fix zone filters fixes #218

README.md

@@ -19,7 +19,7 @@ You see multiple bounding boxes because it draws bounding boxes from all frames
 Run the container with
 ```bash
 docker run --rm \
--name frigate \
+-name blakeblackshear/frigate:stable \
 --privileged \
 --shm-size=512m \ # should work for a 2-3 cameras
 -v /dev/bus/usb:/dev/bus/usb \
@@ -52,13 +52,12 @@ Example docker-compose:
 A `config.yml` file must exist in the `config` directory. See example [here](config/config.example.yml) and device specific info can be found [here](docs/DEVICES.md).
 
 ## Recommended Hardware
-**Note: I may receive commissions for purchases made through links below.**
 |Name|Inference Speed|Notes|
 |----|---------------|-----|
-|[Atomic Pi](https://amzn.to/2FKJHpu)|16ms|Best option for a dedicated low power board with a small number of cameras.|
-|[Intel NUC NUC7i3BNK](https://amzn.to/2RDYZPe)|8-10ms|Best possible performance. Can handle 7+ cameras at 5fps depending on typical amounts of motion.|
-|[BMAX B2 Plus](https://amzn.to/3cjgQ81)|10-12ms|Good balance of performance and cost. Also capable of running many other services at the same time as frigate.|
-|[Minisforum GK41](https://amzn.to/32FyKhG)|9-10ms|Great alternative to a NUC. Easily handiles 4 1080p cameras.|
+|Atomic Pi|16ms|Best option for a dedicated low power board with a small number of cameras.|
+|Intel NUC NUC7i3BNK|8-10ms|Best possible performance. Can handle 7+ cameras at 5fps depending on typical amounts of motion.|
+|BMAX B2 Plus|10-12ms|Good balance of performance and cost. Also capable of running many other services at the same time as frigate.|
+|Minisforum GK41|9-10ms|Great alternative to a NUC. Easily handiles 4 1080p cameras.|
 
 ARM boards are not officially supported at the moment due to some python dependencies that require modification to work on ARM devices. The Raspberry Pi4 gets about 16ms inference speeds, but the hardware acceleration for ffmpeg does not work for converting yuv420 to rgb24. The Atomic Pi is x86 and much more efficient.
 

config/config.example.yml

@@ -78,8 +78,6 @@ save_clips:
 objects:
   track:
     - person
-    - car
-    - truck
   filters:
     person:
       min_area: 5000
@@ -140,6 +138,12 @@ cameras:
     ################
     take_frame: 1
 
+    ################
+    # The number of seconds to retain the highest scoring image for the best.jpg endpoint before allowing it
+    # to be replaced by a newer image. Defaults to 60 seconds.
+    ################
+    best_image_timeout: 60
+
     ################
     # MQTT settings
     ################
@@ -209,11 +213,12 @@ cameras:
       draw_zones: False
 
     ################
-    # Camera level object config. This config is merged with the global config above.
+    # Camera level object config. If defined, this is used instead of the global config.
     ################
     objects:
       track:
         - person
+        - car
       filters:
         person:
           min_area: 5000

detect_objects.py

@@ -267,13 +267,8 @@ def main():
         camera_objects_config = config.get('objects', {})
         # get objects to track for camera
         objects_to_track = camera_objects_config.get('track', GLOBAL_OBJECT_CONFIG.get('track', ['person']))
-        # merge object filters
-        global_object_filters = GLOBAL_OBJECT_CONFIG.get('filters', {})
-        camera_object_filters = camera_objects_config.get('filters', {})
-        objects_with_config = set().union(global_object_filters.keys(), camera_object_filters.keys())
-        object_filters = {}
-        for obj in objects_with_config:
-            object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
+        # get object filters
+        object_filters = camera_objects_config.get('filters', GLOBAL_OBJECT_CONFIG.get('filters', {}))
         config['objects'] = {
             'track': objects_to_track,
             'filters': object_filters
@@ -384,7 +379,7 @@ def main():
             best_object = object_processor.get_best(camera_name, label)
             best_frame = best_object.get('frame', np.zeros((720,1280,3), np.uint8))
             
-            crop = bool(request.args.get('crop', 0))
+            crop = bool(request.args.get('crop', 0, type=int))
             if crop:
                 region = best_object.get('region', [0,0,300,300])
                 best_frame = best_frame[region[1]:region[3], region[0]:region[2]]

frigate/object_processing.py

@@ -28,10 +28,9 @@ for key, val in LABELS.items():
 
 def zone_filtered(obj, object_config):
     object_name = obj['label']
-    object_filters = object_config.get('filters', {})
 
-    if object_name in object_filters:
-        obj_settings = object_filters[object_name]
+    if object_name in object_config:
+        obj_settings = object_config[object_name]
 
         # if the min area is larger than the
         # detected object, don't add it to detected objects
@@ -193,8 +192,8 @@ class CameraState():
                 current_best = self.best_objects[object_type]
                 now = datetime.datetime.now().timestamp()
                 # if the object is a higher score than the current best score 
-                # or the current object is more than 1 minute old, use the new object
-                if obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > 60:
+                # or the current object is older than desired, use the new object
+                if obj_copy['score'] > current_best['score'] or (now - current_best['frame_time']) > self.config.get('best_image_timeout', 60):
                     obj_copy['frame'] = np.copy(self.current_frame)
                     self.best_objects[object_type] = obj_copy
                     for c in self.callbacks['snapshot']: