451 lines
16 KiB
Python
451 lines
16 KiB
Python
import atexit
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import logging
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import time
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from datetime import datetime
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from typing import Optional
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import cv2
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import numpy as np
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from scipy.signal import find_peaks
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import ch340
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import utils
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from utils import History, State, login_to_platform, send_data_to_platform
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class MAT:
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def __init__(self, videoSourceIndex=0, bounce_time=2, sensity=30):
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# 初始化logging
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utils.setup_logging()
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self.system_logger = logging.getLogger("System")
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self.control_logger = logging.getLogger("Control")
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self.endpoint_logger = logging.getLogger("Endpoint")
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self.volume_logger = logging.getLogger("Volume")
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self.system_logger.info("正在初始化MAT系统...")
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self.videoSourceIndex = videoSourceIndex
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self.cap = cv2.VideoCapture(videoSourceIndex, cv2.CAP_DSHOW)
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self.ch340 = ch340.CH340()
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self.total_volume = 0
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self.sensity = sensity
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self.state = State(bounce_time)
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atexit.register(self.ch340.stop)
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self.history = History(bounce_time)
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self.colored_volume = None
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self.colored_time = None
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self.colored_im = None
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def ch340_pull(self):
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"抽取12ml液体"
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self.control_logger.info("开始抽取12ml")
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self.ch340.max_speed()
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self.ch340.pull(vol=12)
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self.control_logger.info("完成抽取")
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def ch340_init(self):
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"初始化电机位置，防止过头导致抽取溶液不准。"
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# self.ch340.push(speed=1,t=1)
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self.ch340.pull(speed=1.2, vol=1.8)
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self.control_logger.info("电极位置已初始化")
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def ch340_push(self, speed: int | float = 0.1):
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"常规推送，保证推送时间为1s，推送体积=speed"
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self.ch340.push_async(speed=speed, t=1)
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def process_left(
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self, now: float, value_only: Optional[bool] = False
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) -> Optional[float]:
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"""
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计算当前时刻剩余体积，并更新到self.total_volume。
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@param now: 当前时间戳
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@param value_only: 如果为True，则只返回剩余体积，不更新状态
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@return: 当前剩余体积，仅当value_only为True时返回
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"""
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if self.state.mode == State.Mode.ABOUT:
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return self.total_volume
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st = self.ch340.start
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if not value_only:
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self.ch340.stop()
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if not abs(self.ch340._get_time() - 1) < 1e-3:
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self.control_logger.warning(
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f"CH340 timing issue detected: {self.ch340._get_time()}"
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)
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return self.total_volume
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r = now - st
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ret = self.total_volume - (1 - r) * self.speeds[self.state.mode.value]
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if value_only:
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return ret
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else:
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self.total_volume = ret
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def _pred(self):
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"""预测当前图像状态，返回'transport'、'middle'、'about'或'colored'"""
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suc, im = self.cap.read()
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if not suc:
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self.control_logger.error("无法从摄像头捕获帧")
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return None
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# 录制当前帧到视频文件
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if hasattr(self, "capFile") and self.capFile.isOpened():
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self.capFile.write(im)
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ret, rate = self.predictor(im)
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if ret is None:
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return None
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now = time.time()
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val = self.process_left(now, value_only=True)
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# 确保val不为None
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if val is None:
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self.control_logger.warning("体积计算返回None，跳过本次记录")
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return ret
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# 更新滑动窗口历史记录 - 维护最近end_bounce_time内的状态
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self.history.add_record(now, ret, rate, val, im)
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if self.history.is_empty():
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self.control_logger.error("未预期的没有可用的历史记录")
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return ret
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# === 状态进入逻辑 ===
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if now - self._start_time < 10:
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self._display_status(im, ret, rate, val)
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return ret
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if (
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not self.edited
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and self.history.base is not None
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and rate > self.history.base * 2
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):
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self.edited = True
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self.speeds[0] /= 2
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# 1. middle: predictor返回middle立即进入slow状态
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if ret == "middle":
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if self.state.is_fast_mode():
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self.process_left(now)
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self.state.enter_middle_state(now)
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self.control_logger.info(
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f"检测到middle，立即进入slow模式，当前体积: {val:.2f} ml"
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)
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# 2. about: 返回about，且处于middle则进入about状态
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elif ret == "about":
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if self.state.is_slow_mode():
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self.process_left(now)
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self.state.enter_about_state(now)
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self.control_logger.info(
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f"检测到about，进入about模式，当前体积: {val:.2f} ml"
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)
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# middle检查: 进入middle的bounce_time后，在最近bounce_time内middle比例<70%返回fast状态
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if self.state.should_check_middle_exit(now) and not self.history.last_end:
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# 计算最近bounce_time内的middle比例
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recent_records = self.history.get_recent_records(
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self.state.bounce_time, now
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)
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if recent_records:
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trans_ratio = self.history.get_state_ratio("transport", recent_records)
