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重建质量报告

概述​

MipMapEngine SDK 在完成重建任务后会生成详细的质量报告，包含设备信息、重建效率、参数设置和成果质量等信息。报告以 JSON 格式存储在 report/report.json 文件中，同时提供可视化缩略图用于快速预览。

报告结构​

1. 设备信息​

记录执行重建任务的硬件配置：

字段类型说明
cpu_namestringCPU 名称
gpu_namestringGPU 名称

示例：

{
  "cpu_name": "Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz",
  "gpu_name": "NVIDIA GeForce RTX 3080"
}

2. 重建效率​

记录各阶段的处理时间（单位：分钟）：

字段类型说明
feature_extraction_timefloat特征提取时间
feature_match_timefloat特征匹配时间
sfm_timefloat光束法平差时间
at_timefloat空三总时间
reconstruction_timefloat重建总时间（不含空三）

示例：

{
  "feature_extraction_time": 12.5,
  "feature_match_time": 8.3,
  "sfm_time": 15.2,
  "at_time": 36.0,
  "reconstruction_time": 48.6
}

3. 重建参数​

记录任务的输入参数和配置：

相机参数​

"initial_camera_parameters": [
  {
    "camera_name": "DJI_FC6310",
    "width": 5472,
    "height": 3648,
    "id": 0,
    "parameters": [3850.5, 2736, 1824, -0.02, 0.05, 0.001, -0.001, 0.01]
  }
]

参数数组顺序：[f, cx, cy, k1, k2, p1, p2, k3]

  • f: 焦距
  • cx, cy: 主点坐标
  • k1, k2, k3: 径向畸变系数
  • p1, p2: 切向畸变系数

其他参数​

字段类型说明
input_camera_countint输入相机数量
input_image_countint输入图像数量
reconstruction_levelint重建精度 (1=超高, 2=高, 3=中)
production_typestring产品类型
max_ramfloat最大内存使用 (GB)

坐标系信息​

"production_cs_3d": {
  "epsg_code": 4326,
  "origin_offset": [0, 0, 0],
  "type": 2
}

坐标系类型：

  • 0: LocalENU（本地东北天）
  • 1: Local（本地坐标系）
  • 2: Geodetic（大地坐标系）
  • 3: Projected（投影坐标系）
  • 4: ECEF（地心地固坐标系）

4. 重建结果​

空三后的相机参数​

记录优化后的相机内参：

"AT_camera_parameters": [
  {
    "camera_name": "DJI_FC6310",
    "width": 5472,
    "height": 3648,
    "id": 0,
    "parameters": [3852.1, 2735.8, 1823.6, -0.019, 0.048, 0.0008, -0.0009, 0.009]
  }
]

图像位置差异​

记录每张图像的位置优化量：

"image_pos_diff": [
  {
    "id": 0,
    "pos_diff": 0.125
  },
  {
    "id": 1,
    "pos_diff": 0.087
  }
]

质量指标​

字段类型说明
removed_image_countint空三后移除的图像数
residual_rmsefloat图像点残差均方根误差
tie_point_countint连接点数量
scene_areafloat场景面积 (平方米)
scene_gsdfloat地面采样距离 (米)
flight_heightfloat飞行高度 (米)
block_countint重建分块数

5. 其他信息​

字段类型说明
sdk_versionstringSDK 版本号

可视化缩略图​

报告目录下的 thumbnail 文件夹包含以下可视化文件：

1. 相机残差图​

camera_{id}_residual.png - 24位彩色图像

  • 良好的标定结果：残差在各位置大小相近，方向随机
  • 较差的标定结果：残差较大且有明显方向性
提示

大残差不一定表示整体精度差，这仅反映相机内部精度。最终精度需综合考虑检查点坐标精度和模型质量。

2. 重叠度图​

overlap_map.png - 8位灰度图像

  • 像素值范围：0-255
  • 可渲染为彩色图显示重叠度分布
  • 用于评估航线设计和图像覆盖质量

3. 测区缩略图​

rgb_thumbnail.jpg - 32位彩色图像

  • 用于项目快速预览
  • 显示测区范围和重建效果

报告解读示例​

完整报告示例​

{
  "cpu_name": "Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz",
  "gpu_name": "NVIDIA GeForce RTX 3080",
  "feature_extraction_time": 12.5,
  "feature_match_time": 8.3,
  "sfm_time": 15.2,
  "at_time": 36.0,
  "reconstruction_time": 48.6,
  "initial_camera_parameters": [{
    "camera_name": "DJI_FC6310",
    "width": 5472,
    "height": 3648,
    "id": 0,
    "parameters": [3850.5, 2736, 1824, -0.02, 0.05, 0.001, -0.001, 0.01]
  }],
  "input_camera_count": 1,
  "input_image_count": 156,
  "reconstruction_level": 2,
  "production_type": "all",
  "production_cs_3d": {
    "epsg_code": 4326,
    "origin_offset": [0, 0, 0],
    "type": 2
  },
  "production_cs_2d": {
    "epsg_code": 3857,
    "origin_offset": [0, 0, 0],
    "type": 3
  },
  "max_ram": 28.5,
  "AT_camera_parameters": [{
    "camera_name": "DJI_FC6310",
    "width": 5472,
    "height": 3648,
    "id": 0,
    "parameters": [3852.1, 2735.8, 1823.6, -0.019, 0.048, 0.0008, -0.0009, 0.009]
  }],
  "removed_image_count": 2,
  "residual_rmse": 0.68,
  "tie_point_count": 125840,
  "scene_area": 850000.0,
  "scene_gsd": 0.025,
  "flight_height": 120.5,
  "block_count": 1,
  "sdk_version": "3.0.1"
}

质量评估指标​

优秀质量标准​

  • residual_rmse < 1.0 像素
  • removed_image_count / input_image_count < 5%
  • tie_point_count > 10000
  • 位置差异平均值 < 0.5米

需要注意的情况​

  • residual_rmse > 2.0 像素：可能存在系统误差
  • removed_image_count > 10%：图像质量或重叠度问题
  • tie_point_count < 5000：特征点不足，影响精度

报告解析工具​

Python 解析示例​

import json
import numpy as np

def analyze_quality_report(report_path):
    with open(report_path, 'r', encoding='utf-8') as f:
        report = json.load(f)
    
    # 计算效率指标
    total_time = report['at_time'] + report['reconstruction_time']
    images_per_minute = report['input_image_count'] / total_time
    
    # 计算质量指标
    removal_rate = report['removed_image_count'] / report['input_image_count']
    avg_pos_diff = np.mean([item['pos_diff'] for item in report['image_pos_diff']])
    
    # 生成分析报告
    analysis = {
        'efficiency': {
            'total_time_minutes': total_time,
            'images_per_minute': images_per_minute,
            'area_per_hour': report['scene_area'] / (total_time / 60)
        },
        'quality': {
            'residual_rmse': report['residual_rmse'],
            'removal_rate_percent': removal_rate * 100,
            'avg_position_diff_meters': avg_pos_diff,
            'tie_points_per_image': report['tie_point_count'] / report['input_image_count']
        },
        'scale': {
            'area_sqm': report['scene_area'],
            'gsd_cm': report['scene_gsd'] * 100,
            'flight_height_m': report['flight_height']
        }
    }
    
    return analysis

# 使用示例
analysis = analyze_quality_report('report/report.json')
print(f"处理效率: {analysis['efficiency']['images_per_minute']:.1f} 张/分钟")
print(f"平均残差: {analysis['quality']['residual_rmse']:.2f} 像素")
print(f"地面分辨率: {analysis['scale']['gsd_cm']:.1f} 厘米")

质量报告可视化​

import matplotlib.pyplot as plt
from PIL import Image

def visualize_quality_report(report_dir):
    # 读取报告数据
    with open(f'{report_dir}/report.json', 'r') as f:
        report = json.load(f)
    
    # 创建图表
    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
    
    # 1. 时间分布饼图
    times = [
        report['feature_extraction_time'],
        report['feature_match_time'],
        report['sfm_time'],
        report['reconstruction_time']
    ]
    labels = ['特征提取', '特征匹配', '光束法平差', '3D重建']
    axes[0, 0].pie(times, labels=labels, autopct='%1.1f%%')
    axes[0, 0].set_title('处理时间分布')
    
