Upload bulid_codebook.py

bulid_codebook.py

@@ -0,0 +1,281 @@
+"""
+3DGS Codebook Builder
+=====================
+使用 KMeans 对 3D Gaussian Splatting 模型的四类特征分别构建 codebook：
+  - scale    (3维)   → 16384 个离散索引
+  - rotation (4维)   → 16384 个离散索引
+  - DC       (3维)   → 4096  个离散索引
+  - SH rest  (45维)  → 4096  个离散索引
+
+每个 codebook 单独保存为 .npz 文件，包含：
+  - codebook  : (K, D) float32  —— 聚类中心
+  - indices   : (N,)  int32     —— 每个高斯点对应的索引
+"""
+
+import os
+import argparse
+import numpy as np
+from plyfile import PlyData
+from sklearn.cluster import MiniBatchKMeans
+import time
+
+
+# ─────────────────────────────────────────────
+# 1. PLY 读取
+# ─────────────────────────────────────────────
+
+def read_ply(ply_path: str) -> dict:
+    """读取 3DGS .ply 文件，返回各属性 numpy 数组。"""
+    plydata = PlyData.read(ply_path)
+    vertex  = plydata['vertex']
+
+    positions  = np.stack([vertex['x'], vertex['y'], vertex['z']], axis=1)          # (N, 3)
+    opacities  = vertex['opacity'][:, np.newaxis]                                    # (N, 1)
+    scales     = np.stack([vertex['scale_0'], vertex['scale_1'],
+                           vertex['scale_2']], axis=1)                               # (N, 3)
+    rotations  = np.stack([vertex['rot_0'], vertex['rot_1'],
+                           vertex['rot_2'], vertex['rot_3']], axis=1)                # (N, 4)
+    dc         = np.stack([vertex['f_dc_0'], vertex['f_dc_1'],
+                           vertex['f_dc_2']], axis=1)                                # (N, 3)
+
+    sh_keys  = sorted(
+        [k for k in vertex.data.dtype.names if k.startswith('f_rest_')],
+        key=lambda s: int(s.split('_')[-1])
+    )
+    sh_rest  = np.stack([vertex[k] for k in sh_keys], axis=1) \
+               if sh_keys else None                                                   # (N, 45)
+
+    # filter_3D 是可选字段（部分版本有，部分没有）
+    filter_3d = None
+    if 'filter_3D' in vertex.data.dtype.names:
+        filter_3d = vertex['filter_3D'][:, np.newaxis]                              # (N, 1)
+
+    print(f"[read_ply] 读取完成：{positions.shape[0]} 个高斯点")
+    if sh_rest is not None:
+        print(f"           SH rest 维度：{sh_rest.shape[1]}  "
+              f"（期望 45 = 15 球谐系数 × 3 通道）")
+
+    return {
+        'positions':  positions,
+        'opacities':  opacities,
+        'scales':     scales,
+        'rotations':  rotations,
+        'dc':         dc,
+        'sh_rest':    sh_rest,
+        'filter_3d':  filter_3d,
+        'plydata':    plydata,
+    }
+
+
+# ─────────────────────────────────────────────
+# 2. KMeans 聚类（MiniBatchKMeans，速度快）
+# ─────────────────────────────────────────────
+
+def build_codebook(
+    features: np.ndarray,
+    n_clusters: int,
+    name: str,
+    random_state: int = 42,
+    batch_size: int = 65536,
+    max_iter: int = 300,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    对 features (N, D) 执行 MiniBatchKMeans，返回：
+      codebook : (K, D) float32
+      indices  : (N,)  int32
+    """
+    N, D = features.shape
+    # 若点数少于 cluster 数，直接把每个点当一个 cluster
+    K = min(n_clusters, N)
+    if K < n_clusters:
+        print(f"[{name}] 警告：高斯点数 ({N}) < 目标 cluster 数 ({n_clusters})，"
+              f"自动调整为 K={K}")
+
+    print(f"[{name}] 开始 KMeans：N={N}, D={D}, K={K} ...")
+    t0 = time.time()
+
+    kmeans = MiniBatchKMeans(
+        n_clusters=K,
+        batch_size=min(batch_size, N),
+        max_iter=max_iter,
+        random_state=random_state,
+        n_init=3,
+        verbose=0,
+    )
+    kmeans.fit(features.astype(np.float32))
+
+    codebook = kmeans.cluster_centers_.astype(np.float32)   # (K, D)
+    indices  = kmeans.labels_.astype(np.int32)               # (N,)
+
+    elapsed = time.time() - t0
+    inertia = kmeans.inertia_
+    print(f"[{name}] 完成！耗时 {elapsed:.1f}s  |  inertia={inertia:.4f}")
+    print(f"         codebook shape: {codebook.shape}  |  "
+          f"索引范围: [{indices.min()}, {indices.max()}]")
+
+    return codebook, indices
+
+
+# ─────────────────────────────────────────────
+# 3. 保存单个 codebook
+# ─────────────────────────────────────────────
+
+def save_codebook(
+    save_dir: str,
+    name: str,
+    codebook: np.ndarray,
+    indices: np.ndarray,
+) -> None:
+    """将 codebook 和 indices 存为 <name>_codebook.npz。"""
+    os.makedirs(save_dir, exist_ok=True)
+    out_path = os.path.join(save_dir, f"{name}_codebook.npz")
+    np.savez_compressed(out_path, codebook=codebook, indices=indices)
+    size_mb = os.path.getsize(out_path) / 1024 / 1024
+    print(f"[{name}] 已保存 → {out_path}  ({size_mb:.2f} MB)")
+
+
+# ─────────────────────────────────────────────
+# 4. 主流程
+# ─────────────────────────────────────────────
+
+CODEBOOK_CONFIG = {
+    #  name        n_clusters
+    'scale':    16384,
+    'rotation': 16384,
+    'dc':        4096,
+    'sh':        4096,
+}
+
+
+def build_all_codebooks(
+    ply_path: str,
+    save_dir: str,
+    random_state: int = 42,
+) -> dict:
+    """
+    读取 PLY → 对四类特征分别聚类 → 分开保存。
+
+    返回字典：
+      {
+        'scale':    (codebook_array, indices_array),
+        'rotation': ...,
+        'dc':       ...,
+        'sh':       ...,
+      }
+    """
+    # ── 读取数据 ──────────────────────────────
+    data = read_ply(ply_path)
+
+    scales    = data['scales']     # (N, 3)
+    rotations = data['rotations']  # (N, 4)
+    dc        = data['dc']         # (N, 3)
+    sh_rest   = data['sh_rest']    # (N, 45)  已去除 DC 的 SH
+
+    if sh_rest is None:
+        raise ValueError("PLY 文件中未找到 f_rest_* 字段，无法构建 SH codebook。")
+
+    feature_map = {
+        'scale':    scales,
+        'rotation': rotations,
+        'dc':       dc,
+        'sh':       sh_rest,   # SH codebook 使用去掉 DC 的 45 维高频分量
+    }
+
+    # ── 逐一聚类并保存 ────────────────────────
+    results = {}
+    for name, n_clusters in CODEBOOK_CONFIG.items():
+        features = feature_map[name]
+        print(f"\n{'='*55}")
+        print(f"  构建 [{name}] codebook  |  特征维度: {features.shape[1]}"
+              f"  |  目标 K: {n_clusters}")
+        print(f"{'='*55}")
+
+        codebook, indices = build_codebook(
+            features,
+            n_clusters=n_clusters,
+            name=name,
+            random_state=random_state,
+        )
+        save_codebook(save_dir, name, codebook, indices)
+        results[name] = (codebook, indices)
+
+    print(f"\n{'='*55}")
+    print("  所有 codebook 构建完毕！")
+    print(f"  输出目录：{os.path.abspath(save_dir)}")
+    print(f"{'='*55}")
+    return results
+
+
+# ─────────────────────────────────────────────
+# 5. 验证：从 codebook 重建特征并计算误差
+# ─────────────────────────────────────────────
+
+def evaluate_codebooks(
+    ply_path: str,
+    save_dir: str,
+) -> None:
+    """
+    加载已保存的四个 codebook，重建特征，
+    计算每个维度的均方根误差（RMSE）。
+    """
+    data = read_ply(ply_path)
+    feature_map = {
+        'scale':    data['scales'],
+        'rotation': data['rotations'],
+        'dc':       data['dc'],
+        'sh':       data['sh_rest'],
+    }
+
+    print("\n[评估] 重建误差（RMSE）：")
+    for name in CODEBOOK_CONFIG:
+        path = os.path.join(save_dir, f"{name}_codebook.npz")
+        if not os.path.exists(path):
+            print(f"  [{name}] 文件不存在，跳过")
+            continue
+
+        npz      = np.load(path)
+        codebook = npz['codebook']   # (K, D)
+        indices  = npz['indices']    # (N,)
+
+        original    = feature_map[name].astype(np.float32)
+        reconstructed = codebook[indices]                    # (N, D)
+
+        rmse = np.sqrt(np.mean((original - reconstructed) ** 2))
+        max_err = np.abs(original - reconstructed).max()
+        print(f"  [{name:8s}]  K={codebook.shape[0]:6d}  D={codebook.shape[1]:3d}"
+              f"  RMSE={rmse:.6f}  MaxErr={max_err:.6f}")
+
+
+# ─────────────────────────────────────────────
+# 6. CLI 入口
+# ─────────────────────────────────────────────
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="为 3DGS .ply 文件构建四个 KMeans codebook"
+    )
+    parser.add_argument('ply_path', type=str,default="./merge/original_3dgs.ply",
+                        help='输入的 3DGS .ply 文件路径')
+    parser.add_argument('--save_dir', type=str, default='./codebooks',
+                        help='codebook 保存目录（默认：./codebooks）')
+    parser.add_argument('--seed', type=int, default=42,
+                        help='随机种子（默认：42）')
+    parser.add_argument('--evaluate', action='store_true',
+                        help='构建完成后计算 RMSE 重建误差')
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    build_all_codebooks(
+        ply_path=args.ply_path,
+        save_dir=args.save_dir,
+        random_state=args.seed,
+    )
+
+    if args.evaluate:
+        evaluate_codebooks(
+            ply_path=args.ply_path,
+            save_dir=args.save_dir,
+        )