632 lines
32 KiB
Python
632 lines
32 KiB
Python
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import ast
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import threading
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from datetime import datetime
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import multiprocessing as mp
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import numpy as np
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import time
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import torch
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from queue import Empty
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from scipy import signal
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from torch.autograd import Variable
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from Device.SunnyLinker import SunnyLinker64
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from SSMVEP.algorithm.tdca import TDCA
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from SSMVEP.algorithm.base import generate_cca_references
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from concentration.algorithm.calculate_focus import Calculate
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from blinkdetection.algorithm.eye_detection import blink_detection
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from Zmq.zmqServer import zmqServer
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from Zmq.zmqClient import zmqClient
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from MI.Algorithm.conformer_2class import onlineTrain
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from PubLibrary.InifileHelper import IniRead
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from SSVEP.dwfbcca import FbccaDw
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from Tools.plot_MI_EEG import plotMain
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from collections import deque
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class Decoder_main(threading.Thread):
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def __init__(self):
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threading.Thread.__init__(self)
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self.Runing=True
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self.decoder = None
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self.fs = 250 # 采样率
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self.energy = 0 # 电量
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self.status_code = 0 # 与采集设备通信的状态码，0为异常，1为正常
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self.decoder_class = None #解码器类别
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self.decodingSteps = 0 # 0=停止解码 1=预热 2=解码中 3=解码完成，发送解码结果
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def connect(self, device_type=None, device_host=None, device_port=None, upper_host=None, upper_port=None):
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self.DeviceType = device_type if device_type is not None else int(IniRead('system', 'Device_type'))
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_device_host = device_host if device_host is not None else str(IniRead('system', 'Device_Host'))
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_device_port = device_port if device_port is not None else int(IniRead('system', 'Device_Port'))
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_upper_host = upper_host if upper_host is not None else str(IniRead('system', 'Upper_Host'))
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_upper_port = upper_port if upper_port is not None else int(IniRead('system', 'Upper_Port'))
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if self.DeviceType == 1:
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self.thread_data_server = SunnyLinker64(_device_host, _device_port, self.fs, 64, method='tcp')
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self.thread_data_server.host = _device_host
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self.thread_data_server.port = _device_port
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self.thread_data_server.toUv = True
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self.thread_data_server.start()
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self.zmqServer = zmqServer()
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self.zmqServer.start()
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self.zmqClient = zmqClient(_upper_host, _upper_port)
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self.zmqClient.set_zmq_server(self.zmqServer)
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self.zmqClient.connect()
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def is_valid_signal(self, data, threshold=1e5): # 判断当前信号是否为有效信号
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# data: (chans, samples)
