Merge branch 'release-0.10.0' of github.com:blakeblackshear/frigate into release-0.10.0

docker/Dockerfile.dev

@@ -22,3 +22,5 @@ RUN pip3 install pylint black
 # Install Node 14
 RUN curl -sL https://deb.nodesource.com/setup_14.x | bash - \
     && apt-get install -y nodejs
+
+RUN npm install -g npm@latest

docs/docs/configuration/index.md

@@ -159,8 +159,9 @@ detect:
   enabled: True
   # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
   max_disappeared: 25
-  # Optional: Frequency for running detection on stationary objects (default: 10x the frame rate)
+  # Optional: Frequency for running detection on stationary objects (default: 0)
-  stationary_interval: 50
+  # When set to 0, object detection will never be run on stationary objects. If set to 10, it will be run on every 10th frame.
+  stationary_interval: 0
 
 # Optional: Object configuration
 # NOTE: Can be overridden at the camera level
@@ -224,6 +225,9 @@ motion:
 record:
   # Optional: Enable recording (default: shown below)
   enabled: False
+  # Optional: Number of minutes to wait between cleanup runs (default: shown below)
+  # This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
+  expire_interval: 60
   # Optional: Retention settings for recording
   retain:
     # Optional: Number of days to retain recordings regardless of events (default: shown below)
@@ -264,7 +268,7 @@ record:
       #       here, the segments will already be gone by the time this mode is applied.
       #       For example, if the camera retain mode is "motion", the segments without motion are
       #       never stored, so setting the mode to "all" here won't bring them back.
-      mode: active_objects
+      mode: motion
       # Optional: Per object retention days
       objects:
         person: 15

docs/docs/guides/getting_started.md

@@ -62,6 +62,8 @@ cameras:
           roles:
             - detect
             - rtmp
+    rtmp:
+      enabled: False # <-- RTMP should be disabled if your stream is not H264
     detect:
       width: 1280 # <---- update for your camera's resolution
       height: 720 # <---- update for your camera's resolution
@@ -71,7 +73,9 @@ cameras:
 
 At this point you should be able to start Frigate and see the the video feed in the UI.
 
-If you get a green image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with RTSP cameras that support TCP connections. FFmpeg arguments for other types of cameras can be found [here](/configuration/camera_specific).
+If you get a green image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with H264 RTSP cameras that support TCP connections. If you do not have H264 cameras, make sure you have disabled RTMP. It is possible to enable it, but you must tell ffmpeg to re-encode the video with customized output args.
+
+FFmpeg arguments for other types of cameras can be found [here](/configuration/camera_specific).
 
 ### Step 5: Configure hardware acceleration (optional)
 

docs/docs/integrations/mqtt.md

@@ -55,7 +55,9 @@ Message published for each changed event. The first message is published when th
     "entered_zones": ["yard", "driveway"],
     "thumbnail": null,
     "has_snapshot": false,
-    "has_clip": false
+    "has_clip": false,
+    "motionless_count": 0, // number of frames the object has been motionless
+    "position_changes": 2 // number of times the object has changed position
   },
   "after": {
     "id": "1607123955.475377-mxklsc",
@@ -75,7 +77,9 @@ Message published for each changed event. The first message is published when th
     "entered_zones": ["yard", "driveway"],
     "thumbnail": null,
     "has_snapshot": false,
-    "has_clip": false
+    "has_clip": false,
+    "motionless_count": 0, // number of frames the object has been motionless
+    "position_changes": 2 // number of times the object has changed position
   }
 }
 ```

docs/package.json

@@ -12,13 +12,13 @@
     "clear": "docusaurus clear"
   },
   "dependencies": {
-    "@docusaurus/core": "^2.0.0-beta.6",
+    "@docusaurus/core": "^2.0.0-beta.15",
-    "@docusaurus/preset-classic": "^2.0.0-beta.6",
+    "@docusaurus/preset-classic": "^2.0.0-beta.15",
-    "@mdx-js/react": "^1.6.21",
+    "@mdx-js/react": "^1.6.22",
     "clsx": "^1.1.1",
     "raw-loader": "^4.0.2",
-    "react": "^16.8.4",
+    "react": "^16.14.0",
-    "react-dom": "^16.8.4"
+    "react-dom": "^16.14.0"
   },
   "browserslist": {
     "production": [
@@ -31,5 +31,8 @@
       "last 1 firefox version",
       "last 1 safari version"
     ]
+  },
+  "devDependencies": {
+    "@types/react": "^16.14.0"
   }
 }

frigate/config.py

@@ -72,9 +72,7 @@ class RetainModeEnum(str, Enum):
 
 class RetainConfig(FrigateBaseModel):
     default: float = Field(default=10, title="Default retention period.")
-    mode: RetainModeEnum = Field(
+    mode: RetainModeEnum = Field(default=RetainModeEnum.motion, title="Retain mode.")
-        default=RetainModeEnum.active_objects, title="Retain mode."
-    )
     objects: Dict[str, float] = Field(
         default_factory=dict, title="Object retention period."
     )
@@ -103,6 +101,10 @@ class RecordRetainConfig(FrigateBaseModel):
 
 class RecordConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable record on all cameras.")
+    expire_interval: int = Field(
+        default=60,
+        title="Number of minutes to wait between cleanup runs.",
+    )
     # deprecated - to be removed in a future version
     retain_days: Optional[float] = Field(title="Recording retention period in days.")
     retain: RecordRetainConfig = Field(
@@ -171,8 +173,9 @@ class DetectConfig(FrigateBaseModel):
         title="Maximum number of frames the object can dissapear before detection ends."
     )
     stationary_interval: Optional[int] = Field(
+        default=0,
         title="Frame interval for checking stationary objects.",
-        ge=1,
+        ge=0,
     )
 
 
@@ -473,7 +476,7 @@ class CameraLiveConfig(FrigateBaseModel):
 
 
 class CameraConfig(FrigateBaseModel):
-    name: Optional[str] = Field(title="Camera name.")
+    name: Optional[str] = Field(title="Camera name.", regex="^[a-zA-Z0-9_-]+$")
     ffmpeg: CameraFfmpegConfig = Field(title="FFmpeg configuration for the camera.")
     best_image_timeout: int = Field(
         default=60,
@@ -763,11 +766,6 @@ class FrigateConfig(FrigateBaseModel):
             if camera_config.detect.max_disappeared is None:
                 camera_config.detect.max_disappeared = max_disappeared
 
-            # Default stationary_interval configuration
-            stationary_interval = camera_config.detect.fps * 10
-            if camera_config.detect.stationary_interval is None:
-                camera_config.detect.stationary_interval = stationary_interval
-
             # FFMPEG input substitution
             for input in camera_config.ffmpeg.inputs:
                 input.path = input.path.format(**FRIGATE_ENV_VARS)

frigate/http.py

@@ -167,6 +167,8 @@ def delete_event(id):
     if event.has_snapshot:
         media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
         media.unlink(missing_ok=True)
+        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
+        media.unlink(missing_ok=True)
     if event.has_clip:
         media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
         media.unlink(missing_ok=True)

frigate/object_processing.py

@@ -178,6 +178,7 @@ class TrackedObject:
             "area": self.obj_data["area"],
             "region": self.obj_data["region"],
             "motionless_count": self.obj_data["motionless_count"],
+            "position_changes": self.obj_data["position_changes"],
             "current_zones": self.current_zones.copy(),
             "entered_zones": self.entered_zones.copy(),
             "has_clip": self.has_clip,
@@ -266,7 +267,13 @@ class TrackedObject:
             box = self.thumbnail_data["box"]
             box_size = 300
             region = calculate_region(
-                best_frame.shape, box[0], box[1], box[2], box[3], box_size, multiplier=1.1
+                best_frame.shape,
+                box[0],
+                box[1],
+                box[2],
+                box[3],
+                box_size,
+                multiplier=1.1,
             )
             best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
 
@@ -732,6 +739,10 @@ class TrackedObjectProcessor(threading.Thread):
         if not snapshot_config.enabled:
             return False
 
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
         # if there are required zones and there is no overlap
         required_zones = snapshot_config.required_zones
         if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
@@ -752,6 +763,10 @@ class TrackedObjectProcessor(threading.Thread):
         if not record_config.enabled:
             return False
 
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
         # If there are required zones and there is no overlap
         required_zones = record_config.events.required_zones
         if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
@@ -773,6 +788,10 @@ class TrackedObjectProcessor(threading.Thread):
         return True
 
     def should_mqtt_snapshot(self, camera, obj: TrackedObject):
+        # object never changed position
+        if obj.obj_data["position_changes"] == 0:
+            return False
+
         # if there are required zones and there is no overlap
         required_zones = self.config.cameras[camera].mqtt.required_zones
         if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):

frigate/objects.py

@@ -20,7 +20,9 @@ class ObjectTracker:
     def __init__(self, config: DetectConfig):
         self.tracked_objects = {}
         self.disappeared = {}
+        self.positions = {}
         self.max_disappeared = config.max_disappeared
+        self.detect_config = config
 
     def register(self, index, obj):
         rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
@@ -28,24 +30,83 @@ class ObjectTracker:
         obj["id"] = id
         obj["start_time"] = obj["frame_time"]
         obj["motionless_count"] = 0
+        obj["position_changes"] = 0
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
+        self.positions[id] = {
+            "xmins": [],
+            "ymins": [],
+            "xmaxs": [],
+            "ymaxs": [],
+            "xmin": 0,
+            "ymin": 0,
+            "xmax": self.detect_config.width,
+            "ymax": self.detect_config.height,
+        }
 
     def deregister(self, id):
         del self.tracked_objects[id]
         del self.disappeared[id]
 
+    # tracks the current position of the object based on the last 10 bounding boxes
+    # returns False if the object has moved outside its previous position
+    def update_position(self, id, box):
+        position = self.positions[id]
+        position_box = (
+            position["xmin"],
+            position["ymin"],
+            position["xmax"],
+            position["ymax"],
+        )
+
+        xmin, ymin, xmax, ymax = box
+
+        iou = intersection_over_union(position_box, box)
+
+        # if the iou drops below the threshold
+        # assume the object has moved to a new position and reset the computed box
+        if iou < 0.6:
+            self.positions[id] = {
+                "xmins": [xmin],
+                "ymins": [ymin],
+                "xmaxs": [xmax],
+                "ymaxs": [ymax],
+                "xmin": xmin,
+                "ymin": ymin,
+                "xmax": xmax,
+                "ymax": ymax,
+            }
+            return False
+
+        # if there are less than 10 entries for the position, add the bounding box
+        # and recompute the position box
+        if len(position["xmins"]) < 10:
+            position["xmins"].append(xmin)
+            position["ymins"].append(ymin)
+            position["xmaxs"].append(xmax)
+            position["ymaxs"].append(ymax)
+            # by using percentiles here, we hopefully remove outliers
+            position["xmin"] = np.percentile(position["xmins"], 15)
+            position["ymin"] = np.percentile(position["ymins"], 15)
+            position["xmax"] = np.percentile(position["xmaxs"], 85)
+            position["ymax"] = np.percentile(position["ymaxs"], 85)
+
+        return True
+
     def update(self, id, new_obj):
         self.disappeared[id] = 0
-        if (
+        # update the motionless count if the object has not moved to a new position
-            intersection_over_union(self.tracked_objects[id]["box"], new_obj["box"])
+        if self.update_position(id, new_obj["box"]):
