mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-30 19:09:13 +01:00
a468ed316d
* Added stop_event to util.Process util.Process will take care of receiving signals when the stop_event is accessed in the subclass. If it never is, SystemExit is raised instead. This has the effect of still behaving like multiprocessing.Process when stop_event is not accessed, while still allowing subclasses to not deal with the hassle of setting it up. * Give each util.Process their own logger This will help to reduce boilerplate in subclasses. * Give explicit types to util.Process.__init__ This gives better type hinting in the editor. * Use util.Process facilities in AudioProcessor Boilerplate begone! * Removed pointless check in util.Process The log_listener.queue should never be None, unless something has gone extremely wrong in the log setup code. If we're that far gone, crashing is better. * Make sure faulthandler is enabled in all processes This has no effect currently since we're using the fork start_method. However, when we inevidably switch to forkserver (either by choice, or by upgrading to python 3.14+) not having this makes for some really fun failure modes :D
360 lines
12 KiB
Python
360 lines
12 KiB
Python
"""Handle creating audio events."""
|
|
|
|
import datetime
|
|
import logging
|
|
import threading
|
|
import time
|
|
from typing import Tuple
|
|
|
|
import numpy as np
|
|
import requests
|
|
|
|
import frigate.util as util
|
|
from frigate.camera import CameraMetrics
|
|
from frigate.comms.config_updater import ConfigSubscriber
|
|
from frigate.comms.detections_updater import DetectionPublisher, DetectionTypeEnum
|
|
from frigate.comms.inter_process import InterProcessRequestor
|
|
from frigate.config import CameraConfig, CameraInput, FfmpegConfig
|
|
from frigate.const import (
|
|
AUDIO_DURATION,
|
|
AUDIO_FORMAT,
|
|
AUDIO_MAX_BIT_RANGE,
|
|
AUDIO_MIN_CONFIDENCE,
|
|
AUDIO_SAMPLE_RATE,
|
|
FRIGATE_LOCALHOST,
|
|
)
|
|
from frigate.ffmpeg_presets import parse_preset_input
|
|
from frigate.log import LogPipe
|
|
from frigate.object_detection import load_labels
|
|
from frigate.util.builtin import get_ffmpeg_arg_list
|
|
from frigate.video import start_or_restart_ffmpeg, stop_ffmpeg
|
|
|
|
try:
|
|
from tflite_runtime.interpreter import Interpreter
|
|
except ModuleNotFoundError:
|
|
from tensorflow.lite.python.interpreter import Interpreter
|
|
|
|
|
|
def get_ffmpeg_command(ffmpeg: FfmpegConfig) -> list[str]:
|
|
ffmpeg_input: CameraInput = [i for i in ffmpeg.inputs if "audio" in i.roles][0]
|
|
input_args = get_ffmpeg_arg_list(ffmpeg.global_args) + (
|
|
parse_preset_input(ffmpeg_input.input_args, 1)
|
|
or get_ffmpeg_arg_list(ffmpeg_input.input_args)
|
|
or parse_preset_input(ffmpeg.input_args, 1)
|
|
or get_ffmpeg_arg_list(ffmpeg.input_args)
|
|
)
|
|
return (
|
|
[ffmpeg.ffmpeg_path, "-vn", "-threads", "1"]
|
|
+ input_args
|
|
+ ["-i"]
|
|
+ [ffmpeg_input.path]
|
|
+ [
|
|
"-threads",
|
|
"1",
|
|
"-f",
|
|
f"{AUDIO_FORMAT}",
|
|
"-ar",
|
|
f"{AUDIO_SAMPLE_RATE}",
|
|
"-ac",
|
|
"1",
|
|
"-y",
|
|
"pipe:",
|
|
]
|
|
)
|
|
|
|
|
|
class AudioProcessor(util.Process):
|
|
def __init__(
|
|
self,
|
|
cameras: list[CameraConfig],
|
|
camera_metrics: dict[str, CameraMetrics],
|
|
):
|
|
super().__init__(name="frigate.audio_manager", daemon=True)
|
|
|
|
self.camera_metrics = camera_metrics
|
|
self.cameras = cameras
|
|
|
|
def run(self) -> None:
|
|
audio_threads: list[AudioEventMaintainer] = []
|
|
|
|
threading.current_thread().name = "process:audio_manager"
|
|
|
|
if len(self.cameras) == 0:
|
|
return
|
|
|
|
for camera in self.cameras:
|
|
audio_thread = AudioEventMaintainer(
|
|
camera,
|
|
self.camera_metrics,
|
|
self.stop_event,
|
|
)
|
|
audio_threads.append(audio_thread)
|
|
audio_thread.start()
|
|
|
|
self.logger.info(f"Audio processor started (pid: {self.pid})")
|
|
|
|
while not self.stop_event.wait():
|
|
pass
|
|
|
|
for thread in audio_threads:
|
|
thread.join(1)
|
|
if thread.is_alive():