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energy = np.mean(np.var(data, axis=1)) # 各通道方差均值
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if energy > threshold:
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return False
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return True
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def init_Decoder(self,decoder_class):
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'''
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初始化解码器
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:param decoder_class: 'ssvep' or 'ssmvep' or 'mi' or 'concentration' or ''
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:return:
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'''
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self.decoder_class = decoder_class
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if decoder_class == 'ssvep' or decoder_class == 'pvs':
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self.n_chan = 8
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self.thread_data_server.interval_inited = False
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DW_cost_method, self.DW_cost_tv = ast.literal_eval(IniRead('system', 'SSVEP_ThresholdValue'))
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self.ListFreq = self.zmqServer.targetFreqs
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self.num_target = len(self.ListFreq)
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if self.num_target == 0:
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return
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# 初始化对象 二代算法
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self.dw = FbccaDw(self.fs, self.num_target, self.n_chan, 5, 5,
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0.2, [2.0, 0.1], [8, 7], 50, DW_cost_method)
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# frequence band
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self.dw.filterFrequenceBank()
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self.dw.setNotchFilterPara()
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self.calculateCount = 0
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self.referenceData = self.dw.reference(self.ListFreq, int(50 * 0.2 * self.fs),
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5)
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self.dw.filterInit()
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self.dw.onlineInit() # 刺激闪烁的第1s重置 --在线数据采集时
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elif decoder_class == 'ssmvep':
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self.thread_data_server.interval_init(decoder_class)
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self.n_chan = 8
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self.interval_epoch = ast.literal_eval(IniRead('system', 'SSMVEP_IntervalEpoch'))
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self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6) # 解码数据长度2s,# 精确到小数点后6位
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self.single_train = 10 # 单类别数量
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self.num_target = 2 # 分类目标数目
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self.list_freqs = np.array([8, 9]) # 刺激频率
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self.list_phase = np.array([0, 0]) # 相位
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self.tdca = TDCA(padding_len=5, n_components=1)
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self.Yf = generate_cca_references(self.list_freqs, srate=self.fs, T=self.sample_length,
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phases=self.list_phase, n_harmonics=5)
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self.parameter_init(5,45)
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elif decoder_class == 'mi' or decoder_class == 'ma':
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self.thread_data_server.interval_init(decoder_class)
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self.n_chan = 21
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self.interval_epoch = ast.literal_eval(IniRead('system', 'MI_IntervalEpoch'))
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self.sample_length = round(self.interval_epoch[1] - self.interval_epoch[0], 6) # 解码数据长度2s,# 精确到小数点后6位
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self.single_train = 40 # 单类别数量
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self.num_target = 2 # 分类目标数目
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self.parameter_init(8, 30)
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elif decoder_class == 'concentration':
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self.thread_data_server.interval_inited = False
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self.n_chan = 6
