区域生长与分水岭算法图像分割

程序员文章站 2024-01-16 08:08:46

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1.区域生长法

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(r'C:\Users\feiyu\Desktop\tmy.jpg',0)
seeds = [Point(766,667),Point(666,667),Point(10,10)]
binaryImg = regionGrow(img,seeds,10)
cv2.imshow(' ',binaryImg)
cv2.waitKey(0)

效果：

区域生长与分水岭算法图像分割

2.分水岭法

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread(r'C:\Users\feiyu\Desktop\tmy.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)#膨胀
# Finding sure foreground area

dist_transform = cv2.distanceTransform(opening,2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.15*dist_transform.max(),255,0)#参数改小了，出现不确定区域
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)#减去前景

import matplotlib.pyplot as plt
plt.figure()
plt.gray()
plt.imshow(sure_fg)
plt.show()

区域生长与分水岭算法图像分割