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算法中,初始种子可自动选择(通过不同的划分可以得到不同的种子,可按照自己需要改进算法),图分别为原图(自己画了两笔为了分割成不同区域)、灰度图直方图、初始种子图、区域生长结果图。
另外,不管时初始种子选择还是区域生长,阈值选择很重要。
import cv2
import numpy as np
import matplotlib.pyplot as plt
#初始种子选择
def originalSeed(gray,th):
ret,thresh = cv2.cv2.threshold(gray,th,255,cv2.THRESH_BINARY)#二值图,种子区域(不同划分可获得不同种子)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))#3×3结构元
thresh_copy = thresh.copy() #复制thresh_A到thresh_copy
thresh_B = np.zeros(gray.shape,np.uint8) #thresh_B大小与A相同,像素值为0
seeds = [ ] #为了记录种子坐标
#循环,直到thresh_copy中的像素值全部为0
while thresh_copy.any():
Xa_copy,Ya_copy = np.where(thresh_copy > 0) #thresh_A_copy中值为255的像素的坐标
thresh_B[Xa_copy[0],Ya_copy[0]] = 255 #选取第一个点,并将thresh_B中对应像素值改为255
#连通分量算法,先对thresh_B进行膨胀,再和thresh执行and操作(取交集)
for i in range(200):
dilation_B = cv2.dilate(thresh_B,kernel,iterations=1)
thresh_B = cv2.bitwise_and(thresh,dilation_B)
#取thresh_B值为255的像素坐标,并将thresh_copy中对应坐标像素值变为0
Xb,Yb = np.where(thresh_B > 0)
thresh_copy[Xb,Yb] = 0
#循环,在thresh_B中只有一个像素点时停止
while str(thresh_B.tolist()).count("255") > 1:
thresh_B = cv2.erode(thresh_B,iterations=1) #腐蚀操作
X_seed,Y_seed = np.where(thresh_B > 0) #取处种子坐标
if X_seed.size > 0 and Y_seed.size > 0:
seeds.append((X_seed[0],Y_seed[0]))#将种子坐标写入seeds
thresh_B[Xb,Yb] = 0 #将thresh_B像素值置零
return seeds
#区域生长
def regionGrow(gray,seeds,thresh,p):
seedMark = np.zeros(gray.shape)
#八邻域
if p == 8:
connection = [(-1,-1),(-1,0),1),(0,(1,-1)]
elif p == 4:
connection = [(-1,-1)]
#seeds内无元素时候生长停止
while len(seeds) != 0:
#栈顶元素出栈
pt = seeds.pop(0)
for i in range(p):
tmpX = pt[0] + connection[i][0]
tmpY = pt[1] + connection[i][1]
#检测边界点
if tmpX < 0 or tmpY < 0 or tmpX >= gray.shape[0] or tmpY >= gray.shape[1]:
continue
if abs(int(gray[tmpX,tmpY]) - int(gray[pt])) < thresh and seedMark[tmpX,tmpY] == 0:
seedMark[tmpX,tmpY] = 255
seeds.append((tmpX,tmpY))
return seedMark
path = "_rg.jpg"
img = cv2.imread(path)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#hist = cv2.calcHist([gray],[0],None,[256],[0,256])#直方图
seeds = originalSeed(gray,th=253)
seedMark = regionGrow(gray,thresh=3,p=8)
#plt.plot(hist)
#plt.xlim([0,256])
#plt.show()
cv2.imshow("seedMark",seedMark)
cv2.waitKey(0)
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