0603-1

Former-commit-id: 79f9eff524ee3778b6e010e5fc56d2060118c0fd

analyze_rate_volume.py

@@ -0,0 +1,223 @@
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import re
+from datetime import datetime
+import main as _main
+import argparse
+
+class RateVolumeAnalyzer:
+    def __init__(self):
+        self.mat = _main.MAT()  # 创建MAT实例以使用predictor方法
+        
+    def parse_log_file(self, log_path):
+        """解析日志文件，提取体积和时间信息"""
+        volumes = []
+        timestamps = []
+        
+        with open(log_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                # 匹配体积信息的行
+                if '当前体积:' in line:
+                    # 提取时间戳
+                    time_match = re.search(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}),\d+', line)
+                    # 提取体积
+                    volume_match = re.search(r'当前体积: ([\d.]+) ml', line)
+                    
+                    if time_match and volume_match:
+                        timestamp = datetime.strptime(time_match.group(1), '%Y-%m-%d %H:%M:%S')
+                        volume = float(volume_match.group(1))
+                        timestamps.append(timestamp)
+                        volumes.append(volume)
+        
+        return timestamps, volumes
+    
+    def extract_frames_from_video(self, video_path, timestamps, start_time):
+        """从视频中提取对应时间点的帧"""
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print(f"无法打开视频文件: {video_path}")
+            return []
+        
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frames = []
+        
+        for timestamp in timestamps:
+            # 计算相对于实验开始的秒数
+            seconds_from_start = (timestamp - start_time).total_seconds()
+            frame_number = int(seconds_from_start * fps)
+            
+            # 设置视频位置到指定帧
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
+            ret, frame = cap.read()
+            
+            if ret:
+                frames.append(frame)
+            else:
+                frames.append(None)  # 如果无法读取帧，添加None
+        
+        cap.release()
+        return frames
+    
+    def calculate_rates(self, frames):
+        """使用predictor方法计算每帧的rate"""
+        rates = []
+        states = []
+        
+        for frame in frames:
+            if frame is not None:
+                try:
+                    state, rate = self.mat.predictor(frame)
+                    rates.append(rate)
+                    states.append(state)
+                except Exception as e:
+                    print(f"计算rate时发生错误: {e}")
+                    rates.append(None)
+                    states.append(None)
+            else:
+                rates.append(None)
+                states.append(None)
+        
+        return rates, states
+    
+    def plot_rate_volume_curve(self, volumes, rates, states, log_filename):
+        """绘制rate-体积曲线"""
+        # 过滤掉None值
+        valid_data = [(v, r, s) for v, r, s in zip(volumes, rates, states) 
+                     if r is not None and s is not None]
+        
+        if not valid_data:
+            print("没有有效的数据点可以绘制")
+            return
+        
+        volumes_valid, rates_valid, states_valid = zip(*valid_data)
+        
+        plt.figure(figsize=(12, 8))
+        
+        # 根据状态用不同颜色绘制点
+        colors = {'transport': 'blue', 'middle': 'orange', 'about': 'purple', 'colored': 'red'}
+        
+        for state in colors:
+            state_volumes = [v for v, s in zip(volumes_valid, states_valid) if s == state]
+            state_rates = [r for r, s in zip(rates_valid, states_valid) if s == state]
+            
+            if state_volumes:
+                plt.scatter(state_volumes, state_rates, 
+                           c=colors[state], label=state, alpha=0.7, s=50)
+        
+        # 绘制连接线
+        plt.plot(volumes_valid, rates_valid, 'k-', alpha=0.3, linewidth=1)
+        
+        plt.xlabel('体积 (ml)', fontsize=12)
+        plt.ylabel('Rate', fontsize=12)
+        plt.title(f'Rate-体积曲线 ({log_filename})', fontsize=14)
+        plt.legend()
+        plt.grid(True, alpha=0.3)
+        
+        # 保存图片
+        output_filename = f"rate_volume_curve_{log_filename.replace('.log', '.png')}"
+        plt.savefig(output_filename, dpi=300, bbox_inches='tight')
+        plt.show()
+        
+        print(f"图片已保存为: {output_filename}")
+        
+        # 打印统计信息
+        print(f"\n统计信息:")
+        print(f"总数据点: {len(valid_data)}")
+        for state in colors:
+            count = sum(1 for s in states_valid if s == state)
+            if count > 0:
+                print(f"{state}: {count} 个点")
+    
+    def analyze_experiment(self, timestamp_str=None):
+        """分析指定时间戳的实验，如果不指定则分析最新的实验"""
+        logs_dir = "logs"
+        videos_dir = "Videos"
+        
+        if timestamp_str:
+            log_file = f"titration_{timestamp_str}.log"
+            video_file = f"{timestamp_str}.mp4"
+        else:
+            # 找到最新的日志文件
+            log_files = [f for f in os.listdir(logs_dir) if f.endswith('.log')]
+            if not log_files:
+                print("没有找到日志文件")
+                return
+            
+            log_files.sort()
+            log_file = log_files[-1]
+            
+            # 提取时间戳来找对应的视频文件
+            timestamp_match = re.search(r'titration_(\d{8}_\d{6})\.log', log_file)
+            if timestamp_match:
+                video_file = f"{timestamp_match.group(1)}.mp4"
+            else:
+                print("无法从日志文件名提取时间戳")
+                return
+        
+        log_path = os.path.join(logs_dir, "titration_20250529_191634.log")
+        video_path = os.path.join(videos_dir, "tmp.mp4")
+        
+        if not os.path.exists(log_path):
+            print(f"日志文件不存在: {log_path}")
+            return
+        
+        if not os.path.exists(video_path):
+            print(f"视频文件不存在: {video_path}")
+            return
+        
+        print(f"分析实验: {log_file}")
+        print(f"对应视频: {video_file}")
+        
+        # 解析日志文件
+        timestamps, volumes = self.parse_log_file(log_path)
+        
+        if not timestamps:
+            print("日志文件中没有找到体积数据")
+            return
+        
+        print(f"找到 {len(timestamps)} 个数据点")
+        
+        # 获取实验开始时间
+        start_time = timestamps[0]
+        
+        # 从视频中提取帧
+        print("正在从视频中提取帧...")
+        frames = self.extract_frames_from_video(video_path, timestamps, start_time)
+        
+        # 计算rate
+        print("正在计算rate值...")
+        rates, states = self.calculate_rates(frames)
+        
+        # 绘制曲线
+        print("正在绘制rate-体积曲线...")
+        self.plot_rate_volume_curve(volumes, rates, states, log_file)
+
+def main():
+    parser = argparse.ArgumentParser(description='分析滴定实验的rate-体积曲线')
+    parser.add_argument('--timestamp', type=str, help='指定实验时间戳 (格式: YYYYMMDD_HHMMSS)')
+    parser.add_argument('--list', action='store_true', help='列出所有可用的实验')
+    
+    args = parser.parse_args()
+    
+    analyzer = RateVolumeAnalyzer()
+    
+    if args.list:
+        # 列出所有可用的实验
+        logs_dir = "logs"
+        if os.path.exists(logs_dir):
+            log_files = [f for f in os.listdir(logs_dir) if f.endswith('.log')]
+            log_files.sort()
+            print("可用的实验:")
+            for log_file in log_files:
+                timestamp_match = re.search(r'titration_(\d{8}_\d{6})\.log', log_file)
+                if timestamp_match:
+                    timestamp = timestamp_match.group(1)
+                    print(f"  {timestamp}")
+        return
+    
+    analyzer.analyze_experiment(args.timestamp)
+
+if __name__ == "__main__":
+    main()