removing motion detection

detect_objects.py

@@ -37,22 +37,16 @@ DEBUG = (os.getenv('DEBUG') == '1')
 
 def main():
     DETECTED_OBJECTS = []
-    recent_motion_frames = {}
+    recent_frames = {}
     # Parse selected regions
     regions = []
     for region_string in REGIONS.split(':'):
         region_parts = region_string.split(',')
-        region_mask_image = cv2.imread("/config/{}".format(region_parts[5]), cv2.IMREAD_GRAYSCALE)
-        region_mask = np.where(region_mask_image==[0])
         regions.append({
             'size': int(region_parts[0]),
             'x_offset': int(region_parts[1]),
             'y_offset': int(region_parts[2]),
             'min_person_area': int(region_parts[3]),
-            'min_object_size': int(region_parts[4]),
-            'mask': region_mask,
-            # Event for motion detection signaling
-            'motion_detected': mp.Event(),
             # array for prepped frame with shape (1, 300, 300, 3)
             'prepped_frame_array': mp.Array(ctypes.c_uint8, 300*300*3),
             # shared value for storing the prepped_frame_time
@@ -81,14 +75,13 @@ def main():
     frame_lock = mp.Lock()
     # Condition for notifying that a new frame is ready
     frame_ready = mp.Condition()
-    # Condition for notifying that motion status changed globally
-    motion_changed = mp.Condition()
-
+    # Shared memory array for passing prepped frame to tensorflow
     prepped_frame_array = mp.Array(ctypes.c_uint8, 300*300*3)
     # create shared value for storing the frame_time
     prepped_frame_time = mp.Value('d', 0.0)
     # Event for notifying that object detection needs a new frame
     prepped_frame_grabbed = mp.Event()
+    # Event for notifying that new frame is ready for detection
     prepped_frame_ready = mp.Event()
     # Condition for notifying that objects were parsed
     objects_parsed = mp.Condition()
@@ -96,6 +89,7 @@ def main():
     object_queue = mp.Queue()
     # Queue for prepped frames
     prepped_frame_queue = queue.Queue(len(regions)*2)
+    # Array for passing original region box to compute object bounding box
     prepped_frame_box = mp.Array(ctypes.c_uint16, 3)
 
     # shape current frame so it can be treated as an image
@@ -106,32 +100,18 @@ def main():
         shared_frame_time, frame_lock, frame_ready, frame_shape, RTSP_URL))
     capture_process.daemon = True
 
-    # for each region, start a separate process for motion detection and object detection
+    # for each region, start a separate thread to resize the region and prep for detection
     detection_prep_threads = []
-    motion_processes = []
     for region in regions:
         detection_prep_threads.append(FramePrepper(
             frame_arr,
             shared_frame_time,
             frame_ready,
             frame_lock,
-            region['motion_detected'],
             region['size'], region['x_offset'], region['y_offset'],
             prepped_frame_queue
         ))
 
-        motion_process = mp.Process(target=detect_motion, args=(shared_arr,
-            shared_frame_time,
-            frame_lock, frame_ready,
-            region['motion_detected'],
-            motion_changed,
-            frame_shape, 
-            region['size'], region['x_offset'], region['y_offset'],
-            region['min_object_size'], region['mask'],
-            DEBUG))
-        motion_process.daemon = True
-        motion_processes.append(motion_process)
-
     prepped_queue_processor = PreppedQueueProcessor(
         prepped_frame_array,
         prepped_frame_time,
@@ -157,24 +137,22 @@ def main():
 
     # start a thread to store recent motion frames for processing
     frame_tracker = FrameTracker(frame_arr, shared_frame_time, frame_ready, frame_lock, 
-        recent_motion_frames, motion_changed, [region['motion_detected'] for region in regions])
+        recent_frames)
     frame_tracker.start()
 