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self.win_len = 10
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self.win_step = 1
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self.low_threshold, self.high_threshold = ast.literal_eval(IniRead('system', 'concentration_ThresholdValue'))
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self.calculate = Calculate(self.low_threshold, self.high_threshold, self.fs, self.win_len)
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self.interval_epoch = [0, 1]
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self.parameter_init(2, 40)
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# self.eegQueue moved to Calculate class
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elif decoder_class == 'blink':
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self.n_chan = 2
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self.l_freq = 0.1 # 带通滤波器低频截止
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self.h_freq = 8.0 # 带通滤波器高频截止
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self.total_samples = 0 # 总采样点数
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self.window_ms = 600 # 检测窗口大小 (ms)
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self.step_ms = 100 # 滑动步长 (ms)
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self.window_samples = int(self.window_ms * self.fs / 1000) # 150个样本点
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self.step_samples = int(self.step_ms * self.fs / 1000) # 25个样本点
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self.buffer_size = self.window_samples + self.step_samples * 5
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self.fp1_buffer = deque(maxlen=self.buffer_size)
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self.fp2_buffer = deque(maxlen=self.buffer_size)
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self.sample_counter = 0
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# 预计算滤波器系数，避免在循环中重复设计
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self.Dmin, self.Dmax, self.EMin, self.EMax, self.jitterwin,self.double_blink_interval,self.double_blink_jitter = ast.literal_eval(IniRead('system', 'blink'))
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self.blink_count = 0 # 单次眨眼的次数
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self.last_blink_time = 0 # 上次检测到单次眨眼的时间（样本索引）
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self.blink_timestamps = deque(maxlen=10) # 记录最近10次 单次眨眼的时间戳
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self.double_blink_count = 0 # 连续两次眨眼的次数
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self.double_blink_events = [] # 连续眨眼事件记录
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self.last_double_blink_time = 0 # 上次检测到连续眨眼的时间戳
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self.blink_events = []
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self.blink_b, self.blink_a = signal.butter(4, [self.l_freq / (self.fs / 2), self.h_freq / (self.fs / 2)], btype='band')
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def parameter_init(self,bandPass_low,bandPass_high):
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self.interval_epoch = [int(i * self.fs) for i in self.interval_epoch] # epoch截取信息
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self.train_epoch = [int(self.interval_epoch[0]), int(self.interval_epoch[1] + 0.1 * self.fs)] # 训练样本epoch
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self.trainData = [] #训练数据
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self.trainLabel = [] #训练标签
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self.plotData = [] #报告分析数据
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self.plotLabel = [] #报告分析标签
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self.currentLabel = -1 #刺激界面当前显示的训练标签
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self.train_started = False #是否开始训练模型
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self.load_model = False # 调用模型是否完成的标志
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self.b_notch, self.a_notch = signal.iirnotch(50 / (self.fs/2), 30) # 50Hz工频陷波，250是采样率，30是质量因子
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self.b_design = signal.firwin(65, [bandPass_low / (self.fs/2), bandPass_high / (self.fs/2)], pass_zero=False) # 设计8-30Hz带通滤波器
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fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
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filePath = './online_Models/'
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self.modelPath = ''.join([filePath, fileName, '.pth'])
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self.mp_data_queue = mp.Queue() #多进程传参队列
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self.mp_result_queue = mp.Queue() #多进程结果队列
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def preprocess(self, signal_data):
