Update README & libraries

MLStructFP/__init__.py

@@ -7,9 +7,9 @@
 # Basic information
 __author__ = 'Pablo Pizarro R.'
 __description__ = 'Machine learning structural floor plan dataset'
-__keywords__ = ['ml', 'ai', 'dataset', 'calc', 'matrix analysis', 'cnn', 'structural analysis', 'structural design']
+__keywords__ = ['ml', 'ai', 'floor-plan', 'architectural', 'dataset', 'cnn']
 __email__ = '[email protected]'
-__version__ = '0.5.9'
+__version__ = '0.6.0'
 
 # URL
 __url__ = 'https://github.com/MLSTRUCT/MLSTRUCT-FP'

MLStructFP/db/image/_rect_binary.py

@@ -36,6 +36,12 @@
 class RectBinaryImage(BaseImage):
     """
     Rect binary image.
+
+    This class creates a segmentation mask iterating all rects from a given  floor;
+    obviously, this can be further extended to create other maps,  for example, for
+    wall joints (points), or can be concatenated to create multiple mask tensor for
+    a given crop  (like wall segments + joints). It is up to your  imagination  and
+    technical abilities =). I'd say that this is the most  important class  of all.
     """
     _crop_px: int
     _initial_backend: str
@@ -53,7 +59,7 @@ def __init__(
 
         :param path: Image path
         :param save_images: Save images on path
-        :param image_size_px: Image size (width/height), bigger images are expensive, double the width, quad the size
+        :param image_size_px: Image size (width/height), bigger images are expensive; double the width, quad the size
         """
         BaseImage.__init__(self, path, save_images, image_size_px)
         self._crop_px = int(math.ceil(self._image_size / 32))  # Must be greater or equal than zero
@@ -82,7 +88,6 @@ def _get_floor_plot(self, floor: 'Floor', rect: Optional['Rect'], store: bool) -
         :return: Figure of the floor
         """
         floor_id = str(floor.id)
-
         if floor_id in self._plot.keys():
             return self._plot[floor_id]
 
@@ -138,7 +143,7 @@ def _convert_image_color(self, im: 'Image.Image') -> 'Image.Image':
         :param im: Image
         :return: Converted image
         """
-        return im.convert('P', palette=Image.ADAPTIVE)
+        return im.convert('P', palette=Image.Palette.ADAPTIVE)
 
     # noinspection PyMethodMayBeStatic
     def _post_process(self, im: 'Image.Image') -> 'Image.Image':
@@ -200,7 +205,6 @@ def make_region(self, xmin: NumberType, xmax: NumberType, ymin: NumberType, ymax
             assert self._path != '', 'Path cannot be empty'
             filesave = os.path.join(self._path, figname + '.png')
             im4.save(make_dirs(filesave), format='PNG')
-            # print('Rect {0} saved to {1}'.format(rect.id, filesave))
 
         # noinspection PyTypeChecker
         array = np.array(im4, dtype=TYPE_IMAGE)

README.rst

@@ -38,7 +38,8 @@ scaling.
 The images can be generated from the real rasterized plan, or by using the polygons stored in the
 JSON file. Both image and wall polygons are consistent in their placement.
 
-See more information in our `published article <https://doi.org/10.1016/j.autcon.2023.105132>`_.
+See more information in our `published article <https://doi.org/10.1016/j.autcon.2023.105132>`_; also,
+check out the `AI segmentation model <https://github.com/MLSTRUCT/MLSTRUCT-FP_benchmarks>`_ that tests this dataset.
 
 
 First steps

setup.py

@@ -19,7 +19,7 @@
     'numpy >= 1.18.5',
     'opencv-python <= 4.5.3.56',
     'Pillow >= 10.4.0',
-    'plotly >= 5.22.0',
+    'plotly >= 5.23.0',
     'requests >= 2.32.3',
     'tabulate >= 0.9.0'
 ]