lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
import open3d as o3d
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import numpy as np
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from scipy.spatial import KDTree
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def compute_keep_ratio_and_density(pcd, filtered_pcd, sample_size=1000):
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"""
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计算直通滤波的保留率和密度变化
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Args:
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pcd: 原始点云
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filtered_pcd: 滤波后点云
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sample_size: 密度计算采样点数（加速计算）
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Returns:
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keep_ratio: 保留率 (%)
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density_change: 密度变化 (%)
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"""
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n_orig = len(pcd.points)
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n_filt = len(filtered_pcd.points)
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# 1. 保留率
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keep_ratio = n_filt / n_orig * 100
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# 2. 密度变化（基于平均最近邻距离）
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points_orig = np.asarray(pcd.points)
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points_filt = np.asarray(filtered_pcd.points)
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# 采样（点数太多时加速）
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if len(points_orig) > sample_size:
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idx_orig = np.random.choice(len(points_orig), sample_size, replace=False)
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points_orig_sample = points_orig[idx_orig]
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else:
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points_orig_sample = points_orig
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if len(points_filt) > sample_size:
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idx_filt = np.random.choice(len(points_filt), sample_size, replace=False)
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points_filt_sample = points_filt[idx_filt]
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else:
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points_filt_sample = points_filt
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# 计算平均最近邻距离（密度指标）
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if len(points_orig_sample) > 5:
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tree_orig = KDTree(points_orig_sample)
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dist_orig, _ = tree_orig.query(points_orig_sample, k=2) # k=2 排除自身
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avg_dist_orig = np.mean(dist_orig[:, 1])
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else:
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avg_dist_orig = 0
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if len(points_filt_sample) > 5:
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tree_filt = KDTree(points_filt_sample)
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dist_filt, _ = tree_filt.query(points_filt_sample, k=2)
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avg_dist_filt = np.mean(dist_filt[:, 1])
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else:
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avg_dist_filt = 0
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# 密度变化（距离增大 = 密度减小）
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if avg_dist_orig > 0:
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density_change = (avg_dist_filt - avg_dist_orig) / avg_dist_orig * 100
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else:
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density_change = 0
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return keep_ratio, density_change
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# ========== 使用示例 ==========
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if __name__ == "__main__":
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# 读取点云
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pcd = o3d.io.read_point_cloud(r"E:\point_distance\data\part_car_head.ply")
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# 直通滤波（Z轴）
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points = np.asarray(pcd.points)
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mask = (points[:, 2] >= 0.0) & (points[:, 2] <= 2.0)
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filtered_points = points[mask]
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filtered_pcd = o3d.geometry.PointCloud()
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filtered_pcd.points = o3d.utility.Vector3dVector(filtered_points)
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# 计算指标
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keep_ratio, density_change = compute_keep_ratio_and_density(pcd, filtered_pcd)
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print("=" * 40)
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print("直通滤波指标")
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print("=" * 40)
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print(f"保留率: {keep_ratio:.2f}%")
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print(f"密度变化: {density_change:+.2f}%")
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# 解释
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if density_change > 0:
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print(f" → 点云变稀疏了 {density_change:.2f}%")
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elif density_change < 0:
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print(f" → 点云变密集了 {abs(density_change):.2f}%")
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else:
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print(f" → 密度基本不变") |