R360-UAVmodelTool/analysis_report.py

import pandas as pd
import re
from pathlib import Path

def load_detection_data(csv_path):
    """加载检测报告CSV数据"""
    df = pd.read_csv(csv_path)
    df['Camera'] = df['Image File'].apply(lambda x: re.search(r'cam_(\d+)_', x).group(1) if re.search(r'cam_(\d+)_', x) else None)
    return df[df['Object Count'] > 0]  # 过滤有效检测记录

def generate_analysis_report(df):
    """生成分析报告"""
    report = {
        'total_detections': df.groupby('Camera')['Object Count'].sum().to_dict(),
        'confirmed_positives': df.groupby('Camera')['Confirmed Positive'].sum().to_dict(),
        'average_sizes': {
            cam: {
                'width': round(group['BBox Width'].mean(), 2),
                'height': round(group['BBox Height'].mean(), 2)
            } for cam, group in df.groupby('Camera')
        },
        'false_positives': {
            cam: len(group[~group['Confirmed Positive']]) 
            for cam, group in df.groupby('Camera')
        },
        'true_positives': {
            cam: len(group[group['Confirmed Positive']]) 
            for cam, group in df.groupby('Camera')
        }
    }
    return report

def save_report(report, output_path):
    """保存分析报告"""
    with open(output_path, 'w', encoding='gbk') as f:
        f.write("摄像头检测分析报告\n=====================\n\n")
        
        # 目标数量统计
        f.write("各摄像头目标数量统计：\n")
        for cam, count in report['total_detections'].items():
            f.write(f"摄像头 {cam}: {count} 个目标\n")
        
        # 确认阳性统计
        f.write("\n确认阳性目标统计：\n")
        for cam, count in report['confirmed_positives'].items():
            f.write(f"摄像头 {cam}: {count} 个确认阳性目标\n")
        
        # 真阳性/假阳性统计
        f.write("\n真阳性/假阳性统计：\n")
        for cam in report['total_detections'].keys():
            total = report['total_detections'][cam]
            true_pos = report['true_positives'][cam]
            false_pos = report['false_positives'][cam]
            f.write(f"摄像头 {cam}:\n")
            f.write(f"  总检测数: {total}\n")
            f.write(f"  真阳性数: {true_pos} ({true_pos/total*100:.2f}%)\n")
            f.write(f"  假阳性数: {false_pos} ({false_pos/total*100:.2f}%)\n")
        
        # 平均尺寸统计
        f.write("\n目标平均尺寸统计：\n")
        for cam, sizes in report['average_sizes'].items():
            f.write(f"摄像头 {cam}: 平均宽度 {sizes['width']}px, 平均高度 {sizes['height']}px\n")
        
        # 总体统计
        total_detections = sum(report['total_detections'].values())
        total_true_positives = sum(report['true_positives'].values())
        total_false_positives = sum(report['false_positives'].values())
        
        f.write("\n总体统计：\n")
        f.write(f"总检测数: {total_detections}\n")
        f.write(f"总真阳性数: {total_true_positives} ({total_true_positives/total_detections*100:.2f}%)\n")
        f.write(f"总假阳性数: {total_false_positives} ({total_false_positives/total_detections*100:.2f}%)\n")

if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description='检测报告分析工具')
    parser.add_argument('input_csv', help='输入CSV文件路径')
    parser.add_argument('--output', '-o', default=None, help='输出报告路径')
    
    args = parser.parse_args()
    
    # 设置默认输出路径
    if not args.output:
        csv_dir = Path(args.input_csv).parent
        args.output = csv_dir / "analysis_report.txt"
    
    # 执行分析流程
    df = load_detection_data(args.input_csv)
    report = generate_analysis_report(df)
    save_report(report, args.output)
    
    print(f"分析完成！报告已保存至：{args.output}")