{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b66179c8",
   "metadata": {},
   "source": [
    "# 1.预处理数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533db061",
   "metadata": {},
   "source": [
    "## 1.1 对点云数据进行处理 - 滤除环视范围外过远的点"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864843b2",
   "metadata": {},
   "source": [
    "1. 解析 images.txt，计算所有相机在世界坐标系下的光心（位姿）。  \n",
    "2.  使用 Open3D 加载 .pcd 点云，并基于与最近相机光心的距离进行滤除。  \n",
    "3.  输出相机光心的范围（坐标轴最小/最大值）以及被滤除的点云数量。  \n",
    "4.  将保留的点云按 COLMAP points3D.txt 格式保存，并额外生成一个 PLY 文件，其中红色标记被删除的点，原色保留未删除点。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc3ac56",
   "metadata": {},
   "source": [
    "#### ipynb内部函数 -- 未进行地面与boundingbox对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc2e8c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import open3d as o3d\n",
    "from scipy.spatial.transform import Rotation as R\n",
    "\n",
    "def filter_point_cloud(images_txt, pcd_path, output_points_txt, output_points_ply, output_ply_path):\n",
    "    \"\"\"\n",
    "    根据相机位姿过滤点云，并保存结果。\n",
    "\n",
    "    Args:\n",
    "        images_txt (str): 包含相机位姿的 images.txt 文件路径。\n",
    "        pcd_path (str): 点云文件路径。\n",
    "        output_points_txt (str): 保存过滤后点云的 points3D.txt 文件路径。\n",
    "        output_ply_path (str): 保存带颜色标记的 PLY 文件路径。\n",
    "    \"\"\"\n",
    "    def load_camera_centers(images_txt):\n",
    "        centers = []\n",
    "        with open(images_txt, 'r') as f:\n",
    "            for line in f:\n",
    "                if line.startswith('#') or len(line.strip()) == 0:\n",
    "                    continue\n",
    "                elems = line.strip().split()\n",
    "                if len(elems) < 10:\n",
    "                    continue\n",
    "                qw, qx, qy, qz = map(float, elems[1:5])\n",
    "                tx, ty, tz = map(float, elems[5:8])\n",
    "                rot = R.from_quat([qx, qy, qz, qw]).as_matrix()\n",
    "                center = -rot.T @ np.array([tx, ty, tz])\n",
    "                centers.append(center)\n",
    "        return np.array(centers)\n",
    "\n",
    "    # 1. 加载相机中心\n",
    "    camera_centers = load_camera_centers(images_txt)\n",
    "\n",
    "    # 2. 计算平均中心和最大偏移\n",
    "    center_mean = camera_centers.mean(axis=0)\n",
    "    offsets = np.abs(camera_centers - center_mean)\n",
    "    max_offset = offsets.max(axis=0)\n",
    "\n",
    "    # 3. 加载点云\n",
    "    pcd = o3d.io.read_point_cloud(pcd_path)\n",
    "    points = np.asarray(pcd.points)\n",
    "    colors = np.asarray(pcd.colors) if pcd.has_colors() else np.ones_like(points)\n",
    "\n",
    "    # 4. 构建包围盒并裁剪点云\n",
    "    min_bound = center_mean - max_offset - 15\n",
    "    max_bound = center_mean + max_offset + 15\n",
    "    aabb = o3d.geometry.AxisAlignedBoundingBox(min_bound, max_bound)\n",
    "    pcd_cropped = pcd.crop(aabb)\n",
    "\n",
    "    # 5. 统计信息\n",
    "    total_points = len(points)\n",
    "    kept_points = np.asarray(pcd_cropped.points)\n",
    "    kept_colors = np.asarray(pcd_cropped.colors) if pcd_cropped.has_colors() else np.ones_like(kept_points)\n",
    "    removed_mask = np.ones(total_points, dtype=bool)\n",
    "    kept_indices = aabb.get_point_indices_within_bounding_box(pcd.points)\n",
    "    removed_mask[kept_indices] = False\n",
    "    removed_points = points[removed_mask]\n",
    "    removed_colors = colors[removed_mask]\n",
    "\n",
    "    print(\"相机中心范围：\")\n",
    "    print(f\"  X: [{min_bound[0]:.3f}, {max_bound[0]:.3f}]\")\n",
    "    print(f\"  Y: [{min_bound[1]:.3f}, {max_bound[1]:.3f}]\")\n",
    "    print(f\"  Z: [{min_bound[2]:.3f}, {max_bound[2]:.3f}]\")\n",
    "    print(f\"总点数：{total_points}\")\n",
    "    print(f\"保留点数：{len(kept_points)}\")\n",
    "    print(f\"删除点数：{len(removed_points)}\")\n",
    "\n",
    "    # 6. 保存 points3D.txt (速度太慢，直接使用 ply )\n",
    "    with open(output_points_txt, \"w\") as f:\n",
    "        f.write(\"# 3D point list with one line of data per point:\\n\")\n",
    "        f.write(\"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[]\\n\")\n",
    "        for idx, (pt, col) in enumerate(zip(kept_points, kept_colors), start=1):\n",
    "            r, g, b = (col * 255).astype(int)\n",
    "            f.write(f\"{idx} {pt[0]:.6f} {pt[1]:.6f} {pt[2]:.6f} {r} {g} {b} 0\\n\")\n",
    "    # 保存为 ply 文件\n",
    "    pcd_kept = o3d.geometry.PointCloud()\n",
    "    pcd_kept.points = o3d.utility.Vector3dVector(kept_points)\n",
    "    pcd_kept.colors = o3d.utility.Vector3dVector(kept_colors)\n",
    "    o3d.io.write_point_cloud(output_points_ply, pcd_kept)\n",
    "    print(\"保留的点云已保存为 {output_points_ply}\")\n",
    "    \n",
    "    # 7. 保存带颜色的 PLY 文件\n",
    "    all_points = np.vstack((kept_points, removed_points))\n",
    "    removed_colors_red = np.tile([1.0, 0.0, 0.0], (len(removed_points), 1))\n",
    "    all_colors = np.vstack((kept_colors, removed_colors_red))\n",
    "    pcd_all = o3d.geometry.PointCloud()\n",
    "    pcd_all.points = o3d.utility.Vector3dVector(all_points)\n",
    "    pcd_all.colors = o3d.utility.Vector3dVector(all_colors)\n",
    "    o3d.io.write_point_cloud(output_ply_path, pcd_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80b924be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "相机中心范围：\n",
      "  X: [-0.200, 28.270]\n",
      "  Y: [-16.082, 1.699]\n",
      "  Z: [-9.392, 0.714]\n",
      "总点数：16404321\n",
      "保留点数：10684495\n",
      "删除点数：5719826\n",
      "保留的点云已保存为 {output_points_ply}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
    "\n",
    "images_txt = os.path.join(folder_path, 'sparse/0/images.txt')\n",
    "pcd_path = os.path.join(folder_path, 'pcd/all_raw_points.pcd')\n",
    "output_points_ply = os.path.join(folder_path, 'pcd/points3D.ply')\n",
    "vis_ply_path = os.path.join(folder_path, 'pcd/vis_filter.ply')\n",
    "output_points_txt = os.path.join(folder_path, 'pcd/points3D.txt')\n",
    "filter_point_cloud(images_txt,pcd_path,output_points_txt, output_points_ply,vis_ply_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4206e8",
   "metadata": {},
   "source": [
    "#### 调用 crop_points.py 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8c55ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总点数：16404321，保留：15254842，删除：1149479\n"
     ]
    }
   ],
   "source": [
    "!python crop_points.py \\\n",
    "    --images_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/sparse/0/images.txt \\\n",
    "    --pcd_path /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/all_raw_points.pcd \\\n",
    "    --output_points_txt /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D.txt \\\n",
    "    --output_ply /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/filtered_colored.ply \\\n",
    "    --margin 10.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651ed72f",
   "metadata": {},
   "source": [
    "## 1.2 利用体素化降采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56f5cba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw points len : 16404321\n",
      "downsample points len : 4671298\n",
      "降采样后的点云已保存到 /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points_0.05.ply，体素大小：0.05\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from tools.points_utils import voxel_downsample_and_save\n",
    "\n",
    "voxel_size = 0.05\n",
    "folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results'\n",
    "input_ply_path = os.path.join(folder_path,'pcd/points3D.ply')\n",
    "output_ply_path = os.path.join(folder_path,f'pcd/points3D_{voxel_size}.ply')\n",
    "input_ply_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points.ply'\n",
