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。
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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( "255" ) > 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 ) |
以上这篇关于初始种子自动选取的区域生长实例(python+opencv)就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持服务器之家。
原文链接:https://www.cnblogs.com/er-gou-zi/p/12016951.html