2D_FloorPlan_to_3D_CubiCasa_ftb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_x5w95tGx_I"
      },
      "source": [
        "# CubiCasa / FloorplantoBlenderlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/rbg-research/Floor-Plan-Detection"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBZBH3JcG3q0",
        "outputId": "9cb0fb0f-887c-4f07-d3b0-5d59f3df687e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Floor-Plan-Detection'...\n",
            "remote: Enumerating objects: 71, done.\u001b[K\n",
            "remote: Total 71 (delta 0), reused 0 (delta 0), pack-reused 71 (from 1)\u001b[K\n",
            "Receiving objects: 100% (71/71), 161.12 MiB | 26.96 MiB/s, done.\n",
            "Resolving deltas: 100% (4/4), done.\n",
            "Updating files: 100% (55/55), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd Floor-Plan-Detection\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59Xsgpw6HAc1",
        "outputId": "aed943b6-a1cc-4c75-9c7c-ea206009f866"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Errno 2] No such file or directory: 'Floor-Plan-Detection'\n",
            "/content/Floor-Plan-Detection\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uxn0dVgXGx_J",
        "outputId": "32b9262f-a510-436d-acae-845768d033ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1gRB7ez1e4H7a9Y09lLqRuna0luZO5VRK\n",
            "From (redirected): https://drive.google.com/uc?id=1gRB7ez1e4H7a9Y09lLqRuna0luZO5VRK&confirm=t&uuid=5cea2f64-90b3-40d0-b1b8-efddb55b4824\n",
            "To: /content/Floor-Plan-Detection/model_best_val_loss_var.pkl\n",
            "100% 209M/209M [00:01<00:00, 121MB/s]\n",
            "mkdir: missing operand\n",
            "Try 'mkdir --help' for more information.\n",
            "--2025-01-23 12:29:09--  https://ftp.nluug.nl/pub/graphics/blender/release/Blender2.93/blender-2.93.1-linux-x64.tar.xz\n",
            "Resolving ftp.nluug.nl (ftp.nluug.nl)... 145.220.21.40, 2001:67c:6ec:221:145:220:21:40\n",
            "Connecting to ftp.nluug.nl (ftp.nluug.nl)|145.220.21.40|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 166685444 (159M) [application/octet-stream]\n",
            "Saving to: ‘blender-2.93.1-linux-x64.tar.xz’\n",
            "\n",
            "blender-2.93.1-linu 100%[===================>] 158.96M  25.8MB/s    in 7.0s    \n",
            "\n",
            "2025-01-23 12:29:17 (22.7 MB/s) - ‘blender-2.93.1-linux-x64.tar.xz’ saved [166685444/166685444]\n",
            "\n",
            "tar: -C: Cannot open: No such file or directory\n",
            "tar: Error is not recoverable: exiting now\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "#downloading weights\n",
        "\n",
        "!gdown 'https://drive.google.com/uc?id=1gRB7ez1e4H7a9Y09lLqRuna0luZO5VRK'\n",
        "\n",
        "#downloading blender\n",
        "\n",
        "blender_url =  \"https://ftp.nluug.nl/pub/graphics/blender/release/Blender2.93/blender-2.93.1-linux-x64.tar.xz\"\n",
        "base_url = os.path.basename(blender_url)\n",
        "!mkdir $blender_version\n",
        "!wget -nc $blender_url\n",
        "!tar -xkf $base_url -C ./$blender_version --strip-components=1\n",
        "!rm blender-2.93.1-linux-x64.tar.xz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install opencv-python\n",
        "!pip install pyfiglet\n",
        "!pip install lmdb\n",
        "!pip install scikit-image\n",
        "!pip install svgpathtools\n",
        "!pip install shapely"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9Ff-N2EHsZK",
        "outputId": "0b5e87d8-4d06-424d-d58b-2b50be481c87"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.11/dist-packages (4.10.0.84)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python) (1.26.4)\n",
            "Requirement already satisfied: pyfiglet in /usr/local/lib/python3.11/dist-packages (1.0.2)\n",
            "Requirement already satisfied: lmdb in /usr/local/lib/python3.11/dist-packages (1.6.2)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.11/dist-packages (0.25.0)\n",
            "Requirement already satisfied: numpy>=1.24 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.11.2 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (1.13.1)\n",
            "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (3.4.2)\n",
            "Requirement already satisfied: pillow>=10.1 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (11.1.0)\n",
            "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2.36.1)\n",
            "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2024.12.12)\n",
            "Requirement already satisfied: packaging>=21 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (24.2)\n",
            "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (0.4)\n",
            "Requirement already satisfied: svgpathtools in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from svgpathtools) (1.26.4)\n",
            "Requirement already satisfied: svgwrite in /usr/local/lib/python3.11/dist-packages (from svgpathtools) (1.4.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from svgpathtools) (1.13.1)\n",
