area_closing(image, area_threshold=64, connectivity=1, parent=None, tree_traverser=None)
Area closing removes all dark structures of an image with a surface smaller than area_threshold. The output image is larger than or equal to the input image for every pixel and all local minima have at least a surface of area_threshold pixels.
Area closings are similar to morphological closings, but they do not use a fixed structuring element, but rather a deformable one, with surface = area_threshold.
In the binary case, area closings are equivalent to remove_small_holes; this operator is thus extended to gray-level images.
Technically, this operator is based on the max-tree representation of the image.
If a max-tree representation (parent and tree_traverser) are given to the function, they must be calculated from the inverted image for this function, i.e.: >>> P, S = max_tree(invert(f)) >>> closed = diameter_closing(f, 3, parent=P, tree_traverser=S)
The input image for which the area_closing is to be calculated. This image can be of any type.
The size parameter (number of pixels). The default value is arbitrarily chosen to be 64.
The neighborhood connectivity. The integer represents the maximum number of orthogonal steps to reach a neighbor. In 2D, it is 1 for a 4-neighborhood and 2 for a 8-neighborhood. Default value is 1.
Parent image representing the max tree of the inverted image. The value of each pixel is the index of its parent in the ravelled array. See Note for further details.
The ordered pixel indices (referring to the ravelled array). The pixels are ordered such that every pixel is preceded by its parent (except for the root which has no parent).
Output image of the same shape and type as input image.
Perform an area closing of the image.
We create an image (quadratic function with a minimum in the center and 4 additional local minima.
This example is valid syntax, but we were not able to check execution>>> w = 12
... x, y = np.mgrid[0:w,0:w]
... f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2)
... f[2:3,1:5] = 160; f[2:4,9:11] = 140; f[9:11,2:4] = 120
... f[9:10,9:11] = 100; f[10,10] = 100
... f = f.astype(np.int)
We can calculate the area closing:
This example is valid syntax, but we were not able to check execution>>> closed = area_closing(f, 8, connectivity=1)
All small minima are removed, and the remaining minima have at least a size of 8.
See :The following pages refer to to this document either explicitly or contain code examples using this.
skimage.morphology.max_tree.diameter_opening
skimage.morphology.max_tree.area_closing
skimage.morphology.max_tree.area_opening
skimage.morphology.max_tree.diameter_closing
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