Python参数配置使用XML文件的教程

if __name__ == "__main__":
    # 设置距离阈值
    voxel_size = 5
    distance_threshold = 4
    print(f"Using voxel size: {voxel_size}")
    print(f"Using distance threshold: {distance_threshold}")

    # 加载模型
    pcd_mri = load_and_convert_to_point_cloud("mri1.stl", num_points=8000)
    pcd_scan = preprocess_point_cloud(load_and_convert_to_point_cloud("scan2.stl", num_points=10000), voxel_size)
    pcd_helmet = load_and_convert_to_point_cloud("helmet2.stl", num_points=6000)   
    ...

# 使用RANSAC进行 mri -> scan 粗配准
result_ransac_mri_to_scan = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
    pcd_mri_down, pcd_scan_down, fpfh_mri, fpfh_scan, True,
    distance_threshold,
    o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
    3,
    [o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.8),
     o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)],
    o3d.pipelines.registration.RANSACConvergenceCriteria(4000000, 500)
)

# 使用RANSAC进行 helmet -> scan 粗配准
result_ransac_helmet_to_scan = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
    pcd_helmet_down, pcd_scan_down, fpfh_helmet, fpfh_scan, True,
    distance_threshold,
    o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
    3,
    [o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.8),
     o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)],
    o3d.pipelines.registration.RANSACConvergenceCriteria(4000000, 500)
)

<?xml version="1.0" encoding="UTF-8"?>
<config>
    <Preprocessing>
        <VoxelSize>5.0</VoxelSize>
    </Preprocessing>
    <Alignment>
        <DistanceThreshold>4.0</DistanceThreshold>
    </Alignment>
    <ModelFiles>
        <MRI>mri_model.stl</MRI>
        <Scan>scan_model.stl</Scan>
    </ModelFiles>
    <NumPoints>
        <MRI>8000</MRI>
        <Scan>10000</Scan>
    </NumPoints>
</config>

import xml.etree.ElementTree as ET

# 从 XML 文件中加载参数
def load_parameters_from_xml(xml_file):
    tree = ET.parse(xml_file)
    root = tree.getroot()

    params = {
        'voxel_size': float(root.find('Preprocessing/VoxelSize').text),
        'distance_threshold': float(root.find('Alignment/DistanceThreshold').text),
        'model_files': {
            'mri': root.find('ModelFiles/MRI').text,
            'scan': root.find('ModelFiles/Scan').text,
        },
        'num_points': {
            'mri': int(root.find('NumPoints/MRI').text),
            'scan': int(root.find('NumPoints/Scan').text),
        }
    }
    return params

def save_results_to_xml(file_name, voxel_size, distance_threshold, ransac_results, icp_results):
    root = ET.Element("Results")

    # 添加基本参数
    parameters = ET.SubElement(root, "Parameters")
    ET.SubElement(parameters, "VoxelSize").text = str(voxel_size)
    ET.SubElement(parameters, "DistanceThreshold").text = str(distance_threshold)

    # 添加 RANSAC 和 ICP 结果
    # 省略具体的添加过程，最后美化 XML 并写入文件

    with open(file_name, "w", encoding="utf-8") as f:
        f.write(pretty_xml)

if __name__ == "__main__":
    try:
        import os
        import sys

        BASE_DIR = os.path.dirname(os.path.realpath(sys.argv[0]))
        xml_file_path = os.path.join(BASE_DIR, 'AlignPoint_input.xml')
        params = load_parameters_from_xml(xml_file_path)

        voxel_size = params['voxel_size']
        distance_threshold = params['distance_threshold']

        # 加载和预处理点云
        mri_file_path = os.path.join(BASE_DIR, params['model_files']['mri'])
        scan_file_path = os.path.join(BASE_DIR, params['model_files']['scan'])

        pcd_mri = load_and_convert_to_point_cloud(mri_file_path, params['num_points']['mri'])
        pcd_scan = preprocess_point_cloud(
            load_and_convert_to_point_cloud(scan_file_path, params['num_points']['scan']), voxel_size)

        # 计算 FPFH 特征和下采样点云
        pcd_mri_down, fpfh_mri = compute_fpfh_features(pcd_mri, voxel_size)
        pcd_scan_down, fpfh_scan = compute_fpfh_features(pcd_scan, voxel_size)

        # 执行 RANSAC 和 ICP 配准
        # ...

        # 保存结果到 XML 文件
        save_results_to_xml("AlignPoint_output.xml", voxel_size, distance_threshold, ransac_results, icp_results)

        # 可视化对齐结果
        visualize_alignment(pcd_mri, pcd_scan, result_icp_mri_to_scan.transformation)

    except Exception as e:
        print("An error occurred:", e)
        with open("error_log.txt", "w") as f:
            f.write(str(e))