基于初始种子自动选取的区域生长（python+opencv)

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。

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("") > 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)