WonRvn/OSP_21_Fall_Detection

1import streamlit as st2import numpy as np3import torch4import librosa5import pyaudio6import pygame7from sklearn.preprocessing import StandardScaler8from sklearn.svm import SVC9from sklearn.model_selection import train_test_split10import os11import pandas as pd12 13# Streamlit app title14st.title('Real-time Fall Detection System')15 16# Load trained model and utilities17vad_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad', force_reload=True)18(get_speech_timestamps, save_audio, read_audio, VADIterator, collect_chunks) = utils19 20# Load trained SVM model and scaler (replace with your own model and scaler paths)21svm_model = SVC(C=64.88135039273247, kernel='linear', gamma='scale')  # Load your trained SVM model here22svm_scaler = StandardScaler()  # Load your trained scaler here23 24# Function to convert int16 audio to float25def int2float(sound):26    abs_max = np.abs(sound).max()27    sound = sound.astype('float32')28    if abs_max > 0:29        sound *= 1 / 3276830    sound = sound.squeeze()31    return sound32 33# Function to process audio for real-time fall detection34def process_audio_for_fall_detection(audio_chunk):35    # Convert the audio to a suitable format36    audio_int16 = np.frombuffer(audio_chunk, np.int16)37    audio_float32 = int2float(audio_int16)38    waveform = audio_float3239 40    # Extract MFCC features (adjust n_mfcc based on your model)41    n_mfcc = 13  # Replace this with the number of MFCCs used in your model42    mfcc_features = librosa.feature.mfcc(y=waveform, sr=16000, n_mfcc=n_mfcc)43    mfcc_aggregated = np.mean(mfcc_features, axis=1).reshape(1, -1)44 45    # Scale features46    mfcc_scaled = svm_scaler.transform(mfcc_aggregated)47 48    # Predict fall detection49    fall_detection_prediction = svm_model.predict(mfcc_scaled)50    return fall_detection_prediction51 52# Streamlit button to start fall detection53if st.button('Start Fall Detection'):54    FORMAT = pyaudio.paInt1655    CHANNELS = 156    RATE = 1600057    CHUNK = 102458    audio = pyaudio.PyAudio()59    stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)60 61    st.write("Listening for falls...")62    try:63        while True:64            audio_chunk = stream.read(CHUNK)65            prediction = process_audio_for_fall_detection(audio_chunk)66            if prediction == 1:  # Assuming 1 indicates a fall67                st.write("Fall detected!")68                break69    except KeyboardInterrupt:70        pass71 72    stream.stop_stream()73    stream.close()74    audio.terminate()75 76# Function to play an audio alert77def play_audio_alert(audio_file):78    pygame.mixer.init()79    pygame.mixer.music.load(audio_file)80    pygame.mixer.music.play()81 82