    "output_ply_path = f'/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points_{voxel_size}.ply'\n",
    "\n",
    "voxel_downsample_and_save(voxel_size, input_ply_path, output_ply_path) # ply downsample to ply\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1df326d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8544bb3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
      "Converted /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points.pcd to /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points.ply\n"
     ]
    }
   ],
   "source": [
    "# pcd 2 ply\n",
    "# 将原pcd点云，转换为ply点云\n",
    "from tools.points_utils import pcd_2_ply\n",
    "pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points.pcd'\n",
    "ply_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/sparse/0/raw/all_raw_points.ply'\n",
    "\n",
    "pcd_2_ply(pcd_file,ply_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bf16bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
      "正在写入COLMAP TXT文件: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n",
      "Converted /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply to Colmap TXT in: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt\n"
     ]
    }
   ],
   "source": [
    "# pcd 2 txt\n",
    "from tools.points_utils import pcd_2_colmap_txt\n",
    "pcd_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/sparse/0/points3D_filter.ply'\n",
    "txt_file = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_livo2/livo2_results/pcd/points3D_filter_white.txt'\n",
    "\n",
    "pcd_2_colmap_txt(pcd_file, txt_file, is_white=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd0b306",
   "metadata": {},
   "source": [
    "选择一个合适的 points3D.ply 文件复制到 sparse/0 下"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e87bd3",
   "metadata": {},
   "source": [
    "## 1.2 Resize for more images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91ce4cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "def resize_images(input_dir, output_dir, extensions):\n",
    "    \"\"\"\n",
    "    读取输入文件夹中的所有图片，调整为1/2大小后保存到输出文件夹\n",
    "    \n",
    "    Args:\n",
    "        input_dir: 输入图片文件夹路径\n",
    "        output_dir: 输出图片文件夹路径\n",
    "        extensions: 支持的图片扩展名列表\n",
    "    \"\"\"\n",
    "    # 确保输出目录存在\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "    \n",
    "    # 获取所有图片文件\n",
    "    image_files = []\n",
    "    for file in os.listdir(input_dir):\n",
    "        if any(file.lower().endswith(ext) for ext in extensions):\n",
    "            image_files.append(file)\n",
    "    \n",
    "    if not image_files:\n",
    "        print(f\"在 {input_dir} 中未找到支持的图片文件\")\n",
    "        return\n",
    "    \n",
    "    print(f\"找到 {len(image_files)} 张图片\")\n",
    "    \n",
    "    # 处理每张图片\n",
    "    count = 0\n",
    "    for file in tqdm(image_files, desc=\"处理中\"):\n",
    "        input_path = os.path.join(input_dir, file)\n",
    "        output_path = os.path.join(output_dir, file)\n",
    "        \n",
    "        try:\n",
    "            # 读取图片\n",
    "            img = cv2.imread(input_path)\n",
    "            if img is None:\n",
    "                print(f\"警告: 无法读取图片 {input_path}，跳过\")\n",
    "                continue\n",
    "                \n",
    "            # 获取原始尺寸\n",
    "            height, width = img.shape[:2]\n",
    "            \n",
    "            # 计算新尺寸\n",
    "            new_width = width // 2\n",
    "            new_height = height // 2\n",
    "            \n",
    "            # 调整尺寸\n",
    "            resized_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)\n",
    "            \n",
    "            # 保存图片\n",
    "            cv2.imwrite(output_path, resized_img)\n",
    "            \n",
    "            # 输出尺寸信息\n",
    "            if count == 0:\n",
