获取高于特定值的二维数组中局部最大值的坐标 [英] Get coordinates of local maxima in 2D array above certain value

问题描述

from PIL 导入图片将 numpy 导入为 np从 scipy.ndimage.filters 导入maximum_filter导入pylab# 图片(256 * 256 像素)包含我想要获取位置的亮点# 问题:数据在值 900 - 1000 附近有高背景im = Image.open('slice0000.png')数据 = np.array(im)# 据我所知，data == maximum_filter 给出了像素的真值# 是他们附近最亮的(这里是 10 * 10 像素)maxima = (data == maximum_filter(data,10))# 我怎样才能得到最大值，突出背景一定的值，比如说 500 ?

恐怕我不太了解 scipy.ndimage.filters.maximum_filter() 函数.有没有办法只在斑点内而不是在背景内获得像素坐标?

乔金顿发现爪印

Ivan 找到局部最大值

from PIL import Image
import numpy as np
from scipy.ndimage.filters import maximum_filter
import pylab

# the picture (256 * 256 pixels) contains bright spots of which I wanna get positions
# problem: data has high background around value 900 - 1000

im = Image.open('slice0000.png')
data = np.array(im)

# as far as I understand, data == maximum_filter gives True-value for pixels
# being the brightest in their neighborhood (here 10 * 10 pixels)

maxima = (data == maximum_filter(data,10))
# How can I get only maxima, outstanding the background a certain value, let's say 500 ?

I'm afraid I don't really understand the scipy.ndimage.filters.maximum_filter() function. Is there a way to obtain pixel-coordinates only within the spots and not within the background?

http://i.stack.imgur.com/RImHW.png (16-bit grayscale picture, 256*256 pixels)

解决方案

import numpy as np
import scipy
import scipy.ndimage as ndimage
import scipy.ndimage.filters as filters
import matplotlib.pyplot as plt

fname = '/tmp/slice0000.png'
neighborhood_size = 5
threshold = 1500

data = scipy.misc.imread(fname)

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

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
    x.append(x_center)
    y_center = (dy.start + dy.stop - 1)/2    
    y.append(y_center)

plt.imshow(data)
plt.savefig('/tmp/data.png', bbox_inches = 'tight')

plt.autoscale(False)
plt.plot(x,y, 'ro')
plt.savefig('/tmp/result.png', bbox_inches = 'tight')

Given data.png:

the above program yields result.png with threshold = 1500. Lower the threshold to pick up more local maxima:

References:

• J.F. Sebastian counts nuclei
• Joe Kington finds paw prints
• Ivan finds local maximums

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