mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
322 lines
11 KiB
Python
322 lines
11 KiB
Python
"""Handle creating audio events."""
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import datetime
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import logging
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import multiprocessing as mp
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import signal
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import threading
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import time
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from types import FrameType
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from typing import Optional, Tuple
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import numpy as np
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import requests
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from setproctitle import setproctitle
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from frigate.comms.inter_process import InterProcessCommunicator
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from frigate.config import CameraConfig, FrigateConfig
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from frigate.const import (
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AUDIO_DURATION,
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AUDIO_FORMAT,
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AUDIO_MAX_BIT_RANGE,
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AUDIO_MIN_CONFIDENCE,
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AUDIO_SAMPLE_RATE,
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FRIGATE_LOCALHOST,
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)
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from frigate.ffmpeg_presets import parse_preset_input
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from frigate.log import LogPipe
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from frigate.object_detection import load_labels
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from frigate.types import FeatureMetricsTypes
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from frigate.util.builtin import get_ffmpeg_arg_list
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from frigate.util.services import listen
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from frigate.video import start_or_restart_ffmpeg, stop_ffmpeg
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try:
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from tflite_runtime.interpreter import Interpreter
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except ModuleNotFoundError:
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from tensorflow.lite.python.interpreter import Interpreter
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logger = logging.getLogger(__name__)
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def get_ffmpeg_command(input_args: list[str], input_path: str) -> list[str]:
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return get_ffmpeg_arg_list(
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f"ffmpeg {{}} -i {{}} -f {AUDIO_FORMAT} -ar {AUDIO_SAMPLE_RATE} -ac 1 -y {{}}".format(
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" ".join(input_args),
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input_path,
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"pipe:",
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)
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)
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def listen_to_audio(
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config: FrigateConfig,
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recordings_info_queue: mp.Queue,
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process_info: dict[str, FeatureMetricsTypes],
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inter_process_communicator: InterProcessCommunicator,
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) -> None:
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stop_event = mp.Event()
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audio_threads: list[threading.Thread] = []
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def exit_process() -> None:
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for thread in audio_threads:
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thread.join()
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logger.info("Exiting audio detector...")
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def receiveSignal(signalNumber: int, frame: Optional[FrameType]) -> None:
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stop_event.set()
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exit_process()
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signal.signal(signal.SIGTERM, receiveSignal)
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signal.signal(signal.SIGINT, receiveSignal)
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threading.current_thread().name = "process:audio_manager"
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setproctitle("frigate.audio_manager")
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listen()
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for camera in config.cameras.values():
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if camera.enabled and camera.audio.enabled_in_config:
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audio = AudioEventMaintainer(
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camera,
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recordings_info_queue,
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process_info,
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stop_event,
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inter_process_communicator,
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)
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audio_threads.append(audio)
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audio.start()
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class AudioTfl:
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def __init__(self, stop_event: mp.Event, num_threads=2):
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self.stop_event = stop_event
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self.num_threads = num_threads
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self.labels = load_labels("/audio-labelmap.txt", prefill=521)
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self.interpreter = Interpreter(
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model_path="/cpu_audio_model.tflite",
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num_threads=self.num_threads,
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)
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self.interpreter.allocate_tensors()
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self.tensor_input_details = self.interpreter.get_input_details()
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self.tensor_output_details = self.interpreter.get_output_details()
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def _detect_raw(self, tensor_input):
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.invoke()
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detections = np.zeros((20, 6), np.float32)
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res = self.interpreter.get_tensor(self.tensor_output_details[0]["index"])[0]
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non_zero_indices = res > 0
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class_ids = np.argpartition(-res, 20)[:20]
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class_ids = class_ids[np.argsort(-res[class_ids])]
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class_ids = class_ids[non_zero_indices[class_ids]]
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scores = res[class_ids]
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boxes = np.full((scores.shape[0], 4), -1, np.float32)
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count = len(scores)
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for i in range(count):
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if scores[i] < AUDIO_MIN_CONFIDENCE or i == 20:
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break
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detections[i] = [
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class_ids[i],
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float(scores[i]),
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boxes[i][0],
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boxes[i][1],
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boxes[i][2],
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boxes[i][3],
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]
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return detections
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def detect(self, tensor_input, threshold=AUDIO_MIN_CONFIDENCE):
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detections = []
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if self.stop_event.is_set():
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return detections
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raw_detections = self._detect_raw(tensor_input)
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for d in raw_detections:
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if d[1] < threshold:
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break
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detections.append(
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(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
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)
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return detections
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class AudioEventMaintainer(threading.Thread):
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def __init__(
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self,
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camera: CameraConfig,
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recordings_info_queue: mp.Queue,
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feature_metrics: dict[str, FeatureMetricsTypes],
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stop_event: mp.Event,
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inter_process_communicator: InterProcessCommunicator,
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) -> None:
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threading.Thread.__init__(self)
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self.name = f"{camera.name}_audio_event_processor"
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self.config = camera
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self.recordings_info_queue = recordings_info_queue
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self.feature_metrics = feature_metrics
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self.inter_process_communicator = inter_process_communicator
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self.detections: dict[dict[str, any]] = feature_metrics
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self.stop_event = stop_event
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self.detector = AudioTfl(stop_event, self.config.audio.num_threads)
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self.shape = (int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE)),)
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self.chunk_size = int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE * 2))
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self.logger = logging.getLogger(f"audio.{self.config.name}")
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self.ffmpeg_cmd = get_ffmpeg_command(
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get_ffmpeg_arg_list(self.config.ffmpeg.global_args)
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+ parse_preset_input("preset-rtsp-audio-only", 1),
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[i.path for i in self.config.ffmpeg.inputs if "audio" in i.roles][0],
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)
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self.logpipe = LogPipe(f"ffmpeg.{self.config.name}.audio")
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self.audio_listener = None
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def detect_audio(self, audio) -> None:
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if not self.feature_metrics[self.config.name]["audio_enabled"].value:
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return
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audio_as_float = audio.astype(np.float32)
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rms, dBFS = self.calculate_audio_levels(audio_as_float)
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# only run audio detection when volume is above min_volume
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if rms >= self.config.audio.min_volume:
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# add audio info to recordings queue
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self.recordings_info_queue.put(
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(self.config.name, datetime.datetime.now().timestamp(), dBFS)
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)
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# create waveform relative to max range and look for detections
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waveform = (audio / AUDIO_MAX_BIT_RANGE).astype(np.float32)
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model_detections = self.detector.detect(waveform)
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for label, score, _ in model_detections:
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if label not in self.config.audio.listen:
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continue
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if score > dict((self.config.audio.filters or {}).get(label, {})).get(
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"threshold", 0.8
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):
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self.handle_detection(label, score)
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self.expire_detections()
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def calculate_audio_levels(self, audio_as_float: np.float32) -> Tuple[float, float]:
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# Calculate RMS (Root-Mean-Square) which represents the average signal amplitude
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# Note: np.float32 isn't serializable, we must use np.float64 to publish the message
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rms = np.sqrt(np.mean(np.absolute(np.square(audio_as_float))))
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# Transform RMS to dBFS (decibels relative to full scale)
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dBFS = 20 * np.log10(np.abs(rms) / AUDIO_MAX_BIT_RANGE)
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self.inter_process_communicator.queue.put(
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(f"{self.config.name}/audio/dBFS", float(dBFS))
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)
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self.inter_process_communicator.queue.put(
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(f"{self.config.name}/audio/rms", float(rms))
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)
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return float(rms), float(dBFS)
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def handle_detection(self, label: str, score: float) -> None:
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if self.detections.get(label):
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self.detections[label][
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"last_detection"
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] = datetime.datetime.now().timestamp()
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else:
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self.inter_process_communicator.queue.put(
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(f"{self.config.name}/audio/{label}", "ON")
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)
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resp = requests.post(
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f"{FRIGATE_LOCALHOST}/api/events/{self.config.name}/{label}/create",
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json={"duration": None, "score": score, "source_type": "audio"},
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)
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if resp.status_code == 200:
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event_id = resp.json()["event_id"]
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self.detections[label] = {
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"id": event_id,
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"label": label,
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"last_detection": datetime.datetime.now().timestamp(),
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}
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def expire_detections(self) -> None:
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now = datetime.datetime.now().timestamp()
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for detection in self.detections.values():
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if not detection:
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continue
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if (
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now - detection.get("last_detection", now)
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> self.config.audio.max_not_heard
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):
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self.inter_process_communicator.queue.put(
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(f"{self.config.name}/audio/{detection['label']}", "OFF")
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)
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resp = requests.put(
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f"{FRIGATE_LOCALHOST}/api/events/{detection['id']}/end",
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json={
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"end_time": detection["last_detection"]
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+ self.config.record.events.post_capture
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},
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)
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if resp.status_code == 200:
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self.detections[detection["label"]] = None
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else:
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self.logger.warn(
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f"Failed to end audio event {detection['id']} with status code {resp.status_code}"
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)
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def start_or_restart_ffmpeg(self) -> None:
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self.audio_listener = start_or_restart_ffmpeg(
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self.ffmpeg_cmd,
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self.logger,
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self.logpipe,
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self.chunk_size,
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self.audio_listener,
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)
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def read_audio(self) -> None:
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def log_and_restart() -> None:
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if self.stop_event.is_set():
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return
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time.sleep(self.config.ffmpeg.retry_interval)
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self.logpipe.dump()
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self.start_or_restart_ffmpeg()
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try:
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chunk = self.audio_listener.stdout.read(self.chunk_size)
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if not chunk:
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if self.audio_listener.poll() is not None:
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self.logger.error("ffmpeg process is not running, restarting...")
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log_and_restart()
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return
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return
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audio = np.frombuffer(chunk, dtype=np.int16)
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self.detect_audio(audio)
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except Exception as e:
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self.logger.error(f"Error reading audio data from ffmpeg process: {e}")
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log_and_restart()
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def run(self) -> None:
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self.start_or_restart_ffmpeg()
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while not self.stop_event.is_set():
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self.read_audio()
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stop_ffmpeg(self.audio_listener, self.logger)
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self.logpipe.close()
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