使用python中的图像处理计算粒子

Question

在变化的背景强度下检测粒子有什么好的算法吗？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一种算法来计算粒子,这些算法会发现这些粒子很重要:

我已经尝试了许多事情的PIL,cv,scipy,numpy,等模块.我从这个非常相似的SO问题中得到了一些提示,乍看之下你可以采取一个简单的阈值:

im = mahotas.imread('particles.jpg')
T = mahotas.thresholding.otsu(im)

labeled, nr_objects = ndimage.label(im>T)
print nr_objects
pylab.imshow(labeled)

但由于背景的变化,你得到了这个:

我也尝试了其他的想法,比如我用来测量爪子的技术,我用这种方式实现了:

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,我得到几乎看起来正确的粒子识别的图像,但根据我的高斯滤波,图像中有太多或太少的粒子在错误的位置(图像有高斯滤波器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,只是在更高密度的粒子上.

编辑:解决方案: 我能够使用以下代码获得一个体面的解决此问题的解决方案:

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

(这是计算的粒子)

并计算粒子数为60.

Answer 1

我已经通过使用一种称为“自适应对比度”的技术，通过调整差异阈值解决了“背景中的可变亮度”问题。它通过对本身具有模糊版本的灰度图像执行线性组合（在情况下有所不同），然后对其应用阈值来工作。

1. 用合适的统计运算符对图像进行卷积。
2. 从卷积图像中减去原始图像，必要时校正强度比例/伽玛。
3. 阈值具有恒定的差异图像。

（原纸）

我scipy.ndimage在浮点域中非常成功地完成了操作，效果比整数图像处理更好，如下所示：

original_grayscale = numpy.asarray(some_PIL_image.convert('L'), dtype=float)
blurred_grayscale = scipy.ndimage.filters.gaussian_filter(original_grayscale, blur_parameter)
difference_image = original_grayscale - (multiplier * blurred_grayscale);
image_to_be_labeled = ((difference_image > threshold) * 255).astype('uint8')  # not sure if it is necessary

labelarray, particle_count = scipy.ndimage.measurements.label(image_to_be_labeled)

希望这可以帮助！！