            "Requirement already satisfied: shapely in /usr/local/lib/python3.11/dist-packages (2.0.6)\n",
            "Requirement already satisfied: numpy<3,>=1.14 in /usr/local/lib/python3.11/dist-packages (from shapely) (1.26.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "scrolled": true,
        "id": "vR8rgR6BGx_K"
      },
      "outputs": [],
      "source": [
        "# Add outer folder\n",
        " # Adds higher directory to python modules path.\n",
        "\n",
        "# Import library\n",
        "from utils.FloorplanToBlenderLib import *\n",
        "\n",
        "# Other necessary libraries\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D7UCvBxIHqLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCY8ajKSGx_K"
      },
      "source": [
        "## Input Image\n",
        "Now we need an example image to work with."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lekwQRfJGx_K"
      },
      "source": [
        "Input image: ![input](https://github.com/rbg-research/Floor-Plan-Detection/blob/main/Images/tv.jpeg?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "FG8rKpkJIaFf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6Gh-PgUGx_K"
      },
      "source": [
        "# Detect Contours (Object Filter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "CjEEefpRGx_K",
        "outputId": "5e368b13-a902-4da3-e546-5e85c1ae1034"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=900x450>"
            ],
            "image/png": 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\n",
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          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Import library\n",
        "from utils.FloorplanToBlenderLib import *\n",
        "\n",
        "import cv2 # for image gathering\n",
        "import numpy as np\n",
        "\n",
        "# for visualize\n",
        "from PIL import Image\n",
        "from IPython.display import display\n",
        "\n",
        "img_path = \"Images/tv.jpeg\"\n",
        "\n",
        "# Read floorplan image\n",
        "img = cv2.imread(img_path)\n",
        "\n",
        "# Create blank image\n",
        "height, width, channels = img.shape\n",
        "blank_image = np.zeros((height,width,3), np.uint8)\n",
        "\n",
        "# Grayscale image\n",
        "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# detect outer Contours (simple floor or roof solution), paint them red on blank_image\n",
        "contour, img = detect.detectOuterContours(gray, blank_image, color=(255,0,0))\n",
        "\n",
        "# Display\n",
        "display(Image.fromarray(img))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4VLU0BIGx_L"
      },
      "source": [
        "# Detect Rooms (Object Filter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "aj9U0LlbGx_L",
        "outputId": "87b5e4cc-30b2-4861-990f-7baa0c336c92"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=900x450>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "img = cv2.imread(img_path)\n",
        "\n",
        "    # grayscale image\n",
        "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# create verts (points 3d), points to use in mesh creations\n",
        "verts = []\n",
        "# create faces for each plane, describe order to create mesh points\n",
        "faces = []\n",
        "\n",
        "# Height of waLL\n",
        "height = 0.999\n",
        "\n",
        "# Scale pixel value to 3d pos\n",
        "scale = 100\n",
        "\n",
        "gray = detect.wall_filter(gray)\n",
        "\n",
        "gray = ~gray\n",
        "\n",
        "rooms, colored_rooms = detect.find_rooms(gray.copy())\n",
        "\n",
        "gray_rooms =  cv2.cvtColor(colored_rooms,cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# get box positions for rooms\n",
        "boxes, gray_rooms = detect.detectPreciseBoxes(gray_rooms, gray_rooms)\n",
        "display(Image.fromarray(colored_rooms))\n",
        "\n",
        " #Create verts\n",
        "room_count = 0\n",
        "for box in boxes:\n",
        "    verts.extend([transform.scale_point_to_vector(box, scale, height)])\n",
        "    room_count+= 1\n",
        "\n",
        "# create faces\n",
        "for room in verts:\n",
        "    count = 0\n",
        "    temp = ()\n",
        "    for pos in room:\n",
        "        temp = temp + (count,)\n",
        "        count += 1\n",
        "    faces.append([(temp)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwopnNEkGx_L"
      },
      "source": [
        "# Detect Walls (Object Filter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "KtRbgCd9Gx_L",
        "outputId": "2861a364-3741-420c-fd1c-a0c1e2e682ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=900x450>"
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "'''\n",
        "    Generate wall data file for floorplan\n",
        "    @Param img_path, path to input file\n",
        "    @Param info, boolean if data should be printed\n",
        "    @Return shape\n",
        "    '''\n",
        "from utils.FloorplanToBlenderLib import *\n",