    # 2. 位置差异直方图
    pos_diffs = [item['pos_diff'] for item in report['image_pos_diff']]
    axes[0, 1].hist(pos_diffs, bins=20, edgecolor='black')
    axes[0, 1].set_xlabel('位置差异 (米)')
    axes[0, 1].set_ylabel('图像数量')
    axes[0, 1].set_title('图像位置优化量分布')
    
    # 3. 重叠度图
    overlap_img = Image.open(f'{report_dir}/thumbnail/overlap_map.png')
    axes[1, 0].imshow(overlap_img, cmap='jet')
    axes[1, 0].set_title('图像重叠度分布')
    axes[1, 0].axis('off')
    
    # 4. 关键指标文本
    metrics_text = f"""
    输入图像: {report['input_image_count']}
    移除图像: {report['removed_image_count']}
    残差RMSE: {report['residual_rmse']:.2f} px
    连接点数: {report['tie_point_count']:,}
    场景面积: {report['scene_area']/10000:.1f} 公顷
    地面分辨率: {report['scene_gsd']*100:.1f} cm
    """
    axes[1, 1].text(0.1, 0.5, metrics_text, fontsize=12, 
                    verticalalignment='center', family='monospace')
    axes[1, 1].set_title('关键质量指标')
    axes[1, 1].axis('off')
    
    plt.tight_layout()
    plt.savefig('quality_report_summary.png', dpi=150)
    plt.show()

自动化质量检查​

质量阈值配置​

QUALITY_THRESHOLDS = {
    'excellent': {
        'residual_rmse': 0.5,
        'removal_rate': 0.02,
        'tie_points_per_image': 1000,
        'pos_diff_avg': 0.1
    },
    'good': {
        'residual_rmse': 1.0,
        'removal_rate': 0.05,
        'tie_points_per_image': 500,
        'pos_diff_avg': 0.5
    },
    'acceptable': {
        'residual_rmse': 2.0,
        'removal_rate': 0.10,
        'tie_points_per_image': 200,
        'pos_diff_avg': 1.0
    }
}

def assess_quality(report):
    """自动评估重建质量等级"""
    
    # 计算指标
    removal_rate = report['removed_image_count'] / report['input_image_count']
    tie_points_per_image = report['tie_point_count'] / report['input_image_count']
    pos_diff_avg = np.mean([item['pos_diff'] for item in report['image_pos_diff']])
    
    # 评估等级
    for level, thresholds in QUALITY_THRESHOLDS.items():
        if (report['residual_rmse'] <= thresholds['residual_rmse'] and
            removal_rate <= thresholds['removal_rate'] and
            tie_points_per_image >= thresholds['tie_points_per_image'] and
            pos_diff_avg <= thresholds['pos_diff_avg']):
            return level
    
    return 'poor'

报告集成应用​

批处理质量监控​

def batch_quality_monitor(project_dirs):
    """批量项目质量监控"""
    
    results = []
    
    for project_dir in project_dirs:
        report_path = os.path.join(project_dir, 'report/report.json')
        
        if os.path.exists(report_path):
            with open(report_path, 'r') as f:
                report = json.load(f)
            
            quality_level = assess_quality(report)
            
            results.append({
                'project': project_dir,
                'images': report['input_image_count'],
                'area': report['scene_area'],
                'gsd': report['scene_gsd'],
                'rmse': report['residual_rmse'],
                'quality': quality_level,
                'time': report['at_time'] + report['reconstruction_time']
            })
    
    # 生成汇总报告
    df = pd.DataFrame(results)
    df.to_csv('batch_quality_report.csv', index=False)
    
    # 统计信息
    print(f"Total projects: {len(results)}")
    print(f"Excellent: {len(df[df['quality'] == 'excellent'])}")
    print(f"Good: {len(df[df['quality'] == 'good'])}")
    print(f"Acceptable: {len(df[df['quality'] == 'acceptable'])}")
    print(f"Poor: {len(df[df['quality'] == 'poor'])}")
    
    return df

最佳实践​

  1. 定期检查报告：每次重建完成后查看质量指标
  2. 建立基准：记录典型项目的质量指标作为参考
  3. 异常预警：设置自动化脚本检测异常指标
  4. 趋势分析：长期跟踪质量指标变化趋势
  5. 优化建议：根据报告指标调整拍摄和处理参数

提示：质量报告是评估和优化重建流程的重要工具，建议将其集成到自动化工作流程中。