0603-1
Former-commit-id: 79f9eff524ee3778b6e010e5fc56d2060118c0fd
This commit is contained in:
2
.gitignore
vendored
2
.gitignore
vendored
@ -10,3 +10,5 @@ logs
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Videos
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*.build
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*.dist
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HCHO
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upx.exe
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14
README.md
14
README.md
@ -0,0 +1,14 @@
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状态管理逻辑：
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- 滑动窗口
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维护一个滑动窗口history，记录最近end_bounce_time内的状态。
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状态包括：时间,浓度,状态,当前截图
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- 进入
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1. middle: predictor返回middle立即进入slow状态。
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2. about: 返回about，且处于middle则进入about状态
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3. end：返回end，置end_check标记开始检查
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- check
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1. middle: 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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2. about：随着middle退出一起退出，不单独处理
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3. end：进入end之后的end_bounce_time，如果end_bounce_time内end比例<80%，则实验终止逻辑。否则从history中找到第二个end并继续check逻辑。
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223
analyze_rate_volume.py
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223
analyze_rate_volume.py
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@ -0,0 +1,223 @@
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import os
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import re
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from datetime import datetime
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import main as _main
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import argparse
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class RateVolumeAnalyzer:
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def __init__(self):
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self.mat = _main.MAT() # 创建MAT实例以使用predictor方法
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def parse_log_file(self, log_path):
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"""解析日志文件，提取体积和时间信息"""
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volumes = []
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timestamps = []
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with open(log_path, 'r', encoding='utf-8') as f:
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for line in f:
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# 匹配体积信息的行
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if '当前体积:' in line:
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# 提取时间戳
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time_match = re.search(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}),\d+', line)
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# 提取体积
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volume_match = re.search(r'当前体积: ([\d.]+) ml', line)
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if time_match and volume_match:
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timestamp = datetime.strptime(time_match.group(1), '%Y-%m-%d %H:%M:%S')
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volume = float(volume_match.group(1))
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timestamps.append(timestamp)
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volumes.append(volume)
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return timestamps, volumes
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def extract_frames_from_video(self, video_path, timestamps, start_time):
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"""从视频中提取对应时间点的帧"""
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"无法打开视频文件: {video_path}")
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return []
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fps = cap.get(cv2.CAP_PROP_FPS)
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frames = []
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for timestamp in timestamps:
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# 计算相对于实验开始的秒数
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seconds_from_start = (timestamp - start_time).total_seconds()
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frame_number = int(seconds_from_start * fps)
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# 设置视频位置到指定帧
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cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
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ret, frame = cap.read()
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if ret:
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frames.append(frame)
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else:
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frames.append(None) # 如果无法读取帧，添加None
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cap.release()
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return frames
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def calculate_rates(self, frames):
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"""使用predictor方法计算每帧的rate"""
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rates = []
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states = []
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for frame in frames:
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if frame is not None:
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try:
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state, rate = self.mat.predictor(frame)
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rates.append(rate)
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states.append(state)
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except Exception as e:
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print(f"计算rate时发生错误: {e}")
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rates.append(None)
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states.append(None)
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else:
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rates.append(None)
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states.append(None)
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return rates, states
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def plot_rate_volume_curve(self, volumes, rates, states, log_filename):
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"""绘制rate-体积曲线"""
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# 过滤掉None值
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valid_data = [(v, r, s) for v, r, s in zip(volumes, rates, states)
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if r is not None and s is not None]
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if not valid_data:
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print("没有有效的数据点可以绘制")
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return
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volumes_valid, rates_valid, states_valid = zip(*valid_data)
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plt.figure(figsize=(12, 8))
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# 根据状态用不同颜色绘制点
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colors = {'transport': 'blue', 'middle': 'orange', 'about': 'purple', 'colored': 'red'}
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for state in colors:
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state_volumes = [v for v, s in zip(volumes_valid, states_valid) if s == state]
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state_rates = [r for r, s in zip(rates_valid, states_valid) if s == state]
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if state_volumes:
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plt.scatter(state_volumes, state_rates,
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c=colors[state], label=state, alpha=0.7, s=50)
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# 绘制连接线
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plt.plot(volumes_valid, rates_valid, 'k-', alpha=0.3, linewidth=1)
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plt.xlabel('体积 (ml)', fontsize=12)
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plt.ylabel('Rate', fontsize=12)
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plt.title(f'Rate-体积曲线 ({log_filename})', fontsize=14)
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plt.legend()
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plt.grid(True, alpha=0.3)
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# 保存图片
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output_filename = f"rate_volume_curve_{log_filename.replace('.log', '.png')}"
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plt.savefig(output_filename, dpi=300, bbox_inches='tight')
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plt.show()
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print(f"图片已保存为: {output_filename}")
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# 打印统计信息
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print(f"\n统计信息:")
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print(f"总数据点: {len(valid_data)}")
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for state in colors:
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count = sum(1 for s in states_valid if s == state)
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if count > 0:
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print(f"{state}: {count} 个点")
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def analyze_experiment(self, timestamp_str=None):
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"""分析指定时间戳的实验，如果不指定则分析最新的实验"""
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logs_dir = "logs"
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videos_dir = "Videos"
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if timestamp_str:
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log_file = f"titration_{timestamp_str}.log"
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video_file = f"{timestamp_str}.mp4"
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else:
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# 找到最新的日志文件
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log_files = [f for f in os.listdir(logs_dir) if f.endswith('.log')]
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if not log_files:
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print("没有找到日志文件")
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return
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log_files.sort()
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log_file = log_files[-1]
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# 提取时间戳来找对应的视频文件
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timestamp_match = re.search(r'titration_(\d{8}_\d{6})\.log', log_file)
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if timestamp_match:
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video_file = f"{timestamp_match.group(1)}.mp4"
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else:
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print("无法从日志文件名提取时间戳")
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return
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log_path = os.path.join(logs_dir, "titration_20250529_191634.log")
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video_path = os.path.join(videos_dir, "tmp.mp4")
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if not os.path.exists(log_path):
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print(f"日志文件不存在: {log_path}")
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return
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if not os.path.exists(video_path):
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print(f"视频文件不存在: {video_path}")
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return
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print(f"分析实验: {log_file}")
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print(f"对应视频: {video_file}")
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# 解析日志文件
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timestamps, volumes = self.parse_log_file(log_path)
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if not timestamps:
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print("日志文件中没有找到体积数据")
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return
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print(f"找到 {len(timestamps)} 个数据点")
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# 获取实验开始时间
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start_time = timestamps[0]
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# 从视频中提取帧
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print("正在从视频中提取帧...")