    "                print(f\"{file}: {width}x{height} -> {new_width}x{new_height}\")\n",
    "            count += 1\n",
    "        except Exception as e:\n",
    "            print(f\"错误: 处理图片 {input_path} 时出错: {str(e)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9b4da36",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps'\n",
    "output_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01/depth_maps_2'\n",
    "exts = ['jpg','jpeg','png']\n",
    "\n",
    "resize_images(input_path, output_path, exts)\n",
    "print(\"所有图片处理完成！\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3830cb91",
   "metadata": {},
   "source": [
    "# 2. LIVO2和Colmap的重建对比实验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd6ff70",
   "metadata": {},
   "source": [
    "colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
    "https://www.cnblogs.com/Todd-Qi/p/15080968.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad9f127",
   "metadata": {},
   "source": [
    "## 2.1 基于Livo2位姿进行稀疏重建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e8c7f1",
   "metadata": {},
   "source": [
    "colmap无法恢复相机位姿；所以这里我们使用livo2恢复位姿后，用colmap 进行三角测量获取关键点\n",
    "https://www.cnblogs.com/Todd-Qi/p/15080968.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497d715a",
   "metadata": {},
   "source": [
    "1. 准备来自Livo2的位姿和相机数据 cameras.txt, images.txt\n",
    "    将内参(camera intrinsics) 放入cameras.txt， 外参(camera extrinsics)放入 images.txt , points3D.txt 为空  \n",
    "    - images.txt 中全部 0.0 0.0 -1 删除;  \n",
    "    - points3D.txt 内容清空;\n",
    "    - cameras.txt 中的内参进行修改 (对输入图像全部进行了 resize 操作，因此需要修改相机内参，将fx, fy, cx, cy 都除以2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497596e0",
   "metadata": {},
   "source": [
    "2. 特征匹配与特征提取  \n",
    "``` bash\n",
    "        colmap feature_extractor \\\n",
    "            --database_path /path/to/project/database.db \\  \n",
    "            --image_path    /path/to/project/images\n",
    "```\n",
    "``` bash\n",
    "        colmap exhaustive_matcher \\\n",
    "            --database_path /path/to/project/database.db\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590c5ba8",
   "metadata": {},
   "source": [
    "3. 三角化重建 (保存的点云和其他文件均为bin格式)\n",
    "``` bash\n",
    "        colmap point_triangulator \\\n",
    "            --database_path /path/to/project/database.db \\\n",
    "            --image_path    /path/to/project/images \\\n",
    "            --input_path    /path/to/sparse_model \\\n",
    "            --output_path   /path/to/triangulated_model\n",
    "\n",
    "```\n",
    "\n",
    "查看txt结果\n",
    "``` bash\n",
    "        colmap model_converter \\\n",
    "            --input_path 0 \\\n",
    "            --output_path 0_txt_from_livo2 \\\n",
    "            --output_type TXT\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2b853f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d359ac68",
   "metadata": {},
   "source": [
    "4. 稠密重建(optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77250b4b",
   "metadata": {},
   "source": [
    "# 3.训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d44f71f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# baseline raw gs for training\n",
    "!CUDA_VISIBLE_DEVICES=1 python train.py \\\n",
    "    -s data/tree_01_livo2 \\\n",
    "    -m data/tree_01_livo2/outputs/3dgs_baseline\n",
    "    \n",
    "# render\n",
    "!CUDA_VISIBLE_DEVICES=1 python render.py \\\n",
    "    -s data/tree_01_colmap \\\n",
    "    -m data/tree_01_colmap/outputs/3dgs_baseline    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a363d15d",
   "metadata": {},
   "source": [
    "# 4.深度实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1de51e3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- depth_maps_2文件夹 ---\n",
      "找到图片：00144.png\n",
      "分辨率：1024 x 768 像素\n",
      "通道数：3\n",
      "通道说明：三通道（RGB）\n",
      "--- images我文件夹 ---\n",
