.get(
+                        "current_attributes", []
+                    )
+                    for attr in attributes:
+                        if attr.get("label") != "license_plate":
+                            continue
 
-                    if license_plate is None or attr.get(
-                        "score", 0.0
-                    ) > license_plate.get("score", 0.0):
-                        license_plate = attr
+                        if license_plate is None or attr.get(
+                            "score", 0.0
+                        ) > license_plate.get("score", 0.0):
+                            license_plate = attr
 
-                # no license plates detected in this frame
-                if not license_plate:
-                    return
+                    # no license plates detected in this frame
+                    if not license_plate:
+                        return
+
+                # we are using dedicated lpr with frigate+
+                if obj_data.get("label") == "license_plate":
+                    license_plate = obj_data
 
                 license_plate_box = license_plate.get("box")
 
@@ -1160,7 +1168,9 @@ class LicensePlateProcessingMixin:
                     or area(license_plate_box)
                     < self.config.cameras[obj_data["camera"]].lpr.min_area
                 ):
-                    logger.debug(f"{camera}: Invalid license plate box {license_plate}")
+                    logger.debug(
+                        f"{camera}: Area for license plate box {area(license_plate_box)} is less than min_area {self.config.cameras[obj_data['camera']].lpr.min_area}"
+                    )
                     return
 
                 license_plate_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
@@ -1239,8 +1249,11 @@ class LicensePlateProcessingMixin:
             )
             return
 
-        # For LPR cameras, match or assign plate ID using Jaro-Winkler distance
-        if dedicated_lpr:
+        # For dedicated LPR cameras, match or assign plate ID using Jaro-Winkler distance
+        if (
+            dedicated_lpr
+            and "license_plate" not in self.config.cameras[camera].objects.track
+        ):
             plate_id = None
 
             for existing_id, data in self.detected_license_plates.items():
@@ -1306,8 +1319,11 @@ class LicensePlateProcessingMixin:
             (id, top_plate, avg_confidence),
         )
 
-        if dedicated_lpr:
-            # save the best snapshot
+        # save the best snapshot for dedicated lpr cams not using frigate+
+        if (
+            dedicated_lpr
+            and "license_plate" not in self.config.cameras[camera].objects.track
+        ):
             logger.debug(
                 f"{camera}: Writing snapshot for {id}, {top_plate}, {current_time}"
             )

frigate/embeddings/maintainer.py

@@ -457,7 +457,11 @@ class EmbeddingMaintainer(threading.Thread):
 
         camera_config = self.config.cameras[camera]
 
-        if not camera_config.type == CameraTypeEnum.lpr:
+        if (
+            camera_config.type != CameraTypeEnum.lpr
+            or "license_plate" in camera_config.objects.track
+        ):
+            # we're not a dedicated lpr camera or we are one but we're using frigate+
             return
 
         try:

frigate/track/tracked_object.py

@@ -442,7 +442,7 @@ class TrackedObject:
 
         if bounding_box:
             thickness = 2
-            color = self.colormap[self.obj_data["label"]]
+            color = self.colormap.get(self.obj_data["label"], (255, 255, 255))
 
             # draw the bounding boxes on the frame
             box = self.thumbnail_data["box"]

frigate/video.py

@@ -15,6 +15,7 @@ from frigate.camera import CameraMetrics, PTZMetrics
 from frigate.comms.config_updater import ConfigSubscriber
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import CameraConfig, DetectConfig, ModelConfig
+from frigate.config.camera.camera import CameraTypeEnum
 from frigate.const import (
     CACHE_DIR,
     CACHE_SEGMENT_FORMAT,
@@ -519,6 +520,7 @@ def track_camera(
         frame_queue,
         frame_shape,
         model_config,
+        config,
         config.detect,
         frame_manager,
         motion_detector,
@@ -585,6 +587,7 @@ def process_frames(
     frame_queue: mp.Queue,
     frame_shape,
     model_config: ModelConfig,
+    camera_config: CameraConfig,
     detect_config: DetectConfig,
     frame_manager: FrameManager,
     motion_detector: MotionDetector,
@@ -612,6 +615,29 @@ def process_frames(
 
     region_min_size = get_min_region_size(model_config)
 
+    attributes_map = model_config.attributes_map
+    all_attributes = model_config.all_attributes
+
+    # remove license_plate from attributes if this camera is a dedicated LPR cam
+    if camera_config.type == CameraTypeEnum.lpr:
+        modified_attributes_map = model_config.attributes_map.copy()
+
+        if (
+            "car" in modified_attributes_map
+            and "license_plate" in modified_attributes_map["car"]
+        ):
+            modified_attributes_map["car"] = [
+                attr
+                for attr in modified_attributes_map["car"]
+                if attr != "license_plate"
+            ]
+
+            attributes_map = modified_attributes_map
+
+        all_attributes = [
+            attr for attr in model_config.all_attributes if attr != "license_plate"
+        ]
+
     while not stop_event.is_set():
         _, updated_enabled_config = enabled_config_subscriber.check_for_update()
 
@@ -805,9 +831,7 @@ def process_frames(
             # if detection was run on this frame, consolidate
             if len(regions) > 0:
                 tracked_detections = [
-                    d
-                    for d in consolidated_detections
-                    if d[0] not in model_config.all_attributes
+                    d for d in consolidated_detections if d[0] not in all_attributes
                 ]
                 # now that we have refined our detections, we need to track objects
                 object_tracker.match_and_update(
@@ -819,7 +843,7 @@ def process_frames(
 
         # group the attribute detections based on what label they apply to
         attribute_detections: dict[str, list[TrackedObjectAttribute]] = {}
-        for label, attribute_labels in model_config.attributes_map.items():
+        for label, attribute_labels in attributes_map.items():
             attribute_detections[label] = [
                 TrackedObjectAttribute(d)
                 for d in consolidated_detections
@@ -836,8 +860,7 @@ def process_frames(
         for attributes in attribute_detections.values():
             for attribute in attributes:
                 filtered_objects = filter(
-                    lambda o: attribute.label
-                    in model_config.attributes_map.get(o["label"], []),
+                    lambda o: attribute.label in attributes_map.get(o["label"], []),
                     all_objects,
                 )
                 selected_object_id = attribute.find_best_object(filtered_objects)
@@ -885,7 +908,7 @@ def process_frames(
             for obj in object_tracker.tracked_objects.values():
                 if obj["frame_time"] == frame_time:
                     thickness = 2
-                    color = model_config.colormap[obj["label"]]
+                    color = model_config.colormap.get(obj["label"], (255, 255, 255))
                 else:
                     thickness = 1
                     color = (255, 0, 0)