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if trans_ratio > 0.4:
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self.process_left(now)
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self.state.exit_middle_check()
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self.control_logger.info(
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f"middle比例{trans_ratio:.2%}<60%，退出middle检查，返回fast模式"
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)
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if self.state.mode == State.Mode.ABOUT and self.state.about_check:
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h = self.history.get_recent_records(self.about_time / 3, now)
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ratio = self.history.get_state_ratio("transport", h)
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self.history.about_history.append(ratio == 1)
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while len(self.history.about_history) > 5:
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self.history.about_history.pop(0)
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if len(self.history.about_history) == 5 and self.history.last_end < 2:
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rate = sum(self.history.about_history) / len(self.history.about_history)
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if rate > 0.8:
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self.state.exit_about()
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self.control_logger.info(
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f"about比例{ratio:.2%}<80%，退出about检查，返回middle模式"
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)
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ret = self.end_check()
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if ret == "colored":
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return ret
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if not self.history.last_end:
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self.history.last_end = max(self.history.last_end, ret)
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self.state.about_check = False
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# 显示状态信息
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self._display_status(im, ret, rate, val)
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return ret
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def end_check(self, dep=0):
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"""终点检测，通过对图像直方图峰值分析，确认是否全局变色。"""
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if not self.running:
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return "colored"
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if dep == 1:
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suc, im = self.cap.read()
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hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
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# 降低饱和度通道 (S通道是索引1)
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hsv[:, :, 1] = hsv[:, :, 1] * 0.8
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# 转换回BGR颜色空间
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result = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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name = f"colored_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg"
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cv2.imwrite(name, result)
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self.colored_im = name
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# 维护一个递归检测，重复4次后认定为终点。
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if dep > 3:
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self.colored_volume = self.total_volume
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self.endpoint_logger.info(f"最终体积: {self.colored_volume:.2f} ml")
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self.running = False
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self.ch340.stop()
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return "colored"
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suc, im = self.cap.read()
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hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
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s = hsv[:, :, 0]
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s = s[s > 0]
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hist = cv2.calcHist([s], [0], None, [256], [0, 256])
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hist = hist.flatten() # 转换为一维数组
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# 峰值检测 - 找到直方图中的峰值
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peaks, properties = find_peaks(
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hist,
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height=np.max(hist) * 0.2, # 峰值高度至少是最大值的10%
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distance=10, # 峰值之间的最小距离
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prominence=np.max(hist) * 0.01,
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) # 峰值的突出度
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if np.any(peaks > 130):
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if dep == 0:
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self.process_left(time.time())
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self.history.end_history.append(True)
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self.control_logger.info(f"检测到colored状态，end_check at {dep}")
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bgn = time.time()
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while time.time() - bgn < 2:
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suc, im = self.cap.read()
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if hasattr(self, "capFile") and self.capFile.isOpened():
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self.capFile.write(im)
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cv2.putText(
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im,
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"ENDCHK",
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.7,
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(0, 255, 0),
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2,
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)
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cv2.imshow("Frame", im)
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return self.end_check(dep + 1)
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# time.sleep(2)
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else:
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self.colored_im = None
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return dep
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def _display_status(self, im, detection_result, rate, volume):
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"""显示状态信息到图像上"""
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mode_color = {
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State.Mode.FAST: (255, 255, 255),
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State.Mode.SLOW: (10, 215, 255),
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State.Mode.ABOUT: (228, 116, 167),
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}
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status_text = (
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f"Stat: {detection_result}, rate: {round(rate,2)}, Vol: {volume:.2f} ml"
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)
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status_text += self.state.get_status_text()
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cv2.putText(
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im,
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status_text,
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.7,
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mode_color[self.state.mode],
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2,
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)
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cv2.imshow("Frame", im)
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cv2.waitKey(1)
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def predictor(self, im):
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"""主预测函数，分析图像并返回当前状态。当前，colored不使用这个函数。"""
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hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
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s = hsv[:, :, 1]
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mask = s > self.sensity
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cv2.imshow("mask", im * mask[:, :, np.newaxis])
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tot = mask.shape[0] * mask.shape[1]
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val = np.sum(mask)
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rate = val / tot
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if self.history.base is not None:
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base = self.history.base
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thr = (min(0.05, base * 5), min(0.2, base * 13), 0.5)
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# thr = (base*5, base*13, 0.5)
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if rate < thr[0]:
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return "transport", rate
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elif rate < thr[1]:
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return "middle", rate
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elif rate < thr[2]:
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return "about", rate
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else:
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return "colored", rate
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else:
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return "transport", rate
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def __del__(self):
