Improve async object detector support (#17712)

* Move object detection to folder

* Add input store type

* Add hwnc

* Add hwcn

* Fix test

benchmark.py

@@ -6,7 +6,7 @@ import numpy as np
 
 import frigate.util as util
 from frigate.config import DetectorTypeEnum
-from frigate.object_detection import (
+from frigate.object_detection.base import (
     ObjectDetectProcess,
     RemoteObjectDetector,
     load_labels,

frigate/app.py

@@ -55,7 +55,7 @@ from frigate.models import (
     Timeline,
     User,
 )
-from frigate.object_detection import ObjectDetectProcess
+from frigate.object_detection.base import ObjectDetectProcess
 from frigate.output.output import output_frames
 from frigate.ptz.autotrack import PtzAutoTrackerThread
 from frigate.ptz.onvif import OnvifController

frigate/detectors/detector_config.py

@@ -25,6 +25,8 @@ class PixelFormatEnum(str, Enum):
 class InputTensorEnum(str, Enum):
     nchw = "nchw"
     nhwc = "nhwc"
+    hwnc = "hwnc"
+    hwcn = "hwcn"
 
 
 class InputDTypeEnum(str, Enum):

frigate/detectors/plugins/hailo8l.py

@@ -1,6 +1,5 @@
 import logging
 import os
-import queue
 import subprocess
 import threading
 import urllib.request
@@ -28,37 +27,11 @@ from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import (
     BaseDetectorConfig,
 )
+from frigate.object_detection.util import RequestStore, ResponseStore
 
 logger = logging.getLogger(__name__)
 
 
-# ----------------- ResponseStore Class ----------------- #
-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, response):
-        with self.cond:
-            self.responses[request_id] = response
-            self.cond.notify_all()
-
-    def get(self, request_id, timeout=None):
-        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)
-
-
 # ----------------- Utility Functions ----------------- #
 
 
@@ -122,14 +95,14 @@ class HailoAsyncInference:
     def __init__(
         self,
         hef_path: str,
-        input_queue: queue.Queue,
+        input_store: RequestStore,
         output_store: ResponseStore,
         batch_size: int = 1,
         input_type: Optional[str] = None,
         output_type: Optional[Dict[str, str]] = None,
         send_original_frame: bool = False,
     ) -> None:
-        self.input_queue = input_queue
+        self.input_store = input_store
         self.output_store = output_store
 
         params = VDevice.create_params()
@@ -204,9 +177,11 @@ class HailoAsyncInference:
     def run(self) -> None:
         with self.infer_model.configure() as configured_infer_model:
             while True:
-                batch_data = self.input_queue.get()
+                batch_data = self.input_store.get()
+
                 if batch_data is None:
                     break
+
                 request_id, frame_data = batch_data
                 preprocessed_batch = [frame_data]
                 request_ids = [request_id]
@@ -274,16 +249,14 @@ class HailoDetector(DetectionApi):
         self.working_model_path = self.check_and_prepare()
 
         self.batch_size = 1
-        self.input_queue = queue.Queue()
+        self.input_store = RequestStore()
         self.response_store = ResponseStore()
-        self.request_counter = 0
-        self.request_counter_lock = threading.Lock()
 
         try:
             logger.debug(f"[INIT] Loading HEF model from {self.working_model_path}")
             self.inference_engine = HailoAsyncInference(
                 self.working_model_path,
-                self.input_queue,
+                self.input_store,
                 self.response_store,
                 self.batch_size,
             )
@@ -364,26 +337,16 @@ class HailoDetector(DetectionApi):
                 raise FileNotFoundError(f"Model file not found at: {self.model_path}")
         return cached_model_path
 
-    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 detect_raw(self, tensor_input):
-        request_id = self._get_request_id()
-
         tensor_input = self.preprocess(tensor_input)
+
         if isinstance(tensor_input, np.ndarray) and len(tensor_input.shape) == 3:
             tensor_input = np.expand_dims(tensor_input, axis=0)
 
-        self.input_queue.put((request_id, tensor_input))
+        request_id = self.input_store.put(tensor_input)
+
         try:
-            original_input, infer_results = self.response_store.get(
-                request_id, timeout=10.0
-            )
+            _, infer_results = self.response_store.get(request_id, timeout=10.0)
         except TimeoutError:
             logger.error(
                 f"Timeout waiting for inference results for request {request_id}"

frigate/events/audio.py

@@ -29,7 +29,7 @@ from frigate.const import (
 )
 from frigate.ffmpeg_presets import parse_preset_input
 from frigate.log import LogPipe
-from frigate.object_detection import load_labels
+from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.video import start_or_restart_ffmpeg, stop_ffmpeg
 

frigate/object_detection/base.py

@@ -15,12 +15,13 @@ from frigate.detectors import create_detector
 from frigate.detectors.detector_config import (
     BaseDetectorConfig,
     InputDTypeEnum,
-    InputTensorEnum,
 )
 from frigate.util.builtin import EventsPerSecond, load_labels
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.services import listen
 
