如何删除由形态学引起的blob的扩展 [英] How to remove an extension to a blob caused by morphology

问题描述

我有一张我正在侵蚀和扩张的图像：

  kernel = np.ones（（5， 5），np.float32）/ 1 
 eroded_img = cv2.erode（self.inpainted_adjusted_image，kernel，iterations = 10）
 dilated_img = cv2.dilate（eroded_img，kernel，iterations = 10）
  

以下是侵蚀和膨胀的结果：

然后我就像这样采取阈值：

  self.thresh = cv2.threshold（dilated_img，0,255，cv2.THRESH_BINARY + cv2.THRESH_OTSU）[1] 
  

但是阈值给了我一个不需要的扩展，我在下面的图像中标记了（红线上方的区域是不需要的区域）：

  import cv2 
 import numpy as np 
 import matplotlib.pyplot as plt 
 
 img = cv2.imread（'image。 png'，0）
 thresh = cv2.threshold（img，210,255，cv2.ADAPTIVE_THRESH_MEAN_C）[1] 
 canny = cv2.Canny（thresh，50,150）
 cimg = cv2。 cvtColor（img，cv2.COLOR_GRAY2BGR）
 
 circles = cv2.HoughCircles（canny，cv2.HOUGH_GRADIENT，1,20，param1 = 50，param2 = 23，minRadius = 0，maxRadius = 0）
 circles = np.uint16（np.around（circles））
 
 
 for i in circle [0，：]：
＃draw the outer circle 
 cv2.circle（cimg，（i [0]，i [1]），i [2]，（255,0,0），3）
＃画圆圈的中心
 cv2。 circle（cimg，（i [0]，i [1]），2，（0,0,255），3）
 
 titles = ['Original Image'，'Adaptive Thresholding'，Canny ，Hough Circle] 
 images = [img，thresh，ca nny，cimg] 
 for x in xrange（4）：
 plt.subplot（2,2，i + 1），plt.imshow（images [i]，'gray'）
 plt.title（titles [i]）
 plt.xticks（[]），plt.yticks（[]）
 plt.show（）
  

如果还不够，请告诉我们。

I have an image that I'm eroding and dilating like so:

kernel = np.ones((5,5),np.float32)/1
        eroded_img = cv2.erode(self.inpainted_adjusted_image, kernel, iterations=10)
        dilated_img = cv2.dilate(eroded_img, kernel, iterations=10)

Here's the result of the erosion and dilation:

and then I'm taking a threshold of it like so:

self.thresh = cv2.threshold(dilated_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

But the threshold gives me an unwanted extension that I've marked in the image below (The region above the red line is the unwanted region):

How do I get rid of this unwanted region? Is there a better way to do what I'm doing?

解决方案

Working with a different type of threshold (adaptive threshold, which takes local brigthness into account) will already get rid of your problem: The adaptive threshold result is what you are looking for.

[EDIT: I have taken the liberty of adding some code on Hough circles. I admit that I have played with the parameters for this single image to get a nice looking result, though I do not know what type of accuracy you are needing for such a type of problem]

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('image.png',0)
thresh = cv2.threshold(img, 210, 255, cv2.ADAPTIVE_THRESH_MEAN_C)[1]
canny = cv2.Canny(thresh,50,150)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(canny,cv2.HOUGH_GRADIENT,1,20, param1=50,param2=23,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))


for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(255,0,0),3)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

titles = ['Original Image', 'Adaptive Thresholding', "Canny", "Hough Circle"]
images = [img, thresh, canny, cimg]
for i in xrange(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

Let us know if this is not yet sufficient.

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