hold_peaks

Replaces each voxel with the last peak seen along the given axis

import matplotlib.pyplot as plt
import numpy as np
from edt import edt

import porespy as ps

ps.visualization.set_mpl_style()

im

The input image will most likely be the distant transform

np.random.seed(0)
im = ps.generators.blobs(shape=[500, 500])
dt = edt(im)

pk = ps.filters.hold_peaks(im=dt, axis=0)

fig, ax = plt.subplots(1, 2, figsize=[16, 8])
ax[0].imshow(dt / im)
ax[0].axis(False)
ax[0].set_title("distance transform")

ax[1].imshow(pk / im)
ax[1].axis(False)
ax[1].set_title("peaks");

axis

Controls the axis of the search:

fig, ax = plt.subplots(1, 2, figsize=[12, 6])

axis = 0
pk1 = ps.filters.hold_peaks(im=dt, axis=axis)
ax[0].imshow(pk1 / im)
ax[0].axis(False)
ax[0].set_title(f"axis = {axis}")

axis = 1
pk2 = ps.filters.hold_peaks(im=dt, axis=axis)
ax[1].imshow(pk2 / im)
ax[1].axis(False)
ax[1].set_title(f"axis = {axis}");

ascending

A boolean that controls the direction of the scanning:

fig, ax = plt.subplots(1, 2, figsize=[12, 6])

ascending = True
pk1 = ps.filters.hold_peaks(im=dt, ascending=ascending)
ax[0].imshow(pk1 / im)
ax[0].axis(False)
ax[0].set_title(f"ascending = {ascending}")

ascending = False
pk2 = ps.filters.hold_peaks(im=dt, ascending=ascending)
ax[1].imshow(pk2 / im)
ax[1].axis(False)
ax[1].set_title(f"ascending = {ascending}");