     # start a thread to store the highest scoring recent person frame
-    best_person_frame = BestPersonFrame(objects_parsed, recent_motion_frames, DETECTED_OBJECTS, 
-        motion_changed, [region['motion_detected'] for region in regions])
+    best_person_frame = BestPersonFrame(objects_parsed, recent_frames, DETECTED_OBJECTS)
     best_person_frame.start()
 
     # start a thread to parse objects from the queue
     object_parser = ObjectParser(object_queue, objects_parsed, DETECTED_OBJECTS)
     object_parser.start()
     # start a thread to expire objects from the detected objects list
-    object_cleaner = ObjectCleaner(objects_parsed, DETECTED_OBJECTS,
-        motion_changed, [region['motion_detected'] for region in regions])
+    object_cleaner = ObjectCleaner(objects_parsed, DETECTED_OBJECTS)
     object_cleaner.start()
 
     # connect to mqtt and setup last will
-    def on_connect(client, userdata, flags, rc): 
+    def on_connect(client, userdata, flags, rc):
         print("On connect called")
         # publish a message to signal that the service is running
         client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
@@ -191,32 +169,16 @@ def main():
     mqtt_publisher = MqttObjectPublisher(client, MQTT_TOPIC_PREFIX, objects_parsed, DETECTED_OBJECTS)
     mqtt_publisher.start()
 
-    # start thread to publish motion status
-    mqtt_motion_publisher = MqttMotionPublisher(client, MQTT_TOPIC_PREFIX, motion_changed,
-        [region['motion_detected'] for region in regions])
-    mqtt_motion_publisher.start()
-
     # start the process of capturing frames
     capture_process.start()
     print("capture_process pid ", capture_process.pid)
 
-    # start the object detection prep processes
+    # start the object detection prep threads
     for detection_prep_thread in detection_prep_threads:
         detection_prep_thread.start()
     
     detection_process.start()
     print("detection_process pid ", detection_process.pid)
-    
-    # start the motion detection processes
-    # for motion_process in motion_processes:
-    #     motion_process.start()
-    #     print("motion_process pid ", motion_process.pid)
-
-    # TEMP: short circuit the motion detection
-    for region in regions:
-        region['motion_detected'].set()
-    with motion_changed:
-        motion_changed.notify_all()
 
     # create a flask app that encodes frames a mjpeg on demand
     app = Flask(__name__)
@@ -259,8 +221,6 @@ def main():
 
             for region in regions:
                 color = (255,255,255)
-                if region['motion_detected'].is_set():
-                    color = (0,255,0)
                 cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
                     (region['x_offset']+region['size'], region['y_offset']+region['size']), 
                     color, 2)
@@ -277,8 +237,6 @@ def main():
     capture_process.join()
     for detection_prep_thread in detection_prep_threads:
         detection_prep_thread.join()
-    for motion_process in motion_processes:
-        motion_process.join()
     detection_process.join()
     frame_tracker.join()
     best_person_frame.join()

frigate/object_detection.py

@@ -47,7 +47,7 @@ def detect_objects(prepped_frame_array, prepped_frame_time,
         objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
         # time.sleep(0.1)
         # objects = []
-        print(engine.get_inference_time())
+        # print(engine.get_inference_time())
         # put detected objects in the queue
         if objects:
             for obj in objects:
@@ -109,7 +109,7 @@ class PreppedQueueProcessor(threading.Thread):
 # should this be a region class?
 class FramePrepper(threading.Thread):
     def __init__(self, shared_frame, frame_time, frame_ready, 
-        frame_lock, motion_detected,
+        frame_lock,
         region_size, region_x_offset, region_y_offset,
         prepped_frame_queue):
 
@@ -118,7 +118,6 @@ class FramePrepper(threading.Thread):
         self.frame_time = frame_time
         self.frame_ready = frame_ready
         self.frame_lock = frame_lock
-        self.motion_detected = motion_detected
         self.region_size = region_size
         self.region_x_offset = region_x_offset
         self.region_y_offset = region_y_offset
@@ -129,9 +128,6 @@ class FramePrepper(threading.Thread):
         while True:
             now = datetime.datetime.now().timestamp()
 
