37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
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import asyncio
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import websockets
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import cnn_classifier
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import donwlink_message
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import numpy as np
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from scipy.io import wavfile
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server_classifier=None
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server_ip="192.168.1.71"
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server_port=8765
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model_struct = 'model_mukcso_batch256.json'
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model_weights = "best_model_mukcso_batch256.h5"
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def background(f):
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def wrapped(*args, **kwargs):
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return asyncio.get_event_loop().run_in_executor(None, f, *args, *kwargs)
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return wrapped
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async def service(websocket, path):
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buf = await websocket.recv()
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decoded = np.frombuffer(buf, dtype=np.int16)
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print("Wav arrived!")
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wavfile.write("arrived.wav", 44100, decoded.astype(np.int16))
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await websocket.send("Wav arrived to Server.")
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prediction=server_classifier.predict("arrived.wav")
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if prediction[0] == 'sturnus':
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alert()
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@background
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def alert():
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donwlink_message.send_to_device("RequestAlert")
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def start_websocket_server():
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global server_classifier
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server_classifier = cnn_classifier.classifier(model_struct,model_weights)
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start_server = websockets.serve(service, server_ip, server_port)
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asyncio.get_event_loop().run_until_complete(start_server)
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asyncio.get_event_loop().run_forever()
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