获取2D数组中局部最大值的坐标超过特定值 [英] Get coordinates of local maxima in 2D array above certain value

问题描述

来自PIL导入的

 图片
从scipy.ndimage.filters导入numpy as np 
 import maximum_filter 
 import pylab 
 
 ＃图片（256 * 256像素）包含我想要获得位置的亮点
＃问题：数据有高背景值900  -  1000 
 
 im = Image.open（'slice0000 .png'）
 data = np.array（im）
 
＃据我所知，data == maximum_filter给出像素的真值
＃是最亮的他们的邻居（这里10 * 10像素）
 
 maxima =（data == maximum_filter（data，10））
＃我怎样才能获得最大值，背景特定值，我们说500？ 
  

我恐怕不太了解 scipy.ndimage。 filters.maximum_filter（）函数。有没有办法只在斑点内而不是在背景中获得像素坐标？

Joe Kington发现爪子印刷品

Ivan找到当地最大值

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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