        "# Read floorplan image\n",
        "img = cv2.imread(img_path)\n",
        "\n",
        "# grayscale image\n",
        "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# create wall image (filter out small objects from image)\n",
        "wall_img = detect.wall_filter(gray)\n",
        "\n",
        "# detect walls\n",
        "boxes, img = detect.detectPreciseBoxes(wall_img)\n",
        "\n",
        "display(Image.fromarray(wall_img))\n",
        "\n",
        "# create verts (points 3d), points to use in mesh creations\n",
        "verts = []\n",
        "# create faces for each plane, describe order to create mesh points\n",
        "faces = []\n",
        "\n",
        "# Height of waLL\n",
        "wall_height = 1\n",
        "\n",
        "# Scale pixel value to 3d pos\n",
        "scale = 100\n",
        "\n",
        "# Convert boxes to verts and faces\n",
        "verts, faces, wall_amount = transform.create_nx4_verts_and_faces(boxes, wall_height, scale)\n",
        "\n",
        "# Create top walls verts\n",
        "verts = []\n",
        "for box in boxes:\n",
        "    verts.extend([transform.scale_point_to_vector(box, scale, 0)])\n",
        "\n",
        "# create faces\n",
        "faces = []\n",
        "for room in verts:\n",
        "    count = 0\n",
        "    temp = ()\n",
        "    for _ in room:\n",
        "        temp = temp + (count,)\n",
        "        count += 1\n",
        "    faces.append([(temp)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9YRAfmsGx_L"
      },
      "source": [
        "# CubiCasa Pre-Trained Object/Room Segmentation          \n",
        "# (Deep Neural Network)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "4vn3xZPKGx_M",
        "outputId": "0c6b34e8-7396-4921-c386-0443d4fd068b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "module 'matplotlib.cm' has no attribute 'register_cmap'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-8bacf7ccd23e>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloaders\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFloorplanSVG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDictToTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCompose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRotateNTurns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msegmentation_plot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpolygons_to_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw_junction_from_dict\u001b[0m\u001b[0;34m,\u001b[0m           \u001b[0mdiscrete_cmap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mdiscrete_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_prosessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msplit_prediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_polygons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes_grid1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxesGrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/Floor-Plan-Detection/utils/plotting.py\u001b[0m in \u001b[0;36mdiscrete_cmap\u001b[0;34m()\u001b[0m\n\u001b[1;32m    589\u001b[0m     \u001b[0mcmap3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mListedColormap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rooms'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m     \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m     cpool = ['#DCDCDC', '#8dd3c7', '#b15928', '#fdb462', '#ffff99',\n\u001b[1;32m    593\u001b[0m              \u001b[0;34m'#fccde5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'#80b1d3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'#808080'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'#fb8072'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'#696969'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.cm' has no attribute 'register_cmap'"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "sys.path.append(\"..\") # Adds higher directory to python modules path.\n",
        "\n",
        "# Import library\n",
        "from utils.FloorplanToBlenderLib import *\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import cv2\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from model import get_model\n",
        "from utils.loaders import FloorplanSVG, DictToTensor, Compose, RotateNTurns\n",
        "from utils.plotting import segmentation_plot, polygons_to_image, draw_junction_from_dict,           discrete_cmap\n",
        "discrete_cmap()\n",
        "from utils.post_prosessing import split_prediction, get_polygons, split_validation\n",
        "from mpl_toolkits.axes_grid1 import AxesGrid\n",
        "\n",
        "rot = RotateNTurns()\n",
        "room_classes = [\"Background\", \"Outdoor\", \"Wall\", \"Kitchen\", \"Living Room\" ,\"Bed Room\", \"Bath\",\n",
        "                \"Entry\", \"Railing\", \"Storage\", \"Garage\", \"Undefined\"]\n",
        "icon_classes = [\"No Icon\", \"Window\", \"Door\", \"Closet\", \"Electrical Applience\" ,\"Toilet\", \"Sink\",\n",
        "                \"Sauna Bench\", \"Fire Place\", \"Bathtub\", \"Chimney\"]\n",
        "\n",
