我按照分水岭分割给出了this教程来分离附着图像上的棕色细胞 . 它进展顺利（细胞被蓝色边界分开）但现在我想计算这些细胞并确定它们的大小（像素数）以绘制分布函数 . 你能帮忙怎么做？
代码如下 .
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)
1 回答
你做了很难的部分！现在只需将结果阈值（彩色）并调用方便的
connectedComponentsWithStats