关于python：使用导数确定二维数组中局部最大值的坐标

Determine the coordinates of local maximas in a two-dimensional array using derivative

我有一个fits图像，我试图在我的图像中找到局部极大值的坐标，但到目前为止，我还不能很好地使它工作。我的照片可以在这里找到。到目前为止我所拥有的是

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import numpy as np
import scipy.nimage as ndimage
from astropy.wcs import WCS
from astropy import units as u
from astropy import coordinates as coord
from astropy.io import fits
import scipy.ndimage.filters as filters
from scipy.ndimage.filters import maximum_filter
hdulist=fits.open("MapSNR.fits")
#reading a two dimensional array from fits file
d=hdulist[0].data
w=WCS("MapSNR.fits")
idx,idy=np.where(d==np.max(d))
rr,dd=w.all_pix2word(idx,idy,o)
c=coord.SkyCoord(ra=rr*u.degree, dec=dd*u.degree)
#The sky coordinate of the image maximum
print c.ra
print c.dec

这就是我如何找到图像的全局最大值的方法，但我想得到具有大于3的意义的局部最大值的坐标。

我在网上查到的是下面这个答案，在我的情况下不能正常工作。更新：我使用了这个函数

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def detect_peaks(data, threshold=1.5, neighborhood_size=5):

data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0 # sets values <= threshold as background
labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
x,y=[],[]
for dy,dx in slices:
x_center = (dx.start + dx.stop - 1)/2
y_center = (dy.start + dy.stop - 1)/2
x.append(x_center)
y.append(y_center)
return x,y

号

我想找到一种使用更好的方法的方法，比如数组中的导数或分而治之的方法。我会选择一个更好的推荐解决方案。

相关讨论

所以我有了这个，使用skimage自适应阈值。希望有帮助：

原件 enter image description here 。

代码

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from skimage.filters import threshold_adaptive
import matplotlib.pyplot as plt
from scipy import misc, ndimage
import numpy as np

im = misc.imread('\Desktop\MapSNR.jpg')

# Apply a threshold
binary_adaptive = threshold_adaptive(im, block_size=40, offset=-20).astype(np.int)
# Label regions and find center of mass
lbl = ndimage.label(binary_adaptive)
points = ndimage.measurements.center_of_mass(binary_adaptive, lbl[0], [i+1 for i in range(lbl[1])])

for i in points:
p = [int(j) for j in i]
binary_adaptive[i] += 5

plt.figure()
plt.imshow(im, interpolation='nearest', cmap='gray')
plt.show()

plt.figure()
plt.imshow(binary_adaptive, interpolation='nearest', cmap='gray')
plt.show()

产量

氧化镁

改变阈值的参数将对局部极大值的发现位置和极大值的发现数量有很大的影响。