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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。
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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)
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