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 ?

from skimage.feature import peak_local_max
xy = peak_local_max(data, min_distance=2,threshold_abs=1500)

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')

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---

---