max_tree(image, connectivity=1)
Component trees represent the hierarchical structure of the connected components resulting from sequential thresholding operations applied to an image. A connected component at one level is parent of a component at a higher level if the latter is included in the first. A max-tree is an efficient representation of a component tree. A connected component at one level is represented by one reference pixel at this level, which is parent to all other pixels at that level and to the reference pixel at the level above. The max-tree is the basis for many morphological operators, namely connected operators.
The input image for which the max-tree is to be calculated. This image can be of any type.
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.
Array of same shape as image. The value of each pixel is the index of its parent in the ravelled array.
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).
Build the max tree from an image.
We create a small sample image (Figure 1 from [4]) and build the max-tree.
This example is valid syntax, but we were not able to check execution>>> image = np.array([[15, 13, 16], [12, 12, 10], [16, 12, 14]])See :
... P, S = max_tree(image, connectivity=2)
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.morphology.max_tree.diameter_closing
skimage.morphology.max_tree.area_opening
skimage.morphology.max_tree.max_tree_local_maxima
skimage.morphology.max_tree.diameter_opening
skimage.morphology.max_tree.area_closing
skimage.morphology.max_tree.max_tree
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