SkyLi
11c5e062c2
Analyzes validation set images using YOLO detection, extracts ROI temperature features, and plots histograms.
317 lines
10 KiB
Python
317 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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验证集目标检测与ROI温度特征直方图分析
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对验证集图像进行YOLO检测，提取ROI温度特征，绘制直方图
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"""
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import sys
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from pathlib import Path
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# 添加项目根目录到路径
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sys.path.insert(0, str(Path(__file__).parent.parent))
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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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from tqdm import tqdm
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from collections import defaultdict
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from src.detect import InfraredDetector
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from src.extract_temp import TempFeatureExtractor, load_infrared_image
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def analyze_validation_set(
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image_dir: str,
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model_path: str,
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conf: float = 0.25,
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output_dir: str = None
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):
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"""
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分析验证集，提取ROI温度特征并绘制直方图
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Args:
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image_dir: 验证集图像目录
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model_path: YOLO模型路径
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conf: 检测置信度阈值
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output_dir: 输出目录
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"""
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image_dir = Path(image_dir)
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if output_dir:
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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else:
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output_dir = Path("output/temp_analysis")
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output_dir.mkdir(parents=True, exist_ok=True)
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# 初始化检测器和温度特征提取器
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detector = InfraredDetector(model_path, conf=conf)
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extractor = TempFeatureExtractor(model_path)
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# 获取所有图像
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image_paths = sorted([
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p for p in image_dir.glob('*')
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if p.suffix.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']
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])
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print(f"找到 {len(image_paths)} 张图像")
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# 收集所有温度特征
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all_features = defaultdict(list)
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class_features = defaultdict(lambda: defaultdict(list))
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for img_path in tqdm(image_paths, desc="处理图像"):
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try:
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# 加载灰度图像（用于温度提取）
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gray_img = load_infrared_image(str(img_path))
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# 进行检测
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detections = detector.detect(str(img_path), conf=conf)
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for det in detections:
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bbox = det['bbox']
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x1, y1, x2, y2 = map(int, bbox)
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# 确保边界在图像范围内
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(gray_img.shape[1], x2), min(gray_img.shape[0], y2)
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if x2 - x1 <= 0 or y2 - y1 <= 0:
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continue
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roi = gray_img[y1:y2, x1:x2]
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if roi.size == 0:
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continue
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# 提取温度特征
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features = extractor.extract_from_roi(roi)
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# 收集全局特征
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all_features['temp_max'].append(features['temp_max'])
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all_features['temp_min'].append(features['temp_min'])
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all_features['temp_mean'].append(features['temp_mean'])
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all_features['temp_std'].append(features['temp_std'])
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all_features['temp_median'].append(features['temp_median'])
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all_features['gradient_mean'].append(features['gradient_mean'])
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all_features['gradient_std'].append(features['gradient_std'])
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# 计算方差（标准差的平方）
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variance = features['temp_std'] ** 2
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all_features['temp_variance'].append(variance)
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# 按类别收集特征
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class_name = det['class_name']
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class_features[class_name]['temp_max'].append(features['temp_max'])
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class_features[class_name]['temp_mean'].append(features['temp_mean'])
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class_features[class_name]['temp_std'].append(features['temp_std'])
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class_features[class_name]['temp_variance'].append(variance)
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except Exception as e:
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print(f"处理 {img_path.name} 时出错: {e}")
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continue
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# 打印统计信息
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print(f"\n总共提取了 {len(all_features['temp_max'])} 个ROI的温度特征")
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# 绘制全局直方图
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draw_global_histograms(all_features, output_dir)
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# 绘制各类别的温度STD直方图
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draw_class_histograms(class_features, output_dir)
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# 绘制温度散点图
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draw_temp_scatter(all_features, output_dir)
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# 保存特征统计到CSV
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save_stats_csv(all_features, class_features, output_dir)
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print(f"\n分析完成! 结果保存至: {output_dir}")
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def draw_global_histograms(features: dict, output_dir: Path):
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"""绘制全局温度特征直方图"""
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fig, axes = plt.subplots(2, 4, figsize=(16, 8))
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fig.suptitle('ROI Temperature Features Distribution (All Classes)', fontsize=14)
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plot_configs = [
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('temp_max', 'Max Temperature', 'red'),
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('temp_min', 'Min Temperature', 'blue'),
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('temp_mean', 'Mean Temperature', 'green'),
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('temp_std', 'Temperature Std Dev', 'orange'),
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('temp_median', 'Median Temperature', 'purple'),
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('temp_variance', 'Temperature Variance', 'brown'),
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('gradient_mean', 'Gradient Mean', 'pink'),
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('gradient_std', 'Gradient Std', 'gray'),
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]
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for idx, (key, title, color) in enumerate(plot_configs):
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ax = axes[idx // 4, idx % 4]
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data = features.get(key, [])
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if data:
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ax.hist(data, bins=30, color=color, alpha=0.7, edgecolor='black')
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ax.axvline(np.mean(data), color='red', linestyle='--',
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linewidth=2, label=f'Mean: {np.mean(data):.2f}')
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ax.axvline(np.median(data), color='blue', linestyle=':',
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linewidth=2, label=f'Median: {np.median(data):.2f}')
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ax.set_title(title)
