Fixes (#18117)

* face library i18n fixes

* face library i18n fixes

* add ability to use ctrl/cmd S to save in the config editor

* Use datetime as ID

* Update metrics inference speed to start with 0 ms

* fix android formatted thumbnail

* ensure role is comma separated and stripped correctly

* improve face library deletion

- add a confirmation dialog
- add ability to select all / delete faces in collections

* Implement lazy loading for video previews

* Force GPU for large embedding model

* GPU is required

* settings i18n fixes

* Don't delete train tab

* webpush debugging logs

* Fix incorrectly copying zones

* copy path data

* Ensure that cache dir exists for Frigate+

* face docs update

* Add description to upload image step to clarify the image

* Clean up

---------

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

frigate/embeddings/__init__.py

@@ -235,7 +235,7 @@ class EmbeddingsContext:
             if os.path.isfile(file_path):
                 os.unlink(file_path)
 
-        if len(os.listdir(folder)) == 0:
+        if face != "train" and len(os.listdir(folder)) == 0:
             os.rmdir(folder)
 
         self.requestor.send_data(

frigate/embeddings/embeddings.py

@@ -21,7 +21,7 @@ from frigate.data_processing.types import DataProcessorMetrics
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
 from frigate.types import ModelStatusTypesEnum
-from frigate.util.builtin import EventsPerSecond, serialize
+from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
 from frigate.util.path import get_event_thumbnail_bytes
 
 from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
@@ -75,8 +75,10 @@ class Embeddings:
         self.metrics = metrics
         self.requestor = InterProcessRequestor()
 
+        self.image_inference_speed = InferenceSpeed(self.metrics.image_embeddings_speed)
         self.image_eps = EventsPerSecond()
         self.image_eps.start()
+        self.text_inference_speed = InferenceSpeed(self.metrics.text_embeddings_speed)
         self.text_eps = EventsPerSecond()
         self.text_eps.start()
 
@@ -183,10 +185,7 @@ class Embeddings:
                 (event_id, serialize(embedding)),
             )
 
-        duration = datetime.datetime.now().timestamp() - start
-        self.metrics.image_embeddings_speed.value = (
-            self.metrics.image_embeddings_speed.value * 9 + duration
-        ) / 10
+        self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
         self.image_eps.update()
 
         return embedding
@@ -220,9 +219,7 @@ class Embeddings:
             )
 
         duration = datetime.datetime.now().timestamp() - start
-        self.metrics.text_embeddings_speed.value = (
-            self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
-        ) / 10
+        self.text_inference_speed.update(duration / len(ids))
 
         return embeddings
 
@@ -241,10 +238,7 @@ class Embeddings:
                 (event_id, serialize(embedding)),
             )
 
-        duration = datetime.datetime.now().timestamp() - start
-        self.metrics.text_embeddings_speed.value = (
-            self.metrics.text_embeddings_speed.value * 9 + duration
-        ) / 10
+        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
         self.text_eps.update()
 
         return embedding
@@ -276,10 +270,7 @@ class Embeddings:
                 items,
             )
 
-        duration = datetime.datetime.now().timestamp() - start
-        self.metrics.text_embeddings_speed.value = (
-            self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
-        ) / 10
+        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
 
         return embeddings
 

frigate/embeddings/onnx/face_embedding.py

@@ -23,10 +23,7 @@ FACENET_INPUT_SIZE = 160
 
 
 class FaceNetEmbedding(BaseEmbedding):
-    def __init__(
-        self,
-        device: str = "AUTO",
-    ):
+    def __init__(self):
         super().__init__(
             model_name="facedet",
             model_file="facenet.tflite",
@@ -34,7 +31,6 @@ class FaceNetEmbedding(BaseEmbedding):
                 "facenet.tflite": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.tflite",
             },
         )
-        self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
         self.tokenizer = None
         self.feature_extractor = None
@@ -113,10 +109,7 @@ class FaceNetEmbedding(BaseEmbedding):
 
 
 class ArcfaceEmbedding(BaseEmbedding):
-    def __init__(
-        self,
-        device: str = "AUTO",
-    ):
+    def __init__(self):
         super().__init__(
             model_name="facedet",
             model_file="arcface.onnx",
@@ -124,7 +117,6 @@ class ArcfaceEmbedding(BaseEmbedding):
                 "arcface.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/arcface.onnx",
             },
         )
-        self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
         self.tokenizer = None
         self.feature_extractor = None
@@ -154,7 +146,7 @@ class ArcfaceEmbedding(BaseEmbedding):
 
             self.runner = ONNXModelRunner(
                 os.path.join(self.download_path, self.model_file),
-                self.device,
+                "GPU",
             )
 
     def _preprocess_inputs(self, raw_inputs):