feat: initial commit - massive-cloud point cloud processing system

alg/point_porj/archive/relative_code/RG.py

@@ -0,0 +1,147 @@
+import numpy as np
+from scipy.spatial import KDTree
+from sklearn.neighbors import NearestNeighbors
+
+
+def estimate_normals(points, k=20):
+    """用 PCA 估计法线"""
+    n_points = len(points)
+    normals = np.zeros_like(points)
+
+    nbrs = NearestNeighbors(n_neighbors=k).fit(points)
+    distances, indices = nbrs.kneighbors(points)
+
+    for i in range(n_points):
+        neighbors = points[indices[i]]
+        centroid = np.mean(neighbors, axis=0)
+        cov = np.cov((neighbors - centroid).T)
+        eig_vals, eig_vecs = np.linalg.eig(cov)
+        normal = eig_vecs[:, np.argmin(eig_vals)]
+        # 统一法线方向
+        if normal[2] < 0:
+            normal = -normal
+        normals[i] = normal
+
+    return normals
+
+
+def compute_curvature(points, normals, k=20):
+    """计算曲率（特征值比值）"""
+    curvature = np.zeros(len(points))
+    nbrs = NearestNeighbors(n_neighbors=k).fit(points)
+    distances, indices = nbrs.kneighbors(points)
+
+    for i in range(len(points)):
+        neighbors = points[indices[i]]
+        centroid = np.mean(neighbors, axis=0)
+        cov = np.cov((neighbors - centroid).T)
+        eig_vals, _ = np.linalg.eig(cov)
+        curvature[i] = np.min(eig_vals) / (np.sum(eig_vals) + 1e-8)
+
+    return curvature
+
+
+def region_growing(points, normals, curvature,
+                   angle_threshold=30.0,  # 法线夹角阈值（度）
+                   curvature_threshold=0.05,  # 曲率阈值
+                   min_cluster_size=100):
+    """
+    区域生长算法
+    Args:
+        points: (N, 3) 点云坐标
+        normals: (N, 3) 法线
+        curvature: (N,) 曲率
+        angle_threshold: 法线夹角阈值（度）
+        curvature_threshold: 曲率阈值
+        min_cluster_size: 最小簇大小
+    Returns:
+        labels: (N,) 每个点的簇标签（-1 表示未分类/噪声）
+    """
+    n_points = len(points)
+    labels = np.full(n_points, -1, dtype=int)
+
+    # 按曲率升序排序（曲率小的作为种子）
+    sorted_indices = np.argsort(curvature)
+
+    # 构建 KD-Tree 用于快速邻域搜索
+    tree = KDTree(points)
+
+    label_id = 0
+    angle_threshold_rad = np.radians(angle_threshold)
+    cos_threshold = np.cos(angle_threshold_rad)
+
+    for seed_idx in sorted_indices:
+        if labels[seed_idx] != -1:
+            continue
+
+        # 开始一个新区域
+        current_region = []
+        seeds = [seed_idx]
+        labels[seed_idx] = label_id
+
+        while seeds:
+            current_seed = seeds.pop(0)
+            current_region.append(current_seed)
+
+            # 搜索半径内的邻居（动态半径或固定半径）
+            neighbors = tree.query_ball_point(points[current_seed], r=0.2)
+
+            for neighbor in neighbors:
+                if labels[neighbor] != -1:
+                    continue
+
+                # 检查法线夹角
+                dot_product = np.abs(np.dot(normals[current_seed], normals[neighbor]))
+                if dot_product > cos_threshold:
+                    labels[neighbor] = label_id
+
+                    # 如果曲率足够小，作为新种子
+                    if curvature[neighbor] < curvature_threshold:
+                        seeds.append(neighbor)
+
+        # 检查区域大小
+        region_size = np.sum(labels == label_id)
+        if region_size < min_cluster_size:
+            # 区域太小，标记为噪声
+            labels[labels == label_id] = -1
+        else:
+            label_id += 1
+
+    return labels
+
+
+# 使用示例
+if __name__ == "__main__":
+    import open3d as o3d
+
+    # 读取点云
+    pcd = o3d.io.read_point_cloud("E:\point_distance\data\\bell_pepper_6.pcd")
+    points = np.asarray(pcd.points)
+
+    # 预处理：体素下采样 + 去噪
+    pcd = pcd.voxel_down_sample(0.0002)
+    pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+    points = np.asarray(pcd.points)
+
+    # 计算法线和曲率
+    print("计算法线...")
+    normals = estimate_normals(points, k=30)
+
+    print("计算曲率...")
+    curvature = compute_curvature(points, normals, k=30)
+
+    # 区域生长分割
+    print("区域生长...")
+    labels = region_growing(points, normals, curvature,
+                            angle_threshold=25.0,
+                            curvature_threshold=0.05,
+                            min_cluster_size=100)
+
+    print(f"分割完成，共 {len(np.unique(labels)) - (1 if -1 in labels else 0)} 个区域")
+
+    # 可视化
+    colors = np.random.rand(len(np.unique(labels)), 3)
+    point_colors = np.array([colors[label] if label != -1 else [0.5, 0.5, 0.5]
+                             for label in labels])
+    pcd.colors = o3d.utility.Vector3dVector(point_colors)
+    o3d.visualization.draw_geometries([pcd])