fix for queue size growing too large

detect_objects.py

@@ -29,9 +29,9 @@ MQTT_USER = os.getenv('MQTT_USER')
 MQTT_PASS = os.getenv('MQTT_PASS')
 MQTT_TOPIC_PREFIX = os.getenv('MQTT_TOPIC_PREFIX')
 
-# REGIONS = "350,0,300,50:400,350,250,50:400,750,250,50"
+REGIONS = "300,0,0,2000,200,no-mask-300.bmp:300,300,0,2000,200,no-mask-300.bmp:300,600,0,2000,200,no-mask-300.bmp:300,900,0,2000,200,no-mask-300.bmp"
 # REGIONS = "400,350,250,50"
-REGIONS = os.getenv('REGIONS')
+# REGIONS = os.getenv('REGIONS')
 
 DEBUG = (os.getenv('DEBUG') == '1')
 
@@ -70,7 +70,7 @@ def main():
         print("Unable to capture video stream")
         exit(1)
     video.release()
-        
+
     # compute the flattened array length from the array shape
     flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
     # create shared array for storing the full frame image data
@@ -95,7 +95,7 @@ def main():
     # Queue for detected objects
     object_queue = mp.Queue()
     # Queue for prepped frames
-    prepped_frame_queue = queue.Queue()
+    prepped_frame_queue = queue.Queue(len(regions)*2)
     prepped_frame_box = mp.Array(ctypes.c_uint16, 3)
 
     # shape current frame so it can be treated as an image

frigate/object_detection.py

@@ -1,4 +1,5 @@
 import datetime
+import time
 import cv2
 import threading
 import numpy as np
@@ -33,7 +34,6 @@ def detect_objects(prepped_frame_array, prepped_frame_time,
     region_box = [0,0,0]
     while True:
         # wait until a frame is ready
-        prepped_frame_grabbed.clear()
         prepped_frame_ready.wait()
 
         prepped_frame_copy = prepped_frame_np.copy()
@@ -41,10 +41,13 @@ def detect_objects(prepped_frame_array, prepped_frame_time,
         region_box[:] = prepped_frame_box
 
         prepped_frame_grabbed.set()
+        # print("Grabbed " + str(region_box[1]) + "," + str(region_box[2]))
 
         # Actual detection.
         objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
-        # print(engine.get_inference_time())
+        # time.sleep(0.1)
+        # objects = []
+        print(engine.get_inference_time())
         # put detected objects in the queue
         if objects:
             for obj in objects:
@@ -90,14 +93,16 @@ class PreppedQueueProcessor(threading.Thread):
         # process queue...
         while True:
             frame = self.prepped_frame_queue.get()
-            print(self.prepped_frame_queue.qsize())
+            # print(self.prepped_frame_queue.qsize())
             prepped_frame_np[:] = frame['frame']
             self.prepped_frame_time.value = frame['frame_time']
             self.prepped_frame_box[0] = frame['region_size']
             self.prepped_frame_box[1] = frame['region_x_offset']
             self.prepped_frame_box[2] = frame['region_y_offset']
+            # print("Passed " + str(frame['region_x_offset']) + "," + str(frame['region_x_offset']))
             self.prepped_frame_ready.set()
             self.prepped_frame_grabbed.wait()
+            self.prepped_frame_grabbed.clear()
             self.prepped_frame_ready.clear()
 
 
@@ -145,11 +150,15 @@ class FramePrepper(threading.Thread):
             # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
             frame_expanded = np.expand_dims(cropped_frame_rgb, axis=0)
 
+            # print("Prepped frame at " + str(self.region_x_offset) + "," + str(self.region_y_offset))
             # add the frame to the queue
-            self.prepped_frame_queue.put({
+            if not self.prepped_frame_queue.full():
-                'frame_time': frame_time,
+                self.prepped_frame_queue.put({
-                'frame': frame_expanded.flatten().copy(),
+                    'frame_time': frame_time,
-                'region_size': self.region_size,
+                    'frame': frame_expanded.flatten().copy(),
-                'region_x_offset': self.region_x_offset,
+                    'region_size': self.region_size,
-                'region_y_offset': self.region_y_offset
+                    'region_x_offset': self.region_x_offset,
-            })
+                    'region_y_offset': self.region_y_offset
+                })
+            # else:
+            #     print("queue full. moving on")

frigate/objects.py

@@ -11,8 +11,18 @@ class ObjectParser(threading.Thread):
         self._detected_objects = detected_objects
 
     def run(self):
+        # frame_times = {}
         while True:
             obj = self._object_queue.get()
+            # frame_time = obj['frame_time']
+            # if frame_time in frame_times:
+            #     if frame_times[frame_time] == 7:
+            #         del frame_times[frame_time]
+            #     else:
+            #         frame_times[frame_time] += 1
+            # else:
+            #     frame_times[frame_time] = 1
+            # print(frame_times)
             self._detected_objects.append(obj)
 
             # notify that objects were parsed
@@ -40,9 +50,11 @@ class ObjectCleaner(threading.Thread):
                 # look for the first object found within the last second
                 # (newest objects are appended to the end)
                 detected_objects = self._detected_objects.copy()
+
+                #print([round(now-obj['frame_time'],2) for obj in detected_objects])
                 num_to_delete = 0
                 for obj in detected_objects:
-                    if now-obj['frame_time']<1:
+                    if now-obj['frame_time']<2:
                         break
                     num_to_delete += 1
                 if num_to_delete > 0: