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https://github.com/blakeblackshear/frigate.git
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Add threaded queue for movement for slower ptzs
This commit is contained in:
Josh Hawkins
parent
27eb2a6088
commit
56d074ec4e
@ -1,6 +1,8 @@
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"""Automatically pan, tilt, and zoom on detected objects via onvif."""
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import copy
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import logging
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import queue
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import threading
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import time
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from multiprocessing.synchronize import Event as MpEvent
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@ -22,7 +24,9 @@ class PtzMotionEstimator:
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self.frame_manager = SharedMemoryFrameManager()
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# homography is nice (zooming) but slow, translation is pan/tilt only but fast.
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self.norfair_motion_estimator = MotionEstimator(
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transformations_getter=TranslationTransformationGetter()
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transformations_getter=TranslationTransformationGetter(),
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min_distance=30,
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max_points=500,
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)
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self.camera_config = config
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self.coord_transformations = None
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@ -31,10 +35,16 @@ class PtzMotionEstimator:
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def motion_estimator(self, detections, frame_time, camera_name):
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if self.camera_config.onvif.autotracking.enabled and self.ptz_moving.value:
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logger.debug(f"Motion estimator running for {camera_name}")
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# logger.debug(
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# f"Motion estimator running for {camera_name} - frame time: {frame_time}"
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# )
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frame_id = f"{camera_name}{frame_time}"
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frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape)
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yuv_frame = self.frame_manager.get(
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frame_id, self.camera_config.frame_shape_yuv
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)
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frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2GRAY_I420)
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# mask out detections for better motion estimation
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mask = np.ones(frame.shape[:2], frame.dtype)
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@ -47,7 +57,7 @@ class PtzMotionEstimator:
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# merge camera config motion mask with detections. Norfair function needs 0,1 mask
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mask = np.bitwise_and(mask, self.camera_config.motion.mask).clip(max=1)
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# Norfair estimator function needs color
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# Norfair estimator function needs color so it can convert it right back to gray
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frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGRA)
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self.coord_transformations = self.norfair_motion_estimator.update(
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@ -56,6 +66,10 @@ class PtzMotionEstimator:
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self.frame_manager.close(frame_id)
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# logger.debug(
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# f"Motion estimator transformation: {self.coord_transformations.rel_to_abs((0,0))}"
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# )
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return self.coord_transformations
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return None
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@ -98,6 +112,10 @@ class PtzAutoTracker:
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self.tracked_object_previous: dict[str, object] = {}
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self.object_types = {}
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self.required_zones = {}
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self.move_queue = queue.Queue()
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self.move_thread = threading.Thread(target=self._process_move_queue)
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self.move_thread.daemon = True # Set the thread as a daemon thread
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self.move_thread.start()
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# if cam is set to autotrack, onvif should be set up
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for camera_name, cam in self.config.cameras.items():
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@ -122,6 +140,42 @@ class PtzAutoTracker:
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f"Disabling autotracking for {camera_name}: FOV relative movement not supported"
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)
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def _process_move_queue(self):
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while True:
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try:
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if self.move_queue.qsize() > 1:
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# Accumulate values since last moved
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pan = 0
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tilt = 0
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while not self.move_queue.empty():
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camera, queued_pan, queued_tilt = self.move_queue.get()
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logger.debug(
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f"queue pan: {queued_pan}, queue tilt: {queued_tilt}"
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)
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pan += queued_pan
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tilt += queued_tilt
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else:
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move_data = self.move_queue.get()
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camera, pan, tilt = move_data
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logger.debug(f"removing pan: {pan}, removing tilt: {tilt}")
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logger.debug(f"final pan: {pan}, final tilt: {tilt}")
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self.onvif._move_relative(camera, pan, tilt, 0.1)
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# Wait until the camera finishes moving
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while self.camera_metrics[camera]["ptz_moving"].value:
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pass
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except queue.Empty:
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pass
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def enqueue_move(self, camera, pan, tilt):
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move_data = (camera, pan, tilt)
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logger.debug(f"enqueue pan: {pan}, enqueue tilt: {tilt}")
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self.move_queue.put(move_data)
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def _autotrack_move_ptz(self, camera, obj):
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camera_config = self.config.cameras[camera]
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@ -139,7 +193,7 @@ class PtzAutoTracker:
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int(size_ratio * camera_height)
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# ideas: check object velocity for camera speed?
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self.onvif._move_relative(camera, -pan, tilt, 1)
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self.enqueue_move(camera, -pan, tilt)
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def autotrack_object(self, camera, obj):
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camera_config = self.config.cameras[camera]
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@ -160,9 +214,11 @@ class PtzAutoTracker:
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and not obj.false_positive
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and self.tracked_object_previous[camera] is None
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):
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logger.debug(f"Autotrack: New object: {obj.to_dict()}")
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logger.debug(
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f"Autotrack: New object: {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
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)
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self.tracked_object[camera] = obj
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self.tracked_object_previous[camera] = obj
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self.tracked_object_previous[camera] = copy.deepcopy(obj)
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self._autotrack_move_ptz(camera, obj)
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return
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@ -184,15 +240,18 @@ class PtzAutoTracker:
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self.tracked_object_previous[camera].obj_data["box"],
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obj.obj_data["box"],
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)
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< 0.05
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> 0.05
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):
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logger.debug(
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f"Autotrack: Existing object (do NOT move ptz): {obj.to_dict()}"
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f"Autotrack: Existing object (do NOT move ptz): {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
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)
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self.tracked_object_previous[camera] = copy.deepcopy(obj)
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return
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logger.debug(f"Autotrack: Existing object (move ptz): {obj.to_dict()}")
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self.tracked_object_previous[camera] = obj
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logger.debug(
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f"Autotrack: Existing object (move ptz): {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
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)
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self.tracked_object_previous[camera] = copy.deepcopy(obj)
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self._autotrack_move_ptz(camera, obj)
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return
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@ -216,9 +275,11 @@ class PtzAutoTracker:
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)
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< 0.2
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):
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logger.debug(f"Autotrack: Reacquired object: {obj.to_dict()}")
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logger.debug(
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f"Autotrack: Reacquired object: {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
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)
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self.tracked_object[camera] = obj
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self.tracked_object_previous[camera] = obj
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self.tracked_object_previous[camera] = copy.deepcopy(obj)
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self._autotrack_move_ptz(camera, obj)
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return
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@ -229,7 +290,9 @@ class PtzAutoTracker:
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self.tracked_object[camera] is not None
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and obj.obj_data["id"] == self.tracked_object[camera].obj_data["id"]
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):
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logger.debug(f"Autotrack: End object: {obj.to_dict()}")
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logger.debug(
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f"Autotrack: End object: {obj.obj_data['id']} {obj.obj_data['box']}"
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)
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self.tracked_object[camera] = None
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self.onvif.get_camera_status(camera)
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@ -249,7 +312,8 @@ class PtzAutoTracker:
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and self.tracked_object_previous[camera] is not None
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and (
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# might want to use a different timestamp here?
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time.time() - self.tracked_object_previous[camera].last_published
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time.time()
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- self.tracked_object_previous[camera].obj_data["frame_time"]
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> autotracker_config.timeout
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)
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and autotracker_config.return_preset
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