+from .util import tensor_transform
+
 logger = logging.getLogger(__name__)
 
 
@@ -30,14 +31,6 @@ class ObjectDetector(ABC):
         pass
 
 
-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)
-
-
 class LocalObjectDetector(ObjectDetector):
     def __init__(
         self,

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)

frigate/stats/util.py

@@ -15,7 +15,7 @@ from frigate.camera import CameraMetrics
 from frigate.config import FrigateConfig
 from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
 from frigate.data_processing.types import DataProcessorMetrics
-from frigate.object_detection import ObjectDetectProcess
+from frigate.object_detection.base import ObjectDetectProcess
 from frigate.types import StatsTrackingTypes
 from frigate.util.services import (
     get_amd_gpu_stats,

frigate/test/test_object_detector.py

@@ -5,7 +5,7 @@ import numpy as np
 from pydantic import parse_obj_as
 
 import frigate.detectors as detectors
-import frigate.object_detection
+import frigate.object_detection.base
 from frigate.config import DetectorConfig, ModelConfig
 from frigate.detectors import DetectorTypeEnum
 from frigate.detectors.detector_config import InputTensorEnum
@@ -23,7 +23,7 @@ class TestLocalObjectDetector(unittest.TestCase):
                         DetectorConfig, ({"type": det_type, "model": {}})
                     )
                     test_cfg.model.path = "/test/modelpath"
-                    test_obj = frigate.object_detection.LocalObjectDetector(
+                    test_obj = frigate.object_detection.base.LocalObjectDetector(
                         detector_config=test_cfg
                     )
 
@@ -43,7 +43,7 @@ class TestLocalObjectDetector(unittest.TestCase):
 
         TEST_DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
         TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
-        test_obj_detect = frigate.object_detection.LocalObjectDetector(
+        test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
             detector_config=parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
         )
 
@@ -70,7 +70,7 @@ class TestLocalObjectDetector(unittest.TestCase):
         test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
         test_cfg.model.input_tensor = InputTensorEnum.nchw
 
-        test_obj_detect = frigate.object_detection.LocalObjectDetector(
+        test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
             detector_config=test_cfg
         )
 
@@ -91,7 +91,7 @@ class TestLocalObjectDetector(unittest.TestCase):
         "frigate.detectors.api_types",
         {det_type: Mock() for det_type in DetectorTypeEnum},
     )
-    @patch("frigate.object_detection.load_labels")
+    @patch("frigate.object_detection.base.load_labels")
     def test_detect_given_tensor_input_should_return_lfiltered_detections(
         self, mock_load_labels
     ):
@@ -118,7 +118,7 @@ class TestLocalObjectDetector(unittest.TestCase):
 
         test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
         test_cfg.model = ModelConfig()
-        test_obj_detect = frigate.object_detection.LocalObjectDetector(
+        test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
             detector_config=test_cfg,
             labels=TEST_LABEL_FILE,
         )

frigate/types.py

@@ -3,7 +3,7 @@ from typing import TypedDict
 
 from frigate.camera import CameraMetrics
 from frigate.data_processing.types import DataProcessorMetrics
-from frigate.object_detection import ObjectDetectProcess
+from frigate.object_detection.base import ObjectDetectProcess
 
 
 class StatsTrackingTypes(TypedDict):

frigate/video.py

@@ -24,7 +24,7 @@ from frigate.const import (
 from frigate.log import LogPipe
 from frigate.motion import MotionDetector
 from frigate.motion.improved_motion import ImprovedMotionDetector
-from frigate.object_detection import RemoteObjectDetector
+from frigate.object_detection.base import RemoteObjectDetector
 from frigate.ptz.autotrack import ptz_moving_at_frame_time
 from frigate.track import ObjectTracker
 from frigate.track.norfair_tracker import NorfairTracker

frigate/watchdog.py

@@ -4,7 +4,7 @@ import threading
 import time
 from multiprocessing.synchronize import Event as MpEvent
 
-from frigate.object_detection import ObjectDetectProcess
+from frigate.object_detection.base import ObjectDetectProcess
 from frigate.util.services import restart_frigate
 
 logger = logging.getLogger(__name__)

process_clip.py

@@ -14,7 +14,7 @@ sys.path.append("/workspace/frigate")
 
 from frigate.config import FrigateConfig  # noqa: E402
 from frigate.motion import MotionDetector  # noqa: E402
-from frigate.object_detection import LocalObjectDetector  # noqa: E402
+from frigate.object_detection.base import LocalObjectDetector  # noqa: E402
 from frigate.track.centroid_tracker import CentroidTracker  # noqa: E402
 from frigate.track.object_processing import CameraState  # noqa: E402
 from frigate.util import (  # noqa: E402