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Start audio process

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This commit is contained in:
Nick Mowen 2023-06-18 13:37:52 -06:00
parent 386e388f75
commit 10e194b0d1
3 changed files with 72 additions and 76 deletions
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10
frigate/app.py
View File

@ -29,6 +29,7 @@ from frigate.const import (
MODEL_CACHE_DIR,
RECORD_DIR,
)
from frigate.events.audio import listen_to_audio
from frigate.events.cleanup import EventCleanup
from frigate.events.external import ExternalEventProcessor
from frigate.events.maintainer import EventProcessor
@ -390,6 +391,14 @@ class FrigateApp:
capture_process.start()
logger.info(f"Capture process started for {name}: {capture_process.pid}")
def start_audio_processors(self) -> None:
audio_process = mp.Process(
target=listen_to_audio,
name=f"audio_capture",
args=(self.config, self.event_queue)
)
logger.info(f"Audio process started: {audio_process.pid}")
def start_timeline_processor(self) -> None:
self.timeline_processor = TimelineProcessor(
self.config, self.timeline_queue, self.stop_event
@ -486,6 +495,7 @@ class FrigateApp:
self.start_detected_frames_processor()
self.start_camera_processors()
self.start_camera_capture_processes()
self.start_audio_processors()
self.start_storage_maintainer()
self.init_stats()
self.init_external_event_processor()

75
frigate/detectors/plugins/audio_tfl.py
View File

@ -1,75 +0,0 @@
import logging
import numpy as np
from pydantic import Field
from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi
from frigate.object_detection import load_labels
try:
from tflite_runtime.interpreter import Interpreter
except ModuleNotFoundError:
from tensorflow.lite.python.interpreter import Interpreter
logger = logging.getLogger(__name__)
DETECTOR_KEY = "audio"
class AudioTfl(DetectionApi):
type_key = DETECTOR_KEY
def __init__(self, labels):
self.labels = load_labels("/audio-labelmap.txt")
self.interpreter = Interpreter(
model_path="/cpu_audio_model.tflite",
num_threads=2,
)
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] < 0.4 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=0.8):
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

63
frigate/events/audio.py
View File

@ -19,9 +19,15 @@ from frigate.const import (
AUDIO_SAMPLE_RATE,
CACHE_DIR,
)
from frigate.detectors.plugins.audio_tfl import AudioTfl
from frigate.detectors.detection_api import DetectionApi
from frigate.object_detection import load_labels
from frigate.util import listen
try:
from tflite_runtime.interpreter import Interpreter
except ModuleNotFoundError:
from tensorflow.lite.python.interpreter import Interpreter
logger = logging.getLogger(__name__)
FFMPEG_COMMAND = (
@ -47,6 +53,61 @@ def listen_to_audio(config: FrigateConfig, event_queue: mp.Queue) -> None:
AudioEventMaintainer(camera, stop_event)
class AudioTfl(DetectionApi):
def __init__(self, labels):
self.labels = load_labels("/audio-labelmap.txt")
self.interpreter = Interpreter(
model_path="/cpu_audio_model.tflite",
num_threads=2,
)
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] < 0.4 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=0.8):
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
class AudioEventMaintainer(threading.Thread):
def __init__(self, camera: CameraConfig, stop_event: mp.Event) -> None:
threading.Thread.__init__(self)