ウォーターシェーダー分割後の細胞カウント - openCV/Python

Question

私は、付着した画像上の褐色細胞を分離するために、流域セグメンテーションについてチュートリアルを続けた。それはうまくいった（細胞は青い境界線で分けられる）が、今はそれらの細胞を数え、その分布関数をプロットするためにその大きさ（ピクセル数）を決定したいと思う。それをどうやって手伝ってくれますか？

コードは次のとおりです。

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

あなたはハード部分をやった！今すぐあなたの結果を色付けして（カラーで）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)