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# # 计算每行的平均值
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row_means = np.mean(signal_data, axis=-1, keepdims=True)
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# 对每一行去均值
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signal_data = signal_data - row_means
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signal_data = signal.lfilter(self.b_notch, self.a_notch, signal_data, axis=-1) # 工频陷波
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signal_data = signal.lfilter(self.b_design, 1, signal_data, axis=-1) # 带通滤波
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return signal_data
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def run(self):
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while self.Runing:
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if self.zmqServer.decoder_switch or self.zmqServer.changeTarget:
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print(f"Decoder_class Switch Detected: {self.zmqServer.decoder_class}")
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self.zmqServer.decoder_switch = False
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self.zmqServer.changeTarget = False
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self.reset_state() # 切换前先统一清理旧状态
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self.init_Decoder(self.zmqServer.decoder_class)
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# 同步信息
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if self.zmqServer.state_mode == 'sync':
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self.zmqClient.send_to_all('sync', self.zmqClient.state)
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self.zmqServer.state_mode = 'rest'
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# 状态异常，报告上位机
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if self.status_code != self.thread_data_server.status_code:
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self.status_code = self.thread_data_server.status_code
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self.zmqClient.send_to_all('status_code', int(self.status_code))
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print('status code')
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# 返回电量
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if self.energy != self.thread_data_server.energy:
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self.energy = self.thread_data_server.energy
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self.zmqClient.send_to_all('energy', int(self.energy))
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print('energy')
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if self.zmqServer.open_Impedance == True: # 开启阻抗检测功能，仅运行一次
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self.thread_data_server.Impedance(True)
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print('Impedance')
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self.zmqServer.open_Impedance = -1
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elif self.zmqServer.open_Impedance == False:
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self.thread_data_server.Impedance(False)
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self.zmqServer.open_Impedance = -1
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if self.zmqServer.get_Impedance: # 返回阻抗值
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# print(self.zmqServer.get_Impedance)
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# print(self.thread_data_server.GetDataLenCount())
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if self.thread_data_server.GetDataLenCount() > 250:
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Impe_data = self.thread_data_server.getData(250)
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# 计算阻抗
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imps = self.thread_data_server.getImpedance(Impe_data,self.zmqServer.decoder_class)
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self.zmqClient.send_to_all('impedance', imps.tolist())
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else:
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pass
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if self.zmqServer.getReport: #返回训练报告内容
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self.zmqServer.getReport = False
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allData = np.array(self.plotData)
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allLabel = np.array(self.plotLabel) + 1
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nTrials = min(len(allLabel),len(allData))
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if nTrials < 30:
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self.zmqClient.send_to_all('miReport',0)
