302 lines
12 KiB
Python
302 lines
12 KiB
Python
"""
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ssmvep_headless.py
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无界面版 SSMVEP 范式通讯流程模拟脚本。
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复现 ssmvep_main.py 的完整指令序列（train 0/1/2, rest, predict, saveData），
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但不依赖 psychopy 也不打开任何窗口/音频，用 time.sleep 替代帧循环等待。
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启动顺序:
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1. runDecoder.py
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2. datamock.py
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3. ssmvep_headless.py
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"""
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import sys
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import os
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import json
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import time
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import threading
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import zmq
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import numpy as np
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from datetime import datetime
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from PubLibrary.InifileHelper import IniRead
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personname = 'demo'
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session = '01'
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DATAMOCK_LABEL_ADDR = 'tcp://127.0.0.1:8101' # datamock 标签命令地址
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# ========== ZMQ 结果接收服务 ==========
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class ZmqResultServer(threading.Thread):
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def __init__(self, port=8088):
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threading.Thread.__init__(self)
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self.port = port
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self.running = True
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self.energy = 0
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self.paradigm = 0 # 0=个体校准, 1=康复训练, 2=等待模型训练
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self.ChoosenNum = -1
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self.context = zmq.Context()
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self.socket = self.context.socket(zmq.ROUTER)
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self.socket.bind(f"tcp://0.0.0.0:{self.port}")
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self.daemon = True
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self.trial_idx = 0
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def run(self):
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print(f"[Server] UpperHost_Server listening on {self.port}")
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while self.running:
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try:
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frames = self.socket.recv_multipart(zmq.NOBLOCK)
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if len(frames) < 3:
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continue
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message = json.loads(frames[2].decode('utf-8'))
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method = message.get('method')
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params = message.get('params')
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if method == 'energy':
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self.energy = params
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elif method == 'paradigm':
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self.paradigm = params
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print(f"[Server] paradigm -> {params}")
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elif method == 'result':
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self.ChoosenNum = params
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self.trial_idx += 1
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print(f"[Server] result={self.ChoosenNum} (trial {self.trial_idx})")
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except zmq.Again:
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time.sleep(0.005)
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except Exception as e:
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print(f"[Server] error: {e}")
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def stop(self):
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self.running = False
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self.socket.close()
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self.context.term()
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# ========== ZMQ 命令发送客户端 ==========
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class ZmqCmdClient:
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def __init__(self, host, port):
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self.host = host
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self.port = port
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self.context = zmq.Context()
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self.socket = self.context.socket(zmq.DEALER)
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# PUSH socket 用于向 datamock.py 发送标签命令
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self._label_sock = self.context.socket(zmq.PUSH)
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self._label_sock.connect(DATAMOCK_LABEL_ADDR)
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print(f"[Client] label PUSH connected to {DATAMOCK_LABEL_ADDR}")
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def connect(self):
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self.socket.connect(f"tcp://{self.host}:{self.port}")
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print(f"[Client] connected to {self.host}:{self.port}")
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def start_recv_thread(self, result_server):
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"""启动后台线程，持续接收 decoder 通过 8099 ROUTER 回发的消息，并更新 result_server 的状态"""
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self._result_server = result_server
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self._stop_recv = threading.Event()
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def _recv_loop():
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while not self._stop_recv.is_set():
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try:
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frames = self.socket.recv_multipart(zmq.NOBLOCK)
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# DEALER 收到的格式: [b'', json_bytes]
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data_bytes = frames[-1]
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message = json.loads(data_bytes.decode('utf-8'))
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method = message.get('method')
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params = message.get('params')
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ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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print(f"[{ts}] [CmdClient] recv: {method}={params}")
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if method == 'paradigm':
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self._result_server.paradigm = params
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print(f"[{ts}] [CmdClient] paradigm updated -> {params}")
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elif method == 'result':
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self._result_server.ChoosenNum = params
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self._result_server.trial_idx += 1
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print(f"[{ts}] [CmdClient] result={params} (trial {self._result_server.trial_idx})")
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elif method == 'energy':
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self._result_server.energy = params
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except zmq.Again:
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time.sleep(0.005)
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except Exception as e:
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print(f"[CmdClient recv] error: {e}")
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time.sleep(0.01)
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self._recv_thread = threading.Thread(target=_recv_loop, daemon=True)
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self._recv_thread.start()
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print(f"[Client] 后台接收线程已启动（监听 decoder 8099 回发消息）")
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def stop_recv_thread(self):
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if hasattr(self, '_stop_recv'):
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self._stop_recv.set()
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def _send_label(self, label_value):
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"""向 datamock.py 发送标签命令"""
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try:
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self._label_sock.send_string(str(label_value), zmq.NOBLOCK)
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except Exception as e:
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print(f"[Client] label send error: {e}")
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def send_data(self, method, params):
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msg = {'method': method, 'params': params}
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try:
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self.socket.send_multipart([b'', json.dumps(msg).encode('utf-8')])
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ts = datetime.now().strftime('%H:%M:%S.%f')[:-3]
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print(f"[{ts}] send_data: {method}={params}")
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# 根据 train/predict 命令向 datamock 发送标签
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if method == 'train':
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if params == 0:
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self._send_label(1)
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print(f"[Label] train 0 -> datamock label=1")
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elif params == 1:
