嗨，我尝试从此图像中检测粒子长度大小
我想得到粒子的长度大小，如下所示。并使用质心获取粒子大小的直方图并检测如下线：
我尝试使用分水岭。但我对这段代码的了解仍然很低。我想问的是：1.如何像下面这张图片一样进行分割，以便我可以评估我的分割..我不知道如何显示它（我会分享我的代码）
- 如何分离与其他粒子结合的粒子。我得到了这个轮廓
这是我的代码：
from __future__ import print_function
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import scalar
import sys
from scipy.spatial import distance as dist
import scipy.ndimage as ndi
import matplotlib.pyplot as plt
from skimage import filters
from sklearn import cluster
from scipy import ndimage
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy.ndimage import label
img = cv2.imread("C:\\Users\\adiyu\Pictures\\snipping\\c3smeasure.png",
cv2.IMREAD_COLOR)
kernel = np.array([[-1,-1,-1],[-1,9,-1], [-1,-1,-1]])
kernel2 = np.ones((3,3),np.uint8)
img=cv2.filter2D(img,-255, kernel)
img = cv2.medianBlur(img,3)
img_np = np.array(img)
img_np_rgb = cv2.cvtColor(img_np,cv2.COLOR_RGBA2RGB)
#Kmeans cluster using Opencv
Z = img.reshape((-1,3))
# convert to np.float32
Z = np.float32(Z)
# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 3
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_PP_CENTERS)
# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
gray= cv2.cvtColor(res2,cv2.COLOR_BGR2GRAY)
gray = cv2.fastNlMeansDenoising(gray, None, 6,7,21)
cv2.imshow('res2',res2)
cv2.imshow('gray',gray)
thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY_INV +
cv2.THRESH_OTSU + cv2.THRESH_OTSU)[1]
cv2.imshow('thresh',thresh)
opening = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
cv2.imshow("opening", opening)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE,kernel2, iterations=2)
cv2.imshow("closing", closing)
fg = cv2.erode(closing,None,iterations = 2)
cv2.imshow("fg", fg)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
cv2.imshow("sure_bg", sure_bg)
# 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)
cv2.imshow("unknown", unknown)
# 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]
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()enter preformatted text here
欢迎任何建议..谢谢你