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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAM 3D Body (3DB) Mesh Alignment to SAM 3D Object Scale\n",
    "\n",
    "This notebook processes a single 3DB mesh and aligns it to the SAM 3D Objects scale.\n",
    "\n",
    "**Input Data:**\n",
    "- `images/human_object/image.jpg` - Input image for MoGe\n",
    "- `meshes/human_object/3DB_results/mask_human.png` - Human mask\n",
    "- `meshes/human_object/3DB_results/human.ply` - Single 3DB mesh in OpenGL coordinates\n",
    "- `meshes/human_object/3DB_results/focal_length.json` - 3DB focal length\n",
    "\n",
    "**Output:**\n",
    "- `meshes/human_object/aligned_meshes/human_aligned.ply` - Aligned 3DB mesh in OpenGL coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from mesh_alignment import process_and_save_alignment\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f\"Using device: {device}\")\n",
    "PATH = os.getcwd()\n",
    "print(f\"Current working directory: {PATH}\")\n",
    "\n",
    "# Please inference the SAM 3D Body (3DB) Repo (https://github.com/facebookresearch/sam-3d-body) to get the 3DB Results\n",
    "image_path = f\"{PATH}/images/human_object/image.png\"\n",
    "mask_path = f\"{PATH}/meshes/human_object/3DB_results/mask_human.png\"\n",
    "mesh_path = f\"{PATH}/meshes/human_object/3DB_results/human.ply\"\n",
    "focal_length_json_path = f\"{PATH}/meshes/human_object/3DB_results/focal_length.json\"\n",
    "output_dir = f\"{PATH}/meshes/human_object/aligned_meshes\"\n",
    "os.makedirs(output_dir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load and Display Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = Image.open(image_path)\n",
    "mask = Image.open(mask_path).convert('L')\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axes[0].imshow(input_image)\n",
    "axes[0].set_title('Input Image')\n",
    "axes[0].axis('off')\n",
    "axes[1].imshow(mask, cmap='gray')\n",
    "axes[1].set_title('Mask')\n",
    "axes[1].axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Process and Save Aligned Mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "success, output_mesh_path, result = process_and_save_alignment(\n",
    "    mesh_path=mesh_path,\n",
    "    mask_path=mask_path,\n",
    "    image_path=image_path,\n",
    "    output_dir=output_dir,\n",
    "    device=device,\n",
    "    focal_length_json_path=focal_length_json_path\n",
    ")\n",
    "\n",
    "if success:\n",
    "    print(f\"Alignment completed successfully! Output: {output_mesh_path}\")\n",
    "else:\n",
    "    print(\"Alignment failed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Interactive 3D Visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mesh_alignment import visualize_meshes_interactive\n",
    "\n",
    "aligned_mesh_path = f\"{PATH}/meshes/human_object/aligned_meshes/human_aligned.ply\"\n",
    "dfy_mesh_path = f\"{PATH}/meshes/human_object/3Dfy_results/0.glb\"\n",
    "\n",
    "demo, combined_glb_path = visualize_meshes_interactive(\n",
    "    aligned_mesh_path=aligned_mesh_path,\n",
    "    dfy_mesh_path=dfy_mesh_path,\n",
    "    share=True\n",
    ")"
   ]
  }
 ],
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