-            # wait until motion is detected
-            self.motion_detected.wait()
-
             with self.frame_ready:
                 # if there isnt a frame ready for processing or it is old, wait for a new frame
                 if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:

frigate/objects.py

@@ -30,114 +30,92 @@ class ObjectParser(threading.Thread):
                 self._objects_parsed.notify_all()
 
 class ObjectCleaner(threading.Thread):
-    def __init__(self, objects_parsed, detected_objects, motion_changed, motion_regions):
+    def __init__(self, objects_parsed, detected_objects):
         threading.Thread.__init__(self)
         self._objects_parsed = objects_parsed
         self._detected_objects = detected_objects
-        self.motion_changed = motion_changed
-        self.motion_regions = motion_regions
 
     def run(self):
         while True:
 
-            # while there is motion
-            while len([r for r in self.motion_regions if r.is_set()]) > 0:
-                # wait a bit before checking for expired frames
-                time.sleep(0.2)
+            # wait a bit before checking for expired frames
+            time.sleep(0.2)
+
+            # expire the objects that are more than 1 second old
+            now = datetime.datetime.now().timestamp()
+            # 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']<2:
+                    break
+                num_to_delete += 1
+            if num_to_delete > 0:
+                del self._detected_objects[:num_to_delete]
+
+                # notify that parsed objects were changed
+                with self._objects_parsed:
+                    self._objects_parsed.notify_all()
 
-                # expire the objects that are more than 1 second old
-                now = datetime.datetime.now().timestamp()
-                # 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']<2:
-                        break
-                    num_to_delete += 1
-                if num_to_delete > 0:
-                    del self._detected_objects[:num_to_delete]
-
-                    # notify that parsed objects were changed
-                    with self._objects_parsed:
-                        self._objects_parsed.notify_all()
-
-            # wait for the global motion flag to change
-            with self.motion_changed:
-                self.motion_changed.wait()
 
 # Maintains the frame and person with the highest score from the most recent
 # motion event
 class BestPersonFrame(threading.Thread):
-    def __init__(self, objects_parsed, recent_frames, detected_objects, motion_changed, motion_regions):
+    def __init__(self, objects_parsed, recent_frames, detected_objects):
         threading.Thread.__init__(self)
         self.objects_parsed = objects_parsed
         self.recent_frames = recent_frames
         self.detected_objects = detected_objects
-        self.motion_changed = motion_changed
-        self.motion_regions = motion_regions
         self.best_person = None
         self.best_frame = None
 
     def run(self):
-        motion_start = 0.0
-        motion_end = 0.0
-
         while True:
 
-             # while there is motion
-            while len([r for r in self.motion_regions if r.is_set()]) > 0:
-                # wait until objects have been parsed
-                with self.objects_parsed:
-                    self.objects_parsed.wait()
+            # wait until objects have been parsed
+            with self.objects_parsed:
+                self.objects_parsed.wait()
 
-                # make a copy of detected objects
-                detected_objects = self.detected_objects.copy()
-                detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
-                # make a copy of the recent frames
-                recent_frames = self.recent_frames.copy()
+            # make a copy of detected objects
+            detected_objects = self.detected_objects.copy()
+            detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
+            # make a copy of the recent frames
+            recent_frames = self.recent_frames.copy()
 
-                # get the highest scoring person
-                new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
+            # get the highest scoring person
+            new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
 
-                # if there isnt a person, continue
-                if new_best_person is None:
-                    continue
+            # if there isnt a person, continue
+            if new_best_person is None:
+                continue
 
