范式相关代码修改完成
This commit is contained in:
240
Decoder.py
240
Decoder.py
@@ -24,7 +24,7 @@ from logs.log import algo_log
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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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from Zmq.filterProcess import SlidingFilter
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def get_root_path():
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"""
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@@ -63,6 +63,8 @@ class Decoder_main(threading.Thread):
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self.zmqServer = zmqServer(device_info=self.device_info)
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self.zmqServer.start()
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self.filter = SlidingFilter()
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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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@@ -188,10 +190,10 @@ class Decoder_main(threading.Thread):
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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.device_info['sample_rate']/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.device_info['sample_rate']/2), bandPass_high / (self.device_info['sample_rate']/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 = os.path.join(get_root_path(), MODEL_FOLDER) + os.sep
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for old_pth in glob.glob(os.path.join(filePath, '*.pth')):
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os.remove(old_pth)
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fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
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self.modelPath = ''.join([filePath, fileName, '.pth'])
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self.mp_data_queue = mp.Queue()
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self.mp_result_queue = mp.Queue()
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@@ -220,54 +222,6 @@ class Decoder_main(threading.Thread):
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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.device_info['sample_rate'])
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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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@@ -276,17 +230,12 @@ class Decoder_main(threading.Thread):
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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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# self.
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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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else:
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if self.zmqServer.open_Impedance == False: # 非阻抗检测状态
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if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
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time.sleep(0.005)
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continue;
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self.zmqServer.paradigmBuffer.getData(25)
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except Exception as e:
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algo_log(f"Decoder Loop Error: {e}")
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time.sleep(0.1) # Prevent CPU spin if error is persistent
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@@ -344,18 +293,18 @@ class Decoder_main(threading.Thread):
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if self.zmqServer.StartTrain:
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self.currentLabel = self.zmqServer.currentLabel
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self.zmqServer.StartTrain = False
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if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.train_epoch[1] \
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+ self.thread_data_server.event_inner_idx:
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+ self.zmqServer.event_inner_idx:
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time.sleep(0.0001)
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return
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print('训练队列数据：', self.zmqServer.paradigmBuffer.GetDataLenCount())
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trainTrial = self.thread_data_server.get_SSMVEPData() # 取出所有数据
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print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.thread_data_server.event_inner_idx])
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trainTrial = self.zmqServer.paradigmBuffer.get_SSMVEPData() # 取出所有数据
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print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.zmqServer.event_inner_idx])
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trainTrial = self.preprocess(trainTrial[:self.n_chan, :]) # 预处理
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trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.train_epoch[
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0]:self.thread_data_server.event_inner_idx + self.train_epoch[1]]
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print('trial: ', self.thread_data_server.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
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trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[
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0]:self.zmqServer.event_inner_idx + self.train_epoch[1]]
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print('trial: ', self.zmqServer.event_inner_idx, self.train_epoch[0], self.train_epoch[1])
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if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
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self.trainLabel, list) \
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and self.trainLabel.count(self.currentLabel) < self.single_train:
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@@ -373,17 +322,17 @@ class Decoder_main(threading.Thread):
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formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
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print('启动预测 ', formatted_time)
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if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.interval_epoch[1] \
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+ self.thread_data_server.event_inner_idx:
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+ self.zmqServer.event_inner_idx:
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time.sleep(0.0001)
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return
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data = self.thread_data_server.get_SSMVEPData() # 读取全部数据
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print('取出的： ', data.shape, 'event: ', data[-2, self.thread_data_server.event_inner_idx])
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data = self.zmqServer.paradigmBuffer.get_SSMVEPData() # 读取全部数据
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print('取出的： ', data.shape, 'event: ', data[-2, self.zmqServer.event_inner_idx])
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data = self.preprocess(data[:self.n_chan, :]) # 预处理
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data = data[:,
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self.thread_data_server.event_inner_idx + self.interval_epoch[
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0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
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self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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pad_eeg_test = np.zeros(
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(data.shape[0], int((self.sample_length + 0.1) * self.device_info['sample_rate'])))
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pad_eeg_test[:, :int(self.sample_length * self.device_info['sample_rate'])] = data
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@@ -392,20 +341,20 @@ class Decoder_main(threading.Thread):
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choosenNum = choosenNum[0]
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print('结果：', choosenNum, 'rho: ', sorted(features_2[0]),
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sorted(features_2[0])[-1] - sorted(features_2[0])[-2])
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self.zmqClient.send_to_all('result', int(choosenNum))
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self.zmqServer.broadcast_message('result', int(choosenNum))
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print('发送给界面完成。')
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else: # 休息状态
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if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
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if self.zmqServer.open_Impedance == False: # 非阻抗检测状态
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if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
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time.sleep(0.005)
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return
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self.thread_data_server.getData(25)
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self.zmqServer.paradigmBuffer.getData(25)
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def decoder_MI(self):
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'''模型训练'''
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if self.train_started == False and all(
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self.trainLabel.count(i) >= self.single_train for i in range(self.num_target)): # 模型尚未训练
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self.zmqClient.send_to_all('paradigm', 2) # 模型训练前，训练集采集完毕，通知上位机
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self.zmqServer.broadcast_message('paradigm', 2) # 模型训练前，训练集采集完毕，通知上位机
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self.train_started = True
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self.trainData = np.array(self.trainData)
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self.trainLabel = np.array(self.trainLabel) + 1
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@@ -431,7 +380,7 @@ class Decoder_main(threading.Thread):
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with torch.no_grad():
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_ = self.model(warmup_data)
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self.load_model = True
