Update README

README.rst

@@ -1,6 +1,6 @@
 
 ======================
-MLSTRUCT_FP-benchmarks
+MLSTRUCT-FP_benchmarks
 ======================
 
 .. image:: https://img.shields.io/github/actions/workflow/status/MLSTRUCT/MLSTRUCT-FP_benchmarks/ci.yml?branch=master
@@ -15,7 +15,7 @@ MLSTRUCT_FP-benchmarks
     :target: https://opensource.org/licenses/MIT
     :alt: License MIT
 
-Benchmarks of MLStructFP dataset.
+Benchmarks of `MLSTRUCT-FP <https://github.com/MLSTRUCT/MLSTRUCT-FP>`_ dataset.
 
 
 Description
@@ -29,22 +29,25 @@ jupyter notebook files (in order) for more information and a quick start:
 - `fp_unet <https://github.com/MLSTRUCT/MLSTRUCT-FP_benchmarks/blob/master/fp_unet.ipynb>`_: Creates U-Net model for wall segmentation
 - `vectorization <https://github.com/MLSTRUCT/MLSTRUCT-FP_benchmarks/blob/master/vectorization.ipynb>`_: Vectorizes a model using Egiazarian et al. method
 
+The weights for the best model (no_rot_256_50) can be downloaded at `this link <https://drive.google.com/file/d/15ufkjoWOFyT0Cm-MEc9zQJCDJIooOgh7/view?usp=sharing>`_.
+
 
 Citing
 ------
 
 .. code-block:: tex
 
     @article{Pizarro2023,
-        title = {Large-scale multi-unit floor plan dataset for architectural plan analysis and recognition},
-        journal = {Automation in Construction},
-        volume = {156},
-        pages = {105132},
-        year = {2023},
-        issn = {0926-5805},
-        doi = {https://doi.org/10.1016/j.autcon.2023.105132},
-        url = {https://www.sciencedirect.com/science/article/pii/S0926580523003928},
-        author = {Pablo N. Pizarro and Nancy Hitschfeld and Ivan Sipiran}
+      title = {Large-scale multi-unit floor plan dataset for architectural plan analysis and
+               recognition},
+      journal = {Automation in Construction},
+      volume = {156},
+      pages = {105132},
+      year = {2023},
+      issn = {0926-5805},
+      doi = {https://doi.org/10.1016/j.autcon.2023.105132},
+      url = {https://www.sciencedirect.com/science/article/pii/S0926580523003928},
+      author = {Pablo N. Pizarro and Nancy Hitschfeld and Ivan Sipiran}
     }
 
 

create_data.ipynb

@@ -125,15 +125,10 @@
    ],
    "source": [
     "from MLStructFP.db import DbLoader\n",
-    "from MLStructFP.db.image import *\n",
     "\n",
     "from MLStructFP_benchmarks.ml.model.core import DataFloorPhoto\n",
     "from MLStructFP_benchmarks.utils import FloorPatchGenerator, FPDatasetGenerator\n",
     "\n",
-    "from matplotlib import pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
-    "import numpy as np\n",
-    "import time\n",
     "import plotly.offline as pyo\n",
     "\n",
     "pyo.init_notebook_mode()"

fp_unet.ipynb

@@ -47,12 +47,11 @@
    "source": [
     "from MLStructFP_benchmarks.ml.model.architectures import *\n",
     "from MLStructFP_benchmarks.ml.model.core import *\n",
-    "from MLStructFP_benchmarks.ml.utils import binary_accuracy_metric, iou_metric\n",
+    "from MLStructFP_benchmarks.ml.utils import iou_metric\n",
     "from MLStructFP_benchmarks.utils import *\n",
     "from MLStructFP.db import DbLoader\n",
     "\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import tensorflow as tf\n",
     "\n",
@@ -434,10 +433,7 @@
    "id": "89120cd3",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "db = DbLoader('../MLSTRUCT-FP/dataset/fp.json')\n",
-    "test_data = data.load_part(2, False)"
-   ]
+   "source": "test_data = data.load_part(2, False)"
   },
   {
    "cell_type": "code",
@@ -452,7 +448,7 @@
     "    print(f'IoU: {iou_metric(img_true, model.predict_image(img_in)):.2f}')\n",
     "    model.plot.plot_predict(img_in, img_true, threshold=False, title=title)\n",
     "\n",
-    "def plot_floor(image_size: float, patch_size: float, floor) -> None:\n",
+    "def plot_floor(image_size: int, patch_size: float, floor) -> None:\n",
     "    patchgen = FloorPatchGenerator(image_size=image_size, patch_size=patch_size, bw=True)\n",
     "    patchgen.plot_patches(floor, photo=1, patches=False, rect=False, axis=False,\n",
     "                          save=f'.out/full/{floor.id}_c.png')\n",
@@ -463,6 +459,8 @@
     "    patchgen.plot_model(floor, model, f'.out/model/{floor.id}.png', outline=False)\n",
     "    patchgen.plot_model(floor, model, f'.out/model/{floor.id}_outline.png', outline=True)\n",
     "\n",
+    "\n",
+    "# noinspection PyProtectedMember\n",
     "def plot_full(plan_idx: int, patch_size: float):\n",
     "    plan_id = data._split[1][plan_idx] # Consider only test floor plans\n",
     "    for f in db.floors:\n",

setup.py

@@ -21,6 +21,7 @@
     'MLStructFP >= 0.6.0',
     'numpy <= 1.18.5',
     'Pillow >= 10.4.0',
+    'plotly >= 5.23.0',
     'scikit-image <= 0.18.1',
     'scikit-learn >= 1.3.2'
 ]