        "model = get_model('hg_furukawa_original', 51)\n",
        "n_classes = 44\n",
        "split = [21, 12, 11]\n",
        "model.conv4_ = torch.nn.Conv2d(256, n_classes, bias=True, kernel_size=1)\n",
        "model.upsample = torch.nn.ConvTranspose2d(n_classes, n_classes, kernel_size=4, stride=4)\n",
        "checkpoint = torch.load('model_best_val_loss_var.pkl')\n",
        "\n",
        "model.load_state_dict(checkpoint['model_state'])\n",
        "model.eval()\n",
        "model.cuda()\n",
        "\n",
        "# Create tensor for pytorch\n",
        "img = cv2.imread(img_path)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # correct color channels\n",
        "\n",
        "# Image transformation to range (-1,1)\n",
        "img = 2 * (img / 255.0) - 1\n",
        "\n",
        "# Move from (h,w,3)--->(3,h,w) as model input dimension is defined like this\n",
        "img = np.moveaxis(img, -1, 0)\n",
        "\n",
        "# Convert to pytorch, enable cuda\n",
        "img = torch.tensor([img.astype(np.float32)]).cuda()\n",
        "n_rooms = 12\n",
        "n_icons = 11\n",
        "\n",
        "with torch.no_grad():\n",
        "    #Check if shape of image is odd or even\n",
        "    size_check = np.array([img.shape[2],img.shape[3]])%2\n",
        "\n",
        "    height = img.shape[2] - size_check[0]\n",
        "    width = img.shape[3] - size_check[1]\n",
        "\n",
        "    img_size = (height, width)\n",
        "\n",
        "    rotations = [(0, 0), (1, -1), (2, 2), (-1, 1)]\n",
        "    pred_count = len(rotations)\n",
        "    prediction = torch.zeros([pred_count, n_classes, height, width])\n",
        "    for i, r in enumerate(rotations):\n",
        "        forward, back = r\n",
        "        # We rotate first the image\n",
        "        rot_image = rot(img, 'tensor', forward)\n",
        "        pred = model(rot_image)\n",
        "        # We rotate prediction back\n",
        "        pred = rot(pred, 'tensor', back)\n",
        "        # We fix heatmaps\n",
        "        pred = rot(pred, 'points', back)\n",
        "        # We make sure the size is correct\n",
        "        pred = F.interpolate(pred, size=(height, width), mode='bilinear', align_corners=True)\n",
        "        # We add the prediction to output\n",
        "        prediction[i] = pred[0]\n",
        "\n",
        "prediction = torch.mean(prediction, 0, True)\n",
        "\n",
        "\n",
        "rooms_pred = F.softmax(prediction[0, 21:21+12], 0).cpu().data.numpy()\n",
        "rooms_pred = np.argmax(rooms_pred, axis=0)\n",
        "\n",
        "icons_pred = F.softmax(prediction[0, 21+12:], 0).cpu().data.numpy()\n",
        "icons_pred = np.argmax(icons_pred, axis=0)\n",
        "\n",
        "heatmaps, rooms, icons = split_prediction(prediction, img_size, split)\n",
        "polygons, types, room_polygons, room_types = get_polygons((heatmaps, rooms, icons), 0.2, [1, 2])\n",
        "\n",
        "wall_polygon_numbers=[i for i,j in enumerate(types) if j['type']=='wall']\n",
        "boxes=[]\n",
        "for i,j in enumerate(polygons):\n",
        "    if i in wall_polygon_numbers:\n",
        "        temp=[]\n",
        "        for k in j:\n",
        "            temp.append(np.array([k]))\n",
        "        boxes.append(np.array(temp))\n",
        "\n",
        "verts, faces, wall_amount = transform.create_nx4_verts_and_faces(boxes, wall_height, scale)\n",
        "\n",
        "# Create top walls verts\n",
        "verts = []\n",
        "for box in boxes:\n",
        "    verts.extend([transform.scale_point_to_vector(box, scale, 0)])\n",
        "\n",
        "# create faces\n",
        "faces = []\n",
        "for room in verts:\n",
        "    count = 0\n",
        "    temp = ()\n",
        "    for _ in room:\n",
        "        temp = temp + (count,)\n",
        "        count += 1\n",
        "    faces.append([(temp)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hT4LiRoLGx_M",
        "outputId": "2d42769f-1188-4243-eca9-4d4af61a1a34"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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qn9+j42Qv4H9oPtMJwO+A9YBtgTcCX+9r+1rgcOC2tu0Vbf+vA56T5IlVdfnkP6ZmiwFLkiRJE5Jk3pDdi6pq/jhO3wZ4bFVdOrD/i8DDgJdW1Vf7rrUmTfA6LMkJVXXtZGpu+9oS2Kl9e/YYzU8DHlRVfx7oY2vgx8CHgGf37X8U8BngT8CO7WhZ/3kb9v38COCzwCJgp6r6Q9+xXYDv0ATR50/g42kpYcCSJEkSwDpJzu17f3hVHT5C2wOH7Ps+MH8c1/nIYLhqQ8tOwDH94QqgqhYnORA4nmaRis+M4xo9+yZZzN8XuXgBsBpwXFX9cLQTq+qPI+z/eZLTgGckWamq7mgP/RvN39YHD4ar9rwr+97+G7AS8Jb+cNW2OzXJCTSjWP8wGPC09DNgSZIkCeD6qtp2PA2rKmO3GtE5Q/Zt327XGGF0bN12u8UEr/WWdls0U/x+AXyZZvRoTEl2Bd5AM8VvHe79t/M6wNXtz09st98aR9e9z7tTkscPOb4eTSh8BHDeeGrV0sOAJUmSpJl0zZB9D2y3T29fI1l9gtfapH8VwYlI8hbgUOAm4LvA5cAtNGHtecDWwCp9p6zZbu8xIjWC3ud95xjtJvp5tRQwYEmSJGkmDVtcovfMrLdU1WEzWcww7YOI59GEwW2q6uqB49sPOW1xu30IYy9T3/u8a1TVnyZfqZZGLtMuSZKk2XZWu91xVqv4u3VoRqTOHBKuVqdZqGNQ7zM8e8ixkdouLZ9XHTJgSZIkaVZV1bk0S7O/oF2+/F6SPDrJejNU0h9ppgM+rg1UvRpWolndb50h5/wPcCdwwLDnZPWvIgh8CrgD+Hi7ouBg25WTGL7uo5wiKEmSpKXBy2iWRv/fJG+mWUZ9MbAh8BhgK5rFIYau7telqro7yWE0z8H6ZZIFwMrAzsDaNM/G2nngnIuSvJFmAY3z23N+R3O/1eNplm/fuW37mzZIHgH8Ksm3gd/SrCy4Ec3I1nXA5tP9WdU9A5YkSZJmXVVdmeRxwJtolmN/Oc1KetcAFwGfBH45gyUdQBNyXgfsTXPf1HeB/YGDhp1QVZ9PciHwDmAuzWIY19OsXviFgbZfTvJz4O00wesZwF+Bq4BjgK91/YE0M1I11kOsJUmSNJMet8GD6+y9Xz6j11xp3n+fN95l2iWNzHuwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSNzRju4cOHCmqlCJEmSljebbrppZrsGSd1yBEuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSerInNkuQJIkSfd0NSvwAR4w22VImgRHsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkpZzSRYlWTTbdSwLDFiSJEkalyQrJtkryfeT3JjkjiR/TPKLJF9I8tzZrnGmJZmfpAZedyW5IclpSV4+2zVqZs2Z7QIkSZK09EuyInAS8CxgMXAycCWwMrAl8DJgc+CEWSpxti0ALmh/Xhn4R+C5wM5JHlVV752twjSzDFiSJEkaj5fShKufAztV1c39B5PcD3jCbBS2lDi+qub370jyOOBc4G1JDq6qW2elMs0opwhKkiRpPJ7UbucPhiuAqrqlqk7v35dkjSTvbKfKXZnk9iTXJTkhyfaDfSTZuJ1iN39YAUnOSFID++a258xL8k9JTk6yOMkt7VTGJw3pZ4Mk70vy4yTXtHVdleToJI+ayJcymqo6D7gRWBX4hyF1bN5OMbyireHatoZHDmnbm4q4cZK9k/wyya3tOYcnWWNYDUk2THJYkt8lWdJO7TwnyQEjtL9/ko8muTzJbUkuSfKuJJni17HcMGBJkiRpPG5ot4+YwDlbAB8A7qaZUvjfwHeBpwI/SPKsDuvbFjiTJsx8gWY64w7AqUMCy1OAd9NMdTwW+DhwFvBC4JwkW3dRUJJtgLWBy6rquoFjzwJ+Brwc+ClwKHAq8IK2hm1G6PYj7evnwKeBPwB7AccNuf62bbs3AVcBhwFHAX8G5g3peyXgFGAP4Fs03+NqwIeA943rQ8spgpIkSRqXbwLvAt6Q5B9o/qA/r6ouG+WcXwMbVNX1/TuTbAicQxNsvt1RfbsCr+mfppdkb+CzwFuAN/a1PQ14UFX9eaCurYEf0wSKZ0/w+s9LsnH788rAxjT3YF0JvHLgOmsBXwFuAZ5SVRf1HduKJux9ARgWsp4IPLqqLm/bz2k/z85Jtquqc9r9KwPfoAl4L6+qowdq2HBI3xvQBLKnV9WStt1BwG+BtyY5pKruGNe3sRwzYOlvFiy5buxGHdp9tXVn9HqSJGlU6yQ5t+/94VV1eO9NVZ2f5BXAJ4BXtC+S3Aj8ADiiqk7s73DYVMJ2/5VJjgHelGSjXliYoh8P3gMFHAF8Cthu4Pp/HKGunyc5DXhGkpUmGCZ2b1/9lgBHA78c2P8qYE1gn/5w1dZwYZLPA/u2i2NcNHDuf/Z/X1V1Z5IjgR1pPuc57aHn0IS8EwbDVXvelSN8jjf3wlXb7o9JFrQ1PxK4cITz1DJgSZIkCeD6qtp2tAZV9fUkxwE700y/e2y7fR7NCM7/AXtW1d/uk0ryZJoRpO2B9WhGd/o9BOgiYJ07uKOq7khyLbDW4LEkuwJvoJlauA73/rt4HeDqCVz/b6Nn7YqLGwKvppmKt3uSbavqL23b3v1nWyeZN6Sv3jTMLYDBgHWvzwlc0W77P+cT2+23xlk/wM1Vdck4+9cIDFiSJEkat3ZU5zvtqxcm9qAZLXoVzdTB49tjzweOAW6lufdqIfBXmnuy5gI7Aat0VNriEfbfCazYvyPJW2juebqpretymul6RRMWt55KXVV1F3AZ8J9JHkFzn9WbgA+2TR7Ybvcao6vVh+xbPGTfne22/3Ou2W7/MMY1xup7pP41AgOWJEmSJq0NE19P8mhgf5oFLI5vDx8M3A5sW1W/7j8vyedoAla/u9vtSH+jrjnVett7luYB1wDbVNXVA8fvtbrhFJ1NE7D6pyn2pk5uXVW/6Ph6PYvb7UOmqX+NwFUEJUmS1IXeghH9y3lvBlw0JFytQDO1cNBN7fahgweSPICJrWA4knVogtqZQ8LV6gxfWGIqetPq+v/uPqvd7tjxtfr1rjHRxTo0RQYsSZIkjSnJS5M8vQ1Hg8cezN+nu/2g79Ai4OFJNuhrG5oRpHs9b6pd1e83wJP7n0fVTkP8b5olw6fqjzTTAR/XBqreNVaiWcBjnQ6u0etzLeA17dsz+g4dSTPCdGCS7QZOI8kKSeZO8fIn0nz/z03y0iHXGLaKoDrgFEFJkiSNxxNoFqu4JsmPgEvb/ZvQLJG+GrCA5p6rno/TLJN+fpJjgTuAJ9OEqxNpVrob9FHgf4EfJ/kGzf1bO9M8o+nnNPdHTVpV3Z3kMJrnYP2yXSFv5fYaawOntz9PVP8y7b1FLp5Dc7/VT2m+h14NNyR5Ic39amclORX4Fc09YA+lWQTjgTTP9JqUqro9yYto7pU7ul2y/qy2zy2AXTALTAu/VEmSJI3HfwG/A54GPAZ4Js0f6zfQjM4cDRzdv4JgVX0uyW3AvjQr6i0BfkgzqrMHQwJWVR3RjnK9rT3nJprgth/NQ4G7cABwHfA6YG+ae6K+S3MP2UGT7HNwmfbeaNyHgU9W1a39javq1CSPAd5B813uSHO/2lU0z7Wa8metqnOT/BNNmHw28KS2rkvwwcHTJn3/G7iXhQsXjnxQkiRJU7Lppptm2P4NNtig9t577xmtZd68eeeNtUy7pLE5giUtg351wG6d9HPJfkf6QGhJkqQJMGBJy6DdXv6kTvo5tJNeNF6b/OYQVtj1CeNuf/fJZ3Pp5vuNu+9a/+DJlgbAojWWTOl8SZKWBwYsSVqK3PWz8Y8+hrPH3XaFXZ/AQSfcOJmSANh/w7Unfa4kScsTl2mXJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6sic2S5AkjT97j75bA587hQ7WbhLJ7VIkrQsM2BJ0nLg0s33g4WzXYUkScs+pwhKkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHXEVQkpYShz5sLzY7ZGJLoW958DQVI2l23W8teOweM3zReTN8PWnZZMCSpKXE7qutCwefNNtlSJKkKXCKoCRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcRVBCVJk7JgyXXse9nnp9zPSUedyZaunihJWkY4giVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRn4N1H7dgyXUTPmf31dadhkokSZIkOYIlSZIkSR0xYEmSJGlaJNkzSSXZc4r9bNz2M7+byqTpY8CSJEnSuCVZMcleSb6f5MYkdyT5Y5JfJPlCkufOdo3SbPIeLEmSJI1LkhWBk4BnAYuBk4ErgZWBLYGXAZsDJ7SnHAecBVw907VKs8WAJUmSpPF6KU24+jmwU1Xd3H8wyf2AJ/Tet8fv0UZa1jlFUJIkSeP1pHY7fzBcAVTVLVV1eu/9SPdgJVnUvu6f5KNJLk9yW5JLkrwrScZTTJIVknyivcY3k6w2lQ8ndcERLEmSJI3XDe32ER30tRJwCrAB8C3gTuB5wIeAVYGDRjs5yarAUcALgE8Db66quzuoS5oSR7AkSZI0Xt8E7gDekORLSV6Q5GGT7GsD4E/AllW1d1X9O/BYmimFb02y0kgnJlkb+B7wfODdVbWP4UpLCwOWJEmSANZJcm7f6/WDDarqfOAVwLXt9lhgUZIbkhyX5DkTvOabq2pJX/9/BBYAawCPHHZCG+h+DGwHvLKqPjzBa0rTyimCkiRJAri+qrYdq1FVfT3JccDOwA40o0470Ezve16S/wP2rKoao6ubq+qSIfuvaLdrDTn2SOAnwP2BZ1fVqWPVK800A5YkadJOOurM2S5B0iyoqjuA77Sv3vLtewBHAK+iWZ79+DG6WTzC/jvb7YpDjj0CWBu4APjZBEqWZowBS5I0Kbuvti4cfNJslyFpKVBVdwFfT/JoYH/gqYwdsCbjROBi4BDg1CRPr6obxjhHmlHegyVJkqSu/LndjmuZ9cmoqg8Cb6WZmnhGkgdN17WkyTBgSZIkaVySvDTJ05Pc62/IJA8G9mrf/mA666iqQ4F/A7YEvp9kg+m8njQRThGUJEnSeD0BeAtwTZIfAZe2+zcBdgVWo1kF8JjpLqSqPpvkVuB/gR8keWpVXT7d15XGYsCSJEnSeP0X8DvgacBjgGfSPBT4BuAM4Gjg6HGsINiJqpqf5Dbg//h7yPr9TFxbGokBS5IkSeNSVVcAn25f42k/H5g/ZP/Go5wzD5g3sG8RI9zXVVVfAb4ynnqkmeA9WJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdWTObBegqdl9tXVnuwRJkiRJLUewJEmSJKkjjmBJmlYLllw3I9dxNFeSJC0NDFiSptVmh7xmRq6zYL8jl8uQ9asDdpuWfrc8+KRp6VeSpGWdUwQlSZIkqSMGLEmSJEnqiFMEJek+7JL9jpyWfrecll4lSVr2GbAkTauZupdneQ0Ey+N9Z5IkLc2cIihJkiRJHTFgSZIkSVJHDFiSJEmS1BHvwZIkSVrKbLDyHPbfcO0Zvea8Gb2atOwyYEmStIxbsOS6cbVz0RRJmjoDliRJy4HNDnnNqMena8l/SVreeA+WJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkadYkWZRk0cC+PZNUkj1npypp8gxYkiRJGlUbdiby2nO2awZIMq+tZ+5s16Llx5zZLkCSJElLvYOG7NsXWAP4BLB44NgFE+h7l0lVJC2lDFiSJEkaVVXNG9zXjlKtARxaVYum0PfCSRcmLYWcIigtg0466szZLkGStBxL8uIkP0hyc5IlSX6Z5D1JVhnS9l73YI3R94ZJPpXk90luS3