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self.zmqClient.send_to_all('paradigm', 1) # 模型调用完毕，通知上位机
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self.zmqServer.broadcast_message('paradigm', 1) # 模型调用完毕，通知上位机
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else:
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print("训练失败:", result['msg'])
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except Empty:
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@@ -444,26 +393,26 @@ class Decoder_main(threading.Thread):
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if self.zmqServer.StartTrain:
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self.currentLabel = self.zmqServer.currentLabel
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self.zmqServer.StartTrain = False
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if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.interval_epoch[1] \
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+ self.thread_data_server.event_inner_idx:
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+ self.zmqServer.event_inner_idx:
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time.sleep(0.0001)
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return
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print('训练队列数据：', self.zmqServer.paradigmBuffer.GetDataLenCount())
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originalTrial = self.thread_data_server.get_MIData() # 取出MI导联数据
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print('取出的： ', originalTrial.shape, 'event: ', originalTrial[-2, self.thread_data_server.event_inner_idx])
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originalTrial = self.zmqServer.paradigmBuffer.get_MIData() # 取出MI导联数据
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print('取出的： ', originalTrial.shape, 'event: ', originalTrial[-2, self.zmqServer.event_inner_idx])
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trainTrial = self.preprocess(originalTrial[:self.n_chan, :]) # 预处理
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trainTrial = trainTrial[:, self.thread_data_server.event_inner_idx + self.interval_epoch[
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0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
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print('trial: ', self.thread_data_server.event_inner_idx, self.interval_epoch[0], self.interval_epoch[1])
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trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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print('trial: ', self.zmqServer.event_inner_idx, self.interval_epoch[0], self.interval_epoch[1])
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if trainTrial.shape[1] == (self.interval_epoch[1] - self.interval_epoch[0]) and isinstance(self.trainLabel,
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list) \
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and self.trainLabel.count(self.currentLabel) < self.single_train:
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self.trainData.append(trainTrial)
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self.trainLabel.append(self.currentLabel)
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print('训练集：', np.shape(self.trainData))
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self.plotData.append(originalTrial[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
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0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
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self.plotData.append(originalTrial[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
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self.plotLabel.append(self.currentLabel)
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elif self.zmqServer.state_mode == 'predict' and self.load_model == True: # 测试状态
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@@ -473,21 +422,21 @@ class Decoder_main(threading.Thread):
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formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
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print('启动预测 ', formatted_time)
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if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.interval_epoch[1] \
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+ self.thread_data_server.event_inner_idx:
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+ self.zmqServer.event_inner_idx:
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time.sleep(0.0001)
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return
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originalData = self.thread_data_server.get_MIData() # 读取全部数据
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print('取出的： ', originalData.shape, 'event: ', originalData[-2, self.thread_data_server.event_inner_idx])
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originalData = self.zmqServer.paradigmBuffer.get_MIData() # 读取全部数据
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print('取出的： ', originalData.shape, 'event: ', originalData[-2, self.zmqServer.event_inner_idx])
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start = time.time()
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data = self.preprocess(originalData[:self.n_chan, :]) # 预处理
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data = data[:,
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self.thread_data_server.event_inner_idx + self.interval_epoch[
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0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]]
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self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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self.plotData.append(
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originalData[:self.n_chan, self.thread_data_server.event_inner_idx + self.interval_epoch[
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0]:self.thread_data_server.event_inner_idx + self.interval_epoch[1]])
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originalData[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
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test_data = data[np.newaxis, np.newaxis, :, :]
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test_data = torch.from_numpy(test_data)
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@@ -497,15 +446,15 @@ class Decoder_main(threading.Thread):
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y_pred = torch.max(Cls, 1)[1]
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self.plotLabel.append(int(y_pred.item()))
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print('运动意图识别: ', y_pred)
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self.zmqClient.send_to_all('result', int(y_pred.item()))
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self.zmqServer.broadcast_message('paradigm', int(y_pred.item()))
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end = time.time()
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print(f'发送给界面完成,耗时{end - start:.3f}s。')
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else: # 休息状态
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if self.zmqServer.get_Impedance == False: # 非阻抗检测状态
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if self.zmqServer.open_Impedance == False: # 非阻抗检测状态
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if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
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time.sleep(0.005)
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return
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self.thread_data_server.getData(25)
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self.zmqServer.paradigmBuffer.getData(25)
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# def decoder_concentration(self):
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# if self.zmqServer.state_mode == 'predict':
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@@ -531,99 +480,6 @@ class Decoder_main(threading.Thread):
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# return
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# self.thread_data_server.getData(25)
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#### Blink detection #####
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# def check_double_blink(self, current_time):
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# """
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# 检查是否检测到连续两次眨眼
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# @param current_time: 当前眨眼时间戳
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# @return: True表示检测到连续两次眨眼
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# """
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# if len(self.blink_timestamps) < 2:
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# return False
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# # 检查是否在去抖期内
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# if self.last_double_blink_time > 0:
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# time_since_last_double_blink = current_time - self.last_double_blink_time
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# if time_since_last_double_blink < self.double_blink_jitter:
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# return False # 在去抖期内，忽略连续眨眼检测
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# last_time = self.blink_timestamps[-1] # 当前眨眼
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# prev_time = self.blink_timestamps[-2] # 上次眨眼
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# interval = last_time - prev_time
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# if interval <= self.double_blink_interval:
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# return True
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# return False
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# def process_blink_detection(self):
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# """
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# 在缓冲区数据上执行,单次眨眼检测
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# """
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# if len(self.fp1_buffer) < self.window_samples:
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# return
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|
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# 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.device_info['sample_rate'], 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.device_info['sample_rate']) * 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):
|
||||
'''
|
||||
|
||||
Reference in New Issue
Block a user