Python实现区域生长算法（RGA），并且使用鼠标选取初始坐标点

RGA的原理

区域生长算法的基本思想是将有相似性质的像素点合并到一起。对每一个区域要先指定一个种子点作为生长的起点，然后将种子点周围领域的像素点和种子点进行对比，将具有相似性质的点合并起来继续向外生长，直到没有满足条件的像素被包括进来为止。这样一个区域的生长就完成了。

• 实现该算法的一个关键问题是给定种子点(种子点如何选取？)

  可以手动输入坐标作为种子点。也可根据自己划分的阈值自动生成种子。当然我感觉最好还是使用人工交互选取种子点。

算法步骤 ：

a> 创建一个空白的图像(全黑)；

b> 将种子点存入vector中，vector中存储待生长的种子点；

c> 依次弹出种子点并判断种子点如周围8邻域的关系(生长规则)，相似的点则作为下次生长的种子点；

d> vector中不存在种子点后就停止生长。

使用人工交互的方法获取种子点（鼠标点击）

import matplotlib.pyplot as plt
from  PIL import Imagedef get_x_y(path,n): #path表示图片路径，n表示要获取的坐标个数im = Image.open(path)plt.imshow(im, cmap = plt.get_cmap("gray"))pos=plt.ginput(n)return pos   #得到的pos是列表中包含多个坐标元组

区域生长算法

#区域生长
def regionGrow(gray, seeds, thresh, p):  #thresh表示与领域的相似距离，小于该距离就合并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)]#四邻域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 = int(pt[0] + connection[i][0])tmpY = int(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]:continueif abs(int(gray[tmpX, tmpY]) - int(gray[pt])) < thresh and seedMark[tmpX, tmpY] == 0:seedMark[tmpX, tmpY] = 255seeds.append((tmpX, tmpY))return seedMark

测试

path = r"H:\Dataset\water_leakage\qietu\train\img\34_01.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=10)
# print(seeds)
seeds=get_x_y(path=path,n=3) #获取初始种子
print("选取的初始点为：")
new_seeds=[]
for seed in seeds:print(seed)#下面是需要注意的一点#第一： 用鼠标选取的坐标为float类型，需要转为int型#第二：用鼠标选取的坐标为（W,H），而我们使用函数读取到的图片是（行，列），而这对应到原图是（H,W），所以这里需要调换一下坐标位置，这是很多人容易忽略的一点new_seeds.append((int(seed[1]), int(seed[0])))#result= regionGrow(gray, new_seeds, thresh=3, p=8)#plt.plot(hist)
#plt.xlim([0, 256])
#plt.show()result=Image.fromarray(result.astype(np.uint8))
result.show()

整合上面的函数，用于一个文件的所有图片

def RGA(img_path,save_path,n):imgs_path = os.listdir(img_path)for r in imgs_path:img=os.path.join(img_path,r)seeds = get_x_y(path=img, n=n)print("选取的初始点为：")new_seeds=[]for seed in seeds:print(seed)new_seeds.append((int(seed[1]), int(seed[0])))img = cv2.imread(img)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)result = regionGrow(gray, new_seeds, thresh=3, p=8)result = Image.fromarray(result.astype(np.uint8))result.show()result.save(save_path+r)img_path=r'H:\Dataset\water_leakage\qietu\val\img'
save_path=r'H:\Dataset\water_leakage\qietu\val\RAG'
RGA(img_path,save_path,3)

网上流行的另一个python版本的区域生长算法，将其改为人工交互模式

这个版本和上那个版本是区别是第一个版本在regionGrow函数中坐标是放在元组中。
而这个版本的坐标放在point函数中，相当于放到一个个的节点中吧

import osimport numpy as np
import cv2
from PIL import Image
import matplotlib.pyplot as pltdef get_x_y(path,n): #path表示图片路径，n表示要获取的坐标个数im = Image.open(path)plt.imshow(im, cmap = plt.get_cmap("gray"))pos=plt.ginput(n)return posclass Point(object):def __init__(self, x, y):self.x = xself.y = ydef getX(self):return self.xdef getY(self):return self.ydef 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 connectsdef regionGrow(img, seeds, thresh, p=1):height, weight = img.shapeseedMark = np.zeros(img.shape)seedList = []for seed in seeds:seedList.append(seed)label = 255connects = selectConnects(p)while (len(seedList) > 0):currentPoint = seedList.pop(0)seedMark[currentPoint.x, currentPoint.y] = labelfor i in range(8):tmpX = currentPoint.x + connects[i].xtmpY = currentPoint.y + connects[i].yif tmpX < 0 or tmpY < 0 or tmpX >= height or tmpY >= weight:continuegrayDiff = getGrayDiff(img, currentPoint, Point(tmpX, tmpY))if grayDiff < thresh and seedMark[tmpX, tmpY] == 0:seedMark[tmpX, tmpY] = labelseedList.append(Point(tmpX, tmpY))return seedMarkdef RGA(img_path,savepath,n):imgs_path = os.listdir(img_path)for r in imgs_path:img=os.path.join(img_path,r)seeds = get_x_y(path=img, n=n)print("选取的初始点为：")seeds_point = []for seed in seeds:print(seed)seeds_point.append(Point(int(seed[1]),int(seed[0])))img = cv2.imread(img)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)seedMark = regionGrow(gray, seeds_point, thresh=3, p=8)seedMark = Image.fromarray(seedMark.astype(np.uint8))seedMark.show()seedMark.save(os.path.join(savepath,r))img_path=r'H:\Dataset\water_leakage\qietu\val\img'
save_path=r'H:\Dataset\water_leakage\qietu\val\RAG'
RGA(img_path,save_path,3)