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
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
import open3d as o3d
|
|
import numpy as np
|
|
from scipy.spatial import KDTree
|
|
|
|
|
|
def evaluate_voxel_filter(pcd, leaf_size=0.05):
|
|
"""
|
|
评估体素滤波效果，计算各项指标
|
|
|
|
Args:
|
|
pcd: 原始点云
|
|
leaf_size: 体素边长
|
|
|
|
Returns:
|
|
metrics: 指标字典
|
|
filtered_pcd: 滤波后点云
|
|
"""
|
|
# 执行体素滤波
|
|
filtered_pcd = pcd.voxel_down_sample(leaf_size)
|
|
|
|
n_orig = len(pcd.points)
|
|
n_filt = len(filtered_pcd.points)
|
|
|
|
# 1. 基础统计指标
|
|
keep_ratio = n_filt / n_orig * 100
|
|
downsample_rate = (n_orig - n_filt) / n_orig * 100
|
|
compression_ratio = n_orig / n_filt if n_filt > 0 else 0
|
|
|
|
# 2. 密度变化指标
|
|
points_orig = np.asarray(pcd.points)
|
|
points_filt = np.asarray(filtered_pcd.points)
|
|
|
|
# 计算平均最近邻距离（采样加速）
|
|
sample_size = min(1000, n_filt)
|
|
if n_filt > sample_size:
|
|
idx = np.random.choice(n_filt, sample_size, replace=False)
|
|
points_sample = points_filt[idx]
|
|
else:
|
|
points_sample = points_filt
|
|
|
|
if len(points_sample) > 5:
|
|
tree = KDTree(points_sample)
|
|
dist, _ = tree.query(points_sample, k=2)
|
|
avg_distance = np.mean(dist[:, 1])
|
|
else:
|
|
avg_distance = 0
|
|
|
|
# 3. 输出指标
|
|
metrics = {
|
|
'leaf_size': leaf_size,
|
|
'n_orig': n_orig,
|
|
'n_filt': n_filt,
|
|
'keep_ratio': keep_ratio,
|
|
'downsample_rate': downsample_rate,
|
|
'compression_ratio': compression_ratio,
|
|
'avg_point_distance': avg_distance,
|
|
'estimated_density': 1 / (avg_distance ** 3) if avg_distance > 0 else 0
|
|
}
|
|
|
|
return metrics, filtered_pcd
|
|
|
|
|
|
# ========== 使用示例 ==========
|
|
if __name__ == "__main__":
|
|
# 读取点云
|
|
pcd = o3d.io.read_point_cloud(r"E:\point_distance\data\part_car_head.ply")
|
|
|
|
# 测试不同体素大小
|
|
leaf_sizes = [2, 15, 20, 2]
|
|
|
|
print("=" * 60)
|
|
print("体素滤波指标评估")
|
|
print("=" * 60)
|
|
print(f"{'体素边长':<10} {'保留率':<12} {'下采样率':<12} {'压缩比':<10} {'平均间距':<12}")
|
|
print("-" * 60)
|
|
|
|
for leaf_size in leaf_sizes:
|
|
metrics, filtered = evaluate_voxel_filter(pcd, leaf_size)
|
|
print(f"{leaf_size:<10} {metrics['keep_ratio']:>8.2f}% "
|
|
f"{metrics['downsample_rate']:>8.2f}% "
|
|
f"{metrics['compression_ratio']:>8.1f}x "
|
|
f"{metrics['avg_point_distance']:>8.4f}m")
|
|
|
|
# 可视化对比
|
|
print("\n可视化对比...")
|
|
orig_colored = o3d.geometry.PointCloud()
|
|
orig_colored.points = pcd.points
|
|
orig_colored.paint_uniform_color([0, 0, 1]) # 蓝色
|
|
# o3d.visualization.draw_geometries([orig_colored],window_name=f"orignal (blue,{len(pcd.points)}")
|
|
metrics, filtered = evaluate_voxel_filter(pcd, leaf_size=20)
|
|
filtered.paint_uniform_color([1, 0, 0]) # 红色
|
|
# o3d.visualization.draw_geometries([filtered], window_name=f"Voxel (red,{len(filtered.points)}")
|
|
o3d.visualization.draw_geometries(
|
|
[orig_colored, filtered],
|
|
window_name=f"Original (Blue, {len(pcd.points)} pts) vs Voxel (Red, {len(filtered.points)} pts)"
|
|
) |