|
|
self.logger.info(f"Waiting for thread {thread.name:s} to exit")
|
|
thread.join(10)
|
|
|
|
for thread in audio_threads:
|
|
if thread.is_alive():
|
|
self.logger.warning(f"Thread {thread.name} is still alive")
|
|
|
|
self.logger.info("Exiting audio processor")
|
|
|
|
|
|
class AudioEventMaintainer(threading.Thread):
|
|
def __init__(
|
|
self,
|
|
camera: CameraConfig,
|
|
camera_metrics: dict[str, CameraMetrics],
|
|
stop_event: threading.Event,
|
|
) -> None:
|
|
super().__init__(name=f"{camera.name}_audio_event_processor")
|
|
|
|
self.config = camera
|
|
self.camera_metrics = camera_metrics
|
|
self.detections: dict[dict[str, any]] = {}
|
|
self.stop_event = stop_event
|
|
self.detector = AudioTfl(stop_event, self.config.audio.num_threads)
|
|
self.shape = (int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE)),)
|
|
self.chunk_size = int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE * 2))
|
|
self.logger = logging.getLogger(f"audio.{self.config.name}")
|
|
self.ffmpeg_cmd = get_ffmpeg_command(self.config.ffmpeg)
|
|
self.logpipe = LogPipe(f"ffmpeg.{self.config.name}.audio")
|
|
self.audio_listener = None
|
|
|
|
# create communication for audio detections
|
|
self.requestor = InterProcessRequestor()
|
|
self.config_subscriber = ConfigSubscriber(f"config/audio/{camera.name}")
|
|
self.detection_publisher = DetectionPublisher(DetectionTypeEnum.audio)
|
|
|
|
def detect_audio(self, audio) -> None:
|
|
if not self.config.audio.enabled or self.stop_event.is_set():
|
|
return
|
|
|
|
audio_as_float = audio.astype(np.float32)
|
|
rms, dBFS = self.calculate_audio_levels(audio_as_float)
|
|
|
|
self.camera_metrics[self.config.name].audio_rms.value = rms
|
|
self.camera_metrics[self.config.name].audio_dBFS.value = dBFS
|
|
|
|
# only run audio detection when volume is above min_volume
|
|
if rms >= self.config.audio.min_volume:
|
|
# create waveform relative to max range and look for detections
|
|
waveform = (audio / AUDIO_MAX_BIT_RANGE).astype(np.float32)
|
|
model_detections = self.detector.detect(waveform)
|
|
audio_detections = []
|
|
|
|
for label, score, _ in model_detections:
|
|
self.logger.debug(
|
|
f"{self.config.name} heard {label} with a score of {score}"
|
|
)
|
|
|
|
if label not in self.config.audio.listen:
|
|
continue
|
|
|
|
if score > dict((self.config.audio.filters or {}).get(label, {})).get(
|
|
"threshold", 0.8
|
|
):
|
|
self.handle_detection(label, score)
|
|
audio_detections.append(label)
|
|
|
|
# send audio detection data
|
|
self.detection_publisher.publish(
|
|
(
|
|
self.config.name,
|
|
datetime.datetime.now().timestamp(),
|
|
dBFS,
|
|
audio_detections,
|
|
)
|
|
)
|
|
|
|
self.expire_detections()
|
|
|
|
def calculate_audio_levels(self, audio_as_float: np.float32) -> Tuple[float, float]:
|
|
# Calculate RMS (Root-Mean-Square) which represents the average signal amplitude
|
|
# Note: np.float32 isn't serializable, we must use np.float64 to publish the message
|
|
rms = np.sqrt(np.mean(np.absolute(np.square(audio_as_float))))
|
|
|
|
# Transform RMS to dBFS (decibels relative to full scale)
|
|
if rms > 0:
|
|
dBFS = 20 * np.log10(np.abs(rms) / AUDIO_MAX_BIT_RANGE)
|
|
else:
|
|
dBFS = 0
|
|
|
|
self.requestor.send_data(f"{self.config.name}/audio/dBFS", float(dBFS))
|
|
self.requestor.send_data(f"{self.config.name}/audio/rms", float(rms))
|
|
|
|
return float(rms), float(dBFS)
|
|
|
|
def handle_detection(self, label: str, score: float) -> None:
|
|
if self.detections.get(label):
|
|
self.detections[label]["last_detection"] = (
|
|
datetime.datetime.now().timestamp()
|
|
)
|
|
else:
|
|
self.requestor.send_data(f"{self.config.name}/audio/{label}", "ON")
|
|
|
|
resp = requests.post(
|
|
f"{FRIGATE_LOCALHOST}/api/events/{self.config.name}/{label}/create",
|
|
json={"duration": None, "score": score, "source_type": "audio"},
|
|
)
|
|
|
|
if resp.status_code == 200:
|
|
event_id = resp.json()["event_id"]
|
|
self.detections[label] = {
|
|
"id": event_id,
|
|
