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harrylincoln/listen-and-notify.py

Last active June 2, 2022 17:24
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Raspberry pi script to listen for baby cries and predict what might need
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listen-and-notify.py
import time
import audioop
import pyaudio
from pushbullet import Pushbullet
import wave
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import audio
from datetime import datetime
# camera bits
import io
import picamera
import logging
import socketserver
from threading import Condition
from http import server
# Initialisation for PyAudio
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
RECORD_SECONDS = 1
threshold = 200
reading = 0
previousreading = 0
wav_output_filename = 'bub-30-sec-clip.wav'
# PyAudio Object
ppAudio = pyaudio.PyAudio()
# Initialisation for Pushbullet
pb = Pushbullet('your-api-key')
PAGE = """\
<html>
<head>
<title>picamera MJPEG streaming demo</title>
</head>
<body>
<h1>PiCamera MJPEG Streaming Demo</h1>
<img src="stream.mjpg" width="640" height="480" />
</body>
</html>
"""
class StreamingOutput(object):
def __init__(self):
self.frame = None
self.buffer = io.BytesIO()
self.condition = Condition()
def write(self, buf):
if buf.startswith(b'\xff\xd8'):
# New frame, copy the existing buffer's content and notify all
# clients it's available
self.buffer.truncate()
with self.condition:
self.frame = self.buffer.getvalue()
self.condition.notify_all()
self.buffer.seek(0)
return self.buffer.write(buf)
class StreamingHandler(server.BaseHTTPRequestHandler):
def do_GET(self):
if self.path == '/':
self.send_response(301)
self.send_header('Location', '/index.html')
self.end_headers()
elif self.path == '/index.html':
content = PAGE.encode('utf-8')
self.send_response(200)
self.send_header('Content-Type', 'text/html')
self.send_header('Content-Length', len(content))
self.end_headers()
self.wfile.write(content)
elif self.path == '/stream.mjpg':
self.send_response(200)
self.send_header('Age', 0)
self.send_header('Cache-Control', 'no-cache, private')
self.send_header('Pragma', 'no-cache')
self.send_header(
'Content-Type', 'multipart/x-mixed-replace; boundary=FRAME')
self.end_headers()
try:
while True:
with output.condition:
output.condition.wait()
frame = output.frame
self.wfile.write(b'--FRAME\r\n')
self.send_header('Content-Type', 'image/jpeg')
self.send_header('Content-Length', len(frame))
self.end_headers()
self.wfile.write(frame)
self.wfile.write(b'\r\n')
except Exception as e:
logging.warning(
'Removed streaming client %s: %s',
self.client_address, str(e))
else:
self.send_error(404)
self.end_headers()
class StreamingServer(socketserver.ThreadingMixIn, server.HTTPServer):
allow_reuse_address = True
daemon_threads = True
# hangs until script is cancelled
while True:
stream = ppAudio.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
frames = []
for i in range(0, int(RATE/CHUNK*RECORD_SECONDS)):
data = stream.read(70, exception_on_overflow=False)
frames.append(data)
time.sleep(0.001)
reading = audioop.max(data, 2)
if reading - previousreading > threshold:
print(reading)
pb.push_note("Raspberry Pi",
f'Baby started crying at: {datetime.now().strftime("%H:%M:%S")}')
stream.stop_stream()
stream.close()
with picamera.PiCamera(resolution='640x480', framerate=24) as camera:
output = StreamingOutput()
camera.start_recording(output, format='mjpeg')
address = ('', 8000)
server = StreamingServer(address, StreamingHandler)
server.serve_forever()
print("camera starting")
pb.push_note(
"Raspberry Pi", f'Camera started at http://your-pis-ip:8000')
thirtySecStream = ppAudio.open(
format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
thirtySecStreamFrames = []
print("recording audio")
# loop through stream and append audio chunks to frame array
for ii in range(0, int((RATE/CHUNK)*30)):
thirtySecData = thirtySecStream.read(
CHUNK, exception_on_overflow=False)
thirtySecStreamFrames.append(thirtySecData)
print("finished recording audio")
pb.push_note(
"Raspberry Pi", f'Audio captured at {datetime.now().strftime("%H:%M:%S")}')
thirtySecStream.stop_stream()
thirtySecStream.close()
# save the audio frames as .wav file
wavefile = wave.open(wav_output_filename, 'wb')
wavefile.setnchannels(CHANNELS)
wavefile.setsampwidth(ppAudio.get_sample_size(FORMAT))
wavefile.setframerate(RATE)
wavefile.writeframes(b''.join(thirtySecStreamFrames))
wavefile.close()
# TF audio classifer
base_options = core.BaseOptions(
file_name=f'./cries_model.tflite')
classification_options = processor.ClassificationOptions(
max_results=2)
options = audio.AudioClassifierOptions(
base_options=base_options, classification_options=classification_options)
classifier = audio.AudioClassifier.create_from_options(options)
# Run inference
audio_file = audio.TensorAudio.create_from_wav_file(
f'./{wav_output_filename}', classifier.required_input_buffer_size)
audio_result = classifier.classify(audio_file)
print(f'Audio result: {audio_result}')
pb.push_note("Raspberry Pi", f'Predictions: {audio_result}')
print("camera stopping")
camera.stop_recording()
previousreading = reading
stream.stop_stream()
stream.close()
# Clearing the resources
stream.stop_stream()
stream.close()
ppAudio.terminate()
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