-            > 0.9
-        ):
             self.tracked_objects[id]["motionless_count"] += 1
         else:
             self.tracked_objects[id]["motionless_count"] = 0
+            self.tracked_objects[id]["position_changes"] += 1
         self.tracked_objects[id].update(new_obj)
 
+    def update_frame_times(self, frame_time):
+        for id in self.tracked_objects.keys():
+            self.tracked_objects[id]["frame_time"] = frame_time
+
     def match_and_update(self, frame_time, new_objects):
         # group by name
         new_object_groups = defaultdict(lambda: [])

frigate/record.py

@@ -497,7 +497,8 @@ class RecordingCleanup(threading.Thread):
             oldest_timestamp = datetime.datetime.now().timestamp()
         except FileNotFoundError:
             logger.warning(f"Unable to find file from recordings database: {p}")
-            oldest_timestamp = datetime.datetime.now().timestamp()
+            Recordings.delete().where(Recordings.id == oldest_recording.id).execute()
+            return
 
         logger.debug(f"Oldest recording in the db: {oldest_timestamp}")
         process = sp.run(
@@ -548,7 +549,7 @@ class RecordingCleanup(threading.Thread):
         # self.sync_recordings()
 
         # Expire tmp clips every minute, recordings and clean directories every hour.
-        for counter in itertools.cycle(range(60)):
+        for counter in itertools.cycle(range(self.config.record.expire_interval)):
             if self.stop_event.wait(60):
                 logger.info(f"Exiting recording cleanup...")
                 break

frigate/test/test_config.py

@@ -1244,6 +1244,30 @@ class TestConfig(unittest.TestCase):
         runtime_config = frigate_config.runtime_config
         assert runtime_config.cameras["back"].snapshots.retain.default == 1.5
 
+    def test_fails_on_bad_camera_name(self):
+        config = {
+            "mqtt": {"host": "mqtt"},
+            "snapshots": {"retain": {"default": 1.5}},
+            "cameras": {
+                "back camer#": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {
+                                "path": "rtsp://10.0.0.1:554/video",
+                                "roles": ["detect"],
+                            },
+                        ]
+                    },
+                }
+            },
+        }
+
+        frigate_config = FrigateConfig(**config)
+
+        self.assertRaises(
+            ValidationError, lambda: frigate_config.runtime_config.cameras
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

frigate/video.py

@@ -3,6 +3,7 @@ import itertools
 import logging
 import multiprocessing as mp
 import queue
+import random
 import signal
 import subprocess as sp
 import threading
@@ -469,6 +470,8 @@ def process_frames(
     fps_tracker = EventsPerSecond()
     fps_tracker.start()
 
+    startup_scan_counter = 0
+
     while not stop_event.is_set():
         if exit_on_empty and frame_queue.empty():
             logger.info(f"Exiting track_objects...")
@@ -512,7 +515,10 @@ def process_frames(
             # if there hasn't been motion for 10 frames
             if obj["motionless_count"] >= 10
             # and it isn't due for a periodic check
-            and obj["motionless_count"] % detect_config.stationary_interval != 0
+            and (
+                detect_config.stationary_interval == 0
+                or obj["motionless_count"] % detect_config.stationary_interval != 0
+            )
             # and it hasn't disappeared
             and object_tracker.disappeared[obj["id"]] == 0
             # and it doesn't overlap with any current motion boxes
@@ -532,16 +538,39 @@ def process_frames(
         region_min_size = max(model_shape[0], model_shape[1])
         # compute regions
         regions = [
-            calculate_region(frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.2)
+            calculate_region(
+                frame_shape,
+                a[0],
+                a[1],
+                a[2],
+                a[3],
+                region_min_size,
+                multiplier=random.uniform(1.2, 1.5),
+            )
             for a in combined_boxes
         ]
 
         # consolidate regions with heavy overlap
         regions = [
-            calculate_region(frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0)
+            calculate_region(
+                frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
+            )
             for a in reduce_boxes(regions, 0.4)
         ]
 
+        # if starting up, get the next startup scan region
+        if startup_scan_counter < 9:
+            ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
+            ymax = int(frame_shape[0] / 3 + ymin)
+            xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
+            xmax = int(frame_shape[1] / 3 + xmin)
