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use numpy for velocity estimate calcs

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This commit is contained in:
Josh Hawkins 2023-10-11 17:10:43 -05:00
parent 4d6bbe0120
commit 560a7e3281
2 changed files with 24 additions and 23 deletions
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44
frigate/ptz/autotrack.py
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@ -530,31 +530,27 @@ class PtzAutoTracker:
camera_height = camera_config.frame_shape[0]
camera_fps = camera_config.detect.fps
# Norfair gives us two points for the velocity of an object represented as x1, y1, x2, y2
x1, y1, x2, y2 = obj.obj_data["estimate_velocity"]
if abs(x2 - x1) > 8 or abs(y2 - y1) > 8:
velocities = obj.obj_data["estimate_velocity"]
vel_mags = np.linalg.norm(velocities, axis=1)
if np.any(vel_mags > 8):
# invalid velocity
return (0,) * 4, -1
return np.zeros((2, 2)), -1
average_velocity = (
(x1 + x2) / 2,
(y1 + y2) / 2,
(x1 + x2) / 2,
(y1 + y2) / 2,
)
average_velocity = np.mean(velocities, axis=0)
# get euclidean distance of the two points, sometimes the estimate is way off
velocity_distance = np.linalg.norm([x2 - x1, y2 - y1])
# get euclidean distance of the points, sometimes the estimate is way off
velocity_distance = np.linalg.norm(velocities[0] - velocities[1])
if (
0 <= abs(average_velocity[0]) < (camera_width / camera_fps / 2)
and 0 <= abs(average_velocity[1]) < (camera_height / camera_fps / 2)
0 <= np.abs(average_velocity[0]) < (camera_width / camera_fps / 2)
and 0 <= np.abs(average_velocity[1]) < (camera_height / camera_fps / 2)
and velocity_distance <= 10
):
return average_velocity, velocity_distance
else:
# invalid velocity
return (0,) * 4, -1
return np.zeros((2, 2)), -1
def _below_distance_threshold(self, camera, obj):
# Returns true if Euclidean distance from object to center of frame is
@ -742,10 +738,14 @@ class PtzAutoTracker:
if distance != -1:
# 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)
]
current_box = np.array(obj.obj_data["box"])
average_velocity = np.tile(average_velocity, 2)
predicted_box = (
current_box
+ camera_fps * predicted_movement_time * average_velocity
)
predicted_box = np.round(predicted_box).astype(int)
centroid_x = round((predicted_box[0] + predicted_box[2]) / 2)
centroid_y = round((predicted_box[1] + predicted_box[3]) / 2)
@ -755,8 +755,10 @@ class PtzAutoTracker:
tilt = (0.5 - (centroid_y / camera_height)) * 2
logger.debug(f'{camera}: Original box: {obj.obj_data["box"]}')
logger.debug(f"{camera}: Predicted box: {predicted_box}")
logger.debug(f'{camera}: Velocity: {obj.obj_data["estimate_velocity"]}')
logger.debug(f"{camera}: Predicted box: {tuple(predicted_box)}")
logger.debug(
f'{camera}: Velocity: {tuple(np.round(obj.obj_data["estimate_velocity"]).flatten().astype(int))}'
)
zoom = self._get_zoom_amount(camera, obj, predicted_box)

3
frigate/track/norfair_tracker.py
View File

@ -278,11 +278,10 @@ class NorfairTracker(ObjectTracker):
min(self.detect_config.width - 1, estimate[2]),
min(self.detect_config.height - 1, estimate[3]),
)
estimate_velocity = tuple(t.estimate_velocity.flatten().astype(int))
obj = {
**t.last_detection.data,
"estimate": estimate,
"estimate_velocity": estimate_velocity,
"estimate_velocity": t.estimate_velocity,
}
active_ids.append(t.global_id)
if t.global_id not in self.track_id_map: