분수령 세분화 후 세포를 계산하는 방법 - openCV/Python

Question

다음은 유역 분할에 대한 자습서로 첨부 된 이미지의 갈색 셀을 구분합니다. 그것은 잘 갔다 (세포는 파란 경계로 분리된다) 그러나 지금 나는 그 세포를 세고 배급 기능을 계획하기 위하여 그들의 크기 (화소 수)를 결정하고 싶으면. 어떻게 할 수 있겠습니까?

코드는 아래와 같습니다.

import numpy as np 
import cv2 

img = cv2.imread('test watershed.tif') 
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) 

# noise removal 
kernel = np.ones((3,3),np.uint8) 
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) 

# sure background area 
sure_bg = cv2.dilate(opening,kernel,iterations=3) 

# Finding sure foreground area 
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5) 
ret, sure_fg = cv2.threshold(dist_transform,0.1*dist_transform.max(),255,0) 


# Finding unknown region 
sure_fg = np.uint8(sure_fg) 
unknown = cv2.subtract(sure_bg,sure_fg) 

# Marker labelling 
ret, markers = cv2.connectedComponents(sure_fg) 

# Add one to all labels so that sure background is not 0, but 1 
markers = markers+1 

# Now, mark the region of unknown with zero 
markers[unknown==255] = 0 

markers = cv2.watershed(img,markers) 
img[markers == -1] = [255,0,0] 

**UPDATE** 

#thresholding a color image, here keeping only the blue in the image 
th=cv2.inRange(img,(255,0,0),(255,0,0)).astype(np.uint8) 


#inverting the image so components become 255 seperated by 0 borders. 
th=cv2.bitwise_not(th) 

#calling connectedComponentswithStats to get the size of each component 
nb_comp,output,sizes,centroids=cv2.connectedComponentsWithStats(th,connectivity=4) 

#taking away the background 
nb_comp-=1; sizes=sizes[0:,-1]; centroids=centroids[1:,:] 

bins = list(range(np.amax(sizes))) 

#plot distribution of your cell sizes. 

numbers = sorted(sizes) 


plt.hist(sizes,numbers) 

cv2.imwrite("test watershed result",img) 

Answer 1

당신은 힘든 부분을했습니다! 이제 결과를 임계 값 (colorwise)으로 바꾸고 편리하게 전화하십시오. connectedComponentsWithStats

#thresholding a color image, here keeping only the blue in the image 
th=cv2.inRange(img,(255,0,0),(255,0,0)).astype(np.uint8) 

#inverting the image so components become 255 seperated by 0 borders. 
th=cv2.bitwise_not(th) 

#calling connectedComponentswithStats to get the size of each component 
nb_comp,output,sizes,centroids=cv2.connectedComponentsWithStats(th,connectivity=4) 

#taking away the background 
nb_comp-=1; sizes=sizes[1:,-1]; centroids=centroids[1:,:] 

#plot distribution of your cell sizes (using matplotlib.pyplot as plt) 
plt.hist(sizes)