diff --git a/Decoder.py b/Decoder.py index 80e0a5d..092a126 100644 --- a/Decoder.py +++ b/Decoder.py @@ -24,7 +24,7 @@ from logs.log import algo_log from SSVEP.dwfbcca import FbccaDw # from Tools.plot_MI_EEG import plotMain from collections import deque - +from Zmq.filterProcess import SlidingFilter def get_root_path(): """ @@ -63,6 +63,8 @@ class Decoder_main(threading.Thread): self.zmqServer = zmqServer(device_info=self.device_info) self.zmqServer.start() + + self.filter = SlidingFilter() # self.zmqClient = zmqClient(_upper_host, _upper_port) # self.zmqClient.set_zmq_server(self.zmqServer) @@ -188,10 +190,10 @@ class Decoder_main(threading.Thread): self.load_model = False # 调用模型是否完成的标志 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 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 - fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S') filePath = os.path.join(get_root_path(), MODEL_FOLDER) + os.sep for old_pth in glob.glob(os.path.join(filePath, '*.pth')): os.remove(old_pth) + fileName = 'Model_' + datetime.now().strftime('%Y-%m-%d-%H-%M-%S') self.modelPath = ''.join([filePath, fileName, '.pth']) self.mp_data_queue = mp.Queue() self.mp_result_queue = mp.Queue() @@ -220,54 +222,6 @@ class Decoder_main(threading.Thread): # self.zmqClient.send_to_all('sync', self.zmqClient.state) self.zmqServer.state_mode = 'rest' - # # 状态异常，报告上位机 - # if self.status_code != self.thread_data_server.status_code: - # self.status_code = self.thread_data_server.status_code - # self.zmqClient.send_to_all('status_code', int(self.status_code)) - # print('status code') - - # # 返回电量 - # if self.energy != self.thread_data_server.energy: - # self.energy = self.thread_data_server.energy - # self.zmqClient.send_to_all('energy', int(self.energy)) - # print('energy') - - # if self.zmqServer.open_Impedance == True: # 开启阻抗检测功能，仅运行一次 - # self.thread_data_server.Impedance(True) - # print('Impedance') - # self.zmqServer.open_Impedance = -1 - # elif self.zmqServer.open_Impedance == False: - # self.thread_data_server.Impedance(False) - # self.zmqServer.open_Impedance = -1 - - # if self.zmqServer.get_Impedance: # 返回阻抗值 - # # print(self.zmqServer.get_Impedance) - # # print(self.thread_data_server.GetDataLenCount()) - # if self.thread_data_server.GetDataLenCount() > 250: - # Impe_data = self.thread_data_server.getData(250) - # # 计算阻抗 - # imps = self.thread_data_server.getImpedance(Impe_data,self.zmqServer.decoder_class) - # self.zmqClient.send_to_all('impedance', imps.tolist()) - # else: - # pass - # if self.zmqServer.getReport: #返回训练报告内容 - # self.zmqServer.getReport = False - # allData = np.array(self.plotData) - # allLabel = np.array(self.plotLabel) + 1 - # nTrials = min(len(allLabel),len(allData)) - # if nTrials < 30: - # self.zmqClient.send_to_all('miReport',0) - # else: - # allData = allData[:nTrials] - # allLabel = allLabel[:nTrials] - # ch_names = ['FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'CP3', 'CP1', - # 'CP2', 'CP4', 'P3', 'P1', 'PZ', 'P2', 'P4'] - # compare_names = ['C3', 'CZ', 'C4'] - # miReport = plotMain(ch_names=ch_names,compare_names=compare_names,Data=allData,labels=allLabel,MI_label=1,Rest_label=2, - # fs=self.device_info['sample_rate']) - # self.zmqClient.send_to_all('miReport',miReport) - - # --- 取数优先：先执行 decoder（消费环形缓冲），再处理 plot/report 等重负载 --- try: if self.decoder_class == 'ssvep' or self.decoder_class == 'pvs': @@ -276,17 +230,12 @@ class Decoder_main(threading.Thread): self.decoder_SSMVEP() elif self.decoder_class == 'mi': self.decoder_MI() - # elif self.decoder_class == 'concentration': - # self.decoder_concentration() - # elif self.decoder_class == 'blink': - # self.decoder_blink() - # else: - # self. - # # if self.zmqServer.get_Impedance == False: # 非阻抗检测状态 - # # if self.thread_data_server.GetDataLenCount() < 25: - # # time.sleep(0.005) - # # continue; - # # self.thread_data_server.getData(25) + else: + if self.zmqServer.open_Impedance == False: # 非阻抗检测状态 + if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25: + time.sleep(0.005) + continue; + self.zmqServer.paradigmBuffer.getData(25) except Exception as e: algo_log(f"Decoder Loop Error: {e}") time.sleep(0.1) # Prevent CPU spin if error is persistent @@ -344,18 +293,18 @@ class Decoder_main(threading.Thread): if self.zmqServer.StartTrain: self.currentLabel = self.zmqServer.currentLabel self.zmqServer.StartTrain = False - if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ + if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ self.train_epoch[1] \ - + self.thread_data_server.event_inner_idx: + + self.zmqServer.event_inner_idx: time.sleep(0.0001) return print('训练队列数据：', self.zmqServer.paradigmBuffer.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.zmqServer.paradigmBuffer.get_SSMVEPData() # 取出所有数据 + print('取出的： ', trainTrial.shape, 'event: ', trainTrial[-2, self.zmqServer.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]) + trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[ + 0]:self.zmqServer.event_inner_idx + self.train_epoch[1]] + print('trial: ', self.zmqServer.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: @@ -373,17 +322,17 @@ class Decoder_main(threading.Thread): 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.zmqServer.paradigmBuffer.GetDataLenCount() < \ + if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ self.interval_epoch[1] \ - + self.thread_data_server.event_inner_idx: + + self.zmqServer.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.zmqServer.paradigmBuffer.get_SSMVEPData() # 读取全部数据 + print('取出的： ', data.shape, 'event: ', data[-2, self.zmqServer.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]] + self.zmqServer.event_inner_idx + self.interval_epoch[ + 0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]] pad_eeg_test = np.zeros( (data.shape[0], int((self.sample_length + 0.1) * self.device_info['sample_rate']))) pad_eeg_test[:, :int(self.sample_length * self.device_info['sample_rate'])] = data @@ -392,20 +341,20 @@ class Decoder_main(threading.Thread): 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)) + self.zmqServer.broadcast_message('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.zmqServer.open_Impedance == False: # 非阻抗检测状态 if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25: time.sleep(0.005) return - self.thread_data_server.getData(25) + self.zmqServer.paradigmBuffer.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.zmqServer.broadcast_message('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 @@ -431,7 +380,7 @@ class Decoder_main(threading.Thread): 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 + self.zmqServer.broadcast_message('paradigm', 1) # 模型调用完毕，通知上位机 else: print("训练失败:", result['msg']) except Empty: @@ -444,26 +393,26 @@ class Decoder_main(threading.Thread): if self.zmqServer.StartTrain: self.currentLabel = self.zmqServer.currentLabel self.zmqServer.StartTrain = False - if self.thread_data_server.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ + if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ self.interval_epoch[1] \ - + self.thread_data_server.event_inner_idx: + + self.zmqServer.event_inner_idx: time.sleep(0.0001) return print('训练队列数据：', self.zmqServer.paradigmBuffer.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]) + originalTrial = self.zmqServer.paradigmBuffer.get_MIData() # 取出MI导联数据 + print('取出的： ', originalTrial.shape, 'event: ', originalTrial[-2, self.zmqServer.