最近學(xué)習(xí)了數(shù)字圖像的分水嶺分割算法，來總結(jié)一下。分水嶺分割算法的詳解這篇文章寫的不錯(cuò)鹏氧。
網(wǎng)址如下：https://zhuanlan.zhihu.com/p/67741538
接下來對(duì)圖1進(jìn)行分水嶺分割
第一步：讀取圖片并使用大津法對(duì)圖像進(jìn)行二值化分割，得到的圖向如下圖所示。
input_image = cv2.imread('E:\\ImageData\\coins.png')?
gray = cv2.cvtColor(input_image,cv2.COLOR_BGR2GRAY)
?ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)?
第二步：使用形態(tài)學(xué)操作延窜，確定前景區(qū)域和背景區(qū)域
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) # 形態(tài)開運(yùn)算
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3) #背景
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5) #距離變換
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
第三步：使用形態(tài)學(xué)操作，確定未知區(qū)域抹锄。
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
第四步：使用分水嶺分割方法逆瑞，其中，watershed的輸入圖像中伙单，0代表未知區(qū)域获高，其它為非零整數(shù)，如1吻育，2念秧，3...
放回邊界為-1.
markers = cv2.watershed(input_image,markers)
input_image[markers == -1] = [255,0,0]
完整代碼如下：
import numpy as np
import cv2
from matplotlib import pyplot as plt
input_image = cv2.imread('E:\\ImageData\\coins.png')
gray = cv2.cvtColor(input_image,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) # 形態(tài)開運(yùn)算
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(input_image,markers)
input_image[markers == -1] = [255,0,0]
plt.subplot(241), plt.imshow(gray),
plt.title('Original'), plt.axis('off')
plt.subplot(242), plt.imshow(thresh, cmap='gray'),
plt.title('Threshold'), plt.axis('off')
plt.subplot(243), plt.imshow(sure_bg, cmap='gray'),
plt.title('sure_bg'), plt.axis('off')
plt.subplot(244), plt.imshow(dist_transform, cmap='gray'),
plt.title('Dist Transform'), plt.axis('off')
plt.subplot(245), plt.imshow(sure_fg, cmap='gray'),
plt.title('sure_fg'), plt.axis('off')
plt.subplot(246), plt.imshow(unknown, cmap='gray'),
plt.title('Unknow'), plt.axis('off')
plt.subplot(247), plt.imshow(np.abs(markers), cmap='jet'),
plt.title('Markers'), plt.axis('off')
plt.show()
cv2.imshow('img',input_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
結(jié)果如下所示：
