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fix typing

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This commit is contained in:
Josh Hawkins 2026-04-14 22:14:52 -05:00
parent 72df583ddb
commit 0baeab7747
1 changed files with 16 additions and 6 deletions
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22
frigate/data_processing/real_time/custom_classification.py
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@ -298,6 +298,9 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
) )
return return
if not self.tensor_input_details or not self.tensor_output_details:
return
input = np.expand_dims(resized_frame, axis=0) input = np.expand_dims(resized_frame, axis=0)
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input) self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
self.interpreter.invoke() self.interpreter.invoke()
@ -355,7 +358,7 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
if topic == EmbeddingsRequestEnum.reload_classification_model.value: if topic == EmbeddingsRequestEnum.reload_classification_model.value:
if request_data.get("model_name") == self.model_config.name: if request_data.get("model_name") == self.model_config.name:
def _do_reload(data): def _do_reload(data: dict[str, Any]) -> dict[str, Any]:
self.__build_detector() self.__build_detector()
logger.info( logger.info(
f"Successfully loaded updated model for {self.model_config.name}" f"Successfully loaded updated model for {self.model_config.name}"
@ -365,7 +368,8 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
"message": f"Loaded {self.model_config.name} model.", "message": f"Loaded {self.model_config.name} model.",
} }
return self._enqueue_request(_do_reload, request_data) result: dict[str, Any] = self._enqueue_request(_do_reload, request_data)
return result
else: else:
return None return None
else: else:
@ -616,6 +620,9 @@ class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
self.classification_history[object_id].append(("unknown", 0.0, timestamp)) self.classification_history[object_id].append(("unknown", 0.0, timestamp))
return return
if not self.tensor_input_details or not self.tensor_output_details:
return
input = np.expand_dims(resized_crop, axis=0) input = np.expand_dims(resized_crop, axis=0)
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input) self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
self.interpreter.invoke() self.interpreter.invoke()
@ -665,7 +672,7 @@ class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
f"{self.model_config.name}: get_weighted_score returned consensus_label={consensus_label}, consensus_score={consensus_score} for {object_id}" f"{self.model_config.name}: get_weighted_score returned consensus_label={consensus_label}, consensus_score={consensus_score} for {object_id}"
) )
if consensus_label is not None: if consensus_label is not None and self.model_config.object_config is not None:
self._emit_result( self._emit_result(
{ {
"type": "classification", "type": "classification",
@ -680,11 +687,13 @@ class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
} }
) )
def handle_request(self, topic: str, request_data: dict) -> dict | None: def handle_request(
self, topic: str, request_data: dict[str, Any]
) -> dict[str, Any] | None:
if topic == EmbeddingsRequestEnum.reload_classification_model.value: if topic == EmbeddingsRequestEnum.reload_classification_model.value:
if request_data.get("model_name") == self.model_config.name: if request_data.get("model_name") == self.model_config.name:
def _do_reload(data): def _do_reload(data: dict[str, Any]) -> dict[str, Any]:
self.__build_detector() self.__build_detector()
logger.info( logger.info(
f"Successfully loaded updated model for {self.model_config.name}" f"Successfully loaded updated model for {self.model_config.name}"
@ -694,7 +703,8 @@ class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
"message": f"Loaded {self.model_config.name} model.", "message": f"Loaded {self.model_config.name} model.",
} }
return self._enqueue_request(_do_reload, request_data) result: dict[str, Any] = self._enqueue_request(_do_reload, request_data)
return result
else: else:
return None return None
else: else: