import numpy as np
from plyfile import PlyData, PlyElement
from sklearn.cluster import AgglomerativeClustering
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.transform import Rotation as R
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
import os
import json


# ============================================================
#  Merge 相关函数
# ============================================================

def read_ply(ply_path):
    plydata = PlyData.read(ply_path)
    vertex  = plydata['vertex']
    positions = np.stack([vertex['x'], vertex['y'], vertex['z']], axis=1)
    opacities = vertex['opacity'][:, np.newaxis]
    scales    = np.stack([vertex['scale_0'], vertex['scale_1'], vertex['scale_2']], axis=1)
    rotations = np.stack([vertex['rot_0'], vertex['rot_1'], vertex['rot_2'], vertex['rot_3']], axis=1)
    filter_3D = np.stack([vertex['filter_3D']], axis=1)
    dc        = np.stack([vertex['f_dc_0'], vertex['f_dc_1'], vertex['f_dc_2']], axis=1)
    sh_keys   = [key for key in vertex.data.dtype.names if key.startswith('f_rest_')]
    sh_rest   = np.stack([vertex[key] for key in sh_keys], axis=1) if sh_keys else None

    # point_id：每个点的唯一标识，原始PLY没有该字段时自动生成 0~N-1
    if 'point_id' in vertex.data.dtype.names:
        point_ids = vertex['point_id'].astype(np.int64)
    else:
        point_ids = np.arange(len(positions), dtype=np.int64)

    return {
        'positions': positions, 'opacities': opacities, 'scales': scales,
        'rotations': rotations, 'dc': dc, 'sh_rest': sh_rest,
        'plydata': plydata, 'filter_3D': filter_3D,
        'point_ids': point_ids,  # shape (N,)，每个点的唯一ID
    }


def quaternion_to_rotation_matrix(q):
    try:
        rot = R.from_quat(q)
    except:
        rot = R.from_quat([q[1], q[2], q[3], q[0]])
    return rot.as_matrix()


def compute_covariance(rotation, scale_log):
    R_mat        = quaternion_to_rotation_matrix(rotation)
    scale_actual = np.exp(scale_log)
    S_mat        = np.diag(scale_actual)
    return R_mat @ S_mat @ S_mat.T @ R_mat.T


def covariance_to_rotation_scale(cov):
    eigenvalues, eigenvectors = np.linalg.eigh(cov)
    eigenvalues = np.maximum(eigenvalues, 1e-7)
    scale       = np.sqrt(eigenvalues)
    if np.linalg.det(eigenvectors) < 0:
        eigenvectors[:, 0] *= -1
    rotation = R.from_matrix(eigenvectors).as_quat()  # [x,y,z,w]
    return rotation, scale


def dc_to_rgb(dc):
    C0 = 0.28209479177387814
    return np.clip(dc * C0 + 0.5, 0, 1)


def build_octree(positions, max_points=5000):
    cells = []
    def subdivide(indices, bbox_min, bbox_max, depth=0):
        if len(indices) <= max_points or depth > 10:
            cells.append({'indices': indices, 'bbox_min': bbox_min, 'bbox_max': bbox_max})
            return
        center = (bbox_min + bbox_max) / 2
        for i in range(8):
            sub_min = np.array([
                center[0] if (i & 1)      else bbox_min[0],
                center[1] if (i >> 1 & 1) else bbox_min[1],
                center[2] if (i >> 2 & 1) else bbox_min[2],
            ])
            sub_max = np.array([
                bbox_max[0] if (i & 1)      else center[0],
                bbox_max[1] if (i >> 1 & 1) else center[1],
                bbox_max[2] if (i >> 2 & 1) else center[2],
            ])
            mask        = np.all((positions[indices] >= sub_min) & (positions[indices] < sub_max), axis=1)
            sub_indices = indices[mask]
            if len(sub_indices) > 0:
                subdivide(sub_indices, sub_min, sub_max, depth + 1)
    subdivide(np.arange(len(positions)), positions.min(axis=0), positions.max(axis=0))
    return cells


def build_knn_connectivity_graph(positions, k=10):
    n_points = len(positions)
    nbrs     = NearestNeighbors(n_neighbors=min(k+1, n_points), algorithm='kd_tree').fit(positions)
    _, indices = nbrs.kneighbors(positions)
    rows, cols = [], []
    for i in range(n_points):
        for j in range(1, len(indices[i])):
            rows += [i, indices[i][j]]
            cols += [indices[i][j], i]
    return csr_matrix((np.ones(len(rows)), (rows, cols)), shape=(n_points, n_points))


def get_connected_clusters(labels, connectivity_matrix):
    refined_labels = labels.copy()
    next_label     = labels.max() + 1
    for cluster_id in np.unique(labels):
        cluster_indices = np.where(labels == cluster_id)[0]
        if len(cluster_indices) <= 1:
            continue
        subgraph = connectivity_matrix[cluster_indices, :][:, cluster_indices]
        n_components, component_labels = connected_components(subgraph, directed=False, return_labels=True)
        if n_components > 1:
            for comp_id in range(1, n_components):
                refined_labels[cluster_indices[component_labels == comp_id]] = next_label
                next_label += 1
    return refined_labels


def cluster_and_merge_cell(data, cell_indices, bbox_min, bbox_max,
                           k_neighbors=5, spread_factor=0.01,
                           aspect_ratio_threshold=5.0, compress_ratio=4,
                           id_counter=None):
    """
    对单个 cell 内的点进行聚类和合并。

    id_counter: 一个长度为1的列表 [int]，用于跨cell生成全局唯一的新point_id。
                每产生一个合并点就将其自增1。

    返回：
        merged_data  : 合并后各属性（含 point_ids 字段）
        cell_lineage : dict { child_id(int): [parent_id(int), ...] }
    """
    if len(cell_indices) < 4:
        return None, None

    n_clusters = max(1, len(cell_indices) // compress_ratio)

    cell_positions  = data['positions'][cell_indices]
    cell_dc         = data['dc'][cell_indices]
    cell_opacities  = data['opacities'][cell_indices]
    cell_scales     = data['scales'][cell_indices]
    cell_rotations  = data['rotations'][cell_indices]
    cell_filter_3D  = data['filter_3D'][cell_indices]
    cell_point_ids  = data['point_ids'][cell_indices]   # 父节点的 point_id

    connectivity_matrix = build_knn_connectivity_graph(cell_positions, k=k_neighbors)

    cell_size      = np.maximum(bbox_max - bbox_min, 1e-6)
    norm_positions = (cell_positions - bbox_min) / cell_size
    rgb            = dc_to_rgb(cell_dc)

    features = np.concatenate([norm_positions * np.sqrt(0.8), rgb * np.sqrt(0.2)], axis=1)
    labels   = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward').fit_predict(features)
    refined_labels   = get_connected_clusters(labels, connectivity_matrix)
    final_n_clusters = len(np.unique(refined_labels))
    print(f"  原始簇数: {n_clusters}, 连通性约束后簇数: {final_n_clusters}")

    merged_data = {
        'positions': [], 'opacities': [], 'scales': [], 'rotations': [],
        'dc': [], 'sh_rest': [] if data['sh_rest'] is not None else None,
        'filter_3D': [], 'point_ids': []
    }
    # 族谱：{child_point_id: [parent_point_id, ...]}
    cell_lineage = {}

    for cluster_id in np.unique(refined_labels):
        idx_in_cell = np.where(refined_labels == cluster_id)[0]
        if len(idx_in_cell) == 0:
            continue

        # 父节点的 point_id（与顺序无关的唯一标识）
        parent_ids = [int(x) for x in cell_point_ids[idx_in_cell]]

        # 为本合并点分配新的唯一 point_id
        child_id = int(id_counter[0])
        id_counter[0] += 1

        scale_actual       = np.exp(cell_scales[idx_in_cell])
        approx_volumes     = np.prod(scale_actual, axis=1, keepdims=True)
        actual_opacities   = 1.0 / (1.0 + np.exp(-cell_opacities[idx_in_cell]))
        weights            = actual_opacities * approx_volumes
        normalized_weights = weights / weights.sum()

        merged_position  = (cell_positions[idx_in_cell] * normalized_weights).sum(axis=0)
        merged_dc        = (cell_dc[idx_in_cell] * normalized_weights).sum(axis=0)
        merged_filter_3D = (cell_filter_3D[idx_in_cell] * normalized_weights).sum(axis=0)

        if data['sh_rest'] is not None:
            merged_sh_rest = (data['sh_rest'][cell_indices][idx_in_cell] * normalized_weights).sum(axis=0)

        covariances = np.array([compute_covariance(cell_rotations[i], cell_scales[i]) for i in idx_in_cell])
        merged_cov  = np.zeros((3, 3))
        for i, orig_idx in enumerate(idx_in_cell):
            diff = cell_positions[orig_idx] - merged_position
            merged_cov += normalized_weights[i, 0] * (covariances[i] + spread_factor * np.outer(diff, diff))

        merged_rotation, merged_scale = covariance_to_rotation_scale(merged_cov)

        min_s = merged_scale.min()
        if merged_scale.max() / (min_s + 1e-8) > aspect_ratio_threshold:
            merged_scale = np.clip(merged_scale, None, min_s * aspect_ratio_threshold)

        merged_opacity_actual = (cell_opacities[idx_in_cell] * normalized_weights).sum(axis=0)
        merged_opacity_actual = np.clip(merged_opacity_actual, 1e-5, 1.0 - 1e-5)
        merged_opacity        = np.log(merged_opacity_actual / (1.0 - merged_opacity_actual))

        merged_data['positions'].append(merged_position)
        merged_data['opacities'].append(merged_opacity)
        merged_data['scales'].append(merged_scale)
        merged_data['rotations'].append(merged_rotation)
        merged_data['dc'].append(merged_dc)
        merged_data['point_ids'].append(child_id)
        if data['sh_rest'] is not None:
            merged_data['sh_rest'].append(merged_sh_rest)
        merged_data['filter_3D'].append(merged_filter_3D)

        # 族谱：child_id → parent_ids（均为 point_id，与顺序无关）
        cell_lineage[child_id] = parent_ids

    for key in merged_data:
        if merged_data[key] is not None and len(merged_data[key]) > 0:
            merged_data[key] = np.array(merged_data[key])

    return merged_data, cell_lineage


def validate_data(merged_data):
    print("\n" + "="*60 + "\n数据验证报告\n" + "="*60)
    total   = len(merged_data['positions'])
    has_nan = np.zeros(total, dtype=bool)
    has_inf = np.zeros(total, dtype=bool)
    for key in ['positions', 'opacities', 'scales', 'rotations', 'dc']:
        arr = merged_data[key]
        if arr.ndim == 1:
            has_nan |= np.isnan(arr)
            has_inf |= np.isinf(arr)
        else:
            has_nan |= np.isnan(arr).any(axis=1)
            has_inf |= np.isinf(arr).any(axis=1)
    print(f"总点数: {total}  NaN点: {has_nan.sum()}  Inf点: {has_inf.sum()}")
    print("="*60 + "\n")
    return {'has_nan': has_nan.sum(), 'has_inf': has_inf.sum(), 'total': total}


def save_ply(merged_data, original_plydata, output_path):
    n_points   = len(merged_data['positions'])
    dtype_list = [
        ('x','f4'),('y','f4'),('z','f4'),('opacity','f4'),
        ('scale_0','f4'),('scale_1','f4'),('scale_2','f4'),
        ('rot_0','f4'),('rot_1','f4'),('rot_2','f4'),('rot_3','f4'),
        ('f_dc_0','f4'),('f_dc_1','f4'),('f_dc_2','f4'),
        ('point_id', 'i8'),   # 每个点的唯一ID，用于族谱对应，与顺序无关
    ]
    n_sh = 0
    if merged_data['sh_rest'] is not None:
        n_sh = merged_data['sh_rest'].shape[1]
        dtype_list += [(f'f_rest_{i}','f4') for i in range(n_sh)]
    if merged_data.get('filter_3D') is not None:
        dtype_list.append(('filter_3D','f4'))

    vd = np.empty(n_points, dtype=dtype_list)
    vd['x']        = merged_data['positions'][:,0]
    vd['y']        = merged_data['positions'][:,1]
    vd['z']        = merged_data['positions'][:,2]
    vd['opacity']  = merged_data['opacities'].flatten()
    vd['scale_0']  = np.log(merged_data['scales'][:,0])
    vd['scale_1']  = np.log(merged_data['scales'][:,1])
    vd['scale_2']  = np.log(merged_data['scales'][:,2])
    vd['rot_0']    = merged_data['rotations'][:,0]
    vd['rot_1']    = merged_data['rotations'][:,1]
    vd['rot_2']    = merged_data['rotations'][:,2]
    vd['rot_3']    = merged_data['rotations'][:,3]
    vd['f_dc_0']   = merged_data['dc'][:,0]
    vd['f_dc_1']   = merged_data['dc'][:,1]
    vd['f_dc_2']   = merged_data['dc'][:,2]
    vd['point_id'] = merged_data['point_ids'].astype(np.int64)
    if merged_data['sh_rest'] is not None:
        for i in range(n_sh):
            vd[f'f_rest_{i}'] = merged_data['sh_rest'][:,i]
    if merged_data.get('filter_3D') is not None:
        vd['filter_3D'] = merged_data['filter_3D'].flatten()

    PlyData([PlyElement.describe(vd, 'vertex')]).write(output_path)


# ============================================================
#  Merge 主流程（含族谱收集）
# ============================================================

def run_merge(data, compress_ratio=4, k_neighbors=5,
              spread_factor=0.0, aspect_ratio_threshold=15.0,
              id_counter=None):
    """
    对整份数据执行一次 merge，返回合并后数据和本级族谱。

    族谱格式（level_lineage）：
        dict { child_point_id(int): [parent_point_id(int), ...] }
        与点的存储顺序完全无关，通过 point_id 唯一定位每个点。

    id_counter: [int]，跨 cell 全局唯一ID生成器，由外部传入以保证跨级唯一性。
    """
    if id_counter is None:
        id_counter = [0]

    n_input = len(data['positions'])
    cells   = build_octree(data['positions'], max_points=5000)
    print(f"划分为 {len(cells)} 个 cells")

    all_merged = {
        'positions': [], 'opacities': [], 'scales': [], 'rotations': [],
        'dc': [], 'sh_rest': [] if data['sh_rest'] is not None else None,
        'filter_3D': [], 'point_ids': []
    }
    level_lineage = {}   # {child_id: [parent_id, ...]}

    for i, cell in enumerate(cells):
        if i % 100 == 0:
            print(f"  处理进度: {i}/{len(cells)}")

        merged, cell_lineage = cluster_and_merge_cell(
            data, cell['indices'], cell['bbox_min'], cell['bbox_max'],
            k_neighbors=k_neighbors, spread_factor=spread_factor,
            aspect_ratio_threshold=aspect_ratio_threshold,
            compress_ratio=compress_ratio,
            id_counter=id_counter,
        )
        if merged is None:
            continue

        for key in all_merged:
            if all_merged[key] is not None and len(merged[key]) > 0:
                all_merged[key].append(merged[key])

        level_lineage.update(cell_lineage)   # 合并各cell的族谱dict

    final_data = {
        key: np.concatenate(all_merged[key], axis=0)
        for key in all_merged
        if all_merged[key] is not None and len(all_merged[key]) > 0
    }
    n_merged = len(final_data['positions'])
    print(f"合并后点数: {n_merged}  压缩率: {n_merged/n_input*100:.2f}%")

    return final_data, level_lineage


# ============================================================
#  Fine-tuning 阶段
# ============================================================

def finetune_merged_gaussians(
    merged_ply_path,
    original_source_path,
    output_ply_path,
    image_resolution=1,
    sh_degree=3,
    num_epochs=500,
    lr_opacity=0.05,
    lr_scaling=0.005,
    lr_rotation=0.001,
    lr_features_dc=0.0025,
    lr_features_rest=0.000125,
    white_background=False,
    kernel_size=0.1,
    gpu_id=0,
    log_interval=50,
):
    """
    冻结高斯点位置，用下采样 GT 图像对其余参数做 fine-tuning。

    original_source_path : 原始分辨率 COLMAP 目录，程序内部自动 in-memory 下采样。
    image_resolution     : GT 图像边长缩小倍率（1=原图, 2=1/2边长, 4=1/4边长, 8=1/8边长）。
    """
    import torch
    import torch.nn.functional as F
    from gaussian_renderer import render, GaussianModel
    from scene.dataset_readers import sceneLoadTypeCallbacks
    from utils.camera_utils import loadCam
    from utils.loss_utils import l1_loss, ssim
    import random

    device     = f'cuda:{gpu_id}'
    torch.cuda.set_device(device)
    bg_color   = [1,1,1] if white_background else [0,0,0]
    background = torch.tensor(bg_color, dtype=torch.float32, device=device)

    # 1. 加载高斯模型
    print("\n[Fine-tune] 加载 merge 后的高斯模型...")
    gaussians = GaussianModel(sh_degree)
    gaussians.load_ply(merged_ply_path)
    print(f"[Fine-tune] 高斯点数: {gaussians.get_xyz.shape[0]}")

    # 2. 冻结位置
    gaussians._xyz.requires_grad_(False)
    optimizer = torch.optim.Adam([
        {'params': [gaussians._features_dc],   'lr': lr_features_dc,   'name': 'f_dc'},
        {'params': [gaussians._features_rest],  'lr': lr_features_rest, 'name': 'f_rest'},
        {'params': [gaussians._opacity],        'lr': lr_opacity,       'name': 'opacity'},
        {'params': [gaussians._scaling],        'lr': lr_scaling,       'name': 'scaling'},
        {'params': [gaussians._rotation],       'lr': lr_rotation,      'name': 'rotation'},
    ], eps=1e-15)

    # 3. 读取相机（原始分辨率）
    print(f"[Fine-tune] 读取相机，GT 将 in-memory 下采样 1/{image_resolution} 边长...")
    if os.path.exists(os.path.join(original_source_path, "sparse")):
        scene_info = sceneLoadTypeCallbacks["Colmap"](
            original_source_path, "images", eval=False, resolution=1)
    elif os.path.exists(os.path.join(original_source_path, "transforms_train.json")):
        scene_info = sceneLoadTypeCallbacks["Blender"](
            original_source_path, white_background, eval=False, resolution=1)
    else:
        raise ValueError(f"[Fine-tune] 无法识别数据集格式: {original_source_path}")

    class _LoadArgs:
        resolution  = 1
        data_device = device

    cameras = []
    for i, ci in enumerate(scene_info.train_cameras):
        try:
            cameras.append(loadCam(_LoadArgs(), i, ci, 1.0, load_image=True))
        except Exception as e:
            print(f"[Fine-tune] 跳过相机 {i}: {e}")

    if not cameras:
        raise RuntimeError("[Fine-tune] 没有可用的训练相机。")

    # 4. in-memory 下采样：GT 图像 + 渲染分辨率同步缩小
    if image_resolution > 1:
        print(f"[Fine-tune] 对 {len(cameras)} 个相机做 1/{image_resolution} 边长下采样...")
        for cam in cameras:
            gt_orig  = cam.original_image.to(device)
            H, W     = gt_orig.shape[1], gt_orig.shape[2]
            new_H, new_W = H // image_resolution, W // image_resolution
            cam.original_image = F.interpolate(
                gt_orig.unsqueeze(0), size=(new_H, new_W),
                mode='bilinear', align_corners=False
            ).squeeze(0).cpu()
            # FoVx/FoVy 不变，渲染器根据新 image_width/height 自动反算 focal length
            cam.image_width  = new_W
            cam.image_height = new_H
        print(f"[Fine-tune] 下采样后尺寸: {cameras[0].image_height} x {cameras[0].image_width}")

    # 5. 训练循环
    class _Pipeline:
        convert_SHs_python = False
        compute_cov3D_python = False
        debug = False

    pipeline     = _Pipeline()
    lambda_dssim = 0.2

    print(f"\n[Fine-tune] 开始优化，共 {num_epochs} epochs，{len(cameras)} 张图像...")
    for epoch in range(1, num_epochs + 1):
        random.shuffle(cameras)
        epoch_loss = 0.0
        for cam in cameras:
            optimizer.zero_grad()
            rendered = render(cam, gaussians, pipeline, background, kernel_size=kernel_size)["render"]
            gt       = cam.original_image.to(device)
            if rendered.shape != gt.shape:
                gt = F.interpolate(gt.unsqueeze(0), size=rendered.shape[1:],
                                   mode='bilinear', align_corners=False).squeeze(0)
            loss = (1.0 - lambda_dssim) * l1_loss(rendered, gt) \
                 + lambda_dssim * (1.0 - ssim(rendered, gt))
            loss.backward()
            optimizer.step()
            epoch_loss += loss.item()
        if epoch % log_interval == 0 or epoch == 1:
            print(f"[Fine-tune] Epoch {epoch:4d}/{num_epochs}  avg_loss={epoch_loss/len(cameras):.6f}")

    # 6. 保存
    print(f"\n[Fine-tune] 保存至 {output_ply_path} ...")
    os.makedirs(os.path.dirname(os.path.abspath(output_ply_path)), exist_ok=True)
    gaussians.save_ply(output_ply_path)
    print("[Fine-tune] 保存完成。")


# ============================================================
#  完整流程入口
# ============================================================

def run_all(
    input_ply,
    original_source_path,
    output_base,
    # merge 参数
    k_neighbors=5,
    spread_factor=0.0,
    aspect_ratio_threshold=15.0,
    # fine-tune 参数
    sh_degree=3,
    num_epochs=500,
    lr_opacity=0.05,
    lr_scaling=0.005,
    lr_rotation=0.001,
    lr_features_dc=0.0025,
    lr_features_rest=0.000125,
    white_background=False,
    kernel_size=0.1,
    gpu_id=0,
    log_interval=50,
):
    """
    完整流程：级联 merge（三级）+ 每级 fine-tune + 族谱保存。

    输出目录结构：
        output_base/
        ├── L1/
        │   ├── merged.ply       # 1/4 点数，merge 后未微调
        │   └── finetuned.ply    # 1/4 点数，微调后
        ├── L2/
        │   ├── merged.ply       # 1/16 点数
        │   └── finetuned.ply
        ├── L3/
        │   ├── merged.ply       # 1/64 点数
        │   └── finetuned.ply
        └── lineage.json         # 完整族谱

    族谱结构（lineage.json）：
        {
          "L1": [[idx, ...], [idx, ...], ...],
            // L1[i] = 原始 PLY 中哪些点合并成了 L1 第 i 个点

          "L2": [[idx, ...], ...],
            // L2[i] = L1 finetuned PLY 中哪些点合并成了 L2 第 i 个点

          "L3": [[idx, ...], ...]
            // L3[i] = L2 finetuned PLY 中哪些点合并成了 L3 第 i 个点
        }

    层级恢复示例（从 L3 追溯到原始点）：
        L3[i] 由 L2 中的 lineage["L3"][i] 合并而来
        其中 L2[j] 又由 L1 中的 lineage["L2"][j] 合并而来
        其中 L1[k] 又由原始点中的 lineage["L1"][k] 合并而来
    """

    # 三级配置：(级别名, image_resolution)
    # compress_ratio 固定为 4，每级压缩 1/4 点数
    levels = [
        ("L1", 2),   # 原始   → 1/4  点，图像边长 1/2
        ("L2", 4),   # L1结果 → 1/16 点，图像边长 1/4
        ("L3", 8),   # L2结果 → 1/64 点，图像边长 1/8
    ]

    finetune_kwargs = dict(
        original_source_path=original_source_path,
        sh_degree=sh_degree,
        num_epochs=num_epochs,
        lr_opacity=lr_opacity,
        lr_scaling=lr_scaling,
        lr_rotation=lr_rotation,
        lr_features_dc=lr_features_dc,
        lr_features_rest=lr_features_rest,
        white_background=white_background,
        kernel_size=kernel_size,
        gpu_id=gpu_id,
        log_interval=log_interval,
    )

    merge_kwargs = dict(
        compress_ratio=4,
        k_neighbors=k_neighbors,
        spread_factor=spread_factor,
        aspect_ratio_threshold=aspect_ratio_threshold,
    )

    # id_counter 跨三级共享，保证所有点的 point_id 全局唯一：
    #   原始点   : point_id = 0 ~ N_original-1  (read_ply 自动生成)
    #   L1合并点 : point_id 从 N_original 开始递增
    #   L2、L3  : 继续递增，绝不与任何已有 point_id 冲突
    current_data     = read_ply(input_ply)
    n_original       = len(current_data['positions'])
    id_counter       = [n_original]  # 合并点编号从原始点数量之后开始
    full_lineage     = {}            # {level: {str(child_id): [parent_id, ...]}}
    current_ply_path = input_ply

    for level, image_resolution in levels:
        print("\n" + "=" * 70)
        print(f"级别: {level}  |  本级压缩 1/4  |  图像边长缩小 1/{image_resolution}")
        print(f"输入 PLY: {current_ply_path}")
        print("=" * 70)

        level_dir     = os.path.join(output_base, level)
        merged_ply    = os.path.join(level_dir, "merged.ply")
        finetuned_ply = os.path.join(level_dir, "finetuned.ply")
        os.makedirs(level_dir, exist_ok=True)

