高斯滤波后合并接近的物体

Question

我试图计算图像中的对象数量，并从另一个问题中偶然发现了以下代码，我将它应用到我的示例图像上，如下所示：

import numpy
import matplotlib.pyplot as plt
from scipy import misc, ndimage
from skimage import feature
from skimage.filters import roberts, sobel

im = misc.imread('/home/nvp/temp/kaw.png',flatten=True)
im = im.astype('int32')
edges1 = feature.canny(im, sigma=3)
plt.imshow(edges1,interpolation='nearest')
dx = ndimage.sobel(im, 1)  # horizontal derivative
dy = ndimage.sobel(im, 0)  # vertical derivative
mag = numpy.hypot(dx, dy)  # magnitude
mag *= 255.0 / numpy.max(mag)
dna = mag
dnaf = ndimage.gaussian_filter(dna, 7)
T = 27 # set threshold by hand to avoid installing `mahotas` or
       # `scipy.stsci.image` dependencies that have threshold() functions

# find connected components
labeled, nr_objects = ndimage.label(dnaf > T) # `dna[:,:,0]>T` for red-dot case
print(dnaf, labeled,len(labeled))
print("Number of objects is %d " % nr_objects)

# show labeled image
####scipy.misc.imsave('labeled_dna.png', labeled)
####scipy.misc.imshow(labeled) # black&white image
import matplotlib.pyplot as plt
plt.imsave('labeled_dna.png', labeled)
plt.imshow(labeled)
plt.show()

然而它输出如下图像：

最终对象存储在 labeled np.array 我想，我想做的是合并此输出数组中的关闭对象。如您所见，最后一行的第二个对象有两个部分，但它们彼此非常接近。

由于我不知道 numpy ，我想要一种方法来设置阈值并合并它们之间距离较小的对象。任何帮助表示赞赏。谢谢:)

Answer 1

您要合并的两个对象与第一行的对象 3 和 4 一样接近...因此仅基于对象之间的接近度的解决方案不会给您想要的结果。

一个解决方案可能是 dilate 根据对象的面积（扩大更多小对象）。

示例（未测试）：

import scipy.ndimage.morphology as morpho

# parameters
max_dilat = 20 # dilation (in number of pixels) for a small object
sz_small = 100 # size of a small object (max dilated)
sz_big   = 10000 # size of a big object (not dilated)

result = labeled*0

# for each detected object
for obj_id in range(1, nr_objects+1):
    # creates a binary image with the current object
    obj_img = (labeled==obj_id)
    # computes object's area
    area = numpy.sum(obj_img)
    # dilatation factor inversely proportional to area
    dfac = int( max_dilat*(1-min(1,(max(0,area-sz_small)/sz_big))) )
    # dilates object
    dilat = morpho.binary_dilation(obj_img, iterations=dfac)

    result += dilat

# result is now an int array with ones where you have a single 
# dilated object, and twos (or more) in overlapping parts
labeled, nr_objects = ndimage.label(result>0)

高斯滤波后合并接近的物体

Merging close objects after gaussian filter

python

numpy

image-processing

computer-vision

python-3.x