porespy.filters.find_peaks#

porespy.filters.find_peaks(dt, r_max=4, strel=None, sigma=None, parallel_kw={'divs': 1})#

Finds local maxima in the distance transform

Parameters:

  • dt (ndarray) – The distance transform of the pore space. This may be calculated and filtered using any means desired.

  • r_max (scalar) – The radius of the spherical element used in the maximum filter. This controls the localness of any maxima. The default is 4 voxels.

  • strel (ndarray) – Instead of supplying r_max, this argument allows a custom structuring element allowing control over both size and shape.

  • sigma (float or list of floats) – If given, then a gaussian filter is applied to the distance transform using this value for the kernel (i.e. scipy.ndimage.gaussian_filter(dt, sigma))

  • parallel_kw (dict) –

    Dictionary containing the settings for parallelization by chunking. The optional settings include divs (scalar or list of scalars, default = [2, 2, 2]), overlap (scalar or list of scalars, optional), and cores (scalar, default is all available cores).

    divs is the number of times to divide the image for parallel processing. If 1 then parallel processing does not occur. 2 is equivalent to [2, 2, 2] for a 3D image. If a list is provided, each respective axis will be divided by its corresponding number in the list. For example, [2, 3, 4] will divide z, y, and x axis to 2, 3, and 4 respectively.

    overlap is the amount of overlap to include when dividing up the image. This value is controlled by the size (i.e. radius) of the structuring element and cannot be controlled in this function using parallel_kw!

    cores is the number of cores that will be used to parallel process all domains. If None then all cores will be used but user can specify any integer values to control the memory usage. Setting value to 1 will effectively process the chunks in serial to minimize memory usage.

Returns:

image – An array of booleans with True values at the location of any local maxima.

Return type:

ndarray

Notes

It is also possible ot the peak_local_max function from the skimage.feature module as follows:

peaks = peak_local_max(image=dt, min_distance=r, exclude_border=0, indices=False)

The skimage function automatically uses a square structuring element which is significantly faster than using a circular or spherical element.

Examples

Click here to view online example.

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