+            regions.append(
+                calculate_region(
+                    frame_shape, xmin, ymin, xmax, ymax, region_min_size, multiplier=1.2
+                )
+            )
+            startup_scan_counter += 1
+
         # resize regions and detect
         # seed with stationary objects
         detections = [
@@ -555,6 +584,7 @@ def process_frames(
             for obj in object_tracker.tracked_objects.values()
             if obj["id"] in stationary_object_ids
         ]
+
         for region in regions:
             detections.extend(
                 detect(
@@ -570,7 +600,7 @@ def process_frames(
         #########
         # merge objects, check for clipped objects and look again up to 4 times
         #########
-        refining = True
+        refining = len(regions) > 0
         refine_count = 0
         while refining and refine_count < 4:
             refining = False
@@ -625,44 +655,49 @@ def process_frames(
 
         ## drop detections that overlap too much
         consolidated_detections = []
-        # group by name
-        detected_object_groups = defaultdict(lambda: [])
-        for detection in detections:
-            detected_object_groups[detection[0]].append(detection)
 
-        # loop over detections grouped by label
+        # if detection was run on this frame, consolidate
-        for group in detected_object_groups.values():
+        if len(regions) > 0:
-            # if the group only has 1 item, skip
+            # group by name
-            if len(group) == 1:
+            detected_object_groups = defaultdict(lambda: [])
-                consolidated_detections.append(group[0])
+            for detection in detections:
-                continue
+                detected_object_groups[detection[0]].append(detection)
 
-            # sort smallest to largest by area
+            # loop over detections grouped by label
-            sorted_by_area = sorted(group, key=lambda g: g[3])
+            for group in detected_object_groups.values():
+                # if the group only has 1 item, skip
+                if len(group) == 1:
+                    consolidated_detections.append(group[0])
+                    continue
 
-            for current_detection_idx in range(0, len(sorted_by_area)):
+                # sort smallest to largest by area
-                current_detection = sorted_by_area[current_detection_idx][2]
+                sorted_by_area = sorted(group, key=lambda g: g[3])
-                overlap = 0
+
-                for to_check_idx in range(
+                for current_detection_idx in range(0, len(sorted_by_area)):
-                    min(current_detection_idx + 1, len(sorted_by_area)),
+                    current_detection = sorted_by_area[current_detection_idx][2]
-                    len(sorted_by_area),
+                    overlap = 0
-                ):
+                    for to_check_idx in range(
-                    to_check = sorted_by_area[to_check_idx][2]
+                        min(current_detection_idx + 1, len(sorted_by_area)),
-                    # if 90% of smaller detection is inside of another detection, consolidate
+                        len(sorted_by_area),
-                    if (
-                        area(intersection(current_detection, to_check))
-                        / area(current_detection)
-                        > 0.9
                     ):
-                        overlap = 1
+                        to_check = sorted_by_area[to_check_idx][2]
-                        break
+                        # if 90% of smaller detection is inside of another detection, consolidate
-                if overlap == 0:
+                        if (
-                    consolidated_detections.append(
+                            area(intersection(current_detection, to_check))
-                        sorted_by_area[current_detection_idx]
+                            / area(current_detection)
-                    )
+                            > 0.9
-
+                        ):
-        # now that we have refined our detections, we need to track objects
+                            overlap = 1
-        object_tracker.match_and_update(frame_time, consolidated_detections)
+                            break
+                    if overlap == 0:
+                        consolidated_detections.append(
+                            sorted_by_area[current_detection_idx]
+                        )
+            # now that we have refined our detections, we need to track objects
+            object_tracker.match_and_update(frame_time, consolidated_detections)
+        # else, just update the frame times for the stationary objects
+        else:
+            object_tracker.update_frame_times(frame_time)
 
         # add to the queue if not full
         if detected_objects_queue.full():