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]) + trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.interval_epoch[ + 0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]] + print('trial: ', self.zmqServer.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.plotData.append(originalTrial[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[ + 0]:self.zmqServer.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 @@ -473,21 +422,21 @@ class Decoder_main(threading.Thread): 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.zmqServer.paradigmBuffer.GetDataLenCount() < \ + if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \ self.interval_epoch[1] \ - + self.thread_data_server.event_inner_idx: + + self.zmqServer.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]) + originalData = self.zmqServer.paradigmBuffer.get_MIData() # 读取全部数据 + print('取出的： ', originalData.shape, 'event: ', originalData[-2, self.zmqServer.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.zmqServer.event_inner_idx + self.interval_epoch[ + 0]:self.zmqServer.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]]) + originalData[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[ + 0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]) test_data = data[np.newaxis, np.newaxis, :, :] test_data = torch.from_numpy(test_data) @@ -497,15 +446,15 @@ class Decoder_main(threading.Thread): 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())) + self.zmqServer.broadcast_message('paradigm', 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.zmqServer.open_Impedance == False: # 非阻抗检测状态 if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25: time.sleep(0.005) return - self.thread_data_server.getData(25) + self.zmqServer.paradigmBuffer.getData(25) # def decoder_concentration(self): # if self.zmqServer.state_mode == 'predict': @@ -531,99 +480,6 @@ class Decoder_main(threading.Thread): # 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.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): ''' diff --git a/README.md b/README.md index 68aa398..d1fcc39 100644 --- a/README.md +++ b/README.md @@ -17,5 +17,12 @@ Debug_64ch_Decoder_Optimize is an updated version that fixes several issues and 2026年6月5日13:55:34 # 遗留问题 -1. 之前当处于阻抗检测状态时，Decoder在空跑。 -2. 当前无法判断是否处于阻抗检测状态。 \ No newline at end of file +1. 之前当处于阻抗检测状态时，Decoder在空跑。当前无法判断是否处于阻抗检测状态。 + - 解决方法，保留之前发阻抗命令 + + +# 常用命令 +source activate 3in1Py310 +python runDecoder.py +python datamock.py +python ZeroMQClient_mock.py diff --git a/Zmq/dataBuffer.py b/Zmq/dataBuffer.py index 9d67df2..f08a35b 100644 --- a/Zmq/dataBuffer.py +++ b/Zmq/dataBuffer.py @@ -11,7 +11,7 @@ class ParadigmRingBuffer: def __init__(self, n_chan, n_points): self.n_chan = n_chan self.n_points = n_points - self.buffer = np.zeros((n_chan, n_points)) + self.buffer = np.zeros((n_chan, n_points), dtype=np.float32) self.currentPtr = 0 self.readPtr = 0 self.nUpdate = 0 @@ -117,6 +117,6 @@ class ParadigmRingBuffer: def resetAllPara(self): self.nUpdate = 0 self.currentPtr = 0 - self.readPtr = 0 # add by lizhenhua 清空读指针 - self.buffer = np.zeros((self.n_chan, self.n_points)) # add by lizhenhua 清空环形缓冲区 + self.readPtr = 0 + self.buffer.fill(0.0) diff --git a/Zmq/filterProcess.py b/Zmq/filterProcess.py index decc620..522e736 100644 --- a/Zmq/filterProcess.py +++ b/Zmq/filterProcess.py @@ -4,6 +4,7 @@ """ import numpy as np import threading +from scipy import signal from logs.log import algo_log class FilterRingBuffer: @@ -16,7 +17,7 @@ class FilterRingBuffer: self.n_chan = n_chan self.n_points = n_points - self.buffer = np.zeros((n_chan, n_points), dtype=np.float64) + self.buffer = np.zeros((n_chan, n_points), dtype=np.float32) self.current_ptr = 0 # 写入指针 self.total_samples = 0 # 已写入总点数 diff --git a/Zmq/zmqServer.py b/Zmq/zmqServer.py index 4cb8b7f..f18d185 100644 --- a/Zmq/zmqServer.py +++ b/Zmq/zmqServer.py @@ -4,6 +4,9 @@ import threading import json import queue from typing import Dict +import datetime +import time + # from Device.SunnyLinker import SunnyLinker64 from Zmq.dataBuffer import ParadigmRingBuffer from Zmq.filterProcess import FilterRingBuffer @@ -74,43 +77,16 @@ class zmqServer(threading.Thread): # 范式buffer参数, 事件检测相关 self._event_lock = threading.Lock() - self._epoch_finished = False - self._event_inner_idx = -1 - self.pack_contain_event = False + self.predict_event = 99 self.events = [1, 2, self.predict_event] - self.count_events = {} self.latency = 50 self.train_latency = 50 - self._interval_inited = False - - @property - def interval_inited(self): - return self._interval_inited - - @interval_inited.setter - def interval_inited(self, value): - self._interval_inited = value - - @property - def epoch_finished(self): - with self._event_lock: - return self._epoch_finished - - @epoch_finished.setter - def epoch_finished(self, value): - with self._event_lock: - self._epoch_finished = value - - @property - def event_inner_idx(self): - with self._event_lock: - return self._event_inner_idx - - @event_inner_idx.setter - def event_inner_idx(self, value): - with self._event_lock: - self._event_inner_idx = value + self.count_events = {} + self.epoch_finished = False + self.pack_contain_event = False + self.event_inner_idx = -1 + self.interval_inited = False def reset_state(self): """清空采集器状态和缓存数据""" @@ -148,10 +124,6 @@ class zmqServer(threading.Thread): self.predict_event = 99 self.events = [1, 2, self.predict_event] self.interval_inited = True - # if getattr(self, 'serial', None) and self.serial.is_open: - # self.serial.close() - # self.serial = serial.Serial(self.serial_port, 460800, timeout=1) # 连接同步器串口 - def broadcast_message(self, method, params): """Put message into queue to be sent to all command clients""" @@ -262,9 +234,25 @@ class zmqServer(threading.Thread): data_np = data_np.reshape(self.device_info['frame_points'], self.device_info['channel_nums']) data_np = data_np.T.astype(np.float64) - # 6. 写入缓冲区 - self.paradigmBuffer.appendBuffer(data_np) + # 6. 写入滤波缓冲区 self.filterBuffer.appendBuffer(data_np) + + # 7. 写入范式缓冲区 + try: + with self.paradigmBufferLock: + if self.interval_inited: + self.epoch_finished = self.detect_event(data_np) + if self.pack_contain_event: + self.paradigmBuffer.resetAllPara() # 检测到当前pack含有event，清除ringbuffer中之前的数据 + self.paradigmBuffer.appendBuffer(data_np) + if self.epoch_finished: + time.sleep(0.005) + algo_log('epoch_finished: ' + datetime.datetime.now().strftime('%H:%M:%S.%f')[:-3], level="DEBUG") + else: + self.paradigmBuffer.appendBuffer(data_np) + except Exception as e: + print("锁：写入异常",e) + self.paradigmBuffer.appendBuffer(data_np) # algo_log(f"数据写入成功：shape={data_np.shape}, 范围=[{data_np.min():.2f}, {data_np.max():.2f}] μV", level="DEBUG") @@ -274,6 +262,38 @@ class zmqServer(threading.Thread): import traceback traceback.print_exc() + # 检测是否含有标签 + def detect_event(self, samples): + self.pack_contain_event = False + events = np.array(samples[-2])[0].tolist() + for idx, event in enumerate(events): + if int(event) in self.events: + new_key = "".join( + [ + str(event), + datetime.datetime.now().strftime("%Y-%m-%d \ + -%H-%M-%S"), + ] + ) + if event == self.predict_event: + self.count_events[new_key] = self.latency + 1 + else: + self.count_events[new_key] = self.train_latency + 1 + self.event_inner_idx = idx + self.pack_contain_event = True + drop_items = [] + for key, value in self.count_events.items(): + value = value - 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