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self.cap.release()
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if hasattr(self, "capFile") and self.capFile.isOpened():
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self.capFile.release()
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cv2.destroyAllWindows()
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def format_date_time(self, timestamp):
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return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
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def run(
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self,
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quick_speed=0.2,
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slow_speed=0.05,
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end_speed=0.02,
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mid_time=0.5,
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about_time=1,
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cap_dir="Videos",
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):
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self.running = True
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self.start_time = time.time()
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self.speeds: list[float] = [quick_speed, slow_speed, end_speed]
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self.need_check = False
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self.edited = False
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self.volume_list = []
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self.color_list = []
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if cap_dir is not None:
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vid_name = f"{cap_dir}/{datetime.now().strftime('%Y%m%d_%H%M%S')}.mkv"
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else:
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vid_name = "Disabled"
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# 记录实验开始
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experiment_params = {
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"视频源索引 ": self.videoSourceIndex,
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"防抖时间 ": self.state.bounce_time,
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"快速模式速度": f"{quick_speed} ml/次",
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"慢速模式速度": f"{slow_speed} ml/次",
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"录制视频 ": vid_name,
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}
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self.system_logger.info("实验参数:")
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for key, value in experiment_params.items():
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self.system_logger.info(f" {key}: {value}")
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fps = int(self.cap.get(cv2.CAP_PROP_FPS)) or 30
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width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# 初始化视频录制器
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if vid_name != "Disabled":
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fourcc = cv2.VideoWriter.fourcc(*"x264") # 使用更兼容的编码器
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self.capFile = cv2.VideoWriter(vid_name, fourcc, fps, (width, height))
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if not self.capFile.isOpened():
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self.system_logger.error(f"无法打开视频录制器: {vid_name}")
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del self.capFile
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else:
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self.system_logger.info(f"视频录制已初始化")
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self.ch340_init()
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cnt = 0
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self._start_time = time.time()
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self.about_time = about_time
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while self.running:
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if not self.ch340.running:
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if 12 * cnt - self.total_volume < 0.5:
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self.ch340_pull()
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cnt += 1
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should_push = False
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if self.state.is_fast_mode():
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should_push = True
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elif (
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self.state.is_slow_mode()
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and time.time() - self.ch340.start > mid_time
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):
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should_push = True
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elif (
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self.state.is_about_mode()
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and time.time() - self.ch340.start > about_time
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):
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if not self.state.about_first_flag:
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self.state.about_check = True
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else:
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self.state.about_first_flag = False
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should_push = True
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if should_push:
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speed = self.speeds[self.state.mode.value]
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self.volume_logger.info(
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f"当前体积: {self.total_volume:.2f} ml, 加入速度: {speed:.2f} ml/次"
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)
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self.ch340_push(speed)
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self.total_volume += speed
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self.volume_list.append(round(self.total_volume, 2))
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try:
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self.color_list.append(self.state.mode.name)
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except Exception as e:
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self.control_logger.error(f"报表失败: {e}")
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if self._pred() is None:
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self.control_logger.error("预测失败，跳过当前帧")
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continue
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# 释放视频录制器
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if hasattr(self, "capFile") and self.capFile.isOpened():
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self.capFile.release()
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self.system_logger.info(f"视频录制完成: {vid_name}")
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# 实验结束，记录结果
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experiment_results = {
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"总体积 ": f"{self.total_volume:.2f} ml",
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"终点体积": f"{self.colored_volume:.2f} ml",
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"理论体积": f"{self.colored_volume:.2f} ml",
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"实验时长": f"{time.time() - self.start_time:.2f} 秒",
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"滴定速率": f"{self.total_volume / (time.time() - self.start_time):.2f} mL/s",
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}
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upload_data = {
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"start_time": self.format_date_time(self.start_time),
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"end_time": self.format_date_time(time.time()),
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"volume_record": f"{self.volume_list}",
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"voltage_record": f"{[]}",
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"color_record": f"{self.color_list}",
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"final_volume": f"{self.colored_volume}",
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}
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self.system_logger.info("实验结果:")
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for key, value in experiment_results.items():
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self.system_logger.info(f" {key}: {value}")
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return upload_data, self.colored_im
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if __name__ == "__main__":
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token = login_to_platform("13504022184", "password")
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# 创建MAT类的实例并运行
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mat = MAT(videoSourceIndex=1, bounce_time=2, sensity=32)
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mat.state.mode = State.Mode.FAST
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final_data, finish_picture = mat.run(
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slow_speed=0.05,
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quick_speed=1.0,
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about_time=3,
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# cap_dir=None
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)
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with open("log.json", "w") as f:
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import json
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json.dump(final_data, f, indent=4)
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send_data_to_platform(token, final_data, finish_picture)
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