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else:
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allData = allData[:nTrials]
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allLabel = allLabel[:nTrials]
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ch_names = ['FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'CP3', 'CP1',
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'CP2', 'CP4', 'P3', 'P1', 'PZ', 'P2', 'P4']
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compare_names = ['C3', 'CZ', 'C4']
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miReport = plotMain(ch_names=ch_names,compare_names=compare_names,Data=allData,labels=allLabel,MI_label=1,Rest_label=2,
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fs=self.fs)
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self.zmqClient.send_to_all('miReport',miReport)
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# --- 取数优先：先执行 decoder（消费环形缓冲），再处理 plot/report 等重负载 ---
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try:
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if self.decoder_class == 'ssvep' or self.decoder_class == 'pvs':
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self.decoder_SSVEP()
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elif self.decoder_class == 'ssmvep':
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self.decoder_SSMVEP()
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elif self.decoder_class == 'mi':
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self.decoder_MI()
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elif self.decoder_class == 'concentration':
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self.decoder_concentration()
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elif self.decoder_class == 'blink':
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self.decoder_blink()
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else:
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if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
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if self.thread_data_server.GetDataLenCount() < 25:
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time.sleep(0.005)
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continue;
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self.thread_data_server.getData(25)
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except Exception as e:
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print(f"Decoder Loop Error: {e}")
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import traceback
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traceback.print_exc()
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time.sleep(0.1) # Prevent CPU spin if error is persistent
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def decoder_SSVEP(self):
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if self.zmqServer.StartDecode:
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self.zmqServer.StartDecode = False
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self.decodingSteps = 1
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self.thread_data_server.ResetAll()
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print('启动预测')
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if self.thread_data_server.GetDataLenCount() < 50:
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time.sleep(0.005)
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return
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if self.zmqServer.get_Impedance != False: # 阻抗检测状态不解码
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return
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data = self.thread_data_server.getDataViaSSVEP(50)
|
|||
|
|
data = data[:self.n_chan, :]
|
|||
|
|
if self.decodingSteps == 1 and hasattr(self,'dw'): # 开始预热
|
|||
|
|
self.dw.onlineInit() # 刺激闪烁的第1s重置 --在线数据采集时
|
|||
|
|
self.dw.warmFilter(data) # 预热
|
|||
|
|
self.decodingSteps = 2
|
|||
|
|
print('预热数据完成。开始预测')
|
|||
|
|
return
|
|||
|
|
if self.decodingSteps == 2 and hasattr(self,'dw'): # 解码中
|
|||
|
|
choosenNum = self.dw.fbccaDWMW(data, self.referenceData, self.DW_cost_tv, self.calculateCount)
|
|||
|
|
self.calculateCount += 1
|
|||
|
|
if choosenNum != -1 and self.is_valid_signal(data):
|
|||
|
|
self.decodingSteps = 3
|
|||
|
|
print('预测结果：' + str(choosenNum) + ',计算次数：' + str(self.calculateCount))
|
|||
|
|
self.calculateCount = 0
|
|||
|
|
if self.decodingSteps == 3: # 发送解码后的信息
|
|||
|
|
self.zmqClient.send_to_all('result', int(choosenNum))
|
|||
|
|
self.decodingSteps = 0
|
|||
|
|
print('发送给界面完成。')
|
|||
|
|
|
|||
|
|
def decoder_SSMVEP(self):
|
|||
|
|
'''模型训练'''
|
|||
|
|
if self.load_model == False and all(
|
|||
|
|
self.trainLabel.count(i) >= self.single_train for i in range(len(self.list_freqs))): # 模型尚未训练完成
|
|||
|
|
self.trainData = np.array(self.trainData)
