forked from Github/frigate
Autotracking improvements and bugfixes (#7984)
* add zoom factor and catch motion exception * reword error message * check euclidean distance of estimate points * use numpy for euclidean distance * config entry * use zoom factor and zoom based on velocity * move debug inside try * change log type to info * logger level warning * docs * exception handling
This commit is contained in:
Josh Hawkins
@@ -2,7 +2,6 @@
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import copy
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import logging
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import math
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import os
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import queue
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import threading
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@@ -105,16 +104,22 @@ class PtzMotionEstimator:
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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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frame, mask
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)
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try:
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self.coord_transformations = self.norfair_motion_estimator.update(
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frame, mask
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)
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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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except Exception:
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# sometimes opencv can't find enough features in the image to find homography, so catch this error
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logger.warning(
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f"Autotracker: motion estimator couldn't get transformations for {camera_name} at frame time {frame_time}"
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)
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self.coord_transformations = None
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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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@@ -172,6 +177,7 @@ class PtzAutoTracker:
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self.calibrating: dict[str, object] = {}
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self.intercept: dict[str, object] = {}
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self.move_coefficients: dict[str, object] = {}
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self.zoom_factor: dict[str, object] = {}
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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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@@ -187,6 +193,7 @@ class PtzAutoTracker:
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self.object_types[camera_name] = cam.onvif.autotracking.track
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self.required_zones[camera_name] = cam.onvif.autotracking.required_zones
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self.zoom_factor[camera_name] = cam.onvif.autotracking.zoom_factor
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self.tracked_object[camera_name] = None
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self.tracked_object_previous[camera_name] = None
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@@ -470,7 +477,7 @@ class PtzAutoTracker:
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tilt = tilt_excess
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zoom = zoom_excess
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def _should_zoom_in(self, camera, box, area):
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def _should_zoom_in(self, camera, box, area, average_velocity):
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camera_config = self.config.cameras[camera]
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camera_width = camera_config.frame_shape[1]
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camera_height = camera_config.frame_shape[0]
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@@ -485,7 +492,15 @@ class PtzAutoTracker:
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#
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# TODO: Take into account the area changing when an object is moving out of frame
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edge_threshold = 0.15
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area_threshold = 0.7
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area_threshold = self.zoom_factor[camera]
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velocity_threshold = 0.1
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# if we have a fast moving object, let's zoom out
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# fast moving is defined as a velocity of more than 10% of the camera's width or height
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# so an object with an x velocity of 15 pixels on a 1280x720 camera would trigger a zoom out
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velocity_threshold = average_velocity[0] > (
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camera_width * velocity_threshold
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) or average_velocity[1] > (camera_height * velocity_threshold)
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# returns True to zoom in, False to zoom out
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return (
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@@ -494,10 +509,12 @@ class PtzAutoTracker:
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and bb_top > edge_threshold * camera_height
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and bb_bottom < (1 - edge_threshold) * camera_height
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and area < area_threshold * camera_area
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and not velocity_threshold
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)
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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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average_velocity = (0,) * 4
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# # frame width and height
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camera_width = camera_config.frame_shape[1]
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@@ -525,12 +542,19 @@ class PtzAutoTracker:
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(y1 + y2) / 2,
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)
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# this box could exceed the frame boundaries if velocity is high
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# but we'll handle that in _enqueue_move() as two separate moves
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predicted_box = [
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round(x + camera_fps * predicted_movement_time * v)
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for x, v in zip(obj.obj_data["box"], average_velocity)
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]
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# get euclidean distance of the two points, sometimes the estimate is way off
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distance = np.linalg.norm([x2 - x1, y2 - y1])
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if distance <= 5:
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# this box could exceed the frame boundaries if velocity is high
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# but we'll handle that in _enqueue_move() as two separate moves
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predicted_box = [
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round(x + camera_fps * predicted_movement_time * v)
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for x, v in zip(obj.obj_data["box"], average_velocity)
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]
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else:
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# estimate was bad
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predicted_box = obj.obj_data["box"]
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centroid_x = round((predicted_box[0] + predicted_box[2]) / 2)
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centroid_y = round((predicted_box[1] + predicted_box[3]) / 2)
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@@ -545,7 +569,15 @@ class PtzAutoTracker:
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if camera_config.onvif.autotracking.zooming == ZoomingModeEnum.relative:
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# relative zooming concurrently with pan/tilt
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zoom = obj.obj_data["area"] / (camera_width * camera_height)
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zoom = min(
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obj.obj_data["area"]
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/ (camera_width * camera_height)
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* 100
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* self.zoom_factor[camera],
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1,
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)
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logger.debug(f"Zoom value: {zoom}")
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# test if we need to zoom out
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if not self._should_zoom_in(
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@@ -554,15 +586,18 @@ class PtzAutoTracker:
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if camera_config.onvif.autotracking.movement_weights
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else obj.obj_data["box"],
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obj.obj_data["area"],
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average_velocity,
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):
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zoom = -(1 - zoom)
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# don't make small movements if area hasn't changed significantly
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# don't make small movements to zoom in if area hasn't changed significantly
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# but always zoom out if necessary
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if (
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"area" in obj.previous
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and abs(obj.obj_data["area"] - obj.previous["area"])
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/ obj.obj_data["area"]
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< 0.1
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< 0.2
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and zoom > 0
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):
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zoom = 0
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else:
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@@ -579,7 +614,7 @@ class PtzAutoTracker:
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if 0 < zoom_level <= 1:
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if self._should_zoom_in(
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camera, obj.obj_data["box"], obj.obj_data["area"]
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camera, obj.obj_data["box"], obj.obj_data["area"], (0, 0, 0, 0)
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):
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zoom = min(1.0, zoom_level + 0.1)
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else:
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@@ -637,10 +672,11 @@ class PtzAutoTracker:
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# more often to keep the object in the center. Raising the percentage will cause less
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# movement and will be more flexible with objects not quite being centered.
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# TODO: there's probably a better way to approach this
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distance = math.sqrt(
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(obj.obj_data["centroid"][0] - camera_config.detect.width / 2) ** 2
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+ (obj.obj_data["centroid"][1] - camera_config.detect.height / 2)
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** 2
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distance = np.linalg.norm(
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[
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obj.obj_data["centroid"][0] - camera_config.detect.width / 2,
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obj.obj_data["centroid"][1] - camera_config.detect.height / 2,
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]
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)
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obj_width = obj.obj_data["box"][2] - obj.obj_data["box"][0]
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