grains.cad.polygonize

grains.cad.polygonize(label_image, neighbor_search_algorithm, connectivity=1, detect_boundaries=True, orientation='ccw', close=False)[source]

Polygon representation of a label image.

Parameters

  • label_image (ndarray) – Labeled input image, represented as a 2D numpy array of positive integers.

  • neighbor_search_algorithm (functools.partial) – Specifies which algorithm to use for constructing the branch-region connectivity. The function to be passed (along with its arguments) is skeleton2regions().

  • connectivity ({1,2}, optional) – A connectivity of 1 (default) means pixels sharing an edge will be considered neighbors. A connectivity of 2 means pixels sharing a corner will be considered neighbors.

  • detect_boundaries (bool, optional) – When True, the image boundaries will be treated as part of the skeleton. This allows identifying boundary regions in the skeleton2regions function. The default is True.

  • orientation ({‘cw’, ‘ccw’}, optional) – Clockwise (‘cw’) or counterclockwise (‘ccw’) orientation of the polygons. The default is ‘ccw’.

  • close (bool, optional) – When True, one vertex in the polygons is repeated to indicate that the polygons are indeed closed. The default is False.

Returns

polygons (dict) – The keys in the dictionary correspond to the labels of the input image, while the values are ndarray objects with two columns, the x and y coordinates of the polygons.

See also

build_skeleton(), skeleton2regions(), region_as_polygon()

Examples

>>> test_image = np.array([
...   [1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
...   [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]],
...  dtype=np.int8)
>>> polygons = polygonize(test_image, search_neighbor(2, np.inf), connectivity=1)