|
|||
|
|
self.trainLabel = np.array(self.trainLabel)
|
|||
|
|
print(np.shape(self.trainData), (self.trainLabel))
|
|||
|
|
# 保存多个数组到文件
|
|||
|
|
# np.savez('20250520_yy.npz', array1=self.trainData, array2=self.trainLabel)
|
|||
|
|
# self.decoder = self.fbtdca.fit(self.trainData, self.trainLabel, Yf=self.Yf)
|
|||
|
|
self.decoder = self.tdca.fit(self.trainData, self.trainLabel, Yf=self.Yf)
|
|||
|
|
now = datetime.now()
|
|||
|
|
formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
|
|||
|
|
print('模型训练完成', formatted_time)
|
|||
|
|
self.load_model = True
|
|||
|
|
self.zmqClient.send_to_all('paradigm', 1)
|
|||
|
|
|
|||
|
|
'''训练阶段采集数据'''
|
|||
|
|
if self.zmqServer.state_mode == 'train': # 训练状态
|
|||
|
|
if self.zmqServer.StartTrain:
|
|||
|
|
self.currentLabel = self.zmqServer.currentLabel
|
|||
|
|
self.zmqServer.StartTrain = False
|
|||
|
|
if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
|
|||
|
|
self.train_epoch[1] \
|
|||
|
|
+ self.thread_data_server.event_inner_idx:
|
|||
|
|
time.sleep(0.0001)
|
|||
|
|
return
|
|||
|
|
print('训练队列数据：', self.thread_data_server.GetDataLenCount())
|
|||
|
|
trainTrial = self.thread_data_server.get_SSMVEPData() # 取出所有数据
|
|||
|
|
print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.thread_data_server.event_inner_idx])
|
|||
|
|
trainTrial = self.preprocess(trainTrial[:self.n_chan, :]) # 预处理
|
|||
|
|
trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.train_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.train_epoch[1]]
|
|||
|
|
print('trial: ', self.thread_data_server.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
|
|||
|
|
if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
|
|||
|
|
self.trainLabel, list) \
|
|||
|
|
and self.trainLabel.count(self.currentLabel) < self.single_train:
|
|||
|
|
self.trainData.append(trainTrial)
|
|||
|
|
self.trainLabel.append(self.currentLabel)
|
|||
|
|
|
|||
|
|
elif self.zmqServer.state_mode == 'predict': # 测试状态
|
|||
|
|
if self.load_model == False: # 模型尚未训练完成
|
|||
|
|
time.sleep(0.01)
|
|||
|
|
return
|
|||
|
|
else: # 已有模型
|
|||
|
|
if self.zmqServer.StartDecode:
|
|||
|
|
self.zmqServer.StartDecode = False
|
|||
|
|
now = datetime.now()
|
|||
|
|
formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
|
|||
|
|
print('启动预测 ', formatted_time)
|
|||
|
|
|
|||
|
|
if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
|
|||
|
|
self.interval_epoch[1] \
|
|||
|
|
+ self.thread_data_server.event_inner_idx:
|
|||
|
|
time.sleep(0.0001)
|
|||
|
|
return
|
|||
|
|
data = self.thread_data_server.get_SSMVEPData() # 读取全部数据
|
|||
|
|
print('取出的： ', data.shape, 'event: ', data[-2, self.thread_data_server.event_inner_idx])
|
|||
|
|
data = self.preprocess(data[:self.n_chan, :]) # 预处理
|
|||
|
|
data = data[:,
|
|||
|
|
self.thread_data_server.event_inner_idx + self.interval_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
|
|||
|
|
pad_eeg_test = np.zeros(
|
|||
|
|
(data.shape[0], int((self.sample_length + 0.1) * self.fs)))
|
|||
|
|
pad_eeg_test[:, :int(self.sample_length * self.fs)] = data
|
|||
|
|
choosenNum, features_2 = self.decoder.predict(pad_eeg_test)
|
|||
|
|
if isinstance(choosenNum, np.ndarray):
|
|||
|
|
choosenNum = choosenNum[0]
|
|||
|
|
print('结果：', choosenNum, 'rho: ', sorted(features_2[0]),
|
|||
|
|
sorted(features_2[0])[-1] - sorted(features_2[0])[-2])
|
|||
|
|
self.zmqClient.send_to_all('result', int(choosenNum))
|
|||
|
|
print('发送给界面完成。')
|
|||
|
|
else: # 休息状态
|
|||
|
|
if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
|
|||
|
|
if self.thread_data_server.GetDataLenCount() < 25:
|
|||
|
|
time.sleep(0.005)
|
|||
|
|
return
|
|||
|
|
self.thread_data_server.getData(25)
|
|||
|
|
|
|||
|
|
def decoder_MI(self):
|
|||
|
|
'''模型训练'''
|
|||
|
|
if self.train_started == False and all(
|
|||
|
|
self.trainLabel.count(i) >= self.single_train for i in range(self.num_target)): # 模型尚未训练
|
|||
|
|
self.zmqClient.send_to_all('paradigm', 2) # 模型训练前，训练集采集完毕，通知上位机
|
|||
|
|
self.train_started = True
|
|||
|
|
self.trainData = np.array(self.trainData)
|
|||
|
|
self.trainLabel = np.array(self.trainLabel) + 1
|
|||
|
|
# print('训练集：',np.shape(self.trainData), (self.trainLabel))
|
|||
|
|
p = mp.Process(target=onlineTrain, args=(self.mp_data_queue, self.mp_result_queue)) # 开启子进程，训练模型
|
|||
|
|
p.start()
|
|||
|
|
self.mp_data_queue.put({'data': self.trainData, 'label': self.trainLabel, 'modelPath': self.modelPath,
|
|||
|
|
'n_chan': self.n_chan})
|
|||
|
|
|
|||
|
|
'''检查模型是否训练完成,调用'''
|
|||
|
|
if self.load_model == False and self.train_started == True:
|
|||
|
|
try:
|
|||
|
|
result = self.mp_result_queue.get_nowait()
|
|||
|
|
if result['status'] == 'success':
|
|||
|
|
print("模型训练完成，加载新模型")
|
|||
|
|
# 调用模型
|
|||
|
|
self.model = torch.load(self.modelPath, weights_only=False)
|
|||
|
|
self.model.eval()
|
|||
|
|
# 模型预热
|
|||
|
|
warmup_data = np.random.uniform(-1, 1, (1, 1, self.n_chan, 1000))