      "找到图片：00144.png\n",
      "分辨率：1024 x 768 像素\n",
      "通道数：3\n",
      "通道说明：三通道（RGB）\n"
     ]
    }
   ],
   "source": [
    "# 一、确认图像与深度图像尺寸是否一致\n",
    "from tools.images_utils import get_first_image_info\n",
    "\n",
    "folder_path = \"/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_save_w_depth/depth_maps_2\"\n",
    "print(\"--- depth_maps_2文件夹 ---\")\n",
    "get_first_image_info(folder_path)\n",
    "folder_path = \"/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3_depth/images\"\n",
    "print(\"--- images我文件夹 ---\")\n",
    "get_first_image_info(folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ee3b672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功生成 JSON 文件：/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3_depth/sparse/0/depth_params.json\n",
      "共处理 539 个 PNG 文件\n"
     ]
    }
   ],
   "source": [
    "# 2.在 sparse/0 下生成一个 depth_params.json\n",
    "\n",
    "# 示例数据:\n",
    "# {\n",
    "# \"DSC_3893\": {\n",
    "#     \"scale\": 50.55648822378238,\n",
    "#     \"offset\": 0.01793079636288747\n",
    "# },\n",
    "# \"DSC_3902\": {\n",
    "#     \"scale\": 56.80526691363168,\n",
    "#     \"offset\": 0.027163276135205097\n",
    "# }\n",
    "# }\n",
    "\n",
    "from tools.images_utils import generate_depth_params_json\n",
    "import os\n",
    "\n",
    "folder_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3_depth/'\n",
    "PNG_FOLDER = os.path.join(folder_path, 'depth_maps')\n",
    "OUTPUT_PATH = os.path.join(folder_path,\"sparse/0/depth_params.json\")\n",
    "# 生成 JSON 文件\n",
    "generate_depth_params_json(PNG_FOLDER, OUTPUT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b337a10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重命名 删去 -final的后缀\n",
    "from tools.images_utils import batch_rename_files\n",
    "\n",
    "\n",
    "FOLDER_PATH = \"/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3_depth/depth_maps\"  # 请替换为实际的文件夹路径\n",
    "batch_rename_files(FOLDER_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f60df4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果有深度mesh缺失\n",
    "# ~/git_project/livo2-data-utils/10-Mesh-acc$ \n",
    "#     python demo-acc.py -i \\\n",
    "#         /home/qinllgroup/hongxiangyu/git_project/livo2-data-utils/10-Mesh-acc/data/delete \\\n",
    "#         -o /home/qinllgroup/hongxiangyu/git_project/livo2-data-utils/10-Mesh-acc/data/delete \\\n",
    "#         --max_edge 15\n",
    "\n",
    "from tools.images_utils import resize_image\n",
    "\n",
    "input_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_save_w_depth/depth_maps/00466-final.png'\n",
    "output_path = '/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_save_w_depth/depth_maps_2/00466.png'\n",
    "resize_image(input_path, output_path, scale=2, quality=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "024d1b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 训练深度mask\n",
    "!CUDA_VISIBLE_DEVICES=1 python train.py \\\n",
    "    -s data/tree_01_livo2 \\\n",
    "    -d data/tree_01_livo2/depth_map \\\n",
    "    -m data/tree_01_livo2/outputs/3dgs_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "254f6278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功将图片从 2048x1536 缩小到 1024x768\n",
      "保存路径: /home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_save_w_depth/depth_maps_2/00466.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_save_w_depth/depth_maps_2/00466.png'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "66d0f159",
   "metadata": {},
   "source": [
    "## 4.1 深度读取"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7406df01",
   "metadata": {},
   "source": [
    "# 5.可视化TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b0acade",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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