有没有什么好的算法可以检测背景强度变化的粒子？例如，如果我有以下图像：
有没有办法计算小的白色颗粒，即使左下方出现明显不同的背景？
为了更清楚一点，我想标记图像并使用发现这些粒子显着的算法对粒子进行计数：
我用PIL, cv, scipy,numpy等模块尝试了很多东西。我从这个非常相似的 SO question中得到了一些提示，乍一看，您可以采用这样的简单阈值：
im = mahotas.imread('particles.jpg')
T = mahotas.thresholding.otsu(im)
labeled, nr_objects = ndimage.label(im>T)
print nr_objects
pylab.imshow(labeled)
但由于不断变化的背景，你会得到这个：
我还尝试了其他想法，例如我发现的一种用于测量 paws 的技术，我以这种方式实现了它：
import numpy as np
import scipy
import pylab
import pymorph
import mahotas
from scipy import ndimage
import cv
def detect_peaks(image):
"""
Takes an image and detect the peaks usingthe local maximum filter.
Returns a boolean mask of the peaks (i.e. 1 when
the pixel's value is the neighborhood maximum, 0 otherwise)
"""
# define an 8-connected neighborhood
neighborhood = ndimage.morphology.generate_binary_structure(2,2)
#apply the local maximum filter; all pixel of maximal value
#in their neighborhood are set to 1
local_max = ndimage.filters.maximum_filter(image, footprint=neighborhood)==image
#local_max is a mask that contains the peaks we are
#looking for, but also the background.
#In order to isolate the peaks we must remove the background from the mask.
#we create the mask of the background
background = (image==0)
#a little technicality: we must erode the background in order to
#successfully subtract it form local_max, otherwise a line will
#appear along the background border (artifact of the local maximum filter)
eroded_background = ndimage.morphology.binary_erosion(background, structure=neighborhood, border_value=1)
#we obtain the final mask, containing only peaks,
#by removing the background from the local_max mask
detected_peaks = local_max - eroded_background
return detected_peaks
im = mahotas.imread('particles.jpg')
imf = ndimage.gaussian_filter(im, 3)
#rmax = pymorph.regmax(imf)
detected_peaks = detect_peaks(imf)
pylab.imshow(pymorph.overlay(im, detected_peaks))
pylab.show()
但这也没有运气，显示了这个结果：
使用区域最大函数，我得到的图像几乎似乎给出了正确的粒子识别，但是根据我的高斯滤波，错误位置的粒子太多或太少（图像的高斯滤波器为 2,3，& 4):
此外，它还需要处理与此类似的图像：
这是上面相同类型的图像，只是粒子密度更高。
编辑：已解决的解决方案： 我能够使用以下代码为这个问题找到一个不错的工作解决方案：
import cv2
import pylab
from scipy import ndimage
im = cv2.imread('particles.jpg')
pylab.figure(0)
pylab.imshow(im)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5,5), 0)
maxValue = 255
adaptiveMethod = cv2.ADAPTIVE_THRESH_GAUSSIAN_C#cv2.ADAPTIVE_THRESH_MEAN_C #cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresholdType = cv2.THRESH_BINARY#cv2.THRESH_BINARY #cv2.THRESH_BINARY_INV
blockSize = 5 #odd number like 3,5,7,9,11
C = -3 # constant to be subtracted
im_thresholded = cv2.adaptiveThreshold(gray, maxValue, adaptiveMethod, thresholdType, blockSize, C)
labelarray, particle_count = ndimage.measurements.label(im_thresholded)
print particle_count
pylab.figure(1)
pylab.imshow(im_thresholded)
pylab.show()
这将显示如下图像：
（这是给定的图像）
和
（这是计数的粒子）
并计算粒子数为 60。