算法中，初始種子可自動(dòng)選擇（通過不同的劃分可以得到不同的種子，可按照自己需要改進(jìn)算法），圖分別為原圖（自己畫了兩筆為了分割成不同區(qū)域）、灰度圖直方圖、初始種子圖、區(qū)域生長(zhǎng)結(jié)果圖。
另外，不管時(shí)初始種子選擇還是區(qū)域生長(zhǎng)，閾值選擇很重要。
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import cv2import numpy as npimport matplotlib.pyplot as plt#初始種子選擇def originalSeed(gray, th): ret, thresh = cv2.cv2.threshold(gray, th, 255, cv2.THRESH_BINARY)#二值圖，種子區(qū)域(不同劃分可獲得不同種子) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))#3×3結(jié)構(gòu)元 thresh_copy = thresh.copy() #復(fù)制thresh_A到thresh_copy thresh_B = np.zeros(gray.shape, np.uint8) #thresh_B大小與A相同，像素值為0 seeds = [ ] #為了記錄種子坐標(biāo) #循環(huán)，直到thresh_copy中的像素值全部為0 while thresh_copy.any(): Xa_copy, Ya_copy = np.where(thresh_copy > 0) #thresh_A_copy中值為255的像素的坐標(biāo) thresh_B[Xa_copy[0], Ya_copy[0]] = 255 #選取第一個(gè)點(diǎn)，并將thresh_B中對(duì)應(yīng)像素值改為255 #連通分量算法，先對(duì)thresh_B進(jìn)行膨脹，再和thresh執(zhí)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值為255的像素坐標(biāo)，并將thresh_copy中對(duì)應(yīng)坐標(biāo)像素值變?yōu)? Xb, Yb = np.where(thresh_B > 0) thresh_copy[Xb, Yb] = 0 #循環(huán)，在thresh_B中只有一個(gè)像素點(diǎn)時(shí)停止 while str(thresh_B.tolist()).count("255") > 1: thresh_B = cv2.erode(thresh_B, kernel, iterations=1) #腐蝕操作 X_seed, Y_seed = np.where(thresh_B > 0) #取處種子坐標(biāo) if X_seed.size > 0 and Y_seed.size > 0: seeds.append((X_seed[0], Y_seed[0]))#將種子坐標(biāo)寫入seeds thresh_B[Xb, Yb] = 0 #將thresh_B像素值置零 return seeds#區(qū)域生長(zhǎng)def regionGrow(gray, seeds, thresh, p): seedMark = np.zeros(gray.shape) #八鄰域 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內(nèi)無元素時(shí)候生長(zhǎng)停止 while len(seeds) != 0: #棧頂元素出棧 pt = seeds.pop(0) for i in range(p): tmpX = pt[0] + connection[i][0] tmpY = pt[1] + connection[i][1] #檢測(cè)邊界點(diǎn) 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 seedMarkpath = "_rg.jpg"img = cv2.imread(path)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#hist = cv2.calcHist([gray], [0], None, [256], [0,256])#直方圖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) |
以上這篇關(guān)于初始種子自動(dòng)選取的區(qū)域生長(zhǎng)實(shí)例（python+opencv)就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://www.cnblogs.com/er-gou-zi/p/12016951.html