        # --- Merge ---
        print(f"\n[{level}] Step 1: Merge")
        final_data, level_lineage = run_merge(
            current_data, id_counter=id_counter, **merge_kwargs)

        result = validate_data(final_data)
        if result['has_nan'] or result['has_inf']:
            print(f"⚠️  NaN={result['has_nan']}  Inf={result['has_inf']}")

        save_ply(final_data, current_data['plydata'], merged_ply)
        print(f"[{level}] Merge PLY 已保存: {merged_ply}")

        # 族谱 key 转 str（JSON 要求 key 必须是字符串）
        # 格式：{"child_point_id": [parent_point_id, ...], ...}
        full_lineage[level] = {str(k): v for k, v in level_lineage.items()}
        lineage_path = os.path.join(output_base, "lineage.json")
        with open(lineage_path, 'w') as f:
            json.dump(full_lineage, f)
        print(f"[{level}] 族谱已保存（当前已完成: {list(full_lineage.keys())}）")

        # --- Fine-tune ---
        print(f"\n[{level}] Step 2: Fine-tune (image_resolution=1/{image_resolution})")
        finetune_merged_gaussians(
            merged_ply_path=merged_ply,
            output_ply_path=finetuned_ply,
            image_resolution=image_resolution,
            **finetune_kwargs,
        )

        # 下一级从 finetuned.ply 出发（fine-tune 不改变点数和 point_id，只改属性）
        current_ply_path = finetuned_ply
        current_data     = read_ply(finetuned_ply)

    # 族谱已在每级完成后实时写盘，此处仅做最终确认
    lineage_path = os.path.join(output_base, "lineage.json")
    print(f"\n✅  族谱完整保存至: {lineage_path}")

    print("\n🎉  所有级别完成！")
    print(f"输出目录: {output_base}")
    print("  L1/merged.ply, L1/finetuned.ply  — 1/4 点数")
    print("  L2/merged.ply, L2/finetuned.ply  — 1/16 点数")
    print("  L3/merged.ply, L3/finetuned.ply  — 1/64 点数")
    print("  lineage.json                      — 完整族谱")


# ============================================================
#  族谱工具函数（供后续训练代码使用）
# ============================================================

def load_lineage(lineage_path):
    """加载族谱文件"""
    with open(lineage_path, 'r') as f:
        lineage = json.load(f)
    return lineage


def trace_to_original(child_point_id, level, lineage):
    """
    从某一级的点（通过 point_id 指定）追溯到原始点的 point_id 集合。

    参数：
        child_point_id : 要追溯的点的 point_id（int 或 str 均可）
        level          : 该点所在级别，"L1" / "L2" / "L3"
        lineage        : load_lineage() 返回的族谱 dict

    返回：
        List[int]，原始 PLY 中的 point_id 列表（即 0 ~ N_original-1 范围内的值）

    示例：
        lineage = load_lineage("low_results/lineage.json")
        # 查询 L3 中 point_id=500100 的点对应的所有原始点
        orig_ids = trace_to_original(500100, "L3", lineage)
    """
    levels    = ["L1", "L2", "L3"]
    level_idx = levels.index(level)

    # 当前层的父节点 point_id 列表
    current_ids = lineage[level][str(child_point_id)]

    # 逐级向上追溯，直到 L1 的父节点（即原始点 point_id）
    for parent_level in reversed(levels[:level_idx]):
        next_ids = []
        for pid in current_ids:
            # 原始点的 point_id 不在族谱里（它们是叶子节点），直接保留
            key = str(pid)
            if key in lineage[parent_level]:
                next_ids.extend(lineage[parent_level][key])
            else:
                next_ids.append(pid)
        current_ids = next_ids

    return [int(x) for x in current_ids]


# ============================================================
#  入口
# ============================================================



if __name__ == "__main__":

    run_all(
        input_ply           = "merge/original_3dgs.ply",
        original_source_path = "data",   # 唯一需要提供的 COLMAP 目录
        output_base         = "outputs",

        # merge 参数
        k_neighbors=5,
        spread_factor=0.0,
        aspect_ratio_threshold=15.0,

        # fine-tune 参数
        sh_degree=3,
        num_epochs=250,
        lr_opacity=0.05,
        lr_scaling=0.005,
        lr_rotation=0.001,
        lr_features_dc=0.0025,
        lr_features_rest=0.000125,
        white_background=False,
        kernel_size=0.1,
        gpu_id=2,
        log_interval=50,
    )