Muqsit · April 23, 2025 20:11
diff --git a/bedrock-skin-stealer.ipynb b/bedrock-skin-stealer.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOoxv8dkC1368O2mLMvq7Mf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/Muqsit/07db65e1ab5802a35ceffde37c9fe7cf/bedrock-skin-stealer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install dependencies"
      ],
      "metadata": {
        "id": "TTkw-y0kOfNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pygltflib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DibV85kHEcwv",
        "outputId": "2ada9e12-1756-4d20-c2ae-16fceff22f5f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pygltflib\n",
            "  Downloading pygltflib-1.16.4-py3-none-any.whl.metadata (33 kB)\n",
            "Collecting dataclasses-json>=0.0.25 (from pygltflib)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
            "Requirement already satisfied: deprecated in /usr/local/lib/python3.11/dist-packages (from pygltflib) (1.2.18)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json>=0.0.25->pygltflib)\n",
            "  Downloading marshmallow-3.26.1-py3-none-any.whl.metadata (7.3 kB)\n",
            "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json>=0.0.25->pygltflib)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.11/dist-packages (from deprecated->pygltflib) (1.17.2)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.11/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json>=0.0.25->pygltflib) (24.2)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json>=0.0.25->pygltflib)\n",
            "  Downloading mypy_extensions-1.1.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.11/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json>=0.0.25->pygltflib) (4.13.2)\n",
            "Downloading pygltflib-1.16.4-py3-none-any.whl (27 kB)\n",
            "Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)\n",
            "Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB)\n",
            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, dataclasses-json, pygltflib\n",
            "Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.1.0 pygltflib-1.16.4 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Download skin (.glb)"
      ],
      "metadata": {
        "id": "-2t_PkmKOhgZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N6Xw6QFED1n",
        "outputId": "f578a925-86b6-4a99-899a-1d9697db58e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-04-23 20:06:20--  https://persona-secondary.franchise.minecraft-services.net/api/v1.0/profile/xuid/2535419200678464/image/modelbinary\n",
            "Resolving persona-secondary.franchise.minecraft-services.net (persona-secondary.franchise.minecraft-services.net)... 13.107.253.69, 2620:1ec:29:1::69\n",
            "Connecting to persona-secondary.franchise.minecraft-services.net (persona-secondary.franchise.minecraft-services.net)|13.107.253.69|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26512 (26K) [application/octet-stream]\n",
            "Saving to: ‘model.glb’\n",
            "\n",
            "model.glb           100%[===================>]  25.89K  --.-KB/s    in 0.004s  \n",
            "\n",
            "2025-04-23 20:06:21 (6.58 MB/s) - ‘model.glb’ saved [26512/26512]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "xuid = 2535419200678464\n",
        "!wget -O model.glb https://persona-secondary.franchise.minecraft-services.net/api/v1.0/profile/xuid/{xuid}/image/modelbinary"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extract standard minecraft .png from .glb"
      ],
      "metadata": {
        "id": "lAnNjbt4OmyH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pygltflib import GLTF2\n",
        "\n",
        "gltf = GLTF2().load(\"model.glb\")\n",
        "image = gltf.images[0]\n",
        "bv = gltf.bufferViews[image.bufferView]\n",
        "with open(\"model.glb\", \"rb\") as f:\n",
        "    f.read(4)  # magic\n",
        "    f.read(4)  # version\n",
        "    f.read(4)  # length\n",
        "    json_chunk_length = int.from_bytes(f.read(4), \"little\")\n",
        "    f.read(4)  # chunk type JSON\n",
        "    f.read(json_chunk_length)  # interesting metadata, but not useful for us\n",
        "    bin_chunk_length = int.from_bytes(f.read(4), \"little\")\n",
        "    f.read(4)  # chunk type BIN\n",
        "    f.read(bv.byteOffset)\n",
        "    data = f.read(bv.byteLength)\n",
        "\n",
        "from PIL import Image\n",
        "import io\n",
        "image = Image.open(io.BytesIO(data))\n",
        "print(image.size)\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "2MGKgLFWEJ8Q",
        "outputId": "e2194e4f-de7f-4e65-eb23-89ffcb9fae95"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=64x64>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Validate"
      ],
      "metadata": {
        "id": "aR7Tp7qrOr9K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile expected.base64\n",
        "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RK5iQ5qKOBOD",
        "outputId": "458c7fb1-26fc-420c-d41e-132fa7fc21da"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing expected.base64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import base64\n",
        "from io import BytesIO\n",
        "from PIL import ImageChops\n",
        "\n",
        "with open(\"expected.base64\", \"rb\") as f:\n",
        "    true_data = f.read()\n",
        "\n",
        "truth = Image.open(BytesIO(base64.b64decode(true_data)))\n",
        "diff = ImageChops.difference(truth, image)\n",
        "print(\"Images are equal\" if diff.getbbox() is None else \"Images are not equal\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VAbfCKKONK_",
        "outputId": "16c64cbe-2d9e-41da-8e7c-2ec78bad783b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images are equal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
        "\n",
        "axes[0].imshow(truth, interpolation=\"nearest\")\n",
        "axes[0].set_title(\"Ground truth (%d x %d)\" % truth.size)\n",
        "axes[0].axis(\"off\")\n",
        "\n",
        "axes[1].imshow(image, interpolation=\"nearest\")\n",
        "axes[1].set_title(\"From mojang (%d x %d)\" % image.size)\n",
        "axes[1].axis(\"off\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "imP0QW8eQH01",
        "outputId": "8e4759e0-3e7a-44d3-ff27-6f5948f31fc1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
 }
No results found