Python实现区域生长算法（regionGrow）

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Python实现区域生长算法（regionGrow）

2024-07-12 14:34| 来源: 网络整理| 查看: 265

区域生长是一种串行区域分割的图像分割方法。区域生长是指从某个像素出发，按照一定的准则，逐步加入邻近像素，当满足一定的条件时，区域生长终止。区域生长的好坏决定于1.初始点（种子点）的选取。2.生长准则。3.终止条件。区域生长是从某个或者某些像素点出发，最后得到整个区域，进而实现目标的提取。

区域生长的原理：

区域生长的基本思想是将具有相似性质的像素集合起来构成区域。具体先对每个需要分割的区域找一个种子像素作为生长起点，然后将种子像素和周围邻域中与种子像素有相同或相似性质的像素（根据某种事先确定的生长或相似准则来判定）合并到种子像素所在的区域中。将这些新像素当作新的种子继续上面的过程，直到没有满足条件的像素可被包括进来。这样一个区域就生长成了。

区域生长实现的步骤如下:

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。

二维平面图像

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 img = cv2.imread('lean.png',0) seeds = [Point(10,10),Point(82,150),Point(20,300)] binaryImg = regionGrow(img,seeds,10) cv2.imshow(' ',binaryImg) cv2.waitKey(0)

三维体素数据：

import numpy as np def grow(img, seed, t): """ img: ndarray, ndim=3 An image volume. seed: tuple, len=3 Region growing starts from this point. t: int The image neighborhood radius for the inclusion criteria. """ seg = np.zeros(img.shape, dtype=np.bool) checked = np.zeros_like(seg) seg[seed] = True checked[seed] = True needs_check = get_nbhd(seed, checked, img.shape) while len(needs_check) > 0: pt = needs_check.pop() # Its possible that the point was already checked and was # put in the needs_check stack multiple times. if checked[pt]: continue checked[pt] = True # Handle borders. imin = max(pt[0]-t, 0) imax = min(pt[0]+t, img.shape[0]-1) jmin = max(pt[1]-t, 0) jmax = min(pt[1]+t, img.shape[1]-1) kmin = max(pt[2]-t, 0) kmax = min(pt[2]+t, img.shape[2]-1) if img[pt] >= img[imin:imax+1, jmin:jmax+1, kmin:kmax+1].mean(): # Include the voxel in the segmentation and # add its neighbors to be checked. seg[pt] = True needs_check += get_nbhd(pt, checked, img.shape) return seg

区域生长涉及种子选取，提供一个获取图像zuo'坐标的函数：

def on_mouse(event, x,y, flags , params): if event == cv2.EVENT_LBUTTONDOWN: print('Seed: ' + 'Point' + '('+str(x) + ', ' + str(y)+')', imger[y, x]) clicks.append((y, x)) cv2.setMouseCallback('input', on_mouse, 0, )

‘input’是你显示图像的命名。

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Python实现区域生长算法（regionGrow）

Python实现区域生长算法（regionGrow）

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