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Autotracking improvements and bugfixes (#7984)

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* 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
2023-09-28 18:21:37 -05:00
committed by GitHub
parent 0048cd5edc
commit ead03c381b
4 changed files with 81 additions and 25 deletions
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6
frigate/config.py
View File

@@ -152,6 +152,12 @@ class PtzAutotrackConfig(FrigateBaseModel):
zooming: ZoomingModeEnum = Field(
default=ZoomingModeEnum.disabled, title="Autotracker zooming mode."
)
zoom_factor: float = Field(
default=0.3,
title="Zooming factor (0.1-0.75).",
ge=0.1,
le=0.75,
)
track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
required_zones: List[str] = Field(
default_factory=list,

84
frigate/ptz/autotrack.py
View File

@@ -2,7 +2,6 @@
import copy
import logging
import math
import os
import queue
import threading
@@ -105,16 +104,22 @@ class PtzMotionEstimator:
# Norfair estimator function needs color so it can convert it right back to gray
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGRA)
self.coord_transformations = self.norfair_motion_estimator.update(
frame, mask
)
try:
self.coord_transformations = self.norfair_motion_estimator.update(
frame, mask
)
logger.debug(
f"Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0,0]])}"
)
except Exception:
# sometimes opencv can't find enough features in the image to find homography, so catch this error
logger.warning(
f"Autotracker: motion estimator couldn't get transformations for {camera_name} at frame time {frame_time}"
)
self.coord_transformations = None
self.frame_manager.close(frame_id)
logger.debug(
f"Motion estimator transformation: {self.coord_transformations.rel_to_abs([[0,0]])}"
)
return self.coord_transformations
@@ -172,6 +177,7 @@ class PtzAutoTracker:
self.calibrating: dict[str, object] = {}
self.intercept: dict[str, object] = {}
self.move_coefficients: dict[str, object] = {}
self.zoom_factor: dict[str, object] = {}
# if cam is set to autotrack, onvif should be set up
for camera_name, cam in self.config.cameras.items():
@@ -187,6 +193,7 @@ class PtzAutoTracker:
self.object_types[camera_name] = cam.onvif.autotracking.track
self.required_zones[camera_name] = cam.onvif.autotracking.required_zones
self.zoom_factor[camera_name] = cam.onvif.autotracking.zoom_factor
self.tracked_object[camera_name] = None
self.tracked_object_previous[camera_name] = None
@@ -470,7 +477,7 @@ class PtzAutoTracker:
tilt = tilt_excess
zoom = zoom_excess
def _should_zoom_in(self, camera, box, area):
def _should_zoom_in(self, camera, box, area, average_velocity):
camera_config = self.config.cameras[camera]
camera_width = camera_config.frame_shape[1]
camera_height = camera_config.frame_shape[0]
@@ -485,7 +492,15 @@ class PtzAutoTracker:
#
# TODO: Take into account the area changing when an object is moving out of frame
edge_threshold = 0.15
area_threshold = 0.7
area_threshold = self.zoom_factor[camera]
velocity_threshold = 0.1
# if we have a fast moving object, let's zoom out
# fast moving is defined as a velocity of more than 10% of the camera's width or height
# so an object with an x velocity of 15 pixels on a 1280x720 camera would trigger a zoom out
velocity_threshold = average_velocity[0] > (
camera_width * velocity_threshold
) or average_velocity[1] > (camera_height * velocity_threshold)
# returns True to zoom in, False to zoom out
return (
@@ -494,10 +509,12 @@ class PtzAutoTracker:
and bb_top > edge_threshold * camera_height
and bb_bottom < (1 - edge_threshold) * camera_height
and area < area_threshold * camera_area
and not velocity_threshold
)
def _autotrack_move_ptz(self, camera, obj):
camera_config = self.config.cameras[camera]
average_velocity = (0,) * 4
# # frame width and height
camera_width = camera_config.frame_shape[1]
@@ -525,12 +542,19 @@ class PtzAutoTracker:
(y1 + y2) / 2,
)
# this box could exceed the frame boundaries if velocity is high
# but we'll handle that in _enqueue_move() as two separate moves
predicted_box = [
round(x + camera_fps * predicted_movement_time * v)
for x, v in zip(obj.obj_data["box"], average_velocity)
]
# get euclidean distance of the two points, sometimes the estimate is way off
distance = np.linalg.norm([x2 - x1, y2 - y1])
if distance <= 5:
# this box could exceed the frame boundaries if velocity is high
# but we'll handle that in _enqueue_move() as two separate moves
predicted_box = [
round(x + camera_fps * predicted_movement_time * v)
for x, v in zip(obj.obj_data["box"], average_velocity)
]
else:
# estimate was bad
predicted_box = obj.obj_data["box"]
centroid_x = round((predicted_box[0] + predicted_box[2]) / 2)
centroid_y = round((predicted_box[1] + predicted_box[3]) / 2)
@@ -545,7 +569,15 @@ class PtzAutoTracker:
if camera_config.onvif.autotracking.zooming == ZoomingModeEnum.relative:
# relative zooming concurrently with pan/tilt
zoom = obj.obj_data["area"] / (camera_width * camera_height)
zoom = min(
obj.obj_data["area"]
/ (camera_width * camera_height)
* 100
* self.zoom_factor[camera],
1,
)
logger.debug(f"Zoom value: {zoom}")
# test if we need to zoom out
if not self._should_zoom_in(
@@ -554,15 +586,18 @@ class PtzAutoTracker:
if camera_config.onvif.autotracking.movement_weights
else obj.obj_data["box"],
obj.obj_data["area"],
average_velocity,
):
zoom = -(1 - zoom)
# don't make small movements if area hasn't changed significantly
# don't make small movements to zoom in if area hasn't changed significantly
# but always zoom out if necessary
if (
"area" in obj.previous
and abs(obj.obj_data["area"] - obj.previous["area"])
/ obj.obj_data["area"]
< 0.1
< 0.2
and zoom > 0
):
zoom = 0
else:
@@ -579,7 +614,7 @@ class PtzAutoTracker:
if 0 < zoom_level <= 1:
if self._should_zoom_in(
camera, obj.obj_data["box"], obj.obj_data["area"]
camera, obj.obj_data["box"], obj.obj_data["area"], (0, 0, 0, 0)
):
zoom = min(1.0, zoom_level + 0.1)
else:
@@ -637,10 +672,11 @@ class PtzAutoTracker:
# more often to keep the object in the center. Raising the percentage will cause less
# movement and will be more flexible with objects not quite being centered.
# TODO: there's probably a better way to approach this
distance = math.sqrt(
(obj.obj_data["centroid"][0] - camera_config.detect.width / 2) ** 2
+ (obj.obj_data["centroid"][1] - camera_config.detect.height / 2)
** 2
distance = np.linalg.norm(
[
obj.obj_data["centroid"][0] - camera_config.detect.width / 2,
obj.obj_data["centroid"][1] - camera_config.detect.height / 2,
]
)
obj_width = obj.obj_data["box"][2] - obj.obj_data["box"][0]