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ax.set_xlabel('Value')
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ax.set_ylabel('Count')
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ax.legend(fontsize=8)
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plt.tight_layout()
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plt.savefig(output_dir / 'global_temp_histograms.png', dpi=150)
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plt.close()
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print(f" 保存: global_temp_histograms.png")
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def draw_class_histograms(class_features: dict, output_dir: Path):
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"""绘制各类别的温度标准差直方图"""
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if not class_features:
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return
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n_classes = len(class_features)
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n_cols = 3
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n_rows = (n_classes + n_cols - 1) // n_cols
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fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 4 * n_rows))
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fig.suptitle('Temperature Std Dev Distribution by Class', fontsize=14)
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axes = axes.flatten() if n_classes > 1 else [axes]
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for idx, (class_name, features) in enumerate(sorted(class_features.items())):
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ax = axes[idx]
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std_data = features.get('temp_std', [])
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if std_data:
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ax.hist(std_data, bins=20, color='steelblue', alpha=0.7, edgecolor='black')
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ax.axvline(np.mean(std_data), color='red', linestyle='--',
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linewidth=2, label=f'Mean: {np.mean(std_data):.2f}')
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ax.set_title(f'{class_name} (n={len(std_data)})')
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ax.set_xlabel('Temperature Std Dev')
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ax.set_ylabel('Count')
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ax.legend(fontsize=8)
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# 隐藏多余的子图
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for idx in range(n_classes, len(axes)):
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axes[idx].axis('off')
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plt.tight_layout()
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plt.savefig(output_dir / 'class_temp_std_histograms.png', dpi=150)
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plt.close()
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print(f" 保存: class_temp_std_histograms.png")
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def draw_temp_scatter(features: dict, output_dir: Path):
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"""绘制温度特征散点图"""
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fig, axes = plt.subplots(1, 2, figsize=(12, 5))
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# temp_max vs temp_std
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ax = axes[0]
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ax.scatter(features['temp_max'], features['temp_std'],
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alpha=0.3, s=10, c='red')
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ax.set_xlabel('Max Temperature')
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ax.set_ylabel('Temperature Std Dev')
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ax.set_title('Max Temperature vs Std Dev')
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ax.grid(True, alpha=0.3)
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# temp_mean vs temp_std
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ax = axes[1]
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ax.scatter(features['temp_mean'], features['temp_std'],
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alpha=0.3, s=10, c='blue')
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ax.set_xlabel('Mean Temperature')
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ax.set_ylabel('Temperature Std Dev')
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ax.set_title('Mean Temperature vs Std Dev')
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(output_dir / 'temp_scatter_plots.png', dpi=150)
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plt.close()
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print(f" 保存: temp_scatter_plots.png")
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def save_stats_csv(all_features: dict, class_features: dict, output_dir: Path):
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"""保存特征统计到CSV"""
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import csv
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# 全局统计
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stats_file = output_dir / 'temp_feature_stats.csv'
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with open(stats_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(['Feature', 'Count', 'Mean', 'Std', 'Min', 'Max', 'Median'])
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for key in ['temp_max', 'temp_min', 'temp_mean', 'temp_std',
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'temp_median', 'temp_variance', 'gradient_mean', 'gradient_std']:
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data = all_features.get(key, [])
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if data:
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writer.writerow([
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key,
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len(data),
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f'{np.mean(data):.2f}',
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f'{np.std(data):.2f}',
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f'{np.min(data):.2f}',
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f'{np.max(data):.2f}',
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f'{np.median(data):.2f}'
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])
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print(f" 保存: temp_feature_stats.csv")
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# 各类别统计
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class_stats_file = output_dir / 'class_temp_stats.csv'
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with open(class_stats_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(['Class', 'Count', 'Mean_Max', 'Mean_Mean', 'Mean_Std', 'Mean_Variance'])
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for class_name in sorted(class_features.keys()):
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features = class_features[class_name]
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writer.writerow([
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class_name,
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len(features['temp_std']),
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f'{np.mean(features["temp_max"]):.2f}',
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f'{np.mean(features["temp_mean"]):.2f}',
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f'{np.mean(features["temp_std"]):.2f}',
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f'{np.mean(features["temp_variance"]):.2f}'
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])
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print(f" 保存: class_temp_stats.csv")
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(
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description='验证集ROI温度特征提取与直方图分析'
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)
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parser.add_argument(
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'--image-dir',
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type=str,
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default='data/valid/images',
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help='验证集图像目录'
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)
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parser.add_argument(
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'--model',
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type=str,
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default='weights/best.pt',
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help='YOLO模型路径'
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)
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parser.add_argument(
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'--conf',
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type=float,
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default=0.25,
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help='检测置信度阈值'
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)
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parser.add_argument(
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'--output',
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type=str,
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default='output/temp_analysis',
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help='输出目录'
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)
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args = parser.parse_args()
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analyze_validation_set(
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image_dir=args.image_dir,
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model_path=args.model,
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conf=args.conf,
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output_dir=args.output
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) |