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self._send_label(2)
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print(f"[Label] train 1 -> datamock label=2")
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elif method == 'predict':
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self._send_label(99)
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print(f"[Label] predict -> datamock label=99")
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except Exception as e:
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print(f"[Client] send error: {e}")
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# ========== 主流程 ==========
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def run_headless():
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server = ZmqResultServer(port=8088)
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server.start()
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_dh = str(IniRead('system', 'Decoder_Host'))
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_dp = int(IniRead('system', 'Decoder_Port'))
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client = ZmqCmdClient(_dh, _dp)
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client.connect()
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client.start_recv_thread(server) # 启动后台接收线程，监听 decoder 8099 回发的 paradigm/result 消息
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time.sleep(1) # 等待连接建立
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client.send_data('decoderClass', 'ssmvep')
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train_time = 2.5 # 每轮训练刺激时长 (s)
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test_time = 2.5 # 每轮测试刺激时长 (s)
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right_rehabilitation = float(IniRead('system', 'Right_rehabilitation'))
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fault_rehabilitation = float(IniRead('system', 'Fault_rehabilitation'))
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rest_time = float(IniRead('system', 'Rest_time'))
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num_blocks = int(IniRead('system', 'Num_blocks'))
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num_trials = int(IniRead('system', 'Num_trials'))
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position = [0, 1]
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truePos_seq = position * int(num_trials / len(position))
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truePos_seq = np.random.permutation(truePos_seq).tolist()
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user_choice = []
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os.makedirs('EEGFiles', exist_ok=True)
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seq_file_path = f'EEGFiles/pos_seq_{personname}{session}_{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}.json'
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seq_info = {
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'position': position,
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'sequence': truePos_seq,
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'start_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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with open(seq_file_path, 'w', encoding='utf-8') as f:
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json.dump(seq_info, f, ensure_ascii=False, indent=2)
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trained = 0
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Num_Total = 0
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Num_Success = 0
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print("=" * 50)
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print("[Headless] 开始运行 SSMVEP 通讯流程（无界面）")
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print(f" num_blocks={num_blocks}, num_trials={num_trials}")
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print(f" train_time={train_time}s, test_time={test_time}s")
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print("=" * 50)
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try:
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while True:
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# -------- 个体校准阶段 --------
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print("\n[Phase] 个体校准阶段 (paradigm=0)")
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client.send_data('rest', 0)
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time.sleep(1)
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# epoch完成需要的额外等待时间：train_latency=120包×20ms=2.4s
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# 在train_time后需再等epoch_wait秒，decoder才能完成epoch采集并取出数据
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epoch_wait = 2.4 # 秒，与train_latency对应
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while server.paradigm == 0:
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# 左腿刺激
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print(f"\n[Train] 左腿刺激 (train 0) trained={trained}")
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client.send_data('train', 0)
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time.sleep(train_time + epoch_wait) # 等待刺激时间+epoch完成时间
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trained += 1
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client.send_data('rest', 0)
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time.sleep(max(0, abs(fault_rehabilitation - train_time) - epoch_wait))
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# 右腿刺激
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print(f"\n[Train] 右腿刺激 (train 1) trained={trained}")
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client.send_data('train', 1)
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time.sleep(train_time + epoch_wait) # 等待刺激时间+epoch完成时间
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trained += 1
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client.send_data('rest', 0)
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time.sleep(max(0, fault_rehabilitation - epoch_wait))
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# 个体校准阶段结束
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print("\n[Phase] 个体校准结束，等待 paradigm=1 ...")
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trained = 0
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time.sleep(1)
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# -------- 康复训练阶段 --------
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while server.paradigm == 1:
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print("\n[Phase] 康复训练阶段 (paradigm=1)")
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for block_idx in range(num_blocks):
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print(f"\n [Block {block_idx+1}/{num_blocks}]")
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time.sleep(10) # 每轮开始前等待
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for trial_idx in range(num_trials):
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true_position = truePos_seq[trial_idx]
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print(f" [Trial {trial_idx+1}/{num_trials}] true_pos={true_position}")
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time.sleep(0.5) # 提示 + 叮声
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server.ChoosenNum = -1
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# 开始测试
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# predict epoch latency = 115包×20ms = 2.3s，需额外等待epoch完成
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predict_epoch_wait = 2.3 # 秒，与predict latency=115包对应
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client.send_data('predict', 1)
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t_start = time.perf_counter()
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while time.perf_counter() - t_start < test_time + predict_epoch_wait:
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if server.ChoosenNum >= 0:
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Num_Total += 1
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user_choice.append(server.ChoosenNum)
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if server.ChoosenNum in [0, 1]:
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Num_Success += 1
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rest_time = right_rehabilitation
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break
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time.sleep(0.02)
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trained += 1
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client.send_data('rest', 0)
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time.sleep(0.5)
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time.sleep(rest_time)
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server.ChoosenNum = -1
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# 训练结束
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print("\n[Phase] 康复训练结束")
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break # 退出康复训练循环
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# 统计结果
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overall_accuracy = Num_Success / Num_Total if Num_Total > 0 else 0
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expected_seq = truePos_seq * num_blocks
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min_len = min(len(user_choice), len(expected_seq))
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same_count = sum(1 for a, b in zip(user_choice[:min_len], expected_seq[:min_len]) if a == b)
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true_accuracy = same_count / min_len if min_len > 0 else 0
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print(f"\n[Result] Overall={overall_accuracy:.3f} ({Num_Success}/{Num_Total})")
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print(f"[Result] TrueAcc={true_accuracy:.3f} ({same_count}/{min_len})")
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break # 完成一个完整流程后退出
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except KeyboardInterrupt:
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print("\n[Headless] 用户中断")
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finally:
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client.send_data('predict', 2) # 关闭系统
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client.send_data('saveData', 0)
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server.stop()
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print("[Headless] 已发送关闭指令，退出。")
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if __name__ == '__main__':
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run_headless()
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