Update object_detection.py

frigate/object_detection.py

@@ -1,4 +1,5 @@
 import datetime
+import time
 import logging
 import multiprocessing as mp
 import os
@@ -8,8 +9,6 @@ import threading
 from abc import ABC, abstractmethod
 
 import numpy as np
-import cv2
-import time
 from setproctitle import setproctitle
 
 import frigate.util as util
@@ -18,7 +17,6 @@ from frigate.detectors.detector_config import (
     BaseDetectorConfig,
     InputDTypeEnum,
     InputTensorEnum,
-    ModelTypeEnum
 )
 from frigate.util.builtin import EventsPerSecond, load_labels
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
@@ -52,8 +50,6 @@ class LocalObjectDetector(ObjectDetector):
             self.labels = {}
         else:
             self.labels = load_labels(labels)
-        
-        self.model_type = detector_config.model.model_type
 
         if detector_config:
             self.input_transform = tensor_transform(detector_config.model.input_tensor)
@@ -91,42 +87,6 @@ class LocalObjectDetector(ObjectDetector):
             tensor_input /= 255
 
         return self.detect_api.detect_raw(tensor_input=tensor_input)
-    
-    def detect_raw_memx(self, tensor_input: np.ndarray):
-
-        if self.model_type == ModelTypeEnum.yolox:
-            
-            tensor_input = tensor_input.squeeze(0)
-
-            padded_img = np.ones((640, 640, 3),
-                    dtype=np.uint8) * 114
-
-            scale = min(640 / float(tensor_input.shape[0]),
-                    640 / float(tensor_input.shape[1]))
-            sx,sy = int(tensor_input.shape[1] * scale), int(tensor_input.shape[0] * scale)
-
-            resized_img = cv2.resize(tensor_input, (sx,sy), interpolation=cv2.INTER_LINEAR)
-            padded_img[:sy, :sx] = resized_img.astype(np.uint8)
-            
-
-            # Step 4: Slice the padded image into 4 quadrants and concatenate them into 12 channels
-            x0 = padded_img[0::2, 0::2, :]  # Top-left
-            x1 = padded_img[1::2, 0::2, :]  # Bottom-left
-            x2 = padded_img[0::2, 1::2, :]  # Top-right
-            x3 = padded_img[1::2, 1::2, :]  # Bottom-right
-
-            # Step 5: Concatenate along the channel dimension (axis 2)
-            concatenated_img = np.concatenate([x0, x1, x2, x3], axis=2)
-
-            # Step 6: Return the processed image as a contiguous array of type float32
-            return np.ascontiguousarray(concatenated_img).astype(np.float32)
-
-        tensor_input = tensor_input.astype(np.float32)  # Convert input to float32
-        tensor_input /= 255.0  # Normalize pixel values to [0, 1]
-
-        tensor_input = tensor_input.transpose(1, 2, 0, 3)  # Convert from NHWC to HWNC (expected DFP input shape)
-
-        return tensor_input
 
 
 def run_detector(
@@ -186,6 +146,8 @@ def run_detector(
         avg_speed.value = (avg_speed.value * 9 + duration) / 10
 
     logger.info("Exited detection process...")
+    return self.detect_api.detect_raw(tensor_input=tensor_input)   
+
 
 def async_run_detector(
     name: str,
@@ -197,7 +159,7 @@ def async_run_detector(
 ):
     # Set thread and process titles for logging and debugging
     threading.current_thread().name = f"detector:{name}"
-    logger.info(f"Starting async detection process: {os.getpid()}")
+    logger.info(f"Starting detection process: {os.getpid()}")
     setproctitle(f"frigate.detector.{name}")
 
     stop_event = mp.Event()  # Used to gracefully stop threads on signal
@@ -221,7 +183,7 @@ def async_run_detector(
         outputs[name] = {"shm": out_shm, "np": out_np}
 
     def detect_worker():
-        """Continuously fetch frames and send them to MemryX."""
+        # """Continuously fetch frames and send them to the detector accelerator."""
         logger.info("Starting Detect Worker Thread")
         while not stop_event.is_set():
             try:
@@ -239,13 +201,12 @@ def async_run_detector(
                 logger.warning(f"Failed to get frame {connection_id} from SHM")
                 continue
 
-            # Preprocess and send input to MemryX
+            #send input to Accelator
-            input_frame = object_detector.detect_raw_memx(input_frame)
             start.value = datetime.datetime.now().timestamp()
             object_detector.detect_api.send_input(connection_id, input_frame)
 
     def result_worker():
-        """Continuously receive detection results from MemryX."""
+        # """Continuously receive detection results from detector accelerator."""
         logger.info("Starting Result Worker Thread")
         while not stop_event.is_set():
             connection_id, detections = object_detector.detect_api.receive_output()
@@ -274,7 +235,8 @@ def async_run_detector(
     while not stop_event.is_set():
         time.sleep(1)
 
-    logger.info("Exited async detection process...")
+    logger.info("Exited detection process...")
+
 
 
 class ObjectDetectProcess:
@@ -386,4 +348,3 @@ class RemoteObjectDetector:
     def cleanup(self):
         self.shm.unlink()
         self.out_shm.unlink()
-