|
|||
|
|
warmup_data = torch.from_numpy(warmup_data)
|
|||
|
|
warmup_data = Variable(warmup_data.type(torch.cuda.FloatTensor))
|
|||
|
|
with torch.no_grad():
|
|||
|
|
_ = self.model(warmup_data)
|
|||
|
|
self.load_model = True
|
|||
|
|
self.zmqClient.send_to_all('paradigm', 1) # 模型调用完毕，通知上位机
|
|||
|
|
else:
|
|||
|
|
print("训练失败:", result['msg'])
|
|||
|
|
except Empty:
|
|||
|
|
pass # 还没完成
|
|||
|
|
except Exception as e:
|
|||
|
|
print('模型调用失败: ', e)
|
|||
|
|
|
|||
|
|
'''训练阶段采集数据'''
|
|||
|
|
if self.zmqServer.state_mode == 'train' and self.train_started == False: # 训练状态
|
|||
|
|
if self.zmqServer.StartTrain:
|
|||
|
|
self.currentLabel = self.zmqServer.currentLabel
|
|||
|
|
self.zmqServer.StartTrain = False
|
|||
|
|
if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
|
|||
|
|
self.interval_epoch[1] \
|
|||
|
|
+ self.thread_data_server.event_inner_idx:
|
|||
|
|
time.sleep(0.0001)
|
|||
|
|
return
|
|||
|
|
print('训练队列数据：', self.thread_data_server.GetDataLenCount())
|
|||
|
|
originalTrial = self.thread_data_server.get_MIData() # 取出MI导联数据
|
|||
|
|
print('取出的： ', originalTrial.shape, 'event: ', originalTrial[-2, self.thread_data_server.event_inner_idx])
|
|||
|
|
trainTrial = self.preprocess(originalTrial[:self.n_chan, :]) # 预处理
|
|||
|
|
trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.interval_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
|
|||
|
|
print('trial: ', self.thread_data_server.event_inner_idx, self.interval_epoch[0], self.interval_epoch[1])
|
|||
|
|
if trainTrial.shape[1] == (self.interval_epoch[1] - self.interval_epoch[0]) and isinstance(self.trainLabel,
|
|||
|
|
list) \
|
|||
|
|
and self.trainLabel.count(self.currentLabel) < self.single_train:
|
|||
|
|
self.trainData.append(trainTrial)
|
|||
|
|
self.trainLabel.append(self.currentLabel)
|
|||
|
|
print('训练集：', np.shape(self.trainData))
|
|||
|
|
self.plotData.append(originalTrial[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
|
|||
|
|
self.plotLabel.append(self.currentLabel)
|
|||
|
|
|
|||
|
|
elif self.zmqServer.state_mode == 'predict' and self.load_model == True: # 测试状态
|
|||
|
|
if self.zmqServer.StartDecode:
|
|||
|
|
self.zmqServer.StartDecode = False
|
|||
|
|
now = datetime.now()
|
|||
|
|
formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
|
|||
|
|
print('启动预测 ', formatted_time)
|
|||
|
|
|
|||
|
|
if self.thread_data_server.epoch_finished == False or self.thread_data_server.GetDataLenCount() < \
|
|||
|
|
self.interval_epoch[1] \
|
|||
|
|
+ self.thread_data_server.event_inner_idx:
|
|||
|
|
time.sleep(0.0001)
|
|||
|
|
return
|
|||
|
|
originalData = self.thread_data_server.get_MIData() # 读取全部数据
|
|||
|
|
print('取出的： ', originalData.shape, 'event: ', originalData[-2, self.thread_data_server.event_inner_idx])
|
|||
|
|
start = time.time()
|
|||
|
|
data = self.preprocess(originalData[:self.n_chan, :]) # 预处理
|
|||
|
|
data = data[:,
|
|||
|
|
self.thread_data_server.event_inner_idx + self.interval_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
|
|||
|
|
self.plotData.append(
|
|||
|
|
originalData[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
|
|||
|
|
0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
|
|||
|
|
|
|||
|
|
test_data = data[np.newaxis, np.newaxis, :, :]
|
|||
|
|
test_data = torch.from_numpy(test_data)
|
|||
|
|
test_data = Variable(test_data.type(torch.cuda.FloatTensor))
|
|||
|
|
with torch.no_grad():
|
|||
|
|
Cls = self.model(test_data)
|
|||
|
|
y_pred = torch.max(Cls, 1)[1]
|
|||
|
|
self.plotLabel.append(int(y_pred.item()))
|
|||
|
|
print('运动意图识别: ', y_pred)
|
|||
|
|
self.zmqClient.send_to_all('result', int(y_pred.item()))
|
|||
|
|
end = time.time()
|
|||
|
|
print(f'发送给界面完成,耗时{end - start:.3f}s。')
|
|||
|
|
else: # 休息状态
|
|||
|
|
if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
|
|||
|
|
if self.thread_data_server.GetDataLenCount() < 25:
|
|||
|
|
time.sleep(0.005)
|
|||
|
|
return
|
|||
|
|
self.thread_data_server.getData(25)
|
|||
|
|
|
|||
|
|
def decoder_concentration(self):
|
|||
|
|
if self.zmqServer.state_mode == 'predict':
|
|||
|
|
if self.zmqServer.StartDecode:
|
|||
|
|
self.zmqServer.StartDecode = False
|
|||
|
|
self.thread_data_server.ResetAll()
|
|||
|
|
now = datetime.now()
|
|||
|
|
formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
|
|||
|
|
print('启动专注力预测 ', formatted_time)
|
|||
|
|
if self.thread_data_server.GetDataLenCount() < int(self.win_step * self.fs): # 每win_step得出一次结果
|
|||
|
|
time.sleep(0.005)
|
|||
|
|
return
|
|||
|
|
if self.zmqServer.get_Impedance != False: # 阻抗检测状态不解码
|
|||
|
|
return
|
|||
|
|
data = self.thread_data_server.get_concentrateData(int(self.win_step * self.fs)) # 修改每次读取的数据
|
|||
|
|
result = self.calculate.queueOpt(data)
|
|||
|
|
if result is not None:
|
|||
|
|
self.zmqClient.send_to_all('result', int(result))
|
|||
|
|
else: # 休息状态
|
|||
|
|
if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
|
|||
|
|
if self.thread_data_server.GetDataLenCount() < 25:
|
|||
|
|
time.sleep(0.005)
|
|||
|
|
return
|
|||
|
|
self.thread_data_server.getData(25)
|
|||
|
|
|
|||
|
|
#### Blink detection #####
|
|||
|
|
def check_double_blink(self, current_time):
|
|||
|
|
"""
|
|||
|
|
检查是否检测到连续两次眨眼
|
|||
|
|
@param current_time: 当前眨眼时间戳
|
|||
|
|
@return: True表示检测到连续两次眨眼
|
|||
|
|
"""
|
|||
|
|
if len(self.blink_timestamps) < 2:
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
# 检查是否在去抖期内
|
|||
|
|
if self.last_double_blink_time > 0:
|
|||
|
|
time_since_last_double_blink = current_time - self.last_double_blink_time
|
|||
|
|
if time_since_last_double_blink < self.double_blink_jitter:
|
|||
|
|
return False # 在去抖期内，忽略连续眨眼检测
|
|||
|
|
last_time = self.blink_timestamps[-1] # 当前眨眼
|
|||
|
|
prev_time = self.blink_timestamps[-2] # 上次眨眼
|
|||
|
|
|
|||
|
|
interval = last_time - prev_time
|
|||
|
|
if interval <= self.double_blink_interval:
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
def process_blink_detection(self):
|
|||
|
|
"""
|
|||
|
|
在缓冲区数据上执行,单次眨眼检测
|
|||
|
|
"""
|
|||
|
|
if len(self.fp1_buffer) < self.window_samples:
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
fp1_data = np.array(list(self.fp1_buffer)[-self.window_samples:])
|
|||
|
|
fp2_data = np.array(list(self.fp2_buffer)[-self.window_samples:])
|
|||
|
|
# 计算FP1和FP2的平均
|
|||
|
|
fp12_mean = (fp1_data + fp2_data) / 2.0
|
|||
|
|
# 带通滤波
|
|||
|
|
try:
|
|||
|
|
fp12_filtered = signal.filtfilt(self.blink_b, self.blink_a, fp12_mean)
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Filter error: {e}")
|
|||
|
|
return
|
|||
|
|
F = np.diff(fp12_filtered)
|
|||
|
|
if len(F) < 3:
|
|||
|
|
return
|
|||
|
|
b, d, e = blink_detection(F, self.fs, self.Dmin, self.Dmax, self.EMin, self.EMax)
|
|||
|
|
|
|||
|
|
if b == 1:
|
|||
|
|
samples_since_last = self.total_samples - self.last_blink_time
|
|||
|
|
time_since_last_ms = (samples_since_last / self.fs) * 1000
|
|||
|
|
if time_since_last_ms >= self.jitterwin: # self.jitterwin 单次眨眼去抖 using time_since_last_ms
|
|||
|
|
self.blink_count += 1
|
|||
|
|
self.last_blink_time = self.total_samples
|
|||
|
|
current_time = time.time()
|
|||
|
|
self.blink_timestamps.append(current_time)
|
|||
|
|
blink_event = {
|
|||
|
|
'count': self.blink_count,
|
|||
|
|
'time': current_time,
|
|||
|
|
'sample_index': self.total_samples,
|
|||
|
|
'duration_ms': d,
|
|||
|
|
'energy': e
|
|||
|
|
}
|
|||
|
|
self.blink_events.append(blink_event)
|
|||
|
|
self.zmqClient.send_to_all('result', 1) # 检测到眨眼信号，通知上位机
|
|||
|
|
if self.check_double_blink(current_time):
|
|||
|
|
self.double_blink_count += 1
|
|||
|
|
interval = self.blink_timestamps[-1] - self.blink_timestamps[-2]
|
|||
|
|
double_blink_event = {
|
|||
|
|
'double_blink_count': self.double_blink_count,
|
|||
|
|
'blink1_time': self.blink_timestamps[-2],
|
|||
|
|
'blink2_time': self.blink_timestamps[-1],
|
|||
|
|
'interval': interval
|
|||
|
|
}
|
|||
|
|
self.double_blink_events.append(double_blink_event)
|
|||
|
|
self.last_double_blink_time = current_time
|
|||
|
|
self.zmqClient.send_to_all('result', 2) # 发送双次眨眼事件
|
|||
|
|
|
|||
|
|
def decoder_blink(self):
|
|||
|
|
if self.thread_data_server.GetDataLenCount() < 50:
|
|||
|
|
time.sleep(0.005)
|
|||
|
|
return
|
|||
|
|
if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
|
|||
|
|
data = self.thread_data_server.get_blinkData(50)
|
|||
|
|
fp1_data = data[0, :] # ch1 (相当于FP1)
|
|||
|
|
fp2_data = data[1, :] # ch2 (相当于FP2)
|
|||
|
|
for i in range(len(fp1_data)):
|
|||
|
|
self.fp1_buffer.append(fp1_data[i])
|
|||
|
|
self.fp2_buffer.append(fp2_data[i])
|
|||
|
|
self.total_samples += 1
|
|||
|
|
self.sample_counter += 1
|
|||
|
|
|
|||
|
|
if self.sample_counter >= self.step_samples:
|
|||
|
|
self.process_blink_detection()
|
|||
|
|
self.sample_counter = 0
|
|||
|
|
|
|||
|
|
def stop(self):
|
|||
|
|
'''
|
|||
|
|
停止运行
|
|||
|
|
@return:
|
|||
|
|
'''
|
|||
|
|
self.zmqServer.stop()
|
|||
|
|
self.Runing=False
|
|||
|
|
|
|||
|
|
def reset_state(self):
|
|||
|
|
"""清空解码器状态和缓存数据"""
|
|||
|
|
# 重置设备层缓存
|
|||
|
|
self.thread_data_server.reset_state()
|
|||
|
|
|
|||
|
|
# 重置解码状态
|
|||
|
|
self.decodingSteps = 0
|
|||
|
|
self.calculateCount = 0
|
|||
|
|
|
|||
|
|
# 重置训练数据
|
|||
|
|
self.plotData = []
|
|||
|
|
self.plotLabel = []
|
|||
|
|
self.trainData = []
|
|||
|
|
self.trainLabel = []
|
|||
|
|
self.currentLabel = -1
|
|||
|
|
self.train_started = False
|
|||
|
|
self.load_model = False
|
|||
|
|
|
|||
|
|
# 重置多进程队列，确保切换 decoder 时旧数据不会泄漏到新队列
|
|||
|
|
if hasattr(self, 'mp_data_queue'):
|
|||
|
|
while not self.mp_data_queue.empty():
|
|||
|
|
try: self.mp_data_queue.get_nowait()
|
|||
|
|
except Empty: pass
|
|||
|
|
if hasattr(self, 'mp_result_queue'):
|
|||
|
|
while not self.mp_result_queue.empty():
|
|||
|
|
try: self.mp_result_queue.get_nowait()
|
|||
|
|
except Empty: pass
|