增强包容性：如何利用Edge ML支持具有特殊需求的个人

Python  import numpy as np import tflite_micro_runtime.interpreter as tflite import sounddevice as sd import pygame import PySimpleGUI as sg import threading import time import os class BullyingDetectionSystem：def __init__(self, model_path)：self.is_running = Falseself.log_file_path = 'bullying_log.txt'self.progress_meter = Noneself.threshold_slider = Noneself.timer_start = None self.model_path = model_pathself.threshold = 0.5 # Use TensorFlow Lite for Microcontrollersself.interpreter = tflite.Interpreter(model_path=model_path)self.interpreter.allocate_tensors() def reset_status(self)： self.is_running = False self.progress_meter.update(0) self.timer_start.update('00：00') self.threshold_slider.update(value=self.threshold) self.window['Status'].update('') self.window['Output'].update('') def playback_audio(self, file_path)： pygame.init() pygame.mixer.init() pygame.mixer.music.load(file_path) pygame.mixer.music.play() while pygame.mixer.music.get_busy()： pygame.time.Clock().tick(10) pygame.quit() def view_log(self)： layout_log_viewer = [[sg.Multiline("", size=(60, 10), key='log_viewer', autoscroll=True)], [sg.Button("Close")]] window_log_viewer = sg.Window("Log Viewer", layout_log_viewer, finalize=True) # Read and display log file content try： with open(self.log_file_path, 'r') as log_file： log_content = log_file.read() window_log_viewer['log_viewer'].update(log_content) except FileNotFoundError： sg.popup_error("Log file not found.") while True： event_log_viewer, _ = window_log_viewer.read() if event_log_viewer == sg.WIN_CLOSED or event_log_viewer == "Close"： break window_log_viewer.close() def simulate_smartwatch_gui(self)： layout = [[sg.Text("Smartwatch Bullying Detection", size=(40, 1), justification="center", font=("Helvetica", 15), key="Title")], [sg.Button("Start", key="Start"), sg.Button("Stop", key="Stop"), sg.Button("Reset", key="Reset"), sg.Button("Exit", key="Exit")], [sg.Text("", size=(30, 1), key="Status", text_color="red")], [sg.ProgressBar(100, orientation='h', size=(20, 20), key='progress_meter')], [sg.Text("Recording Time：", size=(15, 1)), sg.Text("00：00", size=(5, 1), key='timer_start')], [sg.Slider(range=(0, 1), orientation='h', resolution=0.01, default_value=0.5, key='threshold_slider', enable_events=True)], [sg.Button("Playback", key="Playback"), sg.Button("View Log", key="View Log")], [sg.Canvas(size=(400, 200), background_color='white', key='canvas')], [sg.Output(size=(60, 10), key="Output")]] self.window = sg.Window("Smartwatch Simulation", layout, finalize=True) self.progress_meter = self.window['progress_meter'] self.timer_start = self.window['timer_start'] self.threshold_slider = self.window['threshold_slider'] while True： event, values = self.window.read(timeout=100) if event == sg.WIN_CLOSED or event == "Exit"： break elif event == "Start"： self.is_running = True threading.Thread(target=self.run_detection_system).start() elif event == "Stop"： self.is_running = False elif event == "Reset"： self.reset_status() elif event == "Playback"： selected_file = sg.popup_get_file("Choose a file to playback", file_types=(("Audio files", "*.wav"), ("All files", "*.*"))) if selected_file： self.playback_audio(selected_file) elif event == "View Log"： self.view_log() elif event == "threshold_slider"： self.threshold = values['threshold_slider'] self.window['Output'].update(f"Threshold adjusted to： {self.threshold}\n") self.update_gui() self.window.close() def update_gui(self)： if self.is_running： self.window['Status'].update('System is running', text_color='green') else： self.window['Status'].update('System is stopped', text_color='red') self.window['threshold_slider'].update(value=self.threshold) def run_detection_system(self)： while self.is_running： start_time = time.time() audio_data = self.record_audio() self.bullying_detected = self.predict_bullying(audio_data) end_time = time.time() elapsed_time = end_time - start_time self.timer_start.update(f"{int(elapsed_time // 60)：02d}：{int(elapsed_time % 60)：02d}") if self.bullying_detected： try： with open(self.log_file_path, 'a') as log_file： log_file.write(f"Bullying detected at {time.strftime('%Y-%m-%d %H：%M：%S')}\n") except Exception as e： sg.popup_error(f"Error writing to log file： {e}") self.progress_meter.update_bar(10) def record_audio(self)： # Implement audio recording logic using sounddevice # Replace the following code with your actual audio recording implementation duration = 5 # seconds sample_rate = 44100 recording = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1, dtype='int16') sd.wait() return recording.flatten() def predict_bullying(self, audio_data)： # Implement model inference logic using TensorFlow Lite for Microcontrollers # Replace the following code with your actual model inference implementation input_tensor_index = self.interpreter.input_details[0]['index'] output_tensor_index = self.interpreter.output_details[0]['index'] input_data = np.array(audio_data, dtype=np.int16) # Assuming int16 audio data input_data = np.expand_dims(input_data, axis=0) self.interpreter.set_tensor(input_tensor_index, input_data) self.interpreter.invoke() output_data = self.interpreter.get_tensor(output_tensor_index) # Replace this with your actual logic for determining bullying detection return output_data[0] > self.threshold if __name__ == "__main__"： model_path = 'your_model.tflite' detection_system = BullyingDetectionSystem(model_path) detection_system.simulate_smartwatch_gui()