Convert detectors to factory pattern, ability to set different model for each detector (#4635)

* refactor detectors

* move create_detector and DetectorTypeEnum

* fixed code formatting

* add detector model config models

* fix detector unit tests

* adjust SharedMemory size to largest detector model shape

* fix detector model config defaults

* enable auto-discovery of detectors

* simplify config

* simplify config changes further

* update detectors docs; detect detector configs dynamic

* add suggested changes

* remove custom detector doc

* fix grammar, adjust device defaults

frigate/detectors/plugins/openvino.py

@@ -0,0 +1,66 @@
+import logging
+import numpy as np
+import openvino.runtime as ov
+
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig
+from typing import Literal
+from pydantic import Extra, Field
+
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "openvino"
+
+
+class OvDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+    device: str = Field(default=None, title="Device Type")
+
+
+class OvDetector(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, detector_config: OvDetectorConfig):
+        self.ov_core = ov.Core()
+        self.ov_model = self.ov_core.read_model(detector_config.model.path)
+
+        self.interpreter = self.ov_core.compile_model(
+            model=self.ov_model, device_name=detector_config.device
+        )
+        logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
+        self.output_indexes = 0
+        while True:
+            try:
+                tensor_shape = self.interpreter.output(self.output_indexes).shape
+                logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
+                self.output_indexes += 1
+            except:
+                logger.info(f"Model has {self.output_indexes} Output Tensors")
+                break
+
+    def detect_raw(self, tensor_input):
+
+        infer_request = self.interpreter.create_infer_request()
+        infer_request.infer([tensor_input])
+
+        results = infer_request.get_output_tensor()
+
+        detections = np.zeros((20, 6), np.float32)
+        i = 0
+        for object_detected in results.data[0, 0, :]:
+            if object_detected[0] != -1:
+                logger.debug(object_detected)
+            if object_detected[2] < 0.1 or i == 20:
+                break
+            detections[i] = [
+                object_detected[1],  # Label ID
+                float(object_detected[2]),  # Confidence
+                object_detected[4],  # y_min
+                object_detected[3],  # x_min
+                object_detected[6],  # y_max
+                object_detected[5],  # x_max
+            ]
+            i += 1
+
+        return detections