关于初始种子自动选取的区域生长实例（python+opencv)_Python

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。

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									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, kernel, 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, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1)]

									 elif p == 4:

									  connection = [(-1, 0), (0, 1), (1, 0), (0, -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, seeds, thresh=3, p=8)

									#plt.plot(hist)

									#plt.xlim([0, 256])

									#plt.show()

									cv2.imshow("seedMark", seedMark)

									cv2.waitKey(0)