Merge pull request #34 from blakeblackshear/watchdog

0.1.2

config/config.yml

@@ -21,11 +21,14 @@ cameras:
         x_offset: 0
         y_offset: 300
         min_person_area: 5000
+        threshold: 0.5
       - size: 400
         x_offset: 350
         y_offset: 250
         min_person_area: 2000
+        threshold: 0.5
       - size: 400
         x_offset: 750
         y_offset: 250
         min_person_area: 2000
+        threshold: 0.5

frigate/object_detection.py

@@ -38,7 +38,7 @@ def run(self):
             frame = self.prepped_frame_queue.get()
 
             # Actual detection.
-            objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3)
+            objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=frame['region_threshold'], top_k=3)
             # parse and pass detected objects back to the camera
             parsed_objects = []
             for obj in objects:
@@ -59,7 +59,7 @@ def run(self):
 class FramePrepper(threading.Thread):
     def __init__(self, camera_name, shared_frame, frame_time, frame_ready, 
         frame_lock,
-        region_size, region_x_offset, region_y_offset,
+        region_size, region_x_offset, region_y_offset, region_threshold,
         prepped_frame_queue):
 
         threading.Thread.__init__(self)
@@ -71,6 +71,7 @@ def __init__(self, camera_name, shared_frame, frame_time, frame_ready,
         self.region_size = region_size
         self.region_x_offset = region_x_offset
         self.region_y_offset = region_y_offset
+        self.region_threshold = region_threshold
         self.prepped_frame_queue = prepped_frame_queue
 
     def run(self):
@@ -103,6 +104,7 @@ def run(self):
                     'frame_time': frame_time,
                     'frame': frame_expanded.flatten().copy(),
                     'region_size': self.region_size,
+                    'region_threshold': self.region_threshold,
                     'region_x_offset': self.region_x_offset,
                     'region_y_offset': self.region_y_offset
                 })

frigate/video.py

@@ -5,6 +5,7 @@
 import threading
 import ctypes
 import multiprocessing as mp
+import numpy as np
 from object_detection.utils import visualization_utils as vis_util
 from . util import tonumpyarray
 from . object_detection import FramePrepper
@@ -19,6 +20,7 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
     # start the video capture
     video = cv2.VideoCapture()
     video.open(rtsp_url)
+    print("Opening the RTSP Url...")
     # keep the buffer small so we minimize old data
     video.set(cv2.CAP_PROP_BUFFERSIZE,1)
 
@@ -108,6 +110,22 @@ def get_rtsp_url(rtsp_config):
         rtsp_config['password'], rtsp_config['host'], rtsp_config['port'],
         rtsp_config['path'])
 
+class CameraWatchdog(threading.Thread):
+    def __init__(self, camera):
+        threading.Thread.__init__(self)
+        self.camera = camera
+
+    def run(self):
+
+        while True:
+            # wait a bit before checking
+            time.sleep(60)
+
+            if (datetime.datetime.now().timestamp() - self.camera.shared_frame_time.value) > 2:
+                print("last frame is more than 2 seconds old, restarting camera capture...")
+                self.camera.start_or_restart_capture()
+                time.sleep(5)
+
 class Camera:
     def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
         self.name = name
@@ -136,21 +154,24 @@ def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
         # shape current frame so it can be treated as a numpy image
         self.shared_frame_np = tonumpyarray(self.shared_frame_array).reshape(self.frame_shape)
 
-        # create the process to capture frames from the RTSP stream and store in a shared array
-        self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array, 
-            self.shared_frame_time, self.frame_lock, self.frame_ready, self.frame_shape, self.rtsp_url))
-        self.capture_process.daemon = True
+        self.capture_process = None
 
         # for each region, create a separate thread to resize the region and prep for detection
         self.detection_prep_threads = []
         for region in self.config['regions']:
+            # set a default threshold of 0.5 if not defined
+            if not 'threshold' in region:
+                region['threshold'] = 0.5
+            if not isinstance(region['threshold'], float):
+                print('Threshold is not a float. Setting to 0.5 default.')
+                region['threshold'] = 0.5
             self.detection_prep_threads.append(FramePrepper(
                 self.name,
                 self.shared_frame_np,
                 self.shared_frame_time,
                 self.frame_ready,
                 self.frame_lock,
-                region['size'], region['x_offset'], region['y_offset'],
+                region['size'], region['x_offset'], region['y_offset'], region['threshold'],
                 prepped_frame_queue
             ))
 
@@ -171,18 +192,38 @@ def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
         mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects)
         mqtt_publisher.start()
 
+        # create a watchdog thread for capture process
+        self.watchdog = CameraWatchdog(self)
+
         # load in the mask for person detection
         if 'mask' in self.config:
             self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
         else:
             self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
             self.mask[:] = 255
+
+
+    def start_or_restart_capture(self):
+        if not self.capture_process is None:
+            print("Terminating the existing capture process...")
+            self.capture_process.terminate()
+            del self.capture_process
+            self.capture_process = None
+
+        # create the process to capture frames from the RTSP stream and store in a shared array
+        print("Creating a new capture process...")
+        self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array, 
+            self.shared_frame_time, self.frame_lock, self.frame_ready, self.frame_shape, self.rtsp_url))
+        self.capture_process.daemon = True
+        print("Starting a new capture process...")
+        self.capture_process.start()
 
     def start(self):
-        self.capture_process.start()
+        self.start_or_restart_capture()
         # start the object detection prep threads
         for detection_prep_thread in self.detection_prep_threads:
             detection_prep_thread.start()
+        self.watchdog.start()
 
     def join(self):
         self.capture_process.join()