JDkhOSPH6wX+DA9u3p/VMYp/LZpLE4giVNgwVLrmOzQ14zI9e6ZL8j2X21de+xb8uDT+LSDvrevYM+JEnLlySHAO8BrgeOBv4CPBs4BHhmkmdU1e2T7Hsb4DvA2sApwDeBdYDnAT9K8vyq+n9t80Pb/TsBXwQWTeoDSRNkwJIkSRLAOknO7Xt/eFUdPpEOkmxPE66uALarqmva/e8BjgN2A95BE7YmJMkc4OvA6sDOVfX9vmMbAD8F/jfJxlV1W1UdmmRNmoA1v6rOmOg1pckwYEnT5JL9jpztEiRJmojrq2rbKfbx2nb7/l64AqiqO5O8Hfhn4HVMImABuwKbAh/rD1dt/1cl+QjNqNUuwP+79+nSzDBgSdNgcMqeJEnLiW3a7WmDB6rqt0muBDZJskZV3TzBvrdvtw9LMm/I8Ye32y0wYGkWGbAkSZLUlTXa7dUjHL8a2AhYE5howHpgu33RGO1Wn2C/UqcMWJIkSepKLzQ9GBi2/Pr6A+0m0/fuVXXCJM6XZoTLtEuSJKkr57fbuYMHkmwGbAhcWlWLJ9H3We12xwmcc1e7XXES15MmxRGsZcyCJdeNetx7gyRJ0jQ6AvhXYP8kJ1TVdQBJVgQ+RvMf9/93kn0voBkV+/ckp/ctx/437SqGP6+qW9pdN7TbjSZ5TWnCDFjLmNGeveSqdpIkaTpV1Zntan7/AVyY5BjgrzTPwdoK+BHw0Un2fUeSF9A8/+rkJGcCFwC3AA8FHg/8I800xF7AOh24G/hgkq2Am9q+3j+pDyiNgwFLkqTlgP+RTTOlqt6V5HxgH+BVwEo0I0/7A/812YcMt33/IsnWwNtonqn1GpoAdTXN9MQDaR5w3Gv/6ySvpnn21huBVdtDBixNGwOWJEnLOKeHazpU1cajHPsq8NXJ9lNV84H5I7T/I/Du9jWe/r8MfHk8baUuuMiFJEmSJHXEgCVJkiRJHTFgSZIkSVJHvAdLkpZxC5ZcN+oKo7Oty8UXvNdIkjTbDFiStBzY7eVPmu0SRnbZ5zvr6tLN9+usL0mSJsMpgpIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BFXEZQkaRr86oDdlprVGw992F4uYS9JM8QRLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSerInNkuQJIkSQPut5gVtzlptquQNAmOYEmSJElSRxzBkqTlwElHnTnbJcyILQ+e7Qruaan53vfba7YrkKTlhgFLkpZxu6+2LhzsVKOZtuVS9J1vOdsFSNJyxCmCkiRJktQRR7CWMZfsd+RslyBJ6sCCJdd11tfuq63bWV+SpNEZsJYx/p+oJC0b9r3s86yw6xOm3M/dJ5/NpZvv10FFkqTxcIqgJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEXuZAkzZoFS65js0Ne01l/S9OzpyRJyycDliRpQjb5zSGdrG4H8DaAo9/aSV8ACxd21pUkSZPiFEFJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkTYsk85JUkrmzXct4JdmzrXnPGbzmfe57GslIn6Xdd8asFDXDXEVwGbNgyXUjHtt9tXVnsBJJkrSsSVLjaLZzVZ0x3bWMJMl84NXAJlW1aLbqmClJ7gdcBawBfKWqXjbLJS33DFiSJEmaqINGObZopoqYJscBZwFXz3Yh4/QSmnBVwAuSPLCqbpjlmobZArhltouYCQYsSZIkTUhVzZvtGqZLVd0M3DzbdUzA64G7gY8B/0Ezevffs1rREFX1m9muYaZ4D5YkSZJmXJLNk8xPckWS25Ncm+ToJI8cof39krwryblJ/pzkL0l+neSwJA9q2xRNwAC4tL3vp5Is6uvnjHbfyknel+TiJLe1UwtHvQcryYbt9X6XZEmSG5Ock+SAgXY7Jzk8yUVJ/tS2vTDJgUlW7eQLbK6zFfBE4FTgw8DtwOtGaV/t598gyZeS/LGt7bwk95pamGRue868JNsn+V6Sm9vv/5Qk206g1qH3YCWZk+SNSc5qv6tbkpyfZJ8kKwy03bjtZ37781eTXJ/k1vbfxW6jXP8lSU5tf2e3JlmU5CvDPkOSlyY5Pcnitu2vk+yfZJXxfFZHsCRJkjSjkjwL+CawEnAicAmwIfACYNckO1fVz/rarwWcDmwNXAwcQRMmNgVe0/Z1Lc3Uxee17T4BLG676G37HQs8HvgWcDzwxzFq3hY4BVgb+EF7zfsBjwLmAQf3NX8XsDlwJnAysCrw5Lbd3CRPq6q7RrveOL2+3c6vqhuTnAjskWTHqvrhCOes1da1GDgSWBN4MXBUkodU1UeHnPME4D3A94BPA5vR/K6ekuQZo1xrVEl6v/9n0vxejwZuBXYGPtle95VDTn0YcA7we+BLNL+TlwAL2u/29L5rpP2crwaup/m9XUfz723n9rrn9rU/gubf1JU0/0YW04TYg4Fdkjy9qu4c7XMZsCRJkjQhSeaNcOjWqvrQGOeuBXyF5n6cp1TVRX3HtqK5/+kLwDZ9p32aJjR9Fvj3qrq775zVgRWhmbqYZOO27aFjLHLxMGCrqrp+tHrba6wMfIPmD/mXV9XRA8c3HDjljcClVVUD7Q4G9gdeCHxtrOuOUdOqwCtopjMe1+6eD+xBE7xGCj2Pofks/9L7HpN8CDgP+ECSY6vq9wPnPAt4U1V9qu/6u9ME0yOSPLL/dzIB76UJV58C9u2FziQrAocDr01yTFUtGDhvLjCvqv52L2CSo4FvA++kCeM9e9GEq58CT2+ngPbOWRFYr+/9njTh6jia3/OSvmPzgAOBf6cJ7yNyiqAkSZIA1mmnWfVerx+l7YEjvN49juu8imbU5MD+cAVQVRcCnwcem+RRAEnWoxmduBp4x+Af8lX1l/4/mifggPGEq9ZzgI2BEwbDVVvDlQPvfz8Yrlofb7fPnEihI3gRzWjU1/qCwLeBa4AXtkF2mLuAd/V/j1V1KXAYzYjisBGjS4DP9O9oQ8/3aUazdpxo8e30vze19b61f0Sv/fntNAt3vHzI6ZcB7x+o5xTgcmC7gbZvard7D/47qaq7qqp/MZO3AHcCr+0PV62DgRtGqOceHMGSJGkpdNJRZ8JRZ3bS15YHj91GAq6vqnHdU1NVmcJ1tm+3W48wEvaIdrsFcBHNNL4VgB9U1V+ncN1B50yg7RPb7bfG0zjJ/Wn+WH8+zef5B6D/O3vIBK49kl4APrK3o6ruTHIUTTh5JU1oGnR5G6gGnUETkh875NgPRxihOgPYqT3n++OuvPEImhHB3wH7NzP57mUJzb+DQReMMMXyCv7+76v3e9gKuLaqzh+tmDTL3W9NM41w3xHquW2Eeu7BgCVJ0lJoy4NPmu0SpOnywHa71xjtVm+3a7bbP3RcxzUTaDvuGtr7ik6jGUm5kGYq4HXAHW2TA4FxLZYwyjW2AHYAflNVZw0cnk8TsPZieMC6doRue9/HGh2dM5bev4OH03wnI1l9yL7FI7S9k3vO0Fuz3Y7n385aNCF43THqGZMBS5IkSTOpN01r66r6xTjaL263XYz6/M0IU/i6qGF3mnA1v6pe038gyfpM8Y/3Vm/0avOM/PDnrZI8qaoGh8IfNEL7B7fbYdMtJ3POWHrnHFdVL5jE+eOxuN2O5/fWq+f8qtpm1JZjMGBJkiRpJp1FsxDDjsB4AtY5NM95ekqS+49jmmBv6tiKky/xXnqjRM+mWWhjNJu1228OObbTVAtplwp/Jc13Mp/mPqVBG9Lc57UXzYqB/TZKsvGQBUDmttthU+l2SLLCkGmCo50zlt/QrtCXZKWqumOM9hNWVX9NciFN2HzsaNMEq+ovSX4FbJlk7aq6cbLXdZELSZIkzaQjaf6wPjDJ4IIEJFkhydze+6q6DvgqsD7wsSHPRlo9Sf8UtRva7UYd1nwisAh4bpKXDqm5fxXBRe127kCbf6R5VtVU7UEzve6UqvrXqnrd4Itm2fW/Ai8e+G6gCZ4f7v8ek2wCvJlmit2Xh1zz4TQrI/Z/nt1pAuMljLxi4Yjapc4/SfN7PSzJaoNtkqzfW+xkCnrTJD83+F20/9bW79v138DKNCsjrjmknrWSjDm65QiWJGnC7j757NkuYbjNd5ntCqTlwijLtAMcX1UXjHSwqm5I8kKapbDPSnIq8CuakZiH0ixS8ECaZ0f17EOzWMEbaJ4jdQrNc7A2oRmpeS7NggvQPHT3ncDnkxwL/BlY3L/E+ERV1e1JXgR8Bzg6yd40o1qr0ix6sAt//7u691yvtyV5NM3ozkbAbjTPxJpq8OtND/zCKPX+Kck3gD1plnL/dN/hX9A8X+q8JN/h78/BWhP4j6paOKTLbwP/leTZwM/5+3OwbqVZcW8yS7RDszLf1jS/1+ckOY3mfqn1aELdk2mWcr9oxB7G9gWa0dJXAr9LsoDmnrgNgKfSPFNtHkBVHZHkcTRhcmH77+xymsU4NgGeQvMfCN4w2gUNWJKkCbl08/1muwRJs2+0+4gWAReMdnJVnZrkMcA7aALSjjSB6SqaBSKOHWh/U5InAfvSLNn+epqpgFfQ/IF8UV/bU5L0FnnYl2ZE4jKaZy1NWlWdm+SfaJaifzbwJJrwdgnwvr52f03yVOBDNKNYO9I8EPdgmhGSl0y2hiSPoBk1upYmyI3m8zQBay/uGbBuauv/CM0znx5A8/19bNgS9K2zgf+k+Qz70CwGcRrw3qr66WQ+C0BV3ZHkeTQhcE+aELo6TQC6FDgAOGqy/bfXKOBVbVh6PU2YXIVm2f8fAicMtP/3JN+iCVFPowmeN9IErY8yfITvHjLa/X0LFy6cyM1/WgosWHLdiMd2X23dGaxEkiSNZdNNNx26FvS22z6szj33vTNaS7L3eeNdpl33Te2CGN+vqrnjbD+X5qG9B1XVvGkrbBnjPViSJEmS1BGnCEqStJT61QG7ddaXz9WSpJlhwJIkaSm128ufxAq7PmHK/dx98tlc2kE9kqSxGbAkSZKk5UBVDb3nb5T2Z9AsaKEJ8B4sSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSNzZrsASZIkDbj5r9x98tmzXYWkSTBgSZK0FPOPbEm6bzFgSZK0lLp08/1muwRJ0gR5D5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR2ZM9sFSJKWP786YLdO+tnt5U9ihV2f0ElfoznhZR9ny4NPmvbrSJLu+wxYkqQZtWDJdbzt6Ld20tddP9uNg064sZO+RnPg0bBw4bRfRpK0DHCKoCRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJmjZJNk5SSebPdi3STDBgSZIkacKSbJ7kk0kuTHJzktuTXJXk5CT/mmSV2a5xvJLMb0PgxrNdi+77XEVQkiRJE5LkfcCBNP+x/ifAF4G/AA8C5gJfAP4N2HaWSpRmjQFLkiRJ45ZkP+Ag4ArgRVV19pA2uwFvn+napKWBAWsZs/tq6852CZIkaRnVTqGbB9wB/HNVXTisXVWdlOS74+hvfWB/YFdgA+Bm4IfAB6rqvIG2KwNvAPYENgFWAf4I/Bz4ZFV9b6D95sC7gV1oRtZuAk4FDqqqi/vaVd9plybp/XxZVW081meQBhmwJEmSNF6vAVYCvjpSuOqpqttGO55kE+BHNMHqNOArwEOBFwG7Jtmjqk7qO2U+8FLgQuD/gCXtuTsAzwK+19f3s4BvtrWeCFwCbAi8oO1756r6Wdv8IOB5wNbAJ4DF7f7eVpoQA5YkSZLGa4d2e2oHfX2WJiDtX1Uf6O1M8hngB8AXkzysqv6SZA3gX4DzgCdU1V39HSV5YN/Pa9GEtVuAp1TVRX3HtgLOorlHbBuAqprXjsxtDRxaVYs6+GxajrmKoCRJkgDWSXJu3+v1Q9qs326vnMqFkmwIPAO4HPhI/7GqOpMmIK1NM+IEUECA24C7B/urqhv63r4KWBM4sD9cte0uBD4PPDbJo6byGaSROIIlSZIkgOuraqZW/Xtsu/1hVd0x5PhpwCvadv9XVX9KciLwHOCCJMfS3Kt1dlXdMnDu9u126yTzhvT9iHa7BXDRkOPSlBiwJEmSNF5X0wSTh0yxnzX6+hvpOtCMRPW8BHgX8DKa+6YAbk1yDPCOqrq23debLrjXGDWsPu5qpQlwiqAkSZLG60ftdpcp9nNzu33wCMfXH2hHVS2pqnlV9QhgI5oRrh+122OG9L11VWWU1xen+BmkoQxYkiRJGq8jaZZo32Ose5iSrDLK4fPb7Q5Jhs2o2rnd/mzIMarqiqo6CngmzQqBO/QtdHFWu91xtPoG9BbNWHEC50hDGbAkSZI0Lu0Ke/OAlYGTkwy9Z6tdJv1bo/RzJfBdYGNg34Fzn0AzDfAm4Lh237pJHj2kq/vTTPW7E7i93XckzRLrBybZbkhtKySZO7C7t0jGRiPVLI2X92BJkmbU7quty90nn91JXyvuCgdc3U1foznhZWey5cFTnRElLRuq6pB21OlA4KdJzgTOBf5C80DfpwAPb/eN5g3Aj4GPJnlG2773HKy7gddU1Z/btg8Bzk/yS+AXwBXAA4DdaKYZHtZrW1U3JHkhTTg7K8mpwK9oViJ8KM0iGA8EVu2r5VTgncDn2wU0/gwsrqpPTeIr0nIuVTXiwYULF458UJIkSVOy6aabZtj+bR++Tp1z6O4zWsuKux1x3kRWEUyyBfBGmul8G9EElhuAC2juifpyVd3WPmPqUuCLVbXnQB8PAfYH/pnmvqs/0dxX9YGq+mlfuzWBNwNzgUcC6wA3AhcDn6N58HEN9L0x8A6aaYQPpRnhugr4KXBsVR0/0P5tNAtj/CPNCN1lVbXxeL8PqceApWm3YMl1s13CtNt9tXVn5boz/d0uL59ztszW97u0uq//3v19ajzuywFL0nDegyVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUkTmzXYCWfbuvtu5sl7DMWl6+2+Xlc+qe/L1Lku6LHMGSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjrig4YlSZKWMotv/CsnHXXmbJchaRIcwZIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqyJzRDm666aaZqUIkSZIk6b7OESxJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOjLqMu2SJElST5Ia2HU78CfgCuBnwLHAd6rqrpmuTVpaGLAkSZI0UQe12xWBNYEtgVcC/wqcm+TlVfXbWapNmlUGLEmSJE1IVc0b3JfkQcAngRcB30uybVX9caZrk2ab92BJkiRpyqrqWuBfgDOAhwL7DbZJ8vAk/5fkD0luT3JV+/7hw/pMskaSDya5OMmtSW5KckqSpw1pOzdJJZmXZLskJye5sd23cbefVhqZAUuSJEmdqKq7gfe3b1+aJL1jSR4PnAu8Avgp8DHgrPb9ue1x+tqvCZwJvBu4GTiU5h6v7YHvJNl7hDK2B34IrAocAXyR5l4xaUY4RVCSJEld+hFwJ7AesDFwaRu0/g94APCKqjqq1zjJS4CvAl9K8qg2pAF8GHgUcDjwhqqqtv2HaYLaYUlOqapFA9d/Rtv+c9P0+aRROYIlSZIkgHWSnNv3ev1kOqmq24Ab2rfrttsnAZsDP+kPV237r9GEskcCOwAkWZlmZOsvwHt64apt/zvgMGBl4FVDSrjAcKXZZMCSJEkSwPVVtW3f6/Ap9NWbGtgLRtu029NGaN/b/9h2+0jgfsDPq+rGcbTvd84E6pQ6Z8CSJElSZ5KsCqzdvr2u3a7Rbq8e4bTe/jUn2b7fNWMWKU0jA5YkSZK6tAPNff7X9t0fdXO7ffAI56w/0G6i7fsNPgxZmlEGLEmSJHUiyQrAe9u3R/cdOr/dzh3h1J3b7c/a7cXALcDW7WqCY7WXlhoGLEmSJE1ZkvVoVgOcC1wOHNJ3+Mc0oWmHJC8cOO+FwI7Ab2kWu6CqbgeOAv4BOHig/abAm4E7gC9Nw0eRpsRl2iVJkjQhSea1P65Acx/UljRTA1emWWTi5VV1fa99VVWSVwPfBb6WZAHwG5rFLJ4H/Bl4Vd8S7dA8/2pHYJ/2GVmnA+sAL6YJXvtU1aXT9BGlSTNgSZIkaaIObLe304Sjy2iec3Us8J2BoARAVZ3dBqX9gacBzwGuB74CHFxVFw+0vzHJ9sB7gBcAbwOW0AS4j1bVd6bjg0lTZcCSJEnSuFRVxm416vkXA6+cQPvFwLva11htz+Dvy8NLs8Z7sCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjoyZ7YLkCRJ0j3d9uCNuWS/I2f2ol/ZfmavJy2jHMGSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkjBixJkiRJ6ogBS5IkSZI6YsCSJEmSpI4YsCRJkiSpIwYsSZIkSeqIAUuSJEmSOmLAkiRJkqSOGLAkSZIkqSMGLEmSJM24JIuSLJrma+yZpJLsOZ3XkfoZsCRJkjSmJI9rw8rZIxx/aXu8kmwy5PhqSW5NckuSVaa/Yml2GLAkSZI0HucDNwGPS/KAIcd3Aar9+alDjj8ZWAX4UVXd1rbfZToKlWaTAUuSJEljqqq7gTOAFYGdhjR5anv8BoYHrN6+U9v+FlbVws4LlWaZAUuSJEnjdWq7vUeASrIxsEl7/PvAzkPOvUfAGnYPVv89U0l2TnJGkj8n+VOSk5NsMayoJJsl+UaSm5L8NcmZSXYd7YO0Ux6PTfLHJLcluSzJZ5KsP9Dug21NTx/Yf1C7/14hMck1SS4f7fpadhmwJEmSNF6ntdvBqX279B0/HVg/yaN6B9sphdvSTDH82TiusxvwHeBPwGeBHwL/DHw/yTr9DZM8HDgLeCHwE+ATwJXA8cALhnWeZDfgTOA5wPeA/wYuBv4NOHfgHrJeqBzpM/9jGzB7fW8FPKjvPC1n5sx2AZIkSbpvqKpfJ7ka2CrJulV1XXvoqcBfgJ/ShKLevovan3eimVp4RjvVcCzPA55ZVX8LKUk+CLwbeC3wkb62nwYeCOxbVZ/oa787Tci6hySrA1+k+Tt4blX9sO/Yu4APAZ8DntHu/jHQu2esv4/tgO8CT2+P/W97uD9sajnkCJYkSZIA1klybt/r9SO0OxUI95wGuDPww6q6s6p+BfyRe04jvMf0wHH4an+4ah3ebrfr7UiyIU3AuRT4VH/jqlpAM11x0O7A2sDX+sNV67+ARcDTk2zU9rOEZmRsmyRrte2eAqwEHApcxz1Ht3o/O4K1nDJgSZIkCeD6qtq273X4CO16IzNPBWjvi1qfZmpgzxnA3CQr9Ldl/KHj3CH7rmi3a/Xte2y7/VFV3TXknDOG7Num3d5rhKmq7gR+MNB3r+0KwNz2/VOBO2gC3On8/btYkSZ8XVxVVw25tpYDBixJkiRNxOA9ScOmxJ1BE4Qem2Rd4NHAH6rqN+O8xuLBHW34gWaqYc8a7fbaEfq5Zsi+3jlXj3BOb/+affuGfeazq+qv7bEHtfdebdv27+jVcsx7sCRJkjRuVXV5u3LeZkkeSjN6s5jmOVk9vdGspwKX0UwpnI7QcXO7fdAIxx88yjnDjkEzGtffDuAcmnvMnpbkgcDWwH+2x3rB8mnA/Qb2aTnkCJYkSZImqheWnkYzbe77/YtXtCNV19AErIlOD5yIXqjboZ2eN2juKOfc61iSOcCO7du/rXbYN3XwkcAr6QuMVXUJcDnNqNZTgbu553RJLWcMWJIkSZqo3gjNW2mmAg4LFKfThJXeanydB6yqupJmJb9NgH36j7WrCA57IPLxwI3AS5M8ceDYvm1f36uqwedY9T7ze4C/0iwN339sJ+DJwM+r6saJfhYtOwxYkiRJmqjTgKK5t6r3ftDpwP1pAsvFVfWHaarl34EbgEPbhxEfkuTrwDHAiYONq+ovNEu9303zXK0vt+ecAnyUZuRt7yHX6QXE9WhWTLxj4Ng/AKvi/VfLPQOWJEmSJqR9/tUv27fXAxcOadY/qjVtoaOqfgc8ETiWZgTpLcBDaZ6l9c0RzlnQtv1/wDOBdwBb0DzU+HFV9fshp/2c5rPCvQPlaSP8rOWQi1xIkiRpwqpq6zGOX0Jzr9JIxzcesm8+MH+Uc4b2117rhSOcNrS/qvop8PyRrjWkfQHrjnDsKkb5rFq+OIIlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkfmzHYBkiRJuqc1V5jD7qutO6PXfPuMXk1adjmCJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZKkcUlS7euyJKuO0GZR22bONNWwZ9v//OnoX5oqA5YkSZImaiNg39kuQloaGbAkSZI0ETcBNwLvTrLObBcjLW0MWJIkSZqIW4CDgTWAAydyYpIXJ/lBkpuTLEnyyyTvSbJKF4UlWTHJG5L8uO8alyT5QpKHD7RdI8kHk1yc5NYkNyU5JcnThvQ7t52WOC/JPyU5OcniJLck+X6SJ3VRv5YNBixJkiRN1KeBhcDeg8FlJEkOAb4GbAEcDXwKCHAIcEqSladSUHv+t4D/AR7aXuMw4Dzg+cCT+9quCZwJvBu4GTgUOBbYHvhOkr1HuMy27XmrAl8ATgJ2AE5N8sip1K9lx7TcfChJkqRlV1XdkeTdwDeADwMvGK19ku2B9wBXANtV1TXt/vcAxwG7Ae+gCVuTNQ94OnAi8KKquq3v+qsAD+hr+2HgUcDhwBuqqtp2HwbOBQ5LckpVLRq4xq7Aa6pqfl/fewOfBd4CvHEK9WsZ4QiWJEmSANZJcm7f6/WjNa6qY4CfAM9PssMYfb+23b6/F67aPu4E3g7cDbxusoUnWZEm3CyhCUy39R+vqtuq6rq27crAK4C/AO/phau23e9oRr1WBl415FI/7g9XrSOAO4HtJlu/li0GLEmSJAFcX1Xb9r0OH8c5b2+3Hxuj3Tbt9rTBA1X1W+BKYJMka4y/3HvYnOaesF9U1VVjtH0kcD/g51V145DjvRofO+TYuYM7quoO4FpgrfGXq2WZAUuSJEmTUlU/AY4BnpDkJaM07QWnq0c43tu/5iRL6Z33h3G0nUoti0c4505gxXFcW8sBA5YkSZKm4j3AHcAHR1mo4uZ2++ARjq8/0G6iFrfbh4yj7XTXouWcAUuSJEmTVlWXAJ8BNgHeNEKz89vt3MEDSTYDNgQurarFkyzjNzQh6zFJNhij7cU0S81v3a4mOGjndvuzSdai5ZwBS5IkSVP1nzQB573A6kOOH9Fu90+ybm9nuzjFx2j+Jv3fyV68qu6iCXmrAZ8dfK5WkpV7162q24GjgH+geZ5Xf7tNgTfTjMh9abL1aPnmMu2SJEmakqq6sX3O1UdGOH5mko8A/wFcmOQY4K/As4GtgB8BH51iGQcBTwCeA/w2yUnAn2meifUM4J3A/Lbtu4EdgX2SPB44HVgHeDFN8Nqnqi6dYj1aTjmCJUmSpC4cBiwa6WBVvQt4KfA7miXQ30zzt+j+wNPbkaVJa89/Fs00xWuBV7c/b0fzrK0f9bW9keahwh8BHgi8DXgRcA7wrKr6zFRq0fItfUv/S5IkaSnw6Ec/uo4//vgZveZmm212XlVtO6MXlZZBjmBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1BEDliRJkiR1xIAlSZIkSR0xYEmSJElSRwxYkiRJktQRA5YkSZIkdcSAJUmSJEkdMWBJkiRJUkcMWJIkSZLUEQOWJEmSJHXEgCVJkiRJHTFgSZIkSVJHDFiSJEmS1JFU1WzXIEmSpD5Jvg2sM8OXvb6qnjXD15SWOQYsSZIkSeqIUwQlSZIkqSMGLEmSJEnqiAFLkiRJkjpiwJIkSZKkjhiwJEmSJKkj/x9URJikhE9QIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pol_room_seg, pol_icon_seg = polygons_to_image(polygons, types, room_polygons, room_types, height, width)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "ax = plt.subplot(1, 1, 1)\n",
        "ax.axis('off')\n",
        "rseg = ax.imshow(pol_room_seg, cmap='rooms', vmin=0, vmax=n_rooms-0.1)\n",
        "cbar = plt.colorbar(rseg, ticks=np.arange(n_rooms) + 0.5, fraction=0.046, pad=0.01)\n",
        "cbar.ax.set_yticklabels(room_classes, fontsize=20)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "ax = plt.subplot(1, 1, 1)\n",
        "ax.axis('off')\n",
        "iseg = ax.imshow(pol_icon_seg, cmap='icons', vmin=0, vmax=n_icons-0.1)\n",
        "cbar = plt.colorbar(iseg, ticks=np.arange(n_icons) + 0.5, fraction=0.046, pad=0.01)\n",
        "cbar.ax.set_yticklabels(icon_classes, fontsize=20)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUnR-6bZGx_M"
      },
      "source": [
        "# Generate data files and Blender files (Using CubiCasa and Super-Resolution)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShNmaZ7rGx_M",
        "outputId": "b0228feb-a940-4064-f863-08f9d6940152"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/ubuntu/2Dto3D/GIT/ftb_CubiCasa_v4/floortrans/plotting.py:591: UserWarning: Trying to register the cmap 'rooms' which already exists.\n",
            "  cm.register_cmap(cmap=cmap3)\n",
            "/home/ubuntu/2Dto3D/GIT/ftb_CubiCasa_v4/floortrans/plotting.py:597: UserWarning: Trying to register the cmap 'icons' which already exists.\n",
            "  cm.register_cmap(cmap=cmap3)\n",
            "/home/ubuntu/2Dto3D/GIT/ftb_CubiCasa_v4/floortrans/plotting.py:605: UserWarning: Trying to register the cmap 'rooms_furu' which already exists.\n",
            "  cm.register_cmap(cmap=cmap3)\n",
            "/home/ubuntu/2Dto3D/GIT/ftb_CubiCasa_v4/floortrans/plotting.py:611: UserWarning: Trying to register the cmap 'rooms_furu' which already exists.\n",
            "  cm.register_cmap(cmap=cmap3)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ----- Generate  Images/example.png  at pos  None  rot  None  -----\n",
            "Approximated apartment size :  1731843.0\n",
            "Created file : Data/2/floor_verts.txt\n",
            "Created file : Data/2/floor_faces.txt\n",
            "Walls created :  172\n",
            "Created file : Data/2/wall_verts.txt\n",
            "Created file : Data/2/wall_faces.txt\n",
            "Created file : Data/2/top_wall_verts.txt\n",
            "Created file : Data/2/top_wall_faces.txt\n",
            "Number of rooms detected :  22\n",
            "Created file : Data/2/rooms_verts.txt\n",
            "Created file : Data/2/rooms_faces.txt\n",
            "Created file : Data/2/transform.txt\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import FloorplanToSTL as stl\n",
        "import config\n",
        "\n",
        "# can specify or use default paths in config files\n",
        "stl.createFloorPlan(image_path = config.image_path, target_path = config.target_path, SR_Check=True)\n",
        "\n",
        "# Note: USE SR_Check = False for Original Image"
      ]
    }
  ],
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