-                # if there is no current best_person
-                if self.best_person is None:
+            # if there is no current best_person
+            if self.best_person is None:
+                self.best_person = new_best_person
+            # if there is already a best_person
+            else:
+                now = datetime.datetime.now().timestamp()
+                # if the new best person is a higher score than the current best person 
+                # or the current person is more than 1 minute old, use the new best person
+                if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
                     self.best_person = new_best_person
-                # if there is already a best_person
-                else:
-                    now = datetime.datetime.now().timestamp()
-                    # if the new best person is a higher score than the current best person 
-                    # or the current person is more than 1 minute old, use the new best person
-                    if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
-                        self.best_person = new_best_person
-
-                if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
-                    best_frame = recent_frames[self.best_person['frame_time']]
-                    best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
-                    # draw the bounding box on the frame
-                    vis_util.draw_bounding_box_on_image_array(best_frame,
-                        self.best_person['ymin'],
-                        self.best_person['xmin'],
-                        self.best_person['ymax'],
-                        self.best_person['xmax'],
-                        color='red',
-                        thickness=2,
-                        display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
-                        use_normalized_coordinates=False)
-
-                    # convert back to BGR
-                    self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
-
-            motion_end = datetime.datetime.now().timestamp()
-
-            # wait for the global motion flag to change
-            with self.motion_changed:
-                self.motion_changed.wait()
-            
-            motion_start = datetime.datetime.now().timestamp()
+
+            if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
+                best_frame = recent_frames[self.best_person['frame_time']]
+                best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
+                # draw the bounding box on the frame
+                vis_util.draw_bounding_box_on_image_array(best_frame,
+                    self.best_person['ymin'],
+                    self.best_person['xmin'],
+                    self.best_person['ymax'],
+                    self.best_person['xmax'],
+                    color='red',
+                    thickness=2,
+                    display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
+                    use_normalized_coordinates=False)
+
+                # convert back to BGR
+                self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

frigate/video.py

@@ -54,42 +54,34 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
 
 # Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
 class FrameTracker(threading.Thread):
-    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames, motion_changed, motion_regions):
+    def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
         threading.Thread.__init__(self)
         self.shared_frame = shared_frame
         self.frame_time = frame_time
         self.frame_ready = frame_ready
         self.frame_lock = frame_lock
         self.recent_frames = recent_frames
-        self.motion_changed = motion_changed
-        self.motion_regions = motion_regions
 
     def run(self):
         frame_time = 0.0
         while True:
-            # while there is motion
-            while len([r for r in self.motion_regions if r.is_set()]) > 0:
-                now = datetime.datetime.now().timestamp()
-                # wait for a frame
-                with self.frame_ready:
-                    # if there isnt a frame ready for processing or it is old, wait for a signal
-                    if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
-                        self.frame_ready.wait()
-                
-                # lock and make a copy of the frame
-                with self.frame_lock: 
-                    frame = self.shared_frame.copy()
-                    frame_time = self.frame_time.value
-                
-                # add the frame to recent frames
-                self.recent_frames[frame_time] = frame
+            now = datetime.datetime.now().timestamp()
+            # wait for a frame
+            with self.frame_ready:
+                # if there isnt a frame ready for processing or it is old, wait for a signal
+                if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
+                    self.frame_ready.wait()
+            
+            # lock and make a copy of the frame
+            with self.frame_lock: 
+                frame = self.shared_frame.copy()
+                frame_time = self.frame_time.value
+            
+            # add the frame to recent frames
+            self.recent_frames[frame_time] = frame
 
-                # delete any old frames
-                stored_frame_times = list(self.recent_frames.keys())
-                for k in stored_frame_times:
-                    if (now - k) > 2:
-                        del self.recent_frames[k]
-                
-            # wait for the global motion flag to change
-            with self.motion_changed:
-                self.motion_changed.wait()
+            # delete any old frames
+            stored_frame_times = list(self.recent_frames.keys())
+            for k in stored_frame_times:
+                if (now - k) > 2:
+                    del self.recent_frames[k]