"label": label,
|
|
"last_detection": datetime.datetime.now().timestamp(),
|
|
}
|
|
|
|
def expire_detections(self) -> None:
|
|
now = datetime.datetime.now().timestamp()
|
|
|
|
for detection in self.detections.values():
|
|
if not detection:
|
|
continue
|
|
|
|
if (
|
|
now - detection.get("last_detection", now)
|
|
> self.config.audio.max_not_heard
|
|
):
|
|
self.requestor.send_data(
|
|
f"{self.config.name}/audio/{detection['label']}", "OFF"
|
|
)
|
|
|
|
resp = requests.put(
|
|
f"{FRIGATE_LOCALHOST}/api/events/{detection['id']}/end",
|
|
json={"end_time": detection["last_detection"]},
|
|
)
|
|
|
|
if resp.status_code == 200:
|
|
self.detections[detection["label"]] = None
|
|
else:
|
|
self.logger.warning(
|
|
f"Failed to end audio event {detection['id']} with status code {resp.status_code}"
|
|
)
|
|
|
|
def start_or_restart_ffmpeg(self) -> None:
|
|
self.audio_listener = start_or_restart_ffmpeg(
|
|
self.ffmpeg_cmd,
|
|
self.logger,
|
|
self.logpipe,
|
|
self.chunk_size,
|
|
self.audio_listener,
|
|
)
|
|
|
|
def read_audio(self) -> None:
|
|
def log_and_restart() -> None:
|
|
if self.stop_event.is_set():
|
|
return
|
|
|
|
time.sleep(self.config.ffmpeg.retry_interval)
|
|
self.logpipe.dump()
|
|
self.start_or_restart_ffmpeg()
|
|
|
|
try:
|
|
chunk = self.audio_listener.stdout.read(self.chunk_size)
|
|
|
|
if not chunk:
|
|
if self.audio_listener.poll() is not None:
|
|
self.logger.error("ffmpeg process is not running, restarting...")
|
|
log_and_restart()
|
|
return
|
|
|
|
return
|
|
|
|
audio = np.frombuffer(chunk, dtype=np.int16)
|
|
self.detect_audio(audio)
|
|
except Exception as e:
|
|
self.logger.error(f"Error reading audio data from ffmpeg process: {e}")
|
|
log_and_restart()
|
|
|
|
def run(self) -> None:
|
|
self.start_or_restart_ffmpeg()
|
|
|
|
while not self.stop_event.is_set():
|
|
# check if there is an updated config
|
|
(
|
|
updated_topic,
|
|
updated_audio_config,
|
|
) = self.config_subscriber.check_for_update()
|
|
|
|
if updated_topic:
|
|
self.config.audio = updated_audio_config
|
|
|
|
self.read_audio()
|
|
|
|
stop_ffmpeg(self.audio_listener, self.logger)
|
|
self.logpipe.close()
|
|
self.requestor.stop()
|
|
self.config_subscriber.stop()
|
|
self.detection_publisher.stop()
|
|
|
|
|
|
class AudioTfl:
|
|
def __init__(self, stop_event: threading.Event, num_threads=2):
|
|
self.stop_event = stop_event
|
|
self.num_threads = num_threads
|
|
self.labels = load_labels("/audio-labelmap.txt", prefill=521)
|
|
self.interpreter = Interpreter(
|
|
model_path="/cpu_audio_model.tflite",
|
|
num_threads=self.num_threads,
|
|
)
|
|
|
|
self.interpreter.allocate_tensors()
|
|
|
|
self.tensor_input_details = self.interpreter.get_input_details()
|
|
self.tensor_output_details = self.interpreter.get_output_details()
|
|
|
|
def _detect_raw(self, tensor_input):
|
|
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
|
|
self.interpreter.invoke()
|
|
detections = np.zeros((20, 6), np.float32)
|
|
|
|
res = self.interpreter.get_tensor(self.tensor_output_details[0]["index"])[0]
|
|
non_zero_indices = res > 0
|
|
class_ids = np.argpartition(-res, 20)[:20]
|
|
class_ids = class_ids[np.argsort(-res[class_ids])]
|
|
class_ids = class_ids[non_zero_indices[class_ids]]
|
|
scores = res[class_ids]
|
|
boxes = np.full((scores.shape[0], 4), -1, np.float32)
|
|
count = len(scores)
|
|
|
|
for i in range(count):
|
|
if scores[i] < AUDIO_MIN_CONFIDENCE or i == 20:
|
|
break
|
|
detections[i] = [
|
|
class_ids[i],
|
|
float(scores[i]),
|
|
boxes[i][0],
|
|
boxes[i][1],
|
|
boxes[i][2],
|
|
boxes[i][3],
|
|
]
|
|
|
|
return detections
|
|
|
|
def detect(self, tensor_input, threshold=AUDIO_MIN_CONFIDENCE):
|
|
detections = []
|
|
|
|
if self.stop_event.is_set():
|
|
return detections
|
|
|
|
raw_detections = self._detect_raw(tensor_input)
|
|
|
|
for d in raw_detections:
|
|
if d[1] < threshold:
|
|
break
|
|
detections.append(
|
|
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
|
|
)
|
|
return detections
|