如何在python中找到3D数组的局部极大值？

Question

嗨，我正在尝试在一个3D的numpy数组中找到局部极大值，但是我似乎找不到一个简单的方法来使用numpy、scipy或其他任何东西。在

现在我使用scipy.signal.argrelexrema实现了它。但处理大数组的时间很长，而且只在分离轴上工作。在

import numpy as np
from scipy.signal import argrelextrema


def local_maxima_3D(data, order=1):
    """Detects local maxima in a 3D array

    Parameters
    ---------
    data : 3d ndarray
    order : int
        How many points on each side to use for the comparison

    Returns
    -------
    coords : ndarray
        coordinates of the local maxima
    values : ndarray
        values of the local maxima
    """
    # Coordinates of local maxima along each axis
    peaks0 = np.array(argrelextrema(data, np.greater, axis=0, order=order))
    peaks1 = np.array(argrelextrema(data, np.greater, axis=1, order=order))
    peaks2 = np.array(argrelextrema(data, np.greater, axis=2, order=order))

    # Stack all coordinates 
    stacked = np.vstack((peaks0.transpose(), peaks1.transpose(),
                         peaks2.transpose()))

    # We keep coordinates that appear three times (once for each axis)
    elements, counts = np.unique(stacked, axis=0, return_counts=True)
    coords = elements[np.where(counts == 3)[0]]

    # Compute values at filtered coordinates
    values = data[coords[:, 0], coords[:, 1], coords[:, 2]]

    return coords, values

我知道这个解决方案远不是最优的，而且只适用于阶数为1的情况。在python中，有没有更好的方法可以在3D数组中找到局部极大值？在

编辑：

我现在使用以下方法，这实际上要快得多，而且在订单>1时也可以使用：

^{pr2}$

Answer 1

假设你的数据有一些统计表示，你应该能够执行一个三维局部最大，像这样。希望这能回答你的问题。在

import numpy as np
import scipy.ndimage as ndimage

img = np.random.normal(size=(100, 256, 256))

# Get local maximum values of desired neighborhood
# I'll be looking in a 5x5x5 area
img2 = ndimage.maximum_filter(img, size=(5, 5, 5))

# Threshold the image to find locations of interest
# I'm assuming 6 standard deviations above the mean for the threshold
img_thresh = img2.mean() + img2.std() * 6

# Since we're looking for maxima find areas greater than img_thresh

labels, num_labels = ndimage.label(img2 > img_thresh)

# Get the positions of the maxima
coords = ndimage.measurements.center_of_mass(img, labels=labels, index=np.arange(1, num_labels + 1))

# Get the maximum value in the labels
values = ndimage.measurements.maximum(img, labels=labels, index=np.arange(1, num_labels + 1))