# Python简单实现区域生长方式

1. 对图像顺序扫描!找到第1个还没有归属的像素, 设该像素为(x0, y0);

2. 以(x0, y0)为中心, 考虑(x0, y0)的4邻域像素(x, y)如果(x0, y0)满足生长准则, 将(x, y)与(x0, y0)合并(在同一区域内), 同时将(x, y)压入堆栈;

3. 从堆栈中取出一个像素, 把它当作(x0, y0)返回到步骤2;

4. 当堆栈为空时!返回到步骤1;

5. 重复步骤1 - 4直到图像中的每个点都有归属时。生长结束。

Python实现

```import numpy as np
import cv2

class Point(object):
def __init__(self,x,y):
self.x = x
self.y = y

def getX(self):
return self.x
def getY(self):
return self.y

def getGrayDiff(img,currentPoint,tmpPoint):
return abs(int(img[currentPoint.x,currentPoint.y]) - int(img[tmpPoint.x,tmpPoint.y]))

def selectConnects(p):
if p != 0:
connects = [Point(-1, -1), Point(0, -1), Point(1, -1), Point(1, 0), Point(1, 1), \
Point(0, 1), Point(-1, 1), Point(-1, 0)]
else:
connects = [ Point(0, -1), Point(1, 0),Point(0, 1), Point(-1, 0)]
return connects

def regionGrow(img,seeds,thresh,p = 1):
height, weight = img.shape
seedMark = np.zeros(img.shape)
seedList = []
for seed in seeds:
seedList.append(seed)
label = 1
connects = selectConnects(p)
while(len(seedList)>0):
currentPoint = seedList.pop(0)

seedMark[currentPoint.x,currentPoint.y] = label
for i in range(8):
tmpX = currentPoint.x + connects[i].x
tmpY = currentPoint.y + connects[i].y
if tmpX < 0 or tmpY < 0 or tmpX >= height or tmpY >= weight:
continue
grayDiff = getGrayDiff(img,currentPoint,Point(tmpX,tmpY))
if grayDiff < thresh and seedMark[tmpX,tmpY] == 0:
seedMark[tmpX,tmpY] = label
seedList.append(Point(tmpX,tmpY))
return seedMark