Fixes (#20102)

* Catch bird classification resize error

* Improve openvino width detection

* Use auto by default

* Set type

frigate/data_processing/real_time/bird.py

@@ -128,7 +128,11 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         ]
 
         if input.shape != (224, 224):
+            try:
                 input = cv2.resize(input, (224, 224))
+            except Exception:
+                logger.warning("Failed to resize image for bird classification")
+                return
 
         input = np.expand_dims(input, axis=0)
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)

frigate/data_processing/real_time/custom_classification.py

@@ -133,8 +133,12 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             x:x2,
         ]
 
-        if frame.shape != (224, 224):
+        if input.shape != (224, 224):
-            frame = cv2.resize(frame, (224, 224))
+            try:
+                input = cv2.resize(input, (224, 224))
+            except Exception:
+                logger.warning("Failed to resize image for state classification")
+                return
 
         input = np.expand_dims(frame, axis=0)
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)
@@ -254,8 +258,12 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             x:x2,
         ]
 
-        if crop.shape != (224, 224):
+        if input.shape != (224, 224):
-            crop = cv2.resize(crop, (224, 224))
+            try:
+                input = cv2.resize(input, (224, 224))
+            except Exception:
+                logger.warning("Failed to resize image for object classification")
+                return
 
         input = np.expand_dims(crop, axis=0)
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], input)

frigate/detectors/detection_runners.py

@@ -195,9 +195,24 @@ class OpenVINOModelRunner(BaseModelRunner):
 
     def get_input_width(self) -> int:
         """Get the input width of the model."""
-        input_shape = self.compiled_model.inputs[0].get_shape()
+        input_info = self.compiled_model.inputs
-        # Assuming NCHW format, width is the last dimension
+        first_input = input_info[0]
-        return int(input_shape[-1])
+
+        try:
+            partial_shape = first_input.get_partial_shape()
+            # width dimension
+            if len(partial_shape) >= 4 and partial_shape[3].is_static:
+                return partial_shape[3].get_length()
+
+            # If width is dynamic or we can't determine it
+            return -1
+        except Exception:
+            try:
+                # gemini says some ov versions might still allow this
+                input_shape = first_input.shape
+                return input_shape[3] if len(input_shape) >= 4 else -1
+            except Exception:
+                return -1
 
     def run(self, inputs: dict[str, Any]) -> list[np.ndarray]:
         """Run inference with the model.
@@ -354,7 +369,7 @@ class RKNNModelRunner(BaseModelRunner):
 
 
 def get_optimized_runner(
-    model_path: str, device: str, complex_model: bool = True, **kwargs
+    model_path: str, device: str | None, complex_model: bool = True, **kwargs
 ) -> BaseModelRunner:
     """Get an optimized runner for the hardware."""
     if is_rknn_compatible(model_path):
@@ -364,7 +379,7 @@ def get_optimized_runner(
             return RKNNModelRunner(rknn_path)
 
     if device != "CPU" and is_openvino_gpu_npu_available():
-        return OpenVINOModelRunner(model_path, device, **kwargs)
+        return OpenVINOModelRunner(model_path, device or "AUTO", **kwargs)
 
     providers, options = get_ort_providers(device == "CPU", device, **kwargs)
     ortSession = ort.InferenceSession(