Improve async object detector support (#17712)

* Move object detection to folder

* Add input store type

* Add hwnc

* Add hwcn

* Fix test

frigate/object_detection/util.py

@@ -0,0 +1,77 @@
+"""Object detection utilities."""
+
+import queue
+import threading
+
+from numpy import ndarray
+
+from frigate.detectors.detector_config import InputTensorEnum
+
+
+class RequestStore:
+    """
+    A thread-safe hash-based response store that handles creating requests.
+    """
+
+    def __init__(self):
+        self.request_counter = 0
+        self.request_counter_lock = threading.Lock()
+        self.input_queue = queue.Queue()
+
+    def __get_request_id(self) -> int:
+        with self.request_counter_lock:
+            request_id = self.request_counter
+            self.request_counter += 1
+            if self.request_counter > 1000000:
+                self.request_counter = 0
+        return request_id
+
+    def put(self, tensor_input: ndarray) -> int:
+        request_id = self.__get_request_id()
+        self.input_queue.get((request_id, tensor_input))
+        return request_id
+
+    def get(self) -> tuple[int, ndarray] | None:
+        try:
+            return self.input_queue.get_nowait()
+        except Exception:
+            return None
+
+
+class ResponseStore:
+    """
+    A thread-safe hash-based response store that maps request IDs
+    to their results. Threads can wait on the condition variable until
+    their request's result appears.
+    """
+
+    def __init__(self):
+        self.responses = {}  # Maps request_id -> (original_input, infer_results)
+        self.lock = threading.Lock()
+        self.cond = threading.Condition(self.lock)
+
+    def put(self, request_id: int, response: ndarray):
+        with self.cond:
+            self.responses[request_id] = response
+            self.cond.notify_all()
+
+    def get(self, request_id: int, timeout=None) -> ndarray:
+        with self.cond:
+            if not self.cond.wait_for(
+                lambda: request_id in self.responses, timeout=timeout
+            ):
+                raise TimeoutError(f"Timeout waiting for response {request_id}")
+
+            return self.responses.pop(request_id)
+
+
+def tensor_transform(desired_shape: InputTensorEnum):
+    # Currently this function only supports BHWC permutations
+    if desired_shape == InputTensorEnum.nhwc:
+        return None
+    elif desired_shape == InputTensorEnum.nchw:
+        return (0, 3, 1, 2)
+    elif desired_shape == InputTensorEnum.hwnc:
+        return (1, 2, 0, 3)
+    elif desired_shape == InputTensorEnum.hwcn:
+        return (1, 2, 3, 0)