Initial work for adding OpenVino detector. Not functional

frigate/config.py

@@ -54,6 +54,7 @@ class FrigateBaseModel(BaseModel):
 
 class DetectorTypeEnum(str, Enum):
     edgetpu = "edgetpu"
+    openvino = "openvino"
     cpu = "cpu"
 
 

frigate/detectors/openvino.py

@@ -0,0 +1,88 @@
+import logging
+import numpy as np
+import openvino.runtime as ov
+
+from frigate.detectors.detection_api import DetectionApi
+
+
+logger = logging.getLogger(__name__)
+
+
+class OvDetector(DetectionApi):
+    def __init__(self, det_device=None, model_path=None, num_threads=1):
+        self.ovCore = ov.Core()
+        self.ovModel = self.ovCore.read_model(model_path)
+        self.interpreter = self.ovCore.compile_model(
+            model=self.ovModel, device_name=det_device
+        )
+
+        self.interpreter.allocate_tensors()
+
+        self.tensor_input_details = self.interpreter.get_input_details()
+        self.tensor_output_details = self.interpreter.get_output_details()
+
+    def detect_raw(self, tensor_input):
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
+        self.interpreter.invoke()
+
+        boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
+        class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
+        scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
+        count = int(
+            self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
+        )
+
+        detections = np.zeros((20, 6), np.float32)
+
+        for i in range(count):
+            if scores[i] < 0.4 or i == 20:
+                break
+            detections[i] = [
+                class_ids[i],
+                float(scores[i]),
+                boxes[i][0],
+                boxes[i][1],
+                boxes[i][2],
+                boxes[i][3],
+            ]
+
+        return detections
+
+
+class GpuOpenVino(DetectionApi):
+    def __init__(self, det_device=None, model_path=None, num_threads=1):
+        self.interpreter = tflite.Interpreter(
+            model_path=model_path or "/cpu_model.tflite", num_threads=3
+        )
+
+        self.interpreter.allocate_tensors()
+
+        self.tensor_input_details = self.interpreter.get_input_details()
+        self.tensor_output_details = self.interpreter.get_output_details()
+
+    def detect_raw(self, tensor_input):
+        self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
+        self.interpreter.invoke()
+
+        boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
+        class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
+        scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
+        count = int(
+            self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
+        )
+
+        detections = np.zeros((20, 6), np.float32)
+
+        for i in range(count):
+            if scores[i] < 0.4 or i == 20:
+                break
+            detections[i] = [
+                class_ids[i],
+                float(scores[i]),
+                boxes[i][0],
+                boxes[i][1],
+                boxes[i][2],
+                boxes[i][3],
+            ]
+
+        return detections

frigate/object_detection.py

@@ -12,6 +12,7 @@ from setproctitle import setproctitle
 
 from frigate.config import DetectorTypeEnum, InputTensorEnum
 from frigate.detectors.edgetpu_tfl import EdgeTpuTfl
+from frigate.detectors.openvino import OvDetector
 from frigate.detectors.cpu_tfl import CpuTfl
 
 from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
@@ -57,6 +58,10 @@ class LocalObjectDetector(ObjectDetector):
             self.detect_api = EdgeTpuTfl(
                 det_device=det_device, model_config=model_config
             )
+        elif det_type == DetectorTypeEnum.openvino:
+            self.detect_api = OvDetector(
+                det_device=det_device, model_config=model_config
+            )
         else:
             logger.warning(
                 "CPU detectors are not recommended and should only be used for testing or for trial purposes."