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frames = self.extract_frames_from_video(video_path, timestamps, start_time)
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# 计算rate
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print("正在计算rate值...")
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rates, states = self.calculate_rates(frames)
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# 绘制曲线
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print("正在绘制rate-体积曲线...")
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self.plot_rate_volume_curve(volumes, rates, states, log_file)
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def main():
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parser = argparse.ArgumentParser(description='分析滴定实验的rate-体积曲线')
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parser.add_argument('--timestamp', type=str, help='指定实验时间戳 (格式: YYYYMMDD_HHMMSS)')
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parser.add_argument('--list', action='store_true', help='列出所有可用的实验')
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args = parser.parse_args()
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analyzer = RateVolumeAnalyzer()
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if args.list:
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# 列出所有可用的实验
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logs_dir = "logs"
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if os.path.exists(logs_dir):
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log_files = [f for f in os.listdir(logs_dir) if f.endswith('.log')]
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log_files.sort()
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print("可用的实验:")
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for log_file in log_files:
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timestamp_match = re.search(r'titration_(\d{8}_\d{6})\.log', log_file)
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if timestamp_match:
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timestamp = timestamp_match.group(1)
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print(f" {timestamp}")
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return
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analyzer.analyze_experiment(args.timestamp)
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if __name__ == "__main__":
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main()
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3
ch340_gui.exe
Normal file
3
ch340_gui.exe
Normal file
@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc7d1088d4f1edb2c939f12a2d5cd136142744c6ddd23835f91710a0383d295f
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size 21382656
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12
main.py
12
main.py
@ -121,15 +121,15 @@ class MAT:
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recent_history = [(t, state, v, i) for t, state, v, i in self.history if t >= bounce_start_time]
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if recent_history:
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middle_count = sum(1 for _, state, _, _ in recent_history if state == "transport")
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middle_ratio = middle_count / len(recent_history)
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trans_count = sum(1 for _, state, _, _ in recent_history if state == "transport")
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trans_ratio = trans_count / len(recent_history)
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if middle_ratio > 0.3:
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if trans_ratio > 0.3:
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self.process_left(now)
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self.state.exit_middle_check()
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# about状态随middle一起退出
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self.state.exit_about_with_middle()
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self.control_logger.info(f"middle比例{middle_ratio:.2%}<70%，退出middle检查，返回fast模式")
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self.control_logger.info(f"middle比例{trans_ratio:.2%}<70%，退出middle检查，返回fast模式")
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# end检查: 进入end之后的end_bounce_time，如果end比例<80%，则重置；否则终止实验
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if self.state.should_check_end_result(now):
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@ -174,10 +174,6 @@ class MAT:
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self._display_status(im, ret, rate, val)
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return ret
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def _reset_colored_detection(self, current_time, colored_ratio, current_volume):
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"""重置colored检测状态 - 已被新逻辑替代，保留以避免错误"""
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pass
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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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@ -2,3 +2,4 @@ numpy
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opencv-python
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pyserial
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matplotlib
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PySide6
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@ -1,35 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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测试logging系统的功能
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"""
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import utils
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def test_logging():
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"""测试logging系统"""
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# 初始化logging
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log_file = utils.setup_logging()
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print(f"日志文件已创建: {log_file}")
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# 获取各个模块的logger
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system_logger = utils.get_system_logger()
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hardware_logger = utils.get_hardware_logger()
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vision_logger = utils.get_vision_logger()
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control_logger = utils.get_control_logger()
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endpoint_logger = utils.get_endpoint_logger()
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volume_logger = utils.get_volume_logger()
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# 测试各个模块的日志输出
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system_logger.info("系统初始化完成")
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hardware_logger.info("硬件连接正常")
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vision_logger.debug("图像处理开始")
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control_logger.warning("控制逻辑警告")
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endpoint_logger.error("终点检测错误")
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volume_logger.info("体积计算: 10.5 ml")
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print("日志测试完成！请检查logs目录下的日志文件。")
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if __name__ == "__main__":
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test_logging()
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