關(guān)于初始種子自動(dòng)選取的區(qū)域生長實(shí)例（python+opencv)

更新時(shí)間：2020年01月16日 14:51:09 作者：清熱降火

今天小編就為大家分享一篇關(guān)于初始種子自動(dòng)選取的區(qū)域生長實(shí)例（python+opencv)，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過來看看吧

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)域生長結(jié)果圖。

另外，不管時(shí)初始種子選擇還是區(qū)域生長，閾值選擇很重要。

import cv2
import numpy as np
import 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
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 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)